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10.1371_journal.pone.0301029	RESEARCH ARTICLE A systematic review and meta-analysis on the efficacy of vaccination against colibacillosis in broiler production Surya PaudelID C. de Carvalho FerreiraID 7, Alessandra PiccirilloID 6* 1,2☯, Ilias Apostolakos3☯, Ronald Vougat Ngom4,5, Giuditta TilliID 6, Helena a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, 2 Clinic for Poultry and Fish Medicine, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Vienna, Austria, 3 Veterinary Research Institute, Hellenic Agricultural Organization “DIMITRA”, Thessaloniki, Greece, 4 Department of Animal Production, School of Veterinary Medicine and Sciences, University of Ngaoundere, Ngaounde´ re´, Cameroon, 5 Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland, 6 Department of Comparative Biomedicine and Food Science, University of Padua, Legnaro, Italy, 7 Flanders Research Institute for Agriculture, Fisheries and Food, Merelbeke, Belgium ☯ These authors contributed equally to this work. * alessandra.piccirillo@unipd.it Abstract OPEN ACCESS Citation: Paudel S, Apostolakos I, Vougat Ngom R, Tilli G, de Carvalho Ferreira HC, Piccirillo A (2024) A systematic review and meta-analysis on the efficacy of vaccination against colibacillosis in broiler production. PLoS ONE 19(3): e0301029. https://doi.org/10.1371/journal.pone.0301029 Editor: Mohamed Ezzat Abd El-Hack, Zagazig University Faculty of Agriculture, EGYPT Received: December 20, 2023 Accepted: March 8, 2024 Published: March 22, 2024 Copyright: © 2024 Paudel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Colibacillosis, a disease caused by Escherichia coli in broiler chickens has serious implica- tions on food safety, security, and economic sustainability. Antibiotics are required for treat- ing the disease, while vaccination and biosecurity are used for its prevention. This systematic review and meta-analysis, conducted under the COST Action CA18217—Euro- pean Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT), aimed to assess the efficacy of E. coli vaccination in broiler production and provide evidence-based recommendations. A comprehensive search of bibliographic databases, including, PubMed, CAB Abstracts, Web of Science and Agricola, yielded 2,722 articles. Following a defined protocol, 39 studies were selected for data extraction. Most of the studies were experimental infection trials, with only three field studies identified, underscoring the need for more field- based research. The selected studies reported various types of vaccines, including killed (n = 5), subunit (n = 8), outer membrane vesicles/protein-based (n = 4), live/live-attenuated (n = 16), and CpG oligodeoxynucleotides (ODN) (n = 6) vaccines. The risk of bias assess- ment revealed that a significant proportion of studies reporting mortality (92.3%) or feed con- version ratio (94.8%) as outcomes, had “unclear” regarding bias. The meta-analysis, focused on live-attenuated and CpG ODN vaccines, demonstrated a significant trend favor- ing both vaccination types in reducing mortality. However, the review also highlighted the challenges in reproducing colibacillosis in experimental setups, due to considerable varia- tion in challenge models involving different routes of infection, predisposing factors, and challenge doses. This highlights the need for standardizing the challenge model to facilitate comparisons between studies and ensure consistent evaluation of vaccine candidates. While progress has been made in the development of E. coli vaccines for broilers, further research is needed to address concerns such as limited heterologous protection, PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 1 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers practicability for application, evaluation of efficacy in field conditions and adoption of novel approaches. Introduction Poultry meat is expected to hold a substantial share in global meat consumption, accounting for approximately half of the growth in meat production by 2032 [1]. As the poultry industry intensifies, ensuring optimum food safety and animal welfare becomes a top priority. How- ever, bacterial pathogens, such as Escherichia coli pose a major challenge to the poultry indus- try [2]. E. coli is a gram-negative bacterium in the family of Enterobacteriaceae that normally resides in the healthy gut of chickens as a commensal [3, 4]. However, infection with patho- genic strains can lead to colibacillosis, a syndrome that affects chickens of all ages [5]. Coliba- cillosis in broilers can manifest in various clinical forms, including omphalitis, airsacculitis, femoral head necrosis and cellulitis, resulting in high condemnation rates and mortality [6–8]. The avian E. coli isolates are highly heterogenous, with pathogenicity likely involving coordi- nation among several virulence genes, host factors or transfer of genetic elements among E. coli populations [9, 10]. Thus, understanding the pathogenesis of colibacillosis remains a chal- lenge [11]. Colibacillosis is primarily treated with antibiotics. However, recent studies have shown the emergence of antibiotic-resistant strains of E. coli on a global scale [12]. Multidrug resistance in E. coli has become a concerning threat, even in flocks where no antibiotics are used [13–15]. In Europe, E. coli is identified as one of the most relevant antimicrobial resistant bacterial path- ogens from poultry [16]. Consequently, there is a growing need to explore preventive strategies such as vaccination and biosecurity, as opposed to relying solely on antimicrobial treatments. In the past, numerous studies have reported a range of potential vaccine candidates as summa- rized in previous reviews and book chapters [5, 17–20]. However, there have been no system- atic reviews conducted to assess the efficacy of E. coli vaccines in chickens. Such reviews are essential for providing clear, comprehensive evidence that can inform evidence-based recom- mendations. Consequently, this study, conducted under the framework of the COST Action CA18217—European Network for Optimization of Veterinary Antimicrobial Treatment (ENOVAT), aimed to carry out a systematic review and meta-analysis to understand the cur- rent evidence regarding the efficacy of vaccination in preventing colibacillosis in broiler chickens. Methods This review was performed according to the Cochrane Handbook for Systematic Reviews of Interventions [21] and adheres to the structured and reporting guidelines outlined in the Pre- ferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 [22]. Protocol registration A systematic review protocol was developed, registered in the University of Padua Research Archive institutional repository (https://hdl.handle.net/11577/3439974), and published on the Systematic Reviews for Animals and Food (SYREAF) website (https://syreaf.org). PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 2 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Eligibility criteria The primary focus of this systematic review was to include controlled trials with natural dis- ease exposure. However, disease challenge studies and observational studies were also consid- ered. The studies had to be conducted in broiler production chain (Population) and assess the protective efficacy of vaccine candidates (Intervention). The vaccine intervention was com- pared to either an infected and untreated control group or a group that received placebo treat- ment (Comparator). The selected Outcomes of vaccine efficacy for this review were mortality, feed conversion ratio (FCR) and condemnation rate at the slaughterhouse. Articles written in English or Spanish were included, and no restrictions were imposed on publication date or geographical location of the studies. Sources of information To ensure comprehensive coverage of articles, the following bibliographic databases for litera- ture search were used that provide a high level of article recall in the biomedical field [23]: i) MEDLINE (via PubMed, https://pubmed.ncbi.nlm.nih.gov/), ii) CAB Abstracts (via Ovid, https://www.wolterskluwer.com/en/solutions/ovid/cab-abstracts-31), iii) Web of Science (WoS, http://webofknowledge.com/), and iv) Agricola (via ProQuest, https://www.proquest. com/). Searches in CAB Abstracts and Agricola were conducted through the University of Bern (Switzerland), while those in PubMed and WoS through the University of Padova (Italy). All databases of WoS were used, including WoS core collection, BIOSIS Citation Index, KCI-Korean Journal Database, Medline, Russian Science Citation Index and SciELO Citation Index. However, because of their research scopes, certain editions were excluded, namely Arts & Humanities Citation Index (A&HCI), Conference Proceedings Citation Index-Science (CPCI-S), Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH) and Social Sciences Citation Index (SSCI). The initial search was conducted in September 2021 and a second search covering the period from September 2021 to October 2023 was per- formed in October 2023. The databases and search string were the same in both the search events. Search strategy and study selection The search strategy employed a multi-stranded approach, utilizing various combinations of concepts to ensure comprehensive retrieval of relevant research and achieve high sensitivity [21]. The concept and the corresponding search strings are presented in Table 1. Table 1. Bibliographic search strategy to identify studies examining the effect of vaccines against colibacillosis in broiler chickens. Major terms #1 Broilers #2 Vaccination #3 Colibacillosis Key words chicken* OR poultry* OR gallus OR broiler* OR flock vaccination* OR vaccine* OR bacterin* OR sub-unit* OR "killed vaccine" OR "live vaccine" OR "autogenous vaccine*" colibacillosis OR colisepticaemia OR peritonitis OR coli OR Escherichia OR coliform OR colisepticemia OR coligranuloma OR Hjarre’s OR "air sac disease" OR cellulitis OR osteomyelitis OR "brittle bone disease" OR salpingitis OR synovitis OR omphalitis OR enteritis OR "hemorrhagic septicemia" OR "chronic respiratory disease" OR "swollen head syndrome" OR "venereal colibacillosis" OR "coliform cellulitis" OR "yolk sac infection" OR APEC OR "pathogenic E. coli" OR "primary infection" OR "secondary infection" OR multifactorial OR multicausal #1 AND #2 AND #3 records screened https://doi.org/10.1371/journal.pone.0301029.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 3 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers The bibliographic records of the identified articles were downloaded in BibTeX format and imported to Rayyan [24]. To ensure data accuracy, deduplication process was conducted using the built-in function of Rayyan. The screening and evaluation of studies were conducted in two steps. In the first step, at least two independent reviewers screened titles and abstracts. Any conflicts or disagreements were resolved through discussion or with the involvement of a third reviewer. To maintain consistency among reviewers, a calibration exercise was first con- ducted by screening 25 randomly selected studies. The eligibility of studies was evaluated using a set of questions adapted from a previously published protocol [25] as: 1. Is the study original research assessing the use of vaccine(s) to prevent or control colibacil- losis in broilers? YES [PASS], NO [EXCLUDE], UNCLEAR [PASS] 2. Does the study include an eligible comparator via a controlled trial, disease challenge study or observational study? YES [PASS], NO [EXCLUDE], UNCLEAR [PASS] Studies were excluded only if all reviewers unanimously agreed that the answer to any of the screening questions was “no”. The studies that passed the first screening step proceeded to the next, where the full-text articles were retrieved and assessed for eligibility. In the second phase, the following set of questions was applied: i. Is a full text of more than 500 words available? YES [PASS], NO [EXCLUDE] ii. Is a full text available in English and/or Spanish? YES [PASS], NO [EXCLUDE] iii. Is the population of the study broilers? YES [PASS], NO [EXCLUDE] iv. Is the intervention of the study the use of vaccine(s) to prevent or control colibacillosis in broilers? YES [PASS], NO [EXCLUDE] v. Is at least one of mortality, FCR, or condemnations at slaughter due to colibacillosis the out- come(s) described? YES [PASS], NO [EXCLUDE] vi. Is the study design a controlled trial with natural disease exposure or a disease challenge study or an observational field study? YES [PASS], NO [EXCLUDE] Data extraction A Microsoft Excel (2019 version) standardized spreadsheet, developed and validated by the authors, was used for data extraction. Relevant study characteristics, population type (broilers or broiler breeders), group size, year of the study, age of the birds during intervention and out- come assessment, and duration of observation were collected. Detailed data on the interven- tion were also extracted, including the vaccine type and commercial name, route and dose of administration, comparator group, unit of population, and total number of birds included. For studies involving disease challenge, information on challenge day, duration, strain, and admin- istration route were collected. Data extraction focused on mortality, FCR and condemnations at slaughterhouse due to colibacillosis. For mortality, the unit of measurement and assessment period were recorded. For studies reporting FCR and/or condemnations at slaughter, values such as FCR value and/ or age/weight of slaughtered birds were extracted. Risk of bias The risk of bias (RoB) assessment deviated from the original protocol and used instead a recently reported poultry-specific method [26]. Five domains of bias were evaluated, including PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 4 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers bias from randomization (Domain 1), deviations in interventions (Domain 2), missing out- come data (Domain 3), measurement of the outcome (Domain 4), and selection of reported results (Domain 5). Briefly, each domain of bias is composed of several signaling questions that guide the overall risk of bias for each domain. This overall risk of each domain can then be reported as ‘‘low risk” ‘‘unclear”, or ‘‘high risk”. At the end, for each included study, a final overall risk of bias judgment is provided to each outcome based on the results from the five domains. Therefore, a ‘‘low risk of bias” outcome would result from all five domains being classified as ‘‘low risk”; ‘‘unclear” would result when either one or two domains for that out- come have been classified as ‘‘unclear”; and ‘‘high risk of bias” would result from at least three domains being classified as ‘‘unclear” or if at least one domain is classified as ‘‘high risk”. The RoB assessment was conducted only for mortality and FCR outcomes. Data synthesis and statistical analysis The results of the literature search and selection were reported, and descriptive analysis of extracted data was done by using Microsoft Excel (version 2019). After data extraction, included studies were narratively summarized according to the type of vaccines used and study’s setting. The meta-analysis was performed using Revman version 5.4.1 according to Higgins et al. [21]. Considering the data of the selected studies, the meta-analysis was per- formed for two groups of vaccines (i.e. live attenuated vaccine and CpG oligodeoxynucleo- tides) with “mortality” as outcome. Some differences between included studies (age and route of inoculation, E. coli strain, dosage, etc.) were not considered during meta-analysis. The com- parison concerned “infected and vaccinated” and “infected and no vaccinated” groups. Data used consisted of the number of dead animals per each group. The effect measure for outcome were odds ratios (with the 95% confidence levels) with a fixed model visualized through Forest plots. The heterogeneity among studies was evaluated with Cochrane test based on Chi- Squared. Significant heterogeneity was considered when the I2 value was greater than 50% and the p-value was less than 0.05. The sources of heterogeneity between studies were not explored. Reporting bias assessment As recommended by the Cochrane methodology [21], funnel plots followed by the Egger’s test were used to assess publication bias for the outcome ‘‘mortality” using MedCalc version 22.019. This was performed only when sufficient data were available (>10 studies). Certainty was not assessed. Results and discussion Number of eligible studies The results of the selected studies, based on the inclusion criteria, are summarized in Fig 1. Ini- tially, 2,722 studies were identified from the selected databases. After removing non-eligible studies based on the inclusion criteria, 39 studies were deemed suitable for data extraction. Studies characteristics In total, 39 studies were selected for data extraction. Various types of E. coli vaccines with dif- ferent efficacy were reported in the selected studies (Fig 2), including killed (n = 5), subunit (n = 8), outer membrane vesicles/protein based (n = 4), live/live-attenuated (n = 16) and CpG-ODN (n = 6) vaccines. Ten studies evaluated the efficacy of a commercially available vac- cine, while others aimed to assess the suitability of newly reported vaccine candidates. PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 5 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Fig 1. Methodological PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) flowchart for the selection of studies. https://doi.org/10.1371/journal.pone.0301029.g001 Fig 2. E. coli vaccine types, vaccination routes and challenge routes used to assess vaccination efficacy in broilers. “n" refers to number of studies. https://doi.org/10.1371/journal.pone.0301029.g002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 6 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Mortality was considered as an assessment parameter in all studies. Additionally, FCR was included in most studies, except for five. Vaccine types Characteristics of E. coli vaccine types in broiler chickens are described below. Killed vaccines Vaccines using inactivated bacteria can elicit an immune response, mainly through humoral immunity. These vaccines are considered safe as they do not replicate within the host. Hyper- immunization of chickens with intravenous injection of a heat-killed E. coli J5 strain was shown to be effective in preventing mortality and pathological lesions following a challenge [27] (Table 2, S1 Table). In another study formalin treatment, irradiation and ultrasonication methods were employed for bacterial inactivation, all of which were shown effective in pre- venting lesions [28]. Sayed et al. (2021) also showed that formalin-killed E. coli vaccination sig- nificantly reduced mortality after challenge and could be combined with an avian influenza vaccine prepared using the same method, which can also induce a higher antibody response in birds [29]. Recently, autogenous vaccines have been used as a potential solution to address the heterogeneity of E. coli isolates. However, there is limited evidence regarding their effective- ness. In a study conducted by Keita et al. (2022), passive immunization using a bivalent forma- lin-killed autogenous vaccine was found to be effective in reducing mortality when chicks were challenged with one of two E. coli strains [30]. This indicates strain-specific protection provided by the vaccine. Additionally, a combination approach involving parent stock vacci- nation with an autogenous vaccine, along with the supplementation of feed with Enterococcus faecium DSM 7134 and fructo-oligosaccharides in the progeny, showed benefits in terms of improving body weight and gut health. However, FCR was not affected by this combination approach [31]. All of the above-mentioned vaccines have not yet reached to the commercial market. Subunit vaccines These vaccines consist of purified antigenic parts, such as proteins or protein fragments, rather than the entire pathogen [32]. Table 3 provides a summary of the key characteristics of subunit E. coli vaccines in broiler chickens. Details of each study are provided in S1 Table. Vandemaele et al. (2006) investigated the impact of immunization with the biologically active lectin domain Table 2. Important characteristics of killed vaccines against colibacillosis in broiler chickens. Reference Abdul-Aziz & El- Sukhon, 1998 [27] Ibrahim et al., 1997 [28] Day of vaccination 5, 14 & 20 14 Sayed et al., 2021 [29] 21 & 42 Keita et al., 2022 [30] 20 & 22 weeks Fuhrmann et al., 2022 [31] 12 & 17 weeks Route of vaccination IV IM SC IM1 IM1 Dose and route of challenge 0.2 ml of 6x108 CFU/ml (IV) 108 CFU/bird 0.2 ml of 107 CFU (IM) 0.1 ml of 3x108 CFU/ml (SC)2 0.6 ml of 3.2x107 CFU/ml (oral)2 Important findings Chickens hyperimmunized with E. coli J5 showed protection based on mortality, clinical signs and pathological lesions. Formalin-killed, irradiated and ultrasonicated E. coli induced protection. Formalin-killed E. coli prevented mortality from experimental E. coli infection and can be combined with inactivated avian influenza vaccine. Breeders received a bivalent autogenous vaccine; passive immunization was effective in chicks against challenges with one out two E. coli stains only Passive immunization together with administration of pre-and probiotics have beneficial effects on body weight and gut health. IV: intravenous, IM: intramuscular, SC: subcutaneous, CFU: colony forming unit; 1vaccination of breeders for passive immunization; 2 challenge of progenies https://doi.org/10.1371/journal.pone.0301029.t002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 7 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Table 3. Important characteristics of subunit vaccines against colibacillosis in broiler chickens. Reference Day of vaccination Route of Dose and route of challenge Important findings Vandemaele et al., 2005 [34] 10 (single) or 10 & 30 (with booster) vaccination IM or IN Vandemaele et al., 2006 [33] 10 Lynne et al., 2012 [35] 14 Chaudhari et al., 2013 [36] 1 (single) or 1 & 14 (with booster) IM IM Oral 10 ml/group, 3x1010 CFU/ml; prior infection with NDV (nebulization) 10 ml/group, 2.7x1010 CFU/ml (prior infection with NDV; nebulization) or 0.2 ml 104 (intra-airsac) 0.1 ml of 107 CFU (intra-airsac) 50 μl of 107 CFU (intra-airsac) Chaudhari & Lee, 2013 [37] 1 Oral 0.1 ml of 106 CFU (intra-airsac) Ma et al., 2018 [38] Dissanayake et al., 2010 [39] 1–3 & 14–16 Oral 5x1011 CFU (Oral) 7 & 21 IM 106 CFU (subcutaneous) Tuntufye et al., 2012 [40] 10 & 24 Intranasally & IM 2x106 CFU (intra airsac) IM: intramuscular, IN: intra nasal, CFU: colony forming unit https://doi.org/10.1371/journal.pone.0301029.t003 Immunization with sugar-binding domain of FimH (FimH156) effectively induced high levels of adhesion-inhibiting antibodies but did not provide protection against APEC O78 infection. Immunization with the biologically active lectin domain of PapGII could effectively induce high levels of adhesion- inhibiting antibodies but did not provide protection against APEC O78 infection delivered via coarse spray or via intra-air sac challenge. The Iss antigen provided significant protection against challenges with three different APEC strains (O1, O2 and O78). Prime and boost vaccination with an attenuated Salmonella strain carrying P-fimbriae (papA, papG), aerobactin receptor (iutA) and CS31A surface antigen (clpG) genes of E. coli induced immune response and provided protection against E. coli challenge. Coadministration of live attenuated Salmonella strain expressing the heat-liable toxin of E. coli B subunit (LTB) increased the efficacy of the Salmonella-delivered APEC vaccine developed by Chaudhari et al. (2013). Immunization of birds with a recombinant Lactobacillus saerimneri expressing FimA and OmpC antigen of O78 APEC provided protection. Liposome-encapsulated mixture of rough LPSs significantly lowered lesion scores and increased body weight but no difference was observed in mortality. Four ferri-siderophore receptors (FuhE, FepA, IroN, IutA) were expressed in live or bacterial ghost cells; none of the two recombinants were protective. of PapGII on the avian immune response to APEC O78 challenge [33]. The results showed that while immunization effectively stimulated the production of adhesion-inhibiting antibod- ies, it did not provide protection against APEC O78 infection through coarse spray or intra-air sac challenge. Similar results were observed when the sugar-binding domain of FimH (FimH156) was used [34]. However, Lynne et al. (2012) demonstrated that vaccinating birds with increased serum survival gene (iss) fusion proteins provoked the serum and mucosal anti- body response and consequently resulted in broad protection against bacterial challenges with three different APEC strains: O1, O2 and O78 [35]. Similarly, a modified strain of Salmonella carrying multiple genes from E. coli, including P-fimbriae (papA, papG), aerobactin receptor (iutA) and CS31A surface antigen (clpG), elicited mucosal and systemic antibody responses, and stimulated lymphocytic proliferation [36]. Although a single vaccination with this attenu- ated Salmonella strain only provided partial protection against E. coli challenge, repeated vacci- nation significantly enhanced the protective response. Co-administration of this vaccine candidate with a live attenuated Salmonella expressing the heat-labile toxin of E. coli B subunit (LTB) as an adjuvant proved to be more effective in reducing mortality and morbidity rates in challenged birds [37]. In a separate study, a probiotic bacterium called Lactobacillus saerimneri was used as a delivery system to create a recombinant vaccine that expressed fimbrial subunit A (FimA) and outer membrane protein C (OmpC) of O78 APEC. Oral administration of the recombinant L. saerimneri effectively induced an antigen-specific immune response and pro- vided protection, as 70% of vaccinated birds survived while 100% mortality was observed in PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 8 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers the non-vaccinated challenge control group [38]. Additionally, a non-adjuvanted liposome- encapsulated mixture of rough LPSs exhibited a positive dose-dependent effect, especially in terms of antibody level in birds. Immunization with the highest dose (5μg) resulted in lower lesion scores and increased body weight, although the mortality rate did not show a significant difference [39]. Iron uptake system genes play a crucial role in the virulence mechanism of APEC. Four ferri-siderophore receptors namely FuhE, FepA, IroN, IutA were expressed in recombinant live or bacterial ghost cells [40]. However, despite increased IgG titers in birds, neither the intranasal administration of recombinant live E. coli nor the intramuscular infection of recom- binant ghost cells was able to reduce mortality and lesion scores, leading to the conclusion that both vaccine candidates were non-protective. Outer membrane vesicles/proteins-based vaccines Outer membrane vesicles (OMVs) are naturally derived spherical nanovesicles originating from the bacterial outer membrane which contains various bacterial components, such as lipo- polysaccharides, proteins, and other antigens [41]. Their protective efficacy was evaluated in broiler chickens as shown in Table 4 and S1 Table. Immunization of birds with a nanosized OMV-based vaccine derived from APEC O2 demonstrated no adverse effects. Moreover, it significantly increased the survival rate, reduced bacterial loads, and suppressed the produc- tion of proinflammatory cytokines [42]. Immunologically, the vaccine primarily stimulated antigen-specific antibody responses and IFN-γ mediated immune responses in the host. Tak- ing a step further, a combination of multi-serogroup OMVs derived from O1, O2 and O78 E. coli strains induced a robust non-specific and antigen specific immune responses. This was evident from the production of IgG antibodies specific to APEC antigens and resulted in a 90– 100% increase in protection against challenges with APEC O1, O2 or O78 strains compared to the control group [43]. It is difficult to attribute the observed protection solely to specific pro- teins or polysaccharides within the OMVs due to their complex composition. To enhance vaccine uptake and improve pharmacokinetic and pharmacodynamic proper- ties, nanotechnology-based vaccines are beneficial. These vaccines are designed to boost the immune response by providing antigenic targets in a way that mimics natural infection, improving stability and targeting specific immune cells [44, 45]. Mohammed et al. (2021) investigated the potential of chitosan nanoparticles in enhancing the immune response of chickens after vaccination with the outer membrane proteins (OMPs) and flagellar antigens from O1 and O78 serogroups [46]. The study utilized two types of chitosan nanoparticles, Table 4. Important characteristics of outer membrane vesicles/protein-based vaccines against colibacillosis in broiler chickens. Reference Day of vaccination Route of vaccination Hu et al., 2020 [43] 7 & 14 IM Hu et al., 2020 [42] 7 & 14 & 21 IM Mohammed et al., 2020 [46] Abd El-Aziz et al., 2022 [47] 21 14 SC SC Dose and route of challenge 5x108 CFU (intra- airsac) 5x108 CFU (intra- tracheal) 107 CFU (IM) 107 CFU (IM) IM: intramuscular, SC: sub cutaneous, CFU: colony forming unit https://doi.org/10.1371/journal.pone.0301029.t004 Important findings Vaccination with nanosized OMVs had no side effects and efficiently protected chicks against homologous infection with APEC O2. It provoked antibody and IFN-γ mediated immune responses. Combined OMVs from O1, O2 and O78 strains provided robust and broad protection against E. coli challenges with all three strains. Addition of chitosan and ascorbate chitosan nanoparticles improved the immune response induced by outer membrane proteins and flagellin. Chitosan loaded nanoparticles with Montanide adjuvant enhanced immunity for a longer time period. PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 9 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers namely the characterized chitosan (CS) and ascorbate chitosan (AsCS), in both loaded and encapsulated forms. The results demonstrated that both forms of chitosan nanoparticles improved the immune response, in terms of antibody production in chickens and provided protection against infections induced by E. coli O1 and O78, compared to the control group. Subsequent research by Abd El-Aziz et al. (2022) corroborated these findings and further dem- onstrated that the addition of Montanide as an adjuvant to the chitosan nanoparticles pro- longed the humoral and cell-mediated immunological responses, thereby enhancing immunity for an extended period [47]. Live and live attenuated vaccines Live and live attenuated vaccines have advantages for mass application, as they can be deliv- ered as spray or via drinking water. Most of the studies in this review evaluated the efficacy of these vaccines (Table 5, S1 Table). A study by Frommer et al. (1994) found that a non-patho- genic piliated E. coli strain provided broad protection against colibacillosis-induced mortality caused by O1:K1, O2:K1 and O78:K80 [48]. Immunization at 14 or 21 days of age was more effective than at an early age (1 or 7 days) and drinking water or intramuscular administration showed better efficacy than the spray method. Kariyawasam et al. (2002) demonstrated that administering a live E. coli strain of O78 serotype via aerosol route at 18 days of age reduced pathological lesions and systemic bacterial colonization when challenged with the same strain [49]. Advancements in understanding genetic characteristics of E. coli led to the development of live attenuated vaccine candidates by targeting essential genes required for multiplication in the host. Peighambari et al. (2002) tested the efficacy of double mutants created by deletion of cya and crp genes in O2 and O78 E. coli strains [50]. The mutant O2 strain provided moderate protection against air sac lesions when administered via spray, while the mutant O78 strain was ineffective. It was also observed that antibody response was not stimulated in vaccinated birds, indicating the importance of innate or adaptive immunity for protection against coliba- cillosis. Other mutants with galE, purA or aroA deletions showed similar immunogenicity and serogroup-specific protection but they did not provide effective cross-protection against heter- ologous challenge [51]. Currently, there are two licensed live attenuated vaccines against E. coli infection. The crp deletion mutant of E. coli-O78 (Nisseiken Co., Ltd., Tokyo, Japan) is marketed in Japan and is recommended to be administered via fine spray (particle size <20 μm) in day-old chickens. The vaccine has shown effectiveness in reducing lesions following the challenge with homo- and heterologous strains [52, 53], although it may be ineffective against heterologous challenge based on mortality pattern, clinical signs, pathology and bacterial re-isolation [53]. The second live attenuated vaccine is developed by deleting aroA gene of E. coli-O78 strain (Poulvac1, E. coli, Zoetis), and is usually administered via coarse spray in day-old chicks. In a field trial, the Poulvac vaccine did not affect the weight gain in broiler chickens [54]. In experimental condi- tions, several studies have suggested its efficacy against homologous challenge in reducing coli- bacillosis-associated pathological lesions [54–58]. Oral vaccination of birds at day 5 of age reduced morbidity [59] but drinking water application was found to be ineffective in inducing protection [56]. The findings regarding heterologous protection were not consistent among studies with some showing effectiveness against certain strains [54, 60], but not others [56, 57, 60]. The efficacy of the vaccine against homologous challenge was enhanced by supplementa- tion of probiotics Enterococcus faecalis [58] and pre-treatment with Lincospectin improved the vaccine’s response [61]. However, the immune response elicited by the live attenuated vaccine was reported to be interfered by the prior application of ceftiofur sodium antibiotic in layer PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 10 / 22 PLOS ONETable 5. Important characteristics of live and live attenuated vaccines against colibacillosis in broiler chickens. Reference Day of vaccination Route of vaccination Dose and route of challenge Important findings Efficay of vaccination against colibacillosis in broilers Live vaccine Frommer et al., 1994 [48] 1, 7, 14 or 21 IM, spray or per os 1x108 CFU (IM) Kariyawasam et al., 2002 [49] 18 Live attenuated vaccine Peighambari et al., 2002 [50] 14 or 10 and 14 Aerosol 108 CFU (Intra airsac); together with IBV Coase spray 0.1x109 CFU (prior infection with IBV) 1 and 14 Coarse spray Kariyawasam et al., 2004 [51] Asaad et al., 2019 [52] Abd El-Mawgoud et al., 2020 [53] Sadeghi et al., 2018 [54] Galal et al., 2018 [55] Rawiwet et al., 2009 [59] Mohammed et al., 2016 [56] 1 1 1 1 5 1 Fine spray (crp- deletion mutant) Eye drop (aroA deletion mutant) Fine spray 100 ml/group of 109 CFU/ml (aerosol) 6x108 CFU (IT) 0.5 ml of 108 CFU/ml (SC) Coarse spray 108 (IT) Coarse spray 109 CFU (IT) Oral Coarse spray or drinking water 0.5 ml of 1.2x109 CFU/ml (IT) 6x108 CFU (IT) Gharib et al., 2017 [57] 1 or 1 and 14 Elbestawy et al., 2021 [60] 1 Tarabees et al., 2019 [58] 1 and 15 Coarse spray 9x108 CFU (IT) Coarse spray Coarse spray 0.5 ml of 1.2x108 CFU (IT) 0.5 ml of 1x108 CFU (oral) Galal et al., 2021 [61] 1 or 7 Coarse spray 0.1 ml of 109 CFU/ml (IT) Li et al., 2017 [63] 1 day and 12 weeks Coarse spray 0.1 ml of 5x106 CFU/ ml (intrauterine) Vaccination at the age of 14 or 21, but not at 1 or 7 days, via IM or per os elicited protection; mortality was reduced after challenge with O1, O2 and O78 E. coli challenges; spray vaccination provided inadequate protection. Vaccination reduced pathological lesions and systemic bacterial colonization after homologous challenge. Double mutant was created deleting cya and crp genes of O2 and o78 strains; moderate protection was observed with mutant O2 strain as it reduced air sac lesions but the mutant O78 was not effective. Mutants of galE, purA or aroA from O78 strain provided homologous but not heterologous (O2) protection. Both vaccines were effective to minimize the pathological lesions following homo-and heterologous (O1) challenges. The crp deletion mutant vaccine was efficacious to reduce mortality and bacterial colonization after homologous challenge but was not effective against heterologous challenge (O125). aroA deletion mutant (Poulvac) reduced clinical signs and lesions due to O78 and untypeable E. coli strains; no difference was observed in mortality. aroA deletion mutant (Poulvac) provided protection against the homologous challenge; the vaccine did not interfere with humoral immune response induced by other vaccines such as AI, NDV, IBV or IBD. aroA deletion mutant (Poulvac) reduced morbidity (pathological lesions) but no difference was seen following homologous challenge. Spray vaccination of aroA deletion mutant (Poulvac) led to significant reduction in pathological lesions but drinking water application was not effective; homologous protection was observed but not the heterologous protection against O1 challenge aroA deletion mutant (Poulvac) was effective against O78 but not against O125 challenge, protection was associated with cell mediated immunity. aroA deletion mutant (Poulvac) provided protection against O27 and O8 assessed with FCR, mortality, lesions, clinical signs and bacterial re-isolation; protection against O115 was not significant. aroA deletion mutant (Poulvac) decreased the mortality rate and bacterial colonization after homologous challenge; vaccine response was improved by supplementation of probiotics Enterococcus faecalis. aroA deletion mutant (Poulvac) given at 7 days in birds with prior treatment with lincospectin 100 for 3 days was the most effective to prevent mortality and loss of performance due to challenge with O78 strain. aroA deletion mutant (Poulvac) alone was not protective; Poulvac followed by autogenous vaccine delayed the onset of clinical signs for 3–4 days but no signs of protection against homo-and heterologous challenges. (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 11 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Table 5. (Continued) Reference Day of vaccination Route of vaccination Dose and route of challenge Important findings Sˇenk et al., 2022 [73] Poulvac (12 & 20 weeks) with/ without autogenous vaccine (18 weeks) Poulvac: Spray ND Autogenous: IM Vaccinating birds with both commercial live-attenuated (Poulvac) and autogenous vaccines showed some benefits compared to using only the live-attenuated vaccine. IM: intramuscular, CFU: colony forming unit, IT: intra tracheal, ND: not done https://doi.org/10.1371/journal.pone.0301029.t005 birds [62]. Recently, Li et al. (2017) reported that Poulvac administered alone at 1 and 12 weeks did not protect birds against intrauterine challenge despite high antibody titers [63]. However, when the live attenuated vaccine was followed by an autogenous vaccine, the onset of disease was delayed, but there was no evidence of protection against homologous or heterol- ogous challenges. CpG-ODN vaccines CpG oligodeoxynucleotides (CpG ODN) are synthetic DNA molecules that contain specific patterns of cytosine and guanine bases (CpG) recognized by the immune system. They have been used as vaccine adjuvants to enhance the immune response to antigens, including viral or bacterial protein [64]. Several studies investigated the effectiveness of CpG ODN vaccines in reducing mortality caused by colibacillosis (Table 6, S1 Table). Gunawardana et al. (2019) explored the use of CpG ODN administered in ovo to stimulate the immune system of newly hatched chicks and protect them against subcutaneous (SC) bacterial challenge with E. coli ser- ogroup O2 [65]. The study revealed that the administration of synthetic CpG-ODN in freshly hatched chicks led to a rapid increase in immune cells, such as macrophages and dendritic cells, as well as cytokine responses in spleen and lungs. The authors also observed enhanced protection against bacterial challenge in the chickens treated with synthetic CpG-ODN, as indicated by reduced bacterial loads in various tissues and increased survival rates. Similar findings were reported in the studies conducted by Taghavi et al. (2009) and Gomis et al. (2004), which employed similar study designs involving in ovo vaccination, SC challenge with E. coli O2 at comparable doses, and similar observation period for mortality [66, 67]. Two additional studies examined the effects of CpG-ODN vaccines in newly hatched chicks but with different study designs. Allan et al. (2018) also employed in ovo delivery, however the challenge with E. coli O2 was done via the intranavel route and at a much lower dosage (25 CFUs vs 105 CFUs in the aforementioned studies) [68]. Nevertheless, the authors observed increased survival rates in chicks compared to the control group. In a study by Sarfraz et al. (2022) using a similar vaccination route and challenge model, the effectiveness of different innate immune stimulants and their combination was compared [69]. The results showed that the in ovo administration of CpG-ODN in conjunction with polyinosinic-polycytidylic acid was the most efficient in protecting chicks when they were challenged via intranasal route. In another study, one-day-old chicks received intrapulmonary delivery of CpG-ODN followed by SC challenge with E. coli O2, resulting in approximately half the relative risk of mortality com- pared to birds that received saline [70]. Field studies Only a limited number of studies (Table 7, S1 Table) examined the efficacy of E. coli vaccines in field settings compared to the experimental studies mentioned above. The first two studies [71, 72] were not considered for data extraction, as they did not meet the selection criteria. PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 12 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Table 6. Important characteristics of CpG-ODN vaccines against colibacillosis in broiler chickens. Reference CpG-ODN Day of vaccination Route of vaccination Dose and route of challenge Important findings Gomis et al., 2004 [67] in ovo in ovo 105 CFU (SC) Taghavi et al., 2009 [66] in ovo in ovo 104.5 CFU (SC) Goonewardene et al., 2017 [70] 1 intrapulmonary 104.5 CFU (SC) Chickens treated with synthetic CpG-ODN showed enhanced protection against bacterial challenge, indicated by reduced bacterial loads in various tissues and increased survival rates. Chickens treated with synthetic CpG-ODN showed enhanced protection against bacterial challenge, indicated by reduced bacterial loads in various tissues and increased survival rates. SC challenge with E. coli O2, resulting in about half of the relative risk of mortality as did the birds that received saline Allan et al., 2018 [68] in ovo in ovo 25 CFU (intranavel) Challenge with E. coli O2 was done via the intranavel route and at a much lower dosage; increased survival rates of chicks in their experiments compared to the control group. Gunawardana et al., 2019 [65] Sarfraz et al., 2022 [69] in ovo in ovo 104.5 CFU (SC) In rapid increase of immune cells such as macrophages and dendritic cells; enhanced protection against bacterial challenge, indicated by reduced bacterial loads in various tissues and increased survival rates. in ovo in ovo 25–30 CFU (intranavel) Coadministration of CpG (10μg/embryo) and poly I:C 15μg/embryo provided 100% protection against experimental yolk sac infection. SC: subcutaneous, CFU: colony forming unit https://doi.org/10.1371/journal.pone.0301029.t006 One of them evaluated the effectiveness of a commercially available inactivated subunit vac- cine, which contains E. coli fimbrial antigen and flagellar toxin (Nobilis1, MSD Animal Health). The vaccine was administered intramuscularly to commercial broiler breeders [71]. The results showed that the vaccinated flocks experienced lower mortality potentially associ- ated with natural E. coli infection. However, there were no significant differences observed in terms of first week mortality in chicks, slaughterhouse condemnation rates and FCR between birds from vaccinated or non-vaccinated breeder flocks. Another study investigated the effi- cacy of the live attenuated Poulvac1 E. coli vaccine, which was administered in day old chicks as recommended [72]. The findings demonstrated that colibacillosis-like lesions were less fre- quent in vaccinated flocks compared to non-vaccinated flocks. However, no differences were observed in FCR between the two groups. Another study showed that colibacillosis-related lesions were observed less frequently in the flock of birds vaccinated with both live attenuated Poulvac1 and autogenous vaccines compared to the group vaccinated only with live attenu- ated vaccine, indicating some benefits of combining both vaccines in the field [73]. Risk of bias In the evaluation of 39 papers reporting mortality data, the overall RoB revealed ‘‘some con- cerns” for the majority (n = 36, 92.3%) and ‘‘high risk” for a small fraction (n = 3, 7.7%) Table 7. Important characteristics of field studies vaccination against colibacillosis in broiler chickens. Reference Gregersen et al., 2010 [71] Mombarg et al., 2014 [72] Age of vaccination Route of vaccination Dose and route of challenge Important findings 12 & 18 weeks IM day old Spray ND ND Vaccinated breeder flock experienced less mortality due to E. coli natural infection but the vaccination no beneficial impact on the first week mortality of chicks. Colibacillosis associated lesions recorded in slaughterhouse were less frequent in vaccinated flocks compared to non-vaccinated flocks. IM: intramuscular, ND: not done https://doi.org/10.1371/journal.pone.0301029.t007 PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 13 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Fig 3. Risk of Bias (RoB) in the E. coli vaccination studies that reported mortality (A) or feed conversion ratio (B) as one of the assessment parameters. https://doi.org/10.1371/journal.pone.0301029.g003 (Fig 3A). When examining seven studies that included FCR data, the overall RoB was evalu- ated as ‘‘unclear’ (Fig 3B). For mortality outcome, one study (2.6%) exhibited “high risk” of bias, one (2.6%) ‘‘low risk”, and 37 (94.8%) were assessed to have ‘‘unclear” (Fig 3B). The over- all risk of bias of domains 3 to 5 was assessed as ‘‘low” for FCR. In both mortality and FCR, domains 1 and 2 emerged as the primary sources of bias. The overall bias arising from the ran- domization process (domain 1) recorded is mainly related to the lack of information concern- ing concealed allocation sequence of animals in the groups. For example, for mortality as outcome, only seven studies (n = 17.9%) provided this information. The concern with the result of the domain 2 (bias due to deviation from the intended intervention) is due to the absence of information concerning the awareness or not of animal caregivers/researchers about the assigned interventions and whether there were deviations from the intended inter- vention that arose because of the trial. Again, only one study (n = 2.6%) provided these details in the papers with mortality as outcome. Meta-analysis For the live attenuated vaccine, a total of twelve studies were included in the meta-analysis (Fig 4). All studies were performed after the year 2000 and the majority (83.3%) had an overall RoB assessed as “unclear”. When considering the effects on mortality, the comparison between the non-vaccinated and vaccinated groups showed a significant (P < 0.00001) trend favoring vac- cination with a pooled odds ratio of 0.30 (95% CI: 0.19–0.48). A minimal but non-significant level of heterogeneity (P = 0.21; I2 = 24%) among studies was recorded. Due to the absence of a significant heterogeneity among included studies, the subgroup and meta-regression analysis were not performed. As presented in Fig 5, the six studies included in the meta-analysis of the PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 14 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Fig 4. Forest plot of live attenuated vaccine efficacy considering the “mortality” assessment parameter. https://doi.org/10.1371/journal.pone.0301029.g004 CpG-ODN vaccine had an overall RoB assessed as ‘unclear’ and a high level of heterogeneity (P < 0.00001; I2 = 91%). The pooled odds ratio for mortality was 0.64 (95% CI: 0.45–0.90). Due to the limited number of studies included, the sources of heterogeneity were not assessed. The reporting bias was assessed only for the live attenuated vaccine efficacy considering the “mortality” as outcome because more than ten studies were included in the meta-analysis. The funnel plot (Fig 6) and the Egger’s test results (Intercept = 0.43, 95% CI [-1.94 to 2.80], P = 0.69) showed a symmetry of the studies and a non-significant regression test, respectively, indicating an absence of publication bias (studies with no significant results) and validating the analysis as reasonable and reliable. Outlook and conclusion Colibacillosis in broiler chickens poses challenges for animal health and welfare, with serious economic consequences, impacting food safety and security, which can have a clear effect on consumers’ wellbeing and livelihoods. This systematic review examines the efficacy of various vaccines in preventing colibacillosis in broilers, revealing that, while some vaccine candidates have shown promising results, challenges and limitations remain that need to be addressed. Killed and subunit vaccines, while being safe due to only exposing animals to fragments of the pathogen, have limited range of protection and require injections in birds. This ultimately Fig 5. Forest plot of CpG oligodeoxynucleotides (CpG-ODN) vaccine efficacy considering the “mortality” assessment parameter. https://doi.org/10.1371/journal.pone.0301029.g005 PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 15 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Fig 6. Funnel plot of live attenuated vaccine efficacy considering the “mortality” assessment parameter. https://doi.org/10.1371/journal.pone.0301029.g006 raises concerns about the practicality and cost-effectiveness in commercial broiler production. On the other hand, even though live attenuated vaccines can be mass-applied through practical routes, such as drinking water or spray, rigorous safety assessments and investigations into potential long-term effects are still necessary. In this study, meta-analysis was possible only for live-attenuated and CpG ODN vaccines, reflecting great heterogenicity among studies. While the analysis demonstrated a significant trend favoring these vaccination types in reducing mortality, there is a significant variation in challenge models used in vaccine research for E. coli infections, involving various routes of infection, predisposing factors, and a wide range of challenge doses. It shows the difficulty in reproducing the disease experimentally. Nevertheless, standardizing the challenge model is essential for consistent evaluation of vaccine candidates and comparison between studies [74]. Therefore, expanding the portfolio of E. coli vaccines, considering practical feasibility and serotype-independent protection, as well as establishing a robust infection model, are crucial. Field studies offer insights into the real-world vaccine effectiveness, yet the limited number of studies found in this systematic review highlights the need for more research, namely to evalu- ate vaccine efficacy in field conditions and assess additional parameters such as pathological consequences, economic impact and long-term protection. Developing an effective vaccine against colibacillosis in chickens is complex due to the high heterogenicity of E. coli isolates, elusive disease mechanisms, and absence of definitive markers for pathogenic isolates [17, 20]. This complexity is evident in the limited number of vaccines reaching the commercial market, with conflicting reports about their effectiveness [53, 60]. Future vaccine development requires a multi-dimensional approach, focusing on identify- ing conserved antigens that confer broad protection across different APEC serotypes or incor- porate antigens that confer broad protection across different APEC serogroups. Multivalent vaccines targeting multiple serogroups or incorporating diverse antigens may offer enhanced PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 16 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers efficacy and broader coverage. Exploring innovative technology, such as irradiation [75] or glycoconjugate vaccines [76], may hold promise for improving vaccine delivery and bird pro- tection against colibacillosis. Increased investment in research and development along with collaborative efforts between academic, industry and regulatory agencies, can expedite the translation of promising vaccine candidates into commercial products. Public-private partner- ships and funding initiative can also incentivize vaccine development for diseases, such as coli- bacillosis, with significant impacts on animal health and economic sustainability. Despite providing valuable insights, this review has limitations. The focus was primarily on broiler pro- duction chain, excluding vaccine types and studies related to the E. coli vaccination in layer birds. Subgroup analysis was challenging due to variations in challenge models and experi- mental designs. The meta-analysis results should be interpreted with caution considering the diversity of influencing factors and the reduced number of studies considered. Additionally, studies that did not include mortality, FCR and condemnation at slaughter as assessment crite- ria were excluded, although reproducing colibacillosis in experimentally infected birds is chal- lenging, leading to exclusion of some vaccination studies in broilers. In conclusion, while significant progress has been made in the development of E. coli vac- cines for broilers, challenges persist. The benefits of vaccination have been demonstrated in several studies, with meta-analysis showing a positive effect of live attenuated and CpG-ODN vaccination in reducing mortality. However, further research is needed to enhance under- standing of effective vaccines against colibacillosis, considering factors such as antigen selec- tion, adjuvant choice, delivery method, and use of novel approaches. Supporting information S1 Checklist. PRISMA 2020 checklist. Checklist for the Preferred Reporting Items for Sys- tematic Reviews and Meta-Analyses workflow. (DOCX) S1 Table. Extracted metadata of studies included in the analyses of this paper. (XLSX) Acknowledgments This study was carried out within the COST Action CA18217 European Network for Optimi- zation of Veterinary Antimicrobial Treatment (ENOVAT), supported by the European Coop- eration in Science and Technology (COST) (www.enovat.eu and www.cost.eu). The authors wish to thank all participants supporting the COST Action CA18217—ENOVAT, particularly the members of the Working Group 4 (Antimicrobial treatment guidelines; https://enovat.eu/ wg4/). The authors would also like to thank the whole ENOVAT Drafting Group “Veterinary guidelines on antimicrobial use in poultry colibacillosis” for the wise support and valuable expertise provided. A Swiss Government Excellent Scholarship was granted to RVN for the years 2020–21 when he started working in the Drafting Group. Author Contributions Conceptualization: Surya Paudel, Ilias Apostolakos, Alessandra Piccirillo. Data curation: Surya Paudel, Ilias Apostolakos, Ronald Vougat Ngom, Giuditta Tilli, Helena C. de Carvalho Ferreira. Formal analysis: Surya Paudel, Ilias Apostolakos, Ronald Vougat Ngom, Helena C. de Car- valho Ferreira. PLOS ONE \| https://doi.org/10.1371/journal.pone.0301029 March 22, 2024 17 / 22 PLOS ONEEfficay of vaccination against colibacillosis in broilers Investigation: Surya Paudel, Ilias Apostolakos, Ronald Vougat Ngom, Alessandra Piccirillo. Methodology: Ronald Vougat Ngom, Alessandra Piccirillo. Software: Ilias Apostolakos, Ronald Vougat Ngom. Supervision: Helena C. de Carvalho Ferreira, Alessandra Piccirillo. Validation: Helena C. de Carvalho Ferreira, Alessandra Piccirillo. Visualization: Surya Paudel, Ilias Apostolakos. Writing – original draft: Surya Paudel, Ilias Apostolakos. Writing – review & editing: Surya Paudel, Ilias Apostolakos, Ronald Vougat Ngom, Giuditta Tilli, Helena C. de Carvalho Ferreira, Alessandra Piccirillo. References 1. OECD/FAO. 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10.1371_journal.pone.0299141	RESEARCH ARTICLE Symptoms 6 months following SARS-CoV-2 infection in Nepali women 1☯, Sajani Manandhar2, Bimal Sharma Chalise3, Sagar Deepak S. ShresthaID Kumar Rajbhandari3, Anup Bastola3, Parmananda Bhandari3, Santa Kumar Das4, Pankaj Pant4, Sangita Sharma4, Hari Prasad Kattel4, Roshan Kumar Jha5, Mahendra Raj ShresthaID 5, Anil Shrestha5, Richard R. LoveID 6☯* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Shrestha DS, Manandhar S, Chalise BS, Rajbhandari SK, Bastola A, Bhandari P, et al. (2024) Symptoms 6 months following SARS-CoV- 2 infection in Nepali women. PLoS ONE 19(3): e0299141. https://doi.org/10.1371/journal. pone.0299141 Editor: Babatunde Olanrewaju Motayo, Federal Medical Centre Abeokuta, NIGERIA Received: July 19, 2023 Accepted: February 5, 2024 Published: March 11, 2024 Copyright: © 2024 Shrestha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data collected as part of this research will be available to established investigators presenting appropriate scientific and ethical proposal approval documentation. All de- identified subject data and the oral informed consent text will be available with publication of this communication. The contact person for data access committee, ethics committee, or other institutional requests may be sent is: Sanju Maharjan Programme Manager Health Unit Email: sanju@anweshan.org. 1 Department of Internal Medicine, People’s Dental College and Hospital, Kathmandu, Nepal, 2 New ERA, Kathmandu, Nepal, 3 Sukraraj Tropical and Infectious Disease Hospital, Kathmandu, Nepal, 4 Tribhuvan University Teaching Hospital, Kathmandu, Nepal, 5 Nepal Armed Police Forces Hospital, Kathmandu, Nepal, 6 Independent researcher, Madison, United States ☯ These authors contributed equally to this work. * richardibcrf@gmail.com Abstract In Nepal, over 1 million individuals have tested positive for SARS-CoV-2. We sought to describe the frequency of nonrecovery from this infection at 6 months and associated symp- toms. We conducted a retrospective cohort study of 6142 women who had positive and neg- ative PCR tests for this infection 6 months previously at 3 institutions in Kathmandu. In telephone interviews women provided information on 22 symptoms and their intensities, health status and history, and functional status. Of 3732 women who had tested PCR posi- tive, 630 (16.9%) reported that they were unrecovered. These 630 unrecovered women were distinguished statistically from the 3102 recovered women by more frequent histories of allergies, rheumatoid disease, BCG immunization, Covid vaccination, strep throat and recent URIs, and both weight gain and weight losses of more than 5 kg in the 6 months fol- lowing testing, and stressful events in the preceding year. Fatigue, pain, difficulty remember- ing, shortness of breath, heat and cold intolerance and unrefreshing sleep were reported in 41.9% to 10.5% of these 630 unrecovered women. Six months after confirmed SARS-CoV- 2 infection 16.9% of Nepali women have long-COVID manifested as an immune, metabolic, and hormonal systems disruptive and dysfunction syndrome Introduction In Nepal, over 1 million individuals have tested positive for SARS-CoV-2, and this figure may underestimate actual numbers of cases because of limited testing. Data in patients beyond 3 months from diagnosis of this infection about symptoms, their severities, and timelines of these, are limited, particularly in low- and middle-income countries. In high-income coun- tries, investigative journal reports have suggested that 10 to 30 percent of infected individuals, more commonly middle-aged women, have persistent functional capacity-limiting symptoms, 6 months and beyond the time of initial diagnosis [1–4]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 1 / 12 PLOS ONEFunding: The senior author, Dr. Richard Love, provided financial support for the collection of the data reported here. He was involved in all aspects of the study and this manuscript as indicated in the manuscript. Competing interests: The senior author, Dr. Richard Love, provided financial support for the data collection in this report. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Symptoms 6 months following SARS-CoV2 infectio Persistent symptoms following Covid infection, mimic post-infectious disease syndromes reported for multiple viral illnesses, Lyme disease, infectious mononucleosis, viral hepatitis, Q fever and SARS-1, as well as those of chronic fatigue syndrome (CFS)- Myalgic Encephalomy- elitis (ME), a poorly understood, complex and chronic clinical syndrome affecting women four times more often than men [5–14]. CFS/ME is characterized by at least 6 months of men- tal and physical fatigue, muscle weakness exacerbated by physical and social/mental exertion, malaise, pain, non-restorative sleep, and cognitive impairment [14]. CFS/ME is a clinical diag- nosis, with neuroinflammatory, metabolic, and hormonal physiological features [15]. Because of the significant health, social and economic consequences of persistent symptoms following SARS-CoV-2 infection, we designed a study to describe the frequencies and intensi- ties of the commonest reported symptoms, health history correlates and functional status in a convenience sample of women tested for this infection because of suggestive symptoms 6 months previously, in order to evaluate similarities of suggested non-recovery from this infec- tion to Chronic Fatigue Syndrome/Myalgic Encephalomyelitis [14]. Methods We conducted a retrospective cohort study of women who, between August 3, 2020, and Sep- tember 29, 2021, had self-referred themselves and then underwent PCR tests for SARS-CoV-2 done at three referral institutions in Kathmandu (Sukraraj Tropical and Infectious Disease Hospital, Tribhuvan University Teaching Hospital and Nepal Armed Police Forces Hospital) because they were symptomatic with fever, shortness of breath, cough, or anosmia. Between March 21, 2021, and March 24, 2022, trained Nepali interviewers who were unaffiliated with the testing institutions, called 6481 consecutive test-positive case women approximately 6 months (minimum 4 months) following their PCR test. If a family member answered the call and reported that the individual had died, this was recorded. Three attempts were made to contact these women. Participants were briefed about the study, as explained in the ethics committee-approved protocol, informed verbal consent sought with no incentive offered for participating in the study, before enrolling them in the study. During the same study period, attempts were made to call 5940 randomly selected age (in same 5-year age group) and test date-matched women who had tested negative for SARS- CoV-2 at the same institutions 6 months previously (Fig 1). All successfully contacted and consented case (test-positive) and control (test-negative) women were first screened for histories of reinfections (test positive cases) or infections (test negative controls) with SARS-CoV-2, cancer, tuberculosis, pregnancy, mental illness, and HIV. Individuals who did not report these medical conditions were study eligible. Interviewers then asked participants about their current symptoms, health status history and functional sta- tus. The development and validation of the symptom questionnaire has been reported in another publication [16]. The functional status questions were selected from among 90 items of the Common data elements for evaluation of Chronic Fatigue Syndrome/Myalgic Encepha- lomyelitis by the United States National Institutes of Health [17]. A question about Covid-19 vaccination asked whether the patient had ever had such immunization, details of which were not ascertained; The Chinese Sinopharm BIBP, AstraZeneca, and Johnson and Johnson immunizations were available to these women. Any positive response would very likely have indicated receipt of a vaccine after the testing date report which anchored entry into the study. For the patients who had tested positive, we then asked about their recovery. “Do you feel that you have not completely recovered from your Covid-19 infection?” followed by other questions seeking confirmatory or clarifying responses about “feeling unwell” attributed to Covid-19 infection and feeling unreturned to usual level of health following Covid-19 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 2 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio Fig 1. Consort diagram of study populations. https://doi.org/10.1371/journal.pone.0299141.g001 infection. We defined a subgroup of test-positive cases as “unrecovered from SARS-CoV-2 infection” based on affirmative response to this questioning. We used Pearson’s chi square or Fisher’s exact test for categorical data, and mood’s median test for continuous data in comparisons of characteristics between groups. All p values were two sided; p values of <0.05 were considered statistically significant. Statistical analyses were done with SPSS version 25. The study was approved by the Ethics Committee of the Nepal Health Research Council I. D. # l8l / 202l, on 12 March 2021, and amended on 24th August 2021, and subsequently the Institutional Review Board at Marquette University in the United States. The senior author (RRL) provided the funding for the research and was involved in all aspects of this activity as indicated in the section detailing authors’ contributions. Results The test positive (42.4%) and test negative (59%) potential study subjects who were not study eligible occurred because for the majority of these no contact could be made after 3 phone calls, attributed to: no answer, wrong number, left the country, number change and new num- ber unknown, or among significant numbers of potential subjects, the consent and data- obtaining processes were unimplementable because of the associated time burdens. The greater percentage of test negative individuals who were not study eligible occurred because 7.5% of identified subjects had subsequently developed SARS-CoV-2 infection, and a larger percentage of these individuals refused to acknowledge the subject of the call. The age, test site, test date characteristics of the study eligible and ineligible subjects were similar. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 3 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio Among the 3732 test-positive study eligible women (Median age 35 years, age range: 18–96 years; interview timepoint from diagnosis range 4–8 months, median 6.0 months, mean 5.7 months); 630 (16.9%) (Median age 37 years, age range: 18–77 years, interview timepoint from diagnosis range 4–8 months, median 6.0 months, mean 5.7 months) reported that they had not completely recovered from their SARS-CoV-2 infection. The 2436 test negative study eligible women had similar demographic characteristics: (Median age 35 years, age range: 8–82 years, interview timepoint from diagnosis range 3.3–9.6 months, median 6.0 months, mean 6.4 months). The 630 unrecovered women differed statisti- cally from the recovered and the test negative women with greater median age (37 versus 35 and 35 years) (p = 0.002, and p = 0.03). Table 1 shows the characteristics of the three study populations. Unrecovered women are statistically different from the recovered women with more frequent histories of allergies, asthma, BCG immunization, rheumatoid disease, strep throat, and URIs in the preceding year. Unrecovered women were statistically different also from recovered women in reporting both weight gain and weight losses of more than 5 kg in the 6 months after SARS-CoV2 testing, higher usual (before illness) level physical activity which may suppress hormonal fluctuation, and more frequent stressful events in the preceding year. Age, diabetes, tobacco abuse, hyper- tension history, and BMI statistically significant differences were not observed between these two groups. Unrecovered women are statistically different from the test-negative and reportedly SARS- CoV-2 infection history-free women for all of the characteristics listed in the preceding para- graph except asthma, and in having had more frequent dengue, alcohol consumption in the last 30 days and hypertension histories, and fewer pregnancies in the previous year, more fre- quent history of menstrual cycles in the preceding 3 months, and less frequent surgical and general anesthetic procedures in the last year (Table 1). Diabetes, asthma, and three BMI mea- sures did not differ between these groups. Covid-19 immunization was more frequent in unrecovered women than in either recov- ered or test negative women. Table 2 shows the detailed data about symptoms in the 630 test- positive unrecovered women. Higher frequencies of worst pain, fatigue, shortness of breath, poor sleep and difficulty remembering are seen. The low frequencies of self-reported depres- sion, anxiety, chills, or fever, light-headedness or dizziness, cough, and changes in taste and smell are notable. Table 3 shows the frequencies of the 6 most common symptoms in the three study groups of women. The percentages reported for each of these symptoms among the unrecovered women are significantly different from those reported by both the recovered and test-negative women at p = 0.0001. 423 (67%) of the 630 unrecovered women had at least one of the 3 most common symptoms—fatigue, pain or shortness of breath. Table 4 shows the responses to questions about activities of daily living for the 3 studied groups of women. The unrecovered women report more frequent health problem- interfer- ence with their usual activities, but the percentages reporting these problems in the unrecov- ered group seem remarkably low compared with the percentages reporting symptoms in Table 3. Discussion The principal findings from this study are: • 16.9% or 1/6th of middle-aged Nepali women reported themselves as being unrecovered/ unwell/unhealthy following their PCR test confirmed SARS-CoV-2 infections at a median of 6 months, and reported symptoms and functional status information consistent with these PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 4 / 12 PLOS ONETable 1. Demographic and Medical History Characteristics among 3 groups of women: 1) Test positive women self-assessed as unrecovered, 2) Test positive women self-assessed as recovered, and 3) Test negative women who had remained Covid-19 symptoms free. Symptoms 6 months following SARS-CoV2 infectio Group 1 Test + Unrecovered 630 5.7 37.4 37 15.7 18–77 Group 2 Test + Recovered 3102 5.7 37 35 16.9 18–96 Group 3 Test–and Disease-Free 2463 6.4 37.1 35 15.6 18–92 25.3 13.1 50.4 2.3 1.4 77.3 16.8 10.6 12.7 4.4 2.3 6.2 0.2 4.0 0.2 11.6 20.5 5.4 15.2 91.6 0.8 75.7 2.9 5.1 3.2 0.8 12.3 24.9 25 13.1 48.2 3.3 1.4 73.9 8.9 4.5 10.2 4.7 1.1 3.2 0 3.4 0.9 4.2 11.4 3.9 12.9 88.1 1.3 71.4 2.2 3.6 4.3 1.5 3.9 39.7 24.9 11.8 46.2 5.9 1.5 71.4 10.6 6.2 9.9 4.2 1.8 4.1 0 5.1 0.6 6.7 12.5 2.7 18.1 87.8 2.6 60.9 1.4 4.0 15.0 6.3 5.1 38.1 Demographic and Medical History Characteristics Total Time from PCR test (mean months) Mean age (years) Median age b, c (years) Patients aged 50 and > (%) Age range (years) Calculated BMI (kg/m2) BMI > = 30 (%) BMI> = 25 (%) Pregnancy in year before Covid test (% yes)f Pregnancies (mean #) Menstrual period in preceding 3 months before interview (% yes) d Weight increases by 5 or more kg since testing (% yes) e, f Weight decreases by 5 or more kg since testing (% yes) e, f Hypertension history (%) c Diabetes history (%) Asthma history (%) a Allergies history (%) b, d Hepatitis B infection history (%) Family history of serious infectious diseases (% yes) Tobacco smoker (% yes) Stressful event in last year (%) b, d 2 or more URIs in last year (%) b, d Alcohol in last 30 days (% yes) f Child under 5 in home (% yes) c BCG vaccination in past (% yes) a, c Influenza vaccination in in year4 (% yes) Covid vaccination e, f Dengue history d Influenza in last year (% yes) Surgical procedure in last year f (% yes) General anaesthetic in last year f (% yes) Strep throat last year (%) e, f Physical activity <3h/week (usual level before Covid infection) e, f a Statistically significant difference between group 1 and 2 @ p = 0.05. b Statistically significant difference between group 1 and 2 @ p = 0.01. c Statistically significant difference between group 1 and 3 @ p = 0.05. d Statistically significant difference between group 1 and 3 @ p = 0.01. e Statistically significant difference between group 1 and 2 @p = 0.001. f Statistically significant difference between group 1 and 3 @p = 0.001. https://doi.org/10.1371/journal.pone.0299141.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 5 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio Table 2. Presence and intensity of 22 symptoms at a median of 6 months from diagnosis in 630 women self- assessed to be incompletely recovered from SARS-CoV-2 infection. Symptoms Worst pain/ache Pain/ache locations: Muscles = 25 Back/Whole body = 82 Head = 55 Joints = 20 Chest = 75 Feeling sad or depressed Difficulty in word-finding Light-headedness or dizziness on standing Lack of motivation Mental and physical fatigue/tiredness Poor, unrefreshing sleep Fever and/or chills Mental confusion or disorientation Difficulty thinking and concentrating Shortness of breath Reduced physical activity Increased sensitivity to sound or light Rapid or irregular heartbeat Cough Anxious or worried Difficulty remembering Change in sense of smell Increased fatigue the day after more-than-usual physical or social activity Change in sense of taste Numbness in fingers or toes Heat or cold intolerance https://doi.org/10.1371/journal.pone.0299141.t002 0 392 1 46 2 84 3 63 4 31 5 14 594 600 572 587 366 564 618 594 586 454 576 583 581 593 571 523 603 592 615 597 560 4 8 9 6 25 10 2 2 5 35 7 7 8 6 10 13 6 2 3 9 12 11 7 27 11 116 27 3 13 18 71 25 8 14 17 16 44 11 16 4 11 28 7 7 13 18 79 18 5 15 13 45 11 15 16 10 15 36 6 9 6 9 19 10 7 7 7 4 1 2 1 31 13 7 1 3 7 19 6 14 7 3 12 6 2 6 0 4 9 4 1 3 1 6 5 3 4 1 6 8 2 5 2 0 2 Table 3. Frequencies at a median of 6 months after SARS-CoV-2 PCR testing of the 6 most commonly reported symptoms in successfully interviewed women in 3 groups: 1) Test positive women self-assessed as unrecovered, 2) Test positive women self-assessed as recovered, and 3) Test negative women who had remained Covid-19 symptoms free. Demographic and Medical History Characteristics Number and % reporting symptoms Total Cases Fatigue Pain Shortness of breath Difficulty remembering Heat or cold intolerance Poor, unrefreshing sleep https://doi.org/10.1371/journal.pone.0299141.t003 Group 1 Test + Unrecovered 630 264 (41.9) 238 (37.8) 176 (27.9) 107 (17.0) 70 (11.1) 66 (10.5) Group 2 Test + Recovered 3102 39 (1.3) 39 (1.3) 22 (0.7) 25 (0.8) 9 (0.3) 15 (0.5) Group 3 Test–and Disease-Free 2436 86 (3.5) 96 (3.9) 40 (1.6) 37 (1.5) 20 (0.8) 37 (1.5) PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 6 / 12 PLOS ONETable 4. Functional status for activities of daily living at a median of 6 months after Covid-PCR testing in successfully interviewed women in 3 groups: 1) Test posi- tive women self-assessed as unrecovered, 2) Test positive women self-assessed as recovered, and 3) Test negative women who had remained Covid-19 symptoms free. Demographic and Medical History Characteristics Number and % reporting symptoms Symptoms 6 months following SARS-CoV2 infectio Total Cases Health Problems interfering with: Doing the usual work done before your Covid-19 testing 6 months ago a, b Walking upstairs a, b Doing household tasks involving lifting, carrying, or cleaning Taking care of children or adults with health problems a Group 1 Test + Unrecovered 630 13 (2.4) 17 (2.1) 75 (11.9) 15 (3.2) Group 2 Test + Recovered 3102 9 (0.3) 18 (0.7) 128 (4.1) 35 (1.4) Group 3 Test–and Disease-Free 2436 36 (1.7) 31 (1.4) 143 (5.9) 37 (2.2) a Statistically significant difference between groups 1 and 2 @ p<0.05. b Statistically significant difference between groups 1 and 3 @ p<0.01Data about symptoms for test positive recovered and test negative women show no striking excesses of symptoms in the previously infected patients. https://doi.org/10.1371/journal.pone.0299141.t004 assessments. Both groups of demographically matched recovered and never-infected women, interviewed contemporaneously, reported low frequencies of the same major symp- toms. The recovered and uninfected groups of women were very similar in their frequencies of health characteristics—hypertension, diabetes, asthma, allergies, tobacco abuse, strongly suggesting that the specific health characteristics that were different in the unrecovered women are genuinely associated with this condition. These data indicate that non-recovery and chronic illness after 6 months is a consequence of SARS-CoV-2 infection. The 6-month timepoint defining such illness is the metric used to diagnose chronic fatigue syndrome [14]. • The health characteristics data suggest a rich picture of immune, metabolic, and hormonal factors associated with persistence of symptoms and unrecovered status. Previously sug- gested increased frequencies of histories of asthma and allergies were found, but also greater immune system activation or susceptibility histories of dengue, and more frequent strep throat and URIs in the last year, and more frequent BCG vaccination were reported. While BCG vaccination has been suggested to produce protective “trained immunity” beneficial in reducing the severity of Covid-19 illness, a randomized trial of BCG vaccination in health workers to protect against Covid-19 found no evidence of benefit, with a trend suggesting increased risk of infection from this vaccination [18,19]. In the data reported here, BCG vac- cination was associated with increased risk for non-recovery at 6 months. Covid vaccination history was more commonly reported in unrecovered women, but absent further details, a cause-and-effect relationship cannot be suggested. Important and significant metabolic factor differences were reported by the unrecovered women with more frequent reported weight losses and gains of greater than 5 kg. in the period since diagnosis, and more frequent major stressful events in the previous year. Further, it is notable that in these Nepali populations, hypertension, diabetes, and particularly BMI/obesity differences between recovered and unrecovered patients were not observed. • Some of the differences between unrecovered women and women who never developed SARS-CoV-2 infection are notable. The unrecovered women had less frequent pregnancies in the previous year, more frequent history of menstrual cycles in the preceding 3 months, and less frequent surgical and general anesthetic procedures in the last year, as well as more frequent reported weight losses and gains of greater than 5 kg. in the period since diagnosis, and more frequent major stressful events in the previous year. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 7 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio These conclusions should be interpreted in the following contexts. First, the women studied were a convenience sample of individuals living in the Kathmandu valley, who self-referred themselves for SARS-CoV-2 PCR blood testing because of symptoms of infection. Thus, this study group is not randomly selected from the total Nepali population of symptom and symp- tomless individuals and individuals capable and incapable of seeking testing. Further, few of the studied women were likely to have been hospitalized with Covid and have incurred greater specific organ injuries of lung, heart, brain, and blood coagulation tissues and systems. Severity of illness was not otherwise assessed. During this period, the local hospitalization rates for women with Covid-19 were under 5% and limited population variant testing suggested that the prominent variants were alpha variant and delta variant [20]. The studied women were mostly urban area residents, in a country where 80% of inhabitants live in rural areas, and were likely better educated, generally younger, and healthier at the time of diagnosis than their fellow women countrywomen. These considerations signify that the studied populations are selected and are not representative of the total population of Nepal. The details of how items are phrased in Nepali may be critical in some circumstances. Finally, we were able to contact and successfully obtain data from 59% of test positive and 42% of test negative cases or family members. While fractions of the non-recruited women were because of specific study eligibility criteria, larger proportions were non-recruited because of the specific operational conditions of the study—interviews solicited and conducted by tele- phone, the process of acquiring informed consent, the absence of incentives, and the time commitment. These non-interviewed women were demographically similar to those who were successfully interviewed. The strengths of this study lie in the large numbers of women from three testing sites stud- ied, and in the facts and circumstances that: 1. A control group of SARS-CoV-2 test negative and by history never-affected women was studied; these women reported low levels of any symptoms, with much lower frequencies than the unrecovered women, which data offer com- pelling evidence that the association of perceived unrecovered/unwell/unhealthy status and symptoms with SARS-CoV-2 infection is strong. 2. Assessment of recovered status was made by probing questioning about non-recovery (implying a chronic situation), presence of unwellness, and persistent perceived adverse change in health status (unhealthy) following SARS-CoV-2 infection, and symptom data were obtained directly from the patients them- selves. 3. The case status was defined by a laboratory PCR test. 4. The symptoms’ descriptions were for periods of 3 days. And: 5. The symptom questionnaire had reliability and validity information suggesting reasonable credibility for the study population investigated, and the findings are internally consistent—the non-recovered patients clearly report more specific symptoms of important intensities and associated interference with activities of daily living (Tables 2 and 4) [16]. These strengths all support an argument that these observational data for the populations studied are not biased and are of high quality [21]. Finally, these data are important because they describe illness experience in women from a low-middle income coun- try where the frequency of symptomatic and serious illness with SARS-CoV-2 has been sug- gested to be significantly lower than has been observed in high-income countries, and multiple confounding factors such as lower self-reported levels of depression and anxiety, less mood- altering and aspirin drug use, and lower alcohol consumption are not present [22]. Comparing the literature regarding nonrecovery and symptoms 6 months following con- firmed Covid infection is problematic because the majority of reports address patients from western countries (only 2 of 9 studies in a systematic review -reference #4- had patients from non-western countries), concern previously hospitalized patients with severe acute disease, are of older patient groups, and with the exception of a recent study in 3762 volunteers, are small [1,4,23]. While the current report is not of a population-based sample, it comes closer to PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 8 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio describing what a representative group of low- and middle-income country Covid sufferers are likely to be experiencing 6 months after their diagnoses than has been reported to date, spe- cifically in addressing the issue of patient perceptions of their recovery status. The long term most common symptoms’ picture in the reported literature is very similar to that reported here: fatigue, CNS functional problems, and sleep disturbances [1,4,23]. Further what has been the subject of limited study to date regarding long Covid sufferers is the associated co-morbid conditions and metabolic and endocrine changes. The data reported here but begin to untan- gle the full spectrum of these associated factors. Long Covid symptoms and underlying physiologic mechanisms Despite the richness of the reported data, the specific symptom frequencies reported by the non-recovered women do not provide themselves adequate information to propose a defini- tion of “long-COVID” and suggest that somehow our questions missed identification of criti- cal symptoms. Malaise, a defining symptom in chronic fatigue syndrome, was not included because of its problematic translation into Nepali [16]. Questions about energy levels, muscle weakness, appetite, and details of exercise capacities were also omitted. The assignment of non-recovery case status however, along with the specific symptoms data, validate this status assignment, and suggest metabolic and endocrine hormonal systems disruption and that future research should explore details of symptoms associated with these systems. Importantly, these data suggest the parameters of a physiological model for the develop- ment of long- COVID similar to that proposed for chronic fatigue syndrome [14,15]. Long- COVID present at 6 months from time of infection, as seen in these Nepali women, is charac- terized by: 1. Immune system dysfunctional responses associated with allergies, asthma, and BCG vacci- nation histories; increased frequency of URIs, strep throat and dengue with neuroinflam- matory symptoms of pain, difficulty remembering (suggested to be reflective of microglial or dendritic damage), and poor sleep, in the absence of fever and specific signs of active infection. 2. Metabolic and hormonal dysfunction with feelings of being unrecovered, unwell, and per- sistently unhealthy, which are incompletely described by usual specific symptom assessments. 3. Metabolic disturbances with specific symptoms of mental and physical fatigue, shortness of breath (without fever or cough, suggesting exercise capacity loss), pain, poor sleep, difficulty remembering, and heat and cold intolerance; and associated significant weight changes, decreased physical activity, and history of stressful events. 4. Endocrine-hormonal change hypersensitivities associated with recent pregnancy and men- strual cycling, and heat and cold intolerance. Investigations of CFS/ME have suggested that it is a hypometabolic syndrome, and long- COVID has been hypothesized to be a one carbon stress syndrome [24,25]. Together the cur- rent and these reports suggest that long-COVID patients should be investigated for serum ser- ine and markers of oxidative stress such as glutathione, as well as total serum B12, holo- transcobalamin (holoTC), the metabolic markers methylmalonic acid and homocysteine, and plasma formate. Physiological and dynamic assessment of multiple hormones are also sug- gested: ACTH, cortisol, TRH, TSH, thyroid, insulin, epinephrine, serotonin, melatonin, growth hormone, and aldosterone as examples [26]. A recent rigorous small study suggests that Covid infection and type 1 interferon-driven inflammation decrease serotonin levels, and PLOS ONE \| https://doi.org/10.1371/journal.pone.0299141 March 11, 2024 9 / 12 PLOS ONESymptoms 6 months following SARS-CoV2 infectio that this change explains many of the major long Covid symptoms. [27] Interventions with ACTH, Vitamin B-12, folate, glutathione, and serine, directed to re-setting metabolic and hor- monal systems are suggested by these interpretations and models, as have been suggested for CFS/ME [15,28]. Conclusion Six months after PCR test-confirmed SARS-CoV-2, 16.9% of Nepali women reported being unrecovered, and/or unwell, and/or unhealthy, with associated dominantly metabolic and hor- monal systems symptoms, defining for them long-COVID. For these women immune system over-activation factors and dysfunction were associated with this metabolic and endocrine- hormonal disruptive condition. Supporting information S1 Dataset. (CSV) Author Contributions Conceptualization: Deepak S. Shrestha, Richard R. Love. Data curation: Deepak S. Shrestha, Sajani Manandhar, Richard R. Love. Formal analysis: Sajani Manandhar, Richard R. Love. Funding acquisition: Richard R. Love. Methodology: Deepak S. Shrestha, Richard R. Love. Project administration: Deepak S. Shrestha, Bimal Sharma Chalise, Pankaj Pant, Roshan Kumar Jha, Richard R. Love. 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10.3389_fmolb.2022.1074714	B A C Figure S1. Molecular Dynamic simulation of sacsin-ATP complex. (A) Cluster analysis of the loop; the most populated cluster is shown in red, the second in orange, the third in yellow, the fourth in green and the least populated in blue. (B) RMSD analysis of protein C-alpha (blue) and ligand (magenta). (C) Protein-ligand interaction diagram. Interactions are categorized into four types: Hydrogen Bonds (green), Hydrophobic (lilac), Ionic (pink) and Water Bridges (blue). The stacked bar charts are normalized over the course of the entire trajectory. Values over 1.0 are possible as some protein residue may make multiple contacts of same subtype with the ligand. Figure S2. Hsp90 middle and C-terminal regions and corresponding segments of sacsin that do not superimpose. PyMol cartoon of Hsp90 (yellow and blue, residues 386 to 677) and sacsin (green and red, residues 487 to 772). The blue long helix of the Hsp90 middle domain and that of sacsin are superimposed. The rest of the structure shows no immediately recognisable common topology. B A C Figure S3. Molecular Dynamic simulation of sacsin-geldanamycin complex. (A) Cluster analysis of the loop; the most populated cluster is shown in red, the second in orange, the third in yellow, the fourth in green and the least populated in blue. (B) RMSD analysis of protein C-alpha (blue) and ligand (magenta). (C) Protein-ligand interaction diagram. Interactions are categorized into four types: Hydrogen Bonds (green), Hydrophobic (lilac), Ionic (pink) and Water Bridges (blue). The stacked bar charts are normalized over the course of the entire trajectory. Values over 1.0 are possible as some protein residue may make multiple contacts of same subtype with the ligand. B A C Figure S4. Molecular Dynamic simulation of sacsin-AUY922 complex. (A) Cluster analysis of the loop; themost populated cluster is shown in red, the second in orange, the third in yellow, the fourth in green and the least in blue. (B) RMSD analysis of protein C-alpha (blue) and ligand (magenta). (C) Protein-ligand interaction diagram. Interactions are categorized into four types: Hydrogen Bonds (green), Hydrophobic (lilac), Ionic (pink) and Water Bridges (blue). The stacked bar charts are normalized over the course of the entire trajectory. Values over 1.0 are possible as some protein residue may make multiple contacts of same subtype with the ligand.
10.1371_journal.pone.0299456	RESEARCH ARTICLE Automated code development based on genetic programming in graphical programming language: A pilot study Pavel Kodytek, Alexandra BodzasID, Jan Zidek Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, Ostrava, Czech Republic alexandra.bodzas@vsb.cz Abstract Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software devel- opment and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers’ common workflow and decrease the time to market. Even though many code- generating algorithms have been proposed in textual-based programming languages, to the best of the authors’ knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based auto- mated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demon- strated an ability to create satisfying functional and errorless solutions that met all user- defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code’s execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users. Introduction Graphical programming refers to a category of programming languages that use visual repre- sentations, such as icons, symbols, diagrams, or other graphical elements, to facilitate the design and creation of software applications. Unlike traditional text-based programming a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kodytek P, Bodzas A, Zidek J (2024) Automated code development based on genetic programming in graphical programming language: A pilot study. PLoS ONE 19(3): e0299456. https:// doi.org/10.1371/journal.pone.0299456 Editor: Govind Vashishtha, Wroclaw University of Science and Technology: Politechnika Wroclawska, POLAND Received: December 15, 2023 Accepted: February 10, 2024 Published: March 7, 2024 Copyright: © 2024 Kodytek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The code supporting this study is available from https://zenodo.org/ records/10542753. Funding: This work was supported by the “Student Grant System” of VSB-TU Ostrava, project number SP2022/88. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 1 / 20 PLOS ONEA pilot study languages, where the code, i.e., textual commands, are written in text editors or integrated development environments, graphical programming allows users to interactively create pro- grams by manipulating and connecting graphical elements. Since graphical programming does not require a strong understanding of the language and its syntax, these languages are often designed to make programming more intuitive and accessible to non-programmers. Automated code development in LabVIEW or any other graphical programming environ- ment is inspired by reversing a standard software development model. This engineering design process can be perceived as a methodical series of steps that allow programmers to create func- tional products and processes [1]. This process can be highly repetitive, and certain stages often require multiple iterations before proceeding to the next step. Since requirements-based testing and validation, also known as test-driven development, is a common and essential part of software development [2] and a standard procedure for programmers who must verify the code’s functionality, by reversing this process, we can automatically generate code instead of developing programs or unit testing frameworks. In this reverse scenario, we can automatically create programs based on the predefined input requirements, and by backpropagating the input- output differences, we can modify the generated code until all requirements are satis- fied. By transforming this task into a fully automated process, we can therefore fundamentally reshape the development principles for basic programs, and instead of employing human experts for code development and test report validation, we can utilize computers to generate programs and evaluate test reports. Automated code generation in textual-based environments has been used in the software industry for decades [3], and especially in recent years, many novel program generation approaches have been proposed and evaluated on common benchmark problems [4]. These approaches to code generation employ various techniques, including artificial intelligence, machine learning, or genetic evolution methods, to repair or generate efficient and error-free codes. A significant research direction in this field involves the use of machine learning, espe- cially neural network models. Most of these studies employed recurrent neural networks [5– 7], transformer models [8,9], or convolutional neural networks [10,11], which are able to learn patterns and structures from given code samples. By training these models on large code repositories, they can capture syntax, semantics, and even higher-level programming con- structs, which enables them to generate usable code [12]. Another popular direction for automated code generation includes evolutionary algo- rithms, particularly genetic algorithms [13] and genetic programming. Genetic programming (GP) is technically regarded as a special evolutionary algorithm inspired by Darwin’s evolu- tionary theory, where algorithms are characterized by the existence of a population of individ- uals exposed to various environmental circumstances that lead to natural selection. [14] Unlike genetic algorithms, in genetic programming, the individuals in the population are computational programs, which are typically represented as sequences of instructions or expression trees. [15] These populations are iteratively transformed and evolved over genera- tions into other populations by applying genetic operations to aproximate or find a solution to a specific problem. [16] To measure the degree of adaptation of individuals to the environ- ment, usually a fitness function is employed [16]. However, also other alternative measures could be utilized, for example, Wasserstein distance [17] for probability distribution outputs, or Single-valued Neutrosophic Cross-Entropy [18], which measures the dissimilarity in cases of uncertain or incomplete information. This approach to code generation is primarily useful for optimizing codes for specific tasks or constraints and usually require user-defined input/ output examples. However, studies using combinations of input/output examples with natural language descriptions can also be found [19]. One of the biggest advantages of using genetic algorithms based on user-defined requirements, which is the primary focus of this study, is the PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 2 / 20 PLOS ONEA pilot study potential usage of such a system by non-programmers. A well-known case of such a program is, for example, Flash Fill, which is one of the most used data tools integrated into Microsoft Excel that is able to automatically fill out the data in sheets by using predictive technology. [20] According to the comprehensive survey on program synthesis with evolutionary algorithms conducted by Sobania et al., the most frequently used approaches for code generation involve stack-based GP, using mostly Push as a representation language, grammar-guided GP (includ- ing tree-based and linearized grammar-based approaches), and linear GP [21] Although the stack-based GP approach makes up the largest proportion of the identified studies (37 in- scope papers), due to its most common language representation (Forth, Push, or Postscript programming language), it is not considered relevant in real-world software projects, espe- cially from the perspective of software development [21]. Other meta-heuristic algorithms introduced in specific fields of automated programming, especially regarding optimization tasks, may include the particle swarm optimizer, gravitational search algorithm, artificial bee colony algorithm, grey wolf optimizer [22], or differential evolution [23] and slime mould algorithm [24,25]. Even though numerous code generation methods have been proposed for textual-based lan- guages in the last few decades [26,27], to the best of the authors’ knowledge, none of these methods have been implemented in graphical-based programming languages, especially Lab- VIEW. Moreover, the implementation of the actual state-of-the-art methods in a graphical lan- guage is rather inefficient and almost impossible without using text-to-object converters since all existing algorithms are primarily designed for text-based languages. Due to the fact that most of the graphical programming environments, including LabVIEW, do not even have such converters or do not support textual compilers, the main aim of this study is to create a proof of concept for a yet unexplored automated GP-based framework for code generation in the graphical programming environment LabVIEW. The proposed code generation frame- work fully depends on the input requirements, which can be defined even by users without any prior programming knowledge. The entire framework was tested on four basic benchmark problems encompassing fundamental data types, such as string, boolean, numeric, and their combinations. The achieved evaluation results not only demonstrated the algorithm’s ability to generate functional programs in string, numeric and boolean domains, but also proved that the algorithm is able to work in a solution space that isn’t strongly typed, and therefore, can lead to universal solutions. Although the generated solutions showed a significant degree of complexity in comparison to programs written by SW developers, the outcomes of this study prove the feasibility of this idea, where even non-programmers and low-skilled users could efficiently generate programs. The proposed approach in this study is, therefore, the first of its kind in this research field and may serve as a good starting point and inspiration for research- ers and programmers working with graphical programming languages. Since graphically ori- ented programming is recently on the rise with the growing industry 4.0, where PLC-based systems and fast test-developing environments such as LabVIEW or Teststand play a signifi- cant role, introducing such algorithms to these environments would help to solve many human-restricted problems. Materials and methods LabVIEW, as a graphical development environment, utilizes a different code representation in comparison to traditional text-based programming languages. This representation involves indexing functions, which cannot be translated directly into human-readable and understand- able text as in typical text-based environments. This difference in representation complicates the usage of text-based language prediction models like GPT-3 [28] or other commonly used PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 3 / 20 PLOS ONEA pilot study models for generating code. Hence, in this scenario, genetic programming appears to be the most suitable approach, which evolves and optimizes graphical structures toward user-defined requirements. The following chapters are devoted to the LabVIEW programming environ- ment and its language syntax and provide a detailed description of the proposed code genera- tion framework. Labview development environment language syntax LabVIEW, short for Laboratory Virtual Instrument Engineering Workbench, is a visual pro- gramming environment commonly used in measurement, automation, or control applica- tions. Unlike traditional text-based programming languages, LabVIEW utilizes a graphical data flow programming paradigm where the code is represented in the form of interconnected graphical elements called virtual instruments, denoted as VIs, that can be perceived as func- tions or subroutines in conventional programming languages. The abstract syntax is typically represented as a data flow graph or a block diagram, where each VI or block is a self-contained unit of code or a native function. The execution of the program is then conceptualized as a flow of data, where variables are propagated via the wires through a sequential series of con- nected functions, which execute as soon as all data is available on the inputs. The core of each VI is divided into two interdependent parts: the front panel, which repre- sents the user interface, and the block diagram, i.e., the code, responsible for the program’s logic and functionality. The visual representation of both parts of a simple part of a code is demonstrated in Fig 1. If we analyze this part of the code from the programmer’s viewpoint, the created method in LabVIEW carries the name add_pi and has one numeric input as a parameter. The output of this function is then an input value increased by the value of π. However, in a much deeper sense of the language, the created program contains four basic objects (two input objects, a function, and an output), where each object is represented by a specific icon in the block dia- gram. These objects can be considered instances of objects in object-oriented programming, and therefore each of the four elements contains its own private data and methods (such as Fig 1. The created front panel and block diagram for a virtual device called "add_pi". https://doi.org/10.1371/journal.pone.0299456.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 4 / 20 PLOS ONEA pilot study captions, labels, or set-value methods). Since every single object inserted in the block diagram is a child of a prime class called LabVIEW Object, each element created in the block diagram and also on the front panel is a child of this class. Hence, if we are able to refer to any object by using a pointer, we can also programmatically change its publicly accessible data or invoke its publicly accessible methods. It is also possible to get this reference through all objects con- tained in the data of our "main" object, which is our program. Through the reference, we are therefore able to obtain references to all front panel or block diagram objects. Additionally, apart from the four main objects, there are three secondary objects in the given diagram that are presented as connections or wires between the functions, which ensure the data flow of the program. The main difference between a main object and a secondary object is that a secondary object reference can only be obtained from the main object reference to which the secondary object is assigned (connected). Even though for most LabVIEW pro- grammers, this object is just a simple wire connecting two blocks or functions, it is a sophisti- cated class that, in its private parameters, stores information about its description, the connected terminals, state, program pauses, connection points, or references to the main objects. Moreover, this class allows navigating the program from one place to another by using a set of obtained references to various block diagrams or front panel objects. Although most programmers do not use this information and do not need to understand these concepts, it is important knowledge that allows performing automated program develop- ment tasks in the LabVIEW programming environment. LabVIEW scripting. An essential LabVIEW feature that was used in this work is a Lab- VIEW VI Scripting software add-on, which provides a set of functions used to access advanced private methods and information that is normally not available to the user. This includes func- tions that are used to perform code analysis, editing, or even code creation. Nowadays, many leading developers use these features to create templates or automatically generate frameworks for other developers; however, this work deals only with methods allowing to programmati- cally insert and connect objects within the block diagram. The particular LabVIEW scripting processes employed in this work are depicted in Table 1. As it might be seen, scripting is a diverse and powerful tool for modifying the final program, and by combining these functions with built-in LabVIEW functions responsible for the run of the program, assignment of the values to inputs, and their reading or evaluation, we can obtain a tool that is an essential part of automatic program development, and which enables us to cre- ate or modify the particular programs. The proposed framework In this study, we approach the problem of automated code development in a similar way as human evolution works. Each generated VI, which is a final representation of a program, can Table 1. Basic types of LabVIEW scripting functions used for the purpose of automated code generation. Process Type Navigation Detail Function Between function and wire Between wire and function New VI Trace dependencies and connections from the function to the wire Trace dependencies and connections from the wire to the function Creates (insert) new method/function Creation Object on a front panel/block diagram Creates (insert) new objects (native LV functions, controls) in the program Wire Creates (insert) connections between functions Object positioning Changes the position of objects inside a block diagram https://doi.org/10.1371/journal.pone.0299456.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 5 / 20 PLOS ONEA pilot study Fig 2. The entire sequence of the proposed code generation framework. https://doi.org/10.1371/journal.pone.0299456.g002 be seen as a human phenotype that represents the complete characteristics of an individual from the generation. Since only the creation and navigation scripting methods are utilized for the code generation, the whole code information can be obtained in two sets, the Wirer and the Creator, which are thoroughly described in the chapter Genetic structure of the program. The process of the proposed code generation approach, from requirement definition to the formation of a new generation is depicted in Fig 2 and described step by step in the subsequent chapters. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 6 / 20 PLOS ONEA pilot study Genetic structure of the program. The Wirer and the Creator can be perceived as chro- mosomes in human cells, and just as human chromosomes, the Wirer and the Creator set con- tain a collection of genes, where each gene, in our case, carries specific information about the structure (the created functions or other objects such as constants) or binding (connections between the program building blocks). Therefore, whereas the creator is responsible for the insertion of functions on the block diagram by using the creation scripting method, the Wir- erer is responsible for tracing the functions’ inputs and outputs and for creating connections between the particular functions on the block diagram (performed by utilizing the navigation and creation LV scripting methods). The proposed complete genetic structure is demonstrated in Fig 3. Similarly, as the individual genes create cell characteristics by using sub-alleles, the genes contained in the Creator and the Wirer create the final form and the behavior of the generated code. Each gene in both sets, therefore, contains an identifier in the form of an unsigned 8-bit integer that is used to assign the gene a specific function (such as a mathematical operation, equal function, or string function) and data in the form of a byte array, which contains all nec- essary information about the particular element defined by the identifier. For instance, if the gene is mapped as a string constant (defined by the identifier number 0), then the Fig 3. The proposed genetic structure of programs. https://doi.org/10.1371/journal.pone.0299456.g003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 7 / 20 PLOS ONEA pilot study corresponding data within the gene is converted from a byte array back to a string. On the other hand, in the case of a mathematical function (identifier 3), only the first byte of the data carries information about the pointer to a specific numeric function, such as addition (value 0), subtraction (value 1), etc. A detailed explanation of how genetic identifiers are mapped into specific functions is provided in Chapter Initialization process, Table 4. By employing byte array data representation for the data within the Wirer and the Creator, we can furthermore meet the possible requirements for the infinite number of genes in both sets, which ensures no limitations by unnecessary conditions during the program development. Although the Wirerer contains the same genetic structure as a creator, its only function is to create connections between the existing functions. To connect an output of one function with an unconnected input of another function, the navigation LabVIEW scripting method only requires references to the corresponding input and output terminals of the involved func- tions. Due to this fact, the Wirer does not use the information obtained in the identifier, and only the connector’s information is extracted from the data, where the first and second ele- ments of a byte array are converted to an integer value representing the indexes of the corre- sponding output and input terminals. Definition of system requirements. To ensure that the system functions in accordance with the abovementioned goals, it is necessary for the user to have the possibility of defining the input and output variables of the system. According to the requirement definition, a funda- mental requirement for our system was to maintain human readability and be easy to under- stand so that even non-programmers could generate codes. The proposed system in this work supports four basic data types, namely boolean (Bool), string (Str), double (Dbl), and integer (Int). All the user’s functionality requirements are stored in the form of an array of elements, where each element is, like each gene, represented by a control name and an array of values for a given parameter. These parameters are then, at the beginning, loaded into the system and stored in a functional global variable (a frequently used LabVIEW design pattern allowing con- trolled access to data) so they can be reused in the evaluation process. Since we used a byte array for storing the data within the genes, there is no need to address the issue of different functions’ data type compatibility requirements at this program level. The evaluation function then accepts an array of inputs and outputs. The input interface for a common user is realized by using the freely accessible library CLAUDIE_XLSX (Compact Library and Universal Data Import Export xlsx), which allows writing and reading data from Microsoft Excel [29]. The file structure was selected in a way that facilitates the user’s ability to enter his requests in the form of input combinations and their required outputs. An example of an implemented structure for a specific task is demon- strated in Table 2. The first file line defines whether the element is an input (control) or an output (indica- tor), the second line defines its data type (bool, dbl, int, str), the third line declares the name Table 2. Input values example for a numerical problem. in dbl Num_1 5 6 15 17 36 in dbl Num_2 10 11 42 43 44 https://doi.org/10.1371/journal.pone.0299456.t002 out Dbl Num_Ind 15 7 57 60 80 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 8 / 20 PLOS ONEA pilot study Table 3. The used population parameters with the corresponding parameter values. Parameter Population size Complexity Identifier Value Fixed value Randomized with Gaussian distribution–mean selected and sigma 0.1 Randomized in a value range of 0–8 according to Table 4 with a uniform distribution Data within the Creator Generated byte array of U8 values with uniform distribution. Data within the Wirerer Two random U8 values with a uniform distribution in an array https://doi.org/10.1371/journal.pone.0299456.t003 of the element and the following lines create the inputs together with the desired output values. Initialization process. The initialization of default parameters is a critical and challenging problem in evolutionary algorithms. Within this step, the algorithm creates the initial popula- tion by generating individuals with constrained random parameters for the subsequent evolu- tionary process. All population parameters with particular value ranges are depicted in Table 3. Since it is usually not very common to control the size of the population [30], this parame- ter is in this work set to be optional for the users, and for all performed tests, it was set to a con- stant value, usually 1000. The second adjustable population parameter used in this study is the complexity parameter (set in the range from 3 to 20, depending on the problem’s complexity). This number represents the mean value of a Gaussian curve, which is formed by the number of genes generated for each descendant in a population. In other words, the number of genes in a generation likely corresponds to a Gaussian curve with a defined standard deviation of 0.1 and a mean equal to the selected complexity value. This "randomness" ensures a better distri- bution of the generated code possibilities already in the first iteration and prevents the algo- rithm from getting stuck at the local minimum [31]. To assign the gene a LabVIEW object (function, control, or a constant), we proposed a mapping table (refer to Table 4), which takes a randomly generated number of an identifier in a defined range and, based on its value, assigns the identifier a specific LabVIEW building block represented by a native LabVIEW ID class number. These LabVIEW ID numbers can be perceived as inner environment identifiers for particular parent classes, where each LabVIEW building block belongs to a specific parent class. Even though the knowledge of the predefined LabVIEW classes is not required for common programming tasks, it is part of the basic con- cept of the environment. The problem that we encountered with this LabVIEW inner categorization was a multiple occurrence of the class with an identifier of 16400 for significantly different functions. Even Table 4. An overview of the used identifiers and their relation to specific block diagram objects and data usage. Gene identificator Num ID in LV Building block/Object Data usage 0 1 2 3 4 5 6 7 8 16392 16395 16476 16400 16390 16422 16400_1 16429 16400_2 String Constant Control Terminal Bundler Math. Function Converts byte array to string Gene data not used Gene data not used Maps the first byte of the data array to a mathematic function Digital Numeric Constant Converts byte array to a number Boolean constant Bool Function Equal Function Select Function Gene data not used Maps the first byte of the data array to a boolean function Gene data not used Gene data not used https://doi.org/10.1371/journal.pone.0299456.t004 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 9 / 20 PLOS ONEA pilot study though the parent is identical for different descendants, such as mathematical functions, bool- ean functions, and the function select, which is our case, these functions do not have the same inputs, outputs, or meaning. To solve this problem, we divided these descendants into individ- ual subclasses. This means that the gene identifier doesn’t contain the inner LabVIEW class number but instead contains an extended unique identifier that differentiates the problematic classes. The following Table 4 shows the relation between the identification numbers and the corresponding LabVIEW functions and explains the usage of the gene data for the particular identifiers. Another concept that we employed during the initialization process in this work is strongly typed genetic evolution. Unlike classical programming, where any function can be inserted into the code, STGE limits the number of available functions. This means that only functions with a corresponding data type to the selected data type are available. An exception is a bool- ean data type, where, for instance, the "equal" function is a dynamic function that can be used by all data types. It is important to note that since this approach reduces the available func- tions, the mapping table (Table 4) dynamically changes according to the allowed data types. Introducing this procedure directly affects the values that can be written into the gene’s identi- fiers, and so by limiting these values, STGE helps speed up the evolution process and signifi- cantly increases the chances for a successful evolution [32]. Wirer and Creator data generation. Simultaneously with the generation of the identifi- ers for the Wirer and Creator sets, another parallel process produces, for each created iden- tifier, gene data in the form of a U8-byte array. For the generation of the data within Wirer genes, we applied an initialization rule where two numbers from the range of <0, Gauss (complexity; 0,1)> were selected on the basis of the uniform probability. This rule ensures that each element of the program has an equal chance of being joined with any other ele- ment. These generated numbers are then inserted into a U8-byte array that represents the particular gene data. On the other hand, for the data in the Creator, we had to consider all the possibilities that may arise on a theoretical level. Since the Creator assigns information (the generated data) to a created function on the basis of the function’s identifier, there is a probability of assigning data to functions that do not need that information, such as select or equal functions (refer to Table 4 for functions where gene data are not applicable). In a practical application, this case doesn’t seem to be a big problem, but in a worst-case sce- nario, the Creator might assign data to a string constant where the user is expecting a spe- cific input (the infinite monkey theorem). To prevent this occurrence, the algorithm for generating Creator data generates a sequence of numbers in a range of 0–255 based on an even distribution of probabilities until the algorithm meets the defined stopping condition. Unlike in the case of the Wirer, this process, therefore, continues to a potential infinity as long as another randomly generated number from the interval <0; 1> is greater than 0.95i, where i is the length of the currently generated string. This approach theoretically enables infinitely large text and ensures maximal variability. Creation of new child. The creation of a new child from the individual Creator and Wirer genes is implemented in this work sequentially, where each gene is processed separately. At first, all genes from the Creator are processed, which means that all program building blocks, i.e., functions or constants, are created based on the gene identifier and data by calling the LV scripting creation function. All created functions are, during this process, placed on a block diagram at a random place, so the current code doesn’t have to meet the programming standards yet (clean code without unnecessary bends in block diagram wires, top-down and left-right data flow layout, and many more). In the case of block diagram constants, the algo- rithm also handles empty gene data by generating a random constant value. Although this case doesn’t apply to the initialization phase, during the evolution, the genes might lose the genetic PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 10 / 20 PLOS ONEA pilot study material within the data. After creating all the building blocks on the block diagram, the algo- rithm processes the Wirer genes and creates links between the block diagram functions, i.e., between the output of the first object and the input of the second object. During this pro- cess, the algorithm extracts all objects’ input and output terminals by using the LabVIEW scripting navigation method, and from the extracted information (references to terminals), it creates two arrays representing block diagram objects’ inputs and outputs. The final con- nection of two functions or objects on the block diagram is then realized by creating wires between the indexed elements from the above-mentioned arrays, where the array index numbers are obtained from the gene data. To increase the chance of finding a suitable con- nection, we complemented this process with a controlled selection, where the whole array of input terminals is filtered based on the output terminal data type. Since in the LabVIEW programming environment it is possible to connect two different terminals with different data types, which results in a broken wire and non-executable programs, by employing this filtration, only input terminals with a corresponding data type to a selected output terminal are preserved. In the last phase of the process of creating a new child, the generated section of code is eval- uated for its functionality. During this evaluation, the program might not pass for several rea- sons. The main reason for failure is a poor logical interconnection between the functions. This can be caused by unconnected terminals, broken wires, introduced feedback loops (where a function input is linked to the same function output, causing a delay in the output of the exe- cution), or by connecting multiple outputs to one input (which is a problem specific to graphi- cal programming, unlike text-based languages). To eliminate this problem, we implemented an additional sequential algorithm that traces all wires in a block diagram by using a built-in LabVIEW method and then deletes all the incorrectly connected wires. Afterward, for all unconnected user-defined inputs, the algorithm establishes a random connection between a particular input and an existing output with a corresponding data type. Additionally, in the case of any missing required function input, the algorithm creates an appropriate constant with a corresponding data type and connects the constant to a desired input. This process ensures a higher success rate for creating valid and executable code and improves the chances of algorithm convergence. Evaluation of child. A key component that significantly improved the algorithm’s results was the implementation of a back-analysis of the created child. Due to the fact that the above- mentioned process generates random connections and possible constants, it was essential to store this information in a gene pool of the Wirerer and the Creator. Storing this information in the form of newly created genes primarily prevented the descendants with the best results from the development deterioration. The proposed back-analysis algorithm validates the func- tionality of the particular code samples against the user-defined requirements and is based on the following formula: Err ¼ Xinf i¼0 Pð�xiÞ (cid:0) Sð �xi; W; CÞ ð1Þ where the resulting error value Err is the sum of all partial differences between the desired value (output) P for the selected combination of inputs and the obtained values S (actual out- puts), dependent on the values of W and C representing the sets Wirer and Creator. Although this task is not challenging according to the development, the performance of this process is the most time-consuming. Since the program’s user-defined inputs and outputs are in the form of arrays, the evaluation process has to be repeated for each child as many times as many input combinations the user defined. Within the evaluation, we implemented a different PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 11 / 20 PLOS ONEA pilot study evaluation logic for each data type in the program. Operations working with a boolean data type result in a "binary" value of a deviation, where 0 stands for a case of a match (T = T, F = F), and the value of 1 indicates a mismatch. On the other hand, the resulting deviation in numeric data type is equal to the absolute value of the difference between the input and output values. Text strings use an equivalent logic, but the deviation is calculated as the sum of all par- tial results for each ASCII character converted to a U8 data type. All error results for each descendant are then stored in an array for further processing. Creation of a new generation. At the beginning of the evolutionary process, we have to select suitable parents for the new descendants. This task is achieved by sorting the evaluation results by the smallest number and selecting the best available results with the lowest score, i.e., the lowest difference between the achieved and the desired output. Descendants with the best results then become parents. The number of selected descendants for the next evolution was determined by the trial-and-error process and, for the majority of the experiments, was set to 6. To avoid generating similar local maxima due to identical evaluation results, only the first occurrence of the duplicate values is selected. In this study, one-to-one inheritance was employed, so each child has exactly one parent [33]. After selecting the best parents, the algorithms finally proceed to mutation-based evolution by applying mutation to the Creator and Wirer genes. In the case of the mutation of Creator genes, a random value from a range of <0, 100> is generated for each gene in the set. The indi- vidual genes are then modified only if the generated value is less than 25. This modification applies to the identifier, which affects the change of the function, as well as to partial values in the gene data (every single element in the byte array). In the case of the gene modification with a 1/4 probability, there is an additional 25% chance that the current identifier or data element value is decreased by a randomly generated number in a range of 1–3, a 25% chance that the current value is increased by the same number, and a 50% chance that the particular gene value is not changed. The overall probability of changing the gene within the Creator set is, therefore, 12.5%. A similar logic applies also to the evolution of the Wirerer genes, only with different modification probability values. The Wirer genes, in case of a mutation (also a 25% probability), are in one-third of cases incremented by a number from 1 to 100% of their origi- nal value; in 1/3 of cases, the genes are decreased by this number, and there is the same proba- bility that the genes are not changed. The total probability of the mutation of Wirer genes then amounts to approximately 16.6%. This approach to mutation within the Wirer genes ensured higher variability and induced sufficiently large changes in values. Since the Wirer does not use the information obtained in the identifier, this operation affects only the gene data ele- ments, specifically the output and input indexes. The whole process of evolution is depicted in a diagram in Fig 4. The mutation of genes within the Wirer and Creator is followed by the potential creation and elimination of individual genes, which turned out to be a key feature of a functional code- generating system. While the above-mentioned steps completely imitate the human process of mutation (the shrink mutation operator), the creation and elimination of genes slightly deviate from human evolution. Even though we can observe this process in humans, its manifestation is rather physiological (the creation of a new phenotype by combining the changed alleles). On the other hand, the process of code creation is about creating or destroying an existing gene, which prevents future generations from degradation. The elimination process is performed with a probability of 15% for each gene and results in the removal of a particular gene from the gene pool of the specific set. The same probability is also assigned to the process of creating a new gene, in which a full new gene is added to the gene pool of existing genes. This step is per- formed for both the Wirerer and Creator genes. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 12 / 20 PLOS ONEA pilot study Fig 4. The sequence of processing genes during the evolution. https://doi.org/10.1371/journal.pone.0299456.g004 Experimental evaluation To verify the proposed framework, we formulated some basic tasks and benchmark problems. Since the proposed study is designed as a pilot study mainly focused on the feasibility of code generation in a graphical programming environment, the proposed evaluation consists of only simple benchmark problems, dealing mostly with linear and binary tasks. Although there are many benchmark problems available for this purpose, all of them introduce loops and cycles that are not included in this pilot study. Due to this reason, we have chosen simple benchmark problems that do not require more complex loops or cycles. The employment of simpler tasks furthermore allowed us to focus more on the performance evaluation of our algorithms instead of analyzing problems arising from the code’s complexity. Within the evaluation procedure, we defined three main prototype problems, including a string model, a numeric model, and a boolean model. The string prototype problem verification was performed by selecting a function with one input and one output. The particular values for this task are given in Table 5. The desired result of the algorithm is then the addition of a string "ms" to the input value in a numeric format PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 13 / 20 PLOS ONEA pilot study Table 5. Input and output values for a string prototype problem. Input Value Required (Output) Values 100 10 1 500 https://doi.org/10.1371/journal.pone.0299456.t005 100 ms 10 ms 1 ms 500 ms (string formatting). This task is relatively challenging from the perspective of genetic evolution and model testing since the function for concatenating strings has to be evolved, and a string constant with an expected value has to be created at the same time. As we mentioned in previ- ous chapters, the user-defined string can be of any size, and thus it is necessary to mutate and evolve the creation, as well as the destruction of the genes within the data. The second prototype problem refers to a numeric problem and represents a multi-input task where the required output is the creation of a mathematical function between the two inputs. Both the selected input values and the expected output values are listed in Table 6. The most interesting task was the addition of the inputs; hence, this problem was chosen as a typi- cal task of the system. The last prototype problem we defined in this study is the boolean model with two boolean input variables and one boolean output. Within this model, we employed two boolean tasks, including a logical function OR and an EQUAL function. The assignment for the selected task is depicted in Table 7. In this case, the main point was to examine the evolution with a limited ability to verify the outcomes. Results The first problem, which was evaluated to confirm the algorithm’s functionality, was a string problem. The proposed test model involves two problematic key points, which are related to the conversion of the values inside a data element. This process requires size adjustments as well as the evolution of the values within the data. The results of multiple algorithm runs are shown in Fig 5. Table 6. Input and output values for the numeric prototype problem. Input Value 1 Input Value 2 Required output 5 6 15 17 36 10 11 42 43 44 https://doi.org/10.1371/journal.pone.0299456.t006 15 17 57 60 80 Table 7. Input and output values for the boolean prototype problem. Input Value 1 Input Value 2 Required output (Task OR) Required output (Task EQUAL) F T F T F F T T https://doi.org/10.1371/journal.pone.0299456.t007 F T T T T F F T PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 14 / 20 PLOS ONEA pilot study Fig 5. Final solutions of a string problem for multiple algorithms runs. https://doi.org/10.1371/journal.pone.0299456.g005 From these results, we can deduce that we can achieve repeatable solutions even with changes in input parameters. The generated results clearly show the ability of the algorithm to find the "optimal" program. In solution (A), the generated code is burdened with an extra function. Nonetheless, this doesn’t influence the code functionality, and the unused parts are removed during the program compilation. In the case of solution (C), we changed the maxi- mum complexity input parameter. This parameter was set for the basic operations in a range of <0; 10> due to higher computational complexity, but in the case of C, we set this parameter to a value of 30. It is apparent that the resulting solution is already out of the optimal and read- able code; however, the solution still meets the user’s requirements. A key element during the evaluation of the program’s evolution was the verification of sim- ple addition function behavior. Generated solutions, i.e., programs, during the experimental verification led to clear conclusions and findings that weren’t revealed in more complex math- ematical functions. The results of this process are demonstrated in Fig 6. The first problem that can be observed in this solution is the occurrence of different data types in the code, caused by the disabled usage of strict data types. In the first generation, the program included many more objects that were related to the boolean or string data type, but Fig 6. Numeric problem solution. The original form of the generated code (A), the cleaned-up form of the result (B). https://doi.org/10.1371/journal.pone.0299456.g006 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 15 / 20 PLOS ONEA pilot study Fig 7. Solution of OR (A) and EQUAL (B) functions of the boolean problem. https://doi.org/10.1371/journal.pone.0299456.g007 with subsequent generations, the number of these elements decreased until only elements related to the required data type remained. This was caused by the fact that redundant units of code (creator genes) are strongly dominated by useful functions. Genetic evolution, therefore, leads to a selection of genes that do not include these functions. This effect is not strictly domi- nant, but a positive trend has been observed. Another curious behavior we noticed was the cre- ation of a function Y = A + B by using three subtraction functions that resulted in an equation Y = A—((A-B) -B). This result is considered the correct solution to a problem; however, the solution seems to be extremely complex according to its purpose. This occurrence can be restricted by limiting the complexity parameter. In the case of the boolean model, the algorithm was able to achieve satisfying results accord- ing to the program’s functionality. As can be seen in Fig 7, due to a higher complexity parame- ter (a value of 10) and the disabled usage of strict data types, the result of the logical OR function is burdened with a relatively large amount of useless function blocks. On the other hand, for the second test case, including the EQUAL function, we enabled the usage of strict data types while preserving the high complexity parameter. The final solution to this task (B) contains multiple random connections of several equal functions. The result was created within the first generation, where we set a high complexity (10) and enabled strict data types. The additional last step of the evaluation comprises testing the usage of all basic data types. The output of this task should be a function that converts the input numeric value to a logical value on the basis of the required string output value. The proposed input and output values are listed in Table 8. These results (see Fig 8) proved that the proposed algorithm is able to work in a solution space that isn’t strongly typed, and therefore, it can lead to universal solutions. To improve the readability of some codes in this work, we additionally cleaned the code by using a built-in tool that automatically reroutes wires and rearranges block diagram objects. As mentioned Table 8. Input and output values for testing the combination of data types. Input value 0 1 2 3 https://doi.org/10.1371/journal.pone.0299456.t008 Required output Equal to zero Not equal to zero Not equal to zero Not equal to zero PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 16 / 20 PLOS ONEA pilot study Fig 8. Solution of a combinatory problem. (A) the original code of the solution, (B) cleaned-up solution of the task. https://doi.org/10.1371/journal.pone.0299456.g008 before, graphical programming in LabVIEW is based on placing and connecting objects, such as functions, constants, or terminals, on a program’s block diagram. Since during automatic code generation, the user has no control over the functions’ positions or their connections (bends in wires), such automatically generated code does not meet the best coding practices or readability standards, which have to follow the top-to-bottom and left-to-right dataflow para- digm. Therefore, by using the embedded automated cleanup functionality, which is able to adjust the spacing, remove bends in wires, or logically rearrange block diagram objects, the code can meet at least the essential requirements and programming standards. Another important aspect we analyzed during the technical evaluation was the algorithm’s speed and efficiency. This estimation was realized by measuring the independent processing times of the Creator, Wirer, as well as the evaluation process. To get a good estimation, the processing time for each part was averaged over multiple generations with a population size of 1000 and a set complexity value of 10. The estimated processing times for all phases can be seen in Table 9. Discussion The main goal of this pilot study was to create a proof-of-concept for an automated code gen- eration approach within the graphical-based programming language LabVIEW. Since, to the best of the authors’ knowledge, none of the automated code generation methods have been implemented in graphical-based programming languages, especially LabVIEW, this study aims to prove the feasibility and practical potential of this proposed concept. For this purpose, we designed and developed a requirement-based automated code generation algorithm that is Table 9. Average processing times for the individual processes within the evolution. Tested benchmark model Creator process duration (ms) Wirer process duration (ms) Evaluation process duration (ms) String Numeric Boolean Mixed type 20.08 19.40 24.25 28.05 https://doi.org/10.1371/journal.pone.0299456.t009 86.00 63.28 85.92 62.68 19.16 28.86 45.20 30.29 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 17 / 20 PLOS ONEA pilot study able to provide functional solutions on the basis of user-defined requirements. These input requirements can be defined by experienced software developers but also by non-program- mers or users with little programming experience. The proposed framework in this study was tested on four different benchmark problems that were designed to assess the framework’s ability to generate error-free, functional, and efficient codes across various data types. The pro- posed string problem model tested the framework’s capability in text manipulation and string operation tasks, the numeric problem model focused on arithmetic operations and handling numerical data, the boolean model dealt with logical conditions and provided insight into the framework’s decision-making processes, and the last mixed-type problem model tested the framework’s versatility in handling multiple data types. The performance of the proposed framework was then evaluated by assessing the generated codes’ accuracy, complexity, execu- tion speed, or adherence to user requirements. During the experimental evaluation, the designed code generation system achieved success not only in “hill-climbing” tasks, where we were able to find appropriate solutions with a gra- dient ascent algorithm, but also in one-point search problems, such as the boolean problem. Moreover, the mutation genetic operator, in combination with the proposed approach has been identified as a proper strategy for creating connections in graphical programs. Based on the achieved results, we have to point out that even though the algorithm was, in all tasks, able to find a functional and errorless solution that met all input requirements, these solutions were much more complex and were burdened with redundant objects in comparison to program- mer-developed codes. The complexity of the code, in this case, can be interpreted as a higher number of required connected objects that create the final solution. This is mainly caused by the natural behavior of evolutionary algorithms and genetic programming, which focus more on finding the best solution than the optimal solution. Due to this fact, the algorithm proposed in this study might find multiple different solutions that are not optimal, even if they fully sat- isfy the defined requirements. However, this level of complexity did not affect the accuracy or execution speed of the generated code compared to manually written codes. Although we are able to demonstrate the satisfactory functionality of the proposed method and prove the feasibility of this concept, we cannot fully declare that the problem is solved. Furthermore, additional research in this field has to be conducted, especially regarding the optimization and finding optimal solutions. Another remarkable fact revealed by the experi- ments is that a significant portion of the computation time for creating a single child is taken by the Wirer, i.e., by creating interconnections. To optimize this process in the future, it would be beneficial to develop a more sophisticated algorithm that better reflects the actual input and output requirements of the created functions instead of only processing the genetic material in genes and randomizing connections in cases of non-valid solutions. Future research should also be devoted to the implementation of more complex structures, such as cycles or loops, so we can fully exploit all the strengths of the proposed solution and test the methodology on more complex benchmark problems. Moreover, this would allow researchers to compare the proposed approach with existing methods. In addition, exploring multi-parental genetic pro- gramming or some types of polygamy-based algorithms can become an important area for future research. Future research should also consider the potential benefits of using cloud computing since the search space could reach enormous dimensions. This would enable researchers to create thousands or even millions of programs in a second, which could be vali- dated and iterated over the best of the best results to find the final solution to much more com- plex problems.Based on these findings, we can conclude that this pilot study not only proved the feasibility of automated code development in graphically oriented programming languages but also built a strong foundation for further research in this relatively unexplored domain. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299456 March 7, 2024 18 / 20 PLOS ONEA pilot study Author Contributions Conceptualization: Pavel Kodytek. Formal analysis: Alexandra Bodzas. Methodology: Pavel Kodytek. Project administration: Jan Zidek. Software: Pavel Kodytek. Validation: Alexandra Bodzas, Jan Zidek. Writing – original draft: Pavel Kodytek. Writing – review & editing: Alexandra Bodzas. References 1. Gurung G, Shah R, Jaiswal DP. 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10.1371_journal.pwat.0000210	RESEARCH ARTICLE Performance evaluation and application of host-specific Bacteroidales and mitochondrial DNA markers to identify sources of fecal contamination in river water in Japan Bikash MallaID 1, Kazuki Yamamoto2, Kotomi Furukawa2, Eiji HaramotoID 1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Malla B, Yamamoto K, Furukawa K, Haramoto E (2024) Performance evaluation and application of host-specific Bacteroidales and mitochondrial DNA markers to identify sources of fecal contamination in river water in Japan. PLOS Water 3(3): e0000210. https://doi.org/10.1371/ journal.pwat.0000210 Editor: Ricardo Santos, Universidade Lisboa, Instituto superior Te´cnico, PORTUGAL Received: November 7, 2023 Accepted: February 5, 2024 Published: March 6, 2024 Copyright: © 2024 Malla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data can be found in the manuscript and the supporting information file. Funding: This study was financially supported by the Environmental Restoration and Conservation Agency of Japan through the Environment Research and Technology Development Fund (grant number 5-1603 to EH), the Japan Society for the Promotion of Science (JSPS) through Grant-in-Aid for Scientific Research (B) (grant 1 Interdisciplinary Center for River Basin Environment, University of Yamanashi, Kofu, Yamanashi, Japan, 2 Department of Environmental Sciences, University of Yamanashi, Kofu, Yamanashi, Japan * eharamoto@yamanashi.ac.jp Abstract Microbial source tracking (MST) using host-specific Bacteroidales and mitochondrial DNA (mtDNA) markers is an efficient tool to identify the sources of fecal contamination in environ- mental water. This study evaluated and updated the previously reported performances of seven host-specific Bacteroidales markers (three human-, two cattle-, and two pig-specific). Additionally, the performance of a cattle-specific Bovine mtDNA and a pig-specific Swine mtDNA marker were evaluated and then applied to MST of river water samples collected in Yamanashi Prefecture, Japan. We collected 48 fecal-source samples, including raw sew- age, secondary-treated sewage, an effluent of a domestic wastewater treatment tank, pig feces, pig wastewater, and cattle feces, which were quantitatively analyzed using host-spe- cific Bacteroidales and mtDNA markers. BacHum and gyrB markers (human-specific), BacR and Bovine mtDNA markers (cattle-specific), and Pig2Bac and Swine mtDNA markers (pig-specific) were judged the best-performing markers. Then, these selected markers were applied to MST to identify the sources of fecal contamination in 59 river water samples col- lected at 21 sites. Of them, 20 (95%), 21 (100%), and 16 (76%) sites were positive for at least one human, cattle, and pig marker, respectively, indicating the need for immediate action and monitoring to control fecal pollution. 1. Introduction The impact of fecal pollution on the quality of river water is of great concern to human and animal health, as feces are the major sources of waterborne pathogens [1]. Escherichia coli is commonly used as an indicator of fecal pollution. However, its applicability is limited because of its growth in environmental matrices [2] and because it cannot identify the sources of fecal pollution. Microbial source tracking (MST) is an efficient tool to identify the sources of fecal contamination in environmental water to mitigate the problems of fecal contamination [3– 11]. Host-specific Bacteroidales genetic markers are commonly employed for MST because of PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 1 / 15 numbers JP17H03332 to EH; JP20H02284 to EH; and JP23H01536 to EH), and the River Foundation (grant number 26-1263-011 to EH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Bacteroidales and mtDNA for fecal contamination source analysis their high abundance in host feces. However, variations in sensitivity and specificity of these markers among different geographical locations have been reported [12, 13]. Thus, MST using mitochondrial DNA (mtDNA) markers, which target mtDNA shed from epithelial cells in host feces, have been introduced. These markers are proving useful as a potential tool to iden- tify the sources of fecal contamination because they directly target the host DNA rather than detecting the DNA of the microbes harbored by the host [14–19]. Furthermore, these markers are not easily influenced by the hosts’ diets or the season as do the Bacteroidales markers [15, 16, 20, 21]. Although these markers have been reported as species-specific [15, 16], very limited studies have been undertaken across broad geographical regions and applied to environmental water samples [14, 17, 22, 23]. No single marker is effective for source tracking. Consequently, the combined use of different markers targeting the same host is recommended for determin- ing the sources of fecal pollution [23]. Thus, studies are necessary to verify the applicability of these new mtDNA markers as valuable MST markers. In this study, the performances of seven host-specific Bacteroidales markers (three human-, two ruminant-, and two pig-specific) that had been previously evaluated [7] were updated with the addition of new fecal-source samples. Additionally, the performances of a ruminant-spe- cific and pig-specific mtDNA marker were evaluated and compared with the performance of their respective host-specific Bacteroidales markers. The rationale behind selecting human, ruminant/bovine, and pig markers stems from the likelihood that these hosts are the primary sources of fecal contamination at the tested sites. In the vicinity, there is a wastewater treat- ment plant near the river, and numerous domestic wastewater treatment systems are in place, particularly in households not connected to the municipal sewer network. To evaluate the impact of both the wastewater treatment plant and domestic wastewater treatment tanks on water quality, the human marker was chosen. Considering the presence of pig and cow farms, as indicated in a prior study [7], the pig and ruminant/bovine markers were selected to assess the influence of wastewater from these animal farms on the downstream river water quality. Furthermore, upstream of the river, where human activities are minimal, is close to a forest area inhabited by various wild animals such as deer and wild boar. Consequently, ruminant and pig markers were chosen to gauge water quality affected by deer and wild boar feces, respectively. Furthermore, the selected markers were then applied to MST of 59 river water samples collected in Yamanashi Prefec- ture, Japan, to identify the sources of fecal pollution. 2. Material and methods 2.1 Collection of fecal-source and water samples Forty-eight fecal source samples were collected in the Kofu basin, Yamanashi Prefecture, Japan, between 2016 and 2018, as mentioned previously [7, 10]. These samples were used to evaluate the performances of host-specific mtDNA markers and to update the performances of previously analyzed host-specific Bacteroidales markers. The samples included raw sewage (n = 12) and secondary-treated sewage (n = 12) from a wastewater treatment plant (WWTP), effluent of a domestic wastewater treatment tank (n = 3) as human fecal-source samples, cattle feces (n = 15) as cattle fecal-source samples, and pig wastewater (n = 3) as pig fecal-source sam- ples. Three additional samples of secondary-treated sewage (n = 2) and a pig fecal sample were collected in 2017 and 2018 for inclusion in this study. Of the total 48 samples, 35 samples col- lected in 2016 were previously analyzed to assess the performance of Bacteroidales markers [7]. As shown in Fig 1, we selected 20 sites (Sites 1–20) in the Fujikawa River basin, Yamanashi Prefecture, Japan, of which three sites (Sites 4, 12, and 20) were located downstream of a pig farming area and one additional site (Site 21) located in a forest area upstream to where PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 2 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis Fig 1. Location of sampling sites. https://doi.org/10.1371/journal.pwat.0000210.g001 human activities were not present, as mentioned in our previous study [10], for the purpose of MST. River water samples (n = 59) were collected three times over a period of 13 months (November 2017–2018) from each site, except for Sites 15, 16, 19, and 21, where the samples were only collected twice. Of the total 59 river water samples, 58 were previously analyzed for MST using viral markers [10]. The map was created using ArcGIS ArcMap 10.3.1. 2.2 Concentration of bacterial and mtDNA The bacterial and mtDNA in fecal-source and river water samples were concentrated as described previously [7]. Briefly, for the fecal samples, 1% fecal suspension prepared using phosphate-buffered saline (PBS (–)) was vortexed for 5 min, followed by centrifugation of 1 mL of the suspension at 7,000 × g for 10 min at 4˚C. Subsequently, the supernatant was PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 3 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis removed, and 1 ml of PBS (–) was added to the pellet to obtain a bacteria-concentrated sample. For the water samples, raw sewage (10 mL), secondary-treated sewage and effluent from a domestic wastewater treatment tank (100 mL each), pig wastewater (10 mL), and river water samples (500 mL) were filtered through a mixed cellulose ester membrane (pore size, 0.22 μm; diameter, 47 mm; Merck Millipore, Billerica, MA, USA). Next, the membrane was placed in a 50-mL plastic tube containing 10 mL of elution buffer containing 0.2 g/L Na4P2O7 10H2O, 0.3 g/L C10H13N2O8Na3 3H2O, and 0.1 mL/L Tween 80 and vortexed for approximately 5 min. The eluate was then collected into another 50-mL tube. This process was repeated using 5 mL of elution buffer, and the resulting eluate was transferred to the same tube. Following this, the tube underwent centrifugation at 2,000 × g for 10 min at 4˚C, the supernatant was discarded, and the pellet was resuspended in 1 mL of PBS (–), resulting in a bacteria-concentrated sample. 2.3 Extraction of bacterial and mtDNA Bacterial and mtDNA were extracted following the same procedure as described previously [7]. Briefly, 200 μL of a bacteria- or mtDNA-concentrated sample was used to obtain a final volume of 200 μL of bacterial or mtDNA extract using a QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany) on a QIAcube instrument (QIAGEN). 2.4 Quantitative polymerase chain reaction (qPCR) for Bacteroidales and mtDNA markers Table 1 shows the list of primers and probes used in this study. The same set of seven previ- ously tested Bacteroidales markers [7] were tested in this study, namely, three human-specific: gyrB [24], BacHum [3], and HF183 TaqMan [25]; two cattle-specific: BacR [26] and BacCow [3]; and two pig-specific: Pig2Bac [27] and PF163-SYBR [28, 29]. Additionally, two host-spe- cific mtDNA markers, one each for cattle (Bovine mtDNA) and pig (Swine mtDNA) [15] were tested. A Thermal Cycler Dice Real Time System TP800 (Takara Bio, Kusatsu, Japan) was used for DNA quantification. The qPCR reaction mixture was prepared as described previously [7, 10]. Briefly, for all probe-based assays, 25 μL of qPCR reaction was prepared consisting 12.5 μL of Probe qPCR Mix (Takara Bio), 1.0 μL each of forward and reverse primers (10 μmol/L), 1.0 μL of TaqMan (MGB) probe (5 μmol/L), 7.0 μL of PCR-grade water, and 2.5 μL of DNA. For a SYBR-based assay, PF163-SYBR, 22.5 μL of qPCR mixture contained 12.5 μL of SYBR Premix Ex Taq II (Tli RNaseH Plus) (Takara Bio), 1.0 μL each of forward and reverse primers (10 μmol/L), and 8.0 μL of PCR-grade water. Finally, 2.5 μL of DNA was added to the qPCR mixture to prepare final volume of 25 μL. The thermal cycle conditions for all the probe-based assays, including mtDNA markers, were 95˚C for 30 s, followed by 45 cycles of 95˚C for 5 s and 60˚C for 30 s, whereas for the PF163-SYBR assay, it was 95˚C for 30 s, 45 cycles at 95˚C for 5 s, 55˚C for 30 s, and 72˚C for 60 s, followed by a melting curve analysis with steps at 95˚C for 15 s, 60˚C for 30 s, and 95˚C for 15 s. A standard curve was prepared using a 10-fold serial dilutions of synthesized plasmid DNA containing the amplification sequence region and all unknown, standard, and negative control samples were run in duplicate. A cut-off point was set at 40 cycle thresholds and a melting tem- perature of ~79˚C was considered positive for PF163-SYBR assay [7, 30]. Data analysis was performed using Thermal Cycler Dice Real Time System Software 5.11 (Takara Bio). PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 4 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis Table 1. Primer and probe sequences of host-specific Bacteroidales and mtDNA markers. Host Marker Function Sequence (50–30)a Product length (bp) Reference Human gyrB Forward primer Reverse primer GGCGGTCTTCCGGGTAAA CACACTTCTGCGGGTCTTTGT TaqMan MGB probe FAM-TGGCCGACTGCTC-NFQ-MGB BacHum Forward primer Reverse primer TaqMan probe TGAGTTCACATGTCCGCATGA CGTTACCCCGCCTACTATCTAATG FAM-TCCGGTAGACGATGGGGATGCGTT-TAMRA 55 82 [24] [3] HF183 TaqMan Forward primer ATCATGAGTTCACATGTCCG 167 [25] Ruminant BacR Reverse primer TaqMan probe Forward primer Reverse primer CGTAGGAGTTTGGACCGTGT FAM-CTGAGAGGAAGGTCCCCCACATTGGA-TAMRA GCGTATCCAACCTTCCCG CATCCCCATCCGTTACCG TaqMan MGB probe FAM-CTTCCGAAAGGGAGATT-NFQ-MGB BacCow Forward primer Reverse primer TaqMan probe CCAACYTTCCCGWTACTC GGACCGTGTCTCAGTTCCAGTG FAM-TAGGGGTTCTGAGAGGAAGGTCCCCC-TAMRA Bovine mtDNA Forward primer CAGCAGCCCTACAAGCAATGT Pig Pig2Bac Reverse primer TaqMan probe Forward primer Reverse primer GAGGCCAAATTGGGCGGATTAT FAM-CATCGGCGACATTGGTTTCATTTTAG-TAMRA GCATGAATTTAGCTTGCTAAATTTGAT ACCTCATACGGTATTAATCCGC TaqMan MGB probe FAM-TCCACGGGATAGCC-NFQ-MGB PF163 SYBR Forward primer GCGGATTAATACCGTATGA Reverse primer CAATCGGAGTTCTTCGTG Swine mtDNA Forward primer ACAGCTGCACTACAAGCAATGC Reverse primer GGATGTAGTCCGAATTGAGCTGATTAT Probe FAM-CATCGGAGACATTGGATTTGTCCTAT-TAMRA 118 [26] 211 191 [28] [3] [15] 117 [27] 559 197 [29] [28] [15] Abbreviations: FAM, 6-carboxyfluorescein; MGB, minor groove binder; NFQ, nonfluorescent quencher; TAMRA, 5-carboxytetramethylrhodamine. https://doi.org/10.1371/journal.pwat.0000210.t001 2.5 Selection of host-specific Bacteroidales and mtDNA markers for MST qPCR for Bacteroidales and mtDNA markers were performed using DNA extracted from fecal- source samples. Parameters, such as sensitivity, specificity, and accuracy were used to select the best-performing markers [7, 10, 31]. Sensitivity, defined as the ratio of true-positive results, was computed using the formula: Sensitivity = TP/(TP + FN), where TP represents the number of true-positive samples, and FN represents the number of false-negative samples. Specificity, denot- ing the ratio of true-negative samples, was determined by the formula: Specificity = TN/(TN + FP), where TN is the number of true-negative samples, and FP is the number of false-positive samples. Accuracy, reflecting the ratio of the sum of true-positive and true-negative samples to the total number of samples, was calculated as: Accuracy = (TP + TN)/(TP + FP + TN + FN). 2.6 Detection of E. coli A Colilert 18 test kit (IDEXX Laboratories, Westbrook, ME, USA) was used to enumerate E. coli in river water samples by the most probable number (MPN) method as per the manufac- turer’s protocol. Briefly, one pack of Colilert 18 powder was added to a 103 mL of the diluted water sample, and the mixture was poured into a Quanti-Tray 2000. The sealed tray was incu- bated at 37˚C for 18 h before observing for blue fluorescence in large and small wells under ultraviolet light. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 5 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis 2.7 Statistical analysis To determine the concentration of E. coli in water samples, dilution ratios were incorporated into the calculations. Subsequently, the MPN value was computed using MPN-generating soft- ware provided by IDEXX Laboratories. The paired t-test was used to compare the concentra- tions between markers targeting the same host, whereas the independent t-test was used to compare the concentrations of different host-specific markers in target and non-target fecal- source samples. The χ2 test was used to compare the detection frequencies of pig-specific markers in river water samples at pig farming sites and other sites. Statistical analysis was per- formed using Microsoft Office Excel 2013 (Microsoft Corporation, Redmond, USA). Values were considered significant at P < 0.05. 3. Results 3.1 Performance of Bacteroidales markers Table 2 shows an updated result of the detection of seven host-specific Bacteroidales markers (three human, two ruminant, and two pig) reported previously (Haramoto and Osada 2018) after the addition of new fecal-source samples with their sensitivities, specificities, and accura- cies. For this purpose, we tested 13 new fecal-source samples. In the previous study [7], river water samples collected downstream of a pig farm were considered as pig fecal-source samples, whereas in the current study, pig feces and pig wastewater samples were collected (S1 Table). All three human-specific Bacteroidales markers were detected in nine freshly collected human fecal-source samples and the pig feces and pig waste-water samples, except for HF183 TaqMan marker in one pig wastewater sample. The Pig2Bac marker was detected in 100% (4/4) of freshly collected pig fecal-source samples, while PF163 SYBR marker was detected in 50% (2/ 4) of samples. The ruminant-specific BacR marker was not detected in the freshly collected human or pig fecal-source samples. The other ruminant-specific marker, BacCow, was detected in three raw sewage, one secondary-treated sewage, and four pig fecal-source samples. Of the total 29 human fecal-source samples, two human-specific markers, BacHum and HF183 TaqMan, exhibited sensitivity of 100% (29/29), while gyrB exhibited sensitivity of 93% (27/29), with significantly higher concentrations of BacHum marker (8.60 ± 1.58 log copies/L; n = 29) than gyrB marker (6.70 ± 1.54 log copies/L; n = 27) in the human fecal-source samples (t-test, P < 0.05). Surprisingly, HF183 TaqMan marker was detected in 18/19 (95%) non- human fecal source samples. The ruminant-specific BacCow and pig-specific PF163 SYBR markers exhibited specificities of 39% (13/33) and 27% (12/44), respectively, with frequent detection of these markers in raw sewage and effluent of WWTP samples. Surprisingly, all human-specific markers were detected in 100% (4/4) of pig fecal source samples, except for one pig wastewater sample for the HF183 TaqMan marker. The concentrations of the BacHum and HF183 TaqMan markers in positive samples were significantly higher in human fecal-sources (8.52 ± 1.56 log copies/L; n = 29 for BacHum and 8.01 ± 1.39 log copies/L; n = 29 for HF183 TaqMan) than in non-human fecal sources (7.01 ± 0.71 log copies/L; n = 10 for BacHum and 6.91 ± 0.40 log copies/L; n = 14 for HF183 TaqMan) (t-test, P < 0.05), while there was no signif- icant difference in the concentrations of gyrB marker in human (6.70 ± 1.54 log copies/L; n = 27) and non-human fecal sources (6.64 ± 0.96 log copies/L; n = 8) (t-test, P > 0.05). 3.2 Performance of mtDNA markers In this study, Bovine and Swine mtDNA markers were tested as cattle and pig-specific mtDNA markers, respectively (S1 Table). As summarized in Table 2, the Bovine mtDNA marker exhib- ited a sensitivity of 100% (15/15), with concentrations ranging from 8.70 to 9.61 log copies/L. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 6 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis A N ) 0 ( 0 A N ) 0 ( 0 5 3 7 . ) 3 3 ( 1 A N ) 0 ( 0 2 8 9 . ) 0 0 1 ( 1 . 5 4 1 1 ) 0 0 1 ( 1 ± 5 2 9 . ) 0 0 1 ( 3 6 5 0 . 6 3 8 . ) 7 ( 1 . 0 1 0 1 . 1 6 0 ± ± 4 6 7 . 3 3 1 . / 4 ( 0 0 1 ) 4 / 1 3 ( 0 7 ) 4 4 / 5 3 ( 3 7 ) 8 4 ) 0 0 1 ( 3 ) 0 8 ( 2 1 / 4 ( 0 0 1 ) 4 / 2 3 ( 3 7 ) 4 4 / 6 3 ( 5 7 ) 8 4 1 6 0 1 . 7 0 0 1 . ) 0 0 1 ( 1 ) 3 3 ( 1 A N 5 4 9 . ) 0 ( 0 ) 3 3 ( 1 A N A N ) 0 ( 0 ) 0 ( 0 ± 0 2 8 . 8 0 1 . ) 0 0 1 ( 5 1 ± 2 1 9 . 8 2 0 . ) 0 0 1 ( 5 1 ± 0 5 9 . 8 5 0 . ) 3 9 ( 4 1 8 4 0 1 . ) 0 0 1 ( 1 4 6 . ) 0 0 1 ( 1 1 2 7 . ± 6 6 8 . 0 8 0 . ± 3 8 9 . 6 9 0 . ) 0 0 1 ( 3 ) 0 0 1 ( 5 1 ± 5 5 6 . 3 8 0 . ± 8 9 6 . 1 3 0 . ) 7 6 ( 2 ) 0 0 1 ( 5 1 ± 1 1 7 . 7 7 0 . ± 4 1 6 . 4 0 1 . ) d e t s e t s e l p m a s f o . o n / y l t c e r r o c d e g d u j s e l p m a s f o . o n ( e g a t n e c r e P ) 4 / 2 ( 0 5 / 2 1 ( 7 2 ) 4 4 / 4 1 ( 9 2 ) 8 4 / 5 1 ( 0 0 1 ) 5 1 / 2 2 ( 7 6 ) 3 2 / 7 3 ( 7 7 ) 8 4 / 4 1 ( 3 9 ) 5 1 / 2 3 ( 7 9 ) 3 3 / 6 4 ( 6 9 ) 8 4 / 5 1 ( 0 0 1 ) 5 1 / 3 1 ( 9 3 ) 3 3 / 8 2 ( 8 5 ) 8 4 / 9 2 ( 0 0 1 ) 9 2 ) 9 1 / 1 ( 5 / 0 3 ( 3 6 ) 8 4 . c n o C n a e m ( ) D S ± ± 2 2 8 . 2 4 0 . ) 2 9 ( 1 1 A N ) % ( ) 0 ( 0 f o . o N e v i t i s o p s e l p m a s ) % ( . c n o C n a e m ( ) D S ± f o . o N e v i t i s o p s e l p m a s . c n o C n a e m ( ) D S ± ± 0 3 7 . 1 1 1 . ± 0 5 5 . 8 8 0 . ± 4 8 4 . 0 9 0 . ) 5 7 ( 9 ± 6 6 6 . ) 7 6 ( 8 6 6 4 . ) 3 4 ( 6 2 9 0 . 3 2 5 . ) 7 ( 1 d A N ) 0 ( 0 ) 7 6 ( 2 2 3 6 . ) 3 3 ( 1 A N ) 0 ( 0 ) % ( ) 8 ( 1 f o . o N e v i t i s o p s e l p m a s ) % ( . c n o C n a e m ( ) D S ± f o . o N e v i t i s o p s e l p m a s ) % ( . c n o C n a e m ( ) D S ± f o . o N e v i t i s o p s e l p m a s . c n o C n a e m ( ) D S ± ± 7 5 7 . 4 4 0 . ± 9 5 5 . 9 0 0 . ± 5 8 4 . 3 1 0 . f o . o N e v i t i s o p s e l p m a s ) % ( ) 0 0 1 ( 2 1 ) 4 1 ( 2 ) 7 6 ( 2 . c n o C n a e m ( ) D S ± ± 8 5 9 . 9 2 0 . ± 0 8 6 . 2 3 0 . ± 6 3 7 . 9 4 0 . f o . o N e v i t i s o p s e l p m a s ) % ( ) 0 0 1 ( 2 1 ) 0 0 1 ( 4 1 ) 0 0 1 ( 3 . c n o C n a e m ( ) D S ± ± 8 2 8 . 8 4 0 . ± 3 5 5 . 5 5 0 . ± 9 7 4 . 2 7 0 . g i P t n a n i m u R t A N D m e n i w S c a B 2 g i P R B Y S 3 6 1 F P t A N D m e n i v o B R c a B w o C c a B n a M q a T 3 8 1 F H n a m u H B r y g m u H c a B . s e l p m a s e c r u o s - l a c e f n i t s r e k r a m c i t e n e g A N D m d n a s e l a d i o r e t c a B c i f i c e p s - t s o h f o n o i t c e t e D . 2 e l b a T f o . o N e v i t i s o p s e l p m a s ) % ( b . c n o C n a e m ( ) c D S ± f o . o N e v i t i s o p s e l p m a s ) % ( ) 0 0 1 ( 2 1 ± 1 2 0 1 . ) 0 0 1 ( 2 1 ) 3 9 ( 3 1 ) 7 6 ( 2 ) 0 0 1 ( 1 ) 0 0 1 ( 3 ) 7 2 ( 4 / 7 2 ( 3 9 ) 9 2 / 1 1 ( 8 5 ) 9 1 / 8 3 ( 9 7 ) 8 4 3 5 0 . ± 0 2 7 . 8 5 0 . ± 5 9 7 . 9 6 0 . 5 7 6 . ± 9 9 6 . 6 6 0 . ± 1 1 7 . 4 9 0 . ) 0 0 1 ( 4 1 ) 0 0 1 ( 3 ) 0 0 1 ( 1 ) 0 0 1 ( 3 f o . o N s e l p m a s d e t s e t l a c e F a s e c r u o s 2 1 4 1 3 1 3 - y r a d n o c e S d e t a e r t e g a w e s f o t n e u l f f E c i t s e m o d a r e t a w e t s a w t n e m t a e r t k n a t s e c e f g i P r e t a w e t s a w g i P e g a w e s w a R ) 3 3 ( 5 5 1 s e c e f e l t t a C / 9 2 ( 0 0 1 ) 9 2 / 0 1 ( 3 5 ) 9 1 / 9 3 ( 1 8 ) 8 4 s r e t e m a r a P ) % ( y t i v i t i s n e S ) % ( y t i c i f i c e p S ) % ( y c a r u c c A g i p a , t n a l p t n e m t a e r t r e t a w e t s a w c i t s e m o d a f o t n e u l f f e n a , ) 5 = n ( e g a w e s d e t a e r t - y r a d n o c e s , ) 3 = n ( e g a w e s w a r : e r e w d e d u l c n i s e l p m a s w e n 3 1 e h T . ] 7 [ d e t r o p e r y l s u o i v e r p a t a d e h t g n i d u l c n I a . s e c e f t e w g / s e i p o c g o l s i t i n u e h t h c i h w r o f s e c e f g i p d n a e l t t a c r o f t p e c x e , L / s e i p o c g o l , t i n U b . ) 3 = n ( s e l p m a s r e t a w e t s a w g i p d n a , e l p m a s l a c e f 2 0 0 t . 0 1 2 0 0 0 0 . t a w p . l a n r u o j / 1 7 3 1 . 0 1 / g r o . i o d / / : s p t t h . n o i t a i v e d d r a d n a t s , D S c . e l b a c i l p p a t o n , A N d PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 7 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis Furthermore, these markers were also detected in non-target hosts, including raw sewage (67%, 8/12), an effluent sample of a domestic wastewater treatment tank (33%, 1/3), and a pig wastewater sample (33%, 1/3), exhibiting specificity of 67% (22/33). However, the mean con- centration of Bovine mtDNA marker was significantly higher in cattle fecal-source samples (9.12 ± 0.28 log copies/g wet feces; n = 15) compared with fecal-source samples other than cat- tle feces (6.76 ± 1.26 log copies/L; n = 11) (t-test, P < 0.05). Similarly, Swine mtDNA marker exhibited a high sensitivity of 100% (4/4) and a specificity of 70% (31/44), with a high level of detection of this marker in raw sewage samples (92%, 11/12). Interestingly, detection frequen- cies of both markers were highly reduced (60%–92%) by wastewater treatment at the WWTP. 3.3 Application of selected markers to MST of river water samples As summarized in Table 3, we analyzed 59 river water samples collected from 21 sites in Yama- nashi Prefecture for MST using human-specific Bacteroidales markers (BacHum and gyrB), cattle-specific Bacteroidales (BacR) and mtDNA markers (Bovine mtDNA), and pig-specific Bacteroidales (Pig2Bac) and mtDNA markers (Swine mtDNA) (S2 Table). Additionally, the presence of E. coli was analyzed in these samples and was detected in almost all river water samples (98%, 58/59) at concentrations >224 MPN/100 mL. E. coli was only absent in one of the two samples from Site 21, where anthropogenic activities were not present. Both human- specific Bacteroidales markers were detected in the river water samples from all sites, except for Site 21. Out of 59 river water samples, both human-specific markers were detected in 81% of samples. Surprisingly, the BacR marker was detected at all sites, including Site 21. In con- trast, Bovine mtDNA marker was detected only at four sites (4/21, 19%). The Pig2Bac and Swine mtDNA markers were detected in 32% (19/59) and 29% (17/59) of the river water sam- ples, respectively. However, both markers were detected only in 14% of the samples. The detec- tion ratios of Pig2Bac and Swine mtDNA markers were significantly higher in river water samples from the pig farming sites (67%–78%; Sites 4, 12, and 20) compared with the other sites (22%–24%) (χ2-test, P < 0.05). Interestingly, the Swine mtDNA marker was detected in nine samples in which Pig2Bac marker was undetected. 4. Discussion Numerous Bacteroidales and mtDNA MST markers are being evaluated globally to assess their effectiveness in identifying specific hosts responsible for fecal contamination in water environ- ments. The performance of these markers varies significantly due to factors, such as diet, age, geographical location, and environmental conditions [6, 13, 32–37]. Thus, it is essential to vali- date the MST markers for the specific geographical area prior to their use. In this study, the performance of seven Bacteroidales and two mtDNA MST markers were assessed to determine their suitability for effective water quality management. The performance of human- and pig-specific Bacteroidales markers differed from those previously reported [7] after the addition of new fecal-source samples. In this updated study, BacHum and gyrB markers exhibited lower specificities of 53% (10/19) and 58% (11/19), respectively, than reported previously (67% [10/15] for BacHum and 73% [11/15] for gyrB markers) [7]. The BacHum marker demonstrated a sensitivity exceeding 80% in prior studies conducted in Australia, the USA, and Singapore [36, 38, 39]. However, its sensitivity was nota- bly low (<50%), while specificity exceeded 80% in studies conducted in India and Kenya [31, 40]. Conversely, the gyrB marker exhibited both sensitivity and specificity exceeding 80% in the USA [24, 36]. In contrast, in the present study, this marker demonstrated high sensitivity (93%) but lower specificity (58%). It has been reported that a lower mean concentration of gyrB marker compared with BacHum marker corresponded to a lower copy number of gyrB PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 8 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis g o l ( . c n o C ) L / s e i p o c f o . o N e v i t i s o p s e l p m a s g o l ( . c n o C ) L / s e i p o c 8 6 5 . D N D N 0 2 6 . D N 6 0 6 . . 4 3 0 ± 7 0 6 . D N D N . 1 2 0 ± 4 1 6 . . 7 1 2 ± 5 5 7 . . 8 1 0 ± 6 4 6 . 9 1 6 . D N D N 8 1 6 . D N D N 9 2 6 . . 4 2 0 ± 4 3 6 . D N . 4 7 0 ± 6 3 6 . ) % ( ) 3 3 ( 1 ) 0 ( 0 ) 0 ( 0 ) 3 3 ( 1 ) 0 ( 0 ) 3 3 ( 1 ) 7 6 ( 2 ) 0 ( 0 ) 0 ( 0 ) 7 6 ( 2 ) 7 6 ( 2 ) 7 6 ( 2 ) 3 3 ( 1 ) 0 ( 0 ) 0 ( 0 ) 0 5 ( 1 ) 0 ( 0 ) 0 ( 0 ) 0 5 ( 1 ) 0 0 1 ( 3 ) 0 ( 0 ) 9 2 ( 7 1 ) 2 5 ( 1 1 5 5 3 . 6 4 3 . . 1 7 0 ± 6 9 3 . . 2 5 0 ± 9 9 4 . . 0 1 1 ± 0 4 3 . 3 1 4 . D N D N 6 1 4 . D N . 5 1 0 ± 3 4 3 . . 3 5 0 ± 0 2 5 . 3 6 3 . D N 4 2 3 . D N D N D N D N 9 7 4 . D N . 5 8 0 ± 6 1 4 . f o . o N e v i t i s o p s e l p m a s ) % ( ) 3 3 ( 1 ) 3 3 ( 1 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 7 6 ( 2 ) 3 3 ( 1 ) 0 ( 0 ) 0 ( 0 ) 3 3 ( 1 ) 0 ( 0 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 3 3 ( 1 ) 0 ( 0 ) 0 5 ( 1 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 3 3 ( 1 ) 0 ( 0 ) 2 3 ( 9 1 ) 7 5 ( 2 1 t A N D m e n i w S c a B 2 g i P t A N D m e n i v o B R c a B m u H c a B g o l ( . c n o C ) L / s e i p o c f o . o N e v i t i s o p s e l p m a s g o l ( . c n o C ) L / s e i p o c f o . o N e v i t i s o p s e l p m a s ) % ( g o l ( . c n o C ) L / s e i p o c f o . o N e v i t i s o p s e l p m a s ) % ( 4 1 5 . D N 4 8 4 . D N D N 2 5 5 . . 3 1 0 ± 9 1 5 . D N D N D N D N D N D N D N D N D N D N D N D N D N D N . 5 2 0 ± 8 1 5 . ) % ( ) 3 3 ( 1 ) 0 ( 0 ) 3 3 ( 1 ) 0 ( 0 ) 0 ( 0 ) 3 3 ( 1 ) 7 6 ( 2 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 8 ( 5 . 3 8 0 ± 0 6 4 . . 1 8 0 ± 5 6 4 . . 8 2 0 ± 8 5 4 . . 0 5 0 ± 0 5 4 . . 7 0 0 ± 6 8 4 . . 3 4 0 ± 9 4 4 . 9 9 3 . . 0 1 0 ± 5 8 4 . . 0 2 0 ± 5 5 3 . . 7 6 0 ± 7 3 4 . . 3 7 0 ± 8 4 4 . . 8 0 0 ± 3 1 4 . . 6 6 0 ± 6 5 4 . . 2 2 1 ± 6 5 4 . 5 3 3 . 9 5 3 . . 5 0 0 ± 9 7 4 . . 0 2 0 ± 6 6 3 . 9 4 4 . 1 4 3 . . 7 0 0 ± 5 8 4 . . 1 6 0 ± 0 4 4 . ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 3 3 ( 1 ) 7 6 ( 2 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 0 5 ( 1 ) 0 5 ( 1 ) 0 0 1 ( 3 ) 7 6 ( 2 ) 0 5 ( 1 ) 3 3 ( 1 ) 0 0 1 ( 2 ) 0 8 ( 7 4 . 3 4 1 ± 7 3 5 . . 3 2 2 ± 0 6 5 . . 9 9 0 ± 5 3 6 . . 9 2 1 ± 9 0 7 . . 7 1 2 ± 5 7 6 . . 1 3 2 ± 0 7 6 . . 8 6 0 ± 2 6 7 . . 4 8 0 ± 6 3 7 . . 1 7 1 ± 0 3 4 . . 8 7 1 ± 2 5 5 . . 2 3 0 ± 5 7 6 . . 8 1 1 ± 5 8 6 . . 7 4 1 ± 3 2 6 . . 7 6 1 ± 9 6 5 . . 9 7 0 ± 3 9 6 . . 9 2 1 ± 2 8 6 . . 6 8 0 ± 5 3 3 . . 0 0 2 ± 8 3 5 . . 7 2 0 ± 2 6 6 . . 4 6 0 ± 5 0 7 . D N . 6 5 1 ± 4 2 6 . ) 9 1 ( 4 ) 0 0 1 ( 1 2 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 2 ) 0 0 1 ( 2 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 0 0 1 ( 2 ) 0 0 1 ( 3 ) 0 ( 0 ) 5 9 ( 6 5 ) 5 9 ( 0 2 . 3 4 0 ± 2 9 3 . 3 3 4 . . 1 4 0 ± 5 2 4 . . 1 6 0 ± 9 3 4 . . 3 7 0 ± 9 4 4 . . 4 8 0 ± 7 3 4 . . 3 6 0 ± 2 6 4 . . 0 5 0 ± 4 5 4 . 5 7 2 . . 0 3 0 ± 1 2 4 . . 4 1 0 ± 8 1 4 . . 7 5 0 ± 8 4 4 . . 0 3 0 ± 5 5 4 . . 4 8 0 ± 6 7 3 . . 0 2 0 ± 1 5 4 . . 4 7 0 ± 0 6 4 . 8 8 3 . 5 6 4 . . 8 1 0 ± 4 4 4 . . 5 3 0 ± 3 6 4 . D N . 5 5 0 ± 2 3 4 . ) 0 0 1 ( 3 ) 3 3 ( 1 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 3 3 ( 1 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 7 6 ( 2 ) 0 0 1 ( 3 ) 0 0 1 ( 2 ) 0 0 1 ( 2 ) 3 3 ( 1 ) 3 3 ( 1 ) 0 0 1 ( 2 ) 0 0 1 ( 3 ) 0 ( 0 ) 1 8 ( 8 4 ) 5 9 ( 0 2 . 1 8 0 ± 6 3 3 . . 6 7 0 ± 2 3 3 . . 2 4 0 ± 2 9 2 . . 9 6 0 ± 4 4 3 . . 9 1 0 ± 3 0 3 . . 4 0 0 ± 8 0 3 . . 9 5 0 ± 1 5 3 . . 3 8 0 ± 4 4 2 . . 2 4 0 ± 4 8 2 . . 9 2 0 ± 0 5 3 . . 6 4 0 ± 1 8 2 . . 0 6 0 ± 4 5 3 . . 0 3 0 ± 4 0 4 . . 5 4 0 ± 4 6 3 . . 2 2 0 ± 5 5 3 . . 9 1 0 ± 0 7 3 . . 2 4 0 ± 5 3 2 . . 5 1 0 ± 7 1 3 . . 1 3 0 ± 1 6 2 . . 9 4 0 ± 4 4 3 . 9 2 3 . . 9 5 0 ± 1 2 3 . ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 2 ) 0 0 1 ( 2 ) 0 0 1 ( 3 ) 0 0 1 ( 3 ) 0 0 1 ( 2 ) 0 0 1 ( 3 ) 0 5 ( 1 ) 8 9 ( 8 5 ) 0 0 1 ( 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 3 2 1 2 3 4 5 6 7 8 9 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 0 2 1 2 9 5 1 2 l a t o T s e l p m a s l a t o T s e t i s g o l ( . c n o C ) L / s e i p o c B r y g f o . o N e v i t i s o p s e l p m a s ) % ( g o l ( . c n o C ) L / N P M f o . o N e v i t i s o p s e l p m a s ) % ( i l o c . E f o . o N d e t s e t s e l p m a s e t i S . s e l p m a s r e t a w r e v i r n i t s r e k r a m A N D m d n a s e l a d i o r e t c a B c i f i c e p s - t s o h f o n o i t c e t e D . 3 e l b a T i . a e r a g n m r a f g i p a f o m a e r t s n w o d d e t a c o l e r e w 0 2 d n a , 2 1 , 4 s e t i S . d e t c e t e d t o n , D N ; r e b m u n e l b a b o r p t s o m , N P M : s n o i t a i v e r b b A 3 0 0 t . 0 1 2 0 0 0 0 . t a w p . l a n r u o j / 1 7 3 1 . 0 1 / g r o . i o d / / : s p t t h PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 9 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis than 16S rRNA genes, the target of the BacHum marker [7, 10, 41]. The HF183 TaqMan marker exhibited a very low specificity in this study. Similar specificity of <10% was reported by one of five labs that tested the performance of the HF183 TaqMan marker [36]. The sensi- tivity of the PF163 marker dropped to 50% (2/4) in this updated study compared with 88% (15/17) reported previously [7]. This was because we used pig feces and pig wastewater as pig fecal sources instead of using river water samples downstream of a pig farm as per the previous study. Previous studies conducted in Canada and Ireland have reported an exceptionally high sensitivity and specificity, both exceeding 90%, for the PF163 marker [37, 42]. Similarly, in var- ious regions, such as France, the USA, and Nepal, earlier studies have consistently reported a sensitivity of 100% for the Pig2Bac marker, suggesting its prevalence across diverse areas [27, 30, 36]. However, it is noteworthy that this marker was detected in samples from non-target hosts in the current study, as well as in Nepal and the USA [30, 36], but not in samples from non-target hosts in Ireland [37]. In the present study, the ruminant-specific marker, BacR, exhibited both high sensitivity and specificity, surpassing 90%. Consistent with these findings, previous studies have reported a comparable level of sensitivity for this marker, indicating its widespread prevalence across diverse regions [6, 30, 36]. In contrast, the BacCow marker dem- onstrated sensitivity exceeding 90% but exhibited a notably low specificity ranging from 57% to 62% in previous studies [6, 31] and as low as 39% in the current study. These collective find- ings underscore the importance of validating MST markers before their application. A suffi- cient number of samples should be collected for each target and non-target host, encompassing diverse areas, age groups, and environmental conditions to ensure broad repre- sentation. Additionally, the fecal samples must be pure, exclusively containing the feces of the targeted hosts without contamination from other hosts. Contamination risks arise when col- lecting samples from areas where various animals graze together, from waste pits on farms housing different animals, or from downstream river water near animal farms with animal- specific fecal samples. These factors significantly influence the sensitivity and specificity of the tested markers and are important for the validation study. The concentration of host-specific Bacteroidales markers in the target host was several orders of magnitude higher (2–5 log), compared to non-target hosts, as reported previously [37, 43]. This elevated level of the intended markers implies the potential for their detection even after undergoing decay and dilution in water bodies. High detection frequencies of Bovine and Swine mtDNA markers in raw sewage samples but with 1–3 log lower copies of markers than in the target hosts are probably primarily from carry-over mtDNA signals in beef and pork, as these are consumed by humans and excreted in feces [15, 16]. The detection of Bovine and Swine mtDNA markers in influent samples of WWTPs [16] and Bovine mtDNA marker in urban and rural sewered and septic sites have been reported previously [18]. Notably, the concentration of Bovine and Swine mtDNA mark- ers in fecal-source samples was 0.4–1.6 log lower than the corresponding Bacteroidales host- specific markers. Among the human-specific markers, both BacHum and gyrB assays demonstrated a compa- rable level of accuracy, with BacHum at 81% and gyrB at 79%. In contrast, the HF183 TaqMan assay exhibited a notably lower accuracy of 63%. Likewise, within the ruminant-specific Bac- teroidales markers, BacR assay displayed a high accuracy of 96%, while BacCow assay showed a significantly lower accuracy of 58%. The pig-specific Bacteroidales and mtDNA markers both exhibited accuracies below 80%. Consequently, to enhance the precision of fecal contami- nation host identification in river water samples, two markers were employed for each host in the MST process. Among the three targeted hosts, human-specific markers were the most fre- quently detected, followed by ruminant and pig markers. Both human-specific markers were identified in 81% of the samples, representing 95% of the total surveyed sites. Higher detection PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 10 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis frequencies of both pig-specific markers near the pig farming sites (Sites 4, 12, and 20) might suggest that pig farms could pose a risk of pig wastes in rivers if the pig wastes are not properly disposed of. Similar results were previously reported using viral markers (porcine teschovirus) [10] and Bacteroidales markers (Pig2Bac) [7]. In the current study, both pig markers were detected at 7/21 (33%) sites. At Site 21, none of the tested markers were detected, except for BacR (100%, 2/2). More- over, a high concentration of E. coli was observed at this location. The probable source of this contamination is the proximity to a forest where deer are frequently observed. The positive identification of the ruminant-specific marker, BacR, affirms the presence of deer, as they are ruminant animals, in the surrounding area. This presence could be attributed to the existence of wild deer in the forest, as previously documented [7]. Additionally, our prior study [10] revealed the detection of pig-specific viral markers (porcine teschovirus) and potential human-specific fecal markers (pepper mild mottle virus) at this site, confirming the presence of wild boar in the area, which contributes to water source contamination. Furthermore, there are frequent warnings about the sighting of wild boar in the areas near the forest. Regardless of a high sensitivity (100%) and a high marker concentration of Bovine mtDNA in cattle feces, the significantly lower detection frequency of this marker compared with BacR in river water samples may suggest a lower persistence of mtDNA markers than Bacteroidales markers in environmental water. In a prior study, bovine mtDNA was detected in one of the two runoff water samples from a farm where fresh bovine manure had been spread the day before [14]. He et al. (2016) [17] found that the pig-associated Bacteroidales marker (Pig2Bac) exhibited a longer persistence compared to pig-specific mtDNA markers (P-CytB and P-ND5) at both lower and higher temperatures in environmental waters. However, a direct comparison of the persistence of bovine microbial and mtDNA markers in river water samples is lacking. Conse- quently, further investigations are needed to explore the persistence of host-specific mtDNA markers in various aquatic environments across diverse geographical locations. The detection of Swine mtDNA marker in nine samples in which Pig2Bac marker was undetected highlights the importance of using combined markers to identify the same host. The collective findings underscore the significance of MST in pinpointing the specific hosts responsible for potential impacts on river water quality. This importance of MST has also been emphasized in previous studies [44–47]. This study was directed solely at three hosts, human, ruminant, and pig, to discern the ori- gins of fecal contamination as these hosts are likely the primary sources of fecal contamination at the tested sites. It is important to acknowledge the potential for fecal contamination from other hosts not included in this study. This study is subject to certain limitations. Firstly, it did not assess the sensitivity and specificity of the markers in boar and deer feces. Additionally, the small sample size of pig fecal sources could potentially lead to inadequate sensitivity and speci- ficity, introducing considerable variability. To gain a more comprehensive understanding of the various hosts contributing to fecal contamination in river water, our future studies will aim to target these additional sources, providing more detailed insights. 5. Conclusion In summary, this study evaluated and updated the performances of host-specific Bacteroidales markers that were previously reported, evaluated host-specific mtDNA markers in comparison with respective host-specific Bacteroidales markers, and finally applied the results to river water samples to identify the sources of fecal contamination. BacHum and gyrB markers were considered suitable for human, BacR and Bovine mtDNA for cattle, and Pig2Bac and Swine mtDNA for pig fecal-source tracking in the study area. When these markers were applied to PLOS Water \| https://doi.org/10.1371/journal.pwat.0000210 March 6, 2024 11 / 15 PLOS WATERBacteroidales and mtDNA for fecal contamination source analysis river water samples, humans and cattle were found to be the most frequent sources of fecal contamination. Despite both Bacteroidales and mtDNA markers demonstrating similar levels of accuracy, the lower detection frequencies of mtDNA markers compared with Bacteroidales markers in river water samples and significantly high reduction in detection frequencies of mtDNA markers by wastewater treatment indicated that mtDNA markers were less persistent in water environments and easily removed at WWTPs. Thus, the persistence of mtDNA mark- ers in environmental waters under different environmental conditions requires future investi- gation. This study suggested the combined use of different markers targeting the same host to identify the sources of fecal contamination. Supporting information S1 Table. Detection of host-specific Bacteroidales and mtDNA markers in fecal-source samples. (XLSX) S2 Table. Detection of host-specific Bacteroidales and mtDNA markers in river water sam- ples. (XLSX) Acknowledgments The authors would like to thank Mr. Masaaki Ito, Mr. Kosei Koga, Mr. Toshiki Amemiya, Mr. Ryo Koshiishi, Mr. Koki Makise, Mr. Koki Nakaya, Mr. Taizo Mochizuki, Mr. Rui Osada, Ms. Sayaka Sugiyama, and Mr. Takahiro Yamada (University of Yamanashi, Japan) for their sup- port in water sampling and/or laboratory analysis. Author Contributions Conceptualization: Eiji Haramoto. Formal analysis: Bikash Malla. Funding acquisition: Eiji Haramoto. Investigation: Bikash Malla, Kazuki Yamamoto, Kotomi Furukawa, Eiji Haramoto. Resources: Eiji Haramoto. Supervision: Eiji Haramoto. Visualization: Bikash Malla. Writing – original draft: Bikash Malla. Writing – review & editing: Eiji Haramoto. References 1. Santo Domingo JW, Bambic DG, Edge TA, Wuertz S. Quo vadis source tracking? 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10.1371_journal.pwat.0000127	RESEARCH ARTICLE Household, neighbourhood and service provider risk factors for piped drinking-water intermittency in urban and peri-urban Zambia: A cross-sectional analysis Mair L. H. Thomas-PosseeID A. WrightID 1☯ 1,2, Andrew A. Channon3☯, Robert E. S. BainID 4, James a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Thomas-Possee MLH, Channon AA, Bain RES, Wright JA (2024) Household, neighbourhood and service provider risk factors for piped drinking- water intermittency in urban and peri-urban Zambia: A cross-sectional analysis. PLOS Water 3(2): e0000127. https://doi.org/10.1371/journal. pwat.0000127 Editor: Sara Marks, Eawag, SWITZERLAND Received: March 23, 2023 Accepted: October 4, 2023 Published: February 5, 2024 Copyright: © 2024 Thomas-Possee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The original contributions presented in the study are publicly available. The data can be found at the following: Service provider data can be accessed at: https:// database.ib-net.org/countries_results?ctry= 29&years=2018&type=report&ent=country&mult= true&report=1&table=true&chart= false&chartType=column&lang=EN&exch=1. 2018 DHS data is available on application via the public repository: https://dhsprogram.com/methodology/ survey/survey-display-542.cfm. Regulator data is openly available and can be extracted from the 1 Geography and Environmental Science, University of Southampton, Southampton, United Kingdom, 2 WaterAid, London, United Kingdom, 3 Social Statistics and Demography, University of Southampton, Southampton, United Kingdom, 4 UNICEF Middle East and North Africa, Amman, Jordan ☯ These authors contributed equally to this work. mairthomas-poseee@wateraid.org Abstract Given nearly one third of sub-Saharan Africa’s population lack access to an improved water source that is available when needed, service continuity restricts access to safely managed services. Household surveys, water regulators, and utilities all gather data on service conti- nuity, but few studies have integrated these disparate datasets to quantify continuity-related risk factors and inequalities. This study aimed to assess the added value of utility and regu- lator data for international monitoring by assessing factors affecting piped water availability in urban and peri-urban Zambia. Household ‘user’ data from the 2018 Demographic and Health Survey (n = 3047) were spatially linked to provider data from an international utility database and regulator reports. Multilevel modelling quantified provider-related and socio- economic risk factors for households reporting water being unavailable for at least one day in the previous fortnight. 47% (95% CI: 45%, 49%) of urban and peri-urban households reported water being unavailable for at least one full day, ranging from 18% (95% CI: 14%, 23%) to 76% (95% CI: 70%, 81%) across providers. Controlling for provider, home owner- ship (odds ratio (OR) = 1.31; p <0.01), speaking Luvale, Kaonde, Lunda (OR = 2.06; p <0.05) or Tonga (OR = 1.78; p <0.1) as an ethnicity proxy, and dry season interview dates (OR = 1.91; p <0.05) were associated with household-reported interruptions. Households using a neighbour’s tap (OR = 1.33; p <0.1) and in mid-wealth neighbourhoods (OR = 4.31; p <0.1) were more likely to report interruptions. For every $1000 increase in utility-level GDP per capita, the odds of an interruption were 0.51 times less (p<0.01). Substantial inequalities in drinking-water availability were found between provider coverage areas. Spatial integra- tion of user, provider and regulator data enriches analysis, providing a finer-scale perspec- tive than otherwise possible. However, wider use of utility or regulator data requires investment in monitoring of small-scale community supply intermittency and utility coverage area data. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 1 / 26 following report: https://www.nwasco.org.zm/ index.php/media-center/publications/water-supply- and-sanitation-sector-reports. All code and publicly available data has been provided as supplementary materials to allow full transparency and replication of the analysis that has been undertaken. Funding: This research was supported by a South Coast Doctoral Training Partnership (SCDTP) PhD award, awarded to M.L.H.T-P., from the UK Economic and Social Research Council (Grant No. ES/P000673/1). This funding body had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Risk factors for urban intermittent piped drinking-water Introduction The sixth Sustainable Development Goal (SDG) aims to ‘ensure the availability and sustainable management of water and sanitation for all’ by 2030 [1]. The accompanying target 6.1, is to achieve universal and equitable access to safe and affordable drinking-water. The associated indicator, 6.1.1, measures progress towards this target via the proportion of the population using safely managed drinking-water services, requiring that drinking-water from an improved service be available when needed [2]. Additionally, the World Health Organization (WHO) outlines a global drinking-water availability benchmark which recommends that a minimum of 50 litres/capita/day (LPCD) is needed to meet domestic needs, including wash- ing, personal hygiene and cleaning [3, 4]. Between 2000 and 2020 the proportion of the sub-Saharan African (SSA) population using improved drinking-water sources that were available when needed increased from 41% to 59% [5]. Over the same period, the population in SSA using piped water doubled from 185 to 380 mil- lion. However, coverage of piped water services in urban areas has not kept up with population growth, declining from 65% to 59% between 2000 and 2020 [5]. In 2015, an estimated 116 million people in Africa were supplied by an unreliable piped water system prone to interruptions [6]. Water services that are not available when needed may result in damage to water service infra- structure, compromise water safety [7], adversely impact health [8, 9], and lead to additional household expenditure on water storage, treatment and purchasing supplementary water [10], with the latter often sourced from informal service providers and unimproved services [11]. The transition from the Millennium Development Goals (MDGs) to the SDGs saw the spe- cific addition of water availability to the international agenda, resulting in new demand for data sources for monitoring [12]. Multiple sources, including household surveys (hereafter ’user data’), utility companies (hereafter ‘provider data’) and government regulators [13], hold data on water service intermittency. Historically household survey questions on the availability of drinking water services have not been harmonised, thus different countries may not use consistent question and response wording. This complex data landscape is further exacerbated by the challenges of measuring water availability, for which methods vary between studies [14]. Metrics used include hours of service a day alongside service in the last week or month, using household or per capita consumption per day, or the number of interruptions or break- ages in a given time period [15]. In 2018, the Joint Monitoring Programme (JMP) of the WHO and United Nations Children’s Fund (UNICEF), who are responsible for international moni- toring of SDG target 6.1, published a core question, ‘In the last month, has there been any time when your household did not have sufficient quantities of drinking-water when needed?’, for greater harmonisation and incorporation into household surveys for availability monitoring. Since this is a new question and household surveys are implemented only every three to five years, availability of internationally comparable survey data remains patchy. As a result of patchy data and the use of multiple metrics of availability, national and inter- national monitoring has been reliant on numerous data sources, presenting challenges for data integration. Under the MDGs, monitoring was primarily dependent on household surveys and census data [16], whereas more recently under the SDGs, there has been a shift towards using information from regulators of providers alongside these more traditional sources [17]. Regulators often produce annual reports which benchmark levels of service between different providers. An additional data source concerning piped water service levels is available directly from service provider records [18]. Many service providers report their performance data to the International Benchmarking Network for Water and Sanitation Utilities (IBNET). At pres- ent, IBNET provides the most systematic international data on provider reported water avail- ability [18]. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 2 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water To date, most studies that have sought to quantify risk factors for interruptions in water ser- vices have focused almost exclusively on household surveys [19–21]. In comparing provider and user data, one study conducted bespoke household surveys within four case study provider coverage areas (PCAs) in Kenya and Ghana [11], whilst a second compared user-reported and provider-reported service continuity at regional level in Peru [18]. However, since many household surveys are now georeferenced [22], the new household survey question on service continuity provides an opportunity for spatial integration of household reports of water service interruptions with related statistics reported by service providers and regulators. Such spatial integration would enable more detailed assessment of household versus regulator- or pro- vider-reported service continuity for consistency, alongside evaluation of provider-level risk factors for household-reported interruptions such as supply-side service management indicators. Barriers inhibiting household access to uninterrupted water services may exacerbate urban- rural and socio-economic disparities that are evident in drinking-water services. In 2020, only 13% of the rural SSA population used services that were safely managed compared to 54% of the urban population [5]. With the SDGs seeking to ‘leave no one behind’, identifying and addressing drivers of inequalities in water, sanitation and hygiene (WASH) services is critical. In a city-scale study of Lilongwe, Malawi, water rationing of the formal water system led to more irregular supply in low-income informal settlements [23], highlighting one driver of such inequalities. However, whilst wealth quintile was associated with rural household access to continuous water in Bangladesh and Pakistan [19], there is little evidence on urban or per- urban inequalities in household access to continuous piped water in addition to such inequali- ties at city or PCA scale. The primary objective of this study was therefore to assess the added value of utility and regulator data in estimating piped water availability and quantifying risk factors for piped water intermittency and related inequalities. As secondary objectives, the study aims to assess whether household survey data are sufficient in isolation to quantify water availability and related risk factors, alongside identifying barriers to more widespread integrated analyses of household survey, utility, and regulatory data sets. Data on water service availability from users, providers and regulators were first mapped to identify a suitable case study country to examine risk factors for user-reported interruptions in piped water services in SSA. Since Africa shows the least progress towards SDG 6 [5], and Zambia had near contemporaneous data from users, providers and regulators that could be spatially linked, it was selected as the case study. Materials and methods Ethical approval Ethical approval for the use of all data was received from the University of Southampton Ethics Committee (Submission ID # 55516) on 10th March 2020. Study country selection A systematic secondary data audit (S1 Fig; S1–S3 Tables) was undertaken via the JMP’s 2019 country files [24] and IBNET database [25] to identify an African Union (AU) member state for which household survey or population census data, government regulator data and water providers all report at least one water availability metric (i.e. a measure of the quantity of household water supplied, or continuity of water service provision) covering the same year(s). To facilitate subsequent multi-level analysis, countries eligible for inclusion required PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 3 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water household survey or census data geographically disaggregated to sub-provincial level and pro- vider or regulator data disaggregated to at least province level or equivalent coverage area. Half of AU member states (n = 27) lacked relevant household survey or census data on availability and were excluded (Fig 1). Of the remaining 27 that had user data, only five had user, provider and regulator data that each captured water availability. For each of the five shortlisted countries, the three data streams were mapped using ArcMap 10.7.1. This Fig 1. Available data for each of the 54 African Union member states. (GADM 2018 base layer available at: https://gadm.org/index.html). https://doi.org/10.1371/journal.pwat.0000127.g001 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 4 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water determined whether subsequent integration of the three data streams was possible. Zambia was the only country where the administrative geography of drinking-water supply was suffi- ciently simple to facilitate spatial linkage of household survey and provider data since PCA (which are also used by the regulator) coincided with province boundaries. All three Zambian data sources also covered the same year (S1 Table). Study site Located in southern-central Africa, Zambia is a landlocked country with a 2019 population of nearly 19 million [26]; 45% live in urban areas [27]. Zambia’s trans-boundary catchment areas result in fluctuations of surface water availability because of the varying water demands of neighbouring states [28]. Renewable water resources are affected by inconsistent seasonal rain- fall, characterised by periodic drought [29]. Mismanagement and rapid urban growth have also caused considerable stress on groundwater resources [30]. Urban and peri-urban piped drinking-water is supplied to over six million people by 11 commercial Water and Sewerage Companies (WSCs) [31]. Approximately 4,656,375 piped water connections supply drinking-water in 91 towns (S4 Table). In total, an estimated 46% of the urban population have a safely managed water service, that is available when needed [32]. In rural Zambia, 35% have an unimproved service compared to 9% of the urban population [32]. The National Water Supply and Sanitation Council (NWASCO), a statutory body, is responsible for regulating water and sanitation services across Zambia [33]. Data sources and availability metrics We used three main data sources in our study: 1. User data: nationally representative data from the Zambian Demographic and Health Sur- vey (DHS) was used. This was implemented as a multi-stage cluster household survey from 18th July 2018 to 24th January 2019 [34]. Interviews were undertaken concurrently across all provinces, using a stratified two-stage sample design [34]. We used geospatial data, geore- ferenced to cluster level (comprising groups of approximately 25 households, selected at random from a given census enumeration area). Cluster locations are provided as the mean GPS coordinates for all participating households within each cluster, displaced within 2km (for urban areas) to retain anonymity [35]. No sampling clusters were displaced outside of their administrative district. To assess drinking-water availability, participating households are asked ‘In the last two weeks, was the water from your main source not available for at least one full day?’. 2. Provider data: To assess provider-level risk factors for reported interruptions, provider- reported availability of piped services for all 11 WSCs was obtained from the IBNET data- base for 2017, the most recent year available [36]. This includes metrics of availability, such as continuity of service, as yearly average hours of service per day (hrs/day) [37], and yearly average residential consumption in LPCD [38]. Details of non-revenue water, the difference in water supplied and sold as a percentage of net water supplied [39], which inherently affects service availability [40, 41] were also of interest as these reflect illegal use of piped networks and water that has been stolen or leaked from the system. 3. Regulator data: since 2001, NWASCO have also annually reported on each WSC’s perfor- mance based on nine key indicators [33]. The annual average duration of water service (hours/day) at the customer connection, and the average amount of water consumed in LPCD were extracted from the publicly available 2018 NWASCO report [42] for each WSC. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 5 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Household inclusion criteria and data integration With less than 5% of rural households using piped drinking-water services [5], provider and regulator data from NWASCO do not cover rural areas [43]. Given this, 8,117 households in 347 rural clusters (comprising 16% of households nationally reporting piped water as their drinking-water source) were excluded (Fig 2). 47 households in two clusters which had miss- ing GPS coordinates were also excluded. Provider service area boundaries for regulator and Fig 2. Flowchart, showing reasons for excluding households participating in the 2018 Zambian Demographic and Health Survey from analysis (n = number of households). https://doi.org/10.1371/journal.pwat.0000127.g002 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 6 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water provider data were derived through aggregation of district administrative boundaries for Zam- bia from the Global Administrative Areas (GADM) database [44] and spatially joined to DHS household clusters in ArcMap 10.7.1. The final dataset therefore included information on the provider and regulator perspective of water availability at the PCA level (henceforth ‘provider- level’) and the user perspective at household-level. Households that did not drink piped water (whether from a connection in dwelling, yard/ plot, neighbour or public tap/standpipe) were excluded (n = 1574) as they were not asked about water service continuity [45], as were those who did not know about their piped water continuity (n = 46) (Fig 2). Outcome and explanatory variables A binary outcome variable ‘user reported availability of piped water’ was defined as respon- dents reporting at least one full day of interruptions in the two weeks prior to being surveyed. Explanatory variables at the household-, cluster- and provider-level were chosen to represent socio-economic, water source, or neighbourhood characteristics that could constitute risk fac- tors for piped water interruptions (S5 and S6 Tables). Household cluster-level variables were created by aggregating data on households within each DHS cluster. In modelling user-reported availability of piped water, the following household-level explanatory variables were included: • Since different types of connection are prone to different types of interruptions [46], type of piped service (piped into dwelling, piped to neighbour, piped to yard/plot or public tap/ standpipe) was included as a covariate. Users may also consume more water if it is piped to the yard or home, than if located further afield, such as at a neighbour’s or a public tap/ standpipe [47]. • The DHS’s urban/rural wealth index was used as a household wealth variable. This considers assets and services owned by rural populations alongside those owned by urban populations [48]. Wealth relates to the type of piped service used [49] and affects household vulnerability to interruptions. The wealthier pay tariffs more regularly, are less exposed to illegal connec- tions or pipeline breakages, can afford to consume more water [50] and purchase storage tanks which may protect from interruptions [51]. • Month of interview was included as a proxy for seasonal water shortages [52]. Lower bore- hole yields seasonally restrict groundwater-fed piped systems, while lower reservoir levels seasonally affect piped networks drawing on surface waters [53]. Date of interview was grouped into categories to align with the April-September dry season in 2018/19 [54] (dry season: ‘September 2018’; ‘July-August 2018’, rainy season: ‘October-November 2018’; ‘December 2018-January 2019’). • Belonging to a minority ethnic group may restrict water access, since for example, locations of public standpipes and household connections to mains services often disproportionately favour majority ethnic groups [55]. Native language (English, Bemba, Lozi, Tonga, Kaonde/ Lunda/Luvale or other) was therefore included as a proxy for ethnicity. • Household size was included as it correlates with water consumption [51, 56], including depletion rates of household stored water, ultimately affecting continuity. It also relates to choice of service type [57, 58]. • Home ownership relates to the choice of water service and ability to cope with piped water interruptions. For example, home-owners can choose to invest in a water tank as an PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 7 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water important coping strategy [59]. Home ownership as reported by the men’s and women’s DHS questionnaire respondents was included. At DHS household cluster level, we included the following variables: • Degree of urbanisation (classified as: urban centres/cities; urban clusters/towns/suburbs; rural localities; and unpopulated) was included as a systematic review found lower reported availability in rural settings [15]. This DHS variable was derived from 2015 settlement data from the Global Human Settlement Layer (GHSL) project [60]. • Since recently urbanised areas may have water infrastructure that has undergone develop- ment and is less prone to maintenance-related interruptions, we included change in urbani- sation (less urbanised; more urbanised; no change), based on the difference in GHSL settlement class between 1990 and 2015. • Neighbourhood wealth was included to reflect societal factors such as crime levels and disad- vantaged communities [61], which in turn affect neighbourhood water infrastructure and local capacity to cope with water intermittency. Cluster-level averages of the DHS’s urban/ rural wealth index were created. • Newer neighbourhoods are less likely to have water infrastructure that is prone to failure, due to general aging of materials and poor upkeep [11, 62] and neighbourhoods comprising newly arrived migrants may lack sufficient social cohesion to lobby for services [63]. The men’s and women’s DHS questionnaires ask respondents their length of residence in their current home. As a proxy for the age of a cluster’s neighbourhood and social cohesion, we calculated the maximum length of residency for any men or women within a given cluster. Water service provider-level variables were as follows: • Since local economic development affects investment in WASH infrastructure [46, 64], we calculated GDP per capita at the service provider level. 2015 provincial GDP [65] data were converted to GDP per capita using 2015 provincial population statistics [66] (S7 Table), esti- mating this via areal interpolation for those service provider areas that did not match to pro- vincial boundaries. • Non-revenue water was included to account for leakage or illegal use of piped networks [40, 41]. 2017 IBNET data which comprised the difference between water supplied and water sold that is ‘lost’ before it reaches the consumer, expressed as a percentage of net water sup- plied [39]. • Provider and regulator availability–reported average service hours expressed as hrs/day and LCPCD were used and treated as continuous variables. Statistical analysis Stata 16.1 was used for all analysis [67]. Initially, provider, regulator and user-reported avail- ability were compared through scatter plots and calculation of Pearson’s correlation coeffi- cients. The LPCD measures reported by providers and the regulator were also assessed against the WHO benchmark of �50 LPCD to estimate the proportion of population within each PCA not meeting this benchmark. Descriptive and bivariate analysis for the outcome and explanatory variables, and associated 95% confidence intervals (CIs) and chi-square tests, used household survey weights [68] and accounted for the complex survey design. Collinearity and missingness were examined for the explanatory variables. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 8 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Comparison of a single-level unconditional model and a two-level variance components model quantified the clustering in the dataset, confirming the appropriateness of multilevel models. Households (level 1) were nested in household clusters (level 2). Two final models were specified. Model 1 included significant household and household cluster level variables, and accounted for provider-level variation using dummy variables, as the small number of PCAs (<25) made it inappropriate to include the PCA as a level [69]. Model 2 included signifi- cant household, household cluster and provider explanatory variables. Backward elimination with a significance threshold of p = 0.05 was used for both models and type of piped service was retained as a control, despite not reaching this level of significance. An interaction term was considered when a valid hypothesis existed and was subsequently deemed meaningful. Survey weights were not used in the multilevel models, since the DHS only make weights avail- able at the household level due to concerns about disclosure risk [70]. Therefore, they were unavailable at the household cluster or provider level, as was required for this analysis. Lastly, random effects were added to the two models to calculate the proportion of variance within household clusters that could not be attributed to observed variables. Issues of multicollinearity were found between regulator- and provider-reported LPCD or service hours/day. Analysis found regulator and provider reporting for the same year (2017) to be very similar for LPCD, and identical for hrs/day (see section entitled ‘Data Sources and Availability Metrics’). Detailed analysis found a clear relationship between regulator LPCD in 2017 and 2018 (S2 Fig). LPCD was therefore used as the measure of availability. When com- paring regulator and provider perspectives during initial descriptive analysis, 2017 data was used for consistencies of the year. For all modelling however, to eliminate multicollinearity, only 2018 regulator data was used as it matched the year of user-reported availability and reflected the provider perspective. Results Comparison of provider, regulator and user availability In 2017, regulator records for service continuity (hrs/day) perfectly matched the provider data reported to IBNET. No utilities reported a continuous service for 24 hrs/day, with households receiving piped water for 18.4 hrs/day on average. The regulator and providers reported simi- lar quantities of water supplied (in LPCD). Reported quantities were positively correlated (rs = 0.86, p<0.001), though higher average LPCD was reported by providers (S16 Table). 2018 regulator-reported service continuity (hrs/day) is associated with user-reported avail- ability at PCA level (χ(4) = 56.04, p<0.001). Where the regulator reported continuity of 20 hrs/day, 54% of users reported interruptions. 2018 regulator LPCD and user-reported avail- ability were associated (χ(9) = 220.63, p<0.001), with evidence of a significant weak positive correlation (rs = 0.05, p = 0.007). Where the regulator reported supplying �50 LPCD and sub- sequently met the WHO benchmark, 55% of users reported an interruption to their supply. User-reported availability by provider coverage areas Overall, 47% (95% CI: 44%, 49%) of users reported experiencing at least one full day of inter- ruptions in the two weeks prior to being surveyed in 2018. The proportion of households reporting an interruption ranged from 77% (95% CI: 71%, 82%) in Kafubu WSC to 19% (95% CI: 15%, 24%) in Lukanga WSC (S8 Table). Fig 3 shows 2018 user-reported availability along- side regulator/provider-reported hrs/day. Only six of 177 household clusters had no household reports of piped water interruptions. Lusaka WSC supplied piped drinking-water to 44% of households in the sample. Southern WSC supplied a further 14% of sampled households. All other WSCs provided drinking-water to between 2% and 9% of sampled households. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 9 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Fig 3. Household and regulator/provider-reported availability of piped water in Zambia (2018). (Key to Utilities/Water and Sewerage Companies (WSC): NWWSC -North Western WSC; MWSC- Mulonga WSC; NWSC- Nkana WSC; KWSC- Kafubu WSC; LGWSC- Lukanga WSC; LPWSC- Luapula WSC; CWSC- Chambeshi WSC; ESWC- Eastern WSC; LWSC- Lusaka WSC; SWSC- Southern WSC; WWSC- Western WSC) (GADM 2018 base layer available at: https://gadm.org/index.html). https://doi.org/10.1371/journal.pwat.0000127.g003 Bivariate analysis of user-reported water availability 1398 of 3047 households included in analysis reported an interruption to their service, with the proportions of such households varying seasonally and by respondent’s native language (Table 1). Neither household wealth, household size, home ownership nor type of piped service were statistically significantly related to service interruptions (p>0.05). Month of interview was sig- nificantly associated with interruptions to drinking-water service (p<0.05), as was native lan- guage (p<0.05). As expected, type of piped service was significantly associated with household wealth (p<0.01). 68% of households in the richest wealth quintile had a piped service into their dwelling, compared to 2% in the poorest wealth quintile. 45% of those in the poorest wealth quintile used a public standpipe/tap compared to less than 1% in the richest wealth quintile. At the household cluster level, only degree of urbanisation and maximum length of resi- dency were significantly associated with interruptions (p<0.01) (Table 2). The relationship was non-linear, with reported water interruptions lowest at 32.2% for clusters with a PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 10 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Table 1. Proportion of urban Zambian households reporting a piped water service interruption in the preceding fortnight, by socio-economic characteristic (n = 3047). User Characteristics Weighted Percentages (%) of Households (n) Reporting an Interruption Chi squared (df), p- value Household Size 3.54 (4), 0.47 Native Language 1 person 2–3 people 4–6 people 7–9 people 10+ people English Bemba Lozi Lunda, Kaonde, Luvale Household Wealth Tonga Other Poorest Poorer Middle Richer Richest Home Ownership At least partly owns house Does not own No information Type of piped service Piped into dwelling Piped to yard/plot or Public tap/ standpipe Piped to neighbour Month of Interview (Season) July-August 2018 (Dry) September 2018 (Dry) October-November 2018 (Rainy) December 2018- January 2019 (Rainy) Provider Chambeshi WSC Eastern WSC Kafubu WSC Luapula WSC Lukanga WSC Lusaka WSC Mulongo WSC Nkana WSC North Western WSC Southern WSC Western WSC 42.6 (108) 48.2 (358) 47.2 (618) 46.7 (250) 40.3 (64) 25.8 (21) 50.3 (597) 49.1 (117) 50.8 (137) 44.6 (142) 44.1 (384) 45.5 (236) 44.3 (244) 47.0 (273) 48.8 (298) 47.1 (347) 50.8 (477) 45.0 (855) 43.0 (66) 47.7 (322) 47.0 (793) 44.7 (283) 49.8 (653) 43.8 (275) 47.2 (349) 32.6 (121) 57.3 (148) 38.1 (78) 77.0 (166) 58.2 (59) 18.7 (52) 42.4 (377) 58.8 (73) 53.3 (121) 48.2 (105) 41.9 (135) 42.7 (84) 52.69 (8), <0.001 0.84 (4), 0.93 6.19 (2), 0.05 1.40 (2), 0.50 17.56 (3), 0.001 245.87 (10), 0.93 (Continued ) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 11 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Weighted Percentages (%) of Households (n) Reporting an Interruption Chi squared (df), p- value 46.7 (1398) Table 1. (Continued) User Characteristics Total households reporting an interruption: Note: n is an unweighted count * p<0.05 *p<0.01 https://doi.org/10.1371/journal.pwat.0000127.t001 maximum length of residency of 31–40 years. At the provider level, GDP per capita, regulator- and provider-reported service continuity, regulator- and provider-reported LPCD and non- revenue water were all associated with service interruptions (p<0.01). User-reported inequalities in water availability by provider Table 3 presents the socio-economic differences in household characteristics of those reporting interruptions to their service, by provider. Ratios of those reporting an interruption are used as a measure of inequality within each PCA. Inequalities in those experiencing an interruption between providers are sometimes large, though not necessarily significant, when considering a range of household characteristics. Between PCAs, inconsistencies often existed in which household group reported the most interruptions. In 82% of PCAs more one-person households experienced interruptions than households with >10 members. In all PCAs, more households using a public standpipe or a service in their yard experi- enced interruptions than those with a service in their dwelling. In Luapula WSC for example, households using a public standpipe or a service in their yard were 8.9 times more likely to experience an interruption. Households that at least partly owned their home were more likely to report an interruption than those that did not, in all but one PCA. Variation exists in the Table 2. Association between household-reported availability and household cluster level and provider level explanatory variables in urban Zambia (n = 3047). Explanatory variable Chi-squared (df) p-value Household Cluster Level Factors Neighbourhood wealth Maximum length of residency Change in urbanisation Degree of urbanisation Water service provider-level Factors GDP per capita at service provider level Regulator hrs/day Provider hrs/day Regulator LPCD Provider LPCD Non-Revenue Water Note p<0.05 p<0.01 https://doi.org/10.1371/journal.pwat.0000127.t002 1.4 (2) 53.9 (3) 1.4 (2) 15.6 (3) 245.9 (10) 56.0 (4) 144.3 (6) 220.6 (9) 245.9 10) 214.7 (10) 0.50 <0.001 0.49 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001** PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 12 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Table 3. User-reported inequalities in piped water interruptions, by provider. Provider Percentage (%) (weighted) difference of households reporting an interruption between population sub-groups with differing socio- economic characteristics Ratio of households reporting an interruption between population sub- groups with differing socio-economic characteristics Richest versus Poorest HHs Partly Owns Home versus Does not Own 4.8 -29.5 20.6 -2.5 32.3 20.8 One person versus >10 people HHs 0.8 7.0 3.7 15.7 -7.5 -5.4 19.5 3.4 39.0 21.0 7.7 3.7 39.2 19.3 -1.0 44.0 15.4 -0.1 17.9 1.0 6.5 18.0 -19.5 10.7 21.6 -31.1 1.3 Chambeshi WSC Eastern WSC Kafubu WSC Luapula WSC Lukanga WSC Lusaka WSC Mulongo WSC Nkana WSC North Western WSC Southern WSC Western WSC Yard/ Public Standpipe versus Supply in Dwelling Interviewed in Dry (July/ Aug) versus Rainy Season (Dec/Jan) Majority versus Minority Language in WSC Area Partly Owns Home / Does not Own Richest / Poorest HHs One person / >10 people HHs Yard/ Public Standpipe / Supply in Dwelling Interviewed in Dry (July/ Aug) / Rainy Season (Dec/ Jan) Majority / Minority Language in WSC Area 21.4 25.4 19.2 62.6 49.0 44.9 55.9 55.9 40.1 16.2 12.0 16.5 7.6 45.2 32.6 53.8 48.8* 29.8 55.6* 52.0 63.4 29.6 93.0 84.5 63.1 84.4 41.9 46.5 73.7 62.8* 53.5 47.6* 71.8* 1.2 1.8 2.6 1.6 1.8 3.4 3.3 4.0 1.0 1.7 2.4 0.3 0.9 2.9 0.8 1.1 5.0 4.1 3.4 2.3 0.2 0.4 1.1 2.4 2.1 0.2 2.0 2.1 0.8 1.2 3.7 4.5 1.3 1.6 1.8 1.9 8.9 5.1 3.7 7.0 5.8 3.3 1.5 1.3 1.9 1.4 6.3 4.5 4.3 -* 8.8 -* 10.1 9.9 2.2 65.8 28.8 45.4 21.0 14.1 116.1 69.4 -* 44.4 -* -* Negative difference values and ratios below 1.0 indicate lower reported interruptions in the first population sub-group named in column heading. Counts and weighted percentages of the number of households reporting an interruption for each inequality are presented in S9–S14 Tables. S15 Table & S3 Fig present household cluster inequalities in water availability by provider. Household did not report an interruption in one of the socio-economic categories being compared. https://doi.org/10.1371/journal.pwat.0000127.t003 magnitude of this inequality (Table 3). In 55% of PCAs, the richest households experienced interruptions more than the poorest. Differences in household interruptions between those speaking the majority and minority language are consistent between each PCA. Households interviewed in the drier months of July-August were more likely to report an interruption than those interviewed in wetter December-January, regardless of PCA. Some patterns of socioeconomic inequalities in households reporting an interruption may be mediated by PCA characteristics. In urbanised PCAs, the gap between households speaking the majority language that report an interruption, compared to the minority language, is nar- rower than for less urbanised PCAs. For both home ownership and household wealth, the rich- est PCAs had the greatest inequalities in service interruption risk. Conversely, when comparing households using a yard or public tap versus those with water piped to their dwell- ing, or those speaking the majority versus minority language, the richest PCAs had the smallest differences in those reporting an interruption. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 13 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Multi-level logistic regression Table 4 includes the multi-level logistic regression results for two models. Model 1 includes selected household and household cluster factors that explain inequalities in piped-water inter- ruptions with provider accounted for via dummy variables. Model 2 shows the association between socioeconomic characteristics at the household, household cluster and provider-level and the likelihood of households reporting an interruption. When provider is accounted for in model 1, type of piped service is not significant, whereas in model 2 it is. Households using a neighbour’s water had 33% greater odds of reporting an interruption than those with water piped to their dwelling (p<0.1). In model 1, households interviewed at the peak of the dry season had 1.91 times the odds of an interruption than those interviewed in the middle of the rainy season (p<0.05). In model 2, households interviewed outside the wettest months of December-January had 2–3 times the odds of experiencing an interruption (p<0.05). In both models, household ownership is significantly associated with experiencing an inter- ruption. The odds of experiencing an interruption were 1.31–1.32 times higher for those that at least partly owned their home (p<0.001). Availability of services varies significantly with household native language. In both models, those who speak Luvale/Kaonde/Lunda have greater odds of experiencing an interruption than English speaking households (p<0.05). When accounting for provider, compared to English speaking households, Tonga speakers were 1.78 times more likely to have an interruption (p<0.1) whereas in model 2, Bemba speak- ing households had 1.88 time the odds (p<0.1). Households in neighbourhoods classed as having mid-level wealth had 4.31 times the odds of experiencing an interruption than those in poor neighbourhoods (p<0.01) (model 2). Neighbourhood wealth was found to interact with regulator-reported service availability. Compared to poor clusters, for every one litre increase in regulator reported LPCD, house- holds in mid-wealth clusters had 3% lower odds of reporting an interruption (p<0.05). GDP per capita is the only provider-level factor associated with household availability (model 2). For each $1000 increase in GDP per capita, the odds of reporting an interruption are 0.51 times less (p<0.01). GDP per capita at service provider level interacts with regulator- reported LPCD. For every one unit increase in regulator-reported LPCD and GDP per capita at service provider level, the odds of having an interruption were 1.02 times greater (p<0.01). Substantial variations between providers are evident when controlling for home ownership, type of piped service, month of interview and native language (model 1). When comparing the odds of households reporting an interruption to areas supplied by Lusaka WSC, households supplied by Lukanga WSC had 64% lower odds of experiencing an interruption (p<0.05) whilst in Nkana the odds were 2.24 times higher (p<0.05) and Mulongo WSC 3.77 times higher (p<0.05). Similarly, in Chambeshi WSC the odds of having an interruption were 2.43 times higher (p<0.05) and in Luapula they were 2.89 (p<0.05). In Kafubu WSC, households have eightfold increased odds of reporting experiencing an interruption than those whose pro- vision is from Lusaka WSC (p<0.01). The variability in households’ likelihood of experiencing an interruption that was not explained by the household and household cluster factors used in the multilevel models was exam- ined through the Intra-Class Correlation (ICC). The ICC for model 1 shows that 27% of the remaining unexplained variation in reported interruptions lies between household clusters, whilst 73% lies within household clusters. In model 2, 31% of the variation lies between clusters whereas 69% is within clusters. These results suggest that between a quarter and a third of the likelihood of experiencing an interruption is related to the cluster that someone lives in, with the remainder due to the specific household, after accounting for the variables within the models. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 14 / 26 PLOS WATERTable 4. Multi-level logistic regression analysis of user-reported interruptions to piped water services in the urban and peri-urban population of Zambia, 2018 (n = 3047, groups = 177). Risk factors for urban intermittent piped drinking-water Parameter Intercept Household-level Factors Native Language (ref.: English) Bemba Luvale, Kaonde, Lunda Lozi Other Tonga Month of Interview (season) (ref.: Dec 2018-Jan 2019 (Rainy)) Sept 2018 (Dry) Oct-Nov 2018 (Rainy) July-Aug 2018 (Dry) Type of piped service (ref.: Piped into dwelling) Piped to yard/plot or Public tap/standpipe Home ownership (ref: Does not own) Provider-level Dummy Variable Provider (ref.: Lusaka WSC) Piped to neighbour At least partly owns No information Chambeshi WSC Eastern WSC Kafubu WSC Luapula WSC Lukanga WSC Mulongo WSC Nkana WSC North Western WSC Southern WSC Western WSC Household cluster-level Factors Neighbourhood Wealth (ref.: Poor) Provider-level Factors Regulator LPCD GDP per Capita at Service Provider Level Regulator LPCD x Neighbourhood Wealth (ref.: Poor) Regulator LPCD x GDP per Capita at Service Provider Level Random-effects Parameters Between PSU variance Intraclass Correlation Coefficient (ICC) Intra-PSU correlation coefficient Middle Rich Middle Rich Model 1a Model 2b Odds Ratio 0.18 Std.Err. 0.089 Odds Ratio 0.08* Std.Err. 0.05 1.53 2.06** 1.81 1.60 1.78* 1.64 1.61 1.91 1.20 1.32 1.31* 1.10 2.43 0.95 8.02* 2.89 0.36 3.77** 2.24* 1.39 0.70 1.18 - - - - - - - 1.21 0.27 0.50 0.74 0.66 0.52 0.62 0.60 0.53 0.61 0.15 0.20 0.13 0.23 0.94 0.40 3.40 1.45 0.15 1.85 0.90 0.65 0.25 0.54 - - - - - - - 0.19 0.03 1.88* 2.30 1.85 1.77 1.73 2.95* 2.36 2.51* 1.24 1.33* 1.32*** 1.10 - - - - - - - - - - 4.31* 3.22 1.01 0.51* 0.97 0.98* 1.02* 1.47 0.31 0.60 0.81 0.66 0.57 0.59 1.12 0.80 0.84 0.16 0.21 0.13 0.23 - - - - - - - - - - 3.29 2.31 0.01 0.11 0.01 0.01 0.01 0.23 0.03 (Continued ) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 15 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Table 4. (Continued) Log likelihood Deviance Parameter Odds Ratio Std.Err. Odds Ratio Std.Err. Model 1a Model 2b -1787.03 3574.00 -1799.69 3599.00 * p<0.01 ** p<0.05 * p<0.1; PSU: Primary Sampling Unit (household clusters) aOnly significant household and household cluster level variables, plus provider dummy variable bOnly significant household, household cluster level and provider variables https://doi.org/10.1371/journal.pwat.0000127.t004 Discussion Insights into equalities in piped water continuity through integrated analysis of survey, regulator, and provider data sets Integrated analysis of utility, regulator, and household survey data provides some insights into inequalities and risk factors for piped water continuity in urban Zambia that would not be apparent from analysing any one data set in isolation. There are few statistically significant inequalities at the household level (S17 Table, Model 2) and significant cluster- and provider- level predictors. After controlling for household characteristics, this nationally representative analysis of urban DHS data highlights provider-level inequality in service continuity (Fig 3) [13]. 19% of households reported an interruption in Lukanga, compared to 77% in Kafubu WSC (Table 1), with Kafubu, Mulongo, and several other WSCs having significantly higher odds of household-reported interruptions relative to Lusaka after controlling for household characteristics (Table 4). Similarly, a study [18] comparing DHS with IBNET for Peruvian regions also found high inter-provider and inter-province intermittency, with lower house- hold-reported intermittency in Lima, the capital, than most other regions. Whilst there is some tentative evidence in our models that such provider-level variation in water continuity may relate to utility operational indicators such as LPCD, this variation is also associated with regional socio-economic variation such as in GDP per capita (Table 4). Alongside provider-level patterns, variation in household-reported water continuity is evi- dent at the household and household cluster level (Tables 2 & 4). Regulatory reports and pro- vider performance databases such as IBNET do not differentiate the level of service provided to different household groups within their PCAs. However, it is known that ethnic minorities, the poor, and other disadvantaged groups may receive poorer quality services [13]. In this analysis, we integrated PCA boundaries with household survey data to examine such inequali- ties. We find some evidence of urban inequalities in the availability of piped water services in relation to ethnicity (measured via native language), home ownership and seasonality, but not household wealth. We find no significant evidence that such inequalities vary between PCAs. Key findings concerning household- and cluster-level risk factors were as follows: • We found native English speakers less likely to report interruptions compared to respon- dents speaking all other native languages. This could reflect the regional distribution of eth- nicity in relation to service availability. Ethnicity is an important factor contributing to water inequalities globally, with indigenous populations comprising 15% of the world’s poor [1]. • Households in mid-wealth clusters were less likely to experience an interruption than those in poor clusters. This could reflect households in wealthier neighbourhoods adopting PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 16 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water protective measures against interruptions [10, 71] and having greater capacity to address ser- vice network failures. • Counter to our hypothesis, we found households who owned their home were more likely to experience interruptions than those that did not. • Households using a neighbour’s piped water also had higher odds of experiencing an inter- ruption than those with water piped to their dwelling. This could reflect household reluc- tance to sell their water to neighbours during times of water scarcity. • As suggested by other authors [18], reported water continuity varied seasonally, with house- holds interviewed in peak rainy season having lower odds of reporting an interruption (Tables 1 and 4). Although multi-country analyses suggest exposure to microbially contaminated or inade- quately chlorinated water is often greater among poorer households [72, 73], we found Zam- bian households in both poor and wealthy clusters were more exposed to water interruptions relative to those in mid-wealth clusters. Criteria concerning availability and water safety may therefore affect socio-economic inequalities in access to safely managed water services. Comparability and consistency of survey versus provider and regulatory data It is clear the data landscape is complex, which is exacerbated by the contrasting approaches to measuring availability [14, 15]. The extent of our analysis has been limited due to the use of two very different metrics of availability: regulator and provider reported yearly mean service hours or LPCD, whilst DHS household survey respondents reported service interruptions in the past fortnight. Despite this, we find household survey reporting is correlated with provider/regulator reporting (S17 Table). Differences existed between provider and regulator reporting depend- ing on the metric in question, however generally they are highly consistent with one another (S16 Table). This is as expected given NWASCO regulator reports use provider data, but the use of regulator data is preferable for monitoring, given regulator independence from service provision and incentives for providers to report higher service continuity [11]. In linking household survey, regulator and provider data sets to evaluate their consistency, we adopted a different approach to a previous study in Peru [18]. This study aggregated data for PCAs to region level and compared household- and regulator-reported metrics at the region level. In contrast, we used multi-level modelling to assess whether contextual provider- or regulator-reported water continuity metrics were associated with household-level reports of service interruptions. Our approach thus enables simultaneous evaluation of provider-level versus household-level risk factors for supply interruptions, whilst avoiding the known prob- lems of areal aggregation [74]. Particularly for larger service providers, our approach also pro- vides insights into intra-provider socio-economic inequalities in access to an uninterrupted water service via household reports collected independent of the service provider (Table 2). Limitations affecting Zambia study This analysis only captures household respondents who report an interruption of at least one full day in the two weeks prior to being surveyed. As such, recall bias will likely exist in the user data [75] whereby respondents may fail to remember or misremember interruptions in their service. For example, respondents using a 2-week recall period systematically under- reported child diarrhoea relative to a 1-week period [76]. Additionally, respondents who PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 17 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water spend more time away from their homes may be unaware of short duration outages. Thus, there are likely to be households who experience shorter- and longer-term service interrup- tions, that are not captured in household survey data. Source of drinking-water and water used for other purposes form inputs to the DHS wealth index, but not reported supply interrup- tions. This complicates WASH inequality analyses [77] and could have increased collinearity between the wealth index and source type in our models, but we did not find empirical evi- dence for this. The quality of provider and regulator data depends greatly on the accuracy of the data reported by each WSC [14]. Data supplied by providers may be of limited reliability as they may lack any form of independent verification [78]. Between datasets, definitional differences existed in the classification of urban/rural areas [31, 34]. Households were excluded based on the DHS urban/rural classification, despite the regulator or provider potentially classifying them as urban. Additionally, given the DHS used a 2010 rural/urban classification [34, 79], it is likely that some rural areas may have become urbanised. The issue of differing classifications is exacerbated by the use of GHSL population data to create cluster-level locality and change in urbanisation variables. GHSL data uses a finer spatial scale classification [80] that differs to the DHS classification. Thus, despite exclud- ing all rural households from the analysis, inconsistencies between datasets are evident. For both variables, some household clusters are classified as rural or unpopulated areas, despite the DHS classifying them as urban. This could also be a result of the DHS’s displacement of house- hold cluster locations [35], though the effect is likely minimal as GPS clusters were not dis- placed across province boundaries and WSCs are coincident with provinces. The higher-level explanatory variables for wealth also have limitations. The neighbourhood wealth variable was calculated by aggregating household-level DHS wealth index values to household cluster level. This will however mean that closely located extreme differences in wealth are unaccounted for. The provincial wealth variable used provincial GDP data that is based on where industries produce their goods or where their head office is located. Therefore, the data may not be a true representation of wealth in each PCA [65]. Additional explanatory variables were considered for analysis, including ‘voice’ [9], gender of the person collecting water [81], storage tank ownership [82] and blue water scarcity [83]. In all instances, inclusion was not possible as there was either no available data, limited vari- ability across Zambia or reasonable proxies did not exist. Barriers to methodological transferability Our integrated analysis of household survey and water utility databases highlights data-related barriers at present to methodological transferability at both international and national level. In the context of Zambia, comparison has been made possible by a relatively simple data land- scape, where PCAs largely coincide with DHS regions. At present, exploring inequalities else- where via this methodology would only be possible in countries where household surveys have measured service interruptions and for WSCs serving large populations, with known geo- graphic coverage areas. Given that household survey cluster coordinates are randomly dis- placed to preserve confidentiality [22], misallocation of households to providers is more likely where PCAs are small. Thus, our approach is more suited to countries with several large-scale water service providers, as opposed to countries dominated by small-scale providers or a single national provider (e.g. Ghana Water Company Limited). Elsewhere, the availability of data from all three streams is currently restricted to eastern and southern Africa (Fig 1 and S1 Fig; S1–S3 Tables), but analysis will become possible in more countries as data availability increases. By 2030, the data landscape may change considerably with greater availability of PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 18 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water household survey data on water service continuity, from government via regulators, from utili- ties as service providers and potentially other new forms of data such as via social media or sensors [84]. Therefore, whilst the current transferability of methods is reliant on present-day data availability, as monitoring expands, the applicability and transferability of this analysis may broaden. Within Zambia, data were only available for urban and peri-urban services from the pro- vider and regulator, which limited the scope of this analysis to include only the urban and peri-urban third of the Zambian population. Compared to user data which has national cover- age, both datasets only include the population served by large-scale reporting commercial utili- ties and may exclude services from small-scale private schemes/companies [85], micro- operators managing delegated services [86], and community-managed piped services [18]. Implications for international monitoring The JMP only makes estimates for safely managed drinking-water when there are data avail- able on water quality and at least one other element (accessibility or availability) that repre- sents at least half of the population in question (e.g. country) [87]. The development of methods which could help to reach this criterion threshold, for example by using data from multiple perspectives, will be critical in better analysing WASH for international agendas such as the SDGs. Improvements in data, coupled with a standardised process by which data are processed to give nationally representative and internationally comparable insights into drink- ing-water availability are needed, especially in order to understand inequalities between popu- lation groups [16]. Definitions of provider-reported average service hours requires further clarity. For exam- ple, does it represent the hours a pump in a piped network is operated or the average duration of service households receive? Several suggestions have been made to address this, such as reg- ular random household surveys by providers or the use of sensors to detect outages [18]. At present, provider reporting of hrs/day is unclear and it is unknown what the number of days between service is. We recommend further smaller scale studies of drinking-water availability. Whilst our anal- ysis bears similarities to Rawas et al. [18] in its regional/provincial analysis, future work that resonates more closely with Bellaubi et al.’s [11] finer scale case study analysis would give more detailed understanding of the availability of piped services. To achieve this, we recom- mend providers and regulators report availability for smaller geographical units so that house- holds can be better matched with provider jurisdictions. This would also be enhanced by utilities providing their service areas as coverage area boundaries to IBNET to facilitate data integration. Additionally, a future multi-country study could potentially explore other IBNET- derived management indicators (e.g. concerning staff training or adequacy infra-structure investment [88]) alongside those relating to service availability. However, care would be needed to control for other regional covariates that could explain water service continuity, such as regional GDP. Greater uptake of the JMP’s expanded WASH questions by national statistical agencies via household surveys, such as the DHS, would facilitate further, more widespread evaluation of regulator and provider data concerning water availability. Alongside the DHS question ana- lysed in our study, these expanded questions also include asking households ‘how many hours per day is water supplied on average?’ [2]. Whilst uptake such questions depends on survey implementation resources and national priorities [15], it would enable direct comparison with regulator and provider reporting of service availability via a metric common to all data sources. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 19 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water Conclusion This study demonstrates the additional insights into risk factors associated with piped drink- ing-water availability by incorporating utility and regulatory data into household survey analy- sis for urban and peri-urban Zambia. At household and household cluster (neighbourhood) level, when adjusting for confounders through multi-level modelling, inequalities are minimal. Wealth was the only neighbourhood risk factor found to influence service availability. At the household level, home ownership, month of interview, native language and type of service had a modest effect. At provider-level, inequalities between PCAs were found in household report- ing of interruptions to services. Our analysis builds on existing assessments of drinking-water services [11, 18] by including the additional perspective of the regulator. We find correlations between user, provider and regulator reports of service availability, but direct comparison is difficult, due to variations in availability metrics used. Limited data availability also restricts more widespread, integrated analyses of these data in rural areas and across SSA. Moving forward, greater availability of water service continuity data from all three data sources should enable assessment of socio- economic and geographic inequalities in access to uninterrupted water services and potential for understanding how water management indicators relate to household-related interruptions in a wider set of countries. However, more widespread, integrated use of utility or regulator data requires investment in government monitoring of intermittency in small-scale commu- nity supplies to better understand rural service access. It also requires investment in utility cov- erage area map layers to facility spatial data integration with household surveys. Supporting information S1 Fig. Flowchart showing inclusion criteria for identifying study country, with reasons for excluding African Union member states. (TIF) S2 Fig. Scatterplot showing the similarities between 2017 and 2018 regulator reported LPCD. (TIF) S3 Fig. Provider inequalities compared to differences in household socioeconomic charac- teristics, for households that reported experiencing an interruption to their piped water service. (TIF) S1 Table. Year of all available data streams for the 13 shortlisted countries. Colour depicts the data stream with dark green = regulator data, mid-green = user data and light green = provider data; black box indicates where data for all three streams is available for the same year. (TIF) S2 Table. Metrics of availability used by all available data streams for the 13 shortlisted countries. Colour depicts the data stream with dark green = regulator data, mid-green = user data and light green = provider data; black box indicates where the same metric of availability is used for multiple data streams for a given country. (TIF) S3 Table. Level of disaggregation for all available data streams for the 13 shortlisted coun- tries. Colour depicts the data stream with dark green = regulator data, mid-green = user data and light green = provider data; black box indicates where the same metric of availability is PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 20 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water used for multiple data streams for a given country. (TIF) S4 Table. Commercial Water Supply Company (WSC) connections and population ser- viced in 2018. (TIF) S5 Table. Outcome and explanatory variables, and their sources, included in the multi- level model analysis. (TIF) S6 Table. DHS household and men’s survey questions relating to source and continuity of drinking-water supply and socio-economic characteristics. (TIF) S7 Table. GDP and GDP per capita based on 2015 current prices, in Zambian Kwacha (ZKW) and US Dollars (US$) for WSC coverage areas. (TIF) S8 Table. Proportion of households reporting a full day’s interruption in their piped water supply in the preceding fortnight, per provider, with 95% confidence intervals. (TIF) S9 Table. User-reported inequalities, by month of interview, in piped-water interruptions by provider. (TIF) S10 Table. User-reported inequalities, by type of supply, in piped-water interruptions by provider. (TIF) S11 Table. User-reported inequalities, by household size, in piped-water interruptions by provider. (TIF) S12 Table. User-reported inequalities, by household wealth, in piped-water interruptions by provider. (TIF) S13 Table. User-reported inequalities, by tenure, in piped-water interruptions by provider. (TIF) S14 Table. User-reported inequalities, by native language, in piped-water interruptions by provider. (TIF) S15 Table. User-reported inequalities in piped-water interruptions by provider: The per- centage (weighted) of households, by household cluster characteristics, reporting an inter- ruption to their supply in each PCA. (TIF) S16 Table. Assessment of average annual piped water service delivery in urban and peri- urban Zambia in 2017/18 (n = 3047 HHs). (TIF) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000127 February 5, 2024 21 / 26 PLOS WATERRisk factors for urban intermittent piped drinking-water S17 Table. Exploration of clustering in reported service interruptions through comparison of a single-level unconditional model with a two-level variance components model (n = 3047). (TIF) S1 Data. (7Z) Author Contributions Conceptualization: Mair L. H. Thomas-Possee, Andrew A. Channon, Robert E. S. Bain, James A. Wright. Data curation: Mair L. H. Thomas-Possee. Formal analysis: Mair L. H. Thomas-Possee. Funding acquisition: Andrew A. Channon, James A. Wright. Investigation: Mair L. H. Thomas-Possee, Andrew A. Channon, James A. Wright. Methodology: Andrew A. Channon, James A. Wright. Project administration: Mair L. H. Thomas-Possee. Supervision: Andrew A. Channon, Robert E. S. Bain, James A. Wright. Visualization: Mair L. H. Thomas-Possee. Writing – original draft: Mair L. H. Thomas-Possee. Writing – review & editing: Andrew A. Channon, Robert E. S. Bain, James A. Wright. References 1. UN Water. Sustainable Development Goal 6: Synthesis Report on Water and Sanitation. United Nations. New York, USA; 2018. 2. WHO, UNICEF. Core questions on water, sanitation and hygiene for household surveys: 2018 update. New York, NY, USA.; 2018. 3. Howard G, Bartram J. Domestic Water Quantity, Service Level and Health. World Health Organization. Geneva, Switzerland; 2003. 4. Howard G, Bartram J, Williams A, Overbo A, Fuente D, Geere J-A. 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10.1371_journal.pone.0300377
10.1371_journal.pstr.0000095	RESEARCH ARTICLE The macroeconomic money-nature nexus: Are growing money supplies a relevant obstacle on the way to an ecologically sustainable global economy? Stefan Mo¨ ckelID* Research Unit Environment and Society, Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany * stefan.moeckel@ufz.de Abstract Production, consumption and nature depletion have been growing rapidly for more than 300 years, even faster than exponential population growth. A comprehensive understanding of the causes behind this great acceleration is necessary if we are to achieve a sustainability transformation. This paper is intended to draw the attention in the sustainability debate to the amounts of money that have been growing rapidly all over the world. The money supply has not been a main focus so far, since for the economic mainstream it is not a growth driver, as according to the neoclassical view, money growth is largely neutralized by inflation, while for Keynesians it merely follows economic growth. However, the growing money supply means greater liquidity for consumption as well as for investments in production, assets and resource exploitation. An expansion of the money supply is even a prerequisite for a simulta- neous increase in investment and consumer spending. At the same time, the expansion in the supply of raw materials, goods and services keeps inflation rates behind money growth globally. The paper aims to identify and illustrate the causalities of how the money supply and the use of natural resources are interconnected by means of economic activities. This money-nature nexus would explain why, due to high money growth rates, both the real economy and the depletion of natural resources and ecosystems continue to increase despite all efficiency improvements and sustainability efforts to date. It should therefore be a realistic fear that without a global limitation of exponential money growth, ecological sustain- ability cannot be achieved. Author summary The money supply and the use of natural resources are interconnected by means of eco- nomic activities. This money-nature nexus explains why, due to high money growth rates, both the real economy and the depletion of natural resources and ecosystems continue to increase despite all efficiency improvements and sustainable transformation efforts to a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mo¨ckel S (2024) The macroeconomic money-nature nexus: Are growing money supplies a relevant obstacle on the way to an ecologically sustainable global economy? PLOS Sustain Transform 3(1): e0000095. https://doi.org/ 10.1371/journal.pstr.0000095 Editor: Alka Bharat, Maulana Azad National Institute of Technology, INDIA Received: January 23, 2023 Accepted: January 5, 2024 Published: January 31, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pstr.0000095 Copyright: © 2024 Stefan Mo¨ckel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The Fig 1 in the main text as well as in the supplementary information the Figure A in S1 Appendix and the Figures A to D in S2 Appendix are based on publicly available PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 1 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONsources. The data is available through the references [144,151,154–158]. Funding: The manuscript has been elaborated independently and funded by the Helmholtz Centre for Environmental Research – UFZ, the author’s employer. The UFZ had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. The macroeconomic money-nature nexus date. It is therefore to be feared that without a global limitation of exponential money growth, environmental sustainability of the global economy cannot be achieved. 1. Introduction “Rapid growth of output is the distinguishing feature of modern times and contrasts sharply with human history going back to its origins millions of years ago. This is perhaps the cen- tral economic fact of the century.” [1p. 501] Over the past 300 years, humanity, and to an even greater extent the volume of goods and ser- vices produced as well as the use of natural resources as a source of raw materials and as a sink for waste has grown exponentially [2–7]. This development, also known as the Great Accelera- tion, is unique in human history [8,9]. The global output growth per person rose from a level of 0.016%/a before the 18th century to an average of 2.1%/a in the period from 1700 until 2012 [5], which is an increase in the economic output per person by over 1100%. Due to the simulta- neous population growth of over 1000% (from 0.6 to 7.05 trillion), the total output of mankind grew by more than 14000% (from 495 to 71,169 trillion in constant EUR 2012) over the last 300 years. Since 1950, global production output O has grown over 3%/a in real terms [5]. Between 1971 and 2016 the global real GDP (cf. OECD definition [10]), which measures national income Y and the total production output of goods and services produced in a given year in monetary terms (Y = PGDP×O), increased inflation-adjusted annually by 3.06%/a (see Fig 1 and Table B in S1 Appendix). By contrast, the population grew only 1.53%/a. Over this time span, natural resource consumption also increased faster than the population. The global land use for settlements and transport (built-up land) has been the fastest growing environmental factor with rates of 2.88%/a highly correlated with real GDP and the con- sumption expenditures of households and governments (3.03%/a). Global material con- sumption and material extraction have increased by 2.65%/a and 2.64%/a in correlation with GDP, whereby CO2 emissions have increased by 1.89%/a. Even if a relative decoupling of GDP growth from energy, material resources and greenhouse gas emissions can be observed during this time period (cf. [11]), further economic growth in combination with population dynamics brings mankind even closer to the ecological limits of planet Earth [6,11–14]. Although technological progress has enhanced energy and resource efficiency since the beginning of the industrial revolution, a faster growing demand has further increased overall natural resource consumption [15–19]. Up to date, there is no sign of an absolute decoupling of resource consumption from economic growth at the global level as efficiency improve- ment or recycling rates lag behind the growing demand and regional decoupling effects are mainly based on spatial displacements [2,3,14–16,20]. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the OECD therefore con- cluded in 2019: „Yet, existing evidence shows that current strategies and practices have not accomplished a decoupling of economic growth from energy and materials consumption over an extended PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 2 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus Fig 1. Comparison of global growth trends of non-monetary or deflated indices from 1971 to 2016 (45 years). Own presentation using data from the World Bank, the OECD, the UN Environment International Resource Panel/WU Vienna/CSIRO, the Global Footprint Network (GFN) and the WWF/Zoological Society of London (for details see Table B in S1 Appendix). Note: The nominal monetary values were deflated with the implicit GDP deflator, which reflects the ratio of GDP in current US$ to GDP in constant US $ 2010 both from the World Bank. https://doi.org/10.1371/journal.pstr.0000095.g001 time span. Without an adjustment of orientations and priorities, including an effective instrumentation of such policies, a sustainable economy is not going to be achieved.” [4Ch. 6, p. 141] „This Outlook projects that, in the absence of new policies, global materials use would rise from 89 Gt in 2017 to 167 Gt in 2060. This growth is reflected in all major categories of materials: metallic ores (9 to 20 Gt), non-metallic minerals (44 to 86 Gt), biomass (22 to 37 Gt) and fossil fuels (15 to 24 Gt). In addition, the extraction, processing and disposal of materials brings significant environmental consequences, which will be magnified as mate- rials use increases. These include a doubling of greenhouse gas emissions, pollution to the soil, water and air, and toxic effects on humans and aquatic and terrestrial ecosystems.” [21p. 3] Currently, there is a vibrant debate about the causes of the Great Acceleration. A correct and comprehensive understanding of the factors and drivers is crucial for finding ways towards an ecologically sustainable development that ends the exponential increase in the use of natural resources and enables a primarily qualitative development within the planetary boundaries. So far, the most important causes of the great acceleration have been regarded as: scientific and technological progress, the development of free citizenship and free enterprise, PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 3 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus the discovery of fossil fuels, as well as the expanding human population and rising levels of education [1,22–25]. What is striking about the Great Acceleration is that the forerunners are the volumes of money as well as debt on a global scale (see Fig 1 and Table B in S1 Appendix) and in particular in the USA and China (see S2 Appendix). Between 1971 and 2016 the global amount of money increased nominally by 8.94%/a whilst debt increased by 8.68%/a (see Fig A and Table A in S1 Appendix). This was faster than nominal GDP (7.24%/a) and much faster than inflation (4.05%/a), representing the rates of change in consumer price indexes (CPI) and also referred to as the GDP deflator. After 45 years, those differences have increased even further, so that global GDP is now barely half the size of the money supply and the credit volume. In 2017, worldwide public and private debt exceeded US$184 trillion [26], which is more than 225% of the global Gross Domestic Product (GDP). Between 1971 and 2016, money growth in nominal terms was more than 9 times higher than the US Consumer Price Index or the implicit GDP Deflator and more than 47 times higher than world population growth (see Table A in S1 Appendix). This is an important finding as even small differences in annual growth rates lead to large and exponentially growing differences in absolute terms in the long run (see Fig B in S3 Appendix). The exponential expansion of the amount of money is possible because today’s money predominantly exists as immaterial deposit money, which is created by central and pri- vate banks out of nothing when granting loans or buying bonds (see section 2). The paper examines, if and how this globally growing amount of money increases the human use of natural resources. Because if the exponentially growing money supply has been a major driver or at least an enabler of global economic growth, and if economic growth is accompanied by growing resource use in spite of all those advances in efficiency, then rising money supplies are a critical barrier to a global economy that can function within the planet’s boundaries. Therefore, this is a central topic to humanity’s future sustainable transformation policy. It should be noted that for a sustainable global economy, the total money supply and not the per capita money stock is decisive. The latter comes into play in the distribution of wealth and natural resources, which is currently highly unequal due to the very different indi- vidual liquidity conditions [27–29]. Since the introduction of paper money there has been a discussion [30–33] about the conse- quences of money growth for the economy, society and nature [34,35]. With recent financial crises and the impending climate crisis, the discussion has resurfaced, although the debate in ecological macroeconomics focuses primarily on pricing mechanisms, promoting green investments and possible monetary growth imperative [36–43], the ecological impact of grow- ing money supplies receives attention only from few authors [34,44–49]. Generally, it is recognized that money, as freely available capital (liquidity), and the lending thereof are of fundamental importance in modern capitalist economies, since money enables maintenance and growth investments in physical capital and production as well as matches the demand and supply for goods, labor and assets [1,23,24,35,50–53]. Money circulates through the entire economy in exchange for labor, goods, services and assets (see the standard model of economic flows in Fig 2), which is why the growth in credit and liquidity is generally seen as a sign of economic prosperity, but doubts are starting to grow [26,34,54–57]. However, the standard model does not reflect the growth in money and credit volumes (see section 2). The reason for this is that, in spite of its acknowledged importance and general eco- nomic presence, money is not a driver of economic growth according to mainstream eco- nomic opinion (see section 3). The paper points out how both the orthodox assumption of a neutralization of the money supply from inflation within 2–3 years and the heterodox assump- tion of a merely temporary monetary expansion initiated by economic growth do not stand up PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 4 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus Fig 2. General macroeconomic model of mutually balancing economic flows and money circulation. Own presentation. AD = Aggregate Demand of Goods and Services; AS = Aggregate Supply of Goods and Services; C = Consumption Expenditures of Households; I = Financial Investments; IE = Investment Expenditures of Corporations; IM = Imports; G = Government Expenditures; GS = Government Subsidies; L = Labor; Lo = Loans; R = Returns (interests, dividends, repayments etc.); S = Savings (including asset purchase); T = Taxes; W = Wages; X = Exports. https://doi.org/10.1371/journal.pstr.0000095.g002 to empirical verification, since on the global scale inflation rates lag behind money growth by an average of around 5%/a and debt alone increased by over 8.5%/a between 1971 and 2016. It is rather the case that there are strong interactions between monetary expansions, eco- nomic growth, and increasing resource use, whereby: • an increase in the use of natural resources (>2% per annum since 1971) is essential for the growth in production and consumption in spite of all the progress made in resource effi- ciency and recycling (see section 4.1); • the expansion of the money supply is essential for both the demand and supply of goods, ser- vices and assets by enabling more investment in production, R&D as well as resource extrac- tion (see section 4.2); • both of the above result in a self-reinforcing money-nature nexus between money growth and increasing resource consumption (see section 4.3), which is also empirically visible (see section 4.4). States and international institutions should therefore not only focus on the availability and distribution of liquidity in their economic policies, but also include the money supply in their sustainability transformation policies and align their monetary policies with planet boundaries (see the conclusion in section 5). 2. Money and modern money creation Money has always been a social construct whose institutional purpose is to facilitate trade as an artificial universal medium of exchange with inherent asset value, which is at the same time also a measurement unit for the value of other assets [56,58]. Since the establishment of states, they issued money as currency, in order to contain private credit money systems, pay state PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 5 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus employees (e.g. soldiers) and levy monetary taxes [56,59]. Thereby, general acceptance and official recognition as a means of payment distinguishes money from other assets (e.g. gold, bonds, stocks, derivates, Bitcoins, bonus points) or other IOUs [1,35,58]. If some representa- tives of the concept of endogenous money regard nearly every credit, monetary claim and transferable asset as money [60], this goes too far [35] as it equates money as a means of pay- ment with the monetary value of assets, which would makes the entire economy identical to the money supply. For this reason the narrower concept of money will be used in the follow- ing. Money in this sense has experienced two decisive changes and a partial privatization of money creation over the last 300 years. From the 17th century onwards, rulers in Europe and subsequently governments world- wide have allowed the issuance of paper money in addition to precious metal coins to mone- tize the growing national debt and reduce dependence on the availability of gold and silver [35,56,59]. To ensure acceptance of this new fiat money, value guarantees (gold and silver stan- dards, colonial shareholdings) were issued to start with. A second change then followed with the reintroduction of intangible book money without value guarantees [35,56,59]. Today, central banks issue two types of money as legal tender: coins and notes (cash) and book money for commercial banks (reserves) [35,58]. For the provision of reserves, commer- cial banks normally pay interest. However, the interest rate level and thus the price for the Fig 3. Macroeconomic relationships in the economy and with nature. The circles are interlocking gears. The arrows indicate the direction of rotation and at the same time the direction of influence, depending on whether the arrow is before or after the gearing. The gears with titles in italics, are located on a second or third level and are connected to the first level at the toothing points, but not at the crossing points. They illustrate further connections between the individual quantities of the first level (see description in S5 Appendix). Own presentation. LD = Liquidity Demand; LS = Liquidity Supply; IL&C = Interest Level & Creditworthiness; AD = Aggregate Demand of Goods and Services; AS = Aggregate Supply of Goods and Services; I&B = Infrastructure & Buildings; NR = Natural Resources; NRE = Natural Resource Efficiency; R & D = Research & Development; TFP = Total Factor Productivity; CL = Commercialization Level; APP = Aggregate Purchasing Power; AFP = Aggregate Financial Power. https://doi.org/10.1371/journal.pstr.0000095.g003 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 6 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus state provided liquidity is not determined by supply and demand (cf. Fig 3) as in the neoclassi- cal IS-LM model [23,61] for the commercial credit market, since the immaterial money supply of the central banks is unlimited and can be expanded at any time without cost. Rather, the central banks determine the interest rate level with their key interest rates based on political specifications (e.g. an inflation target close to 2%/a) [62] also referred to as policy rates [63]. The expansion of the overall amount of money has been strongly accelerated once more by the sight deposits of the commercial banks on legal tender. The transmission of these imper- sonal IOUs [59] via bank transfer is generally used and also officially accepted as a means of payment (especially for taxes and state wage payments), which is why these intangible bank claims are generally recognized as money too (deposit money) [1,32,34,35,57,58]. Commercial banks expand the money supply by granting a loan amount as a new demand deposit out of nothing [34,35,46,55–57,64–66]. For this commercial banks do not need to hold savings, cash or central bank reserves to the same extent, since demand deposits are regularly only trans- ferred but not paid out and with the central banks there is a last resort lender, ensuring that commercial banks always have sufficient liquidity [35,60,64–66]. The ability to create money distinguishes both commercial banks from other financial insti- tutions, which are merely money intermediaries without access to central bank liquidity (so- called shadow banks) [66,67], and bank loans from other forms of credit based on savings or deferrals [35]. For this purpose, commercial banks must comply with legal requirements on the amount of liquidity reserves and the equity to be maintained. However, the policy rates for the minimum amounts of reserves have been continuously reduced to a few percentage points in most countries and even completely dropped in some states, while the minimum equity rates under Basel III are 10.5% [68]. This enables commercial banks to expand the money sup- ply with newly created deposit money at very little expense in line with the demand for bank loans. It explains why the money supply is growing hand in hand with bank borrowing (Fig 1) and why today deposit money now secures the vast majority of liquidity [35,58,66]. Unlike cash, however, deposit money disappears when the deposit expires (e.g. due to disbursement or loan repayment) [23,64]. Hereby, the credit-based money creation system contains a self-reinforcing loop, because increases in money growth fosters the demand for assets (e.g. real estate, gold, bonds, shares, collateralized debt obligations, cryptocurrencies). Due to a lower elasticity of asset supply, asset prices often rise faster than consumer prices, increasing the net wealth of the owners of physical capital (also referred to as real capital) (cf. [5,69–72]). Increasing asset prices do not take money out of the money circulation. As net wealth rises, the creditworthiness of compa- nies, households and governments improves, as does the collateral and equity of banks. Thus, on the one hand due to asset inflation, the credit demand for asset purchases increases, while on the other hand banks can grant more newly created credit money due to higher collateral and equity without enhancing their default risk [35,53,55]. This self-reinforcing loop explains both the strong positive correlation between asset prices and bank debts [66,70] and the higher growth rates of the money supply and bank credit relative to GDP shown in Fig 1. The self- reinforcing loop is the reason for the recurring house price bubbles and subsequent financial crises, with the prominent example of the subprime market bubble in the U.S. that burst in 2007 leading to the subsequent credit and financial crisis [70,71,73,74]. Overall, the amount of money is thus largely determined by the liquidity demand of com- mercial banks against the central bank on the one hand and by the overall liquidity demand of companies, households as well as governments and their satisfaction by commercial banks on the other (cf. Fig 3). The demand for liquidity and the ability of commercial banks to lend and create deposit money depend largely on the policy rates: key interest rates and the minimum level for reserves and equity. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 7 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus Furthermore, commercial banks also expand the money supply when they buy bonds or other financial products from governments, companies or shadow banks and pay for these assets with new deposit money [66,67]. Since the financial crisis of 2008, central banks have also been using the purchase of bonds (quantitative easing) as a way of increasing the liquidity of governments, commercial banks or even non-financial corporations, averting insolvencies and boosting economic growth [35,64,65]. All in all, unlike 300 years ago, money and thus liquidity is no longer scarce, but can be increased by central banks without limits and by com- mercial banks on a large scale at almost no cost [34,35,57]. To the extent that the standard model of mutually balancing economic flows and money circulation: Y ¼ C þ I þ G þ NX (with Y = National Income = GDP, C = Consumption Expenditures of Households, I = Finan- cial Investments, G = Government Expenditures and NX = Net Exports) excludes money crea- tion (see Fig 2), it does not fully describe national economies and their cycles. It is indeed correct that money circulates throughout the economy as cash or deposit money from buyer to seller to the next vendor, from savers to investors, from consumers to producers, from tax- payers to the state and so on. What is misleading, however, is the assumption made by the eco- nomic mainstream that the financial resources required for maintenance and growth investments are generated solely by greater rates of household savings or export surpluses [1,23]. In reality, it is the opposite, namely that in modern economies it is not savings that make bank loans possible, but rather bank loans and the resulting money creation initially gen- erating the liquidity gains from which savings then arise [35,66]. This becomes very clear when the model is applied to the global economy. Since at the global level, trade surpluses and deficits balance out, for the global economy the standard model is abbreviated to: Y = C+G+I. Or as the OECD put it: “The general equilibrium model brings all these nonlinear trends together into an internally consistent set of developments of all model variables. At the global level, this is a closed sys- tem: global exports equal global imports, global savings equal global investments.” [21p. 48] According to this equation, however, no growth of global GDP would be possible, since higher saving rates in favor of higher investment would reduce global consumptions expendi- tures. Therefore, the world economy can only grow because the total money supply M avail- able for C, I and G is increased globally through the money creation MC of national, central and commercial banks [34,35,51,57,66]). By detaching money from precious metals, money creation and thus global economic growth is no longer limited by the extraction rates of gold and silver. The standard model should therefore be: YGlobal ¼ MC ðCExisting þ IExisting þ GExistingÞ Even in national economies, money growth enables economic growth through the simulta- neous expansion of investment and spending by households and governments. This is espe- cially the case when there are low export surpluses or even trade deficits, like in the USA (see S2 Appendix). The full standard model here is stated as follows: Y ¼ MC ðCExisting þ IExisting þ GExisting þ NXExistingÞ PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 8 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus 3. Do exponentially growing money supplies drive economic growth? “Money makes the world go around” lyricized Fred Ebb in a song written for the movie "Caba- ret" in 1972 [75]. Nevertheless, it remains controversial among economists whether the mone- tary expansion of the money supply also changes the real economy and, in particular, the aggregate demand AD and the aggregate supply AS of goods and services. Despite the day-to- day comprehensive use of money as a quid pro quo, for the economic mainstream the growing money supply is not considered to be a growth driver. According to the orthodox or neoclassi- cal view, it does not even affect the real economy, since money supply growth is largely neu- tralized by inflation with a time lag of 2–3 years [1,23,32,76,77]. For heterodox economists the money supply is not neutral but also no growth driver, as the endogenous money supply only follows economic growth [24,51,52,56,60,65,78]. Both views are contrary to one another and ultimately cannot convincingly explain empirical developments. 3.1. The orthodox view and critique Since Adam Smith, orthodox economists have assumed a far-reaching independence between real and nominal variables, regarding money merely as a neutral lubricant. Unlike previous views [30], classical economists assumed that due to a constant AS an expansion of the money stock merely increases prices and not AD, whereby the purchasing power per monetary unit decreases and money growth is completely neutralized by inflation with a time lag of a few years (cf. reproduced quote from I. Fisher in S4 Appendix). For this neutrality assumption, they refer to the classical Quantity Theory of Money (QTM), which states that the money stock M multiplied by the velocity of money V is equal to the price level P multiplied by the aggregate volume of transactions T in an economy [32,77]: M � V ¼ P � T Note: Many economists shorten the QTM by using the aggregate volume of real production output O [1p. 490,79] labeled as Q) or the real national income Y (alias real GDP) instead of T [23p. 557,80Appendix I,81,82] and formulate: M×V = P×O or M×V = P×Y. However, both equations do not include the transactions and price developments of assets, because O and Y do not record the dealings of existing assets (e.g. purchase of land, shares). Moreover, in the case of Y, the national total income already includes the inflation-adjusted price level for the production output (Y = PGDP×O), which is why the actual formula here is M×V = Y. The QTM is a convincing approach to describe the relationship between the money supply and money use as well as prices in the economy. However, it does not necessarily imply that money growth is almost completely neutralized by inflation. Because if the number and the volume of transactions T increase, then prices must rise more slowly than the money supply if the velocity of money in circulation does not increase sharply. This is precisely the case accord- ing to the empirical data. Although empirical studies have acknowledged a positive correlation between the nominal money growth and inflation, the rates of money growth and inflation are moving at different levels [81,83–88]. The empirical results show that long-term inflation is several percentage points lower than money growth in most of the countries studied. The 45-degree line of correlation for the aver- age rates of change for money quantities and price levels calculated by McCandless and Weber (83) for 110 countries from 1960 to 1990 shifted by about 5 percentage points towards mone- tary growth rates, which can be clearly seen at the starting point with 0% inflation (see reprint Fig A in S3 Appendix). Similar shifts were observed in the studies by Vogel [87] for 16 Latin American Countries (reprinted in Lucas [86Figure 1]) and Barro [88Figure 7.1] for 79 countries (restated in Teles and Uhlig [81Figure 1 and 2]). The development in the United States from PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 9 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus 1867–1995 also indicate higher monetary growth rates over longer periods [84,85]. This corre- sponds to the data for the USA between 1961 and 2016, where money growth was 3.42 per- centage points above the development of the CPI, but also to developments in China from 1991 to 2016, where money growth even exceeded inflation by more than 15 percentage points (see Table B in S2 Appendix). Global trends show that between 1971 and 2016 the growth rate of Broad Money was 8.94%/a, while the global GDP deflator used by the World Bank was only 4.05%/a, which corresponds closely to the Consumer Price Index (CPI) of the USA of 4.03%/a (see Table A in S1 Appendix). The divergence between money growth and (consumer) inflation is based on two cumula- tive effects. One effect is the time delay in price adjustments to changes in the nominal money supply. This has also been recognized by neutrality advocates, who assume that it takes about 2–3 years to adopt prices to the money supply changes due to several reasons (e.g. long-term price and wage agreements, market imbalances) [1,23,53,77]. However, the alleged time period completely negates the fact that in reality the money supply does not grow on a one-time basis, but constantly, resulting in a permanent and cumulative delay. The other more significant effect is the steady expansion of the production output as well as the supply of assets [71,89], whereby among assets, the volume of shares, bonds, collateralized debt obligations as well as build-up land (see Fig 1) and gold in particular increased [5,35p. 114– 119,90,91]. Consumer inflation rates are lower than money growth because production output has expanded over 3% per year in real terms since 1950 [5] (cf. Fig 1 and Table B in S1 Appen- dix), so that the volume of goods and services has steadily increased. Consistent with the QTM, the nominal growth of the money supply of 8.94%/a between 1971 and 2016 is not completely neutralized by inflation resulting in an exponential real money growth of about 4.5%/a (see S1 Appendix). Due to the fact, that the production output O does not comprise the asset trading TAsset and that the developments in asset prices PAsset are not included in the calculation of inflation rates for GDP goods and services (PGDP), the QTM formula should be written as follows: M � V ¼ PGDP � O þ PAsset � TAsset If we rearrange this equation according to PGDP, it becomes clear that the (consumer) infla- tion of the production output prices does not only depend on the money supply. The Quantity Theory of Money therefore explains very well why inflation rates are lower than the rates of money supply growth: PGDP ¼ M � V (cid:0) PAsset � TAsset O All in all, the orthodox belief in an almost complete neutralization of money growth is empirically disproved, as inflation rates lag behind the growth rates of the money supply, not only in the short, but also in the long run. Since 1950, the growth rates of the money supply are on average about 5 percentage points higher than consumer inflation. This “minor” difference between growth rates means that the gap between the money stock and the price level increases exponentially over the years (cf. Fig B in S3 Appendix). As a result, the real money supply also grows exponentially with major implications for the real economy [34,35,92]. This raises the question as to whether the exponential growth in the money supply can stimulate a permanent expansion of the real economy (see section 3.3) and why production output on a global level has been able to grow for such an unusually long time (see section 4). PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 10 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus 3.2. The heterodox view and critique Since Keynes, heterodox economists reject this classic dichotomy and assume that money is endogenous and therefore not neutral for the real economy, whereby the money supply is not the cause but the outcome of economic growth, which increases the demand for liquidity [24,50–52,56,60,65,78]. J.M. Keynes argued that AS can be extended to full employment if there is unemployment, so that AD, which has increased as a result of higher liquidity, is matched by a higher supply (see reproduced quote from Keynes in S4 Appendix). Due to these changes in the real economy, prices do not rise to the same extent as the money supply and the purchasing power of money declines less by comparison. Neo-classical economists adopt this assumption in the AD-AS model and suppose that the expansion in money supply and the cor- responding lowering of interest rates increases AD and thus due to growing investment AS expands until the natural unemployment rate reaches the potential production output and any further monetary expansion then only raises prices (Fig 4a) [1,23,53,61,77]. Unlike Keynes, who merely questioned and corrected the classical assumptions on the dynamics of individual QTM variables, post-Keynesian economists dismiss the QTM arguing with the endogeneity of money [36,60,93,94]. They argue mainly on the basis of the Monetary Circuit, according to which credit money is only created on the basis of credit request and dis- appears when the loan is repaid, which is why the money supply is only increased temporarily —almost as an aid or catalyst (cf. the summary quote from W. Godley [95] in S4 Appendix) [51]. This Monetary Circuit is to be distinguished from the circulation of money in the econ- omy as in Fig 2. For heterodox economists, this circuit with the creation and extinction of deposit money is the reason why the economy drives the money supply and not vice versa. However, the extinction of deposit money accounts at the end of the circuit neither diminishes Fig 4. Effects of the money supply expansion in an AD-AS model according to a) Neoclassical assumptions and b) assumption of a money-nature nexus. In Fig 4a), aggregate demand AD increases with monetary expansion, while potential output remains constant due to the natural unemployment rate, so that when full employment is reached, only prices increase. In Fig 4b), due to the financial investments in technical progress and resource exploitation, potential output will also increase, which is why the aggregate supply can grow in line with growing demand, while prices only rise moderately. Fig 4a) own presentation according to Samuelson & Nordhaus [1p. 488]. Fig 4b own presentation. AD = Aggregate Demand; AS = Aggregate Supply; E = Equilibrium Point of AD-AS; PO = Potential Output. https://doi.org/10.1371/journal.pstr.0000095.g004 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 11 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus its function as a medium of exchange for investment or consumption nor its transfer to other market participants during its temporary existence phase. In this phase, the corresponding debt only reduces the net wealth of the debtors in their balances [66]. Furthermore, the growth effects of the money supply in the economy rise as long as the amount of credit-based deposit money increases in line with the growth of bank credit volumes (Fig 1). Thus, so far, the aggre- gate Monetary Circuit has not yet been closed in most countries all over the world. Also their argument, that “the rise in production takes shape in the mind of producers before money is created and is effectively realized when credit is granted and money is created to finance it” [78] is misleading because as with all economic exchanges the relationship between liquidity demand LD and liquidity supply LS is interdependent [35]. Without a corre- sponding supply of money, the envisaged demand simply remains an idea with no prospect of realization. Thus, without growing demand for liquidity there would be no creation of deposit money; but without the creation of new money by central and commercial banks, there will be no additional liquidity to expand production or consumption. The inflation-adjusted quantity of money and the possibilities of its extension are thus crucial for the realization of investment and consumption desires [34,35,96] and their exclusion in most growth theories (cf. [24]) is incomprehensible. The decisive intermediary between demand and supply is the interest level IL for liquidity (LD = IL×LS), the price level P for goods, services and assets (AD = P×AS) and the wage level WL for labor (Labor Demand = WL×Labor Supply) (cf. Fig 3). By overestimating the question of the exogeneity or endogeneity of money or money crea- tion [35]90, post-Keynesian economists underestimate the economic and ecological signifi- cance of the quantity of money recognized by the state as a means of payment, which Keynes himself still saw [79]Ch.21. Post-Keynesian economists act as if other laws of economics apply to endogenous money. However, exogeneity or endogeneity is merely a question of perspective and not of the economic effects of money: Money is endogenous at the state level, as today the legislative of states create the respective national currency for their economies and allow cen- tral as well as commercial banks to put it into the economy as cash, reserves or deposit money. For the national economy and the free market on the whole, state-accepted deposit money is endogenous due to commercial banks create this money. However, for the level of non-bank- ing corporations and all households both cash and also deposit money remains an exogenous quantity, which they are not able to create. They can only acquire it in exchange for work and services, goods and assets or borrowing it in the case of loans [35]. How money is created must therefore to be separated from the question of what effects existing money supply has. For the economy and the people it is not significant by whom or why money is created, but that money (liquidity) is available for the intended investments and consumption. 3.3. Why money growth stimulates the real economy Due to the circulation of money in an economy (see Fig 2), neither consumer inflation nor asset price increases deactivate money. However, both rising consumer prices and rising asset prices reduce purchasing power in a society, because less can be bought for the same amount of money when prices rise [32]. Conversely, if like in the past the money supply rises faster than prices: the higher the quantity of money compared to the price level, the larger the quanti- ties that can be bought and therefore the higher the aggregate purchasing power of the society (cf. reproduced 2nd quote from I. Fisher in S4 Appendix). The increase in purchasing power not only boosts aggregate demand, but also aggregate financial power for investments in pro- duction, infrastructure, resource extraction and assets. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 12 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus The rising investment volume enables resource extraction and production capacities to be expanded, resulting in an increase in the aggregate supply of goods and services as well as assets (see Fig 1 and S1 Appendix). The Quantity Theory of Money can plausibly represent this effect of the growing money supply by rearranging the equation according to the produc- tion output O: O ¼ M � V (cid:0) PAsset � TAsset PGDP According to this, a growing money supply M or a higher velocity of money V increases production output as long as the expansion in available liquidity is not absorbed [97] by a growth in asset prices PAsset or asset transactions TAsset. Existing inflation minimizes output growth but does not bring it to a halt, provided that liquidity in an economy or in the global economy is expanding faster than inflation rates. Unlike in the days of pure gold and silver currencies, for the past 300 years the very cheap creation of money out of nothing has made it easy to expand the money supply (see section 2), as long as governments or their central banks do not restrict money creation or reduce the demand for liquidity through high key interest rates. At the same time, an expansion of production output causes inflation rates to lag behind nominal money growth. As a result, purchasing power and investment power increase in real terms, constituting a self-reinforcing feedback effect. In contrast, the velocity of money in circulation cannot be increased at will [98]. Therefore, the ideas of Silvio Gesell [99] on the so-called "free money” or”stamp scrip”—with decreasing purchasing power by definition (“shrinkage money”)—and subsequent practical regional monetary experiments could increase economic activity only to a limited extent due to a one- off increase in the velocity of circulation (cf. [100–102]). Contrary to earlier economic assumptions (see section 3.1 and 3.2), the expansion of pro- duction capacities as well as potential output is not (or at most only for a short time) restricted by the available number of the unemployed. On the one hand, global trade has resulted in a global division of labor with a previously non-exhausted and growing number of employable people available [1,35]. On the other hand and even more importantly, labor productivity and total factor productivity (TFP) could be increased on a large scale through automation, upscal- ing and technological progress, replacing or potentiating labor by physical capital and energy [15,22,25,51,103]. As a result, both actual production output and potential output could be steadily increased (cf. Fig 4b). However, both the expansion of employed labor, physical and human capital, as well as TFP require a prior expansion of financial resources to pay for the people, assets, equipment, raw materials, and the energy needed to expand production [1,24,50,51]. The enhancement of TFP and of the availability of raw materials and energy is preceded by global financial invest- ments from governments and companies in the exploration and extraction of natural resources [21] as well as in research and development (R&D) [104,105], whereby the influence of R&D investments on productivity growth is generally overestimated [106]. It is estimated that annual investment in resource extraction worldwide accounts for approx. $1 trillion, with a return on investment of $4 trillion/a (equivalent to 7% of global GDP) [107,108]. The expo- nential creation of liquidity at very low cost has enabled a steady expansion of these invest- ments worldwide [34,35,51,57], also enabling the required maintenance investments, which account for about 10–15% of national income [5]. Overall, in an economy real money growth increases not only the aggregate demand, but also the aggregate supply due to growing investment power [30,33–35,55,57,65]. However, the channels of influence of the money supply are complex in national economies, as almost all PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 13 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus economic transactions (i.e. the purchase of mining licenses, land, raw materials, energy, machinery, equipment, consumer goods, services or labor) are carried out with money (see Fig 3). The more money that is available for transactions (liquidity), the more economic activity is possible, when inflation rates are lower than money growth rates. 4. Strong interactions between the growth of the money supply, the economy and the use of natural resources There are complex interactions between monetary expansion, economic growth and increas- ing resource use, which are essential for the discussion on sustainability, especially on the global level (see Fig 5). A monetary expansion of the world economy exists if statistically the money aggregate or “broad money” (also referred to as M2 in the USA and M3 in Europe) increases globally in nominal terms (cf. [109]). From an economics perspective, the world economy grows when GDP increases on the global level in inflation-adjusted terms, which happens when production output increases on the global level and is taken up by a similar increase in demand (cf. [1]). The use of natural resources increases with every anthropogenic use of land, water and air (e.g. for agriculture, settlements, shipping, wind power), with the extraction of substances and organisms from soils, waters and the biosphere, with the harness- ing of solar energy through photovoltaics, with emissions into the atmosphere and the dis- charge of waste and sewage into waters and soils (cf. [110–112]). Fig 5. Money-nature nexus with money-resource rebound effect. Own presentation. https://doi.org/10.1371/journal.pstr.0000095.g005 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 14 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus 4.1. No production or consumption without natural resources The interdependence between production and natural resources is nowadays generally recog- nized in the economic Production Function: O ¼ A � f ðK; L; H; NRÞ Due to this function, the production output of goods and services O are primarily deter- mined by the supply of the following factors: physical capital K, labor L, human capital H (knowledge and skills) and natural resources NR (including energy) as well as the available production technology A, represented by technological progress and an increase in total factor productivity [1,23,24,34,51]. Contrary to the former Neoclassical Production Function, which only includes K, L and A [113,114], the modern Production Function recognizes that both human skills and natural resources play a crucial role in production. While the abiotic geo- sphere provides land and a multitude of raw materials [3,115], the biosphere is responsible for extensive ecosystem services [111,112,116], without which neither production nor human life would be possible. The use of natural resources in modern economies takes place in connec- tion with consumption and at various levels of production: 1. The construction of production facilities, buildings and machinery as well as the necessary infrastructures in a national economy (roads, railways and waterways, electricity and com- munication networks) requires land as well as raw materials and energy. At the same time, emissions, waste and extensive changes to ecosystems already take place at this stage. This means the greater the expansion of physical capital K, the larger the ecological footprint [3,4,14,15]. 2. The provision of goods and services in production facilities requires biological and mineral raw materials as well as energy, generating emissions and waste. Again, the higher the pro- duction output, the higher the use of natural resources [16,17,117]. 3. The provision of raw materials and energy for production in agriculture, forestry, fisheries, mining and the energy sector (hereinafter referred to as primary production) involves extensive changes to land and ecosystems, as well as causing significant emissions and waste (including greenhouse gases, nutrients, tailings and wastewater) [2,118–120]. Primary production takes up by far the largest areas of land and water [3,121–124], while at the same time requiring a considerable input of energy and raw materials (e.g. for fertilizers, mining machinery, wind turbines). 4. Finally, the consumption and use of produced goods and services has various impacts on natural resources, especially due to the resulting emissions and waste (e.g. the greenhouse gases from cars and heating systems, household waste and sewage). Here, it is also the case that the more goods and services that are consumed, the larger the ecological footprint. To date, technological progress has increased resource efficiency in primary production, commodity production and consumption, whereas recycling has reduced the use of new natu- ral resources. Nevertheless, the total consumption of energy and raw materials has still not fallen, but continues to rise in correlation with GDP (see Fig 1 and S1 Appendix) [3,15,16]. Among the reasons for this are: • Rebound effects, since efficiency improvements regularly lead to lower costs in production and operating, which in turn boosts aggregate demand [2,18–20,125,126]. This efficiency rebound effect correlates closely with the money supply, since the aggregate purchasing power of society is enhanced with the cost reductions as long as the nominal money supply PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 15 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus Fig 6. The efficiency rebound effect and the money supply. With the increase in resource efficiency NRE to NRE´, more goods and services can be produced and consumed with the same amount of resources NR. Since the aggregate demand AD increases with a constant money supply M because of the rebound effect to AD’, the aggregate supply increases to AS´ and the resource savings NR’ are small. The efficiency gains are fully converted into resource savings NRs when the money supply Ms decreases and thus AD remains constant despite efficiency-related cost reductions. Conversely, if the money supply increases to Mg as in reality, then AD grows to ADg and NR to NRg, which is possible due to the efficiency gains NRE’ and the expansion of the potential production output PO to POg due to growing investment in resource exploitation, increasing AS to ASg. Own presentation. Q = Quantity; AD = Aggregate Demand; ADg = unrealizable ADg; AS = Aggregate Supply; M = Money Supply; PO = Potential Output; NR = Quantity of Natural Resource Consumption; s = sustainable; g = growth; NRE = Natural Resource Efficiency (resource consumption per product as a function of resource price). https://doi.org/10.1371/journal.pstr.0000095.g006 is not reduced to the same extent (Fig 6). However, the idea of using higher taxation to coun- teract cost reduction [125,127–129] fails in macroeconomic terms, since higher tax revenues increase government expenditures [130]. • Physical constraints to resource efficiency, since resource productivity in terms of matter and energy cannot be increased above 100 percent resource use, which corresponds to a complete utilization without any waste, emissions or exhaust heat. Furthermore, when approximating this absolute limit, the productivity improvements that can be achieved from constant effort become smaller and smaller, as with all limited growth processes. Practically, use-efficiency lies significantly below this limit due to the laws of thermodynamics, in partic- ular of entropy [49,131,132p. 320] or as Georgescu-Roegen put it: "The impossibility of using machinery that produces no waste is [. . .] an inherent limitation of the human nature." [133p. 191]. Contrary to the pioneering picture drawn by McAfee [134], in the USA the share of lost energy is even more than two thirds of the generated energy [135]. • Physical and social constraints to recycling, since a 100 percent recycling cannot be achieved due to entropy and the mechanical, chemical, and biological aging processes (e.g., wear and tear, oxidation of metals, decay of plastics to micro plastics due to UV radiation) as well as social misconduct in disposal (cf. [136]). Current recycling rates are 60% for iron and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 16 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus copper, 30% for sand, 16–25% for phosphorus and 15% for rare earths, and 0% for fossil fuels [137p. 407] (cf. also [138p. 176,139p. 70]). Thus, the use of natural resources is a key factor of production and can only be substituted to a very limited extent by human labor or skills. Its long-standing absence from the economic Production Function is responsible for many economic and social misconceptions about unlimited growth. 4.2. No increase in production and consumption without growing money supply The economy and real GDP can only grow if the supply of goods, services and assets as well as the demand for these items grow by the same amount as far as possible. Otherwise, too much increase in supply will lead to deflation whereas too much increase in demand will cause inflation. In monetary terms, this means that both consumer spending and financial investment in production and resource extraction must increase. In the global economy, this is only possible if the global amount of money expands, since unlike in national economies, no foreign invest- ment or export surpluses are possible on a global scale (see section 2). The exponential increase in the global money supply has been possible due to the creation of money out of thin air. Growing money supply increases economic activities within an economy as well as globally, since money growth is to date neither neutralized by inflation nor a merely temporary phe- nomenon of a Monetary Circuit (see section 3.1 and 3.2). Similar to the standard model of mutually balancing economic flows, the Production Function excludes money growth as a fac- tor. The expansion of financial investments is hidden in the expansion of the dynamic quanti- ties A, K, L, H and NR. However, if an expansion of financial investments globally is only possible through an expansion of the total money supply, then money creation in the global economy is the decisive reason for the expansion of physical capital, paid labor, available natu- ral resources, and the increase of technical progress and human capital (see section 3.3). Even in national economies, the expansion of the money supply significantly boosts productive investment [50]. In order to highlight this importance for the growth of production output, the Production Function should be supplemented by the factor money creation MC, taking into account that money creation is distributed between production and demand for goods, services and assets (MC = MCProduktion+MCConsumption+MCAsset). The production equation then could look as fol- lows with respect to the production increase Ot+1: Otþ1 ¼ Ot þ MCProduction � A � f ðK; L; H; NRÞ 4.3. Self-reinforcing money-nature nexus If money expansion promotes the growth of production and consumption (4.2) and this eco- nomic growth increases the use of natural resources (4.1), then there is also an impact relation- ship between the money supply and the use of resources (cf. Fig 3). This relationship is not one-way, but reciprocal and self-reinforcing and can be described as follows (cf. Fig 5): • The nominal expansion of the money supply increases the demand for natural resources, but at the same time enables an expansion of investment in the exploration and exploitation of natural resources. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 17 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus • The expansion of resource availability keeps the price of resources from rising, or at a slower rate than the money supply, while allowing production output to expand. • The expansion of production output and resource availability prevent the prices of goods and services from rising as fast as the money supply, leading to an inflation-adjusted increase in the money supply and aggregate purchasing power. • The increase in the money supply and aggregate purchasing power in real terms steadily enhance aggregate demand, so that firms continue to expand production and resource extraction by extending borrowing, which further increases the money supply in nominal terms as well as resource extraction. It should be emphasized that there is an imbalance in this money-nature nexus. While the money supply as an artificial, immaterial construct can be expanded at will, the earth’s natural resources are absolutely limited. Even the steady input of solar energy is limited by the size of the Earth. If production output depends considerably on the use of natural resources and eco- system services (see section 4.1), then without an expansion of the natural resource availability, money growth would only raise inflation rates, since production output could only be increased to a very limited extent through advances in resource efficiency and recycling. How- ever, until now and contrary to the orthodox Hotelling-rule, the exploration and exploitation of raw materials and energy has been steadily increased through financial investment and tech- nical progress by expanding or intensifying mining, drilling, agriculture and forestry [15,16] (Fig 1 and S1 Appendix), which is why resource prices remain low despite a growing demand in reality [16]. Additionally, many ecosystem services can still be used for free [4,57]. There- fore, growing investments in resource exploitation and R&D, not only increase production and TFP, but also potential output [50,51], keeping inflation low according to the AD-AS model (Fig 4b). Already at the beginning of the Industrial Revolution J.W. von Goethe described in his dra- matic work Faust [31] how the expansion of the money supply increases investments in pro- duction and resource exploitation through fiat money and how this simultaneously prevents inflationary development by matching the growing money supply with a correspondingly growing amount of real values (land, raw materials, energy, produced goods), giving the inher- ently worthless paper notes or immaterial deposits a stable monetary value [35,140]. This self- reinforcing relationship termed H.C. Binswanger as “growth spiral” [34]. The money-nature nexus describes the relationship between the total quantity of money and the total use of resources in an economy or in the global economy. Since money circulates within the economy, the money supply therefore determines the total circulation volume that can be used for economic transactions. The money-nature nexus does not describe the use of resources in individual economic transactions. The latter can have a quantitatively and qualita- tively different ecological footprint depending on the scope and subject matter as well as on interests, incentives and other individual reasons for decision-making. For an ecologically sus- tainable global economy, only the total quantity of money is decisive, while the quantity per capita is of relevance for the social question of the distribution of wealth and the individual use of natural resources. The money-nature-nexus can be expressed by combining the Quantity Theory of Money M×V = PGDP×O+PAsset×TAsset (see section 3.1) and the generally recognized Production Func- tion O = A×f(K, L, H, NR) (see section 4.1). However, with regard to the overall ecological impact of mankind IECO, a distinction must be made between the resource use through the production output NRGDP and the direct resource use through subsistence activities NRSubsis- tence without any commercial transactions (IECO = NRGDP+NRSubsistence). The money-nature PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 18 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus nexus is restricted to NRGDP. The monetary influenced resource use at a certain point in time can be expressed by the following formula: NRGDP ¼ M � V (cid:0) PAsset � TAsset PGDP � AðK; L; HÞ 4.4. Empirical visibility of the money-nature nexus According to the QTM formula, real GDP and thus production output as well as global demand and supply of raw materials and the GHG emissions, have been steadily increasing worldwide over recent decades in line with monetary growth, while at the same time biodiver- sity and thus the quality or quantity of ecosystems have been steadily declining, and inflation has been significantly lagging behind monetary growth (Fig 1 and Table B in S1 Appendix). The increase in global GDP has been made possible by the expansion of money supply world- wide by central and commercial banks through money creation out of nothing [34,35,50,66]. Rising national incomes accompanied by rising aggregate demand and production are driving the increase in raw material consumption as well as ecological footprints [16,115,141–143]. Between 1990 and 2008, with every 10% increase in GDP, the average national material foot- print of 186 countries has increased by 6% [17]. Therefore, countries with a high GDP per cap- ita also have a high Ecological Footprint [144]. According to Beck et al. [50], expanding credit to households and businesses by up to 109% of GDP generates economic growth. Since an expansion of credit volumes is enabled globally by money creation alone, the money supply increases along with credit volumes (see Fig 1). The fact that nominal money growth was con- siderably higher than GDP growth and resource utilization (see section 1 and S1 Appendix) can be plausibly explained by the asset price inflation and the liquidity-asset rebound effect (see section 2). The money-nature nexus became highly visible in the 2008 financial crisis, when a global shortage of liquidity from the commercial banks led to a decline in production and consump- tion [145], but also in the use of natural resources (e.g. raw materials, CO2-emissions, built-up land, available fishing biocapacities) (see Fig 1 and Table B in S1 Appendix as well as [146p. 22 regarding global resource trading]), until growth resumed in 2010 due to massive liquidity injections by central banks and increased government spending. Despite a decline in the money supply, the bursting of the Dotcom Bubble in 2000 did not have a major impact on production, con- sumption and resource use, as particularly the trade and prices of intangible assets were affected. All in all, the money-nature nexus appears billions of times every day around the world when companies, households or governments exchange cash or deposit money into newly pro- duced goods or services that have been made by using natural resources, including energy. As with national GDP, the higher the individual income, the higher the resource use [27,28]. Global analyses show that in 2015, the world’s richest 10% were responsible for 49% of CO2 emissions, while the poorest 50% emitted only 7% [29]. Material desires do not seem to be lim- ited [147] but grow with available liquidity (cf. [148,149]). Boosted by the low interest rates on loans worldwide and exponential money growth, e.g. in Europe the production of superyachts grew by 228% from 1998 to 2008 [150]. Even after an individual saturation of even relative needs [147] has been reached, money deposited at financial institutions or invested in compa- nies continues to promote the use of natural resources. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 19 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus 5. Conclusions for future sustainable transformation policy Money is a universal medium of economic exchange. If the money supply increases (whether exogenously or endogenously generated), then people, firms and governments can buy more goods, services and assets as long as inflation lags behind money growth, which is empirically the case on a global level and in most countries. This is based on a self-reinforcing interaction between the amount of money and the use of nature (the money-nature nexus), since the quantity of money in relation to the price level determines the extent of the economic activity and the overall resource use, whereby the possibility of expanding the use of resources in turn determines the price level. As long as growing liquidity increases investments in resource exploitation and the growing availability of resources conversely adds value to the money cre- ated, exponential money growth is not neutralized by inflation resulting in an increase of the aggregate purchasing power. As long as for this growing purchasing power and aggregate demand the necessary resources can still be extracted from nature and the waste can be disposed of there, then eco- nomically it will also happen due to the economic incentive. Even a green purpose in money creation is only significant for the first use of the new money, but not for all subsequent uses during the circulation of money in an economy. Therefore, as long as the new money exists, it increases the overall use of natural resources in economies, even if the new money is to be used initially to improve resource efficiency or climate protection (see exemplary illustration in Fig 7). Therefore, contrary to growing political demands, necessary green investments [2,4] should not be financed by expanding the money supply, but by redirecting existing liquidity. Fig 7. Increase in natural resource use due to money circulation in the case of a green purpose in money creation, illustrated by the simplified example of a wind power investment. Own presentation. https://doi.org/10.1371/journal.pstr.0000095.g007 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 20 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus All in all, the almost unlimited provision of money at very low cost in the form of bank notes, reserves and deposit money for 300 years is the decisive difference to pre-capitalist econ- omies. This liquidity expansion has enabled rising consumption but also growing investments in production, real and human capital, R&D, technical and medical progress, labor productiv- ity, resource efficiency as well as the increase in exploration and exploitation of energy and raw materials, which are all considered to be growth drivers. Therefore, money is not only a lubricant but the fuel in modern economies irrespective of whether the money is provided by the central bank or commercial banks, as both increase liquidity for companies, households and governments. This was clearly illustrated by the financial crisis in 2008, when a global shortage of liquidity by commercial banks led to a decline in production and consumption, but also in the use of resources, until growth resumed in 2010 due to massive liquidity injec- tions by central banks and governments (cf. Fig 1, S1 and S2 Appendices). The growth of the real economy will only come to an end when production and consump- tion can no longer be scaled up quantitatively due to the scarcity of natural resources or liquid- ity. However, money as an artificial construct is unlimited and so far resource exploitation has kept pace with the growing demand, even if finite resources decrease at a faster rate. If the global quantity of money continues to grow exponentially, then there will be a rising depletion of natural resources and ecosystems. The ecological effects of liquidity decline if the nominal amount of money shrinks due to higher interest rates or if the real money supply declines through inflation (e.g. due to political constraints on the availability of resources). As money does not fall from the sky, but is created by central and commercial banks, govern- ments have the power to limit the demand and supply of money to an environmentally com- patible level by changing legal framework for banks and especially policy rates, instead of forcing further exponential liquidity growth to solve social, economic and ecological problems. It is important to note that the growing liquidity cannot be effectively prevented by taxation as some authors [46,65] assume, since taxes do not reduce liquidity in an economy, as long as governments respend the tax revenues on investment, consumption or wage payments rather than eliminating the money. If the money stock is decisive for the use of natural resources, then the money supply should not grow by more than 2%/a in nominal terms in view of the inflation target of 2%/a envisaged by many central banks to ensure that humanity’s use of natural resources does not increase any further in the short term. In the medium term the nominal money supply should be corre- lated with the available resources and ecosystem capacities of earth to ensure a sustainable bal- ance of the world economy. As a balancing monetary rule the money supply could be calibrated to the ratio of the Ecological Footprint and the Biocapacity indicators of the Global Footprint Network [144], both comprising built-up land, atmospheric carbon surplus, crop- lands, fishing grounds, forest products and grazing land, supplemented by a safety buffer for uncertainties and a target value for biodiversity. The calibration formula for the global money supply M could therefore be: � Mtþx ¼ Mt � 0; 5 � Bt � SM EFt þ LPIt LPITV Note: Mt is the global broad money supply measured e.g. by the World Bank in current US$ in the latest available year t; Mt+x is the target global broad money supply for the year t+x with x as the desired transition period; Bt is the Bio- capacity and EFt is the Ecological Footprint in year t (both measured in global hectares (gha) by GFN [144] and SM the safety margin (e.g. 0.75 for a 25% buffer); LPIt is the Living Planet Index representing biodiversity loss and calculated by WWF/ZSL [151] in year t; LPITV is the target value for this index (e.g. 0.8 for a maximum loss of 20% since 1970). In this formula, price changes are indirectly taken into account, since inflation (e.g. from the scarcity of raw materials) PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000095 January 31, 2024 21 / 29 PLOS SUSTAINABILITY AND TRANSFORMATIONThe macroeconomic money-nature nexus reduces purchasing power and therefore economic activity and resource use when the money supply is limited, while deflation has the opposite effect. Since no world currency exists, the calibration of the global money supply with nature’s capacities requires also an international regulation on the allocation of the global money bud- get to the individual currencies. The exact allocation is above all a question of the global distri- bution of wealth and resource consumption among the states. Currently, this distribution is extremely unfair, since encouraged by money growth high-income states disproportionately use globally limited resources at the expense of low-income states [3,117,152]. A global implementation of the suggested money supply rule could lead to a much fairer distribution of wealth and resource use than at present. On the one hand, for states whose Eco- logical Footprint already exceeds their Biocapacity (see [144]), a reduction in the national money supply is required due to the suggested rule. This will reduce the material prosperity of these societies, which raises social problems. Therefore, a money reduction should be gradual with the proposed formula as monetary sustainability target in the long run, allowing for suffi- cient time for economic and social adjustments. On the other side, countries with a low Eco- logical Footprint should be permitted to increase their money supply and economic activities further up to a higher but still sustainable level of prosperity and resources. Calibrating national money supplies to ecological capacities would be a strong incentive to use existing financial resources specifically to reduce the ecological impact and restore ecosys- tems, instead of investing primarily in economic growth. The proposed monetary transforma- tion requires accompanying government measures and adjustments [40,46,57]. Priority should be given to replacing fossil with renewable energies and protecting biodiversity, as cli- mate change and biodiversity loss may irreversibly change current environmental conditions [2,3]. Subsidies and investments that damage the environment have to be abolished completely. To make the change socially acceptable for all people, the reductions in levels of prosperity should be mitigated by a better distribution of income, capital and labor as well as a promotion of non-material wealth factors [20,46,153]. Data availability The Fig 1 in the main text as well as in the supplementary information the Figure A in S1 Appendix and the Figures A to D in S2 Appendix are based on publicly available sources. The data is available through the references [144,151,154–158]. Supporting information S1 Appendix. Global development from 1971 to 2016. (PDF) S2 Appendix. Developments in China and the USA over the past decades. (PDF) S3 Appendix. Inflation rates that lag behind money growth. (PDF) S4 Appendix. Collection of quotations. (PDF) S5 Appendix. Description to Fig 3 \| macroeconomic relationships in the economy and with nature. 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10.3389_fimmu.2021.689397	Supplementary Material for: Crosstalk between CD11b and Piezo1 mediates macrophage responses to mechanical cues Hamza Atcha1,2, Vijaykumar S. Meli1,2, Chase T. Davis1,2 Kyle T. Brumm1,2, Sara Anis1,2, Jessica Chin1,2, Kevin Jiang1,2, Medha M. Pathak1,4,5, and Wendy F. Liu1,2,3,6* 1 Department of Biomedical Engineering, University of California, Irvine 2 The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine 3 Department of Chemical Engineering and Materials Science, University of California, Irvine 4 Department of Physiology and Biophysics, University of California, Irvine 5 Sue and Bill Gross Stem Cell Research Center, University of California, Irvine 6 Department of Molecular Biology and Biochemistry, University of California, Irvine * Correspondence: 2412 Engineering Hall Irvine, CA 92697 Tel: Fax: Email: (949) 824-1682 (949) 824-9968 wendy.liu@uci.edu * To whom correspondence may be addressed. 1 Supplementary Figures Supplementary Material Figure S1: Quantification of cell morphology for macrophages subjected to cyclic uniaxial stretch. Quantification of cell alignment relative to the direction of stretch (A) and perpendicular to the direction of stretch (B), cell aspect ratio or the length of the major axis divided by the length of the minor axis (C), and spread cell area (D) for unstimulated, IFNγ/LPS, and IL4/IL13 stimulated macrophages with no stretch or 20% cyclic uniaxial stretch. Error bars indicate standard deviation for three separate experiments and * p < 0.05 when compared to the indicated condition as determined by Student’s t-test. 2 Figure S2: Cyclic mechanical stretch does not affect macrophage cell viability. Quantification of cell viability, as measured by Cyquant assay, following either 4 (left) or 24 (right) h of adhesion, stimulation, and 18 h of stretch. Cell number was normalized to the unstimulated, 0% stretch, condition for each time point. Error bars indicate standard deviation about the mean for three separate experiments. 3 Supplementary Material Figure S3: Quantification of morphological parameters for macrophages subjected to cyclic uniaxial stretch after 4 h of adhesion. Quantification of cell alignment relative to the direction of stretch (A) and perpendicular to the direction of stretch (B), as defined by the highlighted regions in Figure 1B, cell aspect ratio or the length of the major axis divided by the length of the minor axis (C), and spread cell area (D) for unstimulated, IFNγ/LPS, and IL4/IL13 stimulated macrophages with no stretch or 20% cyclic uniaxial stretch. Error bars indicate standard deviation for three separate experiments and * p < 0.05 when compared to the indicated condition as determined by Student’s t- test. 4
10.1371_journal.pone.0299837	RESEARCH ARTICLE Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022 Tian Yan, Fang LiuID* Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, United States of America a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 * fliu2@nd.edu Abstract Background OPEN ACCESS Citation: Yan T, Liu F (2024) Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022. PLoS ONE 19(3): e0299837. https:// doi.org/10.1371/journal.pone.0299837 Editor: Michal Ptaszynski, Kitami Institute of Technology, JAPAN Received: September 14, 2023 Accepted: February 15, 2024 Published: March 15, 2024 Copyright: © 2024 Yan, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people’s emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre- pandemic level. Method We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post- emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Opti- mized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed- effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. Data Availability Statement: The data are available at https://www.kaggle.com/datasets/avaytt/reddit- messages-from-selected-universities/data. Results Funding: T.Y. is supported by the China Scholarship Council Scholarship (https://www. chinesescholarshipcouncil.com/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. Conclusions Competing interests: NO authors have competing interests. Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 1 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study. Introduction Background While COVID-19 remains a public health priority, many governments have transitioned away from the emergency phase that gripped the globe in 2020 and 2021. With a variety of effective strategies implemented to combat the COVID-19 pandemic, including vaccination, quaran- tine measures, and the adoption of remote work and study routines, the impact of the pan- demic on society has gradually subsided since the second half of 2021. In the U.S., nearly all state-level mask mandates had been lifted by April 2022; many educational institutions from elementary schools to higher education institutes have returned to the pre-pandemic in-person learning mode; social gatherings, conferences, sports, and entertainment events have also wel- comed back participants and fans at full capacity, among others. Despite that physical and in-person activities may have already largely recovered, the post- pandemic world does not mirror the pre-pandemic era in many aspects. Two such changes are people’s social behaviors and the public’s opinions and attitudes toward various domains and subjects and their psychological and emotional status shifts from pre-pandemic. In this work, we focus on the latter topic. Research has been undertaken to examine sentiments and attitudes towards public health in the aftermath of various pandemic waves and post-pandemic periods, leveraging data from social media. For example, Twitter data were collected to study the sentiment distribution in India after the second wave of COVID-19 using deep neural networks [1] and the majority of sentiments were found either neutral or positive. Potential reasons behind the negative senti- ment toward COVID-19 vaccine were investigated using Twitter data [2]. Tweets with nega- tive sentiment were selected and topics implied by these tweets were discovered using topic modeling and manual thematic analysis. A subsequent study indicates that the negative senti- ment towards the COVID-19 vaccine has a detrimental spillover effect on the public’s senti- ment towards other vaccines such as the Measles vaccine [3]. The motivation and inclination to travel in 2021 were studied using thematic analysis, sentiment classification, and word cloud; nature-based travel was the first choice of travel after 2020 [4]. Twitter users’ sentiment change toward COVID-19 vaccination was studied [5] after the first COVID-19 vaccination was implemented in the U.S. It was found that public sentiment towards vaccination became more positive after the first dose of vaccination. Post-pandemic public opinion and sentiment on ports and corporate choice of import and export of goods were positive through frequency verification between public opinions and sentiments analysis of the influence mechanism [6]. Socioeconomic factors that may affect people’s attitude towards reopening the economy post-pandemic were studied using various data sources such as Twitter data, socioeconomic data, and COVID-19 cases [7]. It was found that people with low education levels, low income, in the labor force, and with higher residential rents were more interested in reopening the economy. Twitter data was used to learn people’s attitudes towards remote working [8] through topic modeling and deep neural networks. It found that “work-life balance”, “less stress”, “future” and “engagement” were positive topics; “virtual health”, “privacy concerns”, and “stress” were negative topics; and neutral topics included “new technologies”, “sustainabil- ity”, and “technology issues”. Sentiment analysis of Twitter-based teleworking in a post- PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 2 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? pandemic context showed the prevalence of positive sentiments regarding telework that were generally associated with confidence, anticipation, and joy. [9]. Social media data were col- lected to provide a comprehensive analysis of logistics and transportation trends and showed The overall sentiment toward post-pandemic logistics in Japan was positive [10]. A study was conducted to learn public sentiment towards the government for efforts to restore the econ- omy in Indonesia and suggested a high percentage margin between positive and negative senti- ments of 37% [11]. Public sentiment toward education post-COVID-19 was also studied using Twitter data sector [12] safety was identified as a top concern for students, parents, and educators via senti- ment analysis and machine learning (ML) techniques. Social media data was also used to study the attitude of the Jordanian community towards online and in-person hybrid learning [13] in the post-pandemic era. The study found that 40% of the samples displayed negative senti- ments, 13% were positive, and 24.5% were neutral. A group of students at a university in the Philippines were asked about their thoughts or feelings of students [14] about the implementa- tion of the limited face-to-face classes. The responses are dominated by positive thoughts or feelings (67%), as opposed to negative (23%) and neutral (10%). There is also work that studied general sentiment change at different stages during the pan- demic using other data types, such as surveys. For example, depressive symptoms were mea- sured via the 8-item Center for Epidemiologic Studies Depression Scale in participants of �60 years old, which showed that the prevalence of clinically depressive symptoms was 19.8% dur- ing the pandemic, 7.2%, and 7.2% at waves 4 and 5 respectively [15]. Anxiety and depression symptoms and their recovery and loneliness in 2019 and after the first wave of COVID-19 in 2020 were studied among the general Dutch population using data from the Dutch Longitudi- nal Internet studies for the Social Sciences panel [16]. The study suggested the first wave of the pandemic did not negatively affect the prevalence of anxiety and depression symptoms among the general population during the first four months, but that emotional loneliness increased. In summary, the above work used social media data to study sentiment or attitudes at a sin- gle snapshot in time in 2020, 2021, or 2022, with many focusing on data in a specific domain such as economy, education, import/export, logistics, travel, remote working, and telework. The sub-populations being studied were also diverse, including the elderly populations, college students, and the workforce, or among the general social media users, the demographics of whom are hard to define due to lack of data. Study objective and overview The primary goal of this study is to examine how the sentiment shift from 2019 to 2022 and whether and when the level of negative sentiments has returned to the pre-pandemic era (2019) using college subreddit community data. As secondary objectives, we examine how other factors may affect the sentiment such as region, college type and classification, enroll- ment, etc. Our study is different from the works summarized in Background. First, our study investi- gates general sentiment rather than sentiment in a specific domain or toward a specific topic; in addition, it examines the temporal trend of sentiment from 2019 to 2022, representing the before-pandemic baseline and several phases during the pandemic, rather than a single snap- shot in time. This study is a follow-up study to “COVID-19 sentiment analysis using college subreddit data” [17], which examined the sentiment during the early phase of the pandemic (2020) as opposed to the pre-pandemic (2019) in 8 higher-education institutes (HEI) using college sub- reddit data in the U.S. The current study collected subReddit data associated with 128 HEIs in PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 3 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? the U.S., including the 8 schools studied in [17], over 4 years (August to December in 2019, 2020, 2021, and 2022, respectively), where 2021 and 2022 can be regarded as later stages of the pandemic. In other words, the scope of this study is much broader compared to [17], with a longer study period and many more schools that cover all four regions of the U.S.; the number of messages also increases from 165,570 in [17] to 3,925,509 in this study. In terms of methods, we used a pre-trained model that was an ensemble of two deep neural network models that learn sentiment from semantics, textual data, and relational data to predict sentiments of the Reddit messages collected in this study and applied a generalized linear mixture model to understand the temporal trend of sentiment from 2019 to 2022. The remainder of the paper is structured as follows. In Data Collection, we describe the types of data collected for this study. In Methods, we introduce the ML and statistical proce- dures used to address the objectives of the study. The study results are presented in Results. The study limitations and future work are discussed in Discussion and the main study conclu- sions are presented in Conclusion. Materials and methods Data collection First, the Reddit data collected in this study and how the data are used are in accordance with Reddit’s Terms and Conditions on data collection and usage. We also consulted the research compliance program at the University of Notre Dame (authors’ affiliation) and no Institu- tional Review Board (IRB) approval was needed given that the collected data were publicly accessible on Reddit and we did not collect privately identifiable data nor interact with the Reddit users. More information regarding privacy compliance is provided in S1 File. Our study focuses on Reddit data associated with a subset of HEIs in the U.S. The inclu- sion/exclusion criteria for school “recruitment” into the study is that a school had an active subrededit community from 2019 to 2022 and the number of messages in a subreddit commu- nity is above some threshold. On top of that, we aim for representativeness and diversity while taking the data storage and computational cost into consideration. Specifically, we first com- piled a list of HEIs with subreddits, leading to more than 400 institutions. We then dropped those schools that had a very small amount of messages in their subreddits. If a subreddit had <20 messages in each of the four years from 2019 to 2022 or <10 messages in at least two years, we dropped the school from the initial set. In addition, due to data storage and computa- tional constraints, we further subsetted the schools, retaining the diversity and representative- ness in the chosen subset, including geographical regions within the U.S. and HEI types (e.g, research universities, liberal arts colleges, and institutions specializing in particular fields such as the Naval Academy), academic ranking, level of intercollegiate athletics, among others. This eventually led to a total of 128 schools, as listed in provided in S1 File. Nevertheless, we acknowledge that the selection process, to some extent, involved subjective judgment influ- enced by the authors’ knowledge of the schools, despite our best efforts to maintain objectivity. The Reddit data collection process started with the retrieval of all textual messages from the subreddits associated with each HEI in the final set of the 128 schools. We supplemented this textual corpus with additional attributes specific to each HEI, such as region, Carnegie classifi- cation of HEI (CCHEI), enrollment, graduation rate, faculty headcount, etc. Reddit data collection and pre-processing. To examine the sentiment trend from 2019 and 2022, we downloaded the data from August to November, in the years 2019, 2020, 2021, and 2022 from the subreddit communities of the 128 schools. 2019 is regarded as the pre-pan- demic baseline, 2020 represents the pandemic peak period, while 2021 and 2022 represent the transition to the post-emergency period. We used the Pushshift API (https://github.com/ PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 4 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? pushshift/api) to download the comment data but excluded the submission data due to its non-availability when the study was conducted. In the downloaded message, some comments were deleted by Reddit users themselves, in which cases, “[deleted]” is used as a substitution. Comments may also be removed by Reddit moderators or administrators for various reasons, such as Reddit community rule violations, suspicious messages, spam, bots, or inappropriateness. Those comments are marked as “[removed]”. In both of the cases, we deleted them from the dataset from the subsequent senti- ment analysis as they do not contain “sentiment” information. In addition. the Reddit data contains a large number of emoticons, non-standard spellings, and internet slang. We employed the RoBERTa model which is capable of handling internet slang and other non-stan- dard spelling by using a special tokenizer, before obtaining the embeddings of the semantic information. The messages in each school form a graph. In the graph, each node represents a message, and if one message replies to another one, they are direct neighbors and we draw a directional edge between the two. Large graphs with more than approximately 30,000 nodes would require large memories (RAM larger than 22GB), exceeding our computational ability. For that rea- son, we subsampled a large graph to limit its size. specifically, for those schools with over 30,000 messages, we capped it at 30,000 following the breadth-first search algorithm [18]. As suggested by its name, the algorithm explores all nodes at the present depth in a graph before moving on to the nodes at the next depth level. Specifically, we randomly selected 50 nodes to start with and then added all the neighboring nodes that were connected to each of the 50 nodes (other numbers than 50 can also be used). If there were no direct neighbors to any of the 50 nodes, we randomly selected another 50 nodes to add to the subgraph. We grew the sub- graph by repeating this node-adding process sequentially until the number of the nodes in the subgraph node number got close to but did not exceed 30,000, at which point we randomly selected a set of the newly added nodes from the latest round and then randomly selected a necessary number of their neighboring nodes to make the final subgraph to contain exactly 30,000 messages. We expect this sampling strategy will not cause notable selection bias in the sample data. There are a few reasons. First, as presented in Fig 1 in Results, there are only a small proportion of schools with more than 30,000 messages each year (2.34%, 9.38%, 14.84%, 7.03% in 2019, 2020, 2021, and 2022 respectively). In other words, all messages in most of the school subRed- dit communities are included in the subsequent ML and statistical analysis tasks without being subsampled. Second, nodes with connections were included in the subgroup via a largely ran- dom sample process coupled with the breadth-first search algorithm. Isolated nodes without any connection were also randomly sampled, either in the initial stage or when there were no direct neighbors to any of the already sampled nodes. Fig 1. Histograms of number of messages across schools by year (blue and red lines represent median and mean, respectively). https://doi.org/10.1371/journal.pone.0299837.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 5 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? School-level baseline data. We consider a set of attributes associated with the schools that might impact the sentiment change from 2019 to 2022. The variables are listed in Table 1. The baseline data were collected from multiple sources—the 2020 U.S. census [19], Carnegie Clas- sification Of Institutions Of Higher Education (CCIHE) [20], and Integrated Postsecondary Education Data System (IPEDS) [21]. For the data collected from IPEDS, if there are multiple years of data, we average them across all available years to obtain the final value for these vari- ables. There are no missing values for this part of the collected data. We provided descriptive statistics on these variables to examine whether the schools are balanced in these attributes. We also included these attributes in the statistical model used to estimate the sentiment trend over the 4 years. This not only helps adjust for potential con- founding effects and improve the precision of parameter estimates but also allows us to exam- ine how these factors may affect sentiment in general, aside from the pandemic. Table 1. Descriptive statistics of school-level baseline characteristics. categorical variable: frequency (percentage) summary statistics Source and Year Variable Region West South Northeast Midwest Type Private public D1 (NCAA Division 1 school) Yes No CCHIE (Carnegie classification) Baccalaureate: arts & sciences focus Master’s: larger programs Doctoral: high research activity Doctoral: very high research activity Medical (grants a medical degree?) Yes No 24 (18.75%) 41 (32.03%) 31 (24.22%) 32 (25.00%) 44 (34.38%) 84 (65.63%) 108 (84.38%) 20 (15.62%) 7 (5.47%) 1 (0.78%) 18 (14.06%) 102 (79.69%) 77 (60.16%) 51 (39.84%) - - CCHIE, 2021 NCAA, 2023 CCHIE; 2021 IPEDS; 2021 U.S. 2020 Census IPEDS; 2021 IPEDS; 2019— 2021 IPEDS; 2019— 2021 IPEDS; 2020— 2021 IPEDS; 2020 continuous variable: mean ± SD (min, max) Population (city population in 1,000) Doctoral degrees (No. of doctoral degrees granted) Tenure (tenured/tenure-track faculty count) 564.4 ± 1278.8 (7.2, 8804.2) 315.7 ± 238.7 (0, 876) 1152.3 ± 682.9 (150, 3280) Enrollment (12-month unduplicated student enrollment in 1,000) 30.8 ± 18.1 (1.5, 101.9) Graduate student (12-month unduplicated graduate student enrollment in 1,000) Selectivity (Percent of applicants admitted) 9.020 ± 6.153 (0, 2.920) 52.5% ± 29.1% (5.0, 96.4)% Graduation rate (bachelor’s within 4 yrs) https://doi.org/10.1371/journal.pone.0299837.t001 61.2% ± 20.8% (15.7, 91.3)% IPEDS; 2019— 2021 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 6 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? Methods To analyze the data, we followed the approach in [17] by first predicting the sentiment for the collected messages using ML techniques and then estimating the sentiment trend over 2019 to 2022 using a statistical regression model. In what follows, we introduce the methods used in each step in detail. Sentiment prediction. We applied the pre-trained model in [17] to predict sentiment (negative vs non-negative) for each collected message after pre-processing in this study. The model was an ensemble of a graph neural network model and a pre-trained RoBERT model. We used the pre-trained model for making predictions rather than training a prediction model based on several considerations. First, the pre-trained RoEBRTa in the ensemble was trained on a large amount of data and is widely accepted, reliable, and robust data for learning embeddings from textual data. Second, it is labor-intensive to label sentiment for messages, which is a necessary step for training a good prediction model from scratch. Third, the data used in training/testing the prediction model in [17] have commonalities with the data in this study. In fact, the whole data in the former is a subset of the data in this study; as mentioned in Introduction, this study is essentially a follow-up and scaled-up study of the study in [17] by including more schools and variables and studying a longer period. Lastly, the model trained in [17] achieved top-of-the-line accuracy performances per multiple prediction metrics, rela- tive to the results in the literature in sentiment classification using social media data via ML techniques. All taken together, we decided to leverage the trained model in [17] to predict the sentiment in this study. We now briefly explain the components of the prediction model below, and readers may refer to [17] for more technical details as well as the numerical results of the model performance. RoBERTa [22] is an improved version of the BERT (Bidirectional Encoder Representations from Transformers) [23] and a pretraining framework that’s based on the attention mecha- nism [24]. The original RoBERTa was trained on a dataset of over 160GB of uncompressed text, such as BookCorpus plus English Wikipedia (16GB), and CC-News (76GB), among oth- ers. The model we employed is based on a RoBERTa model [25] that is trained on 58 million messages from Twitter and fine-tuned for sentiment analysis, which is more suitable for our application. The Python code for the RoBERTa framework is adapted based on the existing work. [25] (see S1 File for the link to the code). Embeddings for the Reddit messages were obtained from the RoBERTa model and then used in two downstream learning tasks. First, they were fed to a feed-forward neural network with softmax as the last layer to output the sentiment probabilities for the messages. Second, they were part of the input, along with the relational information among the messages, to the graph attention network (GAT) [26] to output a second set of predicted sentiment probabili- ties for the messages. GAT is a type of GNN model that incorporates the attention mechanism [24] to attend to neighborhoods’ features and use different weights for different nodes in a neighborhood in the graph. We treated the messages in each school as a separate directional graph in this application. GAT and RoBERTa can be different in their sentiment prediction performance. In this par- ticular prediction task, GAT tends to be more accurate in predicting negative sentiment and RoBERTa tends to be more accurate in predicting non-negative sentiment. To improve accu- racy and obtain more robust sentiment predictions, model stacking was used in [17] to com- bine predicted sentiment probabilities from GAT and Roberta to output the final sentiment classification for each message. Regarding the computational cost for running the prediction models, it took about one week to run RoBERTa and one day to run GAT, respectively, across all the messages in 128 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 7 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? schools on a computer with Intel(R) Xeon(R) CPU L5520 @ 2.27GHz and RAM 72.0 GB, and x64-based processor. 98.7 GB was used to store all unprocessed and processed data. Statistical analysis of sentiment trend from 2019 to 2022. After having the sentiment classification for the 3,925,509 messages, we fitted a generalized linear mixed-effects model (GLMM) to examine how sentiment changes from 2019 and 2022. The GLMM is � � log Prðyik is negativeÞ 1(cid:0) Prðyik is negativeÞ Pp ¼ b0 þ j¼1 bjxijk þ zk, where the sentiment label yik (negative vs non- negative) of message i in school k is the binary response; year (categorical) and the set of variables in Table 1 are fixed-effect covariates coded in xijk for j = 1, . . ., p (p is the number of regression coefficients associated with the covariates). zk � N ð0; s2Þ is included as a ran- dom effect to account for the within-school dependency as the messages from the same subreddit community are likely to be correlated. Due to the different scales of magnitude across different numerical covariates x, standardization (shifted by sample mean and scaled by sample standard deviation) was applied to each numerical x before the model fitting. From the GLMM, we estimated the odds ratios (ORs) of negative sentiment in 2020, 2021, and 2022 vs. the baseline 2019, along with 95% confidence intervals based on the Wald’s z-test for the ORs and p-value for testing the ORs against 1. We also estimated the effects of other school-level covariates in the model on the odds of negative sentiment in a similar manner. To correct the multiplicity issue from testing multiple odds ratios against 1 and control the overall false discovery rate (FDR), we also provided the adjusted p-values obtained using the FDR pro- cedure [27]. The main assumptions for the employed GLMM include the normality of the random effect zk, the linearity of the covariates in the model, and no obvious outlying observations. The nor- mality of the random effect is typical in the GLMM setting and is widely accepted and used in the statistical community. We focus on examining the main effect of the covariates in the GLMM and thus did not include any interaction terms among the covariates or higher-order terms of the numerical covariates, which also aids in result interpretation. We also calculated the Pearson residuals and plotted their histogram. The histogram of the residuals suggested the Pearson residuals were approximately normal in each sentiment category and no obvious outlying observations were detected. We included a single random variable zk to account for dependency among the messages from the same subreddit in the GLMM. This implicitly assumes that the messages within the same subreddit were equally correlated. Due to a lack of data and highly unbalanced data (i.e., the number of messages per user), we did not incorpo- rate more random effects to model other types of dependencies. For example, some messages may come from the same user, from different users in the same family, or from different users who were roommates or coworkers, etc. In other words, the dependency, if it is non-ignorable, can be complex and the data needed to account for such dependencies are largely unavailable from Reddit due to privacy concerns. In addition, even if they were available, they would be highly unbalanced across different entities, leading to potential numerical problems when run- ning the GLMM. Since the goal of the study is to examine the temporal trend of sentiment rather than understanding different types of dependency, we believe the current model specifi- cation is sufficiently reasoned to serve the main goal. Also noted is that GLMM was applied to learn the temporal trend of sentiment over 4 years, adjusting for school-level characteristics; it was not intended for for predictive purposes, particularly given the absence of user-level attri- butes, except for an inclusion of a user-level random effect to account for interdependence among the data points. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 8 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? Fig 2. Heatmaps of negative sentiment percentage in all 128 school subreddit communities by year. Each circle represents a school. The right column shows the within-school differences between 2020 to 2022 vs. 2019 (pre- pandemic). The crosses in the 2021 and 2022 plots represent difference values outside the [−20, 20]% range. (30.38% for the University of Notre Dame in 2020; 31.25% for Boise State University, and -28.82% for the University of Idaho in 2021; and 45.72% for the University of Maine, 25.98% for the University of New Mexico-Main Campus, 31.39% for the Tulane University of Louisiana in 2022). https://doi.org/10.1371/journal.pone.0299837.g002 Results School-level characteristics The baseline characteristics of the school-level data are summarized in Table 1. The frequency and percentage of each category are provided in the case of the categorical variables; mean, standard deviation, minimum, and maximum are provided for the continuous variables. Fig 1 depicts the distributions of the number of messages across the 128 schools by year. The number of messages varies by school and year, but most schools have messages <30k across all 4 years. Due to computational constraints, for schools with more than 30k messages, we sampled a subgraph that has 30,000 messages (nodes) using the methods described in Red- dit Data Collection and Pre-processing. This leads to a total of 3,925,509 messages in the study, the sentiments of which are predicted. Sentiment classification We calculate the percentage of negative messages in each school year based on the predicted sentiment from the ML model and present the heatmaps (left column) in Fig 2. In the maps, each circle represents a school and its position on the map represents its geographical location in the U.S. We also plot the difference in the percentage of negative sentiment from 2020 to 2022 vs. 2019 for each school (right column) in Fig 2. Compared to the year 2019, all the other years had higher negative sentiment proportions than 2019. The year 2020 has the highest PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 9 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? Fig 3. Percentage of negative sentiment distribution for all schools in each year (blue and red line represents median and mean, respectively). https://doi.org/10.1371/journal.pone.0299837.g003 percentage of negative sentiment. For years 2021 and 2022, although the negative sentiment proportions are still higher than in 2019, they are lower than in 2020. Similar trends can be observed in Fig 3 that depicts the distribution of negative sentiment percentage across the schools by year. Temporal sentiment trend and school-level covariate effect The GLMM model was run on complete records only (a total of 3,925,509 messages). There is a significant imbalance in the factor CCHIE, with only one school classified as “Master’s Col- leges & Universities: Larger Programs” and seven schools as “Baccalaureate: arts & sciences focus”, which could lead to potential computational and inferential problems in the GLMM setting. We thus combined the two categories as one and referred to it as “Baccalaureate/Mas- ter’s Colleges/Universities”. We used the glmer function in R package lme4 to run the GLMM and the p.adjust function in R package stats to obtain FDR adjusted p-values in testing whether the OR associated with a covariate in the GLMM is one or not. The inferential results are presented in Table 2 and Fig 4. We use adjusted p-value <0.05 as the cutoff for a statistically significant effect based on the adjusted p-value. Year has a statistically significant effect on Sentiment. The odds of having negative sentiments in 2020, 2021, and 2022, are 25.0%, 7.3%, and 6.3% higher, respectively, than that in 2019, suggesting the likelihood of negative sentiment increased significantly dur- ing the pandemic compared to before the pandemic. The negative sentiment proportion in the second half of 2021 almost returned to the pre-pandemic level and further decreased slightly in the second half of 2022. Overall, we may conclude the level of negative sentiment has decreased as the pandemic gradually subsided and everyday life resumed its usual course, although it might remain elevated in 2022 compared to the same period before the pandemic. Among the other covariates examined in the GLMM, Enrollment is statistically significantly associated with sentiment. For every one SD (16,551) increase in enrollment, the odds of hav- ing negative sentiment go up by 11.3%, implying larger enrollment tends to have a negative impact on Sentiment. Compared to Master’s/Baccalaureate universities/colleges, the odds of having negative sentiment is 30.1% higher (adjusted p-value = 0.035) in doctoral schools with very high research activity and 27.6% (adjusted p-value = 0.059) in doctoral schools with high research activity. The observations exhibit rational comprehensibility; there is constant pres- sure on both students and faculty members in HEIs with the requirement of research produc- tivity and excellence, which is likely linked with the higher negative sentiment in those schools and the communities that are associated with them. Private schools tend to have lower odds of negative sentiment (13.6% lower, adjusted p-value = 0.042) than public schools. The rest of the examined covariates do not have a statistically pronounced effect on sentiment, such as region, D1 school or not, a medical school or not, selectivity, etc. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 10 / 15 PLOS ONETable 2. Estimated effects of covariates on the odds of negative sentiment. Covariate Region Type Year D1 Midwest Northwest South West Public Private 2019 2020 2021 2022 No Yes CCHIE Baccalaureate or Master’s Doctoral: high research activity Doctoral: very high research activity No Yes Medical city population‡ enrollment‡ doctoral degrees‡ tenure‡ graduate student‡ selectivity‡ graduation rate‡ Has sentiment returned to the pre-pandemic level? odds ratio (95% CI) p-value - 1.048 (0.930, 1.182) 0.995 (0.898, 1.103) 0.947 (0.836, 1.072) - 0.864 (0.767,0.972) - 1.250 (1.243, 1.258) 1.073 (1.066, 1.079) 1.063 (1.056, 1.069) - 1.007 (0.88,1.151) - 1.276 (1.031, 1.58) 1.301 (1.062, 1.593) - 0.940 (0.858,1.029) 0.997 (0.953, 1.042) 1.113 (1.038, 1.193) 1.030 (0.972, 1.093) 1.003 (0.941, 1.069) 0.984 (0.921, 1.052) 0.996 (0.953, 1.042) 0.981 (0.941, 1.023) raw - 0.442 0.925 0.389 - 0.015 - <0.001 <0.001 <0.001 - 0.923 - 0.025 0.011 - 0.180 0.878 0.003 0.318 0.936 0.641 0.869 0.375 adjusted† - 0.646 0.936 0.616 - 0.042 - <0.001 <0.001 <0.001 - 0.936 - 0.059 0.035 - 0.380 0.936 0.010 0.604 0.936 0.870 0.936 0.616 † The multiplicity-corrected/adjusted p-values were calculated using the FDR procedure [27]. ‡ For numerical variables, the presented OR is associated with one SD increase. The rows with—as entries are the reference categories for the categorical covariates. The bold rows are the covariates/levels that are statistically significant if <0.05 is used for the adjusted p-values. https://doi.org/10.1371/journal.pone.0299837.t002 Fig 4. Forrest plot of estimated odds ratios of negative sentiment for the model covariates (vs. its reference level or with 1SD increase) in the GLMM with 95% confidence intervals. An asterisk * indicates a statistically significant effect per the adjusted p-value (Table 2). https://doi.org/10.1371/journal.pone.0299837.g004 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 11 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? The CIs associated with the ORs of the year comparisons are much narrower compared to those associated with other factors. This is because Year is the only within-cluster factor whereas the others are between-cluster factors, where cluster here refers to subreddit commu- nity or school in the GLMM. The variance of the effect of a between-cluster factor contains both the within-cluster sampling variability and the between-cluster variance (variance across clusters) whereas that for a within-factor factor only contains the former and is thus smaller. The precise estimates for the ORs in the year comparison benefit from the huge number of messages, where the precision on the estimated effects of the between-school factors is more determined by the number of schools, which is 128. Discussion We collected subReddit data from 128 college communities on Reddit in the U.S. and some school-level baseline covariates to study sentiment change from 2019 to 2022 that covered the pre-pandemic period to several stages of the COVID-19 pandemic. To our knowledge, this is the first study to examine the temporal trend of general sentiment from the pre-pandemic (2019) to a near-post-pandemic period (2nd half of 2022). While there exist studies that examine sentiments related to the pandemic (see Introduction), they often focus on a single snapshot in time during the pandemic or on a highly specific domain or topic in the post-pandemic period. While there are no existing studies for a direct side-by-side com- parison with our results, the overall trend in mental recovery post-2020, as indicated by our study, aligns with findings in positive attitudes toward different topics in several studies con- ducted in 2022 and 2023 (e.g., [1, 8–11, 14]). Since we used Reddit data, only schools with active subreddits from 2019 to 2022 are eligi- ble, which are the schools that are relatively well-known and have active online communities on social media. The Reddit users who post in those schools’ subreddit are not restricted to students, professors, or staff members who are affiliated with the schools, and they may also include those who might be interested in the subreddit (e.g. those who live in the city as a school, alumni, family members, and friends of those who are affiliated with the schools, fans of the athletic programs of the schools, etc). While the study results can be generalized to the sub-population the collected data represents and reflects the sentiment changes from 2022 to 2019 in that subpopulation, they would not be immediately generalized to the general popula- tion without understanding the demographics of the users (which we don’t have data on). We opted to employ a pre-trained sentiment classification model [17]. Although the model boasts considerable predictive capabilities and there is some overlap between the training/test- ing data used for that model and the data utilized in the present study, the data in this study are much more comprehensive. The classification model performance may be further improved if it can be fine-tuned using more training data in this study; an interesting topic to be explored in future work. A reviewer pointed out that the binary classification of sentiment used in the ML commu- nity is blunt as sentiment can have many subtypes. While we agree, we chose to use binary classification out of several considerations. First, while a binary classification is not as granular, it is less subject to bias and subjectivity and is thus more robust and generalizable compared to a classification scheme with more subtypes. In addition, training an ML model to predict a cat- egorical outcome with more levels would be more difficult and require a decent amount of training data in each level of the outcome and a larger model with more parameters. For exam- ple, Yan and Liu [17] trained ML models to predict 3-category outcomes (positive, negative, neutral) and 5-category outcomes (very positive, positive, negative, very negative, neutral), the classification accuracy on sentiments dropped to 60% for the former and 50% for the PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 12 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? latter from *85% using 2-category outcomes. Third, a binary categorization is sufficient for achieving the main goal of this study. Taken together, we have chosen to use the negative vs. non-negative sentiment classification in this study. That said, we will continue to work on developing innovative methods and ML approaches aimed at enhancing the classification of sentiment subtypes, collaborating with subject matter experts. Since the collected subreddit data do not contain individual-level demographic information about the users who posted the messages, which can be highly sensitive and pose privacy risks for re-identification, the covariates examined in the GLMM include only school-level public information. The current GLMM does not examine time-varying covariates except for the year itself. A potential interesting extension to the current study is to include time-varying covariates, such as the state-level vaccination percentage, unemployment rate, and inflation rate, in the model. On the other hand, including those time-varying covariates may blunt the temporal signal as observed in the current data due to their correlation with year. Regardless, considering these variables can help interpret the temporal trend of the sentiment qualitatively. For example, the significant drop in negative sentiment in 2021 compared to 2020 may likely be due to the availability of COVID vaccines and more effective treatments for COVID-19, giving people hope and a positive outlook that things would return to normal. Though the neg- ative sentiment level in 2022 is still lower than in 2020, This could be attributed to the possibil- ity that it takes more time for individuals to achieve full mental recovery. Additionally, certain factors might contribute to negative sentiment even in the absence of the pandemic. Examples include the substantial layoffs in major tech companies in 2022, impacting college and univer- sity students, as well as the significant rise in living costs during the same period. Nevertheless, these are conjectures lacking formal causality analysis and primarily serve as hypotheses that require validation through a rigorous study using a well-defined dataset, designed to under- stand the underlying reasons behind the observed emotional shift. Conclusion We collected subreddit messages associated with 128 HEIs in the U.S., covering the pre-pan- demic period (2019) and various stages of the pandemic (2020, 2021, and 2022) to understand how sentiment evolved from 2019 to 2022. The results suggest a notable recovery in the senti- ment composition (negative vs. non-negative) in 2022 and 2011 with a drop of 18% to 19% in the odds of negative sentiment, respectively, from 2020, indicating a positive shift in the overall sentiment landscape compared to the peak period of the pandemic. Compared to the pre-pan- demic era (2019), the odds of negative sentiment were still 6% to 7% higher in 2021 and 2022, a phenomenon possibly influenced by factors such as prolonged mental recovery from the pandemic, external events like tech company layoffs, and increased living costs in 2022. The study’s insights could have implications for policymakers, educational institutions, and mental health practitioners, providing them with a better and more nuanced understanding of the lingering effects of the pandemic on sentiment within the higher education community. Additionally, the methodology employed in this research, combining ML and statistical analy- sis, contributes to the methodological toolkit for sentiment analysis in large-scale social media datasets. Supporting information S1 File. The file contains more information about the data and privacy compliance, the code, and the list of HEIs included in this study. (ZIP) PLOS ONE \| https://doi.org/10.1371/journal.pone.0299837 March 15, 2024 13 / 15 PLOS ONEHas sentiment returned to the pre-pandemic level? Acknowledgments We thank three reviewers for taking the time to review our manuscript and providing insight- ful comments that contributed to its enhancement. Author Contributions Conceptualization: Fang Liu. Data curation: Tian Yan. Formal analysis: Tian Yan. Funding acquisition: Tian Yan. Investigation: Tian Yan, Fang Liu. Methodology: Tian Yan, Fang Liu. Project administration: Fang Liu. Resources: Tian Yan, Fang Liu. Software: Tian Yan. Supervision: Fang Liu. Validation: Fang Liu. Visualization: Tian Yan. Writing – original draft: Tian Yan, Fang Liu. Writing – review & editing: Tian Yan, Fang Liu. References 1. Shopnil MSI, Hasan SM, Srizon MAY, Faruk MF. Post-Pandemic Sentiment Analysis Based on Twitter Data Using Deep Learning. In: 2022 25th International Conference on Computer and Information Tech- nology (ICCIT). 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10.1371_journal.ppat.1012094	RESEARCH ARTICLE The read-through transcription-mediated autoactivation circuit for virulence regulator expression drives robust type III secretion system 2 expression in Vibrio parahaemolyticus Dhira Saraswati Anggramukti1☯, Eiji IshiiID Tetsuya Iida1,2, Toshio Kodama4, Shigeaki MatsudaID 1,2☯, Andre PratamaID 1,2* 1, Mohamad Al Kadi3, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Anggramukti DS, Ishii E, Pratama A, Al Kadi M, Iida T, Kodama T, et al. (2024) The read- through transcription-mediated autoactivation circuit for virulence regulator expression drives robust type III secretion system 2 expression in Vibrio parahaemolyticus. PLoS Pathog 20(3): e1012094. https://doi.org/10.1371/journal. ppat.1012094 Editor: Matthew C. Wolfgang, UNC-Chapel Hill: The University of North Carolina at Chapel Hill, UNITED STATES Received: November 9, 2023 Accepted: March 4, 2024 Published: March 27, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.ppat.1012094 Copyright: © 2024 Anggramukti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 1 Department of Bacterial Infections, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan, 2 Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan, 3 Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan, 4 Department of Bacteriology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, Japan ☯ These authors contributed equally to this work. * matsudas@biken.osaka-u.ac.jp Abstract Vibrio parahaemolyticus is the leading cause of seafood-borne gastroenteritis in humans worldwide. The major virulence factor responsible for the enteropathogenicity of this patho- gen is type III secretion system 2 (T3SS2), which is encoded on the 80-kb V. parahaemolyti- cus pathogenicity island (Vp-PAI), the gene expression of which is governed by the OmpR- family transcriptional regulator VtrB. Here, we found a positive autoregulatory feature of vtrB transcription, which is often observed with transcriptional regulators of bacteria, but the reg- ulation was not canonically dependent on its own promoter. Instead, this autoactivation was induced by heterogeneous transcripts derived from the VtrB-regulated operon upstream of vtrB. VtrB-activated transcription overcame the intrinsic terminator downstream of the operon, resulting in transcription read-through with read-in transcription of the vtrB gene and thus completing the autoregulatory loop for vtrB gene expression. The dampening of read- through transcription with an exogenous strong terminator reduced vtrB gene expression. Furthermore, a V. parahaemolyticus mutant with defects in the vtrB autoregulatory loop also showed compromises in T3SS2 expression and T3SS2-dependent cytotoxicity in vitro and enterotoxicity in vivo, indicating that this autoregulatory loop is essential for sustained vtrB activation and the consequent robust expression of T3SS2 genes for pathogenicity. Taken together, these findings demonstrate that the regulatory loop for vtrB gene expression based on read-through transcription from the upstream operon is a crucial pathway in T3SS2 gene regulatory network to ensure T3SS2-mediated virulence of V. parahaemolyticus. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 1 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Data Availability Statement: All data are available in the article and supporting information. Funding: This study was supported by Grants-in- Aid for Scientific Research from the Japan Society for the Promotion of Science (Grants 20K07428 and 23K06529 to S.M. and Grants 20K15748 and 23K05637 to E.I.), the Institute for Fermentation (to S.M.), the Chemo-Sero-Therapeutic Research Institute (to S.M.), the Joint Usage / Research Center on Tropical Disease, Institute of Tropical Medicine, Nagasaki University (2023-Ippan-14 to S.M.), the Center for Infectious Disease Education and Research (to S.M. and E.I.), and by the Taniguchi scholarship program from BIKEN Foundation (to D.S.A.). The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. Competing interests: The authors have declared that no competing interests exist. Author summary Many bacterial transcription factors undergo autoregulation, a process by which tran- scription factors regulate their own transcription to amplify or reduce the output responses to a changing environment. The common mode of such autoregulation is achieved by the transcription factor acting on its own promoter. In this study, we found that VtrB, a virulence regulator of the T3SS2 genes of the major food-borne pathogen Vib- rio parahaemolyticus, autoregulates its own expression but independently of its own pro- moter. We also demonstrated how this autoregulation occurs: VtrB activates transcription of the upstream operon and transcription extends to the vtrB gene over the relatively less effective intrinsic terminator. Read-through transcription thus underlies the autoregula- tory loop of vtrB expression. Moreover, this autoregulation was essential for the amplifica- tion of V. parahaemolyticus T3SS2 expression and induction of the full virulence of this pathogen. Together, our findings not only offer new insights into how V. parahaemolyti- cus controls its virulence gene expression to ensure pathogenicity but also provide a framework for further exploring the analogous mechanisms for the autoactivation of tran- scription factor gene expression in bacteria. Introduction Vibrio parahaemolyticus is a gram-negative halophilic bacterium that causes acute gastroenter- itis in humans, and infections with this pathogen have been spreading on a global scale over the last quarter century [1–3]. This bacterium inhabits marine and estuarine environments, and its infections are associated with the consumption of raw or undercooked seafood. The major virulence factor responsible for the enteropathogenicity of V. parahaemolyticus is one of the two type III secretion systems, called T3SS2 [4,5]. The T3SS is a multicomponent syringe- like protein secretion apparatus that is widespread in gram-negative pathogens and symbionts and injects bacterial proteins, so-called effectors, directly into target eukaryotic host cells, which results in disturbing the functions of host cells and promoting infections or symbiosis [6–8]. The T3SS2-related genes are encoded into an 80-kb pathogenicity island called Vp-PAI, which is located on the second of two chromosomes in V. parahaemolyticus [4]. The Vp-PAI region contains putative mobile elements and a lower guanine-cytosine content compared with that of the whole genome, which serve as indicators of an exogenous DNA region [4,9], and this region is thought to have been acquired by horizontal gene transfer, an event that is putatively mediated by Tn7-CRISPR [10]. The expression of the T3SS2 gene cluster in the Vp-PAI region is regulated by two OmpR- family transcription factors, V. parahaemolyticus T3SS2 regulator A (VtrA) and V. parahaemo- lyticus T3SS2 regulator B (VtrB), both of which are also encoded within the Vp-PAI region, in a cascade manner [11]. VtrA is a single membrane-spanning protein with an N-terminal OmpR-family DNA-binding domain and a C-terminal periplasmic domain that forms a com- plex with its cotranscribed protein VtrC [12]. VtrA directly activates the gene expression of VtrB through binding to the area around the -35 element within the vtrB promoter region [13]. The activation of vtrB transcription also requires another membrane-spanning regulator, ToxR [14], which also binds upstream of the VtrA-binding region in the vtrB promoter [13]. VtrB is also a membrane-localized regulator protein with an N-terminal OmpR-family DNA- binding domain and a C-terminal transmembrane domain that promotes the expression of T3SS2-related genes [11]. vtrB gene expression is altered by changes in physical conditions such as temperature and salinity, and this alternation is mediated by the histone-like nucleoid- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 2 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator structuring protein through binding upstream of the vtrB gene [13]. Bile acids in bile secreted into the intestinal tract of the host activate vtrB gene expression [15], which promotes VtrA oligomerization, and thus facilitates VtrA binding to the vtrB promoter and subsequent tran- scriptional activation of vtrB [16]. Such expression profiles appear to reflect the adaptation of V. parahaemolyticus to the human body environment during infection, in which transcrip- tional activation of vtrB is thus crucial. Bacteria have complex gene regulatory networks to enable appropriate output responses to changes in the environment. One common motif of such a regulatory network is autoregula- tion, a process by which transcription factors regulate their own transcription, either directly or indirectly, and positively or negatively [17,18]. Indeed, many bacterial transcription factors undergo autoregulation, such as in E. coli, in which half of the known transcription factors are self-regulated [19]. Positive autoregulation generally stimulates more production of the tran- scription factor and thereby amplifies the gene expression response regulated by the transcrip- tion factor. In V. parahaemolyticus, the regulatory cascade through which VtrA activates vtrB transcription has been well described, but the regulatory network involving the transcriptional activation of vtrB and downstream T3SS2 genes remains to be characterized. Here, we investi- gated whether vtrB transcription is autoregulated to refine its own expression. We found that vtrB gene expression is upregulated in an autoregulatory manner, but not by acting on its own promoter. Instead, this autoactivation is induced by transcription from the operon upstream of vtrB through read-through transcription across the intrinsic terminator. Furthermore, this positive autoregulation allows an increase in the expression level of VtrB and thereby ensures the expression of T3SS2 genes in V. parahaemolyticus and hence the full virulence of this pathogen. Results vtrB gene expression has an autoactivating feature To examine the autoregulatory property of vtrB transcription in V. parahaemolyticus, we com- pared vtrB gene expression between the wild type (WT) and the vtrB-deleted (ΔvtrB) strains. To this end, V. parahaemolyticus strains were grown in LB medium with 0.3 M NaCl to inacti- vate vtrB gene expression [13] (henceforth referred to as the nonpermissive condition) or in LB medium with 0.3 M NaCl and 80 μM sodium taurodeoxycholate (TDC) to activate vtrB gene expression [15] (henceforth referred to as the inductive condition). The vtrB gene expres- sion in each strain was measured by quantitative real-time polymerase chain reaction (qRT– PCR) targeting the 5’-untranslated region of the vtrB gene (vtrB 5’-UTR), which corresponds to the 102-bp region starting from the +1 transcription start site (TSS) of the vtrB gene [16] (Fig 1A). Consistent with the previous finding that VtrA activates vtrB transcription in a bile acid-dependent manner [15,16], vtrB 5’-UTR expression was activated in the WT strain under the inductive condition but was abolished in the vtrA-deleted strain (ΔvtrA), supporting the validity of our assay. However, the vtrB 5’-UTR transcript was also decreased in the ΔvtrB strain compared with the WT strain (Fig 1B), and this decrease was restored by expression of plasmid-borne VtrB in the vtrB-deleted strain (ΔvtrB) (Fig 1C). Furthermore, the plasmid- borne expression of VtrB could confer vtrB 5’-UTR expression in the vtrA- and vtrB-deleted strain (ΔvtrA ΔvtrB), which lack vtrB gene activation by VtrA [11] (Fig 1C). Taken together, these results revealed the autoactivating feature of vtrB gene expression. The autoactivation of transcription factor genes is often achieved by acting on their own promoters [17,18]. Therefore, to determine whether VtrB acts on its own promoter to activate vtrB transcription, we employed a pHRP309-derived lacZ transcriptional fusion reporter plas- mid containing a 284-bp upstream promoter region of vtrB (PvtrB), pHRP309-PvtrB [16], to PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 3 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 1. vtrB gene expression exhibits an autoactivating feature. (A) Schematic representation of the Vp-PAI region (VPA1370–VPA1321: top) and the vtrB upstream region with adjacent genes (bottom). The arrows indicate open reading frames (ORFs) and their orientation. The ORFs are colored according to their function: T3SS2 apparatus genes, blue; genes encoding T3SS2-secreted proteins, orange; other T3SS2-associated proteins, yellow; regulator genes, leaf green; hypothetical genes, gray. A long operon upstream of the vtrB gene predicted by Operon-mapper (https://biocomputo.ibt.unam. mx/operon_mapper/) [20] is indicated above the arrows. The region targeted by the qRT–PCR primers used in B and C is indicated by a red line as vtrB 5’- UTR. (B) Relative expression of the vtrB 5’-UTR region in V. parahaemolyticus WT, ΔvtrA, and ΔvtrB strains grown in LB medium containing 0.3 M NaCl at 37˚C with or without TDC. Total RNA was extracted from each culture at an OD600 of 1 and was analyzed by qRT–PCR. The mean fold-change and standard deviation (SD) values are indicated relative to the WT strains (n = 3). , p < 0.05, as revealed by one-way ANOVA followed by Dunnett’s multiple comparison test. (C) Effect of vtrB complementation under the control of the PBAD promoter on vtrB 5’-UTR expression. V. parahaemolyticus WT, ΔvtrB and ΔvtrAΔvtrB strains with pBAD18-Cm empty vector (indicated as p) or pBAD18-Cm-vtrB (indicated as pvtrB) were grown under TDC and arabinose induction. The mean and SD values are indicated relative to the WT strain harboring the empty vector (n = 3). , p < 0.05, as indicated by one-way ANOVA followed by Dunnett’s multiple comparison test. (D) β-galactosidase activity from the lacZ fusion reporter of the vtrB promoter region (PvtrB) in V. parahaemolyticus WT, ΔvtrA, and ΔvtrB strains grown under TDC induction. The values show the means, and the error bars represent S.D. (n = 3). , p < 0.05; ns, not significant, as revealed by one-way ANOVA followed by Dunnett’s multiple comparison test. (E) β-galactosidase activity from the PvtrB- lacZ reporter of the V. parahaemolyticus ΔvtrAΔvtrB strain with pBAD18-Cm empty vector (indicated as p), pBAD18-Cm-vtrA (indicated as pvtrA), or pBAD18-Cm-vtrB (indicated as pvtrB) was grown under TDC and arabinose induction. The values show the means, and the error bars represent S.D. (n = 3). , p < 0.05, as revealed by Student’s t test. https://doi.org/10.1371/journal.ppat.1012094.g001 monitor vtrB promoter activity. pHRP309-PvtrB was introduced into V. parahaemolyticus strains, and PvtrB-lacZ expression was assessed by measuring β-galactosidase activity under the inductive condition (Fig 1D and 1E). The PvtrB-lacZ expression was not observed in the ΔvtrA strain, confirming that VtrA is responsible for the activation of this promoter, while the ΔvtrB strain retained PvtrB-lacZ activity at a level that was comparable to the WT strain (Fig 1D). Moreover, the vtrB promoter was inactive in the ΔvtrA ΔvtrB strain harboring the empty vec- tor but was activated in the ΔvtrA ΔvtrB strain expressing VtrA on the plasmid, whereas the plasmid-borne expression of VtrB did not confer vtrB promoter activity to the ΔvtrA ΔvtrB strain (Fig 1E). Taken together, these results suggest that the vtrB promoter is not activated by VtrB itself. Multiple transcripts of different lengths contain vtrB gene To address the mechanism by which vtrB autoactivation is driven independently of its own promoter, we profiled the vtrB transcript in V. parahaemolyticus strains grown under the inductive condition by northern blotting using a probe against the vtrB promoter region (Fig 2A). Remarkably, two major vtrB transcripts of different lengths were detected in the WT PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 4 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 2. vtrB is transcribed as multiple transcripts of different lengths. (A) vtrB transcript profile in V. parahaemolyticus WT, ΔvtrB, and ΔvtrA ΔvtrB strains grown under TDC induction. Total RNA was extracted once the cells reached an OD600 of 1, and northern blotting was performed using the PvtrB probe. L, 4,000-nt L transcript; M, 2,000-nt M transcript; S’, S transcript after vtrB deletion. 23S rRNA and 16S rRNA served as loading controls. (B) vtrB expression affects the vtrB transcript profile. V. parahaemolyticus WT, ΔvtrB, and ΔvtrA ΔvtrB strains with pBAD18-Cm empty vector (indicated as p) or pBAD18-Cm-vtrB (indicated as pvtrB) were grown in the presence of TDC and 0.1% arabinose, and total RNA was extracted once the cells reached an OD600 of 1. Northern blotting was performed using the PvtrB probe. L, 4,000-nt L transcript; L’, L transcript after vtrB deletion; M, 2,000-nt M transcript; M’, M transcript after vtrB deletion; S’, S transcript after vtrB deletion. (C) Time-course analysis of the vtrB transcript profile in the V. parahaemolyticus WT and ΔvtrB strains. Both strains were grown to an OD600 of 0.8, and TDC was then added. RNA was extracted after 0, 5, 15, 30, 45, and 60 min of TDC induction, and northern blotting was performed using the PvtrB probe. L, 4,000-nt L transcript; M, 2,000-nt M transcript; S, 700-nt S trasncript; S’, S transcript after vtrB deletion. The data are representative of three independent experiments (A–C). https://doi.org/10.1371/journal.ppat.1012094.g002 strain (Fig 2A: lane 1). Based on their migration relative to the RNA markers, the estimated sizes of the long and short transcripts were approximately 4,000 nt and 2,000 nt, respectively, which cannot be attributed to VtrA-dependent transcription from the vtrB promoter because the coding sequence of vtrB has a length of 552 bp and the previously mapped TSS of the vtrB gene from the vtrB promoter region is located 102 bases upstream of the start codon of the vtrB coding sequence (S1A Fig) [16]. In contrast, a smaller vtrB transcript of approximately 200 nt was detected in the ΔvtrB strain (S1B Fig: a 410-bp deletion in the vtrB coding sequence) [11] (Fig 2A: lane 2) and was hypothesized to be a transcript from the vtrB promoter based on its length. No vtrB transcript was observed in the ΔvtrA strain and the ΔvtrA ΔvtrB strain (both of which lack vtrB transcription from the vtrB promoter) (Fig 2A: lanes 3 and 4), sup- porting the notion that these vtrB transcripts are generated in a VtrA-dependent manner. The expression of VtrB on the plasmid in the ΔvtrB strain yielded two high-molecular-weight tran- scripts (~3,600 nt and ~1,600 nt), which corresponded to the lengths of two transcripts observed in the WT strain minus the length of the vtrB gene deletion, but the smaller transcript of ~200 nt was not observed (Fig 2B: lane 3). A similar transcript profile was found in the ΔvtrA ΔvtrB strain with plasmid-borne expression of VtrB (Fig 2B: lane 5). To further investi- gate the temporal expression patterns of these transcripts, we performed a time-course analysis in which RNA samples were extracted at multiple time points after TDC induction (Fig 2C). In the WT strain, an ~700-nt transcript putatively derived from the vtrB promoter appeared 5 min after TDC induction, and high-molecular-weight transcripts (~4,000 nt and ~2,000 nt) were detected starting 15 min after TDC induction (Fig 2C: lanes 1–3). Thus, three different transcript sizes were observed after TDC induction in the WT strain. Herein, the ~4,000-nt, ~2,000-nt, and ~700-nt transcripts are referred to as L, M, and S transcripts, respectively. The levels of the L and S transcripts were decreased 30 min after TDC induction, whereas the M transcript showed sustained expression up to 60 min after TDC induction (Fig 2C: lanes 4–6), PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 5 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator which explains the transcript pattern of the WT strain shown in Fig 2A. In the ΔvtrB strain, the ~200-nt transcript containing a deletion in the vtrB coding sequence was consistently detected until 60 min after TDC induction (Fig 2C: lanes 7–12). To further characterize the multiple species of vtrB transcripts, we mapped the 5’-end of the transcripts in the WT strain 15 min after TDC induction by rapid amplification of cDNA ends (RACE) using a gene-specific primer to amplify the transcripts from the 3’-region of the vtrB gene. Consistent with the lengths of the three vtrB transcripts observed by northern blotting, three 5’-RACE products of different sizes were observed by agarose gel electrophoresis (S2A Fig), and from these, four 5’-ends were determined (S2B Fig). The 5’-end of the smaller RACE product, which was expected to correspond to the S transcript, was mapped to 102 bases upstream of the start codon of the vtrB gene, which is consistent with the previously mapped TSS of the vtrB gene (S2C Fig: top). Moreover, the 5’-end of the larger RACE product, which was expected to correspond to the M transcript, was mapped to two sites, and both of these sites were located approximately 1,200 bp upstream of the start codon of the vtrB gene, which is upstream of VPA1350 and within the coding sequence of VPA1351 (S2C Fig: middle). The 5’-end of the largest RACE product, which was expected to correspond to the L transcript, was mapped to 39 bases upstream of the VPA1353 start codon, which is within the coding sequence of VPA1354 (S2C Fig: bottom). Together, these results revealed multiple species of vtrB tran- scripts, and among these, the L and M transcripts could not be induced by transcription from the vtrB promoter. The long transcripts originate by transcription from the promoter of the VPA1356–VPA1349 operon upstream of the vtrB gene Upstream of the vtrB gene, a predicted operon encompasses the VPA1356 to VPA1349 genes, and the expression of these genes is dependent on VtrB [11]. Because the 5’-ends of the L and M transcripts were mapped to the interior of this operon, we first hypothesized that the inter- nal transcription start induced the transcripts. To test this possibility, the transcription from upstream of the 5’-ends of the L and M transcripts was examined. The 318-bp upstream region of the VPA1353 gene or the 304-bp upstream region of the VPA1350 gene (Fig 3A) was inserted upstream of the promoterless lacZ gene on the pHRP309 plasmid, yielding the UPVPA1353-lacZ or UPVPA1350-lacZ transcriptional fusion reporter to assess the transcriptional activation of this region in V. parahaemolyticus. However, both the UPVPA1353-lacZ and UPVPA1350-lacZ reporters were inactive in the WT strain (Fig 3B: Fragments 2 and 3), suggest- ing the absence of an internal TSS around the 5’-end of the L and M transcripts within these regions. To seek the TSSs of the L and M transcripts, we prepared a series of lacZ reporter plas- mids containing the further upstream region of the vtrB gene (Fig 3A). The expression of UPVPA1356-lacZ, the lacZ gene fused to the upstream region of the VPA1356 gene at the start of the predicted operon, was activated in the WT but not the ΔvtrB strain (Fig 3B: Fragment 5), supporting the existence of VtrB-activated transcription from upstream of the start of the operon. VtrB-dependent lacZ activity was similarly observed with the UPVPA1356-UPVPA1350- lacZ, UPVPA1356-VPA1349-lacZ, and UPVPA1356-DNVPA1349-lacZ reporters (Fig 3B: Fragments 6, 7, and 8), whereas the VPA1356-UPVPA1350-lacZ reporter showed no lacZ activity (Fig 3B: Fragment 4), suggesting that the transcription of this region is dependent on the single VPA1356 promoter at the start of the operon. The UPVPA1356-PvtrB -lacZ reporter containing both the VPA1356 and vtrB promoters was also activated at a higher level than the reporters containing either the VPA1356 or vtrB promoters, but it should be noted that the lacZ activity obtained with the UPVPA1356-PvtrB-lacZ reporter under this condition was lower than the cumulated activities of the UPVPA1356-lacZ and PvtrB-lacZ reporters (Fig 3B: Fragments 1, 5 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 6 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 3. VtrB activates its own expression from the upstream promoter of the VPA1356–VPA1349 operon. (A) Schematic representation of lacZ fusion reporters containing vtrB upstream regions of various lengths. The lengths of the long fragments are shown at the bottom of each fragment schematic. (B) β-galactosidase activity of the various constructs of lacZ fusion reporters in V. parahaemolyticus WT and ΔvtrB strains. The values show the means, and the error bars represent SDs. (n�3). , p < 0.05; ns, not significant, as revealed by Student’s t test. https://doi.org/10.1371/journal.ppat.1012094.g003 and 9). Taken together, these results suggest that the transcription initiation of the L and M transcripts depends on the promoter of the VPA1356–VPA1349 operon. The inconsistent lengths of the L and M transcripts (~4,000 and ~2,000 nt) with an ~7.5-kb distance from VPA1356 to vtrB indicated that these transcripts were generated from the VPA1356–vtrB tran- script through some modification. Read-through transcription results from incomplete transcription termination at the intrinsic terminator of VPA1349 The transcription of the operon is generally terminated at a terminator positioned at the end of the operon. Among two well-known types of terminators, Rho-dependent and Rho-inde- pendent (intrinsic) terminators [21], we indeed predicted the presence of a typical intrinsic terminator with a 13-nt stem–loop and a 4-nt U-tract at the end of the VPA1356–VPA1349 operon as the VPA1349 terminator (VPA1349T) using ARNold (http://rssf.i2bc.paris-saclay.fr/ toolbox/arnold/) [22] and Mfold (http://www.unafold.org/mfold/applications/rna-folding- form.php) [23] (S3A Fig, VPA1349T). Therefore, our observation that the L and M transcripts spanned across the predicted VPA1349T to the vtrB gene (Figs 2 and 3) indicates read-through transcription over VPA1349T. To validate the transcription termination or read-through at the VPA1349 terminator, we performed northern blotting using probes against the VPA1350 and VPA1349 coding regions. Multiple transcripts in the WT but not ΔvtrB strain were detected on the blots with both probes, and among these, the longer RNAs with sizes of ~4,000 nt and ~2,000 nt were expected to be identical to the L and M transcripts, respectively (Fig 4A and 4B), as observed with the probe against the vtrB promoter region (Fig 4C). Additional transcripts of ~3,000 nt and approximately 1,000 nt were also detected, which is consistent with the lengths expected when the transcription of the L and M transcripts is terminated at VPA1349T. A doublet of approximately 1,000 nt was presumably due to two 5’ ends at a dis- tance of 122 bp from the M transcript mapped in the 5’-RACE (S2 Fig). Thus, these results indicate that transcription of the VPA1356–VPA1349 operon is indeed terminated at PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 7 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 4. Incomplete transcription termination at the VPA1349 terminator (VPA1349T). (A–C) V. parahaemolyticus WT and ΔvtrB strains were grown in LB medium containing 0.3 M NaCl to an OD600 of 0.8, and TDC was then added. After further incubation for 15 min, total RNA was extracted, and northern blotting was performed using a probe for the VPA1350 coding region (A), a probe for the VPA1349 coding region (B), and the PvtrB probe (C). L, L transcript; LT, L transcript terminated at VPA1349T; M, M transcript; MT, M transcript terminated at VPA1349T; S, S transcript; S’, S transcript after the vtrB deletion. 23S rRNA and 16S rRNA were used as loading controls. https://doi.org/10.1371/journal.ppat.1012094.g004 VPA1349T, but a portion of the transcription reads through the terminator and extends to the coding sequence of the vtrB gene. To examine the termination efficiency of VPA1349T, the terminator region was cloned and inserted into the pBAD-lacZ plasmid (pBAD-VPA1349T-lacZ). For comparison, we generated pBAD-lacZ carrying the termination-deficient mutant, in which the nucleotide substitution (GGGGC > CCCCG) was introduced to disrupt hairpin structure formation in VPA1349T (HP mutant, pBAD-VPA1349T-HP-lacZ) or pBAD-lacZ carrying the rplL terminator (rplLT) of E. coli, which is an established strong Rho-independent terminator [24] (S3A Fig: rplLT) (pBAD-rplLT-lacZ). These constructs were introduced into the V. parahaemolyticus ΔvtrB strain, and the effect of the terminator on lacZ expression was assessed by measuring β-galac- tosidase activity under the inductive condition. VPA1349T exhibited ~75% less β-galactosidase activity than the HP mutant, which showed moderate β-galactosidase activity (S3B Fig). In contrast, rplLT showed almost no β-galactosidase activity. Similar results were observed under the nonpermissive condition (S3B Fig), indicating that the transcription termination was not affected by TDC induction. The intrinsic terminator consists of a hairpin structure with a stem of GC-rich bases and an internal loop, which is followed by a sequence of U-rich tracts (S3C Fig) [25], and the strength of the terminator is generally associated with its thermodynamic stability as indicated by Gibbs free energy (ΔG), the higher this value is, the more difficult it is for the sequence to form a stable secondary structure [21,26,27]. We predicted the free energy of VPA1349T according to each element of the terminator’s secondary structure: the stem structure (ΔGS), the U-tract structure (ΔGU), the loop structure (ΔGL), and the total free energy (ΔGT), using the Mfold program [23] (S1 Table) and compared them with those of rplLT. The computed ΔGU of VPA1349T was identical to that of rplLT (both ΔGU = −1.2 kcal/mol) because they contain almost a similar number of U-rich nucleotides following the stem–loop structure, which suggests that the U-rich tract may not contribute to the discrepancy between the termination abilities of VPA1349T and rplLT. Nevertheless, the other thermodynamic parameters support the notion that VPA1349T is markedly less stable terminator than rplLT: VPA1349T has higher ΔGT, ΔGS, and ΔGL values (−8.80 kcal/mol, −13.30 kcal/mol, and 5.70 kcal/mol, respectively) than rplLT (−21.60 kcal/mol, −25.20 kcal/mol, and 4.80 kcal/mol, respectively). Taken together, these results suggest incomplete transcription termination by VPA1349T, presumably due to the moderate thermodynamic stability of the hairpin structure. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 8 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Read-through transcription of vtrB results in robust activation of the T3SS2 genes We then determined whether read-through transcription mediates vtrB autoactivation and subsequent T3SS2 gene activation. To this end, we used a double terminator system [28,29] in which rplLT was placed downstream near the original intrinsic terminator to block read- through transcription at VPA1349T (S4A Fig). To first validate the system, we constructed pBAD-lacZ harboring the VPA1349 coding region and VPA1349T (pBAD-VPA1349-T-lacZ) or harboring an additional rplLT adjacent to VPA1349T in pBAD-VPA1349-T-lacZ (pBAD- VPA1349-DT-lacZ). Both constructs were introduced into the V. parahaemolyticus ΔvtrB strain, and read-through transcription of the lacZ gene was assessed by measuring β-galactosi- dase activity under inductive and nonpermissive conditions. The strain carrying the pBAD- VPA1349-DT-lacZ plasmid exhibited ~60% less β-galactosidase activity than the strain with the pBAD-VPA1349-T-lacZ plasmid under both conditions (S4B Fig), validating the efficient termination of transcription by the double terminator system. We then constructed a V. parahaemolyticus strain carrying the double terminator (DT strain), in which rplLT was inserted downstream of VPA1349T on the chromosome of the WT or POR-2 strain (TDH- and T3SS1-defective V. parahaemolyticus) [30]. In the WT-derived DT strain, an ~700-nt vtrB transcript was consistently detected 5–60 min after TDC induction, whereas multiple vtrB transcripts were observed in the WT strain (S4C Fig). We also observed ~3,000-nt, ~1,000-nt, and ~900-nt transcripts that were terminated at VPA1349T but not ~4,000-nt and ~2,000-nt read-through transcripts on the northern blots with VPA1349 and VPA1350 probes (S4D Fig), which was different from the results found for the WT strain. The qRT–PCR analysis also indicated that the DT strain exhibited defects in the expression of the intergenic region between VPA1349 and the vtrB promoter (downstream of VPA1349), which differed from the results found for its parental POR-2 strain (Fig 5A), validating that read- through transcription over VPA1349T was indeed diminished by the double terminator on the chromosome. The DT strain showed impairments in the expression of vtrB, and in that of the VtrB-regulated T3SS2 genes vopD2 (encoding a T3SS2-secreted protein), vscJ2 (encoding a T3SS2 IM-ring apparatus protein), and VPA1349 (located upstream of the double terminators) compared with the POR-2 strain. To further examine the effect of read-through transcription of vtrB on the T3SS2 gene activation, the protein production of T3SS2-associated proteins was determined by immunoblotting. In agreement with the qRT–PCR results, the protein levels of VtrB, VopD2, and VscJ2 in the DT strain were lower than those in the parental POR-2 strain (Fig 5B). The T3SS2-mediated secretion of VopD2 into the culture medium from the DT strain was also reduced compared with that from the POR-2 strain, indicating that the DT strain showed compromised T3SS2 secretion activity. Thus, these results revealed that read-through transcription increases vtrB expression, leading to subsequent T3SS2 gene activation. Read-through transcription of vtrB ensures the pathogenicity of V. parahaemolyticus Given the crucial role of T3SS2 in the pathogenicity of V. parahaemolyticus, we wondered whether T3SS2 expression induced by vtrB autoactivation contributes to V. parahaemolyticus pathogenicity. T3SS2 causes cytotoxicity against some cultured cell lines, which is one of the T3SS2-mediated virulence traits of V. parahaemolyticus [31]. To determine whether read- through transcription of vtrB activates T3SS2-mediated cytotoxicity, human colon adenocarci- noma Caco-2 cells were infected with V. parahaemolyticus strain POR-2 or its derivative DT, and the level of cytotoxicity was evaluated (Fig 6A). The POR-2 strain caused complete cell death at 6 hours postinfection, whereas vtrB deletion from the POR-2 strain showed attenuated PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 9 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 5. Read-through transcription extending to vtrB is needed for activation of T3SS2 gene expression. (A) Effect of the double terminator on the transcription read-through and expression of VtrB-regulated genes. V. parahaemolyticus POR-2, POR-2 DT (DT) and POR-2 ΔvtrB (ΔvtrB) strains were grown under the inductive condition, and total RNA was extracted from each culture once the culture reached an OD600 of 1. Relative expression of downstream of VPA1349 (between VPA1349T and the transcription start site of vtrB), VPA1349, vtrB 5’-UTR, vtrB, vscJ2, and vopD2 with the housekeeping gene recA was analyzed by qRT–PCR. The values represent the means ± SDs from a minimum of three independent experiments. , p < 0.05, compared with POR-2 by Student’s t test. (B) Effect of the double terminator on the production of VtrB and T3SS2-related proteins. Bacterial whole-cell lysates and culture supernatants of V. parahaemolyticus POR-2, DT, and ΔvtrB strains grown under the inductive condition to an OD600 of 1.8 were analyzed by immunoblotting with the indicated antibodies. Whole-cell lysate proteins on the blotted membrane were visualized with Ponceau S staining for a loading control (LC). https://doi.org/10.1371/journal.ppat.1012094.g005 Fig 6. Read-through transcription of vtrB is needed for the ability of V. parahaemolyticus to induce pathogenicity. (A) Cytotoxicity was evaluated in Caco-2 cells infected with POR-2, DT, and ΔvtrB strains grown under the inductive condition prior to infection at a multiplicity of infection of 10. After 6 hours of infection, the cytotoxic activity was evaluated by determining amount of the lactate dehydrogenase release. The values are the means ± SDs (n = 4). nd, not detected; , p < 0.05 compared with POR-2 by Student’s t test. (B) Fluid accumulation in rabbit ileal loops infected with the indicated V. parahaemolyticus strains. Each ligated loop was infected with bacteria at 109 colony-forming units or was not infected (mock), and the fluid accumulation in the loop was assessed 16 hours after infection. The FA ratio represents the amount of accumulated fluid (ml) per length (cm) of ligated rabbit small intestine. The values are the means ± SDs (n = 4). , p < 0.05, compared with POR-2 by Student’s t test. https://doi.org/10.1371/journal.ppat.1012094.g006 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 10 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator cytotoxicity due to lack of T3SS2 gene activation. Notably, the DT strain exhibited markedly reduced cytotoxicity at 6 hours postinfection. The diarrhea-inducing activity of V. parahaemo- lyticus also depends on T3SS2 [5,31]. We evaluated the effect of read-through transcription of vtrB on diarrhea-inducing activity in the rabbit ileal loop model. The ligated ileal loops of rab- bits were inoculated with V. parahaemolyticus strains, and at 16 h postinfection, the fluid accu- mulation in each loop was measured. The DT strain impaired fluid accumulation in rabbit ileal loops, compared with the parental POR-2 strain (Fig 6B). Taken together, these results support the notion that read-through transcription of vtrB mediates elevated T3SS2 gene expression and ensures the T3SS2-mediated pathogenicity of V. parahaemolyticus. Discussion Bacteria have evolved transcription regulatory networks for the appropriate control of gene expression in response to environmental changes [32,33]. The autoregulation of transcrip- tional regulators is a common strategy consisting of regulatory networks for positive or nega- tive feedback loops that amplify or reduce the output [18,34,35]. The typical mode of autoregulation of a transcription factor is achieved by acting on its own promoter and thereby enhancing or repressing its own gene transcription [17,18]. Indeed, the autoregulation of a transcription factor depending on its own promoter is often observed with master regulators of T3SS gene expression in gram-negative pathogens, such as Salmonella HilD [36,37], Shigella flexneri VirB [38], and enteropathogenic E. coli Ler [39]. In this study, we found that V. para- haemolyticus VtrB, the master regulator of T3SS2 gene regulation, also positively autoregulates its own gene expression, but not in a canonical manner dependent on its own proximal pro- moter. The emerging picture from this study is that the nascent VtrB resulting from the initial activation of the vtrB gene secondarily activates its own expression by generating heteroge- neous transcripts from the distal promoter of the upstream operon that read through the intrinsic terminator, which connects the autoregulatory loop for vtrB gene expression and can lead to robust expression of the T3SS2 genes for virulence (Fig 7). Among multiple transcrip- tion units containing the vtrB gene, one (S transcript) was initiated from the vtrB promoter, and the others (L and M transcripts) were initiated with its 5’-end located further upstream of the vtrB promoter at the internal sites within the VPA1356–VPA1349 operon, the transcription of which is activated by VtrB. An operon is generally defined as a transcription unit containing multiple genes with a single promoter positioned just upstream of the first gene in the operon, whereas operons are often split into suboperons by internal promoters [40–43]. However, because the L and M transcripts were expected to result from transcription initiated from the VPA1356 promoter (L and M transcript precursor) (Fig 3B), the precursor is likely to undergo some modification to be truncated to these transcripts. Such modification may occur simulta- neously with transcription, because VPA1356–vtrB (~7.5 k nt) and VPA1356–VPA1349 (~6.7 k nt) transcripts were not detected in our northern blotting assay (Figs 2 and 4, S4C and S4D). Indeed, bacterial RNA undergoes many cleavage events, which are often operated by ribonu- cleases (RNases) [44]. Because the L and M transcript retain their 3’-end with the vtrB gene, the 3’ to 5’ exonucleases are likely not associated with the processing of the precursor of these transcripts. Moreover, given the absence of 5’ to 3’ exonucleases in γ-proteobacteria [45,46], the processing event is likely endonucleolytic cleavage catalyzed by endonucleases. Bacteria have a repertoire of endonucleases [45,47], and the RNase responsible for this processing needs to be addressed in a future study. In addition, the L transcript exhibited a gradual decline starting 30 min after TDC induction (Fig 2C), suggesting that the L transcript may undergo further truncation into the M transcript. The decline in the S transcript starting 30 min after TDC induction observed in the WT strain is a read-through transcription-dependent process PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 11 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Fig 7. A working model depicting an autoregulatory loop of vtrB expression connected by read-through transcription. VtrA complexed with VtrC initiates activation of vtrB transcription at the vtrB promoter in cooperation with ToxR (a), which leads to primary expression of VtrB (b). VtrB then activates transcription from the promoter upstream of VPA1356 (c), part of which reads through the intrinsic terminator downstream of VPA1349, resulting in an extended transcript that reaches vtrB (read-in transcription of vtrB) and increased VtrB expression (d). VtrB induces further activation of transcription from the promoter upstream of VPA1356 and activates transcription of T3SS2-related genes (e), which can lead to robust expression of T3SS2 genes for virulence (f). OM, outer membrane; IM: inner membrane. https://doi.org/10.1371/journal.ppat.1012094.g007 because it was not observed in the ΔvtrB and DT strains (Figs 2C and S4C), suggesting the pos- sibility that transcription elongation from upstream might interfere with transcriptional acti- vation by VtrA on the vtrB promoter. Alternatively, a potential factor encoded in the VtrB- regulated regulon may antagonize VtrA-activated vtrB transcription. These issues warrant fur- ther study. Our data demonstrated read-through transcription across the intrinsic terminator located at the end of the VPA1356–VPA1349 operon. Indeed, terminator read-through is a more wide- spread event than previously thought, as observed in E. coli [48] and B. subtilis [49]. The notion that VPA1349T with a computed ΔGT value of −8.80 kcal/mol is a less stable terminator is con- sistent with the results previously obtained by Zhai, et al. who classified the 214 characterized terminators in E. coli into strong and weak terminators, with the strong terminators having a low ΔGT of −17 to −14 kcal/mol [50]. A strong and stable hairpin is expected to cause efficient termination due to a greater ability to push the RNAP away from the DNA strand [51]. The lower stability of the hairpin formed by VPA1349T may explain why read-through transcrip- tion is able to occur over this terminator. Alternatively, the distance between the intrinsic ter- minator and the stop codon of the gene also affects the termination efficiency, which is reduced when the stop codon is only a few base pairs upstream of the terminator, in which case the translating ribosome may directly repress the hairpin folding of the terminator of the nascent mRNA, resulting in read-through transcription due to an insufficient termination capacity [52,53]. VPA1349T is separated from the stop codon of the VPA1349 gene by only one A nucleotide (S3A Fig), suggesting the possibility that the proximity between the termina- tor and the stop codon of the VPA1349 gene may affect the transcription read-through. Trans- acting factors also control the termination efficiency by directly regulating hairpin folding or by interfering with RNAP pausing at the termination site [54]. The involvement of trans-acting factors in transcription termination in V. parahaemolyticus is not yet evident, but these hypotheses regarding VPA1349T deserve future studies. Our observation with the double terminator mutant indicates that much of the VtrB expres- sion induced by bile acid stimulation is derived from autoactivation (Fig 5). This PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 12 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator autoactivation loop was found to be essential for enabling the T3SS2 gene expression response to exert pathogenicity, which suggests the necessity of this response for adaptation to the envi- ronment in the human gastrointestinal tract. Autoactivation theoretically involves a slower induction rate but increases the sensitivity of the circuit response to the input signal and amplifies the longitudinal output; this process is favored by the prescribed response [55]. This property may be advantageous for pathogenic bacteria in the stage of infection that lasts hours, in terms of increasing the sensitivity to host-derived signals and maintaining a virulence gene expression level that is sufficient for virulence. Indeed, positive autoregulation of a transcrip- tion factor is often seen with regulatory genes for virulent T3SS, such as HilD of Salmonella enterica, VirB of Shigella flexneri, and ExsA of Pseudomonas aeruginosa [38,56,57]. Salmonella HilD binds to its own promoter for self-activation [56], whereas Shigella virB is activated by the transcriptional activator VirF, and VirB then acts on its own promoter but also on the virF promoter to activate virF transcription, forming positive feedback loops [38]. In contrast to these canonical autoactivation mechanisms, Pseudomonas aeruginosa exsA is initially activated by Vfr in an exsA promoter (PexsA)-dependent manner, and ExsA then binds to the promoter of the upstream exsCEB operon (PexsC) to generate a polycistronic exsCEBA transcript; during this process, ExsA does not bind to or activate its own promoter [58]. The PexsC promoter is approximately 400-fold more active than PexsA and is thus expected to contribute to higher exsA transcript levels [58]. exsA-like autoactivation (transcribed as a distal promoter-depen- dent operon) is observed with another AraC family virulence regulator in gram-negative bac- teria, RegA, the global virulence regulator of Citrobacter rodentium [59]. Thus, the case of exsA autoactivation appears to be similar to that of vtrB in terms of polycistronic transcription from the distal promoter rather than the proximal promoter, which may represent an efficient strat- egy to simultaneously activate its own gene and its target genes from a single promoter but can be considered a polymorphism of the operon structure composed of exsCEBA genes due to the lack of predicted intrinsic terminator downstream of exsCEB. In terms of autoactivation via read-through transcription beyond the terminator, greater similarity is found in the regulation of the toxT gene encoding the transcription factor ToxT of V. cholerae, a causative agent of the severe diarrheal disease cholera, and a closely related spe- cies of V. parahaemolyticus. ToxT directly activates the tcp operon encoding the Tcp pilus needed for V. cholerae intestinal colonization [60,61], and the ctxAB gene encoding the cholera toxin, the absolute virulence factor of V. cholerae for causing severe watery diarrhea [60,62]. The activation of toxT transcription is initiated by binding of the membrane-bound transcrip- tion factor TcpP and its cotranscribed factor TcpH just upstream of the RNAP-binding site on the toxT promoter, which is promoted by binding of an additional membrane-bound tran- scription factor, ToxR, to the toxT promoter [63,64]. This regulatory pathway is quite similar to the initial activation of vtrB transcription: the VtrA/VtrC complex binds around the -35 ele- ment on the vtrB promoter, and the ToxR homolog binds further upstream, indicating func- tional homology between the two regulatory pathways without sequence homology. Notably, this transcription from the tcpA promoter reads through a relatively inefficient terminator downstream of tcpF (upstream of the toxT gene), resulting in the transcription extending to the toxT gene, which allows amplification of toxT gene expression and optimal production of Tcp pilus and cholera toxin [61,65]. Interestingly, V. cholerae and V. parahaemolyticus thus share not only functional homology in the virulence regulatory pathway but also the mecha- nism by which the virulence regulator is positively autoregulated, suggesting convergent evolu- tion of virulence regulation in Vibrio species. Although it is difficult to discuss the utility of read-through transcription-mediated autoactivation for bacteria based only on these limited examples, one possible inference is that moderate transcription termination at the upstream operon terminator fine-tunes the rate of downstream read-through transcription, resulting in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 13 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator sustained activation of the regulator and subsequent target genes. Because both Tcp pilus and T3SS2 are membrane-localized macromolecular apparatuses whose assembly is temporally sequential and hierarchical [66,67], gradual increases in protein amounts may facilitate accu- rate assembly. Alternatively, read-through transcription with moderate termination may opti- mize the amount of VtrB production for T3SS2 gene expression, which may be supported by the observation that a V. parahaemolyticus strain carrying VPA1349T-HP mutation on the chromosome, which is defective in transcription termination at the end of the upstream operon, showed an increased vtrB expression level but no significant change in T3SS2 gene expression compared with the parental strain (S5 Fig). In summary, we propose that positive autoregulation of V. parahaemolyticus VtrB, which involves read-through transcription from the upstream operon in the vtrB gene, is essential for T3SS2 gene expression and virulence. Therefore, our study offers new insights into how V. parahaemolyticus controls virulence gene expression to ensure pathogenicity. A similar regula- tory mechanism may be applied to any transcription factor with the following prerequisite for autoactivation by read-through transcription: located downstream of its target operon with a weak terminator in the same orientation as transcription. Materials and methods Ethics statement All animal experiments in this study were conducted in strict accordance with the guidelines for the Care and Use of Laboratory Animals in the Research Institute for Microbial Diseases, Osaka University, and were performed following an experimental protocol approved by the Animal Care and Use Committee of the Research Institute for Microbial Diseases, Osaka University. Bacterial strains and plasmids All bacterial strains and plasmids used in this study are listed in S2 and S3 Tables. The clinical isolate RIMD2210633 of V. parahaemolyticus was used as the wild-type strain (WT) in this study [4]. All V. parahaemolyticus strains were grown at 37˚C in Luria–Bertani (LB) medium with modified NaCl concentrations (tryptone, 1%; yeast extract, 0.5%; NaCl, 0.3 M). Appropri- ate antibiotics were added to grow the plasmid-carrying strains: 20 μg/ml gentamycin for pHRP309 and 15 μg/ml chloramphenicol for pBAD18-Cm plasmid backgrounds. Arabinose at a final concentration of 0.1% was added to the strains harboring pBAD18-Cm-based plasmids at the early exponential growth phase to induce PBAD-driven expression. The primers used for plasmid construction are listed in S4 Table. Escherichia coli DH5α and BW19851 strains were used for the general manipulation of plasmids and the mobilization of plasmids into V. parahaemolyticus. Mutant construction A four-primer PCR technique was used to construct insertion and deletion mutants as previ- ously described [30]. The primers used are listed in S4 Table. Briefly, the DNA fragment of the deletion or insertion target was cloned and inserted into the pCRII-TOPO vector. This frag- ment was extracted from the pCRII-TOPO vector by digestion with the restriction enzymes BamHI and PstI, and then cloned into an R6K-ori suicide vector, pYAK1, which contains the sacB gene that confers sensitivity to sucrose. The resulting deletion or insertion plasmid was introduced into E. coli BW19851 and transferred into V. parahaemolyticus strains by conjuga- tion. The resulting conjugates were selected on thiosulfate citrate bile sucrose agar containing PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 14 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator chloramphenicol at a concentration of 5 μg/ml and then screened for mutants on LB plates supplemented with 10% sucrose. The desired deletion/insertion in the V. parahaemolyticus genome was confirmed by PCR. RNA extraction Total RNA was extracted using the hot acidic phenol RNA isolation method [68]. For qRT– PCR and northern blotting analysis, V. parahaemolyticus cells were grown in LB broth con- taining 0.3 M NaCl and 80 μM TDC at 37˚C until the optical density at 600 nm (OD600) reached 1.0. For mapping the 5’-ends of transcripts, V. parahaemolyticus cells were grown in LB broth containing 0.3 M NaCl at 37˚C until the OD600 reached 0.8 and then treated with 80 μM TDC for 15 min. The cultures were harvested by centrifugation at 3,000 × g for 10 min, and the supernatant was removed. The remaining pellets were lysed with lysis buffer (0.5% SDS, 20 mM sodium acetate, and 10 mM EDTA at pH 5.5), followed by the addition of acid phenol. The mixture was then heated at 60˚C for 10 min. After centrifugation, the upper aque- ous layer was collected. The RNA was precipitated with ethanol and dried until the resulting pellets became clear. The precipitated RNA was dissolved in nuclease-free water and treated with Turbo DNase (Thermo Fisher) according to the manufacturer’s instructions. The concen- tration of RNA was measured using a NanoPhotometer spectrophotometer (Implen, USA). Quantitative real-time polymerase chain reaction (qRT–PCR) qRT–PCR was performed as previously described [13], with a slight modification. Briefly, the purified RNA samples were diluted to 20 ng/μL with nuclease-free water, and the reactions were performed using SYBR Green RNA-direct Real-time PCR Master Mix (Toyobo) and a QuantStudio 5 real-time PCR system (Thermo Fisher), according to the manufacturer’s instructions. Relative quantification was performed using the threshold cycle (2-ΔΔCT) method and was normalized to that of recA as the housekeeping gene. Transcriptional reporter assay V. parahaemolyticus strains harboring pHRP309-derived lacZ reporter plasmids were grown in LB broth containing 0.3 M NaCl at 37˚C to an OD600 of 0.8. The bacterial cultures were then supplemented with 80 μM TDC and further incubated until reaching an OD600 of 1.8. For pBAD-derived reporter plasmids, V. parahaemolyticus strains were grown in LB broth containing 0.3 M NaCl with or without 80 μM TDC at 37˚C to an OD600 of 1. Arabinose was added at the early exponential growth phase to a final concentration of 0.1%. The β-galactosi- dase activity of the bacterial cell lysates was measured using Miller’s method with the substrate o-nitrophenyl-β-D-galactopyranoside (ONPG), as described previously [69]. Mapping of 5’-end of the transcript The 5’-ends of the transcripts (cDNA fragments) were determined using the SMARTer RACE 5’/3’ Kit (Takara Bio, Shiga, Japan) according to the manufacturer’s instructions. Total RNA from V. parahaemolyticus was isolated as described above. Sanger DNA sequencing was out- sourced to Genewiz (Azenta Life Science). The sequence information obtained was analyzed using GENETYX ver. 18.0.4 software (GENETYX, Tokyo, Japan). Northern blot analysis Extracted total RNA (2.5 μg) was heated at 60˚C and subjected to 1.2% agarose gel electropho- resis in the presence of formaldehyde, with Dyna Marker (Prestain Marker for RNA High, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 15 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator BioDynamics Laboratory Inc., Japan). After electrophoresis, the RNA was transferred from the agarose gel to a positively charged nylon membrane (Roche, Germany) overnight with 20× SSC buffer (3 M NaCl, 0.3 M sodium citrate dehydrate) and cross-linked by UV irradiation (1200 × 100 μJ/cm2). Hybridization was performed at 50˚C for 4 h in DIG Easy Hyb buffer (Roche, Germany) with a DIG-labeled probe. DIG-labeled probes were amplified using a PCR DIG probe synthesis kit (Roche, Germany). The primers used for probe synthesis are listed in S4 Table. The membrane was rinsed with a high-stringency buffer (0.1× SSC buffer, 0.1% SDS) and incubated with a blocking solution (Roche, Germany) for 30 min. Anti-digoxigenin-alka- line phosphatase (anti-DIG-AP) (Roche, Germany) was added to the blocking buffer-soaked membrane. The membrane was incubated for 1 h, equilibrated with detection buffer (0.1 M Tris-HCl at pH 9.5, 0.1 M NaCl), developed with the chemiluminescent substrate CDP-STAR (Roche, Germany) and visualized using an Amersham ImageQuant 800 system (Cytiva, USA). Ribosomal RNA was stained with Gel Red (Biotium, USA) and visualized by UV irradiation as a loading control. Protein sample preparation V. parahaemolyticus strains were grown in LB broth containing 0.3 M NaCl and 80 μM TDC at 37˚C for 3 h to an OD600 of 1.8. The cultures were centrifuged at 15,000 × g for 2 min to sep- arate the supernatants and the resulting pellets, which were used as whole-cell lysates. The secreted proteins were precipitated from the supernatants with ice-cold trichloroacetic acid at a final concentration of 10% on ice for 1 h and then centrifuged at 15,000 × g for 30 min at 4˚C. The resulting pellets were washed with cold acetone and centrifuged at 15,000 × g for 30 min at 4˚C. The precipitate was dried at room temperature for 20 min. Proteins of whole cell lysates and supernatants were solubilized in Laemmli buffer, sonicated for 5 min, and dena- tured at 95˚C for 5 min. The samples were then subjected to SDS–PAGE and immunoblot analysis. Immunoblot analysis For immunoblot analysis, protein samples were separated by SDS–PAGE and transferred to a PVDF membrane by semidry electroblotting. The membranes were blocked in TBST (20 mM Tris-HCl at pH 7.4, 150 mM NaCl, and 0.1% Tween 20) containing 5% skim milk for 1 h and then incubated overnight with primary antibodies against the protein of interest. The anti- VtrB, anti-VscJ2, and anti-VopD2 polyclonal antibodies were prepared in-house by immuniz- ing New Zealand White rabbits, as described elsewhere [11,30]. The membranes were then probed with horseradish peroxidase-conjugated goat anti-rabbit antibody (code: 62–1820, Invitrogen) for 2 h at room temperature. The blots were then developed using an ECL Prime Western Blotting Detection Reagent (Cytiva, USA) and visualized using the Amersham Image- Quant 800 system (Cytiva, USA). For the loading control, whole cell lysates on the blotted membrane were stained with Ponceau S staining solution (Thermo Fisher) for visualization before the blocking procedure. Cell culture and cytotoxicity assay Caco-2 cells (ECACC 86010202) supplied by the European Collection of Authenticated Cell Cultures were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100 μg/ml gentamicin at 37˚C with 5% CO2. The cyto- toxicity assay was performed as previously described [31,70], with some modifications. Briefly, 2× 104 Caco-2 cells were seeded into each well of a 96-well plate and grown to confluence for 48 hours. The cells were washed twice with phosphate-buffered saline, and the medium was PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 16 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator then replaced with fresh phenol red-free DMEM. Prior to infection, V. parahaemolyticus strains grown in LB broth containing 0.3 M NaCl and 80 μM TDC for 3 hours were harvested by centrifugation and suspended in PBS. The cells were then infected with each V. parahaemo- lyticus strain at a multiplicity of infection (MOI) of 10 for 6 hours, and the cytotoxicity assay was performed by measuring the release of lactate dehydrogenase (LDH) into the culture supernatants with a Cytotoxicity LDH assay kit-WST (Dojindo) according to the manufactur- er’s instructions. The cytotoxicity percentage was calculated with the following equation: [opti- cal density at 490 nm (OD490) of experimental release–OD490 of spontaneous release] / [OD490 of maximum release–OD490 of spontaneous release] × 100. Spontaneous release refers to the amount of LDH released from uninfected cells, whereas maximum release is the total amount of LDH released after the complete lysis of uninfected cells by detergent. Rabbit ileal loop assay Rabbit ileal loop tests were performed as previously described [70]. Briefly, 1 ml of the bacterial suspensions (109 colony-forming units) was injected into the ligated ileal loops of a 1.5-kg female New Zealand White rabbit (the length of a loop is approximately 8 cm), and the fluid accumulation in each loop was measured 16 h after inoculation. The fluid accumulation (FA) ratio represents the amount of accumulated fluid (ml) per length of ligated rabbit small intes- tine (cm). Statistical analysis GraphPad Prism 9.5.1 software was used for the statistical analysis of all the data. A two-tailed Student’s t test or one-way ANOVA followed by Dunnett’s multiple comparison test was used for the statistical analysis, and p values < 0.05 were considered to indicate statistical significance. Supporting information S1 Fig. Schematic representation of the vtrB upstream region with adjacent genes in the WT (A) and ΔvtrB strains (B). The arrows indicate genes with their orientation. The nucleotide position is based on the transcriptional start site of vtrB (indicated as +1). The coding sequence of vtrB has a length of 552 bp, whereas the ΔvtrB strain contains a 410-bp deletion in the cod- ing sequence. (TIF) S2 Fig. Mapping of the 5’-ends of vtrB transcripts. (A) Agarose gel electrophoresis of 5’- RACE PCR products. (B) Schematic representation of the position of the mapped 5’-end of each vtrB transcript from 5’-RACE PCR. (C) Nucleotide sequence around the 5’-ends of 5’- RACE products. The determined 5’-ends are indicated in red. The transcriptional start site of the vtrB is indicated as +1, and putative −35 and −10 elements are underlined. The shading indicates the region of coding sequences, and the names of the genes are shown above. (TIF) S3 Fig. Transcription termination ability of VPA1349T. (A) The secondary structure of the VPA1349 terminator (VPA1349T) and the E. coli rplL terminator (rplLT) predicted using Mfold [22]. Gray shading indicates the substituted nucleotides (GGGGC > CCCCG) for dis- rupting the hairpin structure formation in VPA1349T. (B) Evaluation of the transcription ter- mination ability using the terminator-fused lacZ reporters. The V. parahaemolyticus ΔvtrB strain harboring each reporter plasmid with VPA1349T, rplLT, or the VPA1349 terminator hairpin mutant (VPA1349T-HP) was grown in LB medium containing 0.3 M NaCl with or PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 17 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator without TDC induction, and the β-galactosidase activity was monitored. The values show the means and error bars represent the SDs (n = 3). nd, not detected; , p < 0.05, as revealed by one-way ANOVA followed by Dunnett’s multiple comparison test. (C) Schematic structure of the general intrinsic terminator (adapted from [50] with modifications). (TIF) S4 Fig. Effective transcription termination by the double terminator system. (A) The sec- ondary structure of the double terminator system composed of VPA1349T and rplLT was pre- dicted using Mfold [22]. (B) Transcription termination ability of the double terminator system. V. parahaemolyticus ΔvtrB with lacZ reporter plasmids containing the VPA1349 gene with VPA1349T or with the double terminator (VPA1349-DT) was grown in LB medium con- taining 0.3 M NaCl with or without TDC, and the β-galactosidase activity was evaluated. The values show the means and error bars represent the SDs (n = 3). nd, not detected; , p < 0.05, compared with VPA1349-T by Student’s t test. (C) Effect of the double terminator on the vtrB transcript profile in V. parahaemolyticus. The WT and DT strains were grown to an OD600 of 0.8, and TDC was then added. RNA was extracted after 0, 5, 15, 30, 45, and 60 min of TDC induction, and northern blotting was performed using the PvtrB probe. L, L transcript; M, M transcript; S, S transcript. 23S rRNA and 16S rRNA served as loading controls. (D) Transcrip- tion termination at the double terminator. V. parahaemolyticus WT and DT strains were grown in LB medium containing 0.3 M NaCl to an OD600 of 0.8, and TDC was then added. After further incubation for 15 min, total RNA was extracted, and northern blotting was per- formed using the indicated probes: the VPA1350 coding region (left), the VPA1349 coding region (center) and PvtrB (right). L, L transcript; LT, L transcript terminated at VPA1349T or the double terminator; M, M transcript; MT, M transcript terminated at VPA1349T or the double terminator; S, S transcript. The data are representative of three independent experi- ments (C, D). (TIF) S5 Fig. Disrupting the hairpin structure of VPA1349T in the chromosome increases vtrB expression but not VtrB-regulated gene expression. (A) V. parahaemolyticus strains WT, WT-derived DT (DT), ΔvtrB, and a strain carrying the hairpin mutation in VP1349T (S3A Fig: VP1349T-HP) on the chromosome of the WT strain (HP strain) were grown in LB medium containing 0.3 M NaCl to an OD600 of 0.8, and TDC was then added. After further incubation for 15 min, total RNA was extracted, and northern blotting was performed using the probe for the VPA1349 coding region. The HP strain was defective in transcription termination at the end of the upstream operon, as observed by the absence of L and M transcripts terminated at VP1349T. L, L transcript; LT, L transcript terminated at VPA1349T; M, M transcript; MT, M transcript terminated at VPA1349T. The data are representative of three independent experi- ments. (B) Effect of the hairpin mutation in VPA1349T on vtrB and VtrB-regulated gene expression. V. parahaemolyticus strains POR-2, POR-2 DT (DT), POR-2 HP (HP), and POR-2 ΔvtrB (ΔvtrB) were grown under the inductive condition, and total RNA was extracted from each culture once the culture reached an OD600 of 1. Relative expression of vtrB, vopD2, and vscJ2 with the housekeeping gene recA was analyzed by qRT–PCR. The values represent the means ± SDs from a minimum of three independent experiments. *, p < 0.05; ns, not signifi- cant, compared with POR-2 by Student’s t test. (C) Effect of the hairpin mutation in VPA1349T on the production of VtrB and T3SS2-related proteins. Bacterial whole-cell lysates and culture supernatants of V. parahaemolyticus POR-2, HP, and ΔvtrB strains grown under the inductive condition to an OD600 of 1.8 were analyzed by immunoblotting with the indi- cated antibodies. Whole-cell lysate proteins on the blotted membrane were visualized with PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012094 March 27, 2024 18 / 22 PLOS PATHOGENSAutoregulatory circuit for V. parahaemolyticus virulence regulator Ponceau S staining for loading control (LC). (TIF) S1 Table. Gibbs free energy (ΔG) values of VPA1349T and rplLT, calculated using Mfold [22]. (PDF) S2 Table. Bacterial strains used in this study. (PDF) S3 Table. Plasmids used in this study. (PDF) S4 Table. Primers used in this study. (PDF) S1 Data. Source data for graphs in this study. (XLSX) Acknowledgments We thank the members of the Department of Bacterial Infections for helpful discussions. Author Contributions Conceptualization: Dhira Saraswati Anggramukti, Eiji Ishii, Shigeaki Matsuda. Funding acquisition: Eiji Ishii, Shigeaki Matsuda. Investigation: Dhira Saraswati Anggramukti, Eiji Ishii, Shigeaki Matsuda. Project administration: Eiji Ishii, Shigeaki Matsuda. 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10.3389_fncom.2022.917786	11 FIG. 8. log-normal distribution of rates Each black line represents the cumulative distribution of the log-rates for the repertoire for an odorant after subtracting the mean and normalising by the variance (no. of lines is equal to the number of odorants tested). The red line is the cumulative distribution for a standard normal distribution. Data are for Anopheles [46] (right) and Drosophila [45] (left) receptors to all odors agrees well with a Gaussian dis- tribution once we subtract the mean and normalize by the standard deviations of the responses for that odor (z-scoring). Thus in the space of log-rates, we model the responses as a multi-dimensional Gaussian described with the same mean and covariance between receptors as in the Drosophila data. To extend the response matrix along the odorant dimension we simply sample from this multi-dimensional Gaussian and exponentiate the Gaus- sian sample to get the rates. In order to extend along the receptor dimension, we want to create new receptors which share some features of the original data but are not simply duplicates. To do this, if we want to expand the number of receptors by a factor F , say, we create F nrec) which a new covariance matrix (of size F nrec ⇥ is a randomly rotated version of a block diagonal matrix which has the receptor covariance matrix of the Hallem & Carlson data replicated F times along the diagonal. We generate a synthetic rate matrix by generating Gaussian samples using this covariance matrix and then exponen- tiating the samples to get the rates. There are small number of outlier rates which we ﬁnally set to the maxi- mum value in the Drosophila dataset. To parametrically control the amount of structure in the dataset, we use the fall-o↵ of the eigenvalues of the covariance matrix of the log-rates as a measure of struc- ture – faster fall-o↵ indicates most of the variance is along a few directions and thus corresponds to more structure since there is more redundancy in the receptor responses. We vary this fall-o↵ while keeping the overall variance r the same. Speciﬁcally, we ﬁt an exponential ↵ + e ⇤ to the eigenvalue fall-o↵ in the Drosophila dataset, where r is a number between 0 and 1 which speciﬁes the nor- malised rank of the sorted eigenvalue. The choice of this ﬁt was motivated by observing an approximately expo- nential form for the intial fall-o↵ of eigenvalues. To in- crease/decrease the structure we increase/decrease re- FIG. 7. Pairwise distance distortion using Gaussian sensing matrices The pairwise distance distortion thresh- old, ✓ as a function of the input dimension for 25-sparse vec- M Gaussian matrix with i.i.d en- tors projected using a N tries with zero mean and variance 1/N . The ration N/M is ﬁxed to be 24/110. ⇥ III. SUPPLEMENT A. Decoding odor composition To reconstruct ~xfrom measurements ~y= R~x, we used the Iteratively Reweighted Least Squares (IRLS) algo- rithm [80] to ﬁnd the vector that minimizes the L1 norm of ~xsubject to the constraint ~y= R~x, with 500 maxi- mum iterations and a convergence tolerance (in norm) of 10 6. B. Distortion using Gaussian sensing matrices We ﬁrst generate 200 vectors ~xi with 25 non-zero el- ements uniformly sampled between 0 and 2. We then M project these vectors by a matrix R of dimension N where the ratio between N and M is ﬁxed such that N/M = 24/110. The elements of matrix are independent samples from a Gaussian distribution with zero mean and variance 1/N . The distortion measure ✓ is deﬁned as in eq. 1. ⇥ C. Matrix extension and controlling structure The starting point for generating extended ORN re- sponse matrices is a log-normal model for the rates. Fig. 8 shows that the logarithm of the responses of all the 12 [55, 81], the quality of the olfactory code should not de- pend on the details of how speciﬁc receptors respond to di↵erent odorants. Rather, the key determinant should be the overall distribution of responses. To test whether this is the case, we scrambled the receptor and odor- ant labels in the ORN response matrix (top inset in Fig. 10B), thus constructing an artiﬁcial response ma- trix with the same overall distribution of ﬁring rates, but with no odor- or receptor-dependent correlations (second inset in Fig. 10B). We found that decoding performance was essentially identical when using the scrambled and unscrambled response matrices ( Fig. 10B), consistent with the notion that the olfactory system seeks to employ disordered and unstructured sensing. Interestingly, sep- arate scrambling of the receptor labels and odor labels either improved or degraded the decoding, presumably because such scramblings removed correlations that were either detrimental or beneﬁcial for decoding ( Fig. 10B). These opposite e↵ects compensated each other when the sensing matrix was fully scrambled. E. Divisive normalization in the Antennal Lobe and decoding glomerular responses In the Antennal lobe, a network of inhibitory interneu- rons reorganizes the receptor responses for transmis- sion downstream [59]. In the ﬂy, the inhibitory net- work is well-described as e↵ecting a divisive normaliza- tion [57, 58] that scales the responses of each ORN type in relation to the overall activity of all types . This lat- eral inhibition in the can be modelled with the following transformation [57]: R(2) i = Rmax · (R(1) i )1.5 (2) 1.5 + (R(1) i )1.5 + (m " R(1) i )1.5 # · i X i i is the response of the ith ORN type, R(2) where R(1) is the response of the ith glomerulus, parametrizes spon- taneous activity, and m controls the amount of normal- ization. We use Rmax = 165.0, = 10.5, and m = 0.05 [57]. We constructed an artiﬁcial glomerular response matrix R(2) by applying this transformation separately to the ORNs responding to each of the 110 odorants stud- ied in [45]. Thus R(2) ij represented the response of the ith glomerulus to the jth odorant. Applying this transformation to the Drosophila re- sponse matrix, the glomerular responses become more widely distributed and less correlated than their ORN inputs as has been described before. Does this increased disorder improve the representation of odor information? Because the divisive normalization is nonlinear, we can- not, strictly speaking, use the aforementioned decod- ing algorithm to evaluate the information content of the glomerular representation. However, we can instead cre- ate an artiﬁcial benchmark in which mixtures ~xlead to FIG. 9. Covariance matrix eigenvalues for struc- tured matrices Eigenvalues of the covariance matrix for the extended response matrices (black lines) compared with the eigenvalue fall-o↵ for the Drosophila dataset (red line). A structure factor of 1.0 implies a fall-o↵ similar to the Drosophila dataset spectively by multiplying it with a structure factor, while keeping the overall sum of the eigenvalues the same (for a given no. of receptors and odors). Fig. 9 shows the eigenvalue fall-o↵ for three di↵erent structure factors – a structure factor of 1.0 indicates a fall-o↵ similar to the Drosophila dataset, and higher values indicate more structured responses. D. Robust decoding from ORN responses In the main text, we considered a simple linear model of the responses of 24 ORN types in Drosophila respond- ing to odor mixtures. Speciﬁcally, we extracted a ﬁring rate matrix R from the data in [45] (i.e. Rij is the re- sponse of receptor i to odorant j), and we assumed that the response to a mixture could be written as a linear combination of responses to single odorants. We deﬁned a mixture by the composition vector x whose elements specify the concentration of individual odorants in the mixture. The ORN ﬁring rates y could then be written as ~y= R~x . We then attempted to decode composition vectors ~xfrom responses ~yusing the optimal algorithm of [55, 80]. We regarded the reconstruction as a failure if the average squared di↵erence between components of the reconstructed odor vector and the original exceeded 0.01. Decoding error was deﬁned as the failure proba- bility over an odorant mixture ensemble. This criterion for successful reconstruction is equivalent to saying that the reconstruction ˆx of the odor composition vector ~x fails if the norm of the di↵erence exceeds a tol- erance parameter of t = 1.1 (here we used the fact that the odor composition vector ~xhas 110 components). To test the robustness of our conclusions we varied this tol- erance parameter ten-fold, and found that the decoding error curves were largely unchanged (Fig. 10A). Qualita- tively, we observed this robustness because the decoding of odors tends to either succeed very well, or fail very badly. As a result, a broad range of criteria for deﬁning a successful reconstruction will give similar measures of decoding error. ~x k ˆx k According to our general theory, and the results of 13 FIG. 10. Odor decoding from Drosophila ORN responses is robust. (A) Decoding error is robust to ten-fold variations in odor reconstruction tolerance. Mixture complexity = number of component odorants drawn from 110 possibilities. (B) Decoding performance is unchanged after complete scrambling of the Drosophila response matrix, because of opposite e↵ects of scrambling receptors vs. odors. Insets: Response matrices showing ﬁring rates for 24 receptors (rows) responding to 110 monomolecular odorants (columns) without scrambling (solid green) and for models randomly scrambling receptors, odorants, or both (dashed green). (C) Decoding performance is unchanged after complete scrambling of the divisively normalized responses in the Antennal Lobe. Separately scrambling receptors or odors also has no e↵ect on performance. Insets: Response matrices showing activity for 24 glomeruli (rows) responding to 110 monomolecular odorants (columns) without scrambling (solid blue) and after scrambling receptors, odorants, or both (dashed blue). Results shown are averages over 100 iterations over model scrambled response matrices. Decoding error is measured as the probability of decoding failure (see text) over an ensemble of 500 randomly chosen odor mixtures of a given complexity. responses ~yvia ~y= R(2)~x, where R(2) represents a ma- trix of artiﬁcial glomerular responses obtained by trans- forming experimentally measured ORN responses to an odor panel in [45] via divisive normalization. Quanti- tatively, 67% of mixtures with 7 or fewer components drawn from 110 odorants can be accurately decoded from the responses of 24 glomeruli, while similar accuracy was achieved for mixtures with only 5 components when decoding from ORNs (Fig. 10C). Because the number of possible mixtures increases combinatorially with the number of mixture components, this is a substantial im- provement. When we scrambled the identity of odors and receptors, all scramblings left the decoding performance unchanged ( Fig. 10C). We thus conclude that after cor- relations are removed by divisive normalization, the over- all distribution of responses is the sole determinant of the quality of the olfactory information representation. We tested how our results for decoding error would be a↵ected by changing the parameter m, which controls the amount of inhibition in the Antennal Lobe, or the exponent a, which controls the shape of the nonlinearity. In order to simplify our presentation, we study depen- dence on the parameters of the normalization for two i) K = 3, a value where values of mixture complexity: odor reconstruction from Antennal Lobe responses with experimentally-measured parameters is near perfect (see main text), and ii) K = 7, a value where a similar recon- struction starts to degrade. We found that in both cases, the experimentally measured values of m and a led to the lowest decoding error (Fig. 11). F. Linear classiﬁcation To measure how well a particular odor representation (responses of ORNs, glomeruli, or Kenyon cells) facil- itates learning ﬂexible associations between odors and valences, we randomly split the representation of input mixtures into two classes and then trained a linear clas- siﬁer (SVM with linear kernel [82]) to classify the inputs. G. Generating Mushroom Body responses We took each Kenyon cell to have non-zero connection weights drawn uniformly between 0 and 1 with 8 ran- domly selected glomeruli (see Results). Then, following [20], we took the input to the ith Kenyon cell, evoked by an odor with glomerular responses ~yin the Antennal Lobe, to be hi = is an h inner-product, ~wi is the vector of connection strengths, and ~µis the average Antennal Lobe response vector over all odors, normalized to unit length. We chose a response threshold so that a fraction f of neurons with inputs hi ~µ) ~µ, ~y i i , where ~wi, (~y ·i h· h , 14 FIG. 11. The empirically determined divisive normalization in the Antennal Lobe is optimal assuming a linear model for mixtures Decoding error (see main text for deﬁnition) shown as a function of the exponent a, and the inhibition parameter m in the divisive normalization carried out by the Antennal Lobe. Left and right plots correspond to mixtures with K = 3 and K = 7 components drawn randomly from 110 odorants, respectively. The experimentally measured operating point is indicated by a cross in each plot (m = 0.05 and a = 1.5). Decoding error (deﬁnition in main text) is averaged over 500 iterations of mixture ensembles of a given complexity. exceeding threshold are considered active, and normal- ized the thresholded responses so that the maximum ﬁr- ing rate is 5 Hz, on the order of the maximum observed Kenyon cell responses. We averaged results over 100 ran- dom choices of connection strengths. The global inhibi- tion required in this model for generating the disordered responses observed in the Mushroom Body [20] could be implemented by the APL neuron which makes inhibitory connections to all the Kenyon cells H. Structured vs. random connectivity and its interaction with response variability We constructed structured connectivity matrices be- tween glomeruli in the Antennal Lobe and Kenyon cells in the Mushroom Body by reordering the columns of the corresponding random connectivity matrix so that the two matrices model synapses with the same connection strengths feeding into each Kenyon cell, but they sam- ple di↵erent glomeruli. The reordering of the columns was done so that the structured connectivity matrix ex- hibited a block-diagonal structure as shown in Fig. 6C. For analyses we chose the number of blocks to be 3. We then permuted the rows and columns of the structured connectivity matrix. To parametrically vary the amount of structure between the block diagonal connectivity ma- trix and the random matrix, we speciﬁed the probability p that a Kenyon cell could connect to any glomerulus and not just the ones in its preferred group. Now, we discuss how the e↵ect of the structured and random projections on the ability to learn arbitrary asso- ciations in using Mushroom Body neurons (Fig. 6 B,D). The main reason for the reduced classiﬁcation perfor- mance with the structured projection matrix from the Antennal Lobe to Mushroom Body, relative to the ran- dom projection matrix, is due to a higher “e↵ective noise” in the most active neurons. This can be understood by viewing the responses in the Mushroom Body as a linear projection + thresholding of the responses in Antennal Lobe. The e↵ect of increased structure in the projec- tion matrix is to focus the projections from the Anten- nal Lobe into a smaller subspace. Another way to see this is by considering the fall-o↵ of the singular values of the random and the structured projection matrices. The structured matrix has a steeper fall-o↵ and larger out- lier singular values compared to the random projection matrix (Fig. 12, left ). A consequence of focusing the Antennal Lobe responses into a smaller subspace is that the elements of the modes corresponding to this subspace will be larger (compare Fig. 12 right, top and bottom panels), and thus response variability in the Antennal Lobe will lead to a larger variability in the tail responses (most active neurons) in the Mushroom Body for the structured projection matrix. This can be seen in Fig. 13 left, where the histogram of residual responses (noise- less response minus response with noise) of Mushroom Body neurons is larger in magnitude, on average for the structured projection matrix compared to the random projection matrix. How does this result depend on the form of sensing em- ployed by the ORNs? Basically, the higher e↵ective-noise property for structured matrices from Antennal Lobe to Mushroom Body will hold regardless of the form of ORN sensing. As an example, in Fig. 13 right, we show the residual responses for random Gaussian sensing by the ORNs, and even in this case, the structured matrix leads to larger magnitude residual responses. What changes with the sensing method is the quality of representation of the external input in the ORN and Antennal Lobe 15 FIG. 12. Left: Example plot showing ranked singular values of the random and structured projection matrices from Stage 2 (antennal lobe/bulb) to Stage 3 (cortex/mushroom body). For this example, the size of Stage 2 was 72 and the size of Stage 3 was 300 and there were 4 preferred groups in the structured projection matrix (see main text for the construction of the 4 structured matrix). Right: the components of the sum of the ﬁrst four dominant singular vectors : i=1 iui, where the full 70 projection matrix P is given by P = i=1 iuiv†i . The components of the projection give a sense of how the input power is distributed in the dominant subspace. From the top panel, we see that the structured projection matrix directs more of the input power to the subspace. P P FIG. 13. Histogram of residual responses at Stage 3 that are larger than a threshold (0.5 in this case), for random and structured projection matrices. The residual response is deﬁned as the response at Stage 3 without any noise in Stage 1 responses minus the response at Stage 3 with noise in Stage 1 responses. Larger magnitudes for the residual response means that a classiﬁer trained on noiseless responses will have a higher error for the noisy responses. We see that on average, the structured projection matrix leads to larger magnitude residual responses. The left plot corresponds to ORN-like sensing in Stage 1 (see main text; matrix extension), and the right plot corresponds to a hypothetical random Gaussian sensing at Stage 1. In both cases, the structured projection matrix from Stage 2 to Stage 3 leads to larger magnitude residual responses. responses. So a poorer sensing method will lead to an overall reduction in the ability to learn arbitrary associ- ations. I. Classiﬁcation using Kenyon cells: role of sparsity of responses and connections Here, we studied the error in a 2-way classiﬁcation task for 5-component mixtures with varying readout popula- tion sizes (n) and the number of Kenyon cells used as readout in the Mushroom Body (details of classiﬁcation procedure and task in the main text). For a given pop- ulation size n, increasing the fraction of active neurons f barely changes the classiﬁcation performance (bottom panel of Fig. 14A). The classiﬁcation error with a given active fraction f decreases with the number n of neu- rons being read out (left panel of Fig. 14A). However, there is a law of diminishing returns – excellent perfor- mance is achieved for relatively small n, and further in- creasing the population size makes little di↵erence. The disordered projections from the Antennal Lobe to the Mushroom Body suggest that any subset of a given size should be statistically equivalent. We tested this by comparing the classiﬁcation error obtained from di↵er- ent subsets of Kenyon cells. The narrowness of the his- 16 FIG. 14. A) Classiﬁcation error from responses of model Kenyon cells in the Mushroom Body (MB) for arbitrarily separating 300 5-component mixtures into two classes as a function of the readout size (n) and the fraction (f ) of active neurons. The horizontal and vertical sections correspond to n = 105 and f = 0.2, respectively (section shown in panels below and to the left, respectively). Bottom left panel: histogram of classiﬁcation errors for 10000 di↵erent subsets of size n = 105 and f = 0.2. The narrowness of the histogram shows that any two subsets of a given size are roughly equivalent for odor classiﬁcation purposes. B) Classiﬁcation error at the Mushroom Body as a function of the number of glomeruli sampled by each Kenyon cell. Minimum error is found for sparse sampling of glomeruli. All results shown are averages over 100 iterations over mixture ensembles, 100 labelings into appetitive/aversive classes, and 100 iterations over model connectivity matrices between the Antennal Lobe and Mushroom Body (each using a di↵erent instantiation of noise). (See text for details regarding the generation of connectivity matrices and noise.) togram of classiﬁcation error for 10000 di↵erent popu- lations (n = 105, f = 0.2) (lower left panel, Fig. 14A) shows that any subset of a given size is indeed equally good at supporting ﬂexible classiﬁcation. 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10.1371_journal.pstr.0000094	RESEARCH ARTICLE Dynamic modeling of African elephant populations under changing climate and habitat loss across the Greater Virunga Landscape Simon Nampindo1, Timothy O. Randhir2* 1 WCS Uganda Program, Wildlife Conservation Society, Kampala, Uganda, 2 Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, United States of America a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 * randhir@umass.edu Abstract OPEN ACCESS Citation: Nampindo S, Randhir TO (2024) Dynamic modeling of African elephant populations under changing climate and habitat loss across the Greater Virunga Landscape. PLOS Sustain Transform 3(1): e0000094. https://doi.org/ 10.1371/journal.pstr.0000094 Editor: Alka Bharat, Maulana Azad National Institute of Technology, INDIA Received: September 5, 2022 Accepted: January 5, 2024 Published: January 31, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pstr.0000094 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Elephants in Africa are declining rapidly due to habitat loss and human-wildlife conflicts, with these problems worsening with climate change. Understanding how age classes respond to such events is crucial to designing and implementing mitigation strategies and developing the adaptive capacity of wildlife managers to respond to these challenges adequately. This study builds a dynamic simulation model of the age classes of elephants and their interac- tion with habitat, water, and climate. The dynamic response of elephant populations to habi- tat change, water resources, and climate change is assessed. It is observed that climate change affects older elephants more than young ones in terms of survivability and migration. It is also likely that the undetected direct climate change impact on the elephant population is due to changes in habitats, particularly forests and wetlands used for thermal regulation. An improvement in the habitat type and availability of water resources improved the age classes of populations. The results suggest that if the environmental and anthropogenic stressors are not mitigated, Greater Virunga Landscape (GVL) will face a change in popula- tion demography for younger elephants and impact overall populations. Such age-class- specific stress could substantially affect African elephants’ long-term population viability and sustainability. Conservation of elephants requires a transboundary management approach to climate change mitigation, cooperation among conservation agencies, and effective part- nerships with all relevant stakeholders for conservation. Author summary Transboundary wildlife like elephants requires a regional approach to assessment and conservation. This requires a better understanding of the elephant age-specific responses to landscape-level changes in habitat, water availability, and climate change to enable con- servationists to develop landscape-wide conservation strategies. More importantly, water availability and its distribution within the landscape will be critical to the survival of ele- phants amidst the effects of climate change. Long-term simulations of age-class-specific PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 1 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Funding: WCS Benecke Scholarship received by SN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. elephant populations are used in a high biodiversity landscape in Africa. The dynamic impacts of climate and habitat changes on the African elephant population demography are significant. Loss of habitat and water resources can be critical to the long-term survival of elephant populations. With elephants dependent on large landscapes, transboundary cooperation becomes vital for long-term survival and regional sustainability. Introduction Elephants are of global conservation concern due to a dramatic reduction in numbers over the last 100 years and now occur in specific locations with highly discontinuous populations [1]. The 2016 IUCN African Elephant Status Report estimates about 415,428±20,111 elephants in surveyed areas over ten years ending in 2015, using a variety of databases in elephant range states. The Great Elephant Census led by the Wildlife Conservation Society and conducted in 18 African range states in 2016 reported 352,271 elephants, representing a decline of 30%, with Tanzania recording the highest loss on the African continent and the majority occurring in southern Africa. The African elephant is listed as Vulnerable [1] on the IUCN Red List, with multiple environmental stressors contributing to its population decline. Consequently, there is a need to assess the impacts of climate and hydrologic changes on habitats supporting elephant populations on a landscape. The elephants play a critical role in modifying the landscape through seed dispersal of some plant species, felling down trees, and soil fertility enrichment from the dung, creating suitable habitats for insects to thrive. In many African cultures, the ele- phant symbolizes strength, resilience, and the ability to overcome challenges. It is a source of pride for some ethnic groups. On the other hand, elephants cause extensive damage to crops and sometimes threaten human life, resulting in Human Elephant Conflicts (HEC). Elephant decline is mainly attrib- uted to poaching. HEC has resulted in ecosystem change from savanna grassland to wood- lands, contributing to disruptions in predator-prey dynamics, the spread of native species, and recolonization by other species. Consequently, it affects the tourism benefits to African econo- mies. This study aimed to determine the effects of habitat, water resources, and climate change on age-class-specific elephant population dynamics. These results inform conservation inter- ventions and policy changes to enhance elephant population recovery. The Greater Virunga Landscape (GVL), a part of the Albertine Rift (Africa), is a biodiverse landscape with three world heritage sites and a Ramsar site. By 1979, the African elephant range declined from three million square miles to just one million in 2007, distributed across Africa, namely the southern, eastern, western, and central parts of Africa in 37 countries occu- pying mainly savanna woodlands and forests. Elephants are an essential component of this vast ecosystem and play a significant role in vegetation distribution and change [2–9]. Several elephant studies have been conducted specifically regarding ivory poaching [10–18]. However, despite the effort to understand the ecology of elephants, fewer studies have focused on the dynamics of the environment, climate change, habitat change influences on the elephant popu- lation, and demography over long periods. For example, the effect of human population, land use change, civil wars, and overexploitation of resources in historically suitable areas, including the loss of elephants’ preferred plant species, must be better understood. To manage large wild herbivores like elephants effectively, wildlife managers need information on how habitat qual- ity and climate influence the wildlife population. It also requires long-term planning based on a deep understanding of how population processes such as birth and death rates and age struc- ture are affected by habitat size and quality changes, climate, and how they influence PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 2 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss management decisions. The differences in governance and management regimes in the Trans- frontier Conservation Areas (TFCA) make combatting wildlife crime challenging. For exam- ple, poaching conducted by nationals from one country on a foreign territory can only be prosecuted if legal extradition mechanisms exist. Furthermore, the differences in human resource capacity (numbers, training, equipment, and financial resources) make it very difficult to achieve effective conservation. Yet, the ele- phants move back and forth from one country to another. In GVL, insecurity in DR Congo and poor infrastructure make it very difficult to achieve transboundary collaboration and management of elephants between Uganda and DR Congo. Ethnic heterogeneity, differences in cultural value attachments, and varied legal regimes complicate the management of TFCA as well. The general objective of this study was to assess the effects of changes in habitat, water resources, and climate on the elephant population in GVL. Specific objectives were to: 1) develop a comprehensive system dynamic model of age class-specific elephant population; 2) assess the impact of habitat change on elephant populations in GVL; and 3) assess the effect of climate change on the elephant population. The hypotheses (alternate) tested were: (i) conver- sion of habitat from forest to savanna grassland substantially influences age-class specific ele- phant population structure and dynamics; (ii) significant correlations exist between elephant population size, water resources, and climate change. Materials and methods Study area The Greater Virunga Landscape (GVL) (Fig 1) straddles Uganda, Rwanda, and the Democratic Republic of Congo (DRC). The GVL covers an area of 15,700 km2, of which 13,200 km2 (88%) is protected [19]. The protected area includes seven national parks, three large tropic high for- est reserves, and three wildlife reserves, including Bwindi Impenetrable National Park, which became isolated about 50 years ago. It is one of the six critical landscapes in the Albertine Rift and is among the most species-rich of any landscape in the world [20–21]. This landscape includes the Virunga Volcanoes, famous for their population of mountain gorillas (Gorilla ber- ingei beringei), the savanna parks of Virunga and Queen Elizabeth, the Kibale National Park, having high diversity and biomass of primates, and the Rwenzori massif, also known as the ‘Mountains of the Moon.’ Altitude ranges from 5,109m at the top of the Rwenzori massif to 600m in Semliki Park. Consequently, the landscape supports a wide variety of habitats [22]. These habitats include alpine moorland, giant heather, bamboo, montane, and submontane forest, savanna woodland and grassland [20], high and low-altitude wetlands, lakes, and vegetation types in specific lava colonization and thermal pools around the active volcanoes of Nyamluagira and Nyiragongo in Virunga Park. In addition, Papyrus and Carex wetlands are found around the lakes and some streams, and the lakes have habitat types varying from rocky and sandy edges to the pelagic zones in their depths. Virunga, Rwenzori Mountains, and Bwindi Impenetrable national parks are World Heritage Sites, Queen Elizabeth Park is a Biosphere Reserve, and Lake George is a Ramsar Site. Virunga National Park is in the Albertine Rift, a part of the Great Rift Valley, designated as a National Park in 1925, covering an original area of 8,090 km2. The Queen Elisabeth National Park (QENP), which covers 2,080 km2, is a crucial component of GVL [22]. It is connected to Kigezi Wildlife Reserve (265 km2), Kyambura Wildlife Reserve (154 km2), Kibale National Park (795 km2) in Uganda, and linked to Virunga National Park in the Democratic Republic of Congo. It also is contiguous with Uganda’s central forest reserves of Kasyoha-Kitomi Forest Reserve (399 km2), Kalinzu and Maramagambo Forest Reserves (428 km2) in the east and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 3 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 1. Greater Virunga Landscape with vegetation map [20]. Developed by authors in collaboration with the WCS Uganda program. https://doi.org/10.1371/journal.pstr.0000094.g001 borders Lake George and Edward, which are both connected by the Kazinga channel and a range of crater lakes and a significant wetland included on the Ramsar Convention’s list of wetlands of international importance. The Rwenzori Mountains National Park is shared between Uganda and the Democratic Republic of Congo and is less than a kilometer from the equator. It is the third-highest mountain in Africa at 5,109 m (after Kilimanjaro and Mount Kenya). The park is contiguous with the Virunga National Park in the Democratic Republic of Congo (DRC). It forms part of the Queen Elizabeth Conservation Area in Uganda, covering an area of 996 km2, of which the most substantial portion (70%) lies over an altitude of 2,500 m. Bwindi Impenetrable National Park (BINP), a world heritage site, is located on the eastern side of the Albertine Rift Valley, covering 32,092 ha (331 km2), the most extensive Afromon- tane lowland forests in East Africa. Volcanoes National Park (Parc National des Volcans) lies in northwestern Rwanda and borders Virunga National Park in the Democratic Republic of Congo and Mgahinga Gorilla National Park in Uganda. Conceptual model The model presents elephant population–habitat dynamics with water resources and climate change effects (Fig 2). By modeling vegetation cover change and type, elephant–habitat PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 4 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 2. Conceptual model for population dynamics of elephants in GVL. https://doi.org/10.1371/journal.pstr.0000094.g002 interactions, stochastic environmental variables such as precipitation and temperature, and spatial variability of water supplies, including glacier melt contribution on top of Mt. Rwenzori and runoff, the model is used to analyze changes in elephant population acting via natality and mortality over the last 55 years. In the conceptual model, the elephant population is influenced by several factors or stressors. First, the density-dependent factors, mainly food, space, and water resources, limit population growth beyond the area’s carrying capacity [23–26]. Second, climate change affects the elephant population both directly and indirectly. In high tempera- tures, the direct consequence is physiological stress, resulting in a diminished reproduction rate and increased mortality risk among calves and juvenile elephants. Climate change also affects the elephant population by reducing water resources and biomass production due to increased evapotranspiration and loss of suitable habitat. Habitat change is also driven by human activities, particularly the expansion of agricultural land, settlement in wildlife corridor areas, and burning suitable habitats. Similarly, climate change negatively impacts the habitat, including accelerating the loss of native vegetation or increasing the colonization and spread of native invasive species, mainly where temperatures are high and rainfall is declining. In such scenarios, habitat loss and frag- mentation are disproportionately severe, although impacts vary across vegetation types. For example, elephants depend mainly on savanna and forest vegetation, occasionally relying on wetlands/swamps for water, salt, body temperature regulation through wallowing, and migra- tion to other landscape patches. The habitat component of the model simulates long-term vegetation conversions associated with emergent declines in precipitation and Mount Rwenzori glacial stock. The water supply and climatic changes modules within the elephant age-specific population-land cover change model aim to model the expected effect of increasing water availability and food abundance, which are essential for elephant reproduction success. However, with a vegetation cover change of nearly one percent and an elephant population density of 1.0 elephant/km2, savanna woodlands have become the most suitable habitat for elephant survival. The baseline model PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 5 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss consists of both climate and habitat as interacting drivers. The scenarios of climate and habitat models additional individual effects of these stressors as future scenarios. Empirical model In the dynamic model, Ai,t represents the population in ith age class at time t. The number of ele- phants in each particular age class for the entire landscape at a given time (t) was calculated as Ait ¼ Aiðt(cid:0) 1Þ þ N þ Iit (cid:0) Sit (cid:0) Mit ð Þ where, N is the natality rate (elephants/km2/year), Iit is immigrating population into age class i at time t, Sit is the number of surviving individuals in age class i at time t, and Mit is the mortality in age class i at time t (elephants/km2/year). Natality and mortality are density-dependent, while natality is influenced by precipitation, water, and habitat quality. The natality rate N for the entire population is adjusted based on the maximum reproduction rate (MaxN) in the popula- Nit, Where Nit is the adjusted natality in age class i in time t, a is the tion as N ¼ MaxN = starting age class of reproduction, and b is the last reproducing age class in the population. Xb i¼a The survival rate of elephants in each age class is specified as Sit = (Ait−Mit) * γit. Mortality rates for each age class were represented as Mit ¼ MNit ∗ Ait ∗ MIt ∗ Zit ð Þ ∗ ð1 þ P ∗ PIÞ ∗ ð1 þ WP ∗ WIÞ where MNit is the minimum mortality rate for the respective age classes, MI is the mortality rate index, Z is the calf risk (suppressed for age classes 2 and higher), WP is the war pulse func- tion, and WI is the war influence coefficient. The habitat at time t (Ht) is represented Ht = Ht-1 + (HIt−HDt) * dt, where HIt is habitat increase in km2, HDt is habitat decrease in km2, but HIt = Ht * FPt, and HDt = Ht * SPt where FPt and SPt are proportions of the forest, and savanna grassland and woodlands in the entire landscape, respectively. The elephant population dynamics model was specified using Structural Thinking, Experi- mental Learning Laboratory with Animation (STELLA) software (High Performance Systems, Inc.). The STELLA is a dynamic systems software for visual simulation that uses differential equations of stocks and flows. This software has been used for understanding population dynamics and economic fluxes [27–30]. The converters represent input parameters, and the arrows represent mathematical relationships between the elements. A Fourth Order Runge- Kutta method was used to perform the integration because it estimates stock changes by mak- ing more flow forecasts, unlike the other two techniques. Runge-Kutta-4 is also known to pro- vide the best results with relatively large tolerances when simple, functional evaluations are conducted [31–33]. Data compilation The initial values of the elephant population under Ai are presented in Table 1. The total annual reproduction rate (TOPR) for the entire population was 40 percent, and the natality rate index was modeled as a graphic function of the whole observed population. Its values ran- ged from 1 to 2.5 for the period 1960–2010. Where γit is assigned 10 percent for i = 1,4,5, and 0.05 for i = 2,3. The survival rate of age classes A2 and A3 was slightly lower because the ele- phants in this age group are more susceptible to poaching and killed by humans in retaliation for lost human lives or physical injury and crop loss. This age class is when sub-adult males abandon their social groups in search of mates [28]. It was reported that the survival probabili- ties for females were slightly higher (89%) than for males (82%) [28]. The annual population PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 6 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Table 1. Population change by age group. Age structure Elephant population 1960 Elephant population 2006 Change (Proportion) 0–10 11–30 31–40 41–50 >50 Total population 660 531 319 212 38 1,760 https://doi.org/10.1371/journal.pstr.0000094.t001 1,240 998 599 398 72 3,307 0.375 0.302 0.181 0.120 0.022 1.000 growth, including immigration and emigration, averaged 0.17% and 2.8% when migration is excluded over 14 years for the Samburu National Park (Kenya) population [29], the most accu- rately studied population in East Africa. The mean annual mortality was 4.71% and a maxi- mum of 14.1 percent, while the mean yearly natality was 7.21% (maximum 14.4% and minimum 2.1%). Reproduction starts at ages 11–30 and lasts 50 years and above. The observed (census) pop- ulation of 2006 published by UWA, ICCN, WCS, & WWF) was used to assign the number of elephants in each age class for the year 1960 based on the protected area management agencies (UWA, ICCN, and ORTPN) reports and peer-reviewed publication [34–35]. The proportions of each age class are based on research on savanna elephants by specialized scientists, as shown in (Table 2) [32–34]. Of the total population in 2006, 37% of the elephants observed were in the age class 0–10, and the rest of the distribution was as follows: 11–30 (30%), 31–40 (18%), 41–50 (12%) and �50 years (2%). Therefore, 2006 was treated as the base year, and the observed population was used in 1960 for model calibration. The information collected includes observed elephant numbers, habitat change, hydrocli- matology, and water resources. Elephant population data were obtained from the large mam- mal census databases and associated government reports of protected area authorities of Uganda (Uganda Wildlife Authority), DRC (ICCN), and Rwanda (RDB), and conservation organizations such as WCS and WWF that have worked in the region and supported conserva- tion programs in the area for a long time [35]. Reviews of published survey reports in peer- reviewed articles were also conducted to validate the database information [36–39]. From the early 1950s till the late 1970s, most of the census of large mammals was performed by a ground survey by walking along transects systematically designed for this purpose and identifying all scats such as dung, footmarks, and physical count of live animals. After the 1970s, most large mammal counts were done by aerial means, specifically using a four-person carrier light air- craft, and animal counts were done following the standard method. Survival data for ten-year intervals is based on elephant counts by wildlife authorities in the region, including the Uganda Wildlife Authority (UWA) and Institut Congolais pour la Con- servation de la Nature (ICCN) of the Democratic Republic of Congo. The first-age class values are modified for males and females with a value of 0.388, based on an estimate of natality and Table 2. Scenarios and parameters selected for model simulation over a period of 51 years. Scenarios Description Parameter values Climate change according to IPCC AR5 RCP2.6, RCP6.0, & RCP8.5 scenarios Temperature Precipitation Low 1.6˚C 2% increase Medium High 2.8˚C 10% 4.3˚C 18% Habitat change Water resources https://doi.org/10.1371/journal.pstr.0000094.t002 Increase in forest & savannas 50% Increase Determined RCP scenarios PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 7 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss extrapolation from adult mortality rates but excluded from the age classes contributing to natality [40]. However, this consideration is essential for tracking when the sudden influx in births associated with the second age class is accounted for. This value suggests that 37.5% of the population would be comprised of 0-10-year age class graduates. During the twenty-year interval, individual birth and death were simulated using the Leslie method [41]. A random built-in function is used to generate uniform random numbers. The reproductive probabilities are evaluated using the counter and delay built-in functions based on the total population size from the last time step. Individual females give birth to zero or two offspring with density-dependent probabilities. A random number then determines the state of the environmental parameters and the corresponding survival values generated from the dynamic population submodel. Individuals survive to the next age class or die based on the mortality rate index driven by habitat quality (habitat quality index). This yields the state of the population at the end of a ten-year step. A higher number of simulations were conducted to attain acceptable statistical confidence levels. Habitat change As part of the long-term vegetation change mapping for Virunga National Park and Queen Elizabeth National Park, the Wildlife Conservation Society [42] calculated the woody cover change and estimated increases and decreases in woody cover in different parts of the GVL between the 1950s and 2006. Woody cover changes generally ranged from a rise of 1,579 km2 in some parts to a decrease of 334 km2 in others [42]. They attributed the net gain in woody vegetation cover (1,245 km2) to a reduction of large mammals in the landscape from the 1970s (as demonstrated by the observed elephant population trend), the continued recovery of the vegetation from the human resettlement away from the landscape in 1880s [43]. Increasing rainfall, climatic variability, and changes in fire frequency. In Virunga National Park alone, 98.2 km2 was encroached upon by humans, and the new settlement within 2 km of QENP was recorded to have 179,200 people. The land cover map produced by WCS in 2006 [42] was reclassified into four significant land uses: forest, savanna woodland and grasslands, wetlands and water, and human settle- ment and agriculture. This broad classification was made because elephants depend greatly on savanna woodland, grasslands, and forest habitats for food but need water for drinking and body temperature regulation. Elephants, however, use less dense human settlements and agri- cultural areas during migration and occasionally feed on the crops during their movement to other patches within the landscape. Hydroclimatology According to the study conducted by the WCS [42], rainfall in and around the GVL has mostly stayed the same since the early 1900s. Climate variability and change are simulated with the prior knowledge that there are two drought seasons in a year: December to February and June to August. Analysis of climatologic data from nine different sites with at least 20 years of con- tinuous data showed an increasing trend in annual rainfall [42]. The study results revealed sig- nificant (P<0.05) increases in total annual rainfall in Beni (1974–2007), Mweya (1958–2007), Kabale (1918–1996), Kiamara (1982–2007), and Ruhengeri airstrip in Rwanda (1928–1986) over time but the rest of the stations did not show any significant trends. At the local scale, rainfall varies greatly across the landscape. The driest parts of the landscape are in the savanna areas north and south of Lake Edward, recording an average monthly rainfall of 30–40 mm. The Albertine Rift climatological assessment studies show that precipitation and temperature in the region will increase over the next 100 years. According to this study, the mean annual PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 8 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss temperature in the base year (1990) was recorded as 22.7˚C (max 26˚C and min 15˚C), and the modeled temperature for 2030, 2060, and 2090 were 23.6, 24.7, and 26.3˚C, respectively. Simi- larly, the mean annual precipitation in 1990 was noted to be 1199 mm, 1233 mm (2030), 1287 mm (2060), and 1406 mm in 2090 [44]. Across the modeling period, precipitation variation ranges from 821 mm– 2098 mm. For this study, weather data from the Lwiro weather station provided by the Observatoire Volcanoligue de Goma was selected for use in the computation of ET. Data from other weather stations, such as Beni, Butembo, and Mweya, were available but had several missing data for some years. Other sources of hydroclimatological data are the Climate Research Unit (CRU) at the University of East Anglia, the UK, and NOAA CIRES Twentieth Century Global Reanalysis Version 2, Research Data Archive at the National Center for Atmospheric Research, Computa- tional and Information Systems Laboratory [45]. However, the CRU data was summarized to historical monthly averages for the years, and the variability at an annual scale needed to be improved to ensure data quality. This study used 51 years of historical climate observations of daily temperature and precipi- tation from two of the eight weather stations–Lwiro and Butembo in DRC located within the study region. The climate data from these two weather stations were selected for use because they had the highest consistent record of observations from 1960 to 2010 compared to the rest of the stations in the region. Following the description of the IPCC AR5 climate change results based on the four RCP scenarios earlier discussed, East Africa was one of the five regions iden- tified by the IPCC for analyzing regional climate change [42]. Of the four scenarios, future cli- mate change values based on RCP2.6, RCP6.0, and RCP8.5 were selected to implement the climate change effect on elephant population dynamics. Before integrating future climate change prediction, the annual historical precipitation data were detrended to incorporate cli- mate change induced by temperature increase. This procedure was achieved by performing a regression analysis to estimate the temporal trend in the time series. For example, if P is pre- cipitation and t is the time in years, the fitted regression Ṕ = α+βt. The time scale, in this case, was from 1960 to 2010. The fitted regression equation P´ = 1.4024x – 1493.9 was used to esti- mate the average historical precipitation (β) of 1254.80 mm for annual precipitation and an average historical annual precipitation increase of 25.86 mm. The detrended equation is given as P–β(t) + RCPP´/100), where P is the historical precipitation, t is the year, and RCPP´ is the future annual precipitation change percentage. The recorded precipitation trends calculated from the regression analysis were removed from the observed temperature data to isolate cli- mate change trends from natural variability. This was done by subtracting the trend compo- nent derived from the regression model from actual observation. In so doing, it allowed various climate change scenarios to be incorporated into the model. The baseline assessment (BL scenario) is a 51-year scenario without the influence of historical warming and helps eval- uate the exclusive effects of climate change scenarios. The detrended baseline data from 1960 to 2010 was then recalculated to reflect the three precipitation change scenarios identified as RCP2.6, the lowest, RCP6.0 (medium with a 10% precipitation increase), RCP8.5, the highest with an 18% precipitation increase. The warming trends predicted by IPCC represent long-term increases over the current tem- perature data over the next 100 years. The IPCC warming predictions were added to the base- line data as a linear trend for mean annual precipitation. Over the next 100 years, the future temperature change was modeled as an absolute value for each RCP [42]. The future tempera- ture change values for East Africa selected were RCP2.6 = 1.6˚C, RCP6.0 = 2.8˚C, and RCP8.5 = 4.3˚C [46]. As such, incorporating temperature change did not require detrending, which would be necessary if observation time series data were used. Instead, a straightforward PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 9 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss calculation of the annual increment to the historical values was done as per the equation below: � � T ¼ Th þ � t Tf 100 where T is the new calculated temperature for the elephant modeling scenario, Th is the histori- cal temperature, Tf/100 is the RCP scenario temperature per year, and t is the year. To account for the available water resources in the landscape, surface runoff, glacier melt contribution to river discharge, and water in reservoirs, mainly lakes and rivers, were consid- ered. Surface runoff refers to water flow over the land surface. Runoff flow comprises two main elements: base flow, which originates in groundwater, and surface runoff, which accu- mulates rainfall that drains into the stream. Several models for computing runoff and soil loss do exist such as the Rational model commonly used to compute the peak runoff rate from small watersheds and assumes the uniformity of rainfall intensity for the duration at least equal to the time of concentration of and throughout the watershed, Cook’s Method, which requires an evaluation of four watershed characteristics, i.e., relief, infiltration rate, vegetal cover and surface storage to determine the runoff rate, Soil and Water Assessment Tool (SWAT), and Curve Number method. The SWAT developed by the USDA Agriculture Research Service (USDA-ARS) is a small watershed to river basin-scale model used to simulate the quality and quantity of surface and groundwater and predict the environmental impact of land use, land management practices, and climate change. SWAT is widely used in assessing soil erosion prevention and control, non-point source pollution control, and regional manage- ment in watersheds. The SWAT model operates on a daily time step and is designed to predict the impact of land use and management on water, sediment, and agricultural chemical yields in ungauged watersheds. However, several of these hydrological models, including the Envi- ronmental Policy Integrated Climate (EPIC) model [47] and SWAT [48], use the SCS curve number method for estimating storm runoff. Ponce and Hawkins [49] provided a detailed account of the conceptual and empirical foundations of the curve number method, emphasiz- ing its wide use in the United States and worldwide. For this study, the Soil Conservation Ser- vice (SCS) Curve Number Method was used to compute the runoff in ESRI’s ArcGIS with the help of the arc hydrology tools. The Soil Conservation Service (SCS) Curve Number Method is a versatile and widely used procedure for runoff estimation because it gives consistently usable results [50–53]. The Curve Number (CN) measures the relationship between initial abstraction and poten- tial maximum retention of an area after a storm. The CN method is based on the relationships between rainfall depth, P (inches), runoff depth, stormflow, Q in inches, and storage factor [50–51]. The Q is represented as: Q ¼ ðP (cid:0) 0:2StÞ2 ðP þ 0:8StÞ and St ¼ 1000 CN (cid:0) 10: The maximum potential retention (S) is related to the watershed’s soil and land cover con- ditions through the curve number equation above, and S is a dimensionless watershed parame- ter ranging from 0 to 100. The USDA SCS developed tables of runoff curve numbers corresponding to various land use and land cover types available in the SCS-SA User Manual [54]. A CN of 100 represents a limiting condition of an entirely impermeable watershed with zero retention; thus, all the rainfall becomes runoff [55]. Conceptually, a CN of zero represents the other extreme, with the watershed abstracting all rain with no runoff regardless of the rain- fall amount. The water balance is computed using the Thornthwaite- Mather approach [56– 57]. Thornthwaite’s method underestimates potential evapotranspiration during the summer PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 10 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss when the solar radiation received at the surface is at its annual maximum [58]. This method also does not capture local soil moisture patterns that vary with slope and aspect and are essen- tial state variables in capturing the ecological differences in high-altitude and forest-dominated areas. Penman-Monteith equation or Hargreaves method are best alternative methods widely used, but are data-intensive. The Penman-Monteith equation is a combined equation considering the energy supply and mass transfer of water vapor from the evaporating surface derived from the leaf energy balance equation [59–60]. The Penman-Monteith method is very rigorous. However, it was noted to have limitations when applied to highly forested areas because the canopy resistance term can- not be easily parameterized [59–61]. It was also developed to predict evaporation from open water, bare soil, and grass. Furthermore, since it is derived from the energy balance of a leaf, the Penman-Monteith equation ignores the fluxes of water vapor to and from the soil [61]. Despite its shortcomings, the Thornthwaite method was selected for the compilation of evapo- transpiration (ET) because of several reasons, namely: 1) scarcity of climatic and adequate land-atmospheric data required for the compilation of ET; 2) the Thornthwaite method has been used extensively in North America and is proved to produce consistent results regardless of its shortcomings. In Africa, this method was used to evaluate the effects of soil water holding capacity assumptions on estimates of African evapotranspiration rates, moisture deficit, and moisture surplus conditions. Thornthwaite’s method is as follows: 8 >>>< >>>: E0 p ¼ 0; � �a 16 10T I (cid:0) 415:85 þ 32:24T (cid:0) 0:43T2; ; T < 0�C 0 � T < 26:5�C T � 26:5�C Where E’p is monthly unadjusted potential evapotranspiration in mm, T is mean monthly surface air temperature (oC), and I, the annual heat index, is given by the equation I ¼ X12 i¼1 � �1:514 Ti 5:0 Where a ¼ 6:75∗10(cid:0) 7 I3 (cid:0) 7:7 ∗10(cid:0) 5 I2 þ 1:79∗10(cid:0) 2 I þ 0:49: If the air temperature is measured on a monthly scale, then potential evapotranspiration is adjusted for the variable day (h) and month (θ) lengths as follows: Ep ¼ E0 p y 30 h 12 : In this study, the temperature data used was already compiled on an annual basis, and no adjustment of ET was needed. After processing the data in Microsoft Excel, the calculated ET was used in the STELLA model to implement the water balance computation. This region’s primary water source is precipitation, which ends up as runoff, groundwater, and reservoirs through river discharge. The other water source is glacier melt, which contrib- utes less than 1% toward river discharge. In STELLA, water stock was computed as the differ- ence between sources of water (i.e., runoff, glacier contribution) minus the loss through evapotranspiration (ET) and expressed in the form of an equation: Y = Y(t -dt) + (Yi−Yo) * dt where Yo is water loss in the form of ET, Yi is water available in the landscape given as: Yi = R + (Y * gm) where R is the runoff in mm, gm is the contribution from glacial melt from mountain Rwenzori ice fields. Runoff was compiled following the Curve Number (CN) PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 11 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss methodology already described in the methodology section implemented in both ArcGIS, spe- cifically to assign the curve numbers to the land cover matched with the corresponding soil hydrologic group, and in STELLA where a composite curve number (COMPCN) was calcu- lated as: COMPCN ¼ CNFOA∗wFOA ð wFOD þ CNSB ∗ wSB ð ð ð CNWETB ∗ wWETB Þ þ CNSC ∗ wSC ð Þ þ CNFOB ∗ wFOB ð ð Þ þ CNSD ∗ wSD ð ð Þ þ CNFOC ∗ wFOC ð Þ þ CNFOD ∗ Þþ Þ þ CNWETA ∗ wWETA Þ þ þ CNWETC ∗ wWETC ð CNHB ∗ wHB Þ þ CNHC ∗ wHC Þ þ þ CNHA ∗wHA Þ þ þ CNWETD∗wWETD Þ Þ þ CNHD ∗ wHD ð ð ð Þþ CNFO, CNS, CNWET, and CNH are curve numbers of the forest, savanna, wetland, human settlement, and agriculture land cover classes associated with the soil hydrologic group represented by the subscripts A to D. Policy scenarios Scenarios for the anthropogenic factors were assessed for the impact of climate change, specifi- cally precipitation and temperature, under three different RCP scenarios, changing water vol- ume and habitat impacts. To determine the effect of habitat quality on age-specific elephant population dynamics, three scenarios of habitat loss (low, medium, and high), particularly savanna grassland and forests/woodlands, were conducted over 20 years using a monthly sim- ulation scale for each year. Similar simulation experiments were conducted for water resources to assess the changes in age-specific dynamics associated with each experiment (Table 2). Quantitative models presented here provide a valuable tool for exploring the consequences of management decisions involving manipulating habitats and watershed ecosystems to achieve viable elephant population densities. Dynamic modeling allows the evaluation of feedback loops and stock changes related to climate and habitat conditions that will enable the identifi- cation of interventions that protect and enhance elephant populations and the development of management strategies under changing climate and habitat conditions. Quantitative tools allow the assessment of critical needs and evaluation of strategies for the effective conservation of transboundary populations like elephants. Results Calibration and validation of baseline results The simulated population was plotted and compared with the observed population (Fig 3). Furthermore, a scatter plot fitted with a linear regression of simulated vs. observed values and the coefficient of determination (R2) was used to assess the fitted model. A high R2 = 0.78 showed that the model explained the observed data very well, providing a high degree of confi- dence in the model. The model’s performance validation was done using observed data from QENP (Uganda), a savanna park within the GVL. Forty-nine years of census data for this park were accessed from Uganda Wildlife Authority, thoroughly cross-checked with the reports from Uganda National Parks currently housed by the Ministry of Tourism and Antiquities, and corroborated with census figures published in peer-reviewed journals, mainly the African Journal of Ecology. The observed and simulated populations are well explained by the model (R2 = 0.68), and the trend was well represented. The computed PBIAS for the simulation model was PBIAS = -2.86, and the Root Mean Square Error-observations standard deviation ratio (RSR) = 0.48, and for the validation model was PBIAS = 17.21(0.02); RSR = 0.74, further confirming the validity of simulation results. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 12 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 3. Calibration of the elephant population model for GV. https://doi.org/10.1371/journal.pstr.0000094.g003 Baseline results The baseline results from the calibrated model (Fig 4) show that the elephant population peaked in the early 1960s, declined tremendously from 1970 to the 1990s, and steadily rose in the 2000s. The decline in elephant population from 1970 until 1990 is attributed to the collapse of Uganda National Park management and the large-scale slaughter of large mammals during the regime of Idi Amin [42]. The recovery after 1990 can be attributed to improved security in Uganda [42]. In terms of individual age-specific classes, baseline results indicate that age clas- ses 0–10, 11–30, and 31–40 years are recovering from the dramatic decline that occurred in the mid-1990s, while the age classes 41–50 and >50 years completed phased out during the same catastrophic period. Baseline results also show that the number of elephants in age classes 0–10 and 11–30 is the highest or greatest across the modeling period (1960–2010). It is reasonable to suggest that the declining trend would not be expected to behave the same way, given that a large proportion of the elephants at the baseline level is high among these classes compared to other classes. However, this argument is weak because adult elephants are expected to have a lower risk of death compared to juveniles and calves. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 13 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 4. Baseline results for the elephant population dynamics. https://doi.org/10.1371/journal.pstr.0000094.g004 Climate change impacts The results of the climate change analyses based on the IPCC RCP2.6, RCP6.0, and RCP8.5 scenarios showed that climate change has the potential to eliminate elephants in the age classes 41–50 and >50 years (Fig 5) attributed to direct and indirect effects. Large mammals are at risk of succumbing to the effects of climate change from both direct and indirect causes. Indi- rect causes include resource depletion, habitat change, competition and disease [62], low adaptability. Old elephants are expected to be highly vulnerable to diseases, and drought induced deaths such as fire and risk of predation. The remediation interventions include pro- vision of watering points for thermal regulation, habitat management, and prevention of haz- ards and risks such as fire. However, a small number of elephants in the age groups 31–40 and 41–50 have the potential to survive under these climatic conditions. The numbers of elephants in the age group 0–10 and 11–30 showed an initial increase followed by a sharp decline from the late 1970s to the 1990s, and in the late 1980s, the numbers in these classes began to rise again. Increase in the numbers could be attributed to migrating elephants with a reasonable number of calves. The total population of elephants simulated under different RCP scenarios was similar. Climate change is a slow-acting environmental process whose effects take years to influence the species or system [63]. Once the impacts of climate change become eminent, they cause long-term effects such as fire occurrence risk, the emergence of zoonotic diseases, invasive species, and suitable habitat degradation and loss, which conservationists must plan for early by implementing fire management practices, disease monitoring to enable early detection and control spread, and habitat manipulation to avoid disastrous events. However, it demands that the spatial and temporal scales of conservation be aligned with the scales of cli- mate-change projections to develop management strategies that enhance the resilience of the ecosystems. The GVL is characterized by spatially varied climatic conditions attributed to the diversity in ecosystem types, vegetation, and topography. These conditions enhance the ele- phants’ ability to deal with local-level climatic changes by switching habitats from savannas to forests and wetlands during extreme drought conditions and returning to savannas during the wet seasons. Local-scale variations mentioned above often override the projections of broad- scale climate models, resulting in high uncertainty. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 14 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 5. Age class-specific impacts under climate change scenarios. https://doi.org/10.1371/journal.pstr.0000094.g005 Mann–Whitney U test is a nonparametric test of the null hypothesis that two populations are the same against an alternative hypothesis [10–13]. The same test was applied to test the impact of climate change under the extreme representative concentration pathway scenario 8.50 on the elephant population, and the results did not show a significant influence of climate change on elephant population dynamics (RCP8.5>B0, p<0.5; W = 1300.5, P = 0.5), making it consistent with the baseline simulated population response representing a conservative climate change scenario. This confirms the results observed by examining the graphic population trend presented under the RCP8.5 scenario. Thus, with the baseline, climate change impact did not show such dramatic change In elephant population dynamics. Habitat change Habitat change in this landscape is mainly driven by frequent fires inside the savanna parks, and agricultural expansion, especially commercial plantation crops such as tea, sugarcane, tobacco, palm oil, and cocoa, and potentially climate change as evidenced by the displacement of native vegetation with native invasive plant species such Dichrostachys cinerea, Lantana camara, and Imperata cylindrica [64–65]. The protected area authorities have developed fire management plans, including the maintenance of fire lines, early or pre-emptive burning and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 15 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss removal of biomass. To mitigate agricultural expansion, the management has opened and erected permanent pillars and planted trees along the park boundary and some locations con- structed an electric fence or elephant barrier. Elephants spend nearly 80% of their time in savanna woodlands and grasslands, only moving to forest areas during the dry season to feed on tree leaves and search for water and salt leaks [4,8]. They also frequently move to the wet- lands in search of water. Therefore, a fifty percent increase in the proportion of woody vegeta- tion (forest and savannas) was implemented to investigate the impact of an immediate increase in the suitable habitat for elephants. Results suggest that an increase in woody vegeta- tion may increase populations of all age classes (Fig 6) through habitat management to favor palatable plant species, enrichment planting, and suppression of fires, allowing the transition to happen. However, the rise in population for age classes 41–50 and >50 years was slightly low, declining to zero in 1990. Discussion This study aimed to assess the effect of poaching, habitat management, and climate change on the elephant population dynamics in GVL. This was unique because it involved testing three policy changes: biodiversity conservation, water resources management, and carbon sequestra- tion about the elephant population response amidst climate change considered at low to high- temperature change scenario. Furthermore, this study tested three options: strengthening law enforcement, implementing community-focused livelihood interventions, and iii) a market- based solution to stop the supply and demand for elephant ivory using a multicriteria decision support model. These study results are interesting and very helpful to the protected area man- agers in ensuring that the elephant population in GVL is stable and well-protected. Aerial surveys conducted in Queen Elizabeth Protected Area (QEPA) showed that elephant numbers fluctuated between 1,300 and 4,000 during the 1960s and early 1970s [4,63]. During that period, elephants migrated not only between QEPA and Virunga National Park, but also northward to Kibale National Park, Rwenzori Mountains National Park, and the grasslands surrounding these areas [59]. Similarly, surveys of elephants in Virunga in the 1960s estimated about 3,500 elephants [64], only to drop drastically in the 1990s and 2000s because of the inse- curity and presence of rebel groups in the park. In the 1970s, heavy poaching in Queen Eliza- beth National Park is reported to have led to elephants fleeing into Virunga [63]. Conversely, the instability and rise in poaching in Virunga since 1996 resulted in elephants fleeing into Queen Elizabeth [64]. There is undisputed evidence that the population in Queen Elizabeth National Park rose from 150 individuals to 2,950 in 2006 over 25 years, an increase that could not have been achieved solely by births alone [64]. The increase in human population, coupled with a decline in large mammal populations due to poaching [38,65–67], partly contributed to the increase in woody vegetation cover and a reduction in savannah habitats suitable for elephants and other mammals. For example, Plumptre et al. [68] assessed the land cover land use change in GVL and showed that the grass- land cover registered the highest net loss by 33% followed by wooded grassland at 29%. These changes were further emphasized when overall woody cover was assessed where QENP regis- tered a 25% increase between 1954 and 2006. In terms of the entire landscape, there was a 14% increase in woody cover between 2006 and 2017, which is a slightly higher rate of loss over 11 years compared to the period of 52 years [68]. Similarly, the increase in human population has also contributed to the loss of connectivity between protected areas. Yet, these wildlife corri- dors are crucial to elephant movements seeking food and promoting genetic diversity [21,62]. These changes are predicted to increase with climate change, resulting in increased elephant movements in GVL and exacerbating human-wildlife conflicts because of the increased PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 16 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss Fig 6. Impact of doubling habitat on elephant population by age class. https://doi.org/10.1371/journal.pstr.0000094.g006 human population and agricultural increase around protected areas. The study results showed that the protected area managers need to create an appropriate balance between achieving con- servation and meeting the needs of the people, reconciling economic development with wild- life conservation. Fundamentally, conservation delivers economic benefits to the citizens in GVL through tourism revenue, construction of supportive infrastructure such as roads, crea- tion of incentives and compensation models to mitigate human-wildlife conflicts, and initia- tion of nature-based solutions such as carbon credit projects or schemes to secure financing for the management and protection of wildlife. Private landowners living adjacent to the parks must be given fiscal and monetary incentives to maintain some of their land under conserva- tion to create or maintain wildlife corridors to allow elephants to move from one protected area to another. It is a known fact that frontline communities are economically disenfran- chised poor, and yet they depend on the park resources for their livelihoods. In such circum- stances, the park managers are unlikely to receive conservation support from conflicted communities. As such, economic empowerment and the development of community capacity to engage in income-generating activities is vital, an aspect noted from this study’s results. The climate of the GVL varies because of its changing altitude and habitats. Ranging from glaciers and afro-alpine vegetation around 5,100 meters above sea level (a.s.l.) to humid low- land rainforest at 600 meters a.s.l., there are extremes in temperature and rainfall within the same landscape, which is what creates the diversity of niches for the many species that occur here [62]. Climate change modeling results for the region predict that the GVL will become wetter and warmer over time [69]. Our study results reinforce the necessity to secure and maintain wildlife corridors and restore degraded forests and savannah woodlands to guarantee elephant adaptability to climate change. Habitat quality and condition are critical to the PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 17 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss survival of elephants by providing a reliable and sustainable source of food, water, and shelter for thermal regulation. On the other hand, if the suitable habitat is degraded or heavily fragmented, it predisposes the ecosystem to invasive colonization, fire risk, and flooding triggered by the climate change scenario. For example, under the RCP8.5, temperature is expected to be high, resulting in increased biomass accumulation, low elephant adaptability, and death, as witnessed in Tanza- nia in 2022, displacement of species, and competition for resources between people and wild- life. Conversely, suppose precipitation is very high in the case of RCP2.5. In that case, the elephant conservation area may suffer from flooding, vegetation cover modification, and reduced elephant mobility, making the population vulnerable to diseases and other risks. In partnership with conservation organizations, national governments have established policies and legal frameworks, the development of National Elephant Action plans, wildlife corridor protection plans, and climate change mitigation and adaptation plans. These measures have created considerable attention to climate change impacts through conservation planning pro- grams, policy adjustments, increased awareness, and education to mitigate potential climate change impacts and developing new management and financing strategies. This study considered these challenges as nested, multiscaled, and multidisciplinary, demanding a holistic approach. It was also recognized that hierarchical conservation planning helps prioritize the places with the greatest conservation need and focuses on species popula- tion management. The conventional management system, with wildlife managers and researchers examining these inextricably linked problems in total isolation, needs new approaches. As such, an integrated approach was necessary to explore these challenges collec- tively. Under this approach, policy options and conservation efforts can be evaluated, strategic decisions made to allocate scarce resources effectively, and critical players persuaded to inter- vene on a broader scale. In GVL, the presence of landscape species such as elephants, lions, and Mountain Gorillas created the impulse for transboundary resource management and col- laboration. It also catalyzed the creation of the Greater Virunga Transboundary Collaboration (GVTC) Secretariat established under a tripartite agreement among Rwanda, DR Congo, and Uganda to coordinate investments, secure cooperation and collaboration among the three states together with the protected area institutions (UWA, ICCN, and Rwanda Development Board). In addition, an independent entity, the International Gorilla Conservation Program (IGCP), was established to handle tourism development and monitoring of the species in the region. The transboundary nature of the GVL landscape has resulted in strong partnerships between and among stakeholders such as governments, Civil Society Organizations, Conserva- tion NGOs, development partners, donors, academia, and researchers, as well as helped to unlock funding for implementing the GVL strategic plan. In turn, it has helped to protect and conserve biodiversity, including elephants, created the need for regional peace-building and economic integration, and helped to support the sustainable socio-economic development of rural communities. Cross-border collaboration and eco-tourism became a vehicle for building institutional capacity and reducing poverty at the regional scale. Another strategy is establishing community-led programs like community wildlife scouts, eco-guards, or lion guardians focusing on protecting and conserving species at the local level. There are three broad advantages of establishing community wildlife scouts, eco-guards, or lion guardians as an incentive for communities to participate in conservation and develop community-led ecotourism namely a) it entrenches local communities’ active participation in protected area conservation and facilitates information sharing between conservation manag- ers and stakeholders, and b) it results in greater appreciation of the need for protected areas by communities and promotes the integration of indigenous ecological knowledge as well build community-led conflict management, and c) is less costly. The community wildlife scouts, or PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 18 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss the eco-guardians have been tested and rolled out in Kenya and Tanzania, mainly in the con- servancies. The Human Gorilla (HUGO) Conflict Resolution around Bwindi Impenetrable National Park, and Community Wildlife Scouts established around Queen Elizabeth National Park, and Murchison Falls National Park have proved to be a big success in catalyzing commu- nity participation in conservation in Uganda. Several policy experiments were conducted in this study to sustain or improve the elephant populations of GVL, these include suitable habitat increase through the mitigation of forest and savanna vegetation loss to human settlement and agriculture. Results of the policy experi- ment suggest that increasing the current suitable habitat by 50% would significantly improve elephant population numbers. This could be achieved mainly by reducing forest and savanna vegetation loss and conversion to human settlement and agriculture, as well as properly man- aging fires and invasive species. In addition, it supports private landowners to protect wildlife on their land to secure wildlife corridors and mitigate human-wildlife conflicts. The dynamic model can be used to simulate the adaptation potential of elephants to stressors, which is often difficult to research over long periods. Conclusions and recommendations Elephants in Africa are disappearing at an alarming rate, mainly due to habitat degradation and loss and human-wildlife conflicts. The situation is expected to worsen with the advent of climate change impacts, resulting in a high occurrence of prolonged droughts in both arid and semiarid regions. Understanding how animal populations will respond to such dramatic events is crucial to designing and implementing mitigation strategies and developing the adap- tive capacity of wildlife managers to respond to these challenges adequately. Therefore, this study explores how GVL age-class-specific elephant populations will likely respond to habitat change, water resources, and climate change. Also, climate change affected the old elephants more than the young ones regarding survival abilities, but this could be due to immigration from other areas. It is also likely that the undetected direct climate change impact on the ele- phant population is due to a desirable features of suitable habitat, particularly forests and wet- lands used for thermal regulation. However, this does not rule out the idea that indirect impacts, such as thermal and latent flux impacts, are occurring already. On the other hand, an improvement in the habitat type and availability of water resources resulted in a slight increase in all age class populations. In all the analyses, the results suggest that if the environmental and anthropogenic stressors are not mitigated, GVL will have only a very young population of elephants. Studies elsewhere have shown that elephants are suscepti- ble to drought, and calf mortality was higher among young mothers than the more experi- enced mothers. Elephants in GVL are transboundary resources requiring a transboundary management approach, cooperation between conservation agencies, and effective partnerships with relevant stakeholders. The stakeholders include local and regional governments, wildlife protected area authorities of DRC, Rwanda, and Uganda, and law enforcement agencies such as the military, police, judiciary, customs, and border control authorities. Countries in the Albertine Rift region have established collaborative management frameworks that allow all interested parties to develop management plans [70] and implement them jointly for the common good [14]. Collaboration is fundamental to effective conservation [15,20], and regional governments must be willing to develop institutional frameworks to allow it to happen. The major limitation of this study was securing quality data collected at the regional scale, format, and historical veg- etation cover data for some countries that overlap GVL. There are few functioning weather sta- tions, hydrometeorological stations, and cloud-free satellite imagery. The elephant population PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000094 January 31, 2024 19 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONSustaining elephant population under changing climate and habitat loss survey data was also inconsistent and lacked some attributes such as age and sex, which forced us to rely on data from other countries where it has been collected over time using the same methodology. The methodology developed for this study and the analyses conducted can be replicated in other sites such as the Kidepo complex (Kidepo Valley National Park (Uganda) and Kidepo Reserve (South Sudan)), and Mt Elgon shared between Kenya and Uganda, both landscapes that in habit elephants. Similarly, the study approach can be applied to other species, such as lions and Mountain Gorillas. The future applications of the study can include stochasticity in the model, spatial dynamics and optimization, societal valuation of conservation and gover- nance arrangements. Supporting information S1 Text. Model info. (DOCX) S1 Data. Elephant population baseline data. (XLSX) Author Contributions Conceptualization: Simon Nampindo, Timothy O. Randhir. Data curation: Simon Nampindo. Formal analysis: Simon Nampindo, Timothy O. Randhir. Investigation: Simon Nampindo, Timothy O. Randhir. Methodology: Simon Nampindo, Timothy O. Randhir. Supervision: Timothy O. Randhir. Validation: Simon Nampindo. 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10.1371_journal.ppat.1012032	RESEARCH ARTICLE A tick saliva serpin, IxsS17 inhibits host innate immune system proteases and enhances host colonization by Lyme disease agent Thu-Thuy NguyenID Samuel Kiarie Gaithuma1, Moiz Ashraf Ansari1, Tae Kwon Kim2, Lucas Tirloni3, Zeljko Radulovic4, James J. Moresco5, John R. Yates, III6, Albert MulengaID 1, Tae Heung Kim1, Emily Bencosme-Cuevas1, Jacquie Berry1, Alex 1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Veterinary Pathobiology, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America, 2 Department of Diagnostic Medicine/ Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, United States of America, 3 Tick-Pathogen Transmission Unit, Laboratory of Bacteriology, NIAID, Hamilton, Montana, United States of America, 4 Department of Biology, Stephen F. Austin State University, Nacogdoches, Texas, United States of America, 5 Center for Genetics of Host Defense, UT Southwestern Medical Center, Dallas, Texas, United States of America, 6 Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California, United States of America * amulenga@cvm.tamu.edu Abstract Lyme disease (LD) caused by Borrelia burgdorferi is among the most important human vec- tor borne diseases for which there is no effective prevention method. Identification of tick saliva transmission factors of the LD agent is needed before the highly advocated tick anti- gen-based vaccine could be developed. We previously reported the highly conserved Ixodes scapularis (Ixs) tick saliva serpin (S) 17 (IxsS17) was highly secreted by B. burgdor- feri infected nymphs. Here, we show that IxsS17 promote tick feeding and enhances B. burgdorferi colonization of the host. We show that IxsS17 is not part of a redundant system, and its functional domain reactive center loop (RCL) is 100% conserved in all tick species. Yeast expressed recombinant (r) IxsS17 inhibits effector proteases of inflammation, blood clotting, and complement innate immune systems. Interestingly, differential precipitation analysis revealed novel functional insights that IxsS17 interacts with both effector proteases and regulatory protease inhibitors. For instance, rIxsS17 interacted with blood clotting prote- ases, fXII, fX, fXII, plasmin, and plasma kallikrein alongside blood clotting regulatory serpins (antithrombin III and heparin cofactor II). Similarly, rIxsS17 interacted with both complement system serine proteases, C1s, C2, and factor I and the regulatory serpin, plasma protease C1 inhibitor. Consistently, we validated that rIxsS17 dose dependently blocked deposition of the complement membrane attack complex via the lectin complement pathway and pro- tected complement sensitive B. burgdorferi from complement-mediated killing. Likewise, co- inoculating C3H/HeN mice with rIxsS17 and B. burgdorferi significantly enhanced coloniza- tion of mouse heart and skin organs in a reverse dose dependent manner. Taken together, our data suggests an important role for IxsS17 in tick feeding and B. burgdorferi colonization of the host. OPEN ACCESS Citation: Nguyen T-T, Kim TH, Bencosme-Cuevas E, Berry J, Gaithuma ASK, Ansari MA, et al. (2024) A tick saliva serpin, IxsS17 inhibits host innate immune system proteases and enhances host colonization by Lyme disease agent. PLoS Pathog 20(2): e1012032. https://doi.org/10.1371/journal. ppat.1012032 Editor: Catherine A. Brissette, University of North Dakota School of Medicine and Health Sciences, UNITED STATES Received: June 9, 2023 Accepted: February 6, 2024 Published: February 23, 2024 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: This research was supported by National Institutes of Health grants (AI093858, AI074789, AI138129, and AI119873) to AM, National Center for Research Resources (5P41RR011823) and National Institute of General Medical Sciences (8P41GM103533) to JRY, and Intramural PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 1 / 31 PLOS PATHOGENSResearch Program of the National Institute of Allergy and Infectious Diseases (Z01 AI001337-01) to LT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Tick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Author summary Ticks feed on animals and humans for their survival. During blood meal feeding, ticks inject saliva along with disease causative agents into the hosts. Here, we demonstrate that I. scapularis tick saliva protein, IxsS17 inhibits host innate immune system proteases and enhances B. burgdorferi colonization of the host. Recombinant IxsS17 (rIxsS17) inhibits blood clotting and inflammation systems serine proteases including pancreatic trypsin and trypsin IV (~100%), blood clotting factor Xa and XIa (~60–80%), plasmin and cathep- sin G (~50%). Similarly, rIxsS17 interacts with complement system factors, C1s, C2 and factor I and blocks complement membrane attack complex via the lectin complement pathway by up to 97%. We found that, in the mouse model for Lyme disease, rIxsS17 sig- nificantly increases B. burgdorferi colonization of mouse heart and ear tissues by 5.7 and 2.3 times. Taken together, we conclude that IxsS17 is a key protein in tick feeding and B. burgdorferi colonization of the host, and thus, a potential target antigen for developing tick antigen-based vaccines against Lyme disease agent transmission. Introduction Ticks and tick-borne diseases (TBD) impact public and veterinary health globally. Among those, Lyme disease (LD) caused by Borrelia species is one of the most important human TBD that has the most world-wide public health impact. The spirochete, Borrelia burgdorferi that is transmitted by Ixodes spp. ticks is responsible for LD in United States (US) and Europe, while B. afzelii and B. garinii are responsible for LD in Eurasia [1–3]. Recently, a second LD patho- gen B. mayonii was described in the US [4]. Like other TBD agents, except a vaccine against tick-borne encephalitis approved by FDA in United States in 2021, there is currently no effec- tive human vaccine against the LD agent. In the absence of effective vaccines against the LD agent, avoidance of infectious tick bites is the only prevention method against LD currently. Despite a plethora of methods aimed at reducing infectious tick bites [5–7], LD cases have continued to increase. Confirmed and prob- able LD cases reported to the US Centers for Disease Control and Prevention have steadily risen from just under 20,000 in 1996 to more than 40,000 annual cases since 2008 (www.cdc. gov). According to insurance database, between 2010–2018, 476,000 LD cases were diagnosed and treated each year, with economic losses estimated at ~$786 million annually [8,9]. Given the ongoing rise in LD cases and search for better preventative measures, tick-anti- gen based vaccines have emerged among the most promising LD prevention approaches. This is based on evidence that repeatedly infested model animals that acquire immunity against tick feeding are protected against transmission of TBD agents including B. burgdorferi [10–13]. Similarly, in a recent study, repeatedly infested primates were also protected against B. burg- dorferi transmission [14]. Likewise, active immunization of mice with tick saliva proteins con- ferred immunity that reduced transmission of LD agents [15–17]. Similarly, tick saliva and tick salivary gland extracts promoted LD agent replication [18,19] and innate immunity eva- sion ex vivo [20], and enhanced organ colonization in needle inoculated mice [21,22]. From these perspectives, tick saliva factors that promote feeding and transmission of TBD agents have been highly sought after [23–27]. To date, there is no evidence of transovarial transmission (or passed from female ticks to larval ticks) of LD agents. Larval ticks acquire the spirochetes during feeding on infected reser- voir hosts and then transtadially transmit to nymphs, which in turn transtadially transmit to PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 2 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent adult ticks [28]. Major transmission events of B. burgdorferi occur after the tick has fed for more than 48 h [29–31]. The small size of the nymph tick and pain suppressants in its saliva that mask its presence on human skin allows the tick to go unnoticed and feed long enough for more than 36–48 h to transmit the LD agent [32]. For that reason, although both nymph and adult ticks are capable of transmitting LD agents to the human host, most reported LD cases were associated with infectious nymph bites [3,33]. On this basis, we recently identified tick saliva proteins of B. burgdorferi infected I. scapularis nymphs that were secreted every 12 h throughout feeding [25]. This study was initiated to understand functional roles of I. scapularis tick saliva serine pro- tease inhibitor (Serpin; GenBank accession# EEC18973.1 or XP_002415308.5) in tick feeding and B. burgdorferi colonization of the host. We later found that IxsS17 was among homologs (orthologs) to Amblyomma americanum serpin (AAS) 19 that were characterized by the func- tional domain reactive center loop (RCL) being 100% conserved in all tick species according to currently available data [34,35]. We also reported that IxsS17 and its homologs are among the proteins being injected into animals by adult I. scapularis [23], A. americanum [35], and Rhipi- cephalus microplus [27] ticks. In our recent study, we found that B. burgdorferi infected I. sca- pularis nymphs predominantly secreted IxsS17 at 48h feeding time point when major B. burgdorferi transmission events are expected [25]. Additionally, we showed that RNAi silenc- ing of IxsS17 [36] and its A. americanum homolog, AAS19 [23] caused mortality and reduced tick feeding efficiency. This evidence suggested that functions of IxsS17 and its homologs are related to tick feeding and transmission of tick-borne pathogens including B. burgdorferi. Con- sistent with functional analyses of IxsS17 homologs in A. americanum (AAS19; [35]), R. micro- plus (RmS-15; [37,38]), and recently in R. haemaphysaloides (RHS8; [39]) and I. ricinus (Iripin-8;), we provide new information that IxsS17 is an anticoagulant that is potentiated by binding heparin. Significantly, we further show that IxsS17 promotes B. burgdorferi coloniza- tion of the host by inhibiting host inflammation, blood clotting, and complement system effec- tor proteases. Results I. scapularis serpin (IxsS) 17 is not redundant and is conserved across Ixodidae tick species BLASTP search of IxsS17 (EEC18973.1 or XP_002415308.5) amino acid sequence against entries in GenBank retrieved one sequence match of more than 77% amino acid identity per tick species except for Dermacentor silvarum, which has two matches (S1 Fig). The next highest matches to IxsS17 in I. scapularis and other tick species showed amino acid identity levels of less than 50%. This indicates that IxsS17 and its homologs, in other tick species, are not redun- dant except for D. silvarum, which has two matches that differ by an 11 amino acid deletion to IxsS17: KAH7955208.1 and XP_049521536.1 (S1 Fig). With Homo sapiens antithrombin III (CAA48690.1) set as an outlier, neighbor joining phylogeny tree segregated IxsS17 in group A with other Ixodes spp. tick serpins: I. ricinus (ABI94058.1) and I. persulcatus (KAG0414503.1) that show 99% amino acid identity to IxsS17 (Fig 1A, group A). IxsS17 is 80% identical to its homologs in metastriata ticks including D. andersoni (XP_050039672.1) and D. silvarum, (KAH7955208.1 and XP_049521536.1) in group B. Likewise, IxsS17 is 77–79% identical to Hyalomma asiaticum (KAH6936909.1), Rhipicephalus microplus serpin 15 (RmS15; AHC98666.1), R. sanguineous (XP_037506920.1), and R. haemaphysoloides (QHU78941.) in cluster C (Fig 1A). Finally, in group D, IxsS17 is 78–80% identical to Amblyomma americanum (AAS19; GAYW01000076.1), A. maculatum (AEO34218.1), A. triste (A0A023GPF9), A. cajee- nense (A0A023FM57), and Haemaphysalis longicornis (KAH9373177.1) (Fig 1A, group D). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 3 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 1. Amino acid sequence analysis of IxsS17. (A) Phylogenetic tree was constructed using the MEGA-X software, Maximum Likelihood method and Le_Gascuel_2008 model with Bootstrap set to 1,000 replications. Group A, B, C and D represent amino acid identity levels of IxsS17 to its homologs in percentage. (B) Multiple sequence alignment of IxsS17 reactive center loop (EEGSEAAAVTGFVIQLRTAAF) and its homologs as well as the antithrombin III outlier was done in MacVector using T-Coffee specifications. Amino acids in the grey box are identical. https://doi.org/10.1371/journal.ppat.1012032.g001 Although overall amino acid identity is below 100% (S1 Fig), the 21 amino acid sequence of IxsS17 functional reactive center loop (RCL: EEGSEAAAVTGFVIQLRTAAF) is 100% con- served in all tick serpins analyzed in this study (Fig 1B). IxsS17 was initially described among 45 I. scapularis serpin sequences that were extracted genome contigs [40]. In this manuscript, we show that the I. scapularis genome (RefSeq GCF-016920785.2) encode for 62 serpins including IxsS17 as revealed by unique serpin RCLs (S1 Table). Pairwise and global alignment of the 62 RCLs with coverage set to between 80–100% confirmed that IxsS17 RCL was not redundant as all other RCLs are 29–52% identical to IxsS17. When compared to its homologs in other tick species both EEC18973.1 [41, 42] and XP_002415308.5 [43] have a 53 amino acid sequence extension at the amino terminus end. SignalP 6.0 software did not identify the signal peptide in EEC18973.1 or XP_002415308.5 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 4 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent unless the first 53 amino acids were removed (S2A Fig). However, the subcellular localization prediction software DeepLoc-2.0 indicated that EEC18973.1 or XP_002415308.5 were an extra cellular protein with signal peptides at the position 50 to 70 (S2B Fig). In this study we charac- terized mature EEC18973.1 sequence with the first 70 amino acid sequences removed. Yeast expressed rIxsS17 inhibits trypsin-like proteases and its inhibitory function is affected by hexa-histidine tag location Canonical mode of serpin inhibitory activity is mechanical disruption of the target protease which starts with the C-terminal reaction center loop (RCL) irreversibly trapping the target protease [44]. Determined to investigate the effect of the hexa-histidine (His) fusion tag on inhibitory functions of rIxsS17, we successfully expressed, and affinity purified three rIxsS17 constructs: (1) the hexa-histidine tag located at the N- terminal or (2) C-terminal ends or (3) cleaved off using the inhouse produced Tobacco etch virus (TEV) protease (Fig 2). The rIxsS17 are glycosylated like other tick (IxsS-1E1 [AID54718.1], AAS19 [JAI08902.1], AAS27 [GAGD01011247.1], and AAS41 [JAI08957.1]) and human serpins (antithrombin III and vas- pin) [24,35,45–48]. After deglycosylation treatment, protein sizes reduced ~ 2.5–5.0 kDa (S3 Fig). Glycosylation is the most common post-translational modification of proteins when the carbohydrate units are attached to the protein backbone either by N- or O-glycosidic bonds or both [49,50]. In serpins, glycosylation is important for proper protein secretion, sta- bility, and their half-life extension [46,51]. We initially used the C-terminal hexa-His-tagged rIxsS17 in substrate hydrolysis assays of 17 serine proteases related to host responses against tick feeding (Fig 3A and S2 Table). This screen showed that rIxsS17 (1 μM) inhibited pancreatic trypsin (1.5 nM) by 96–100%, rat skin trypsin IV (2.0 nM; in house expressed) by 90–99%, blood clotting factor (f) Xa (2.3 nM) by 79–80% followed by inhibition of blood clotting fXIa (3.7 nM), plasmin (33.7 nM), and Fig 2. Expression and affinity purification of recombinant (r) IxsS17. (A) Graphical illustration of three different rIxsS17 expression constructs that were custom synthesized: (1) C-terminal hexa-histidine tag, (2) N-terminal hexa- histidine tag and Tobacco Etch Virus (TEV) cutting site (ENLYFQG) included, and (3) hexa-histidine tag is cleaved off at the TEV cutting site in the non-tagged rIxsS17. Please note all three recombinant constructs contain full-length sequence of rIxsS17. (B) Western blotting analysis of daily expression of rIxsS17 in Pichia pastoris culture. Culture (1 mL) were precipitated by ammonium sulfate saturation and resolved on 10% SDS-PAGE. rIxsS17 were detected in western blot using the HRP-conjugated monoclonal antibody to the hexa-histidine tag. (C) Silver staining and (D) Western blotting analysis of affinity purified rIxsS17. The hexa-histidine tag was detected in the C-terminal (Lane 1) and N-terminal-His-rIxsS17 (Lane 2) but not in the non-tagged rIxsS17 (Lane 3). https://doi.org/10.1371/journal.ppat.1012032.g002 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 5 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 3. Inhibition profiling of rIxsS17 against 17 serine proteases related to host responses during tick feeding. (A) Inhibition rates of C-terminal-Histidine tagged rIxsS17 (1 μM) against 17 serine proteases (with indicated concentrations) in the substrate hydrolysis assays. Substrate hydrolysis was monitored at A405nm every 11s for 30 min at 30˚C. Inhibition rate was calculated using the formula: 100-Vi/V0 x 100, where Vi = activity in the present of, and V0 in the absence of rIxsS17. (B) Inhibition activity of C-terminal, N-terminal and non-Histidine tagged rIxsS17 against trypsin, factor Xa and human thrombin was determined using the substrate hydrolysis assay. Data represents mean ± SEM calculated from 3 biological replicates. The difference was analyzed using ANOVA in GraphPad Prism 9 and is statistically significant when P value � 0.05. https://doi.org/10.1371/journal.ppat.1012032.g003 cathepsin G (281 nM) by 52–65%, 55–61%, 56–61% respectively. Next, rIxsS17 also inhibited human chymase (21 nM) by 26–31%, as well as native purified rat and mouse chymase by 26 and 10–25% respectively. Finally, rIxsS17 also inhibited blood clotting fXIIa (15 nM) by 18– 33%, neutrophil elastase (22 nM) by 18–25%, pancreatic chymotrypsin (1.4 nM) by 10–27%, human thrombin (19 nM) by 28–34%, fIXa (311.4 nM) and pancreatic kallikrein (20 nM) by ~10%. Lastly, rIxsS17 had no inhibitory activity against bovine thrombin (undefined) and pan- creatic elastase (19 nM). Heat-inactivated rIxsS17 did not inhibit serine proteases (inhibition rate = 0%) suggesting that its inhibitory activity is heat sensitive. Next, we tested if proximity of the hexa-His-fusion tag to the RCL or its absence affected inhibitory activity of rIxsS17 against selected proteases (Fig 3B). As shown, rIxsS17 with N-ter- minal hexa-His-fusion tag had an 8.6% decrease in the inhibitory activity against trypsin. This suggests that beside the C-terminus domain that contains RCL, extension of the N-terminus region of the serpin might affect its inhibitory activity against trypsin. For both factor Xa and thrombin, inhibitory activity of the three constructs were similar. Since C-terminal histidine and non-tagged rIxsS17 have equal inhibitory activity, either of them was used in our down- stream assays. To determine the efficiency and rate at which rIxsS17 inhibits pancreatic trypsin, trypsin IV, and factor Xa, stoichiometry of inhibition (SI) and association rate of constant (ka) were calculated (Fig 4). As shown, the SI (amount of rIxsS17 needed to inhibit one molecule of pro- tease) for C-terminal His-tagged rIsS17 against trypsin, trypsin IV, and factor Xa was esti- mated at 12.9, 10.5, and 68 respectively (Fig 4A–4C). The rate of rIxsS17 (ka) inhibition of trypsin, trypsin IV, and factor Xa was 2.7 ± 0.003 x103 M-1 s-1, 3.9± 0.0001 x103 M-1 s-1, and 5.4 ± 1.1 x 102 M-1 s-1, respectively (Fig 4D–4F). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 6 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 4. rIxsS17 is a moderate inhibitor of trypsin, rat trypsin IV and factor Xa. Stoichiometry inhibition (SI) analysis calculated the amount of rIxsS17 needed to inhibit one molecule of bovine trypsin (A), rat trypsin IV (B), and factor Xa (C). Various molar ratios of rIxsS17 to proteases (0, 2.5, 5, 10, 20, 25, 50) were incubated for 15 min at 37˚C with constant concentration of bovine trypsin (1.5 nM) or rat trypsin IV (2.0 nM) or factor Xa (13.9 nM). In the presence of appropriate substrates, residual enzymatic activity was measured and plotted against rIxsS17: protease molar ratio. The SI was determined by extrapolating to the rIxsS17: protease ratio where protease activity is zero (Y axis = 0). The inhibition rate (ka) of rIxsS17 was determined against bovine trypsin (D), rat trypsin IV (E) and factor Xa (F). Different concentrations of rIxsS17 (50, 100, 200, 400, 600 and 1000 nM) were incubated with constant amounts of bovine trypsin (14.6 nM), trypsin IV (12.5 nM) or factor Xa (13.9 nM) for different periods of time (0, 1, 2, 4, 6, 8, 10 and 15 min) at 37˚C. The residual protease activity was measured and plotted against time to determine the pseudo-first order constant, kobs. Consequently, the second-rate constant (ka) was determined by the best fit line slope of the kobs values that were plotted against rIxsS17 concentration. https://doi.org/10.1371/journal.ppat.1012032.g004 The concentration of rIxsS17 (1 μM) used in the inhibitory assays may not reflect the physi- ological levels of this protein in tick saliva, however, is at optimal concentration of a single tick salivary recombinant protein to be biologically active in-vitro or ex-vivo (1–6 μM) according to Chmelař et al., 2016 [52]. The reason is in the complex salivary mixture, this high concentra- tion could be achieved by combination with numerous redundant proteins. rIxsS17 interacts with both innate immune system effector proteases and regulatory protease inhibitors as revealed by protein-to-protein interaction analysis Fig 5A–5C, and Table 1 and S1 File summarize the differential precipitation protein-protein interaction analysis of rIxsS17 and human plasma proteins. Since low amounts of rIxsS17 were detected in fractions 1–6, we pooled fractions 1–3, and 4–6 while fractions 7–10 where individ- ually analyzed in LC-MS/MS analysis (S1 File). Next, we used PSOPIA (prediction server of protein-to-protein interactions; https://mizuguchilab.org/PSOPIA/) to analyze the interaction if NSAF (normalized spectral abundance factor) value was higher in rIxsS17 and human plasma mixture compared to plasma only controls. The interactions that were predicted with more than 75% likelihood by PSOPIA were considered true (Table 1). This analysis confirmed substrate hydrolysis results and revealed novel insights that rIxsS17 likely interacts with both effector proteases and regulatory protease inhibitors of the innate immune system (Table 1). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 7 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 5. Protein-to-protein interaction using differential precipitation of proteins (DiffPOP) analysis reveals novel IxsS17 functional insights. 25 μg of affinity purified rIxsS17 (A-C) or rIxsS4 (D-F) was incubated with human plasma in reaction buffer (20mM Tris-HCL and 150mM NaCl pH 7.4) overnight at 37˚C. The reaction was stabilized using Phosphoprotein Kit- Buffer A and subjected to repeated precipitation (X10) using methanol and acetic solution (90% methanol to 1% acetic acid). Appropriately washed precipitates of each fraction were resolved on 10% SDS-PAGE and transferred onto PVDF membrane for western blot analysis using monospecific antibodies to rIxsS17. A and D = human plasma only, B and E = human plasma mixed with rIxsS17 or rIxsS4, and C and F = rIxsS17 or rIxsS4 alone. Ladder (L), Number (1–9) represents each fraction from differential precipitation. Please note that, fraction 10 for rIxsS17 is not shown in this figure; however, its LC-MS/MS data analysis is available in S1 File. https://doi.org/10.1371/journal.ppat.1012032.g005 Differential precipitation and PSOPIA analysis revealed that rIxsS17 interacted with blood clotting system factors (f) II (prothrombin), fX, fXII, plasma kallikrein, and plasminogen alongside blood clotting regulatory protease inhibitors; antithrombin III (serpin), heparin Table 1. Fast fractionation and LC-MS/MS analyses identification of human plasma proteins that interacted with rIxsS17 and validation using in silico protein to protein interaction prediction PSOPIA software. Accession Description P00742 P00748 P01042 H0YAC1 A8K9A9 H0VJK2 P00734 P06681 P09871 P05156 P01023 P01008 P05546 P05155 P01019 P36955 P00739 Coagulation factor X Coagulation factor XII Kininogen-1 Plasma kallikrein Plasma kallikrein B Plasminogen Prothrombin Complement C2 Complement C1s subcomponent Complement factor I Alpha-2-macroglobulin Antithrombin-III Heparin cofactor 2 Functional Classification Protease—Blood Coagulation Protease—Blood Coagulation Protease—Blood Coagulation Protease—Blood Coagulation Protease—Blood Coagulation Protease—Blood Coagulation Protease—Blood Coagulation Protease–Complement proteins Protease—Complement proteins Protease—Complement proteins Protease Inhibitor—Blood Coagulation Protease Inhibitor—Blood Coagulation Protease Inhibitor—Blood Coagulation Plasma protease C1 inhibitor Protease Inhibitor—Complement Angiotensinogen Protease inhibitor—Non inhibitory Serpin Pigment epithelium-derived factor Protease inhibitor—Non inhibitory Serpin Haptoglobin-related protein Metal Binding Proteins Q5VY43 Platelet endothelial aggregation receptor 1 Receptor PSOPIA score of 0.75 to 1.0 represent likely protein to protein interactions (Murakami and Mizuguchi, 2014) https://doi.org/10.1371/journal.ppat.1012032.t001 PSOPIA P2P prediction score 0.8573 0.7918 0.9505 0.8573 0.8573 0.891 0.996 0.8204 0.8054 0.8054 0.7279 0.9794 0.9794 0.9794 0.9695 0.9794 0.8442 0.7513 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 8 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent cofactor II (serpin), alpha-2 macroglobulin (non-serpin inhibitor), and Kininogen-1 (non-ser- pin inhibitor) (Table 1). Similarly, rIxsS17 interacted with complement system serine prote- ases, C1s, C2, and factor I alongside the complement system regulatory serpin, plasma protease C1 inhibitor (Table 1). Complement component C3, C4 and C5 were detected in the differential precipitation of proteins analysis (S1 File); however, PSOPIA predicted weak likeli- hood for interaction. We also found that rIxsS17 interacted with non-protease blood clotting system proteins (fibronectin and fibrinogen), non-inhibitory serpins (angiotensinogen, and pigment endothelium derived factor), and non-proteases (haptoglobin and platelet endothelial aggregation receptor 1) (Table 1 and S1 File). Notably rIxsS4 (XP_040066711.2 or XP_040066712.2), an inhibitor of trypsin that similar to IxsS17 has basic amino residue (R) at its P1 site did not interact with human plasma in the differential precipitation protein-protein interaction (Fig 5D–5F). This finding confirmed that the rIxsS17 and plasma protein-to-pro- tein interactions were specific. rIxsS17 binds glycosaminoglycans (GAGs) Homology modeling predicted that IxsS17 secondary structure, which was scored at Coulom- bic electrostatic values of -10 to 10 on ChimeraX server has a single basic positive patch located near the RCL (Fig 6A and 6B). Basic positive patch could potentially bind negatively charged ligands such as GAGs [53,54]. Consistently, docking analysis conducted by AutoDock Vina and ADT v1.5.4 demonstrated that the IxsS17 secondary structure is likely to bind with hepa- rin (Fig 6B). For the docking, nine poses were predicted and the result with the binding affinity of -12.6 Kcal/mol and the lower bound and upper bound RMSD as 0 were selected to be the best docked conformation. Further, the result generated was visualized by PyMOL, which Fig 6. IxsS17 binds heparin and the putative binding sites are located on the positive basic patch. (A) Comparative modeling of IxsS17 secondary structure was predicted on Chimera X server and heparin binding predicted using Autodock Vina and Auto Dock Tools. Heparin ligand (in blue) was arranged accordingly to be flexible to rotate and to explore the most probable binding positions (in red) while the receptor was kept rigid. RCL = reactive center loop. (B) Two heparin binding sites at Lysine 188 and 210 are located on the positive basic patch (Electrostatic potential is color coded: positive is blue; neutral is white and negative is red). (C) Binding affinity of rIxsS17 to 4 different GAGs: heparin (black circle), heparan sulphate (red square), dermatan sulphate (green triangle) and chondroitin sulphate (yellow upside-down triangle). The rIxsS17 was added into 96-well microplates previously coated with different GAG at the concentration of 0, 1, 2, 5, 10 and 20 μg/mL. Binding was detected using HRP-conjugated antibody to the histidine tag and documented as A450nm. The data represent mean ± SEM from 3 biological replicates. (D) Silver staining of rIxsS17, rAAS19 (positive control), and r1E1 (negative control) eluted from heparin column. Recombinant proteins (~300 μg) were applied to the heparin column. After washing, the proteins were eluted using a gradient concentration of NaCl (0.25–0.5–1.0–2.0–3.0 M). Serpins = the proteins before applying to the column, FT = Flow through. https://doi.org/10.1371/journal.ppat.1012032.g006 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 9 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent shows that 5 amino acids of IxsS17 (Asp185, Lys188, Lys210, Ser211, Thr212) were interacting with heparin; and out of 5 binding sites, 2 heparin binding sites (Lys 188 and 210) lies within the positively charged or basic patch which was predicted by Chimera X. Finally, we confirmed that rIxsS17 has high binding affinity for heparin followed by chondroitin sulphate and derma- tan sulphate (Fig 6C). However, it was interesting to observe that rIxsS17 bound to heparin, but not to heparan sulphate (Fig 6C). It might be because of structural variations between hep- arin and heparan sulphate, such as the chain of heparan sulphate is generally longer, with higher molecular weight (30kDa) than heparin (15kDa). Furthermore, l-iduronic acid pre- dominates in heparin while d-glucuronic acid represents most of the uronic acid found in heparan sulfate. This changes the structure configuration resulting into alteration in binding affinity. Most importantly, heparin is the complete modified version of heparan sulphate and contains highest negative charge density of any known biological macromolecule which will increase its binding affinity to the positive patch of rIxsS17 [55]. The relative binding affinity to heparin was further determined showing that rIxsS17 bound on the heparin column and was eluted at 0.25-1M of NaCl (Fig 6D). For positive control, IxsS17 A. americanum homolog, rAAS19 which is known for high binding affinity to heparin and having 4 basic patches [35] was eluted at higher concentrations of NaCl (1-3M). The negative control r1E1 (KF990169) does not bind heparin and does not have a basic patch [45], therefore, came out in the flow- through. Binding of heparin significantly enhances anti-blood clotting effects of rIxsS17 In preliminary studies, we empirically determined that 2 μM of rIxsS17 delayed plasma clot- ting by more than 60 seconds compared to buffer control. Next, we tested if the combination of rIxsS17 and 17 kDa heparin had synergistic anti-plasma clotting effect. As heparin is an approved blood clotting disorder therapeutic [56–58], it is interesting to note that pre-incubat- ing plasma with the rIxsS17 and heparin mixture significantly delayed plasma clotting up to 532.9 seconds compared to clotting time for buffer control (64.5 seconds), rIxsS17 only (184 seconds), and heparin only (407 seconds) (Fig 7). It is also notable that plasma clotting was also delayed to 474 seconds when a reaction was assembled from plasma that was incubated separately with rIxsS17 and heparin. rIxsS17 inhibits complement activation via the mannose-binding lectin pathway and rescues B. burgdorferi from complement-mediated killing Consistent with protein-to-protein interaction (in silico and ex vivo) showing that rIxsS17 interacted with complement system serine proteases (C2, C1s and factor I), our data shows that IxsS17 is an inhibitor of the complement system (Table 1 and Fig 8). We successfully used the WIESLAB complement system kit to independently assess three complement activation pathways. The results demonstrated that rIxsS17 significantly inhibited deposition of the com- plement membrane attack complex (MAC) via the mannose-binding lectin (MBL) comple- ment activation pathway and moderately via the classical and alternative complement activation pathways (S4 Table). In the initial screen, rIxsS17 molar excess (4 μM) reduced MAC deposition by ~40, 62, and 99% via the classical, alternative and MBL pathway, respec- tively (S4 Table). Moreover, we found that rIxsS17 dose dependently reduced by more than 55% MAC deposition through 31 nM of rIxsS17 (Fig 8). In this study, dose response analysis was not done for the classical and alternative complement activation pathway. B. burgdorferi can activate three complement pathways resulting in several host defense mechanisms that include: opsonization, phagocyte recruitment, priming of the adaptive PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 10 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 7. Heparin binding enhances rIxsS17 anti-coagulant activity. Anti-blood clotting effects of heparin binding on rIxsS17 was determined in the recalcification time assay. Universal coagulation reference human plasma was pre- incubated with (1) reaction buffer control, or (2) heparin only, or (3) rIxsS17 only, or (4) rIxsS17 and heparin pre- incubated together, or (5) rIxsS17 and heparin pre-incubated separately. CaCl2 was added to trigger blood clotting and the reaction was monitored at A650nm every 20s for 20 min. The A650nm data were then fitted in the Sigmoidal dose- response lines: blue (buffer control), red (heparin only), green (rIxsS17 only), black (rIxsS17 and heparin pre- incubated together), and brown (rIxsS17 and heparin pre-incubated separately). Clotting time was interpolated from the sigmoid line when A650nm increases by 10% with 95% confident interval. Drop vertical lines A, B, C, D, and E = clotting time for buffer only (circle), rIxsS17 only (triangle), heparin only (square), rIxsS17 and heparin pre- incubated with plasma separately (upside-down triangle), rIxsS17 and heparin pre-incubated with plasma together (diamond). https://doi.org/10.1371/journal.ppat.1012032.g007 immune system, and bacteriolysis [59,60]. Serum-mediated bacteriolysis has been used to test the sensitivity of LD spirochetes to normal human serum [61,62]. Next, we tested if rIxsS17 was able to rescue B. burgdorferi from complement-mediated killing in vitro (Fig 9A–9D). Consistent with its inhibitory effect against complement activation (Fig 8), rIxsS17 dose dependently rescued the complement sensitive B. burgdorferi strain B314/pBBE22luc from complement killing. At 1 h post incubation, B. burgdorferi survival rates ranged from 73–100% and were not different among the tested groups (Fig 9A). At 2 and 2.5 h, only the positive con- trol (complement resistant B. burgdorferi strain B314/pCD100) and the 1μM rIxsS17 groups had higher survival rates than negative control, heat-inactivated rIxsS17 and PBS (protein buffer control) (Fig 9B and 9C). At 3 h of incubation, 0–14% of the negative control survived while survival increased to 19–21% (22 ± 2.7%), 25–31% (28 ± 1.8%), 35–47% (42 ± 3.7%) and 55–70% (64 ± 7.9%) in the presence of 0.25, 0.5, 0.75 and 1 μM rIxsS17, respectively (Fig 9D). The positive control had survival rates of 43–67% (56 ± 12.1%). In heat-inactivated normal human serum, survival rates of all the tested groups ranged from 97–100%. Co-injecting rIxsS17 enhances B. burgdorferi colonization of C3H/HeN heart and skin (ear) Next, we tested if rIxsS17 supported B. burgdorferi colonization of the C3H/HeN Lyme disease mouse model. We initially inoculated six groups of mice (4 mice per group) with BSK-II PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 11 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 8. rIxsS17 dose dependently inhibited membrane attack complex deposition of the Mannose-Binding Lectin pathway. A Blank, Negative control, Positive control or Positive control incubated with 2-fold-serial dilution of rIxsS17starting from 4 μM were added to the Mannan binding lectin (MBL) pathway Kit and incubated at 37˚C for 60 min. After the washing step, conjugate and substrate were subsequently added following the instructions of the manufacturer. Mac deposition rate was calculated using the formular: (Sample-NC)/(PC-NC) x100%, where NC is negative control and PC (or 0.0 μM of rIxsS17) is positive control. Data is presented as percent inhibition of MAC deposition mean ± SEM calculated from 3 biological replicates. https://doi.org/10.1371/journal.ppat.1012032.g008 medium (negative control), B. burgdorferi in BSK-II (positive control) and B. burgdorferi mixed with various amounts of rIxsS17 (1, 2, 5 and 10 μM). B. burgdorferi infection was con- firmed in all treated groups by in-vitro cultivation and conventional PCR (S5 and S6 Tables). Spirochete burden in the ear, heart, joint and bladder tissues did not show significant differ- ences between the groups (S4 Fig). However, we observed a pattern of B. burgdorferi load decreasing with increasing concentration of rIxsS17 in the heart and bladder (S4 Fig). More- over, IgG antibody titer against B. burgdorferi was statistically lower in the higher rIxsS17 con- centration groups (S5 Fig). We decided to repeat the experiment with reduced concentrations of rIxsS17: 0.06, 0.125, 0.25 and 0.5 μM (Fig 10). We show that the spirochete load in the heart tissue of rIxsS17 injected mice with 0.06 and 0.125 μM of rIxsS17 was 5.7 and 4.3 folds significantly higher than B. burgdorferi only group (Fig 10A). Likewise, there is an apparent trend (P < 0.1) that the spi- rochete load in 0.06 and 0.125 μM rIxsS17 injected mice was 1.8 and 2.3 folds higher than the B. burgdorferi only group in ear tissues (Fig 10C). Similarly, there is an apparent high B. burg- dorferi load in joints of mice that were co-injected with 0.125 and 0.25 μM rIxsS17 than control (Fig 10B) and there is no apparent difference in the bladder (Fig 10D). To determine whether PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 12 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 9. rIxsS17 impaired complement mediated killing of Borrelia burgdorferi. Normal human serum (NHS) was pre-incubated with serial dilutions of rIxsS17 (0.25, 0.5, 0.75, and 1.0 μM) or heat-inactivated rIxsS17 (1.0 μM) or Phosphate buffered saline (PBS) at 37˚C for 30 min prior to addition of 85 μl of 106 cells/mL of B. burgdorferi B314/ pBBE22luc (complement sensitive strain) and incubated in a bio-shaker at 32˚C, 100 rpm. NHS incubated with B. burgdorferi B314/pPCD100 (complement resistant strain) were used as positive control. Survival rates of B. burgdorferi were assessed at 1.5 h (A), 2 h (B), 2.5 h (C) and 3 h (D) post incubation. Data represents mean ± SEM of 3 biological replicates. Statistical significance was evaluated using t test in GraphPad Prism 9 (ns: no significance, :P value � 0.05, : P value � 0.01, : P value � 0.001). Negative control: black circle, PBS: red diamond, HI-rIxsS17: green cross, 0.25 μM rIxsS17: maroon square, 0.5 μM rIxsS17: green triangle, 0.75μM rIxsS17: purple upside-down triangle, 1μM rIxsS17: blue hexagon, Positive control: orange star. https://doi.org/10.1371/journal.ppat.1012032.g009 high concentrations of rIxsS17 affected the survival of B. burgdorferi in the inoculum, we incu- bated B. burgdorferi with 0, 0.06, 0.125, 0.25, 0.5, 1, 5, and 10 μM of rIxsS17 in vitro. This analy- sis revealed rIxsS17 did not have a negative effect on B. burgdorferi in culture as spirochete survival ranged from 95–98% up to 24h of observation (S7 Table). It is also notable that IgG titers to B. burgdorferi lysate antigen detected in ELISA of the B. burgdorferi control and rIxsS17-treated groups were not statistically different (S6 Fig). How- ever, IgM titers of the 0.06 and 0.125 μM rIxsS17 co-injected groups were significantly higher than 0.25 and 0.50 μM of rIxsS17 co-injected groups. Interestingly the IgM antibody of mice that were co-injected with 0.25 and 0.50 μM of rIxsS17 did not show any significant difference with B. burgdorferi control mice (Fig 11A and 11B). Furthermore, immune sera of 0.06 and 0.125 μM rIxsS17 co-inoculated mice bound multiple bands on western blots of lab cultured B. burgdorferi lysate (Fig 11C). Discussion This study provides data showing that I. scapularis serpin (IxsS) 17 regulates key functions that are important to tick feeding and B. burgdorferi colonization of the host. It builds on previous studies done by our lab that characterized the A. americanum tick serpin 19 as the only tick serpin that has its functional domain RCL perfectly conserved (100%) in all tick species as per available data [34,35]. Like most parasites, ticks have the propensity for encoding redundant molecular systems. For tick serpins, it is common for multiple paralogs or isoforms showing more than 70% amino acid identity being transcribed by the same tick species [34,40,63] sug- gesting redundancy. Thus, the finding that the IxsS17 amino acid sequence does not show any matches to other I. scapularis serpins with more than 50% amino acid identity suggests that PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 13 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 10. Mouse groups co-inoculated with low dose of rIxsS17 have higher B. burgdorferi load in organs than high dose injected mice. Four mice/group were inoculated with B. burgdorferi only (104 cells) mixed with or without various amounts of rIxsS17 (0.060, 0.125, 0.250, 0.500 μM). At 21 days post inoculation, B. burgdorferi burden in mouse heart (A), joint (B), ear (C) and bladder tissues (D) was quantified by real-time qPCR method. The data were presented as fold change of the rIxsS17 treated groups in comparison with B. burgdorferi group (2 -ΔΔCt = [(Ct Flab—Ct β-Actin) B. burgdorferi-rIxsS17 co- injected group—(Ct Flab—Ct β-Actin) Bb only group]). Blue: B. burgdorferi only group, Red: B. burgdorferi-rIxsS17 co-injected group. https://doi.org/10.1371/journal.ppat.1012032.g010 this protein represents a non-redundant tick serpin. It is notable that except for D. silvarum which encodes for two homologs to IxsS17 with more than 77% amino acid identity, 12 other tick species encoded single serpin homologs to IxsS17, with the RCL being 100% conserved. It is important to note that the two IxsS17 homologs in D. silvarum have the same RCL and the difference is limited an 11 amino acid deletion. We would like to note while amino acid sequence of IxsS17 suggested no redundancy, we are unable to know in this study if IxsS17 is also not functionally redundant. The RCL plays an important role in the inhibitory functions of serpins [44], and thus it is not surprising that the functional roles of IxsS17 is similar to its homologs in A. americanum, AAS19 [35], R. microplus, RmS15 [37,38], and recently I. ricinus, Iripin-8 [64,65]. Collectively, substrate hydrolysis and protein-to-protein interaction data in this study indicate that IxsS17 is an inhibitor of innate immunity effector proteases associated with inflammation, nocicep- tion (pain sensing), hemostasis, and complement innate immune defenses all of which must be blocked by ticks to feed and transmit tick-borne pathogens. In addition to food digestion [66], pancreatic trypsin which was highly inhibited by rIxsS17 is also found in blood circula- tion, accelerates blood clotting in the presence of calcium ions, pro-thromboplastic lipid, factor V, VII, and X [67], and it is also the major activator of protease-activated receptor 2 (PAR2) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 14 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Fig 11. Mouse groups co-inoculated with low dose of rIxsS17 have higher IgM titers compared to high dose injected mice. ELISA plates previously coated with B. burgdorferi crude antigen (200 ng/well) were tested with mouse sera that diluted at 1:250 (A) and 1:500 (B). IgM titers were determined using anti-mouse IgM monospecific antibody conjugated with HRP and absorbance were read at 450 nm. The data were presented as mean ± SEM; each dot is individual mouse. NC = negative control; Bb = B. burgdorferi. Statistical significance was evaluated using t test in GraphPad Prism 9 (:P value � 0.001, **: P value � 0.0001). (C). Western blot analysis of Bb lysate (2 μg) incubated with antisera from mice (diluted to 1:200) and anti-mouse IgM antibody-HRP conjugate (diluted to 1:5000). Images were taken at 18 seconds of exposure. Asterisks indicate extra and intense bands detected in mice challenged with Bb plus 0.06 and 0.125 μM rIxsS17. https://doi.org/10.1371/journal.ppat.1012032.g011 that initiates inflammation signaling [68]. Likewise, trypsin IV, also highly inhibited by rIxS17 is associated with signaling of cutaneous local inflammation and nociception by activation of PAR2 on cutaneous neurons [69]. Cathepsin G regulates the inflammatory responses by stim- ulating production and maturation of cytokines and chemokines and controls the functional state of immune cells [70]. It is interesting to note that like I. ricinus Iripin-8 and A. ameri- canum AAS19 [35,65], rIxsS17 inhibited plasmin. At a glance, IxsS17 inhibition of plasmin is counterintuitive because plasmin is known for degradation of fibrin clots and preventing platelet aggregation by cleaving PAR1 [68,71], which will benefit tick feeding. However, plas- min has additional functions on inflammation and wound healing by directly interacting with various cell types including leukocytes (monocytes, macrophages, and dendritic cells) and cells of the vasculature (endothelial cells, smooth muscle cells) as well as soluble factors of the immune system and components of the extracellular matrix [72,73]. In these interactions, plas- min contributes to inflammation and thus, its inhibition by IxsS17 will help tick feeding through prevention of inflammatory processes. While substrate hydrolysis reveals that rIxsS17 weakly inhibited human thrombin and did not inhibit bovine thrombin, our protein-to-pro- tein interaction data show that this protein interacted with thrombin (or blood clotting factor II) similar to its homologs; AAS19, Iripin-8, and Rm-15 [38,65,74]. Our protein-to-protein interaction data also revealed functional insights of rIxsS17. The finding that rIxsS17 interacted with both effector proteases and regulatory protease inhibitors of the innate immune system is intriguing because serpins are known for their role in inhibit- ing functions of effector serine and cysteine proteases [44]. Thus, it is surprising to note that rIxsS17 interacted with both effector proteases and serpin regulators of the blood clotting path- way, antithrombin III (AT), heparin cofactor II (HCII), kininogen-1 and protease C1 inhibitor (C1 inhibitor) of the complement system. Glycosaminoglycans (GAGs) such as heparin [75] serve as cofactors to enhance the activity of some mammalian serpins such as AT [76,77] and HCII [78–80]. Comparative secondary structure modeling predicted basic patches in IxsS17, which were confirmed as functional heparin binding sites using GAG plate binding and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 15 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent heparin column assays. As IxsS17 and host serpins (AT and HCII) bind heparin, it is poten- tially possible that the observed interaction between IxsS17 and host serpins was because of heparin serving as a bridge between IxsS17 and host serpins. Likewise, as C1 inhibitor is heavily glycosylated [81,82], we hypothesize that IxsS17 may interact with the C1 inhibitor by binding onto GAGs linked to this protein. It is important to also note that in silico analysis pre- dicted direct interactions between rIxsS17 and protease inhibitors with more than 95% chance. These observations warrant further investigations. The function of serpins is effected by mechanical disruption of the target protease that starts with the target protease being trapped at P1 residue in the RCL [44]. Our results demonstrated that positioning the histidine fusion tag at the amino-terminus end and not the C-terminus end reduced the mechanical efficiency of rIxsS17 which was restored by cleaving off the Histi- dine tag. The observed stoichiometry inhibition of rIxsS17 against trypsin, rat trypsin IV, and factor Xa were high and not close to the ideal 1:1 serpin-to-target protease ratio. These findings could be explained by evidence some of the serpins such as blood clotting regulatory serpins antithrombin III and heparin II that require binding of glycosaminoglycans (GAGs) to enhance their inhibitory potency [58]. Similarly, we have shown that heparin binding potenti- ated functions of AAS19, the IxsS17 homolog in A. americanum [74]. Likewise in this study, we determined that the putative basic patch in IxsS17 comparative tertiary structure was func- tional and bound GAGs including heparin, an approved therapeutic against blood clotting dis- orders [56,71]. Consistent with AAS19 [74], heparin binding by rIxsS17 significantly increased its anti-coagulation activity. The finding that heparin binding potentiated the function of rIxsS17 suggests that when tick injects this protein into the host, native IxsS17 binds GAGs to enhance its anticoagulant functions. Our protein-to-protein interaction findings showing that rIxsS17 interacted with comple- ment system proteases. rIxsS17 likely blocks complement activation by potentially interfering with complement component C2 (associated with classical and MBL pathway), C1s (classical pathway), and factors I (alternative pathway). The evidence led us to investigate the effect of this protein on complement activation. Complement system activation can be initiated via binding of specific antibodies (Classical pathway), mannose binding lectins (MBL pathway) or small-scale activation of complement component C3 (Alternative pathway) [59,60]. B. burg- dorferi can activate all 3 complement pathways and results in direct complement-mediated killing of the spirochete [83]. Moreover, Schuijt et al., [84] showed that the MBL pathway is of paramount importance in the eradication of B. burgdorferi. Here, we showed that rIxsS17 facil- itated B. burgdorferi survival and promoted localization via inhibition of MBL pathway. By inhibiting the deposition of MAC in MBL pathway, rIxsS17 rescued B. burgdorferi from com- plement-mediated killing in vitro. Prompted by evidence that IxsS17 is highly secreted by B. burgdorferi infected nymphs [25] within 24-48h of tick feeding, an open-window for transmission of LD agent, suggested that this protein is important to the transmission of B. burgdorferi. Kota´l et al., [65] reported that Iripin-8 significantly influenced nymphal I. ricinus feeding but did not promote B. afzelii transmission as revealed by RNAi silencing. Here, we took a different approach and assessed the effect of co-injecting C3H with rIxsS17 and B. burgdorferi and show that this protein pro- moted colonization of C3H mice. rIxsS17 co-inoculation increased B. burgdorferi loads in heart and ear tissue, but not distal tissues like joint and bladder of C3H/HeN mice at 21 days post inoculation. This finding is relatively in agreement with in vivo experiment of Coumo et al., [85] that MBL deficiency mice had higher antibody titers and harbored significantly higher B. burgdorferi in skin tissue than deeper tissue (heart, joint and bladder) at 14 days post inoculation. Interestingly, our results showed that rIxsS17 effects on B. burgdorferi localization of mouse tissue is inverse dose dependent as revealed by B. burgdorferi load and IgM titers PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 16 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent being higher in mice that were injected with low dose (0.06 and 0.125 μM) than high dose (0.25, 0.5, 1, 2, 5 and 10 μM) of rIxsS17. According to a study of Fikrig et al., [86], IgM to B. burgdorferi whole cell in infected mice peaks at day 14 post inoculation while IgG increases continuously even at day 180 after infection. Since IgG response to B. burgdorferi is much later during infection, it would explain why IgG titer in our experiment which we detected at day 21 (3 weeks) post inoculation did not show significant difference. Although actual amount of IxsS17 that the tick injects into the mammalian host during feeding is unknown, it is estimated to be in picomolar range. Thus, it makes logical sense that low concentrations of rIxsS17 supported B. burgdorferi colonization and vice versa for the high dose. We hypothesize that the effects of low dose rIxsS17 used in this study better resemble the dose of the native protein in tick saliva. It will also be interesting to further investigate if the low spirochete load in mice that received the high dose of rIxsS17 was due to direct toxicity of this protein to B. burgdorferi or that the high dose over stimulated the innate immune system leading to clearance of the spirochetes. LD control and prevention is challenging. The rise and spread of LD, and the fact that indi- viduals can get LD more than once when bitten by an infected tick requires the development of novel effective vaccine against this vector-borne disease [87]. The search for vaccine target antigens is shifting from the pathogen toward tick molecules, with the purpose of reducing tick density and B. burgdorferi infection among tick population and blocking the transmission of LD agents [17,87,88]. In line with this, our data show that IxsS17 is an important protein in both tick feeding and B. burgdorferi colonization of the host, and it represents a possible target antigen in vaccines to prevent transmission of tick-borne disease agents. The mechanism by which IxsS17 protected B. burgdorferi from host innate immune response warrants future investigations. Materials and methods Ethics statement The use of animals strictly followed the animal use protocol approved by Texas A&M Univer- sity Animal Care and Use Committee under the number IACUC 2020–0089. Phylogeny and comparative sequence analysis Amino acid sequence of IxsS17 was obtained from NCBI protein database (Accession # EEC18973.1) and blasted using the protein-protein BLAST (blastP) tool (non-redundant pro- tein sequences (nr) and transcriptome shotgun assembly proteins (tsa_nr) database), resulting in 12 IxsS17 homolog sequences from different tick species. A homo sapiens antithrombin (Accession # CAA48690.1) was used as the out group in phylogenetic analysis of the sequences using MEGA X software [89]. Pairwise sequence alignment analysis between IxsS17 and its homologs was used to determine protein identities. Based on the multiple sequence alignment analysis, the best protein model prediction and overall mean distance calculation, a phyloge- netic tree was generated using maximum likelihood statistical method, bootstrap 1000 repli- cates, Le_Gascuel_2008 model and Gamma distributed with invariant sites (G+I) [90]. In previous studies, we showed that IxsS17 sequence was likely not redundant because its func- tional domain RCL did not show amino acid identity of more than 55% to other I. scapularis serpins [34,35]. To confirm these analyses, we compared the amino acid sequence of IxsS17 RCL to other I. scapularis serpin RCLs that were annotated in the recently updated I. scapularis genome [43]. Briefly, the extracted RCL regions from previously published serpins [40] were used as a query database to search each of the 34,235 protein sequences from the current I. sca- pularis reference genome (ASM1692078v2) using DIAMOND blastp v2.0.15 [91]. Each of the PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 17 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent proteins with a positive hit to the RCL query was manually curated to confirm that it is a serpin with a complete RCL region. Finally, RCL regions from each of the confirmed I. scapularis ser- pins was compared to IxsS17 RCL using Needleman-Wunsch Global Alignment tool from NCBI. The protein signal peptides and subcellular localization of IxsS17 were predicted using the software SignalP 6.0 (https://services.healthtech.dtu.dk/service.php?SignalP-6.0) and deepLoc 2.0 (https://services.healthtech.dtu.dk/service.php?DeepLoc). Expression of recombinant (r) IxsS17 Expression of mature rIxsS17 (without signal peptide) was done in Pichia pastoris using the pPICZαA yeast expression plasmid as described [35, 92]. The coding domain for mature IxsS17 nucleic acid sequence was retrieved from GenBank (Accession # EEC18973.1) and opti- mized for expression in P. pastoris. Two rIxsS17 expression constructs with His-fusion tag placed at amino- or carboxyl-terminus were custom synthesized and cloned into pPICZαA (Biomatik, Wilmington, Delaware). To facilitate cleaving off the His-fusion-tag, the Tobacco etch virus (TEV) protease cutting site (ENLYFQG) was inserted between His-fusion tag and the IxsS17 coding sequence. The TEV protease was produced in house (see below). Routinely, rIxsS17 expression plasmid was transformed into P. pastoris X33 (Thermo Fisher Scientific, Hanover Park, IL, USA), cultured at 28˚C in a bio-shaker (MaxQ 400, Thermo Fisher Scientific), and expression of rIxsS17 induced by feeding cultures daily with 5% metha- nol as a carbon source. For large scale expression (1 L batches), cultures were incubated for 5 days. The pPICZαA yeast expression plasmid secretes the recombinant protein into culture media. Thus, spent media was collected and rIxsS17 was precipitated out by ammonium sul- fate saturation (525 g/L) at 4˚C overnight with stirring. Subsequently, precipitated rIxsS17 was dialyzed against His-tag affinity column binding buffer (100 mM Tris, 500 mM NaCl, 5mM imidazole, pH 7.4) and processed for affinity purification using the Hi-Trap Chelating HP col- umn (Cytiva, Marlborough, MA, USA) under native conditions. Affinity purification was then confirmed by standard sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), silver staining using the Pierce Silver Stain Kit staining kit (ThermoScientific, USA), and western blotting analysis using the mouse monoclonal anti-Histidine antibody (GenScript, Piscataway, NJ) to determine purity. The concentration of rIxsS17 was determined using a bicinchoninic acid (BCA) kit (ThermoScientific, USA). The protein was stored at -80˚C until use. Cleaving off the histidine tag from affinity purified rIxsS17 The TEV protease was produced in house using expression vector MBP-TEVcs (ENLYFQ/G)- His6-TEVΔ (220–242)-R5, a gift from Alice Ting (Addgene plasmid # 135456; http://n2t.net/ addgene:135456; RRID: Addgene_135456) and Escherichia coli BL21 (DE3) (ThermoScientific, USA). In brief, the expression vectors were transformed into the E. coli BL21 strain. The trans- formed E. coli cells were grown in SOB medium (RPI Research Product International, Mount Prospect, IL) at 37˚C until the OD600 reached 0.4–0.6. The TEV expression was then induced by cultivation with 1 mM isopropyl-β-d-thiogalactopyranoside (IPTG) at 30˚C for 5 h. The cells were collected following by sonication in binding buffer (100 mM Tris, 500 mM NaCl, 5mM Imidazole, 10% glycerol, pH 7.4) and centrifugation at 10,000 × g, 4˚C for 30 min. After that, TEV was purified from the supernatants using the Hi-Trap Chelating HP column (Cytiva, Marlborough, MA, USA) under native conditions as described previously. The purity of the protein was assessed by Coomassie blue staining following SDS-PAGE gel electrophoresis. The PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 18 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent concentration of TEV was determined using a NanoDrop 8000 spectrophotometer (Thermo- Scientific, USA). Finally, TEV was stored at -80˚C until use. To cleave off the His fusion tag, affinity purified rIxsS17 (3 μg) and TEV (1 μg) were mixed and incubated at 4˚C overnight in cleaving buffer (50 mM Tris-HCl, 0.5 mM EDTA, 1 mM DTT, pH 8.0). The rIxsS17 and TEV ratio was empirically determined in preliminary studies. Subsequently, the reaction mix was dialyzed into Hi-Trap chelating column binding buffer and then purified as described above. Fractions of non-tagged-rIxsS17 were expected to elute into flow through and column wash fractions while both His-tagged TEV and non-cleaved rIxsS17 bound onto the affinity column matrix and were recovered into elution fractions. SDS-PAGE with silver staining and western blotting analysis using the His-tag antibody were used to confirm purification of His-tag free rIxsS17. Quantification was done as described above. rIxsS17 deglycosylation. In a 50 μl reaction, 10 μg of rIxsS17 was deglycosylated using 2 μl of protein deglycosylation mix II (NEB, MA, USA) under denaturing condition following the instructions from the manufacturer. The reaction was incubated at 37˚C for 16 h. Deglyco- sylated rIxsS17 was analyzed by SDS-PAGE and silver staining. Profiling inhibitor functions against innate immune system proteases Inhibitory activity of rIxsS17 against 17 serine proteases related to host responses against tick feeding was determined using substrate hydrolysis assays as described [35,48,93]. The serine proteases and their substrates used in this study are listed in S2 Table. 1μM of rIxsS17 (His- tagged at N- or C-terminal or His-tag removed) was incubated with empirically verified serine protease amount at 37˚C for 15 min in reaction buffer (20 mM Tris–HCl, 150 mM NaCl, 0.1% BSA, pH 7.4). Subsequently, 200 nM of the appropriate peptide substrate was added to the final reaction volume of 100 μL and hydrolysis was monitored at 405 nm wavelength every 11s for 30 min at 30˚C using microplate reader (Biotek Synergy H1, Winooski, VT, USA). The assay was performed in duplicates of 3 biological replications. Data were subjected to one- phase decay analysis PRISM 9 to determine plateau values as proxies for initial velocity of sub- strate hydrolysis or residual enzyme activity. Stoichiometry of inhibition (SI). Preliminary substrate analysis revealed that molar excess of rIxsS17 inhibited pancreatic trypsin, trypsin IV, and blood clotting factor Xa by more than 70%–nearly 100%. To estimate the molar ratio of rIxsS17 to the target protease (pancre- atic trypsin, trypsin IV, and factor Xa) required for 100% inhibition of enzyme activity of the target protease, stoichiometry of inhibition (SI) analysis was done as described [35,48,74]. Var- ious molar ratios of His-tagged rIxsS17 to proteases (0, 2.5, 5, 10, 20, 25, 50) were incubated for 15 min at 37˚C with constant concentration of bovine trypsin (1.5 nM) or rat trypsin IV (2.0 nM) or factor Xa (13.9 nM). The colorimetric substrate was added; and residual protease activity was determined as described above. SI or the molar ratio of rIxsS17 to protease was determined by plotting the percentage residual protease activity against serpin to protease ratios, fitting data onto the linear regression in PRISM 9, and extrapolation to the ratio which resulted in total loss of protease activity [94]. Affinity constant (ka) calculation. The rate of rIxsS17 inhibiting bovine trypsin, trypsin IV and fXa was determined using the discontinuous method [93, 94]. Different concentrations of rIxsS17 (50, 100, 200, 400, 600 and 1000 nM) were incubated with constant amounts of bovine trypsin (14.6 nM), trypsin IV (12.5 nM) or fXa (13.9 nM) for different periods of time (0, 1, 2, 4, 6, 8, 10 and 15 min) at 37˚C. The colorimetric substrate was added; and residual pro- tease activity was assayed as described above. The pseudo-first order constant, kobs, was deter- mined from the slope of a semi-log plot of the residual protease activity against time. The PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 19 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent second-rate constant (ka) was determined by the best fit line slope of the kobs values that were plotted against rIxsS17 concentration [94]. Homology modeling and prediction of basic patches and docking. Secondary structure modeling of IxsS17 was done on ChimeraX molecular modeling server [95]. The mature IxsS17 protein amino acid sequence was pasted into Alphafold comparative modeling software on ChimeraX server, and the best IxsS17 comparative secondary structure model was reported. Putative basic patches were predicted using the electrostatic potential calculator in ChimeraX. Molecular docking study using the ligand molecule heparin (PubChem ID 22833565) with IxsS17 protein was conducted using Autodock Vina and Auto Dock Tools (ADT) v 1.5.4 from the Scripps Research Institute [96, 97]. The ligand was oriented suppositionally to allow flexi- ble rotation and thus explore the most probable binding positions, while the receptor was kept rigid. The grid maps were calculated by Autogrid which represents the center of active site pocket for the ligand. The generated results were visualized by using PyMOL viewer (https:// pymol.org/2/). Glycosaminoglycan (GAG) binding and effect on rIxsS17 function. Secondary structure modeling predicted at least one basic patch in IxsS17 comparative modeling structure. To test if the rIxsS17 basic patch is functional, rIxsS17 binding affinity of glycosaminoglycans (GAGs): heparin (Sigma-Aldrich, MO, USA), chondroitin sulphate A (Sigma-Aldrich), heparan sul- phate (Galen Laboratory Supplies, North Haven, CT) and dermatan sulphate (Galen Labora- tory Supplies) was done as previously described [48]. A GAG-binding microplate (Galen Laboratory Supplies) was coated with 200 μL of GAG at the concentration of 25 μg/mL in binding buffer (100 mM NaCl, 50 mM Na-acetate, 0.2% Tween, pH 7.2) and incubated over- night at room temperature. After washing with binding buffer, the plate was blocked with 250 μL of 1% bovine serum albumin in PBS for 1 h at 37˚C. Thereafter, different concentra- tions of rIxsS17 (0, 1, 2, 5, 10 and 20 μg/mL) in 200μL of blocking buffer was added and incu- bated for 2 h at 37˚C. After the wash, 200 μL of HRP-conjugated anti-histidine antibody (1:5,000 dilution) was added. Following by addition of 200 μL of 1-step Ultra TMB ELISA sub- strate (Thermo Scientific), 100 μL of hydrochloric acid (1N) was used to stop the reaction; and the OD450 nm was determined using a microplate reader (Biotek Synergy H1). Heparin binding assay. Approximately 300 μg of rIxsS17 or rAAS19 (positive control) or r1E1 (negative control) was applied to the Hi-Trap heparin column (Cytiva). After washing with 10 mM phosphate buffer pH 7.4, the proteins were eluted using a gradient concentration (0.25–0.5–1.0-.2.0–3.0 M) of NaCl. Samples included the protein before binding, flow- through, wash, and elution were collected, resolved on 10% gel of SDS-PAGE, and subjected to silver staining for analysis. Recalcification time assay. Prompted by preliminary findings that rIxsS17 was an inhibi- tor of blood clotting factors and it bound GAGs including heparin, we assayed the effect of heparin and rIxsS17 mixture on plasma clotting in a recalcification time assay, which evaluates the blood clotting system holistically [98]. Five groups: (1) Buffer control (20 mM Tris-HCl, 150 mM NaCl pH 7.4), (2) 17 kDa heparin sodium salt (0.5 μg/mL) (Sigma-Aldrich, USA), (3) rIxsS17 (2 μM: empirically determined to delay plasma clotting by more than 60 seconds), (4) rIxsS17 and heparin mixture incubated separately, and (5) rIxsS17 and heparin incubated together were incubated in 40 μL of 20 mM Tris-HCl, 150 mM NaCl, pH 7.4 buffer for 5 min at 37˚C. Subsequently, 50 μL of pre-warmed (37˚C) universal coagulation reference human plasma (UCRP) (ThermoScientific, USA) was added to each group and incubated at 37˚C for an additional 5 min. After adding 10 μL CaCl2 (final concentration of 150 mM) to trigger plasma clotting, optical density (A) was monitored at 650 nm wavelength every 20s for 20 min using the microplate reader (Biotek Synergy H1). Data from the recalcification time assay were plotted onto sigmoid line in PRISM 9. Initiation of plasma clotting (or clotting time) was PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 20 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent interpolated from the sigmoid line when A650nm increased by 10%, with 95% confident interval as published [74]. Differential Precipitation of Proteins (DiffPOP) and in silico protein to protein interac- tions. Differential precipitation of protein-to-protein interaction between rIxsS17 and human plasma was done as described [99]. In a 1.5 mL vial, a reaction mix of 150 μL reaction containing 25 μg rIxsS17 and 20 μL of human plasma in reaction buffer (20 mM Tris–HCl, 150 mM NaCl, pH 7.4) were incubated at 37˚C overnight. Human plasma only and rIxsS17 only were also incubated in reaction buffer as negative controls. After the incubation, 100 μL of Phosphoprotein Kit- Buffer A (Clontech Laboratories, New York, NY) was added to stabilize the reaction prior to fractionation. To fractionate, precipitation solution (90% methanol/ 1% acetic acid) was added to the stabilized reaction mix, vortexed and incubated at room tempera- ture for 5 min. Precipitates were collected by centrifugation at 14,000 rpm (or max speed) at 4˚C. The supernatant was transferred into a new 1.5 mL tube and process repeated until desired fractions are obtained (10 fractions in total). The pellet was washed in 400 μL of ice- cold acetone, air-dried, re-suspended in 100 μL reaction buffer and stored in -80˚C until use. The expectation for this approach is that rIxsS17 will co-precipitate with its interactors. To determine fractions that co-precipitated with rIxsS17, each fraction was resolved on 10% SDS-PAGE gels and subjected to standard western blotting analysis using the monospecific antibody to rIxsS17. The monospecific antibody to rIxsS17 was purified from immune serum of rabbits that were repeatedly fed on by I. scapularis as previously described [24,48]. The posi- tive signal was developed using chemiluminescent substrates (ThermoScientific, USA). Fractions that contain potential complexes with rIxsS17 were processed for LC-MS/MS analysis using the method published previously [25]. To identify proteins, extracted tandem mass spectra was searched against the database of non-redundant human proteins from Gen- Bank using the Prolucid program in the Integrated Proteomics Pipeline (IP2) as published [100]. The parameters used to identify potentially true rIxsS17 interactors included detecting at least two peptides in two of three independent LC-MS/MS runs and normalized spectral abundance factors (index for relative protein abundance in exceeded plasma only control val- ues). Subsequently, these interactions were validated using in silico methods on the PSOPIA (prediction server of protein-to-protein interactions; https://mizuguchilab.org/PSOPIA/) server [101] and readouts with more than 75% likelihood to interact were considered as true. Complement activity assay. Following up on protein-to-protein interaction results that rIxsS17 also interacted with complement system factors, we investigated its effect on comple- ment pathway activations using the WIESLAB Complement Classical, Alternative and Man- nose-binding Lectin (MBL) pathway Kits (Malmo¨, Sweden). The kits allowed for independent assessment of rIxsS17 on the three complement activation pathways as measured by C5b-C9 or Membrane Attack Complex (MAC) deposition. For initial screening of rIxsS17 possible effect on the complement pathways, we started with high dose of rIxsS17, at 4 μM (20 μg) in 100 μL reactions. Subsequently, inhibition activity assessment of a serially dilute rIxsS17 (0.0078–4 μM) was done. First, rIxsS17 was pre-incubated with positive control (human serum provided with the kit) at 37˚C for 30 min. Then, the samples were added to the wells (provided in the kits) along with a blank (diluent only), negative control and positive control, and incu- bated at 37˚C for 60 min. The assay was performed in duplicates. After the washing step, con- jugate and substrate were subsequently added following the instructions of the manufacturer. Finally, absorbance was read at 405 nm on a microplate reader (Biotek Synergy H1, Winooski, VT, USA). The effect of rIxsS17 on MAC deposition was calculated as follow: (Sample-NC)/ (PC-NC) x100 where NC is negative control and PC is positive control. Borrelia burgdorferi complement sensitivity assay. Prompted by preliminary findings that rIxsS17 dose dependently reduced deposition of the MAC, we assessed its effect on PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 21 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent rescuing complement sensitive spirochete as previously described [61,62,102]. The comple- ment sensitive B. burgdorferi (B314/pBBE22luc) and complement resistant (B314/pCD100) strains were kindly gifted by the Skare lab (TAMU Health Science Center). Both strains were propagated in BSK-II media at 32˚C, 1% CO2. For the assay, 15 μL of normal human serum (NHS) (Complement technology, TX, USA) was pre-incubated with serial dilutions of either C-terminal-His/non-tagged rIxsS17 (0.25, 0.5, 0.75, and 1μM), heat-inactivated rIxsS17 (1μM) or protein buffer (PBS; Phosphate buffered saline) at 37˚C for 30 min prior to addition of 85 μl of B. burgdorferi B314/pBBE22luc at the concentration of 106 cells/mL and inoculated in a bio- shaker at 32˚C, 100 rpm. NHS with B. burgdorferi B314/pBBE22luc or B314/pPCD100 were included as negative and positive controls. Survival of spirochetes was assessed at 1.5, 2, 2.5 and 3 h post incubation. Spirochetes were counted from randomly chosen fields (10–15 fields) under dark-field microscope. Spirochete viability was judged based on cell mobility, mem- brane integrity, and cell lysis as described [102]. Spirochete survival rates were calculated from 3 biological replicates. Heat-inactivated NHS (hiNHS) was used as the no complement-activity control and for normalization. Effect of IxsS17 on B. burgdorferi colonization of C3H/HeN Lyme disease mouse model. Routinely, B. burgdorferi strain B31 (MSK5; kindly gifted by the Skare lab) were cul- tured in BSK-II medium and virulence plasmid Ip25 and Ip28-1 were verified using PCR primers (S3 Table) as described [103]. Groups of C3H/HeN mice (Charles River Laboratories, Wilmington, MA) (4 mice/group) were intradermally inoculated with 104 B. burgdorferi spiro- chetes or 104 B. burgdorferi spirochetes with various amounts of rIxsS17 (0.06, 0.125, 0.25, 0.5, 1, 2, 5 and 10 μM). Another group of 4 mice were inoculated with BSK-II + PBS as the negative control. At 21 days post inoculation, blood and tissue samples were collected from all mice. Serum was extracted from blood; and genomic DNA was extracted from tissues using DNeasy Blood and Tissue kit (Qiagen, MD, USA). B. burgdorferi infection was assessed by ELISA, western blot, in-vitro cultivation, PCR, and real-time qPCR methods. ELISA. ELISA was used to determine IgM and IgG antibody titer to B. burgdorferi in mouse sera. Ninety-six-well-microplates (Nunc MaxiSorp, ThermoScientific) were coated with 200 ng/well of B. burgdorferi lysate antigen, blocked with 5% skim milk at 4˚C overnight and incubated with serially diluted mouse sera (at 1:250-500-1,000–2,000) at room tempera- ture for 2 h. Signal was detected using either goat-anti mouse IgM antibody-HRP conjugated (ThermoScientific) or Clean-Blot IP detection reagent (ThermoScientific) at a 1:5,000 dilution, following by addition of the TMB substrate (3,3’,5,5’-tetramethylbenzidine) (ThermoScienti- fic). The reaction was stopped using 2N sulfuric acid and absorbance was read at 450 nm using the microplate reader (Biotek Synergy H1). Western blotting. Two μg of Bb lysate was resolved by SDS-PAGE and transferred to PDVF membrane. The membrane was blocked in 5% skim milk and then incubated with mouse antisera (diluted to 1:200) overnight at 4˚C. The anti-mouse IgM-HRP conjugates (ThermoScientific) at 5,000-fold dilution was used to detect primary antibodies. Finally, signal was detected using SuperSignal West Femto Maximum Sensitivity Substrate (ThermoScienti- fic) under Biorad ChemiDoc MP Imaging system. In-vitro cultivation of B. burgdorferi. In-vitro cultivation of B. burgdorferi was used to confirm colonization in mice organs. Within 1 h of completing necropsy, mice organs were submerged in 3–5 mL of BSK-II with appropriate antibiotics and antifungals and incubated at 32˚C, 1% CO2. The culture was examined bi-weekly for the presence of B. burgdorferi under dark-field microscope. Real-time quantitative PCR. Real-time qPCR was used to quantify B. burgdorferi (Bb) in mouse organs targeting Bb flab; and Murine β-Actin was used as an internal control and for normalization (Primer sequences are listed in S3 Table) [103,104]. The qPCR assays were PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 22 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent performed in 10 μL reactions with 5 μL iTaq Universal SYBR Green Supermix (Bio-rad, Her- cules, CA), 300 nM each primer and 10–50 ng mouse organ gDNA on a Bio-rad CFX96 real time system (Bio-rad). Data was analyzed by the comparative (Ct) method with the equation: Fold change = 2-ΔΔCt = [(Ct Flab—Ct β-Actin) Bb-rIxsS17 co-injected group—(Ct Flab—Ct β- Actin) Bb only group] [105]. The spirochete burden in mouse organs was expressed as the fold change of B. burgdorferi load in mice that were co-injected with rIxsS17 compared with mice injected with B. burgdorferi only. Statistical analysis Data was analyzed using GraphPad Prism 9 software and represented as mean ± SEM with sta- tistical significance (P < 0.05) detected using the Student’s t-test and two-tailed ANOVA. Supporting information S1 Fig. Multiple sequence analysis of IxsS17 and its homologs. Amino acid sequences of IxsS17 and its homologs as well as antithrombin III were aligned in MacVector using T-Coffee specifications. The broken line red box denotes the functional domain reactive center loop. Please note that accession numbers are indicated. (TIF) S2 Fig. Prediction of signal peptides and subcellular localization for EEC18973.1. Subcellu- lar localization software DeepLoc-2.0 predicted extracellular location for EEC18973.1 (A). Sig- nalP 6.0 software predicted the signal peptide for EEC18973.1 after first 53 amino acid were removed (B). The predicted signal peptides were marked with broken green line. (TIF) S3 Fig. rIxsS17 under native and deglycosylated forms. (A) Native C-terminal-his and non- tagged-IxsS17 were resolved in clear native PAGE following by silver staining analysis. (B) Sil- ver staining image of C-terminal-his and non-tagged-IxsS17 before and after treatment with deglycosylation enzyme under denaturing condition. (TIF) S4 Fig. Quantitative real-time PCR analysis of B. burgdorferi load in mice co-inoculated with 1–10 μM of rIxsS17. Four mice/group were inoculated with B. burgdorferi only (104 spi- rochetes) with or without different amounts of rIxsS17 (1-2-5-10 μM). At 21 days post inocula- tion, B. burgdorferi burden in mouse heat, ear, joint and bladder tissues was quantified by real- time qPCR method. The data were presented as fold change of the rIxsS17 treated groups in comparison with Bb group (2 -ΔΔCt = [(Ct Flab—Ct β-Actin) Bb-rIxsS17 co-injected group— (Ct Flab—Ct β-Actin) Bb only group]). Red arrows indicate decrease on B. burgdorferi load. Bb: B. burgdorferi, Bb + rIxsS17: B. burgdorferi co-inoculated with rIxsS17 groups. (TIF) S5 Fig. IgG titers against B. burgdorferi in mice co-inoculated with 1–10 μM of rIxsS17. IgG titers against B. burgdorferi lysate antigen were detected using ELISA. Mouse sera was tested at 1:500 dilution. The data were presented as mean ± SEM; each dot is individual mouse. NC = negative control; Bb = B. burgdorferi group. Bb = B. burgdorferi, Bb + rIxsS17 = B. burgdorferi co-inoculated with rIxsS17. Statistical significance was evaluated using Student’s t-test in GraphPad Prism 9 (:P value � 0.05, ns: no significance). Red arrows indicate decrease on IgG titers. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 23 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent S6 Fig. IgG titers against B. burgdorferi in mice co-inoculated with 0.06–0.50 μM of rIxsS17. IgG titers against B. burgdorferi lysate antigen were detected using ELISA. Mouse sera was tested at 1:250 and 500 dilutions. The data were presented as mean ± SEM; each dot is individual mouse. NC = negative control; Bb = B. burgdorferi group. Bb = B. burgdorferi, Bb + rIxsS17 = B. burgdorferi co-inoculated with rIxsS17. (TIF) S1 Table. Amino acid residue identity between IxsS17 RCL and other I. scapularis serpins with coverage above 80%. (DOCX) S2 Table. List of proteases and substrates used in the substrate hydrolysis assay. (DOCX) S3 Table. List of oligonucleotide primers used in the study. (DOCX) S4 Table. Percentage complement activity of the human serum treated with rIxsS17. (DOCX) S5 Table. B. burgdorferi positive rate of mouse tissues by in-vitro cultivation. (DOCX) S6 Table. B. burgdorferi positive rate of mouse tissues by PCR. (DOCX) S7 Table. Survival rates B. burgdorferi incubated with different concentrations of rIxsS17 in-vitro. (DOCX) S1 File. LC-MS/MS analysis of 10 fractions resulted from differential precipitation of pro- tein assay of IxsS17 with human plasma. (XLSX) Acknowledgments The author would like to thank Drs. Jon T Skare and Alexandra D Powell-Pierce for providing the B. burgdorferi strains and sharing their complement sensitivity assay protocols. We are grateful to Texas A&M core facility on Molecular genomics workspace and Dr. Andrew Hill- house for their technical assistance with real-time qPCR experiment. Author Contributions Conceptualization: Thu-Thuy Nguyen, Tae Kwon Kim, Lucas Tirloni, Zeljko Radulovic, Albert Mulenga. Data curation: Thu-Thuy Nguyen. Formal analysis: Alex Samuel Kiarie Gaithuma, Moiz Ashraf Ansari, Tae Kwon Kim, Lucas Tirloni, Zeljko Radulovic. Funding acquisition: James J. Moresco, John R. Yates, III, Albert Mulenga. Investigation: Thu-Thuy Nguyen, Tae Heung Kim, Emily Bencosme-Cuevas, Jacquie Berry, Tae Kwon Kim, Lucas Tirloni, Zeljko Radulovic. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012032 February 23, 2024 24 / 31 PLOS PATHOGENSTick saliva serpin inhibits innate immune responses and enhances colonization of Lyme disease agent Methodology: Thu-Thuy Nguyen, Tae Kwon Kim, Lucas Tirloni, Zeljko Radulovic, Albert Mulenga. Supervision: Albert Mulenga. Validation: Thu-Thuy Nguyen, Tae Kwon Kim, Lucas Tirloni, Zeljko Radulovic, James J. Moresco, Albert Mulenga. Visualization: Thu-Thuy Nguyen, Moiz Ashraf Ansari. Writing – original draft: Thu-Thuy Nguyen, Albert Mulenga. 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10.1371_journal.pstr.0000081	RESEARCH ARTICLE Laying foundations for transformation: Insights from local government engagement on climate-resilient rural water services in Nepal Jeremy KohlitzID 1 Heman Paneru2, Min Prasad Basnet2, Sunetra Lala2, Gabrielle Halcrow2, Naomi CarrardID 1, Melita Grant1, Ratan Bahadur Budhathoki2, Shova Chhetri2, 1 Institute for Sustainable Futures, University of Technology Sydney, Sydney, New South Wales, Australia, 2 SNV Netherlands Development Organisation, Kathmandu, Nepal Jeremy.Kohlitz@uts.edu.au Abstract Transformative change in how local governments support rural water services is required to accommodate the increasingly extreme effects of climate change on water service systems. This study explores the potential for contextualised soft systems thinking activities to pre- pare local government officials with responsibilities pertaining to rural water services in Nepal to shift towards more transformative thinking. First, the study presents the findings of focus group discussions in two rural districts of Nepal that identified common climate-related problems for rural water access including water shortages, contamination, and unequal bur- den of impacts. Second, we facilitated workshops with local government and non-govern- ment stakeholders, drawing on the focus group findings to frame the challenges for rural water linked to climate change that require local government response. We designed the workshops drawing on ‘transformative spaces’ concepts and included soft systems thinking activities to foster systemic perspectives. Participants learned about worldviews, leverage points, rich pictures, root cause analysis, and theory-of-change based action planning. Fol- lowing the workshops, the study team participated in reflective sensemaking in which they deliberated on their experiences and notes from facilitating the workshops to assess the extent to which the participants demonstrated transformative thinking about rural water sys- tems. The workshop approach showed promise in shifting how local government partici- pants think about rural water services beyond technical fixes towards addressing deep- seated issues. However, further work is required to foster new relationships necessary to support transformation and grapple with ethical dilemmas pertaining to power dynamics at community and government levels. Nevertheless, the approach presented here is a replica- ble, low-cost way to prepare local government stakeholders in Nepal for transforming their thinking and systems to ways that enable sustainable rural water service delivery under threats of climate change. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kohlitz J, Grant M, Budhathoki RB, Chhetri S, Paneru H, Basnet MP, et al. (2024) Laying foundations for transformation: Insights from local government engagement on climate- resilient rural water services in Nepal. PLOS Sustain Transform 3(3): e0000081. https://doi.org/ 10.1371/journal.pstr.0000081 Editor: Musa Manga, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, UNITED STATES Received: September 8, 2023 Accepted: February 13, 2024 Published: March 12, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pstr.0000081 Copyright: © 2024 Kohlitz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The methods used to implement workshop activities are described in this PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 1 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services paper. Qualitative data from focus group discussions and notes from workshops are not publicly available due to institutional restrictions on the sharing data that includes potentially identifying information. The Institute for Sustainable Futures Ethics Committee may be contacted with inquiries about the data at ISF-Ethics@uts.edu.au. Funding: This work was funded by SNV Netherlands Development Organisation through a paid contract to JK, MG, and NC. Staff from the funder (RBB, SC, HP, MPB, SL, and GH) contributed to the study design, data collection and analysis, decision to publish, and preparation of the manuscript. Competing interests: RBB, SC, HP, MPB, SL, and GH are staff of the funding organisation who worked closely with JK, MG, and NC in the study design, data collection and analysis, decision to publish, and preparation of the manuscript. Author summary Addressing climate change impacts on rural water services in Nepal requires local govern- ments in Nepal to drastically re-think water services. Strategies and tools that support actors to transform how they think about complex situations exist, but actors have to be ready to engage in transformation processes. The strategies and tools must also be contex- tualised. This study presents a process for facilitating local government staff in Nepal to take steps towards transforming how they consider problems and solutions for climate impacts on rural water services. We first conducted focus group discussions (FGDs) with community members, gaining insight into current climate impacts on water services across two local government areas. We then facilitated workshops with local governments, presenting FGD findings and using well-established systems thinking and transformations tools to prompt and inspire thinking about problems and solutions in new ways. The approach shows promise in supporting governments to engage in transformation pro- cesses, but more work is needed to build new relationships and create space for diverse participants to drive ethical transformations. 1 Introduction Climate change is significantly disrupting hydrological patterns in Nepal. Historical climate and disaster trends show that the average temperature in Nepal has risen 1.0˚C– 1.3˚C since 1900, and the intensity and frequency of extreme rainfall events, droughts, and incidences of glacial lake outburst floods, have increased significantly in different areas of the country since 1960 [1,2]. Projections indicate that Nepal will experience an increase in extreme rainfall events and a reduction of rainfall in dry winter seasons over the course of the 21st century [1,2]. Although there is significant uncertainty surrounding the projections of long-term pre- cipitation trends [2], it is imperative to consider the potential impacts of these changes on households and water resources. Dramatic changes in rainfall will worsen risks for household access to safe drinking water in rural areas of Nepal. Changes in hydrometeorological patterns and land uses may already be contributing to the drying of spring sources commonly used by communities in hilly areas for drinking water [3,4]. Over the next two decades, the frequency of river flooding is expected to increase throughout South Asia due to an increase in heavy rainfall events [5,6], which can contribute to contamination of shallow groundwater sources and/or cause people to use alter- native unsafe water sources when their primary water becomes damaged by extreme weather [7,8]. Other climatic impact drivers, such as landslides, windstorms, hailstorms, fires, and gla- cial lake outburst floods, also have potential to disrupt drinking water services in Nepal [9]. The compound challenge of ensuring safe water services for households in the context of climate change requires institutional responses at multiple levels, especially at the local/district scale. Local governments are the primary duty-bearers responsible for progressive realisation of the human right to water for their constituents [10]. In low- and middle-income countries, responsibility for delivering water services is often decentralised to local governments that are financially under-resourced to carry out their mandate [11]. Yet, long-term local government support and oversight are critical to the success of water supplies that are commonly managed by communities in rural areas of low- and middle-income countries [12]. Hence, strategies that strengthen local governments’ capacity to carry out their mandates to oversee water ser- vice delivery in settings where they are poorly resourced are required. For Nepal, these strate- gies must improve local governments’ capacity to understand and respond to climate-related PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 2 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services risks because, while the coverage of households with access to a basic water service is high [13], water scarcity and contamination due to variable rainfall threaten the quality and reliability of these services [14], which undermines the achievement of the safely managed water target of Sustainable Development Goal 6. To address the climate-related water scarcity and contamination risks in Nepal, researchers suggest technological, environmental and social interventions, that are challenging for local governments to implement. Suggestions include promotion of rainwater harvesting and re-use of household greywater [15], water source protection and siting of latrines a safe distance from water abstraction points [16], and watershed-scale spring rejuvenation [3]. As other research- ers point out, women and marginalised groups are likely to be disproportionately affected by the impacts of climate change on water, and call for raised awareness and action to address social exclusion and marginalisation [17,18]. However, capacity at the rural municipality and provincial levels to deliver effective climate resilience responses is constrained, due in part to their poor financial position and recent governance reforms that are still overcoming confu- sion and uncertainty about the new roles of officials at different levels of government [16,19]. Further, national policies and laws relating to climate change have scantly addressed rural water services [9], hence there is little coherent guidance for local governments to follow. To effectively implement actions to reduce climate-related risks to rural water services, Nepal must address the deep-seated institutional and resourcing deficits of its local government. The development and testing of novel approaches that strengthen local government efforts to advance climate-resilient rural water services can help overcome barriers to implementing adaptations in Nepal. Critically, water user and community experiences and needs must inform such approaches to ensure adaptations are appropriate and inclusive [20,21]. In partic- ular, methods are needed that draw together community and scientific knowledge, grapple with the complexity of the systems in question, and inspire and shape actions in line with the profound governance shifts required to achieve climate resilience and broader sustainability. One emerging approach is the creation of ‘transformative spaces’ that generate individual and collective learning towards sustainability transformations. These spaces are defined as “‘safe-enough’ collaborative environments where actors invested in transformation can experi- ment with new mental models, ideas, and practices that can help shift social-ecological systems onto alternative pathways” [22]. Transformative space-making sits at the interface of research and action, drawing from sustainability transformations scholarship and focusing on the ‘how to’ of achieving transformations in diverse Global South contexts [22]. A critical characteristic of transformative spaces is that they are conceived as ongoing engagement processes rather than discrete participatory events [22], as such, taking diverse shapes depending on context, intentions, and participants. Transformative space-making as an approach to driving positive change has been highlighted as a potentially powerful approach in the Global South, with opportunities for researchers to explore how this can catalyse change [23]. Yet, to avoid the risk of failure, it is important to avoid initiating change processes too early, which have a higher risk of failure, and take the time to develop commitment among relevant actors to transform systems they are part of [23]. This study contributes insights from a pilot process that demonstrates how local govern- ment stakeholders in Nepal can be ‘warmed up’ to the idea of transformative spaces that can lead to more sustainable rural water services under climate change. We describe how our focus group discussions (FGDs) in rural communities in Nepal characterise how community mem- bers experience the impacts of climate hazards on their water rural water services, and how we designed a workshop for local government and non-government representatives to raise the awareness of the participants about transformative change processes, drawing on theories and tools from transformative spaces and systems thinking. We then share our findings on how PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 3 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services the workshop approach influenced the participants’ perspectives on development and explore further ways to fully mobilise transformative spaces in rural Nepal to enable transformative change. 2 Methods The insights presented here draw from a qualitative, collaborative research process undertaken by a research institution (the Institute for Sustainable Futures, University of Technology Syd- ney) and a non-government organisation (SNV Nepal) supporting government achievement of area-wide water services in two rural districts in Nepal. The SNV Nepal project, entitled Beyond the Finish Line, aimed to use the opportunity of decentralisation in Nepal to develop inclusive, sustainable and resilient rural water supply services and hygiene behaviour change communication for households, schools and health facilities in two districts as a role model for inclusive water, sanitation and hygiene (WASH) services. Building on its experience in strengthening gravity-fed rural water supply services, SNV engaged with the new local bodies to address gender and social inclusion within WASH governance; equality, sustainability, and resilience in existing water supply services; and improved hygiene for women, men, boys, girls, and people with disabilities. There were three aims of the research process. First, to facilitate participants (researchers, civil society and local government) to explore water service and climate-related issues from a systems perspective, grappling with complexity while charting action towards resilient, safely managed services. Second, to engage the participants in the transformative reflection and learning with the aim of generating the deeper shifts in thinking and action required to drive system change. And third, to develop a practical and replicable process for government and non-government actors to jointly accomplish each of the above aims that could enable trans- formative change processes on a wider scale. Two methods were used to engage two participant groups: 1. A series of FGDs with community members from two rural municipalities to learn from their experiences of climate-related impacts on water services. 2. Systems-focused workshops with local government and non-government actors in two dis- tricts, designed for learning, deliberation, and inspiring action towards climate-resilient water services. Each of these methods is presented in turn, including the rationale for their application, how they were implemented, and sensemaking processes involving research and practitioner members of the study team. Fig 1 summarises the data collection and analysis steps. The research design was reviewed and approved in line with the University of Technology Sydney Human Research Ethics Committee requirements (UTS HREC REF NO. ETH18-2599). 2.1 Community focus group discussions FGDs were used to gather in-depth insights from community and water committee members about climate impacts on rural water access and responses. A body of research shows that, in addition to outside expert identification of climate risks (such as those listed in the introduc- tion section of this paper), community-based perspectives on climate risks and impacts are critical to consider because of the unique ability of community members to 1) communicate the nuance of their lived experiences and 2) frame issues in social, environmental and physical terms that are important to them, both of which influence subsequent identification of appro- priate solutions [24,25]. FGDs were chosen as a method to elicit multiple perspectives on a PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 4 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services Fig 1. Flowchart of data collection and analysis steps. https://doi.org/10.1371/journal.pstr.0000081.g001 shared experience (e.g. a flooding event) and to enable the community members to hear stories and learn from their peers. The FGDs were conducted in April 2021 within the two target districts of SNV Nepal’s Beyond the Finish Line project: Mahabu Rural Municipality in Dailekh district (Karnali Prov- ince) and Ramnagar Rural Municipality in Sarlahi district (Madhesh Province). Mahabu and Ramnagar were purposively chosen as study sites because they are especially prone to climate hazards. Ramnagar is located in a flood-prone area situated between two rivers and was affected by major floods in 2017 and 2019. Mahabu is situated near the base of a hilly area and is exposed to flash and river floods. The FGDs were conducted with separate groups of women, men, poor and marginalised people, people with disabilities, and water user committees. We held FGDs with women sepa- rate from men to learn about potential gendered differences in managing and accessing water under climate stress. Poor and marginalised people in Nepal often receive lower-quality water services and face discrimination in accessing waterpoints [26], so to represent poor and mar- ginalised groups, we conducted FGDs that included householders from the poorest families in the municipality and people belonging to the lowest (Dalit) caste to further understand these inequalities. We further sought to better understand inequalities in water access for people with disabilities who also face discrimination in water access [27]. FGDs were also conducted with water user committees to learn about the operation and maintenance of water supplies. Nine FGDs were held in Dailekh and eight were held in Sarlahi with seven to ten partici- pants in each FGD. The FGDs generally lasted about one hour each. The FGD facilitators (SNV Nepal staff) were native Nepali speakers who used prepared FGD guides to ask questions pertaining to: • types of climate hazards experienced locally and perceptions of how they were changing • impacts of climate hazards on water supply functionality, access, and use • impacts of climate hazards on well-being and health • proactive preparation for expected climate impacts and reactive coping responses • norms around household and community decision-making relating to water during extreme weather • community cohesion and conflict about water management during extreme weather. A COVID-19 safety protocol was followed to protect the health of FGD participants and facilitators. Facilitators travelled to the communities in Dailekh and Sarlahi when the national PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 5 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services government permitted travel to those areas. Facilitators also gained permission of Rural Municipality government officials to hold the FGDs. The FGD participants were provided with face masks and hand sanitizers. The FGDs were held outdoors in Sarlahi and indoors with social distancing and good ventilation in Dailekh. The FGD participants were informed of the purpose of the study, how their information would be used, and that the questions could potentially elicit painful memories. The participants were instructed to discontinue their par- ticipation if they felt the discussion was too upsetting for them. The FGD data were analysed through thematic coding of the facilitators’ notes. A dedicated note-taker attended each FGD and took detailed notes and quotes in Nepali. The note-takers later translated their notes into English. Researchers qualitatively analysed the notes in NVivo using a deductive coding method, with codes drawn from the analytical framework on responding to climate impacts on community-managed water systems developed by [28]. Excerpts from the notes were coded into the following parent codes: physical climate impacts on water supply functionality, physical climate impacts not directly related to water, climate impacts on access or use of water supplies, climate impacts on health and well-being, reactive coping responses to climate impacts, proactive adaptations, agency to respond climate impacts, access to resources for responding to climate impacts, and participation in household and community decision-making. Among the coded data, researchers looked for themes pertain- ing to how inequalities in access to resources, agency (e.g. holding knowledge and ability to influence decision-making), and social norms affected how people experienced climate impacts in line with the empowerment framework developed by [29]. These themes were explicated through writing findings into narratives that were reviewed and validated by SNV Nepal staff who conducted the FGDs and had experience working with the communities. Excerpts of the FGD notes and narratives are presented and discussed in this study. 2.2 Systems-focused workshops The objective of the workshops was to create a space for participants to consider what changes could be made within local government to address climate change impacts on rural water ser- vices, with a focus on government systems and attitudes and values underpinning decisions and systems. Workshops–settings in which people are brought together to learn, problem- solve, and innovate with respect to a central topic–have a long history of serving a dual purpose of filling a need of the participants (e.g. providing knowledge or training on a subject of inter- est) and producing evidence or data to fill a knowledge gap [30]. The workshop design in this study was informed by transformative space-making concepts, albeit on a pilot scale, with the intent for researchers and civil society partners to learn together about the effectiveness of sys- tems thinking tools in stimulating transformative thinking in the context of local governments in Nepal. The workshops further supported participants to: (a) learn about possibilities for cre- ating deeper change in attitudes and governance using systems thinking tools, (b) consider how climate change affects water supply and access in their district, and (c) co-develop a vision and plan for changing government systems to strengthen water services in a changing climate. Section 2.2.1 explains the concepts and theories behind how the workshops were designed. Section 2.2.2 then describes how facilitators conducted the workshop activities. 2.2.1 Methodological foundations. Our process of engaging with local government offi- cials drew on theories from transformative spaces and systems thinking. Transformative spaces provide an overall approach to engaging stakeholders (i.e. how the workshops should be run and the intended outcomes). Meanwhile, systems thinking–an approach to understand- ing the complexity of systems through examining the emergent properties and governing mechanisms of the relationships between system components [31]–provides a suite of practical PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 6 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services tools and activities to collaboratively explore different ways of thinking about the climate- related problems for, and solutions to, rural water services. The two are complementary because the theory of transformative spaces comes from sustainability transformations and transitions scholarship, which is founded on the need for systemic change articulated through systems thinking [32]. We chose transformative spaces and systems thinking theories intend- ing to engage participants in thinking beyond implementing costly technical fixes that are the norm in discourses on climate-resilient water services [33]. We sought to create spaces that tapped into deeper questions of values, normative commitments, and plural ways of knowing– foundational considerations for driving societal shifts towards a more sustainable future [34]. Work to date on transformative spaces has highlighted their potential to be a starting point for institutionalising transformative change and, to achieve this outcome, the importance of assembling diverse methodological frameworks and tools [23]. The creation of transformative spaces can be distinguished as five design phases: (a) problem definition, (b) operationalisa- tion, (c) tactical, (d) outcome, and (e) reflection [23]. Drawing on these phases, we designed the workshops as follows. In the problem definition phase, the transformative space seeks to open up new ways of problematisation through the reframing of issues and lifting perspectives on the problem that often go unheard [23]. Prior to the workshop, the study team conducted the aforementioned FGDs to develop a picture of how different segments of society in Dailekh and Sarlahi districts experienced climate impacts on water access. Quotations from the FGDs were presented to the workshop participants and discussed in small groups. In particular, the participants were guided to consider the gendered dimensions of climate impacts, issues confronting people with disabilities, and their personal experiences with climate impacts. This discussion helped participants see the multi-dimensional nature of climate change impacts on water and learn from the experiences of diverse perspectives. The participants dug deeper in the problematisa- tion phase through the ‘rich pictures’ and ‘five whys’ activities that are detailed further in the following phases. The operationalisation phase involves deliberately designing in opportunities to hear a range of different perspectives [23]. We invited women and men from each local government area (rural municipality) in the district, non-government development organisations (NGOs), and organisations for people with disabilities (OPDs) to the workshop. The workshops facili- tated knowledge co-production, which is “an iterative and collaborative process involving diverse types of expertise, knowledge and actors to produce context-specific knowledge and pathways towards a sustainable future” [35]. Knowledge co-production can foster self-reflec- tion, shared understandings, and practical ideas towards sustainability transformations in the water service sector [36]. We facilitated co-production through mixing participants from dif- ferent agencies in team-based problematising and problem-solving activities. Finally, during the workshop, the participants were guided to do a ‘worldviews’ activity to understand their own worldviews and those of others. Worldviews are a person’s overarching system of mean- ing that influences how they “interpret, enact, and co-create reality” [37]. Shifting individuals towards ‘pluralist’ or ‘strategist’ perspectives, where they appreciate a diversity of perspectives about the world, may be an enabler for more transformative action on climate change [38]. This activity was held at the beginning of the workshop to support participants to appreciate how assumptions we make shape the way an issue is framed and how assumptions differ across diverse people. The tactical phase comprises the activities that support the work that will be done because of the transformative space [23]. Here, we used systems thinking tools to guide workshop par- ticipants towards modes of thinking that could lead to actions with transformational out- comes. Many sustainability interventions target tangible, short-term changes that are easier to PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 7 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services implement, but have limited potential for bringing about transformational change [39]. Hence, we developed the ‘leverage points’ activity that utilised the leverage points framework to guide participants to consider activities or feedback processes in a system where a small change could lead to an eventual overall change in the system’s behaviour [40]. The leverage points framework encourages decision-makers and stakeholders to consider broader and higher-level (paradigmatic) factors, as well as lower-level inputs and parameters when deciding where to focus efforts when trying to create positive societal change [39,40]. A second systems thinking tool used was rich pictures [41,42]. In the rich pictures activity, participants use pic- tures and draw their ideas related to a particular question or problem to represent what is included, important, and what the relationships are between the elements. The benefit of this approach lies in the combination of creative and visual expression, being able to see connec- tions between the elements, and the conversations and explanations associated with the pro- cess. Data is obtained not only from the picture itself but also from the way the picture is then explained by the creator(s). Follow-up deliberations by participants also helped to illustrate the alignments and divergence in the understanding of a particular problem. Finally, the five whys activity was used to help reveal underlying causes of issues or problems [43]. In this activity, participants considered the layers of causality for a particular problem and were encouraged to go beyond superficial cause and effect. When used carefully, the five whys can play a powerful role in helping to illustrate the need for depth of inquiry into causes of a particular issue and in analysing complex problems [44]. In the outcome phase, the outcomes of the activities from the transformative space are con- sidered [23]. To understand the immediate effect of how the workshop influenced the thinking of the participants, we facilitated a ‘theory-of-change based action planning’ activity. A theory of change is “A particular approach for making underlying assumptions in a change project explicit, and using the desired outcomes of the project as a mechanism to guide project plan- ning, implementation, and evaluation” [45]. Theories of change are commonly used in the development sector to plan out course of action, linked to anticipated changes and outcomes [46]. Hence, participants were asked to create a theory of change to encourage them to articu- late their newfound assumptions and desired outcomes for the group to reflect upon. Lastly, the reflection phase involves reflecting on what worked and what did not in enabling transformative change [23]. ‘Sensemaking’ refers to the process through which people come together to understand issues or events together [47]. Through this process, actors create and shape a shared meaning of the issue or event [48]. The study team took part in a ‘sensemaking and practice reflection’ to consider whether the workshop showed promise in reshaping how the participants viewed the climate change problem for rural water access and the possible solutions. 2.2.2 Workshop activities. Two workshops were facilitated by SNV Nepal staff in April and May 2022 with participants from local governments, NGOs, and OPDs. One workshop was held in Sarlahi district with 14 participants, and one was held in Dailekh district with 19 participants. Local government staff invited to the workshops were those who had technical or planning responsibilities related to water service provision and were working in rural munici- palities that were included in the SNV Beyond the Finish Line project. NGO and OPD staff invited were those from organisations that were working with SNV on the Beyond the Finish Line project in Sarlahi and Dailekh. The workshop activity design was informed by the intent to foster a systemic perspective. To achieve this, we drew on soft system methodologies and related approaches. Soft systems thinking sees the world as complex and confusing and influenced by worldviews and human values. Through the process of inquiry and exploration, a learning system emerges [49]. The tools we chose were Worldviews [50], Leverage Points [40], Rich Pictures [41], Five Whys [43] PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 8 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services Table 1. Systems-related activities of workshops. Activity What was involved and how it was conducted Reference to literature (a) Problem definition Focus group discussions Elicitation of experiences from rural community members about climate-related impacts on water access, use and management. (b) Operationalisation Worldviews (c) Tactical Leverage points (c) Tactical Rich pictures (c) Tactical Five whys (d) Outcome Theory-of-change based action planning (e) Reflection Reflective sensemaking https://doi.org/10.1371/journal.pstr.0000081.t001 Introduction to concepts and theory, modified worldviews quiz, discussions about four worldviews, and self-reflection. Introduction to concepts and theory, group ordering of leverage points, discussion, and reflection. [37,50] [39,40] Responding to focus group discussions findings through drawing rich pictures in groups, presentations, and discussion. [41,42,49] Participants chose a challenge identified in focus groups, and then conducted a five whys worksheet to more deeply consider the root causes of the issue, including the social/cultural, environmental, economic and political underpinnings. Participants were asked to consider “what rural water services in your district should be like in the future” to develop an action plan based on what they had learned and discussed during the workshop, based on theory-of-change thinking and processes. [43] [46] The sensemaking process we employed for this project was between SNV partners themselves, and also with the UTS-ISF research team through a number of facilitated workshops. [47] and theory-of-change enablers and causal thinking processes [49]. The workshop incorporated additional activities related to climate change, such as a video on the impacts of climate change on WASH and water management, and a presentation of the FGD findings. This study, how- ever, focuses on the systems thinking activities of the workshops as summarised and described in Table 1. Worldviews. We developed a modified version of Hedlund-de Witt’s worldviews question- naire [37], in a printed format, asking participants to circle the responses that best matched their view. Participants completed the exercise privately, and were not required to share their results. After they conducted the survey, participants were asked to read the four types of worldviews as defined by [37]. They were then asked to discuss which worldviews category they felt best reflected themselves and what they thought of the different worldview categories. Finally, the participants discussed in plenary what revelations this activity invoked for them. The worldviews activity aimed to raise the awareness of participants about the existence of dif- ferent perspectives on climate change, which researchers argue can be foundational for trans- formative climate action [38]. Leverage points. We gave participants an overview of the purpose of the Meadows’ Lever- age Points framework–a framework indicating 12 leverage points that can identify specific activities or feedback processes in a system where a small change could lead to an eventual overall change in the system’s behaviour [40]–then provided them with twelve cards including pictures and simple text relating to each of the 12 leverage points. Participants in small groups were then asked to order the cards from what they believed would be the lowest impact lever- age point to the highest impact leverage point. Participants then reflected on the order that they placed their cards, considering how Meadows orders them, and discussed the similarities and differences in their opinions about the ordering. The cards were placed on the wall and could be easily seen and engaged with by participants, generating robust and inspiring discus- sions. The leverage points were used to consider how interventions in a system can create transformations for sustainability [51]. Rich pictures. We presented quotations from the FGDs to the participants and asked them to draw out climate-related impacts on WASH in Nepal, based on the quotations and their own experiences. In small groups, participants drew symbols and pictures on a flip chart to represent what they had heard and the connections between the ideas, objects, feelings, and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 9 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services concepts. After the pictures were drawn by the groups, a title was given to each picture and a representative explained the picture to the group. This helped participants to draw out diverse causes of a problem and subsequently diverse solutions that go beyond the most apparent fixes [52]. Five whys. We asked participants to choose one of the major problems that they heard from the FGDs and write that problem or challenge at the top of their worksheet. We then asked peo- ple individually to think about what the cause was for that particular problem and write that in the line below the title issue. Participants were then asked to repeat the process so that they had a sequence of five whys. Reflecting on the cause they identified at the bottom of their list, we then asked them to consider what might be the social/cultural, economic, political, and the envi- ronmental ‘why’ underpinning the list. This helped participants to think through some of the root causes of the challenges or issues they identified from a range of perspectives (to avoid too narrow or single causal pathways), which supported thinking beyond financial or physical barri- ers. The five whys tool is helpful for theorising the root causes of a problem, challenging unsub- stantiated assumptions, and considering multiple entry points for solutions [53]. Theory-of-change based action planning. We introduced participants to the concept and processes related to developing a theory of change, drawing from examples of our work in a range of contexts. We demonstrated that overarching goals were clearly and logically linked to outcomes, which were linked to activities based on a power analysis and understanding of how change happens within a particular context. Participants were then asked to develop an action plan based on what they had learned and discussed during the workshop, responding to the question “what should rural water services in your district should be like in the future?” and based on theory-of-change processes. Participants were provided a template to note their ideas and link various actions to intermediate and higher-level outcomes. Overall, this process helped the participant to articulate the outcomes they believed their actions will achieve [46]. 2.2.3 Reflective sensemaking. Workshop data were analysed through a qualitative collab- orative analysis approach [54]. Particularly, results of the workshop were analysed through dis- cussion of written notes and interviews with the workshop facilitators. Workshop facilitators were provided templates showing what kinds of data needed to be collected for each activity. Facilitators in Nepal took notes, collected worksheets, and wrote-up the results of the work- shops into two reports that they then shared with the research partners. A sensemaking session was held with the Nepali facilitators and Australian researchers to discuss and interrogate the results reported in the workshop reports, and to draw out key findings. During the session, the researchers interviewed the facilitators to understand what changes they observed in partici- pants, how the work process impacted on the facilitators personally, and their reflections on the content and process of the workshops. Specifically, the facilitators were asked: • How local governments usually discuss rural water supply issues and if this changed after the workshops. • If participants demonstrated an appreciation of the need for deeper understanding to change to how water systems are supported and governed, and how this can be achieved. • Which activities, tools, or concepts were especially effective during the workshops, and what made them effective. • Reflections on power dynamics during the workshop, and how that affected participation. • If there were any cases of resistance to the ideas and concepts introduced in the workshop. • Recommendations for improving the workshops and to help participants think about deeper transformations they could make to support climate-resilient water services. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 10 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services The interviews were conducted in pairs (one researcher as an interviewer and one work- shop facilitator as an interviewee) and responses were documented in a template. Interviewers also asked probing questions to support the facilitators to elaborate on their responses and fur- ther explain why they perceived effects (or lack thereof) on the mindsets and attitudes of the workshop participants. 3 Results and discussion This section is divided into a presentation and discussion of the results of each method: the community-based FGDs and the ensuing systems-focused workshops. 3.1 Common impacts of climate hazards on water access in Mahabu and Ramnagar The FGDs involved community members from two different geographic and water supply contexts. The Mahabu Rural Municipality is in a mountainous area. The primary water sup- plies for drinking and domestic purposes are usually gravity-fed piped schemes that source water from springs or streams found at higher elevations. Water taps from the piped scheme may be on the household premises or at public standpipes shared by multiple households. The piped schemes are usually operated and maintained by volunteer community water commit- tees. The Ramnagar Rural Municipality is in a flat, lowland region. Community members in Ramnagar usually get water for drinking and domestic purposes from private or public hand- pumps that are typically maintained by the households that used them. Community members expressed that, in the dry season, water shortages occurred in Mahabu and Ramnagar because the yield of the water source was less than demand. This forced community members to arduously seek out alternative water sources, some of which were owned by other community members who restricted their usage (for example, when the owner’s family was eating at the compound on which the water point was located: In the summer, water does not come regularly from the pipe. We have to bring water from a well which is far from home. It takes two to three hours. (Female poor and marginalised group participant, Mahabu) Sometimes we go at 1.00 AM to collect water. We must search for water from three or four wells due to lack of water in the wells. . .whoever can go first, they get the water. (Female poor and marginalised group participant, Mahabu) We have to go to another hand pump to bring water in the dry season. If family members are eating there at that time then we have to wait until the person finishes eating. Then only are we permitted to take water from their hand pump. (Women’s group participant, Ramnagar) Community members, particularly those from Mahabu, shared that water supplies became contaminated when there was heavy rainfall. Participants described the water as becoming tur- bid or malodorous and, in Mahabu, participants sometimes observed dead animals in the water supply. Wealthier households could afford to buy packaged water during such contami- nation events, while relatively poorer households continued to use the degraded water: In the rainy season mud and dust get mixed in water supply and the water quality is very dirty. Sometimes worms, frogs, and snakes die in the water and we have to drink such dirty water. (Female poor and marginalised group participant, Mahabu) PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 11 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services People with disabilities were often disproportionately burdened by these climate impacts. They may have been dependent on family members or neighbours for accessing water when the primary water supply was not working due to a climate hazard, or otherwise had to access an alternative water source with great difficulty: Kids are not always available at home so we request to our neighbour to bring water. If nobody is available, we go by crawling on hands and legs to bring water in the dry season. (Woman with a disability, Mahabu) I don’t have sufficient water to wash clothes, take a bath, go to the toilet. . .we have to go to the river for these purposes which is very difficult for me. Sometimes, even when I feel so thirsty, I can’t get water to drink when family members are not available. (Woman with a disability, Mahabu) Water shortages during dry spells could create conflict over water access, especially in Ram- nagar where some householders competed for scarce water or resisted others from accessing handpumps that they perceived to own: When there is a water shortage, there is conflict within the community over accessing water. . .women throw other’s water pot, fight, use rude words to each other at the water source. . .neighbours stop speaking to each other. (Female poor and marginalised group participant, Ramnagar) On hot days, many hand pumps are dry. I have to bring water from the neighbour’s hand pump and they usually call me [a pejorative nickname for a person with a disability]. If we send the children to bring water, the neighbour scolds the children and does not allow them to take water. (Man with a disability, Ramnagar) Local governments in Nepal are on the frontline of supporting the resolution of these cli- mate-related issues. Community-based water committees are frequently challenged to sustain rural water supplies [55] and climate change heightens the risks of water supply failure [56]. Hence, local governments in Nepal will need to have an increasingly greater role in supporting communities to overcome the hydrological and technological impacts of more extreme weather. Our findings on the burden of climate impacts on water access for marginalised groups, and the contribution of climate hazards to water conflict and competition, aligns with research on climate and water use elsewhere in Nepal [17,18]. As duty-bearers of the human rights to water and sanitation, local governments must take policy, financial and institutional measures to fulfil everyone’s right to water, notwithstanding the stresses of climate change [57]. This includes the implementation of laws, regulations and policies that enforce affirma- tive action for marginalised individuals and groups [58]. The following section explores the extent to which the workshop activities contributed to changing how local government partici- pants perceive their role in ensuring water access is protected against climate hazards and how they imagine solutions. 3.2 Engaging local government in transformative processes: promising outcomes and unresolved questions The workshop approach has shown promise in supporting local government officials to engage with transformative modes of thinking, but further work is needed. One such further need that was beyond the scope of this study is the documentation of subsequent actions taken to under- stand and provide evidence for how supporting transformative modes of thinking translates PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 12 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services into further processes of transformation and, eventually, real-world impact. In this section, we discuss some of the progress made in gaining buy-in from local government on transformative processes, the need to foster new relations with people not present at the workshop to expand opportunities for change, and ethical dilemmas relating to power dynamics that will need to be addressed along the way. 3.2.1 Laying foundations for transformational thinking. A core aim of the workshops was to influence local government staff in Nepal to shift how they thought about supporting rural water services towards systemic changes that will enable water system transformations towards sustainability under climate change. Catalysing actors to take pathways to transforma- tion may be achieved through assembling diverse methodological tools and frameworks [23] that build the actors’ knowledge and competences [59]. Our workshop approach that deployed a variety of adapted systems thinking tools shows promise in building the knowledge and com- petences of local government staff in Nepal regarding transforming water services. Experimenting with multiple kinds of knowledge and ways of knowing can create the con- ditions for learning about and eventually enacting transformative change [60], which the workshop participants showed openness to. The participants were attracted to the idea of shift- ing mindsets to reimagine what could contribute to strengthening water systems against cli- mate change. Participants in both workshops remarked that the worldviews and leverage points activities helped them understand the idea of looking at a problem from different per- spectives. One participant remarked, “This was one of the best workshops I have ever partici- pated in. The content was very insightful and encouraged us to brainstorm. . .honestly it forced us to think and think and think.” The workshop facilitators later reflected that they felt the activities invoked more ‘out of the box’ thinking; local government staff usually focus on technological solutions to water supply problems, but the workshops generated energising dis- cussions among the participants about a wider range of solutions. For example, participants proposed actions related to broader water resources management considerations and the need to collaborate with other government agencies. The facilitators also remarked they felt a change in the way they think about rural water supply sustainability. One facilitator said, “Through this workshop, I myself am transformed.” The workshop facilitators further reported that the participants showed an appreciation of some of the contributing root causes of the local governments’ shortcomings in addressing cli- mate impacts on water services. The facilitators stated that, in their experience, local govern- ment staff typically cite inadequate availability of funds as the primary culprit for their struggles in resolving climate-related issues for water services. However, the five whys activity prompted the participants to consider other drivers, for example, gaps in local policy and lead- ership challenges that contributed to an absence of direction for local government staff to address climate-related issues. One benefit of realising these other root causes was that some of them are more in the sphere of control of the local government staff compared to funding shortfalls. The workshop participants and facilitators agreed that advocating for policy changes and stronger leadership to address climate change impacts was more actionable for local gov- ernment staff than requesting a higher budget for infrastructure upgrades. While the five whys activity helped participants to identify practical leverage points that cre- ate bigger changes in the management and governance of rural water supplies, the workshop approach itself could be a way of acting on leverage points. In her Leverage Point framework, Donella Meadows argues that the power to question, reflect upon and transcend paradigms is generally the most effective leverage point for driving deeper systems change [40]. Above all else, our workshop approach aimed to shift the thinking of the participants by making them aware of their ability to view the problem of climate change for water services in different ways PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 13 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services and reimagine what they could feasibly do to affect systems change. The appropriate framing, discussion, and communication of systems thinking concepts are a pathway to achieving this. Another common mechanism for paving a path to sustainability transformations is the co- creation of knowledges about the state of the system, the desired future development, and the changes needed to reach the desired future [61]. Each workshop culminated in the final activ- ity in which participants worked together to create an action plan based on a theory-of-change process that included co-creating a goal, reaching desired outcomes, and designing practical activities to support the outcomes. One participant reflected, “We all are a mixture of different worldviews and have to work together in a team for a common mission, sometimes it is easy and sometimes challenging.” The facilitators noted that the action plans incorporated elements of the workshop discussions, such as activities to shift the mindsets of local decision-makers, which were distinguished from usual government project planning documents. The facilitators were confident that the workshop was effective at influencing the participants to think more systemically about climate-related issues for water services, although further work is needed to follow-up on the action plans developed, in order to translate this into action on the ground. A barrier that we sought to overcome was making systems thinking and sustainability transformation concepts accessible, relevant, and useful for local government staff in Nepal. Much of the literature and discourse on these topics are academic and at a level of abstraction that can be difficult for newcomers to the topics to grasp. Our experience suggests that the approach of using simple, participatory activities for facilitating the discussion of where to place energy and action to change a system, as well as what sits behind our assumptions, holds promise for shifting thinking in this context. The workshop facilitators felt that local govern- ment participants were challenged to understand new concepts like leverage points and world- views, but through discussions, developed a comprehension and appreciation of them. Local government participants were also enthusiastic about replicating the workshop activities with their leaders at the rural municipality and provincial levels. Although more work is needed to translate discussions and new insights into sustainable action, shifts in perceptions and think- ing are a necessary prerequisite. 3.2.2 Fostering new relations to catalyse change. Transformative spaces can form oppor- tunities for establishing, strengthening or repositioning social networks that enable new ways of working and build collective agency for creating transformations [23,59]. Although, as an exploratory process, our workshops did not fully facilitate the interrogation and re-negotiation of social relationships, we learned about the current state of rural water management relation- ships that informs further work to support systemic change. Engagement of local government decision-makers in the workshop approach is key to affecting real change given the power and influence they have over WASH systems in their context. The workshop participants and facilitators agreed that the local government partici- pants have limited influence to steer department strategies within their hierarchical institu- tions. The workshop participants stressed the importance of holding a similar workshop for rural municipality and provincial leaders who decide about budgeting and strategising on rural development. These leaders are elected representatives and do not necessarily have train- ing or high-level awareness of water and climate issues, so the workshop approach would need to be tailored accordingly. There is also often a high turnover of elected leaders at local govern- ment levels in Nepal that makes it challenging to institutionalise new ways of thinking. The workshop participants suggested they could advocate to their leaders, who have many respon- sibilities and therefore may be reluctant to join a full-day workshop, to give attention to responding to climate impacts on water. Future transformative spaces should carefully con- sider the recruitment of influential participants and accounting for power dynamics (discussed further in the following section). PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 14 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services Collective actions that deliberately aim to change systems at scale are important for trans- formative adaptation to climate change [62]. Hence, any approach to shift local governments in Nepal towards transformative change ideally would be scalable so that a wide range of actors could be reached, and new relations established. Our workshop approach has the potential to be replicated or transferred into other local government contexts. Each of the workshop activi- ties was low-cost and did not require any sophisticated technical skills or resources for facilita- tors. However, the process required experienced facilitators who were comfortable working with systems thinking concepts and translating them into local languages and teaching modali- ties. For example, the workshop facilitators drew on a local story of an insect emerging from a pond and turning into a butterfly to communicate the concept of transformation. Building the capacity of local experts to facilitate such processes is critical for reaching a critical mass of col- lective action. Crucially, people may not transform the way they think about a problem because of a single workshop, and changing knowledge or thinking does not automatically lead to behaviour change. Although paradigm shifts in individuals could happen rapidly in response to a new insight or epiphany, the local government participants of this project would probably require sustained engagement with facilitators to continue to reshape how they problematise and think about solutions to climate impacts on a day-to-day basis. While local government partic- ipants appreciated the systems thinking concepts, the workshop facilitators noted that some workshop participants still struggled at times to think of creative new ways of addressing prob- lems. There is also frequent staff turnover within local governments in Nepal, so refresher activities may be needed over the time it takes to actualise proposed new and innovative solu- tions that go to the heart of the problems. 3.2.3 Ethical dilemmas. Ethical dilemmas can arise from attempts to cultivate transfor- mative change that involve challenging the status quo of dominant systems and in the decision of who to invite to deliberations about systemic change [23]. Power dynamics within institu- tions and communities must be better addressed to enable sustainability transformations for water services in Nepal. The participants of the workshops were overwhelmingly male, which reflects the overall local government workforce in rural Nepal. Representatives from local OPDs joined the workshops, but the workshop facilitators noted their level of participation in discussions was less than their government counterparts. Given that people’s experiences of climate impacts are strongly shaped by their socio-economic status [63], the meaningful inclu- sion and participation of diverse people in problem framing and proposing solutions is critical for a comprehensive identification of key challenges and solutions that provide equitable bene- fits. Our workshop approach could be improved to better incorporate spaces for rights-holders organisations representing women, people with disabilities, and marginalised groups to join the activities and contribute to the discussions. For example, meeting with these groups indi- vidually before the workshop and familiarising them with the concepts and language could help to build their confidence during workshops with local government representatives in attendance. Further, as the focus group discussion results presented in this study showed, power dynamics within communities influences climate responses, for instance, in terms of competi- tion over water during times of scarcity. The local government participants, who were primar- ily technical staff with responsibilities pertaining to infrastructure provision and maintenance, were not trained to deal with community conflict, equality and inclusion issues that crucially must be addressed to reduce the vulnerability of marginalised groups to climate change. Han- dling complex social matters requires another dedicated line of training and support to institu- tionalise social remediation within local governments in Nepal, which would complement and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 15 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services enhance efforts related to systems thinking approaches to climate change and water management. Workshop participants and facilitators raised the need to engage government decision- makers with more influence in such workshops. Introducing participants from varying levels of power within an institution, and across organisations that each might have their own agen- das, could make equal and honest deliberation on desired changes and recognition of power imbalances difficult to achieve. Again, capacitating local experts to facilitate these difficult dis- cussions, through the creation of safe spaces for people to speak and explicitly gaining the agreement of participants upfront to reach consensus [23], is critical. 4 Conclusions This study presented an approach for influencing local government staff in Nepal to shift their thinking towards transformative changes that would support the sustainability of rural water services under climate change. It laid out a low-cost workshop methodology that could be practically implemented in rural areas of Nepal through local facilitators. Our initial experi- ence with this approach showed promise in stimulating local government participants to appreciate concepts that elsewhere have demonstrated potential to lead towards transformative change. Yet, more work is needed to build upon this approach. Systems must be adequately ‘ready’ to be changed, otherwise there is a higher risk of failure if change processes are initiated too early and resistance is met [23]. Our approach was an attempt to prime local government staff to engage with more intensive transformation pro- cesses, such as those that seek to form transformative spaces that fully deal with establishing and renegotiating relationships, navigating political dynamics, and building new competences on the path towards creating real changes in systems [59]. The practice of warming up institu- tions to transformative change processes raises the likelihood that they will succeed. The systemic change needed to achieve transformative adaptation to climate change is unprecedented [64]. More work needs to be done to re-think how rural water services are delivered to ensure they will provide sustainable and equitable benefits under climate change. Practicable approaches that gain the buy-in of critical actors to reimagining what development of rural water services could look like will lay the foundation for the transformative change that is desperately needed. Acknowledgments The authors thank the following groups for giving their time to participate in the study and contribute their ideas: Mahabu Rural Municipality, Dungeshwor Rural Municipality, Gurans Rural Municipality, Everest Club, Ramnagar Rural Municipality, Parsa Rural Municipality, Chandrangar Rural Municipality, Rural Women Upliftment Association (RWUA), Nepal Apanga Sangh, Panchakoshi Disabilities Development Forum, OPD network Mahabu, Mahabu (Dailekh) community, and Ramnagar (Sarlahi) community. Author Contributions Conceptualization: Jeremy Kohlitz, Melita Grant, Sunetra Lala, Gabrielle Halcrow, Naomi Carrard. Formal analysis: Jeremy Kohlitz. Funding acquisition: Ratan Bahadur Budhathoki, Gabrielle Halcrow, Naomi Carrard. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000081 March 12, 2024 16 / 20 PLOS SUSTAINABILITY AND TRANSFORMATIONFoundations for transformation towards climate-resilient rural water services Investigation: Jeremy Kohlitz, Melita Grant, Ratan Bahadur Budhathoki, Shova Chhetri, Heman Paneru, Min Prasad Basnet, Naomi Carrard. Methodology: Jeremy Kohlitz, Naomi Carrard. Project administration: Jeremy Kohlitz, Shova Chhetri, Heman Paneru, Min Prasad Basnet. Resources: Melita Grant, Ratan Bahadur Budhathoki. Supervision: Melita Grant, Ratan Bahadur Budhathoki, Naomi Carrard. Validation: Ratan Bahadur Budhathoki, Shova Chhetri, Heman Paneru, Min Prasad Basnet, Sunetra Lala, Gabrielle Halcrow. Writing – original draft: Jeremy Kohlitz, Melita Grant, Naomi Carrard. Writing – review & editing: Jeremy Kohlitz, Melita Grant, Ratan Bahadur Budhathoki, Shova Chhetri, Heman Paneru, Min Prasad Basnet, Sunetra Lala, Gabrielle Halcrow, Naomi Carrard. References 1. 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10.7554_elife.87146	Reviewed Preprint Published from the original preprint after peer review and assessment by eLife. About eLife's process Reviewed Preprint posted 12 April 2023 Sent for peer review 21 February 2023 Posted to bioRxiv 20 February 2023 Biochemistry and Chemical Biology, Microbiology and Infectious Disease A tRNA modification in Mycobacterium tuberculosis facilitates optimal intracellular growth Francesca G. Tomasi, Satoshi Kimura, Eric J. Rubin, Matthew K. Waldor Department of Immunology and Infectious Diseases Harvard T. H. Chan School of Public Health, Boston, MA , USA • Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA , USA • Department of Microbiology, Harvard Medical School, Boston, MA , USA • Howard Hughes Medical Institute, Boston, MA , USA (https://en.wikipedia.org/wiki/Open_access) (https://creativecommons.org/licenses/by/4.0/) Abstract Diverse chemical modifications fine-tune the function and metabolism of tRNA. Although tRNA modification is universal in all kingdoms of life, profiles of modifications, their functions, and physiological roles have not been elucidated in most organisms including the human pathogen, Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. To identify physiologically important modifications, we surveyed the tRNA of Mtb, using tRNA sequencing (tRNA-seq) and genome-mining. Homology searches identified 18 candidate tRNA modifying enzymes that are predicted to create 13 tRNA modifications across all tRNA species. Reverse transcription-derived error signatures in tRNA-seq predicted the sites and presence of 9 modifications. Several chemical treatments prior to tRNA-seq expanded the number of predictable modifications. Deletion of Mtb genes encoding two modifying enzymes, TruB and MnmA, eliminated their respective tRNA modifications, validating the presence of modified sites in tRNA species. Furthermore, the absence of mnmA attenuated Mtb growth in macrophages, suggesting that MnmA-dependent tRNA uridine sulfation contributes to Mtb intracellular growth. Our results lay the foundation for unveiling the roles of tRNA modifications in Mtb pathogenesis and developing new therapeutics against tuberculosis. eLife assessment This is a valuable addition to the literature as it helps us understand the role of tRNA modifying enzymes in Mycobacterium tuberculosis. By knocking out one of the enzymes, the authors convincingly demonstrate the importance of tRNA-modifying enzymes for intra-host growth of tubercle bacteria. Some of the claims regarding modification as well as the role in virulence could be strengthened through further bioinformatics and phylogenetic analyses as well as experimental approaches. The work will be of interest to microbiologists. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 1 of 24 Introduction tRNA is an adaptor molecule that enables protein synthesis by converting the triplet genetic code in mRNA into amino acids. The fidelity of base paring between mRNA codons and tRNA anticodons is monitored within ribosomes and is critical for properly incorporating the amino acids bound to the 3’ ends of tRNAs into growing polypeptides. For optimal translation, the abundances, and properties of tRNA isoacceptors are fine-tuned by diverse mechanisms, including chemical modifications[(1)-(4)] Dysregulation of tRNA abundance and/or structure leads to defective decoding and results in ribosome pausing and collisions, protein misfolding, stress responses and can have detrimental or lethal effects on the cell[(5)-(10)]. Chemical modifications of tRNA (tRNA modifications) are found in all kingdoms of life and fine-tune tRNA properties including mRNA decoding efficiency, recognition by aminoacyl- tRNA synthetases, half-life and structural stability[(2), (10)-(12)]. Modifications are prevalent in the anticodon loop, particularly at the first letter of the anticodon. Modifications of the anticodon loop directly modulate codon recognition, whereas modifications in the tRNA body region primarily stabilize tRNA tertiary structure, protecting them from degradation in the cell. tRNA modifications are generated by dedicated site-specific enzymes referred to as tRNA modifying enzymes. tRNA modifications have been extensively characterized in a few model organisms(2; 13), but their profiles, regulation, and functions in non-model organisms, including bacterial pathogens, are understudied(13) tRNA sequencing (tRNA-seq) allows for the rapid and systematic prediction of many tRNA modification sites(14; 15). We recently developed a comparative tRNA-seq protocol to profile tRNA modifications in organisms with uncharted tRNA modification profiles; in Vibrio cholerae, this approach led to the discovery of a new RNA modification and RNA editing process(16). tRNA-seq enables rapid prediction of modified sites through detection of reverse transcription-derived signatures, such as nucleotide misincorporation and early termination, both of which occur more frequently at modified sites. Furthermore, several chemical treatments of tRNA can convert modifications that are not recognizable as reverse transcription-derived signatures into detectable signals, expanding the repertoire of modifications that can be distinguished by tRNA-seq[(17)-(20)]. Mycobacterium tuberculosis (Mtb), the agent of tuberculosis (TB), is a global pathogen that caused >10.5 million cases and over 1.5 million deaths worldwide in 2020(21). Several studies have uncovered roles for non-Mtb mycobacterial tRNA modifications in stress responses, adaptation to environmental changes, and persister formation(22). Mycobacterium bovis BCG, an organism closely related to Mtb, responds to hypoxia by reprogramming 40 ribonucleoside modifications in tRNA to facilitate translation of a subset of proteins that promote survival in hypoxic conditions(22). To date, studies of the profiles and functions of Mtb tRNA modifications have been limited. Here, we conducted tRNA-seq in Mtb. We assigned modifications to many of the reverse transcription-derived signatures identified, using information on the presence of homologs of known modifying enzymes in Mtb. Chemical treatments of tRNAs carried out prior to tRNA-seq increased the detectability of certain modifications. We constructed two Mtb deletion mutant strains, with deletions of mnmA and truB, and confirmed that the absence of these modification enzymes eliminated the predicted signals in tRNA-seq data. Furthermore, while deletion of mnmA in Mtb did not affect the pathogen’s growth in in vitro laboratory growth conditions, the mnmA knockout strain’s growth was attenuated in a macrophage infection model. Our findings suggest that tRNA modifications warrant further study as we unravel the complexity of Mtb infections, as they may serve as targets for new therapeutics. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 2 of 24 Results In silico prediction of Mtb tRNA modifying enzymes To predict tRNA modifications in Mtb, we used Basic Local Alignment Search Tool (BLAST) (23) to identify homologues of all tRNA modification enzymes registered in Modomics in the Mtb genome(24). With a stringent threshold (E-value <1×10−10), 20 Mtb genes homologous to genes encoding known RNA modification enzymes were identified. 18 of these genes are predicted to synthesize 13 tRNA modifications in Mtb (Supplementary Table 1), including miaA and miaB for 2-methylthio-6-isopentenyl-adenosine (ms2i6A), tsaD, tsaB, tsaE, and sua5 for N6 threonylcarbamoyladenosine (t6A), mnmA for 2-thiouridine (s2U), truB, truA, and pus9 for pseudouridine (Ψ), trmD for 1-methylguanosine (m 1G), trmI for 1-methyladenosine (m1A), trmL for 2’-O-methylcytidine (Cm) or 2’-O-methyluridine (Um), trmB for 7- methylguanosine (m7G), trmH for 2’-O-methylguanosine (Gm), dusB for dihydrouridine (D), tadA for inosine (I), and tilS for lysidine (k2C). While two additional genes, Rv1713 and Rv2338c, met the threshold for homology to modification enzymes, they exhibited greater similarity to a ribosome associated GTPase (Der) and a molybdopterin biosynthesis protein (MoeW) respectively, and likely do not correspond to tRNA modification enzymes. We mined data from genome-wide Tn-seq and CRISPRi screens(25; 26) to assess the impacts of Mtb tRNA modifications on its growth. Five genes encoding Mtb tRNA modifying enzymes, trmD, tilS, tadA, sua5, and miaA, were reported to be essential for Mtb growth in both Tn- seq(25) and CRISPRi screens(26) (Supplementary Table 1), suggesting that the modifications they produce are critical for tRNA functions. Indeed, E. coli trmD, tilS, tadA, and sua5 are also essential for growth and critical for codon decoding, aminoacylation, and reading frame maintenance[(27)-(30)]. The Mtb modifications synthesized by these enzymes likely have similar impacts on tRNA functions. Unexpectedly, one modifying enzyme, MiaA, is non- essential in E. coli, but apparently essential in Mtb, suggesting that i6A, the modification introduced by MiaA, may have more profound roles in Mtb translation than in E. coli. Several of the putative Mtb tRNA modifying enzymes are conserved across all three domains of life (e.g., TruB and TsaD) (Fig. 1). By contrast, some enzymes were limited to bacterial species closely related to Mtb, possibly suggesting their species-specific physiological roles, including pathogenesis. For example, TrmI (Rv2118c) homologs are widely present in Archaea and Eukaryotes, but are sparsely distributed in bacteria, where they are primarily limited to Actinomycetia, including Mtb, and several thermophilic bacterial species (e.g., Thermus thermophilus). TrmI synthesizes m1A at position 58 in eukaryotes(31) and at position 57/58 in archaea(32); indeed, TrmI in Mtb has been proven to generate m1A at position 58(33). Pus9 (Rv3300c) has many homologs exhibiting weak similarity across eukaryotes and bacteria. However, some species, including Pseudomonas aeruginosa, Acinetobacter baumannii, Neisseria gonorrhoeae, and several species of Actinomycetia, encode proteins showing strong homology to Mtb Pus9, suggesting that this set of pseudouridylases have a distinctive property such as substrate specificity. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 3 of 24 Fig. 1. Phylogenetic distribution of Mtb tRNA modifying enzyme homologs. Heat map of log10 E-values from BLAST search re- sults. BLAST searches were conducted against 118 manually picked organisms using Mtb tRNA modi- fying enzymes as queries. When one organism has multiple hits, the lowest log10(Eval) values among hits is shown. iTol(34) was used to depict the results. Profiling Mtb tRNA modification sites by tRNA sequencing To begin profiling detectable Mtb tRNA modifications, we sequenced tRNAs isolated from wild type Mtb strain H37Rv grown in 7H9 medium. In this protocol, tRNAs are first reversed transcribed to cDNA(16). During cDNA synthesis, chemical modifications on tRNA nucleotides disrupt Watson-Crick base pairing and increase the frequency of reverse transcriptase errors, leading to incorporation of the incorrect nucleotide or early termination of cDNA synthesis(35). These reverse transcription derived ‘signatures’ typically correspond to modified sites(14; 15) and are depicted in the heat map in Fig. 2. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 4 of 24 Fig. 2. Heat map of misincorporation and early termination frequency in sequencing of tRNAs from wild type Mtb. (A, B) Heatmaps show misin- corporation (A) and termina- tion (B) frequencies at all po- sitions across tRNAs (read 5’ to 3’). Predicted modifica- tions are labeled based on similarity to known modifica- tions in other organisms and the presence of the tRNA modifying enzyme homologs (Supplementary Table 1). The positions with more than 10% misincorporation in Mtb but not in E. coli are depicted in white in A. (C)M. tuberculo- sis tRNA modifications pre- dicted in this study. Schematic tRNA secondary structure with sites of modifi- cations identified either by the presence of modifying enzymes and/or tRNA-seq. Modifications and tRNA species that are not observed in E. coli are shown in red. Modifications and positions that are pre- dicted by both RT-derived signature and the presence of the homologs of tRNA modifying enzymes are shown in yellow (without chemical treatment) and green (with chemical treatment), whereas modifications that are only predicted by the presence of the homologs are shown in light blue. Genes reported to be essential in Mtb are shown in bold. Comparison of the reverse transcription derived signatures observed in Mtb to E. coli, in which tRNA modifications are previously well characterized(16) enables the prediction of the presence of common modifications, including ms2i6A, m1G, I, and k2C (Fig 2). These predictions are strongly supported by the set of tRNA modification enzymes identified in the Mtb genome (Fig. 1 and Supplementary Table 1), including miaA and miaB (ms2i6A), trmD (m1G), tadA (I), and tilS (k2C). Some tRNA modifications were observed in Mtb but are not present in E. coli. In other actinobacteria, A58 and A59 are likely modified to m1A(36), and since trmI, the methylase that generates this modification is present in Mtb(33), most Mtb tRNAs likely contain this modification as well. Nucleoside variation in the sequence of tRNA genes can account for some of the variations in modified sites between E. coli and Mtb. For example, in Mtb, termination signatures derived from G at position 37 were detected in Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 5 of 24 during reverse transcription, these positions are likely modified to m1G, as observed in the Bacillus subtilis tRNA-Arg2 gene position 37 G(37). tRNA samples were also treated with several chemical treatments prior to sequencing, to expand the set of tRNA modifications detectable by tRNAseq. These treatments included iodoacetamide (IAA), for detection of sulfur modifications,1-cyclohexyl-(2-morpholinoethyl) carbodiimide (CMC) for detection of Ψ, and alkali for detection of D and m7G. The chemical treatment protocols were first carried out with E. coli, to validate the methods. IAA is a thiol-reactive compound that covalently attaches carboxyamidomethyl to thiolated uridines via nucleophilic substitution(38) and modified s4U is detected as C instead of U. In IAA-treated samples, position 8 and 9, corresponding to s4U in many tRNA species, had high misincorporation frequencies, confirming that IAA treatment modifies s4U, leading to elevated misincorporation (Supplementary Fig. 1). Furthermore, we observed higher misincorporation and termination signals at the positions corresponding to other sulfur modification, s2C, s2U and their derivatives, such as position 32 in tRNA-Arg3, -Arg5, -Ser3, and Arg4, and 34 in tRNA-Glu, -Gln1, and -Lys (Fig. 3 and Supplementary Fig. 1), revealing that IAA treatment facilitates the detection of not only s4U but also additional sulfur modifications, which are weakly detected without the IAA treatment. Figure 3. IAA treatment promotes detection of sulfur modifications on tRNAs by enhancing termination signals. (A, B) Heatmaps of the termina- tion signals of E. coli tRNAs treated with (A) or without (B) IAA. Known modification sites, including sulfur modifications (s4U, s2C, s2U in white) are shown. (C) Termination fre- quency at s 4U, s2C, and s2U sites of tRNAs treated with or without IAA. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 6 of 24 Next, we applied IAA treatment to Mtb tRNA-seq. IAA treatment increased termination signals from position 34 in tRNA-Glu1, -Gln1, and -Lys, which contain s2U derivatives in E. coli (Fig. 4A). The 2-thiouridine modification is carried out by MnmA in E. coli(39), and a homolog, Rv3024c, of this enzyme was identified in the Mtb genome (Supplementary Table 1) (40). We used double stranded DNA-based recombineering to delete Rv3024c in Mtb, yielding strain MtbΔmnmA(41). Sequencing of tRNA isolated from MtbΔmnmA with prior IAA treatment showed reduced termination signals from position 34 in tRNA-Glu1, -Gln1, and - Lys in treated samples, indicating that Rv3024c plays a critical role in the modification responsible for increased termination frequency derived from position 34 in these tRNAs (Fig. 4). Together, these observations strongly suggest that Rv3024c encodes an MnmA-like enzyme that sulfurates position 34 uridines in three Mtb tRNA isoacceptors. Fig. 4. Heat map and plot of early termination frequency from sequencing of tRNAs from wild type and MtbΔmnmA with and without RNA alkylation. (A, B) Heat map of early ter- mination frequencies across tRNA molecules and posi- tions for WT (A) and MtbΔmnmA (B). Sulfur modi- fication are shown in white. (B) Plot of termination fre- quencies at position 37 in wild type (WT) Mtb and MtbΔmnmA for lysine_UUU, glutamate_UUG, and glutamine_UUC isoaccep- tors. IAA; iodoacetamide. As shown previously(42), CMC treatment increased both misincorporation and termination signatures at a subset of Ψs in E. coli tRNAs (Fig 5, and Supplementary Fig. 2 and 3). In addition, CMC-treated samples showed increased frequencies of both misincorporation and termination at position 16, 17, 20, and 20A corresponding to D, and m7G at position 46. Both modifications are known to undergo base elimination in mild alkali conditions(43). Since these signals were also observed in the reaction condition in which CMC was not added, these signals are likely attributable to the alkali treatment that is common to both conditions. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 7 of 24 Fig. 5. CMC and alkali treatment facilitate detection of Ψ, D, and m7G modifications in E. coli. (A, B) Heatmaps of the misincorpora- tion signals of E. coli tRNAs treated with (A) or without (B) CMC. In both conditions, tRNAs are incubated in al- kali condition. Known Ψ, D, and m7G sites are shown. Ψ are shown in white. (C) Misincorporation frequency at known Ψ, D, m7G sites of tRNAs treated with CMC+/alkali+ or CMC-/alkali+, and tRNAs without treatment. Reverse transcription-derived signatures derived from alkali-treated D showed a distinctive pattern. With this treatment, when two Ds are at consecutive positions, e.g., D16 and D17, termination signals were elevated at the following position (Supplementary Fig. 2 and 4). Furthermore, alkali treatment also led to higher misincorporation frequencies at singlet Ds (Fig. 5 and Supplementary Fig. 4). Thus, termination and misincorporation signatures enabled the prediction of known E. coli tRNA sites modified to D. CMC/alkali treatment facilitated the identification of additional modifications in Mtb tRNAs. Regardless of CMC treatment, alkali treated samples showed increased misincorporation and termination frequencies derived from U located at position 16, 17, 20, and 20A (Fig. 6 and Supplementary Fig. 5). As observed in E. coli, termination signals at position 18 and 21 likely correspond to consecutive Ds at position 16 and 17, and 20 and 20A, respectively. Mtb Rv0823c is a homolog of dihydrouridylase DusB (Supplementary Table 1), which likely accounts for the synthesis of D at these positions. Furthermore, alkali treatment increased the misincorporation frequencies at G at position 46 (Supplementary Fig. 6). Since Rv0208c is a homolog of TrmB, which synthesizes m7G at position 46 in E. coli, multiple Mtb tRNA species likely contain m7G at position 46 (Fig. 6 and Supplementary Fig. 6). Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 8 of 24 Fig 6. Heat map of early termination frequency from sequencing of tRNAs isolated from WT and MtbΔtruB following CMC treatment. Heat map of early termina- tion frequencies across tRNA molecules and posi- tions for WT (left) and MtbΔtruB (right). Termination signals derived from position 55 are shown in white. CMC treatment also increased the termination frequency at several sites. Termination signatures derived from position 55 increased in most tRNA species, suggesting that Mtb tRNAs contain pseudouridines at this position(37). Rv2793c is an Mtb homologue of E. coli TruB and deletion of Rv2793c reduced the termination frequencies at this position in tRNAs isolated from MtbΔtruB (Fig. 6 and Supplementary Fig. 7)(41). Together, these observations suggest that Rv2793c encodes a TruB-like enzyme that modifies position 55 uridines to Ψ across tRNA species. Furthermore, the presence of a TruA homolog in Mtb (Rv3455c) suggests that in multiple tRNA species U at positions 38-40 can be modified to Ψ. Indeed, the termination signatures derived from positions 38 and 39 increased depending on CMC treatment, strongly suggesting that these positions are modified to Ψ (Fig. 6 and Supplementary Fig. 7). In total, among 13 tRNA Mtb tRNA modifications predicted by the presence of tRNA modifying enzymes (Fig 1 and Supplementary Table 1), 9 species of modifications were detected based on reverse-transcription derived signatures (Fig. 2C). Growth of MtbΔmnmA is attenuated in a macrophage infection model To address whether Mtb tRNA modifications impact the pathogen’s growth in the host environment, we used the MtbTnDB transposon insertion sequencing (Tn-seq) database(44; 45) to determine if transposon insertions in genes encoding tRNA modifying enzymes have been associated with in vivo growth defects. Transposon insertions in mnmA were reported to attenuate Mtb growth in mice infected with a library of Mtb transposon mutants, suggesting that mnmA facilitates Mtb growth in vivo. We found that the growth of WT and MtbΔmnmA were similar in 7H9 medium (Fig. 7), suggesting the absence of s2U modification at position 34 does not impair Mtb growth in culture. In contrast, the MtbΔmnmA mutant was significantly impaired for growth in a macrophage infection model(45) (Fig. 7). Defective growth of the MtbΔmnmA mutant was also observed in macrophages treated with all-trans retinoic acid (ATRA), which promotes macrophage control of Mtb infection(46). These Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 9 of 24 observations strongly suggest that modification of U to s2U by MnmA facilitates Mtb growth in macrophages. Fig. 7. MtbΔmnmA is attenuated in a macrophage infection model. Top: Wild type and MtbΔmnmA do not display growth differences in 7H9 medium. Bottom: Auto-luminescent wild type and ΔmnmA Mtb strains were diluted to a multiplicity of infec- tion of 2 bacteria per mouse bone marrow-de- rived macrophage with or without all-trans retinoic acid (ATRA). Survival was measured by luminescence and normalized to luminescence at time 0. Discussion Here, we profiled Mtb tRNA modifications with tRNA sequencing to provide the first maps of the tRNA modification landscape in this global pathogen. In total, nine modifications, including six modifications without chemical treatment, were identified based on reverse transcription derived signatures. CMC/alkali treatment and IAA treatment further identified Ψ and m7G and sulfur modifications, respectively. Although we did not chemically validate the modifications predicted by tRNA-seq with mass spectrometry, the identification of Mtb homologs of tRNA modifying enzymes strongly bolsters the RT-signature-based predictions. Furthermore, the deletion of truB and mnmA genes in Mtb eliminated the respective modification signatures of pseudouridine and s2U, validating that the enzymes encoded by these genes synthesize these modifications. Finally, the growth defect of the ΔmnmA strain within macrophages but not in a nutrient-rich medium suggests that s2U tRNA modification facilitates Mtb adaptation to the host intracellular environment. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 10 of 24 additional sulfur modifications as well, including s2C and s2U, indicating that IAA should have general utility in tRNA-seq-based profiling of sulfur modifications, including in studies tracking changes in tRNA sulfuration(47; 48). Multiple modifications do not cause reverse transcriptase errors. Although tRNA-seq provides a simple and rapid method for profiling the landscape of modifications in all tRNA species, this approach does not enable comprehensive identification of modifications. Here, several modifications, including t6A, Cm, Um, and Gm, predicted by the presence of Mtb homolog of tRNA modifying enzymes, such as Sua5, TsaB, TsaD, TsaE, TrmL, and TrmH, were not detected in tRNA-seq. Mapping and further analysis of these modifications will require different approaches such as tRNA purification and RNA mass spectrometric analysis. Several strong signatures were detected in Mtb tRNAs but not in E. coli (Fig. 2A), including G45 in tRNA-Gly2. Most of these signals are strict misincorporations of C or T at A or G position, respectively. Since modifications at a purine position, e.g., m1G and m 1A, generally cause random misincorporation of the other three nucleosides, strong Mtb-specific misincorporation signatures are not likely to be derived from modification. Further mass spectrometric analysis of purified tRNAs will be necessary to assess whether these signals are derived from Mtb-specific modifications. The role of s2U in Mtb appears to be unusual. s2U is a universally conserved modification observed in all three domains of life at the wobble position in the anticodon. This modification enhances the stacking of s2U with U35 to stabilize the anticodon structure, facilitating codon-anticodon interactions. s2U is also recognized by multiple aminoacyl-tRNA synthetases for efficient amino acylation(12). Although elimination of this sulfur modification causes severe growth phenotypes in most organisms(39; 49), unexpectedly, the deletion of mnmA in Mtb did not attenuate growth of the pathogen in vitro, suggesting that the Mtb requirement for s2U modification differs from other organisms. The marked growth retardation of ΔmnmA strain within macrophages indicates the specific requirement of this modification within host cells. This modification may be necessary for maintaining general translation efficiency inside host cells and/or facilitate the expression of specific genes that are necessary for survival within macrophages. In most organisms, s2U is further modified into derivatives containing an additional chemical moiety at position 5(2; 50; 51). However, Mtb does not contain apparent homologs of the tRNA modifying enzymes that introduce the additional modifications to s2U. Thus, Mtb may contain s2U or s2U derivatives synthesized by other types of enzymes. Additional analyses to elucidate the structures of modified s2U in Mtb are warranted. Our findings serve as a valuable starting point for the research community to continue characterizing the physiological roles and mechanisms of Mtb tRNA modifications. Since tRNA-seq offers an efficient and scalable platform for surveying changes in tRNA modifications, this approach will be valuable across growth conditions, and may be extended to growth inside host cells. Finally, further studies elucidating the mechanisms by which tRNA modifications facilitate Mtb growth in host cells should be valuable for designing new therapeutics for tuberculosis. Acknowledgements We appreciate all the members of Waldor lab for fruitful discussion and comments on the manuscript. We also thank Gregory Babunovic for his valuable assistance setting up the macrophage infections used here. This work is supported by NIH/NIAID grants to M.K.W. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 11 of 24 Materials and Methods Bacterial strains and growth conditions Mtb strains were grown from frozen stocks into Middlebrook 7H9 medium supplemented with 0.2% glycerol, 0.05% Tween-80, and ADC (5 g/L bovine serum albumin, 2 g/L dextrose, 3 μg/ml catalase). Cultures were incubated at 37 °C. Strains were grown to mid log-phase for all experiments (OD600 0.4-0.6). Growth was measured on a BioTek plate reader for in vitro growth by measuring OD600 every 24 hours. Bacterial strain construction Supplementary Table 2 depicts the strains, plasmids, primers, and recombinant DNA used for this study. Plasmids were built by restriction digest of a parental vector and inserts were prepared by Gibson assembly(52). Plasmids were isolated from E. coli and confirmed via Sanger sequencing carried out by Genewiz, LLC (Massachusetts, USA). Deletion mutants The knockout strain MtbΔmnmA::zeo (zeocin) was built using double-stranded recombineering in the parental Mtb strain H37Rv. A linear dsDNA fragment was constructed using stitch PCR with the primers listed in Supplementary Table 2 which consisted of a 500bp region upstream of mnmA (Rv3024c)), 500 bp downstream region, and a lox-zeo-lox fragment. This cassette was transformed into an H37Rv recombineering strain as described(53) and plated on 7H10 + zeocin plates. Homology search Local BLAST was performed to search for Mtb homologs of tRNA modifying enzymes. First, the uniport IDs of tRNA modifying enzymes were obtained from Modomics(24), and seven proteins were manually added to the list, including Q47319/TapT, P24188/TrhO, P76403/TrhP, O32034/TrhP1, O32035/TrhP2, P36566/CmoM, and Q87K36/TrcP. Uniprot ID provides a fasta file of tRNA modifying enzymes from the Uniprot database(54). A blast database file was generated by ‘makeblastdb’ script using a fasta file of Mtb proteins (H37Rv strain) retrieved from NCBI. Then, the homologs of tRNA modifying enzymes were searched against the Mtb protein database using the fasta file of tRNA modifying enzymes as a query. Output format is defined by the following script: -outfmt “7 qacc sacc stitle score qcovs evalue pident” -evalue 1e-10. The output file was modified by excel (Supplementary Table 1). Phylogenetic analysis of Mtb tRNA modifying enzyme homologs Local BLAST was conducted to search for homologs of Mtb tRNA modifying enzymes in 118 manually picked organisms across three domains of life. A custom database was generated by combining the fasta files of organisms’ proteins retrieved from NCBI. Homologs of eighteen Mtb tRNA modifying enzymes were searched against the custom protein database using local blast. Log10Evalues were visualized by iTol(34) with a phylogenetic tree generated by phyloT(55). Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 12 of 24 tRNA sequencing Extraction of total RNA Strains were grown to mid-log phase with the appropriate antibiotics and inducing agents described above. RNA was collected at the same OD600 for each strain (between 0.4-0.6). Cells were left on ice for 20 minutes, then pelleted by centrifuging at 4,000 rpm for 10 minutes at 4 °C. Pellets were resuspended in 0.5-1 mL of TriZol (Life Technologies) and lysed using a BeadBug microtube homogenizer (Millipore Sigma). 200 μL of chloroform was added to each tube, after which samples obtained from Mtb strains were removed from biosafety level 3 precautions. Samples were centrifuged at 15,000 rpm for 15 minutes at 4 °C and the aqueous layer was collected into a fresh tube. To the original tube, 250 μL of sodium acetate buffer (300 mM sodium acetate pH 5.2 and 10 mM EDTA pH 8.0) was added, and samples were vortexed at 4 °C for 5 minutes then centrifuged at 15,000 g for 15 minutes at 4 °C. The aqueous layer was added to the fresh sample-containing tubes. 400 μL chloroform was added, and tubes were briefly vortexed and then centrifuged at 15,000 rpm for 1 minute at 4 °C. The aqueous phase was collected into a fresh tube and RNA recovered by ethanol precipitation. RNA pellets were resuspended in 10 mM sodium acetate pH 5.2 and stored at -80 °C until processed for sequencing. Total RNA samples were alkali-treated prior to tRNA extraction to deacylate all tRNAs (1 hour at 37 °C in 100 mM Tris-HCl pH 9.0). Isolation of tRNA fraction 1−2 μg of total RNA was run on a 10% TBE-UREA gel (ThermoFisher Scientific) at 250 V for 1 hour. Gels were stained with SYBR Gold (ThermoFisher Scientific), and tRNA was excised. Excised gels containing tRNA fractions were mashed in RNAse-free tubes, and 300 μL elution buffer (300 mM NaOAc pH 5.5, 1 mM EDTA pH 8.0, 0.10% SDS) was added to each tube. Samples were shaken on a thermoshaker (Eppendorf) for 1−4 hours at 37 °C and supernatant was collected using an Ultrafree filter column (Millipore Sigma). tRNA was recovered by isopropanol precipitation. tRNA dephosphorylation tRNA was dephosphorylated using QuickCIP (New England BioLabs) according to manufacturer instructions, and tRNA was collected by phenol-chloroform extraction followed by isopropanol precipitation. Iodoacetamide (IAA) treatment IAA treatment was performed as described(38). Briefly, 500 ng of total RNA is combined with 10 mM of iodoacetamide, 50 mM NaPO4 pH 8.0, and 50% DMSO in a final volume of 50 μL. Reactions were incubated at 50 °C for 15 minutes and quenched with DTT. CMC treatment CMC treatment was carried out as described in ref. Briefly, 2.5 μg tRNA fraction in 0.5 μl was mixed with 15 μl CMC-BEU buffer with or without CMC (0.34 M or 0 M CMC, 7 M urea, 4 mM EDTA, and 50 mM bicine pH 7.9) and incubated at 37 °C for 20 min. Adding 100 μl CMC stop solution (0.3 M NaOAc pH 5.2 and 100 mM EDTA) quenched the reaction. RNA was desalted with PD-10 desalting column (Cytiva) and recovered by ethanol precipitation. RNA was dissolved in 40 μl of 50 mM sodium carbonate buffer (pH 10.4) and incubated at 37 °C for 4 hours, followed by ethanol precipitation. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 13 of 24 Adapter ligation 0.5 μL RNase inhibitor was added to 3.5μL dephosphorylated tRNA (200−250 ng tRNA) and samples were boiled at 80 °C for 2 minutes. Boiled tRNA was mixed with 12 μL PEG buffer mix (10 μL 50% PEG8000, 2 μL 10 × buffer B0216S; New England Biolabs). 3 μL of 5’ adenylated linkers (Supplementary Table 2) were added (33 pmol/μL) along with 1 μL T4 RNA ligase 2 truncated (New England BioLabs) and incubated at 25 °C for 2.5 hours. Samples were recovered by isopropanol precipitation and run on a 10% TBE-Urea PAGE gel for 40 minutes at 250 V. Ligated products were recovered by gel excision as described above. Reverse transcription Identical quantities of samples with different adapter sequences were pooled for reverse transcription for a total of 200−250 ng tRNA. Reverse transcription was performed by combining 2.1 μL dephosphorylated tRNA with 100 mM Tris-HCl pH 7.5, 0.5 mM EDTA, 1.25 μM RT primer (Supplementary Table 2), 450 mM NaCl, 5 mM MgCl2, 5 mM DTT, 500 nM TGIRT (InGex), and 15% PEG8000 in a final volume of 9 μL. Samples were incubated at 25 °C for 30 minutes, after which 1 μL 10 mM dNTPs (New England BioLabs) were added and reactions incubated at 60 °C for 1 hour. 1.15 μL NaOH was added, and samples were boiled for 15 minutes and run on a 10% TBE Urea PAGE gel at 250V for 1 hour. Reverse transcription products were excised from gels and cDNA recovered by isopropanol precipitation. Linear single-stranded cDNA was circularized using CircLigase II (Lucigen) in accordance with manufacturer instructions. PCR of tRNA libraries PCR reactions were set up using HF Phusion according to the manufacturer’s instructions using a universal reverse primer (Supplementary Table 2) and a different index primer for each pool of samples. PCR reactions were aliquoted into 4 tubes and collected after 6, 8, 10, and 12 cycles. Samples were run on a Native TBE PAGE gel (ThermoFisher Scientific) at 180 V for 50 minutes, and amplified products were cut from the same cycle for each sequencing run. Samples were recovered by gel excision and isopropanol precipitation. Sequencing Sequencing was performed on a MiSeq instrument (Illumina) using 150 bp single end reads with a version 3, 150 cycle kit. Analysis 3’ linker sequences and two nucleotides at the 5’ end were trimmed using cutadapt and fastx-trimmer. Bowtie v1.2.2 was used with default settings to map reads to reference Mtb tRNA sequences retrieved from Mycobrowser(40) (Supplementary Table 3). Mpileup files were generated using samtools (samtools mpileup -I -A --ff 4 -x -B -q 0 -d 10000000). For analysis of termination frequencies, 5’ end termini of mapped reads were piled up using bedtools genomecov (option, -d -5 - ibam). The number of 5’ termini at each tRNA position was divided by the total number of mapped termini at that position plus all upstream (5’) positions. Macrophage infection Auto-luminescent wild type and ΔmnmA Mtb strains grown to the same OD600 were pelleted by centrifugation and prepared in RPMI media by soft spinning as described(56). Briefly, Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 14 of 24 cells were washed, pelleted, resuspended, and centrifuged at 121 g, with the top half of the centrifuged supernatant used. Suspensions were diluted to a multiplicity of infection of 2 bacteria per mouse bone marrow-derived macrophage by determining the OD600. Macrophages were infected for 6 hours, followed by a PBS wash and addition of RPMI with or without all-trans retinoic acid (ATRA). ATRA promotes macrophage control of Mtb infection(46) and was used to assess strain survival in an increasingly restricted macrophage environment. Survival was measured by luminescence in a BioTek plate reader and normalized to luminescence reads at time 0. References 1. Huang H.Y. , Hopper A.K. 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Satoshi Kimura Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA, Department of Microbiology, Harvard Medical School, Boston, MA, USA, Howard Hughes Medical Institute, Boston, MA, USA For correspondence: s.kimura.res@gmail.com ORCID iD: 0000-0003-3555-5877 Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 18 of 24 3. Eric J. Rubin Department of Immunology and Infectious Diseases Harvard T. H. Chan School of Public Health, Boston, MA, USA ORCID iD: 0000-0001-5120-962X 4. Matthew K. Waldor Department of Immunology and Infectious Diseases Harvard T. H. Chan School of Public Health, Boston, MA, USA, Division of Infectious Diseases, Brigham and Women’s Hospital, Boston, MA, USA, Department of Microbiology, Harvard Medical School, Boston, MA, USA, Howard Hughes Medical Institute, Boston, MA, USA ORCID iD: 0000-0003-1843-7000 Editors Reviewing Editor Rajan Sankaranarayanan Centre for Cellular and Molecular Biology, India Senior Editor Bavesh Kana University of the Witwatersrand, South Africa Reviewer #1 (Public Review): Tomasi et al. performed a combination of bioinformatic, next-generation tRNA sequencing experiments to predict the set of tRNA modifications and their corresponding genes in the tRNAs of the pathogenic bacteria Mycobacterium tuberculosis. Long known to be important for translation accuracy and efficiency, tRNA modifications are now emerging as having regulatory roles. However, the basic knowledge of the position and nature of the modifications present in a given organism is very sparse beyond a handful of model organisms. Studies that can generate the tRNA modification maps in different organisms along the tree of life are good starting points for further studies. The focus here on a major human pathogen that is studied by a large community raises the general interest of the study. Finally, deletion of the gene mnmA responsible for the insertion of s2U at position 34 revealed defects in in growth in macrophage but in test tubes suggesting regulatory roles that will warrant further studies. The conclusions of the paper are mostly supported by the data but the partial nature of the bioinformatic analysis and absence of Mass-Spectrometry data make it incomplete. The authors do not take advantage of the Mass spec data that is published for Mycobacterium bovis (PMID: 27834374) to discuss what they find. Important points to be considered: 1. The authors say they took a list of proteins involved in tRNA modifications from Modomics and added manually a few but we do not know the exact set of proteins that were used to search the M. mycobacterium genome. 2. The absence of mnmGE genes in TB suggested that the xcm5U derivatives are absent. These are present in M. bovis (PMID: 27834374). Are the MnmEG gene found in M. bovis? If yes, then the authors should perform a phylogenetic distribution analysis in the Mycobacterial clade to see when they disappeared. If they are not present in M. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 19 of 24 bovis then maybe a non-orthologous set of enzymes do the same reaction and then the authors really do not know what modification is present or not at U34 without LC- MS. The exact same argument can be given for the xmo5U derivatives that are also found in M.bovis but not predicted by the authors in M. tuberculosis. 3. Why is the Psi32 predicted by the authors because of the presence of the Rv3300c/Psu9 gene not detected by CMC-treated tRNA seq while the other Psi residues are? Members of this family can modify both rRNA and tRNA. So the presence of the gene does not guarantee the presence of the modification in tRNAs 4. What are tsaBED not essential but tsaC (called sua5 by the authors) essential? Reviewer #2 (Public Review): In this study, Tomasi et al identify a series of tRNA modifying enzymes from Mtb, show their function in the relevant tRNA modifications and by using at least one deleted strain for MnmA, they show the relevance of tRNA modification in intra-host survival and postulate their potential role in pathogenesis. Conceptually it is a wonderful study, given that tRNA modifications are so fundamental to all life forms, showing their role in Mtb growth in the host is significant. However, the authors have not thoroughly analyzed the phenotype. The growth defect aspect or impact on pathogenesis needs to be adequately addressed. - The authors show that ΔmnmA grows equally well in the in vitro cultures as the WT. However, they show attenuated growth in the macrophages. Is it because Glu1_TTC and Gln1-TTG tRNAs are not the preferred tRNAs for incorporation of Glu and Gln, respectively? And for some reason, they get preferred over the alternate tRNAs during infection? What dictates this selectivity? - As such the growth defect shown in macrophages would be more convincing if the authors also show the phenotype of complementation with WT mnmA. An important consideration here is the universal nature of these modifications across the life forms. Any strategy to utilize these enzymes as the potential therapeutic candidate would have to factor in this important aspect. Reviewer #3 (Public Review): The work presented in the manuscript tries to identify tRNA modifications present in Mycobacterium tuberculosis (Mtb) using reverse transcription-derived error signatures with tRNA-seq. The study identified enzyme homologs and correlates them with presence of respective tRNA modifications in Mtb. The study used several chemical treatments (IAA and alkali treatment) to further enhance the reverse transcription signals and confirms the presence of modifications in the bases. tRNA modifications by two enzymes TruB and MnmA were established by doing tRNA-seq of respective deletion mutants. Ultimately, authors show that MnmA-dependent tRNA modification is important for intracellular growth of Mtb. Overall, this report identifies multiple tRNA modifications and discuss their implication in Mtb infection. Important points to be considered: - The presence of tRNA-based modifications is well characterised across life forms including genus Mycobacterium (Mycobacterium tuberculosis: Varshney et al, NAR, 2004; Mycobacterium bovis: Chionh et al, Nat Commun, 2016; Mycobacterium abscessus: Thomas Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 20 of 24 et al, NAR, 2020). These modifications are shown to be essential for pathogenesis of multiple organisms. A comparison of tRNA modification and their respective enzymes with host organism as well as other mycobacterium strains is required. This can be discussed in detail to understand the role of common as well as specific tRNA modifications implicated in pathogenesis. - Authors state in line 293 "Several strong signatures were detected in Mtb tRNAs but not in E. coli". Authors can elaborate more on the unique features identified and their relevance in Mtb infection in the discussion or result section. - Deletion of MnmA is shown to be essential for E. coli growth under oxidative stress (Zhao et al, NAR, 2021). In similar lines, MnmA deleted Mtb suffers to grow in macrophage. Is oxidative stress in macrophage responsible for slow Mtb growth? - Authors state in line 311-312 "Mtb does not contain apparent homologs of the tRNA modifying enzymes that introduce the additional modifications to s2U". This can be characterised further to rule out the possibility of other enzyme specifically employed by Mtb to introduce additional modification. Author Response: Reviewer #1 (Public Review): Tomasi et al. performed a combination of bioinformatic, next-generation tRNA sequencing experiments to predict the set of tRNA modifications and their corresponding genes in the tRNAs of the pathogenic bacteria Mycobacterium tuberculosis. Long known to be important for translation accuracy and efficiency, tRNA modifications are now emerging as having regulatory roles. However, the basic knowledge of the position and nature of the modifications present in a given organism is very sparse beyond a handful of model organisms. Studies that can generate the tRNA modification maps in different organisms along the tree of life are good starting points for further studies. The focus here on a major human pathogen that is studied by a large community raises the general interest of the study. Finally, deletion of the gene mnmA responsible for the insertion of s2U at position 34 revealed defects in in growth in macrophage but in test tubes suggesting regulatory roles that will warrant further studies. The conclusions of the paper are mostly supported by the data but the partial nature of the bioinformatic analysis and absence of Mass-Spectrometry data make it incomplete. The authors do not take advantage of the Mass spec data that is published for Mycobacterium bovis (PMID: 27834374) to discuss what they find. Important points to be considered: 1. The authors say they took a list of proteins involved in tRNA modifications from Modomics and added manually a few but we do not know the exact set of proteins that were used to search the M. mycobacterium genome. Thank you for pointing out this issue. We will add the complete list of proteins used for the BLAST query. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 21 of 24 2. The absence of mnmGE genes in TB suggested that the xcm5U derivatives are absent. These are present in M. bovis (PMID: 27834374). Are the MnmEG gene found in M. bovis? If yes, then the authors should perform a phylogenetic distribution analysis in the Mycobacterial clade to see when they disappeared. If they are not present in M. bovis then maybe a non-orthologous set of enzymes do the same reaction and then the authors really do not know what modification is present or not at U34 without LC- MS. The exact same argument can be given for the xmo5U derivatives that are also found in M.bovis but not predicted by the authors in M. tuberculosis. The reviewer raises a valid point. In M. bovis mnm5U and cmo5U derivatives were observed in LC-MS analysis. However, we did not identify candidate genes known to be involved in the biogenesis of mnm5U and cmo5U in the Mycobacteriaceae, including M. bovis and Mtb, suggesting that if these modifications are indeed present, they are not synthesized through a canonical biogenesis pathways in this family. There are several examples where the same modification is generated by distinct modification enzymes (Kimura, 2021). These observations raise the interesting possibility that in the Mycobacteriaceae and most species in actinomycetota (except for Bifidobacterium, Corynebacterium and Rhodococcus species), major wobble modifications are generated by biosynthesis pathways that are distinct from those employed by well-characterized organisms. Future studies will examine this hypothesis. 3. Why is the Psi32 predicted by the authors because of the presence of the Rv3300c/Psu9 gene not detected by CMC-treated tRNA seq while the other Psi residues are? Members of this family can modify both rRNA and tRNA. So the presence of the gene does not guarantee the presence of the modification in tRNAs Thank you very much for the careful read. We did not include RluA in the list of query proteins because it is not classified as a tRNA modification enzyme in Modomics. Additionally, the CMC-coupled tRNA-seq is imperfect for detection of all pseudouridylated positions. Due to this limitation, we only assigned modifications that are both predicted by the presence of putative biosynthetic enzymes and RT-derived signatures. As the reviewer points out, we cannot rule out that this homolog targets only rRNAs. We will clarify this possibility in the revised manuscript. Also, RluA will be added to the query and the name of Rv3300c will be changed to RluA in the text and related figures. 4. What are tsaBED not essential but tsaC (called sua5 by the authors) essential? Thank you for pointing out this interesting observation. We are also curious about differences in the essentiality among t6A biogenesis genes. We speculate that TsaC potentially has critical roles in cell viability other than t6A synthesis. TsaC synthesizes a compound, threonylcarbamoyl-AMP, as an intermediate for t6A biogenesis. Thus, it is possible that this intermediate has a role in other essential cellular activities besides t6A biogenesis. Further study of these factors in Mtb could reveal interesting crosstalk between modification synthesis and other cellular activities. Reviewer #2 (Public Review): In this study, Tomasi et al identify a series of tRNA modifying enzymes from Mtb, show their function in the relevant tRNA modifications and by using at least one deleted strain for MnmA, they show the relevance of tRNA modification in intra-host survival and postulate their potential role in pathogenesis. Conceptually it is a wonderful study, given that tRNA modifications are so fundamental to all life forms, showing their role in Mtb growth in the host is significant. However, the authors have not thoroughly analyzed the phenotype. The growth defect aspect or impact on pathogenesis needs to be adequately addressed. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 22 of 24 - The authors show that ΔmnmA grows equally well in the in vitro cultures as the WT. However, they show attenuated growth in the macrophages. Is it because Glu1_TTC and Gln1- TTG tRNAs are not the preferred tRNAs for incorporation of Glu and Gln, respectively? And for some reason, they get preferred over the alternate tRNAs during infection? What dictates this selectivity? Thank you very much for raising this excellent point. As the reviewer suggests, the attenuation of DmnmA Mtb growth inside of macrophages could be caused by disparate codon usage between genes required for in vitro growth and intracellular growth. Among multiple codons encoding Glu, Gln, or Lys, s2U modification-dependent codons might be preferentially distributed in genes associated with intracellular growth. For example, Mtb has two tRNA isoacceptors, Glu1_TTC and Glu2_CTC, to decipher two Glu codons, i.e., GAA and GAG. According to the wobble pairing rule, GAA is only decoded by Glu1_TTC, whereas GAG is decoded by both Glu1_TTC and Glu2_CTC; i.e., GAG can be deciphered by an s2U- independent tRNA. Thus, genes required for intracellular growth might be enriched with GAA, an s2U-dependent codon. The same thing can happen to other Gln and Lys codons deciphered by s2U-containing tRNAs. In the revised manuscript, we will include the perspective of codon usage for explaining the intracellular fitness defect of the ΔmnmA Mtb mutant. - As such the growth defect shown in macrophages would be more convincing if the authors also show the phenotype of complementation with WT mnmA. The reviewer raises a valid point. We note however, that Rv3023c, a putative transposase, is downstream of MnmA and unlike MnmA, Rv3023c appears to be dispensable for in vivo growth, according to the Tn-seq database. Therefore, it is likely that the intracellular growth defect is caused by loss of mnmA. An important consideration here is the universal nature of these modifications across the life forms. Any strategy to utilize these enzymes as the potential therapeutic candidate would have to factor in this important aspect. This is a valid point. Targeting a pathogen-specific system enables avoidance of the adverse side effects caused by many therapeutic reagents. There are a couple of Mtb modification enzymes that are specific to bacteria and critical for Mtb fitness (e.g., TilS). These enzymes represent ideal potential therapeutic targets to suppress Mtb intracellular growth. Reviewer #3 (Public Review): The work presented in the manuscript tries to identify tRNA modifications present in Mycobacterium tuberculosis (Mtb) using reverse transcription-derived error signatures with tRNA-seq. The study identified enzyme homologs and correlates them with presence of respective tRNA modifications in Mtb. The study used several chemical treatments (IAA and alkali treatment) to further enhance the reverse transcription signals and confirms the presence of modifications in the bases. tRNA modifications by two enzymes TruB and MnmA were established by doing tRNA-seq of respective deletion mutants. Ultimately, authors show that MnmA-dependent tRNA modification is important for intracellular growth of Mtb. Overall, this report identifies multiple tRNA modifications and discuss their implication in Mtb infection. Important points to be considered: - The presence of tRNA-based modifications is well characterised across life forms including genus Mycobacterium (Mycobacterium tuberculosis: Varshney et al, NAR, 2004; Mycobacterium bovis: Chionh et al, Nat Commun, 2016; Mycobacterium abscessus: Thomas et al, NAR, 2020). These modifications are shown to be essential for pathogenesis of multiple Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 23 of 24 organisms. A comparison of tRNA modification and their respective enzymes with host organism as well as other mycobacterium strains is required. This can be discussed in detail to understand the role of common as well as specific tRNA modifications implicated in pathogenesis. The reviewer raises a fair point. However, with the exception of Chionh et al., the other studies cited here are not genome-wide characterization of tRNA modification. We will add a discussion of the distribution of tRNA modification enzymes across multiple mycobacterium species and the implications of this distribution for pathogenesis to the revised manuscript. - Authors state in line 293 "Several strong signatures were detected in Mtb tRNAs but not in E. coli". Authors can elaborate more on the unique features identified and their relevance in Mtb infection in the discussion or result section. Thank you for the suggestion. We will lengthen the discussion of the RT-derived signatures observed in Mtb but not in E. coli but the relevance of these modifications for Mtb pathogenicity remains speculative at this point. - Deletion of MnmA is shown to be essential for E. coli growth under oxidative stress (Zhao et al, NAR, 2021). In similar lines, MnmA deleted Mtb suffers to grow in macrophage. Is oxidative stress in macrophage responsible for slow Mtb growth? This is an excellent hypothesis which we will raise in the revised manuscript. - Authors state in line 311-312 "Mtb does not contain apparent homologs of the tRNA modifying enzymes that introduce the additional modifications to s2U". This can be characterised further to rule out the possibility of other enzyme specifically employed by Mtb to introduce additional modification. The reviewer raises a valid point. As discussed above (Reviewer #1, pt 2), Mtb may employ distinct enzymes to generate certain tRNA modifications. Future mass spec-based analyses of Mtb tRNAs will be carried out to identify the precise chemical structure of the sulfurated uridine, and subsequent studies will attempt to determine the enzymes that account for the biogenesis of these modifications. Francesca G. Tomasi et al., 2023 eLife. https://doi.org/10.7554/eLife.87146.1 24 of 24
10.1371_journal.pwat.0000213	RESEARCH ARTICLE Assessing inequalities in urban water security through geospatial analysis Juliana Marc¸ alID 1,2* Jan HofmanID 1,2, Junjie Shen3, Blanca Antizar-Ladislao4,5, David Butler6, 1 Water Innovation and Research Centre (WIRC), Department of Chemical Engineering, University of Bath, Bath, United Kingdom, 2 Water Informatics in Science and Engineering (WISE) Centre for Doctoral Training, University of Bath, Bath, United Kingdom, 3 University Library, University of Bath, Bath, United Kingdom, 4 Isle Utilities Ltd., London, United Kingdom, 5 Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom, 6 Centre for Water Systems, Department of Engineering, University of Exeter, Exeter, United Kingdom jm2842@bath.ac.uk (JM); j.a.h.hofman@bath.ac.uk (JH) Abstract Water security, which is key for sustainable development, has been broadly investigated through different spatial scales, time frames and perspectives, as a multi-dimensional con- cept. Fast growth and the diversity of the urban environment add to the challenges of reach- ing good levels of water security in cities. Yet, few studies have focused on evaluating the heterogeneous distribution of water security in urban areas, which is a key step to highlight where inequalities in large cities are present and how to best guide interventions. The objec- tive of this research is to investigate the spatial heterogeneity of urban water security as well as quantifying inequalities using the new assessment presented in this paper. A holistic indi- cator-based evaluation framework to intra-urban sectors of the city of Campinas in Brazil is applied, followed by an inequality analysis to describe the distribution of water security aspects. A spatial correlation analysis is then carried out to identify patterns for high inequal- ity indicators. Results show that even though Campinas has established good overall water security conditions, spatial heterogeneity is still noticeable in the urban area. Quantification of inequality by the Theil index highlighted aspects, such as vegetation cover, social green areas, and wastewater collection, that are inequitably distributed in the urban area. The sub- sequent analysis of spatial patterns exposed areas on the outskirts of the city where infra- structure challenges and social vulnerability coincide. This novel approach has been therefore successfully validated in a city in Brazil, and it has been demonstrated that our water security assessment framework identifies what are the main water security challenges and where they are in the city. For the first time we show that associating spatial and inequality analysis with conventional evaluation of urban water security has the potential to help target areas in need and tackle specific water security issues in the urban area. This is crucial to inform urban planning and policy making for a sustainable and inclusive urban water management strategy. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Marc¸al J, Shen J, Antizar-Ladislao B, Butler D, Hofman J (2024) Assessing inequalities in urban water security through geospatial analysis. PLOS Water 3(2): e0000213. https://doi.org/ 10.1371/journal.pwat.0000213 Editor: Venkatramanan Senapathi, Alagappa University, VIET NAM Received: February 17, 2023 Accepted: December 13, 2023 Published: February 1, 2024 Copyright: © 2024 Marc¸al et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data supporting this study is free access data and the sources and collections details are provided as supplementary information accompanying this paper. The code used for the analysis and results figures is also made available at: https://github.com/J-Marcal/ WSF_IneqAnalysis. Funding: This study was conducted as part of the Water Informatics Science and Engineering (WISE) Centre for Doctoral Training (CDT), funded by the UK Engineering and Physical Sciences Research Council, grant number EP/L016214/1. JM is PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 1 / 25 supported by a research studentship from this CDT. The content is solely the responsibility of the authors. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Assessing inequalities in urban water security Introduction Urban areas around the world are facing increasing water security challenges associated with rapid growth and climate change. In 2022, we saw cities around the globe experience extreme weather, particularly severe droughts [1] with significant impacts on water availability affecting food and energy production and human well-being [2]. Additionally, urban areas are an intri- cate system of water and other infrastructures that coexist and interact in heterogeneous spaces. This heterogeneity and complexity increase with the size of cities, alongside pressures on the water system and resources. These conditions reinforce the need to investigate urban water security especially from a multi-dimensional perspective, considering the different aspects involved but also its dependence on space and time. With several definitions, perspectives, approaches and assessment methodologies, water security is acknowledged as a broad concept and has been object of interest of scholars for decades [3–7]. The UN considers water security as the “capacity of a population to safeguard sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, and socio-economic development, for ensuring protection against water- borne pollution and water-related disasters, and for preserving ecosystems in a climate of peace and political stability” [8]. This all-encompassing and well-accepted definition [7] provides an interpretation that includes not only supply and accessibility but also environmental, hazard, economic, social and well-being elements. In the urban context, rapid growth and governance issues may lead to opportunities, infra- structure and services to be unevenly distributed in the urban area [9]. As a consequence, the benefits of city life may not be equally available for all, leading to varying water security experi- ences for its inhabitants. The marginalisation of people in informal settlements and slums, inequality, insufficiency and urban poverty compromise water security [10, 11]. Therefore, in an urban environment, certain areas and communities can be more vulnerable to water- related issues [6]. It is thus very important to develop policies that consider the spatial hetero- geneity of the urban area. Being spatially explicit allows the identification of city districts or areas that require strategies for increasing water security. Incorporating a spatial approach to urban water security evaluation can help identify inequalities and provide information to iden- tify areas at risk, helping to establish effective policies to protect the most vulnerable people, making sure that no one is left behind [12]. Previous studies have been interested in the question water security for whom? [13, 14] through investigation of the spatial distribution of different water-related aspects. At a global level, Gain et al. [15] highlighted the importance of spatial and temporal assessment of perfor- mances to identify specific needs and persistent problems in different countries. Doeffinger and Hall [14] worked on evaluating water security across states and counties in the United States, showing evidence of how spatial analysis can reveal the heterogeneity across the coun- try. The work by Stuart et al. [16] discovered geographical patterns and the spatial heterogene- ity of water insecurity in rural Uganda as well as their implications for community water interventions. In terms of urban water security, the study by Tholiya and Chaudhary [17] pro- vides a geospatial assessment of water supply services in Pune in India. While the investigation highlights the differences that were found within the city boundary, the evaluation focuses on water supply performance indicators. Other water security related aspects such as water infra- structure inequalities [18], ecological security [19], alternative water supply [20] and domestic water consumption [21] have also been spatially investigated in the literature, showing the importance of looking within the traditional boundaries as a way to capture disparities. Although the importance of studying a smaller scale has been highlighted by different authors [11, 14, 20, 22], few works in the literature have assessed urban water security PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 2 / 25 PLOS WATERAssessing inequalities in urban water security holistically at intra-city level. The study by Mukherjee et al. [22] provided an evaluation at micro-level for 16 administrative regions in Kolkata, India focusing on availability, accessibil- ity quality and risks as components of an urban water security index. Assefa et al. [23] devel- oped a domestic water security framework applied to the city of Addis Ababa in Ethiopia, subdivided into ten administrative regions. The authors included water supply, sanitation and hygiene indicators in their assessment and the analysis showed considerable disparities in domestic water security within the city, indicating opportunities for local development. How- ever, these studies tend to focus on the drinking water safety aspects of urban water security and lack the explicit incorporation of a spatial approach to their analysis. An in-depth and holistic evaluation of urban water security accounting for spatial patterns and inequality mea- sure is not found in the literature. In this study we present an urban water security assessment that explicitly accounts for the spatial distribution and patterns of water security elements. The main contributions are two- fold: (1) we explore the spatial variability of water security from an intra-urban perspective fol- lowing a framework that includes not only water supply and accessibility but also environmental, hazards, economic, social and well-being elements and (2) we further explore the heterogeneity of urban water security by including an inequality measure in the analysis. In this way we investigate the diversity of the urban area by downscaling the assessment to urban districts and neighbourhoods, and visualising how the results are distributed in the area. This provides a more detailed vision of the city and allows the investigation of where inequali- ties lie. We investigate the ‘what’ and ‘where’ of the water security challenges in the urban area. This could lead to important information to help establish priorities for either monitoring or acting upon local issues, potentially leading to more equality and inclusiveness for water secu- rity in a city. We offer an exploratory analysis of such approach by using the city of Campinas in Brazil as a case study. The paper is structured as follows: the next section describes the methods used in the devel- opment of the assessment framework, including the dimensions considered and the corre- sponding indicators, as well as the context of the city of Campinas and how data was obtained for the case study. We also present the data processing and analysis methods that are used in the framework. This is followed by a section presenting the results of the application of the framework to the city of Campinas, where we discuss the findings and highlight how inequali- ties emerge from the qualitative and quantitative analysis of spatial variation. Finally, we pro- vide some perspectives on the approach and end with concluding remarks. Materials and methods Assessment framework Based on the analysis of gaps in existing water security assessment frameworks reported in lit- erature [11, 24–27], an indicator based framework was created to evaluate urban water secu- rity. The choice and classification of indicators was guided by the United Nations definition [28] of water security—considered as an interdisciplinary, holistic and well-accepted view of the concept [7, 24, 29]. Indicators were divided into different hierarchical levels: first the four dimensions, following the UN water security infographic [8], then categories characterised by one or more indicators. The aspects included in the framework are presented in the Table 1 that also provide references of works adopting similar variables to the assessment of water security. Dimension A: Drinking water and human well-being encompasses some of the funda- mental aspects of water security such as having enough water in terms of quantity and quality available for basic needs. We also include in this dimension measures to indicate access to basic urban water services such as piped drinking water and wastewater collection at the PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 3 / 25 PLOS WATERTable 1. List of selected indicators used in the framework. Dimension Category Indicator Measure Assessing inequalities in urban water security A Drinking water and well- being A1 Water quantity A1.1 Water demand [26, 30, 31] Domestic water consumption (L/cap/day) A1.2 Water availability [32, 33] A1.3 Diversity of sources [25, 34, 35] Ratio between the average flow of renewable freshwater resources and population (in m3/cap/year) Shannon Index accounting for the proportion of water coming from different sources A1.4 Reserve/storage capacity [25, 34] Storage volume in terms of days of supply A1.5 Water stress [26, 36, 37] Freshwater withdrawn as a percentage of the total available A2 Water quality A2.1 Drinking water quality [23, 25, 37] Proportion of drinking water samples meeting local standards A3 Accessibility to services A3.1 Piped water coverage [23, 36, 38] Percentage of population with access to residential piped water supply A3.2 Sewage coverage [23, 25] A3.3 Affordability [23, 34, 39] Percentage of population with access to residential wastewater collection network Proportion of the household budget spent on water and sanitation services A4 Infrastructure reliability A4.1 Service discontinuity [39–41] Proportion of households affected by supply discontinuity A4.2 Service reliability [41, 42] Ratio of the number of sewer corrective maintenance operations to the extension of the sewage network A4.3 Metering level [36, 43, 44] Percentage of households with metered water A4.4 Water loss [36, 37, 45] Percentage of produced water lost in distribution A5 Public health and well-being A5.1 Incidence of water-borne diseases [26, 36, 41] Occurrence of gastrointestinal diseases in number of cases per year per 100.000 people A5.2 Recreational opportunities [46] Area of the sector contained within a radius of 2 kilometres from a social green area B Ecosystems B1 Environment B1.1 Green areas [36, 43] Proportion of area covered by vegetation C Water related hazards and climate change B1.2 Environmental safety Incidence of vector-borne diseases (cases per year per 100.000 people) B2 Pollution control B2.1 Groundwater quality [30, 47, 48] Assessment based on pollutants concentration, according to local standards B2.2 Surface water quality [11, 41, 48] Assessment based on local standards to protection of aquatic life B2.3 Wastewater treatment rate [36, 49, 50] Percentage of collected wastewater treated before discharge B3 Usage efficiency B3.1 Energy usage efficiency [37, 51] B3.2 Wastewater reuse (recycling) [36, 43, 52] Energy consumption by the removal efficiency of wastewater treatment plants Ratio of wastewater reused to wastewater treated B4 Solid waste B4.1 Solid waste collection [30, 48] Coverage of door-to-door solid waste collection B4.2 Recyclable waste collection [48] Coverage of door-to-door recyclable waste collection C1 Water-hazards C1.1 Flood frequency [25, 26, 36] Flood occurrences over a decade C1.2 Drought frequency [43, 47] Drought occurrences over a decade C1.3 Flood- prone areas [47, 53] Percentage of area susceptible to flooding C1.4 People living under hazardous zones [52] Percentage of people living under hazardous zones C2 Preparedness C2.1 Risk Management [25, 54, 55] Qualitative measurement to evaluate disaster prevention and risk management C2.2 Urban drainage [26, 37, 56] Storm drains coverage C2.3 Paved streets Pavement coverage C2.4 Drainage investment [55] Percentage of budget destined to rainwater management C3 Climate change C3.1 Greenhouse gas emissions [43, 48, 57] Emission of greenhouse gases expressed in tonnes of CO2 equivalent per capita C3.2 Temperature increase [43, 58] Average annual temperature increase C3.3 Extreme rainfall events Number of extreme rain events over a decade PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 (Continued ) 4 / 25 PLOS WATERTable 1. (Continued) Dimension D Economic and social development Category D1 Governance Assessing inequalities in urban water security Indicator Measure D1.1 Communication and access to information [59] Qualitative assessment over effective government communication and information access D1.2 Public participation opportunities [26, 60] D1.3 Equality and non- discrimination Qualitative assessment on significant participation opportunities Qualitative assessment on representation diversity in decision making groups D1.4 WASH investment [31, 49] Percentage of the GDP invested in water and sanitation D1.5 Water self-sufficiency [37, 49] Proportion of water withdrawal taken from within own territory D1.6 Organisational structure [26, 47, 61] Qualitative assessment on organisational structure. D2 Social aspects D2.1 Literacy rate [30] D2.2 Population density [56, 62] D2.3 Inequality [30] Percentage of population more than 15 years old that is able to read Population density in the urban area (inhabitants per km2) Gini coefficient, representing the degree of inequality in the distribution of income D2.4 Income [62] Ratio of average income and minimum wage D2.5 Informal dwellings [26, 30] Percentage of population living in informal settings D2.6 Gender equality Ratio of average income from households headed by women and men D3 Economic development D3.1 Per capita GDP [63] Ratio of GDP and population D3.2 Water productivity Ratio of GDP and total freshwater withdrawal https://doi.org/10.1371/journal.pwat.0000213.t001 household, as well as measures of how reliable these services are in the urban area. Finally, we consider the safeguard of health and well-being [8] in the city by including indicators of the incidence of water-borne diseases and access to social green spaces. The status of water resources, pollution-related aspects (including wastewater treatment), vegetation cover, effi- ciency of resource use and solid waste management are grouped under dimension B: Ecosys- tems. Dimension C: Water related hazards and climate change includes water hazards, resilience and protection infrastructure as well as indicators related to changing climate. Finally, social, economic and governance aspects of water use are included under dimension D: Economic and social development. Once populated, since originally expressed in different units, the indicators were normal- ised between 0 and 1 following thresholds based on references from the literature and regional values [23, 47]. Detailed information on the measures for each indicator and the normalisation procedure is presented as supplementary material (see S1 File). Scores range from 1 to 0, with desirable characteristics given ‘1’ and undesirable values, ‘0’. In order to calculate sub-indexes, the indicators are aggregated first by category and then by dimension, using the arithmetic mean of the indicators’ scores. Study area The framework was applied to the city of Campinas in Brazil (see Fig 1A), the third most popu- lous municipality in São Paulo state with an estimated population in 2020 of 1,213,792 people in a territory of 794,571 km2 [64]. One of the richest cities in Brazil, Campinas has gone through an accelerated urbanisation process in the last decade. Campinas, as many other cities in Brazil, is challenged by fast growth and urbanisation—between 1990 and 2018, the popula- tion of Campinas grew by 70% [64] and the urban area increased by 72% [65]. It has nonethe- less resources to monitor its infrastructure and potential to improve its urban water security. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 5 / 25 PLOS WATERAssessing inequalities in urban water security Fig 1. Study area. Location of the municipality of Campinas, Brazil and its territorial division: (A) Country and State and (B) Campinas territorial units. Country and State basemaps source: IBGE (Brazilian Institute of Geography and Statistics) https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais/15774-malhas.html [67, 68] available under open license. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g001 In addition, Campinas has five water treatment plants and, located at the meeting of three river basins, it has a collection system divided into 15 sewer catchments relying on over 20 wastewater treatment plants to serve the urban area [66], which makes this city an interesting case study for geospatial analysis of water security. The municipality recognises 77 territorial units within the urban perimeter and eight in the rural area [70]. These territorial units are defined by the city’s development plan [70, 71] as the smallest territorial divisions (Fig 1B) that configure portions of the urban space that maintain a significant degree of homogeneity in terms of patterns or use of land and socio-economic characteristics [71]. Already used by the local government, considering these sectors would facilitate communication with stakeholders, therefore, we adopted these as spatial units for application of the framework and study of urban security distribution in the urban area. Data collection and processing To quantify the indicator variables, secondary data were collected from reliable official data- bases, government agencies and organisations. Sources such as activity reports from the local water utility [72], surveys from the Brazilian National Institute of Statistics [64], municipal diagnostic reports [66], etc, were used for data collection. The use of public data renders the process transparent and reproducible by other parties. The data used in the application ranged between 2010 and 2014 as a consequence of availability. We have chosen to take a snapshot in time to have a consistent relationship between indicators. Using too large a time range could lead to an inconsistent view of the situation. The data sources and time frame can be found in the supplementary material (see S1 File), along with further details on data collection and normalisation. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 6 / 25 PLOS WATERAssessing inequalities in urban water security Data were collected for the city scale and when possible, to sectors within the city. Nonethe- less, data were not always available at the scale of the sectors. In these cases, data were gathered at the smallest possible intra-urban scale and then transformed to the scale of the territorial units for the calculation of the sub-indexes (level of categories and dimensions). This transfor- mation to the required sector scale was carried out using free and open-source software QGIS (version 3.16). Data analysis, normalisation, aggregation, and visualisation was carried out using GeoPandas (version 0.10.2) package for Python. To deal with missing data, a spatial interpolation using the k-nearest neighbours’ method was carried out using the Scikit-learn (version 1.1.1) Python machine learning library. Once the data for all the indicators have been represented in the same scale, sub-indexes were calculated and urban water security maps for each category and dimensions were created to convey their spatial variability. Data analysis The number of divisions inside the city boundaries for the original data scale was considered as the sample size (n) for that measure. For example, an indicator where only one measure was available for the entire city boundary had a sample size of 1, while indicators for which data were available at a small scale, and specific measurements were available for all territorial units had a sample size of 77. The sample size was important to study the distribution of data. A minimum of five points was required for inequality analysis. The Theil entropy index [73], a measure of regional disparities, was adopted as an inequal- ity measure and calculated for the indicators across the sectors. This index measures an entro- pic distance between groups and an ideal state of equality, where all regions would have the same income, for example. It ranges between 0 (for ideal equality) and 1, with higher values indicating higher inequality. Usually adopted to measure economic inequality—used by the OECD to evaluate inequality in terms of productivity (GDP per worker at place of work) and GDP per capita for instance [74]—the Theil index can be employed to measure any variable of interest, from income inequality, to carbon intensity disparities across countries [75] and inequality in access to improved water source [76]. It is calculated according to Eq 1. Theil ¼ � � yi m yi m ln 1 n Xn i¼1 ð1Þ with N as the sample size, yi the indicator (variable of interest) in the sector and μ the mean across the regions. The analysis of inequality is carried out at the indicator level in order to investigate what causes the observed variation in each dimension, but only when a sample size equal or larger than 5 is available. Indicators with higher levels of inequality were selected for an analysis of spatial autocorrelation. This allows us to evaluate how the score of an indicator in a sector correlates with neighbouring observations and to investigate the existence of pat- terns in the geographical distribution of the indicators. The global spatial correlation is a measure of aggregation of an attribute in the entire study area. Derived from the Pearson correlation coefficient, the statistic used is Moran’s I [77]. The null hypothesis tested is that a certain attribute is randomly distributed in the study area and the computation of an empirical p-value allows us to reject or accept the null hypothesis. A sta- tistically significant p-value (we adopt p = 0.05) indicates a spatial distribution of the variable more spatially clustered than expected if the values were randomly allocated. Similar to corre- lation coefficients, the Moran’s I can be positive or negative, between -1 to 1, with the higher correlation strength to values closest to 1 in absolute value. The positive spatial correlation indicates tendency to clustering of similar values while a negative coefficient, the clustering of PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 7 / 25 PLOS WATERAssessing inequalities in urban water security dissimilar values. The global Moran’s I statistic is given by Eq 2. P i I ¼ P i n P jwij P P jwijzizj iz2 i ð2Þ with n the number of observations (spatial units, indexed by i and j), zi the standardised value of the variable of interest at location i, and wij the spatial weight (i-th row and j-th column). Following the analysis of global spatial correlation, a further spatial analysis of local correla- tion was carried out. Using local Moran’s I (or LISA—Local Indicators of Spatial Association), we can identify clusters where unusual values are concentrated in space. Areas where values are above or below the mean are clustered and four situations can be identified: two when regions with high/low indicators are surrounded by regions with similar values (High/High and Low/Low, HH and LL respectively) and two when regions with high/low indicators are close to regions with opposite values (High/Low and Low/High, HL and LH, respectively) [78]. Derived from Moran’s I, the local Moran’s Ii is given by Eq 3: zi m2 wijzj; where m2 ¼ Ii ¼ X P ; ð3Þ j i iz2 n with n the number of units, zi the standardised value of the variable of interest at location i, and wij the spatial weight (i-th row and j-th column). The spatial correlation analysis was car- ried out using PySAL: Python Spatial Analysis Library (version 2.6.0). The code used for the data analysis and result figures presented in this paper is available at: https://github.com/J-Marcal/WSF_IneqAnalysis. Inclusivity in global research Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the supporting information (see S2 File). Results and discussion Urban water security evaluation The task of populating the list of indicators revealed different levels of data availability and granularity for the city of Campinas. Several indicators only had values for the entire city, espe- cially for water quantity, climate change and governance. This process allowed us to audit the accessibility of free data for this case study and to note the impacts on the following assess- ment. Data at a small scale may be further available within stakeholders’ organisations, how- ever, for transparency reasons only freely accessible data were used in this study. Most the of granular data available issued from a decennial national survey carried out by the Brazilian Institute of Geography and Statistics [64]. Incorporating small scale monitoring to the local agenda and making that information available is important to better investigate certain aspects, especially in terms of governance and risks and climate change. Information such as temperature differences in the urban space can provide insights on urban heat island fluctuations for instance. These have been found to be related to urbanisation pattern and hav- ing influence on public health [79], therefore, detailed information on spatial distribution of temperature in urban areas can prompt public action and help improve different dimensions of water security. Nevertheless, small-scale free information from the state or municipality was difficult to find. Data for some indicators, such as diversity of sources (A1.3), metering level (A4.3) and water loss (A4.4) (Dimension A), were only available for the city scale, therefore, all the sectors received the same score and a study of inequality in the city was not possible. This PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 8 / 25 PLOS WATERAssessing inequalities in urban water security was also the case for several aspects of dimensions C and D, for which data at a small scale was less available. This hinders the assessment on the urban water security heterogeneity since it is difficult to conclude if this is related to homogeneity of the urban area or if there was not enough data to translate the existing variability. The results of the assessment at city and sector scales are presented to each of the four dimensions in Figs 2–5. These show the scores attributed for the city as bars and the scores cal- culated for sectors as circular markers. The size of the circular marker indicates the population living in each sector. The scores range from 1 to 0, with desirable characteristics given ‘1’ and undesirable values, ‘0’. This visualisation shows the interest of our framework since it high- lights the dispersion existent within the studied area for high scoring indicators, such is the case of affordability (A3.3) and access to wastewater collection (A3.2). When aggregating the categories for the four dimensions for the sectors in the city, the spa- tial distribution of the results can be visualised, as seen in Fig 6. Different scores are visibly dis- tributed in the urban area, given an indication of existing spatial inequalities of water security. These results show less differentiation for dimensions C and D, for which granular data was less available. Nonetheless, even with the challenge of data availability, adding the spatial dimension to water security assessment allowed us to show, for all four dimensions consid- ered, some variability in the aggregated scores. The results support the need to investigate inequality within the city boundary rather than considering the average value for the entire urban area. The results for the Drinking water and human well-being dimension (A) show that, in gen- eral, few districts have a below average score (Fig 6A), while diversity can be observed within the municipality when investigating separate categories and indicators (Fig 2). Water quantity Fig 2. Results of assessment for city and sector scales for Drinking water and human well-being (Dimension A). https://doi.org/10.1371/journal.pwat.0000213.g002 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 9 / 25 PLOS WATERAssessing inequalities in urban water security Fig 3. Results of assessment for city and sector scales for Ecosystems (Dimension B). https://doi.org/10.1371/journal.pwat.0000213.g003 Fig 4. Results of assessment for city and sector scales for Water related hazards and climate change (Dimension C). https://doi.org/10.1371/journal.pwat.0000213.g004 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 10 / 25 PLOS WATERAssessing inequalities in urban water security Fig 5. Results of assessment for city and sector scales for Economic and social development (Dimension D). https://doi.org/10.1371/journal.pwat.0000213.g005 (A1) was found to be the most concerning category for the case study, with the lowest scores in the dimension, and water stress (A1.5) being the main challenge for the city (see Fig 2). The high concentration of people and economic activities in the region, associated with decreasing water availability over the years results in constant pressure in the basin’s water resources and a low score for the city. The region has faced water crises in 2014 and 2016, while the available water quantity is a continuous concern of local organisations [80]. Regarding accessibility to services (A3), Campinas has been able to establish very good con- ditions in the urban area. Yet, it is possible to see markers with low score, representing sectors where challenges are still present as shown in Fig 2. Data on sewage coverage (A3.2) for instance, showed some deficiency in the infrastructure of certain sectors in the outskirts of the city. For the last decade a plan to achieve universal sanitation has been implemented by the water utility [66]: for the time scale of this study, 83% of the population had access to sewage collation, a percentage that increased to 94% in 2020 [72]. According to the Sustainable Cities Program, Campinas has achieved the goals for water supply and sewage collection and treat- ment from the SDG 6 but still faces challenges regarding water loss [81]. In terms of reliability of services (infrastructure reliability (A4)), measures of non-scheduled maintenance services (service reliability (A4.2)) were found for the different sewage collection systems, allowing visualisation of some variability between the sectors, especially highlighting low scores in the outskirts and south of the municipality. As for public health and well-being (A5), with little incidence of gastrointestinal infections (incidence of water-borne diseases (A5.1)) throughout the territory, the main component leading to diversity in this category was accessibility to green social areas (recreational opportunities (A5.2)). A very dispersed set of results showed an unequal distribution of scores, with districts in the centre having good PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 11 / 25 PLOS WATERAssessing inequalities in urban water security Fig 6. Spatial distribution of water security. Aggregated results for (A): Drinking water and human well-being (B): Ecosystems (C): Water related hazards and climate change and (D): Economic and social development. Labels on the maps show the highest and lowest scores found for each dimension. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g006 access to parks and gardens and therefore high scores while sectors at the outskirts of the city received low scores. The heterogeneity of scores was more prominent for the dimension Ecosystems (B)(see Fig 6B) that also had the lower score, ranging between 0.34 and 0.74 for the urban sectors. Investi- gation of the categories of this dimension showed that indicators related to green coverage and environmental diseases, from the Environment (B1) category, presented relative low average scores and high dispersion within the city boundary (Fig 3). Campinas, as many other urban areas in tropical and subtropical regions, faces challenges with environmental safety (B1.2)—or water-vectored—diseases, such as dengue fever. These are related to high population density, irregular supply, waste management, etc [82, 83]. The results also demonstrate challenges regarding green coverage (green areas (B1.1)). These are common to the urban context, due to the urbanisation process and high urbanisation rate in the city (in Campinas, of about 98%) [64]. In terms of the pollution control (B2) category, intra-city granular data for groundwater and surface water quality (B2.1 and B2.2) were not available, and therefore, little differentiation was PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 12 / 25 PLOS WATERAssessing inequalities in urban water security observed for these aspects. As for wastewater treatment rate (B2.3), data from wastewater col- lection systems allowed us to verify diversity within the city. For the time scale analysed, improvement was required in some sectors, especially in the south of the city. However, sub- stantial investment has taken place in the last decade which improves the score for this indica- tor. The wastewater treatment rate in the city increased from 72% in 2010 to 89% in 2020, with the water utility goal expected to be reaching 100% by 2025 [72]. A reuse water station, using membrane bioreactor (MBR) technology, is installed and in operation since 2012 in the south of the city. For this area, high removal efficiency is accompa- nied by high energy consumption, leading in some sectors to relatively low scores for the energy usage efficiency (B3.1) indicator [72] (Fig 3). Other districts that have their wastewater treated by energy demanding activated sludge and aerated ponds technology, also had lower scores for this indicator. As to wastewater reuse (B3.2), the practice is still limited due to legis- lation restrictions, resulting in a very low score overall. However, with a second water reuse station inaugurated in 2021, there is great potential to improve usage efficiency in the city of Campinas for the next decade [72]. As for dimension C: Water related hazards and climate change, in terms of water hazards (C1), Campinas did not face any drought during the decade preceding the evaluation date [84], and, although it has faced several flood events, the proportion of flood prone areas varies considerably in the sectors (see Fig 4). As for preparedness (C2), a wide distribution of drainage infrastructure and people living in hazardous areas was found. Nonetheless, due to lack of available granular data for other indicators in the dimension, possible existing spatial variation was attenuated and rendered virtually invisible in the final visualisation map (see Fig 6C). Related to the SDG 13—urgent action to combat climate change and its impacts [85], the scores of dimension C are supported by the results found in the Sustainable Cities Program of which Campinas has taken part since 2012 [81]. This program monitors participant cities in Brazil and evaluates them in terms of the Sustainable Development Index, adopting SDG indi- cators. According to their results, Campinas scores highly in terms of climate change perfor- mance, which also included greenhouse gas emissions and strategies for risk management and prevention of natural disasters. For dimension D—Economic and social development, the spatial distribution of the aggre- gated score was similar to dimension C. It is less noticeable but still exists (see Figs 5 and 6D). This is expected in view of data collection challenges and low sample sizes obtained for some indicators in these dimensions: the lack of data granularity prevents the grasp of urban inequalities. Governance (D1) aspects in particular were only feasible at the city scale and therefore, no distinction is made for the sectors. Granularity was available for social aspects (D2) indicators and therefore, it was possible to observe a distribution of scores in the city for this category (see Fig 5). Gender equality (D2.6) results showed low scores throughout the municipality with only few sectors with a scores above 0.5. This was confirmed by the a similar low score received by the city of Campinas in the Sustainable Cities Program [81] for the SDG 5—Achieve gender equality and empower all women and girls, considering participation of women in decision making positions, wage inequality among others, major challenges were identified in order to achieve this specific goal. Interestingly, the score for income inequality (D2.3) was smaller for the city than for the sectors, an indication that the sectors are somewhat homogeneous, but differences can be found between them. This is supported by the results of average income (D2.4) that show a great dispersion of results (see Fig 5). As for economic devel- opment (D3) indicators, data were available only for the city scale, and translated the favour- able economic position of the city—Campinas is a relatively wealthy city with one of the highest GDPs of the state [64]. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 13 / 25 PLOS WATERAssessing inequalities in urban water security The use of granular data and spatial visualisation clearly highlights the intra-urban variabil- ity for the different water security aspects. Similar to the results of Tholiya and Chaudhary [17] on the performance water supply services and Doeffinger and Hall [14] on sub-national water security assessment, the geospatial visualisation demonstrates the heterogeneity of the studied area. This helps to expose vulnerable regions, and therefore, could inform and support effec- tive decision making. Assessing inequality The inequality of the water security indicators is measured in terms of the Theil entropy index. Results are presented in Fig 7. This figure shows the results of the inequality index against the scores for the sectors, with the ideal setting being high scores and low inequality index (0 would be ideal equality)—the bottom right quadrant, where most indicators are placed for Campinas. Among the indicators from dimension A, data for recreational opportunities (A5.2) show a high inequality score (see Fig 7). Recreational green areas are important for well-being and life quality in urban spaces, nevertheless, intense urbanisation can often neglect this aspect. Cam- pinas, in 2010, had 23 parks and other public green spaces for a population of 1,080,113 people [64], nonetheless, these were concentrated in certain areas and according to the local Environ- mental Office, 70% of the districts had no local social green area [86]. In our study we consider the proximity of people to these areas, but we still find almost 20% of districts with no public Fig 7. Inequality vs scores quadrant plot of inequality indexes versus scores for the analysed indicators. https://doi.org/10.1371/journal.pwat.0000213.g007 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 14 / 25 PLOS WATERAssessing inequalities in urban water security green area within a 30-minute walk. Considering the distance to these local areas also has an effect on the distribution of the results. Even so, the presence of a range of scores shows inequality and consequently different levels of well-being resulting from the access to green areas. The disparity is being addressed by the local government—a municipal Green Plan, established in 2016, targets the deficit of social green areas and aims to implement linear parks in the city [86]. For the accessibility to services category (A3), very high scores were obtained overall, with water supply coverage (A3.1) specially clustered with low inequality index (see Fig 7) associated with high scores, indicating a very favourable situation for the city—99.5% of the urban popu- lation is connected to the drinking water supply network [66]. The results for wastewater col- lection coverage (A3.2), on the other hand, show a higher dispersion and larger range of scores. Over 80% of the urban population had access to sewage collection in 2010 [72], and a Sanita- tion Program is in place aiming to provide the entire city with this service [66]. Nonetheless, the data set in this study shows areas, especially at the urban edges, where the population still lacks sewage connection, relying on individual solutions [66]. Data are especially unequal for green areas (B1.1) and environmental safety (B1.2), for which data on the occurrence of environmental safety diseases are not only scattered but also tending to low scores, resulting in the highest inequality index of the dimension. In 2010, Campinas faced a large dengue fever epidemic with the majority of cases in health centres in the Northwest area of the city [87]. In this study, low scores were attributed to several districts based on data from 61 local health centres, which, overall contributed to the resulting low and disperse score of the indicator and, therefore, of dimension B. Despite that, Campinas has resources to carry out prevention and warning actions and in 2015 the municipal government established a committee for combating arbovirus infections (such as dengue, yellow and Zika fevers) and coordinate prevention and response actions between different stakeholders [88]. Also a concerning aspect for the dimension B, the overall percentage of green areas (B1.1) to the total area is low in Campinas and in addition, the data show an unequal distribution regarding vegetation coverage, with specially low percentage in the city centre. This is closely related to the urbanisation process, high urbanisation rate (about 98%) [64] and population density [86]. Since 2013, the municipality has worked on the recovery of green areas by plant- ing trees and improving the inspection to promote natural regeneration [86]. In contrast, solid waste collection (B4.1) presented a very clustered and high score result, with lower inequality index (see Fig 7). As for dimension C, flood-prone areas (C1.3) and presence of storm drains (urban drainage (C2.2)) presented average scores and the highest inequality results for the dimension. The flood-prone areas (C1.3) are often related to insufficient drainage systems, increase of imper- meable areas and occupation of valleys [66]. In terms of urban drainage (C2.2), data show that only 57% of the public roads have underground storm drains in the urban area [89]. Even if one argues that not all roads need storm water drains due to the geography of the watersheds, the results still show an important variation in the urban zone that can increase the vulnerabil- ity of certain areas. The other indicators analysed for this dimension (paved streets (C2.3) and people living in hazardous zones (C1.4)) are located at the bottom right quadrant, showing an overall good score and low inequality measure. This is compatible with the situation in Campi- nas, where a total of 2% of the of the households living at risk according to the municipal civil defence [89] and the majority of the streets in the urban area are paved (95% [66]). Concerning social aspects (D2), literacy rate (D2.1) presented the highest overall score amongst the analysed indicators of dimension D. Literacy is crucial for the understanding of water issues and therefore the success of collective action. With a very clustered data set (low inequality index, as seen in Fig 7), the analysis shows a very favourable and consistent situation PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 15 / 25 PLOS WATERAssessing inequalities in urban water security for Campinas, yet, when considering the large number of inhabitants of the city, in 2010 the number of people above 15 years old who were not able to read and write was over 28 thou- sand people [64]. Since 2014 a campaign to end illiteracy has been carried out by the munici- pality, showing great progress in the last decade: the illiteracy rate dropped 46% by 2019 [90]. In terms of income, analysis of the Gini Coefficient (inequality (D2.3)) showed that income inequality inside the districts (comparing incomes inside the same sector) resulted in a rather clustered data set. Interestingly, the results for average income (D2.4) in the city showed a more spread-out behaviour with higher inequality index. This indicates that, while inside the sectors a more homogeneous situation in terms of income may be found, different sectors are living different realities: results showed an average income ranging between 2.5 and 35 mini- mum wages [64]. The lowest average incomes were found to be in the south, southwest and north edges of the city, somewhat coinciding with areas where deficit of infrastructure was observed in the other dimensions. The population living in informal settlements (informal dwellings (D2.5)), considered in the assessment of SDG 11- Make cities and human settlements inclusive, safe, resilient and sustain- able, is identified by the Sustainable Cities Program as a big challenge for Campinas [81]. The results in this study showed a generally clustered data set for this indicator (D2.5). This is due to the vast majority of districts having no or a small percentage of people living in such settings and therefore, high scores for this indicator. Nonetheless, the outliers in this case are signifi- cant: a few districts, especially in the south of the city, have higher proportions with up to 80% of the residents living in informal settlements [64]. These areas are classified as highly vulnera- ble by the São Paulo Social Vulnerability Index, an assessment tool to identify areas most vul- nerable to poverty [91]. Another social aspect that deserves attention is gender equality (D2.6), with low scores across the city (see Fig 7). Related to SDG 5—Achieve gender equality and empower all women and girls, this indicator shows great challenges for the city of Campinas (SDG 5 in Campinas received the lowest score in the Sustainable Cities Program evaluation [81]), translating the inequality of incomes in households headed by women and men. The present analysis placed this indicator in the bottom left quadrant of Fig 7 indicating a deficient and considerably uni- form situation with low scores and low dispersion and inequality measures. The quantification of inequality for water security indicators provides a valuable tool for decision making. It raises flags on which indicators show a wide, non-uniform distribution in the urban area. In addition, including this aspect allows us to quantitatively consider water security equity in the city, informing decision makers on aspects that require action to tackle inequalities. Spatial variation. Dimension A, on drinking water and human well-being showed impor- tant variability for certain aspects such as access to recreational areas (A5.2) and wastewater collection (A3.2). The results from the spatial analysis showed some overlay between the low scoring regions for these indicators (Fig 8). Wastewater collection (Sewage coverage(A3.2)) scores showed a significant positive spatial correlation, with a Moran’s I value of 0.522 and p-value of 0.001. This indicates a tendency of similar values being clustered in space. The results for local spatial correlation analysis showed the spatial association around each individual sector. For (A3.2), sectors with high scores for wastewater collection, near neighbourhoods that also have a high score (high/high score), are located in the city centre as seen in Fig 8A. This area is therefore composed of a group of sec- tors that have a very good infrastructure in terms of wastewater collection, while a cluster of low scoring areas near other low areas (LL) are found in the northern and southern outskirts of the city (see Fig 8A). The deficient areas (Low/low association, or cold spots) identified make up 8% of the urban area analysed and take in 4% of the population of Campinas. These PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 16 / 25 PLOS WATERAssessing inequalities in urban water security Fig 8. Moran cluster maps for dimension A. (A): Moran cluster map for indicator (A3.2) Sewage coverage and (B): Moran cluster map for indicator (A5.2) Recreational opportunities. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g008 results are in agreement with the diagnostics obtained in the Municipal Sanitation Plan of 2013 [66], particularly with respect to the neighbourhoods that lack sewage collection infra- structure. The cluster in the south of the city encompasses vulnerable neighbourhoods charac- terised by high population density, low income, and informal settings. The north cluster units do not include informal settlements and, although not as socially vulnerable as the ones in the south cluster, consist of isolated urban patches across the rural area. This entails certain infra- structure shortcomings such as households relying on individual solutions for wastewater management. A similar trend is found for recreational opportunities (A5.2) (see Fig 8B), for which low/ low areas (cold spots) are situated on the suburbs (covering 8.9% of the urban area and near 5% of the population) while more central areas appear as a high scoring cluster. The develop- ment of green social areas was found to be associated with public or private interests during the urbanisation process of the city [92]. That led to parks and other social green areas being located in more developed areas, where there was interest of capital, contributing to the observed inequalities. For this indicator, a negative local association is observed: a unit with low score, that is, a sector with little access to green social areas but surrounded by districts with accessible parks and other recreational opportunities. Despite that, overall, the indicator shows a positive global spatial correlation (similar regions tending to cluster) with Moran’s I of 0.659 and p-value of 0.001. Contrary to the trends observed with the indicators belonging to dimension A, areas with a low score for green areas (B1.1) appear clustered in the centre of the municipality (see Fig 9A). With a positive overall correlation (Moran’s I of 0.303 and p-value of 0.002), the local analysis showed clusters of low/low association (cold spots) in the highly urbanised and dense city cen- tre. The areas included in this cluster house 17% of the urban population and count for 8% of the area. This situation, connected to the urbanisation rate and process in the city, is closely related to the environmental pressures and the need to increase green areas in the city. The most recent municipal plans for conservation and recovery of native vegetation targets urban PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 17 / 25 PLOS WATERAssessing inequalities in urban water security Fig 9. Moran cluster maps for dimension B. (A): Moran cluster map for indicator (B1.1) Green areas and (B): Moran cluster map for indicator (B1.2) Environmental safety. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g009 green areas as well as plans for the construction of several linear parks within the urban area have been announced [86]. In terms of environmental safety (B1.2), areas with low scores (high incidence of environ- mental related diseases, such as dengue fever) show tendency to gather (positive spatial Moran ´s I of 0.467, p-value of 0.001) in the northwest and south of the city (see Fig 9B). The contrib- uting factors to the observed clustering pattern may be related to heterogeneity of infrastruc- ture, land occupation or life habits [88]. The clusters of low scores are consistent with the areas of high incidence of dengue fever identified by Johansen et al. [82] when analysing the rela- tionship between social inequality and dengue fever incidence in Campinas. They emphasise the expansion of the peri-urban areas as a cause of spatial segregation and inequality in the access to urban resources and services. The low values of dispersion and inequality observed for dimension C is also translated to the analysis of spatial autocorrelation. The indicator on the presence of storm drains (urban drainage (C2.2)) presents a data set with a low tendency to cluster with Moran´s I of 0.193 (p- value of 0.015). A small area of cold spots is observable in the northern outskirts of the city in a small area corresponding to 2.3% of the urban area housing a little over 0.33% of the urban population (see Fig 10A). The region is also an area of low/low scores association for wastewa- ter collection (A3.2) and recreational opportunities (A5.2), indicating a set of challenges in the area. As for dimension D, income (D2.4) showed higher values of dispersion and Theil index. Presenting a positive tendency to cluster (Moran´s I of 0.575, p-value of 0.001), a large cold spot (low/low) is found in the southwest of the city (see Fig 10B), covering 25% of the area and 23% of the urban population. The area in the south of the city is also part of the identified low/ low scoring association clusters for wastewater collection (A3.2) and recreational opportunities (A5.2). This is a region where informal settings (D2.5) are predominant and communities are highly social vulnerable according to the São Paulo Social Vulnerability Index [91]. It is also noticeable that the identified cluster of high income areas (high/high score association) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 18 / 25 PLOS WATERAssessing inequalities in urban water security Fig 10. Moran cluster maps for dimensions C and D. (A): Moran cluster map for indicator (C2.2) Urban drainage and (B): Moran cluster map for indicator (D2.4) Income. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g010 presents some overlap with high scoring areas for access to recreational areas (A5.2) (as seen in Fig 8B). These overlays and regional disparities identified show that areas rarely face one specific water security challenge. As the dimensions of water security are interconnected, so are the challenges and advantages brought by infrastructure, policies, and management strate- gies. Therefore, a holistic and in-depth water security evaluation is crucial for sustainable urban water management. Perspectives on the approach We applied a holistic framework to assess water security in the city of Campinas, Brazil, and investigate the heterogeneity of its aspects in the urban context. This was done by incorporat- ing inequality and spatial analysis to the assessment in order to reveal what the challenges are and how they are distributed in the urban area. This study was also presented to experts and water professionals in the field that have provided valuable feedback and perspectives on this approach. Although data availability is viewed as a challenge to such detailed analysis, the potential of this downscaled assessment lies in the visual component which enables identifying what and where the urban water security problems are. This is considered to be an important asset to communication with policymakers. A follow up on possible solutions for local issues and their costs could then lead to regenerative actions. This type of approach can help raise ‘red flags’ in terms of what areas are being overlooked and realities that are getting lost in averages. There is also potential in learning from within the city: sharing experiences and successes between different sectors or neighbourhoods on local initiatives, as well between stakeholders from different areas, equivalent to city-to-city learning [93]. The work for such detailed analysis is more labour-intensive than traditional water security assessment frameworks, but the involvement of stakeholders, when applying such approach can help obtain data and determine priorities. It is also important to consider the flexibility in terms of choice of indicators: this approach can be carried out for any indicator, depending on PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 19 / 25 PLOS WATERAssessing inequalities in urban water security local issues and data availability. This flexibility should still be guided by the concept of water security and the different dimensions and aspects involved. Certain indicators adopted in this study, especially the inclusion of solid waste management, drew attention to its importance in water management within urban areas. Including the inequality index as an extra measure and the spatial analysis to assess water security is therefore an asset to reveal hidden issues and tackle inequality in a local and targeted manner. Conclusions Including spatial and inequality analysis deepens the assessment of water security in the urban context. Downscaling the water security assessment presents both an opportunity and a chal- lenge. Increasing the granularity of the evaluation allows incorporating the spatial dimension in the assessment and therefore investigating inequalities within urban boundaries. On the other hand, large data availability is required for evaluation. In general, adding the spatial component to water security assessment enriches the evalua- tion allowing identification of spatial inequalities. The hierarchical approach allows each level to be uncovered to investigate where the differences lay. Challenges can then be pinpointed, and solutions proposed. For that, information at a smaller scale is key. Downscaling water security assessment is therefore a way to also audit the accessibility of data. Since large scale data can mask variability, downscaled assessment has the potential to encourage small-scale monitoring in urban areas, which, in turn, can promote the analysis of water security inequali- ties. Including measures of inequality in the urban water security assessment helps to identify aspects for which the city has reached an overall positive situation and where important differ- ences still linger. This will then create incentives and opportunities to leave no-one behind. The presented case study analysis allowed identification of local challenges for Campinas. While infrastructure challenges still remain in sectors in the north and south of the city, the highly urbanised centre lacks green coverage. Despite being a rich city, income inequality is present and the connection between economic and social vulnerability with other aspects of water security was identified. There is potential to achieve a more sustainable water cycle, espe- cially in terms of wastewater reuse. Actions by the municipality, such as the Sanitation Pro- gram, show great effort to ensure equitable water services in the city. The assessment for Campinas represents a snapshot in time, with more recent data having been delayed due to the COVID-19 pandemic. Incorporating the temporal aspect in the analysis would allow compari- son of the progress of the city in each water security dimension. This would be a valuable con- tribution for future work. Ultimately, the proposed assessment delivers a visual tool to communicate regional dispari- ties and challenges in the urban area. This can help facilitate communication with different stakeholders by including what and where in the outcomes of the urban water security assessment. Supporting information S1 File. Normalisation of indicators. Description of indicators, metrics, data sources, and normalisation thresholds. (PDF) S2 File. Inclusivity in global research. PLOS Inclusivity in global research questionnaire. (DOCX) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000213 February 1, 2024 20 / 25 PLOS WATERAssessing inequalities in urban water security Author Contributions Conceptualization: Juliana Marc¸al, Jan Hofman. Data curation: Juliana Marc¸al. Formal analysis: Juliana Marc¸al. Investigation: Juliana Marc¸al, Jan Hofman. Methodology: Juliana Marc¸al, Jan Hofman. Supervision: Junjie Shen, Blanca Antizar-Ladislao, David Butler, Jan Hofman. 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10.1371_journal.ppat.1011871	RESEARCH ARTICLE Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection Dat Mai1, Ana Jahn1, Tara Murray1, Michael Morikubo1, Pamelia N. Lim2,3, Maritza M. Cervantes2, Linh K. Pham2,4, Johannes Nemeth1¤, Kevin Urdahl1, Alan H. Diercks1, Alan Aderem1, Alissa C. RothchildID 2* 1 Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, United States of America, 2 Department of Veterinary and Animal Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America, 3 Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America, 4 Animal Biotechnology and Biomedical Sciences Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America ¤ Current address: University Hospital Zurich, University of Zurich, Division of Infectious Diseases and Hospital Epidemiology, Zu¨rich, Switzerland * arothchild@umass.edu Abstract Alveolar macrophages (AMs) play a critical role during Mycobacterium tuberculosis (Mtb) infec- tion as the first cells in the lung to encounter bacteria. We previously showed that AMs initially respond to Mtb in vivo by mounting a cell-protective, rather than pro-inflammatory response. However, the plasticity of the initial AM response was unknown. Here, we characterize how previous exposure to Mycobacterium, either through subcutaneous vaccination with Mycobac- terium bovis (scBCG) or through a contained Mtb infection (coMtb) that mimics aspects of con- comitant immunity, impacts the initial response by AMs. We find that both scBCG and coMtb accelerate early innate cell activation and recruitment and generate a stronger pro-inflamma- tory response to Mtb in vivo by AMs. Within the lung environment, AMs from scBCG vaccinated mice mount a robust interferon-associated response, while AMs from coMtb mice produce a broader inflammatory response that is not dominated by Interferon Stimulated Genes. Using scRNAseq, we identify changes to the frequency and phenotype of airway-resident macro- phages following Mycobacterium exposure, with enrichment for both interferon-associated and pro-inflammatory populations of AMs. In contrast, minimal changes were found for airway-resi- dent T cells and dendritic cells after exposures. Ex vivo stimulation of AMs with Pam3Cys, LPS and Mtb reveal that scBCG and coMtb exposures generate stronger interferon-associated responses to LPS and Mtb that are cell-intrinsic changes. However, AM profiles that were unique to each exposure modality following Mtb infection in vivo are dependent on the lung environment and do not emerge following ex vivo stimulation. Overall, our studies reveal signifi- cant and durable remodeling of AMs following exposure to Mycobacterium, with evidence for both AM-intrinsic changes and contributions from the altered lung microenvironments. Com- parisons between the scBCG and coMtb models highlight the plasticity of AMs in the airway and opportunities to target their function through vaccination or host-directed therapies. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mai D, Jahn A, Murray T, Morikubo M, Lim PN, Cervantes MM, et al. (2024) Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection. PLoS Pathog 20(1): e1011871. https://doi.org/10.1371/journal. ppat.1011871 Editor: Padmini Salgame, New Jersey Medical School, UNITED STATES Received: August 3, 2023 Accepted: November 27, 2023 Published: January 18, 2024 Copyright: © 2024 Mai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Raw and processed RNA-sequencing data can be accessed from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under accession number GSE212205. Funding: This work was supported by National Institute of Allergy and Infectious Disease of the National Institute of Health under Awards U19AI135976 (A.A.), R01AI032972 (A.A.), 75N93019C00070 (K.U., A.C.R., A.A.), and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 1 / 28 PLOS PATHOGENSR21AI163809 (A.C.R.). J.N. was supported by the Swiss National Foundation under grant 310030_200407. P.L. was supported by National Research Service Award T32 GM135096 from the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.N. received honoraria for presentations from Oxford Immunotec, Gilead and ViiV. Alveolar macrophage remodeling by Mycobacterium Author summary Tuberculosis, a disease caused by the bacteria Mycobacterium tuberculosis (Mtb), claims around 1.6 million lives each year, making it one of the leading causes of death worldwide by an infectious agent. Based on principles of conventional immunological memory, prior exposure to either Mtb or M. bovis BCG leads to antigen-specific long-lasting changes to the adaptive immune response that can be effective at protecting against subsequent chal- lenge. However, how these exposures may also impact the innate immune response is less understood. Alveolar macrophages are tissue-resident myeloid cells that play an impor- tant role during Mtb infection as innate immune sentinels in the lung and the first host cells to respond to infection. Here, we examined how prior Mycobacterium exposure, either through BCG vaccination or a model of contained Mtb infection, impacts the early innate response by alveolar macrophages. We find that prior exposure remodels the alveo- lar macrophage response to Mtb through both cell-intrinsic changes and signals that depend on the altered lung environment. These findings suggest that the early innate immune response could be targeted through vaccination or host-directed therapy and could complement existing strategies to enhance the host response to Mtb. Introduction Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis (TB), claimed more than 1.6 million lives in 2021. For the first time since 2005, the number of TB deaths worldwide is increasing [1,2]. These trends highlight the urgent need for new vaccine and therapeutic strategies. Traditionally, vaccine design has focused on generating a rapid, robust, and effective adaptive immune response. However, recent studies suggest that the innate immune system can undergo long-term changes in the form of trained immunity [3], which affect the outcome of infection and could function as important components of an effective TB vaccine [4,5]. Ini- tial trained immunity studies focused on central trained immunity, long-term changes to hematopoietic stem cells that lead to functional changes in short-lived innate cell compart- ments (i.e., monocytes, NK cells, dendritic cells) [3]. More recent studies have examined innate training in tissue-resident macrophages and demonstrated that these cells are also affected by prior exposures. Tissue-resident macrophages can respond to remote injury and inflammation [6], undergo long-term changes [3], and display altered responses to bacteria after pulmonary viral infection [7–9]. Lung resident alveolar macrophages (AMs) are the first cells to become infected with inhaled Mtb and engage a cell-protective response, mediated by the transcription factor Nrf2, that impedes their ability to effectively control bacterial growth [10,11]. In this study, we exam- ined how prior mycobacterial exposure reprograms AMs and alters the overall innate response in the lung to aerosol challenge with Mtb. To evaluate the range of AM plasticity, we chose to compare the effects of subcutaneous BCG vaccination (scBCG) with those arising from a con- tained Mtb-infection (coMtb) model. BCG, a live-attenuated TB vaccine derived from M. bovis and typically given during infancy, provides protection against disseminated pediatric disease but has lower efficiency against adult pulmonary disease [12–14]. In addition to enhancement of Mtb-specific adaptive responses, based on shared antigens, BCG vaccination also leads to changes in hematopoiesis and epigenetic reprogramming of myeloid cells in the bone marrow [15], early monocyte recruitment and Mtb dissemination [16], and innate acti- vation of dendritic cells critical for T cell priming [17]. Intranasal BCG vaccination protects PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 2 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium against Streptococcus pneumoniae and induces long term activation of AMs [18]. A recent study has shown that one mechanism by which BCG vaccination can elicit innate training effects on AMs, separate from alterations to the monocyte population, is through changes to the gut microbiome and microbial metabolites [19]. BCG vaccination is also associated with trained immunity effects in humans [20–22], including well-described reductions in all-cause neonatal mortality and protection against bladder cancer [3,23]. The coMtb model is generated by intradermal inoculation with virulent Mtb into the ears of mice and leads to a contained but persistent lymph node Mtb infection [24,25]. The model replicates observations in both humans and non-human primates (NHPs) that prior exposure to Mtb infection provides protection against subsequent exposure, through a form of concomi- tant immunity [26,27]. In a previous study, we found that coMtb leads to protection against challenge with aerosol Mtb infection and protects mice against heterologous challenges, including infection with Listeria monocytogenes and expansion of B16 melanoma cells, results which suggest there is substantial remodeling of innate immune responses [25]. We found that AMs from coMtb mice mount a more inflammatory response to Mtb infection compared to AMs from control mice, and the enhancement in AM activation after infection, as measured by MHC II expression, was dependent on IFNγR signaling [25]. Here, we show that while both coMtb and scBCG protect against low dose Mtb aerosol challenge, they remodel the in vivo innate response in different ways. In AMs, scBCG elicits a very strong interferon response in AMs, while coMtb promotes a broader pro-inflammatory response that is less dominated by Interferon Stimulated Genes. Prior exposure to Mycobacte- rium also remodels the frequency and phenotype of AM subsets in the lung prior to aerosol challenge and leads to significant changes in the early dynamics of the overall innate response. While changes in the AM responses that are unique to each exposure (scBCG, coMtb) depend on the lung environment, stronger interferon-associated responses following both LPS and Mtb stimulation ex vivo reveal cell-intrinsic changes. Results Prior exposure to Mycobacterium accelerates activation and innate cell recruitment associated with Mtb control We first determined the earliest stage of infection when the immune response was altered by prior exposure to Mycobacterium. Mice were vaccinated with scBCG or treated with coMtb, rested for 8 weeks, and then challenged with low-dose H37Rv aerosol infection. We measured both the cellularity and activation of innate immune cells in the lung at 10, 12 and 14 days fol- lowing infection, the earliest timepoints when innate cells are known to be recruited [10,11,28]. We observed a significant increase in MHC II Median Fluorescence Intensity (MFI) as early as day 10 for AMs from coMtb mice and day 12 for AMs from scBCG mice compared to controls (Figs 1A and S1). There were no significant differences in MHC II expression prior to challenge on day 0 (Fig 1A). There were also significant increases in the numbers of monocyte-derived macrophages (MDM), neutrophils (PMN), dendritic cells, and Ly6C+ CD11b+ monocytes by day 10 in coMtb mice compared to controls, with further increases by days 12 and 14 (Figs 1B and S1). scBCG elicited similar increases in these popula- tions starting at day 10, but the increases were not as robust or rapid as those observed in coMtb. Significant differences between scBCG and coMtb groups were found at days 10, 12, and 14 in MDM, day 14 in PMN, days 12 and 14 in dendritic cells, and day 14 in Ly6C+ CD11b+ monocytes (Fig 1B). While there were not significant differences in AM cell number between the three conditions, there was a modest drop in viability for both AMs from scBCG and coMtb mice by day 14 (Fig 1C). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 3 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 1. Prior exposure to Mycobacterium leads to faster activation and innate cell recruitment following aerosol Mtb challenge. Control, scBCG, and coMtb mice, 8 weeks following exposure, challenged with standard low-dose H37Rv. Lungs collected on day 10, 12, and 14 post-infection. A) AM MHC II MFI. B) Total numbers of MDMs, PMN, DC, and Ly6C+CD11b+ monocytes. C) AM viability (% Zombie Violet-). D) Total numbers of CD44+ CD4+ T PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 4 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium cells, ESAT6-tetramer+ CD4+ T cells, CD44+ CD8+ T cells, and TB10.4-tetramer+ CD8+ T cells. Mean +/- SEM, 5 mice per group, representative of 3 independent experiments. One-way ANOVA with Tukey post-test. * p< 0.05, p< 0.01, p < 0.001. B, C) , , and * scBCG or coMtb vs control; +, + + scBCG vs coMtb. https://doi.org/10.1371/journal.ppat.1011871.g001 In addition to early changes in innate cell activation and recruitment, we observed early recruitment of activated CD44+ CD4+ and CD8+ T cells in the lungs of both coMtb and scBCG mice starting at day 10 as well as TB antigen-specific T cells, ESAT6-tetramer+ CD4+ T cells and TB10.4-tetramer+ CD8+ T cells in coMtb mice starting at day 10 compared to con- trols and scBCG mice (Figs 1D and S1). The differences in the recruitment of ESAT6-tetra- mer+ CD4+ T cells between scBCG and coMtb were expected, as the ESAT6 antigen is expressed by H37Rv but not by BCG. We also evaluated whether these cell recruitment differences correlated with changes in bacterial burden. To compile CFU results from three independent experiments, each with slightly different bacterial growth (S2A Fig), we calculated a ΔCFU value that compared the bacterial burden of each sample to the average for the respective control based on timepoint, organ, and experiment. We found that both modalities generated a significant reduction in bacterial burden compared to controls in the lung, spleen, and lung-draining lymph node (LN) at day 14 and at day 28, as previously reported [16,25,29] (S2A–S2D Fig). At day 10, we observed no difference in lung bacterial burden in scBCG or coMtb mice compared to controls and a small increase in coMtb mice over scBCG. The majority of control mice had undetect- able bacteria in spleen and LN at this time. There was a significant reduction in bacterial bur- den in the lung by day 12 in coMtb but not scBCG mice and a significant reduction in CFU in the LN in both models compared to controls (S2B Fig). Our results demonstrate that prior Mycobacterium exposure leads to accelerated innate cell activation and recruitment, alongside an increase in activated T cells, within the first two weeks of infection, with coMtb generating a faster and more robust response compared to scBCG. These early immune changes are asso- ciated with reductions in bacterial load in the lung. Differences in bacterial burden in the LN and spleen suggest delays in bacterial dissemination, which first appear in the LN at day 12 and then in the spleen at day 14 (S2A Fig). Mycobacterium exposure alters the in vivo alveolar macrophage response to Mtb infection To examine the earliest response to Mtb, we measured the gene expression profiles of Mtb- infected AMs isolated by bronchoalveolar lavage and cell sorting, as previously described [10], 24 hours following aerosol challenge with high dose mEmerald-H37Rv (depositions: 4667, 4800) in scBCG-vaccinated mice and compared these measurements to previously generated profiles of AMs from control (unexposed) mice [10] and coMtb mice [25] (S1 Table). As pre- viously observed for the high dose infection, an average of 1.79% (range: 0.91–3.18%) of total isolated AMs were Mtb infected 24 hours after infection. Changes induced by Mtb infection were measured by comparing gene expression between Mtb-infected AMs and respective naïve AMs for each of the three groups (control, scBCG, coMtb). Principal Component Analy- sis on Mtb infection-induced changes showed that each of the three conditions led to distinct expression changes (Fig 2A) and the majority of up-regulated Differentially Expressed Genes (DEG) (fold change > 2, FDR < 0.05) were unique to each condition (control: 151 unique/257 total DEG, scBCG: 222/289, coMtb: 156/229) (Fig 2B). The divergence in the responses of Mtb-infected AMs from each of the 3 conditions was also reflected in the diversity in the Top 20 Canonical Pathways identified by Ingenuity Pathway Analysis (S3 Fig). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 5 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 2. Mycobacterium exposure alters the alveolar macrophage transcriptional response to Mtb infection in vivo. Bulk RNA-seq profiles of Mtb-infected AMs 24 hours following high-dose mEmerald-H37Rv infection. Gene expression changes are compared to respective naïve samples: Mtb-inf control vs naïve control; Mtb-inf scBCG vs naïve scBCG; Mtb-inf coMtb vs naïve coMtb (controls- reported in Rothchild et al, 2019 [10]; coMtb- reported in Nemeth et al, 2020 [25]). A) Principal Component Analysis using DEG (\|fold change\| > 2, FDR< 0.05) in Mtb-infected AMs compared to respective naïve AMs (control, scBCG, or coMtb). B) Venn Diagram and Intersection plot of overlap in up-regulated DEG between the 3 conditions. C) Gene Set Enrichment Analysis of 50 Hallmark Pathways. Pathways shown have \|NES\| > 1.5 and FDR< 0.05 for at least one of the conditions. * FDR< 0.05, FDR< 0.01, FDR< 0.001. D) Heatmap of 131 original in vivo DEG at 24 hours in Mtb-infected AM (left), Interferon Stimulated Genes, derived from macrophage response to IFNα (fold change >2, p- value < 0.01) Mostafavi et al, 2016 [30] (center-left), IL6 JAK STAT3 hallmark pathway (center-right) and selected coMtb signature genes (right, FDR< 0.05, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 6 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium FC> 2). E) Scatterplots depicting fold change (log2) for Mtb-infected AMs over naïve AMs for scBCG versus coMtb. Highlighted pathways: Nrf2-associated genes out of 131 original in vivo DEG (56 genes, purple), shared leading edge genes for scBCG Interferon Alpha Response and Interferon Gamma Response pathways (61 genes, orange), and leading edge genes for coMtb IL6 JAK STAT3 pathway (23 genes, green). Compiled from 4 independent experiments per condition for control, 2 independent experiments per condition for scBCG and coMtb. https://doi.org/10.1371/journal.ppat.1011871.g002 To identify trends between groups, we performed Gene Set Enrichment Analysis using a set of 50 Hallmark Pathways. As we’ve shown previously, Mtb-infected AMs from control mice at 24 hours had strong enrichment for “Xenobiotic Metabolism” and “Reactive Oxygen Species” pathways, indicative of the Nrf2-associated cell-protective response (Fig 2C). While these two pathways were not among the most enriched pathways in the exposed groups, Mtb-infected AMs from all groups upregulated genes associated with the 131 in vivo DEG that make up the cell-protective Nrf2-driven response at 24 hours [10] (Fig 2D). Expression profiles for Mtb- infected AMs from scBCG mice showed the strongest enrichment for “Interferon Alpha Response” and “Interferon Gamma Response” pathways, which contain many shared genes (Fig 2C). The strength of the interferon response was further highlighted by examining gene expression changes in a set of Interferon Stimulated Genes (ISGs) identified from macro- phages responding to IFNα (fold change > 2, p-value < 0.01) [30] (Fig 2D). Expression pro- files for Mtb-infected AMs from coMtb mice showed a weaker enrichment for interferon response pathways with fewer up-regulated ISGs compared to scBCG, and instead showed enrichment across a number of inflammatory pathways including “IL6 JAK STAT3 signaling” in comparison to the other groups (Fig 2C and 2D). A direct comparison between the gene expression patterns for AMs from scBCG versus coMtb mice could be visualized more readily by scatterplots highlighting either Nrf2-associated, Interferon Alpha and Gamma Response, or IL-6 JAK STAT3 pathway genes (Fig 2E). In summary, Mycobacterium exposures alter the initial in vivo response of AMs to Mtb infection 24 hours after challenge and remodel the AM response in distinct ways. AMs from scBCG vaccinated animals mount a strong interferon-associated response, while AMs from coMtb mice express a more diverse inflammatory profile consisting of both interferon-associ- ated genes as well as other pro-inflammatory genes, including those within the IL-6 JAK STAT3 pathway. Mycobacterium exposure modifies the baseline phenotype of alveolar macrophages in the airway Although scBCG and coMtb exposures alter the AM responses to Mtb infection in vivo, tran- scriptional effects are not widely evident prior to infection as measured by bulk RNA-sequenc- ing of naïve AMs from control, scBCG, or coMtb mice, including expression of innate receptors and adaptors (S4 Fig). However, we posited that remodeling effects were likely not homogenous across the entire AM population and that small heterogenous changes to baseline profiles might be detectable using a single cell approach. We therefore analyzed pooled BAL samples taken from 10 age- and sex-matched mice from each of the three conditions (control, scBCG, coMtb) eight weeks following Mycobacterium exposure by single cell RNA-sequencing (scRNAseq). Gross cellularity was unaffected by mycobacterial exposure as measured by flow cytometry analysis of common lineage markers with AMs being the dominant hematopoietic cell type (57.4–85.8% of CD45+ live cells), followed by lymphocytes (5.26–22.7% of CD45+ live cells) with smaller contributions from other innate cell populations (S5 Fig). Six samples, with an average of 2,709 cells per sample (range: 2,117–4,232), were analyzed together for a total of 17,788 genes detected. The most prominent expression cluster mapped to an AM profile, with smaller clusters mapping to T and B lymphocytes, dendritic cells, and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 7 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium neutrophils (Fig 3A). All cells that mapped to a macrophage profile were extracted and reclus- tered into 11 macrophage subclusters (Fig 3B and 3C). All but two of the macrophage subclus- ters (clusters 6 and 8) expressed AM lineage markers (Siglecf, Mertk, Fcgr1 (CD64), Lyz2 (LysM), and Itgax (CD11c) and had low expression of Itgam (CD11b) (Fig 3D). Cluster 6 showed high Itgam and Lyz2 expression and lower Siglecf expression, likely representing a small monocyte-derived macrophage population in the airway, while cluster 8 displayed high Lyz2 expression, low expression for other AM markers, and expression of Sftpa1 and Wfdc2 (S2 Table), genes most commonly expressed by pulmonary epithelial cells, suggesting that this cluster represents a small population of epithelial cells, To interpret the various expression subclusters, we identified the genes that most distin- guished each cluster from the others (S6 Fig and S2 Table). As has been reported by other groups [31,32], a small proportion of the AMs in two clusters (Clusters 4, 9) had high expres- sion of cell cycle genes (i.e., Top2a, Mki67), indicative of cell proliferation (Fig 3E and S2 Table). Cluster 0 was the most abundant macrophage cluster with high expression of lipid metabolism genes (i.e., Abcg1, Fabp1) (Fig 3F and S2 Table). Cluster 2 was significantly increased in relative frequency for scBCG samples compared to coMtb (p = 0.032, One-way ANOVA with Tukey post-test) and associated with oxidative stress response genes (Hmox1, Gclm). Several Cluster 2 associated genes, Slc7a11, Hmox1, and Sqstm1 also had higher overall expression level in scBCG samples compared to either control or coMtb (Fig 3G and S2 Table). Cluster 7 was the only cluster with an increase in relative frequency trending for both scBCG and coMtb (p = 0.076, One-way ANOVA). Cells in this cluster had high expression of Interferon Stimulated Genes (Ifit1, Isg15) and within this cluster, cells from scBCG samples had higher expression of Axl and Ifi204 than cells from coMtb samples. (Fig 3H and S2 Table). Cluster 3 had significantly higher relative frequency for coMtb samples compared to control and scBCG samples (p = 0.021, 0.039, respectively, One-way ANOVA with Tukey post- test) and was distinguished by expression of macrophage-associated transcription factors (Cebpb, Zeb2, Bhlhe40) [33,34], mitochondrial oxidative phosphorylation (mt-Co1, mt-Cytb, mt-Nd2), chromatin remodeling (Ankrd11, Baz1a), and immune signaling including the CARD9 complex (Malt1, Bcl10, Prkcd) (Figs 3I and S7 and S2 Table). This expression profile closely matches a subcluster of AMs previously described by Pisu et al, as an “interstitial mac- rophage-like” AM population (labeled “AM_2”) that expanded in relative frequency in lung samples 3 weeks following low-dose H37Rv infection [31]. Relative expression level for Cebpb, Mt-Cyb, and Lars2 within Cluster 3 was higher for cells from coMtb samples compared to either control or scBCG samples. Interestingly, Cluster 2 (higher relative frequency in scBCG) and Cluster 3 (higher relative frequency in coMtb) represent divergent endpoints of a pseudotime plot generated by a trajec- tory inference analysis, regardless of whether the starting point is the most abundant cluster in the control group (Cluster 0) (Fig 3J, top) or the cluster of proliferating cells (Cluster 4) (Fig 3J, bottom). This result suggests that scBCG and coMtb may drive AM phenotypes in diver- gent directions and indicates that AM responses can be remodeled into more than one flavor, rather than only a binary “on/off” state. To further investigate whether a sub-cluster of AMs might be responsible for the increased enrichment for Interferon Alpha/Gamma Response pathways in the in vivo Mtb response in scBCG and coMtb mice, we scored each cluster based on the ISG gene module, previously used in Fig 2D. As expected, we observed that only Cluster 7 showed strong enrichment for ISGs, which trended up in frequency for both scBCG and coMtb samples (Fig 3K). To investigate potential reprogramming of non-AM macrophages, we examined Cluster 6, the macrophage cluster with low Siglecf and high Itgam expression that is consistent with a monocyte-derived macrophage population. We observed no statistically significant differences PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 8 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 3. Mycobacterium exposure modifies the alveolar macrophage phenotype in the airway pre-challenge. Single-cell RNA-sequencing of BAL samples from control, scBCG, and coMtb mice pre-aerosol challenge. A) Compiled scRNAseq data for all BAL samples, highlighted by major clusters, annotated based on closest Immgen sample match. B) Highlighting of the two clusters used for macrophage subcluster analysis. C) The 11 clusters generated by the macrophage subcluster analysis, separated by condition. D) Expression of major macrophage-specific markers: Siglecf, Mertk, Fcgr1, Lyz2, Itgam (CD11b), and Itgax (CD11c). E-I) Relative frequency of each macrophage subcluster by condition. (violin plots by cluster) Expression level of representative genes distinguished by that cluster compared to other clusters. One-way PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 9 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium ANOVA with Tukey post-test, * p< 0.05. (3-way violin plots by condition) Differentially expressed genes within Clusters 2, 7, and 3 between control vs scBCG vs coMtb samples. Wilcoxon Rank Sum Test, Bonferroni adjusted p-value. adj-p< 0.05, adj-p< 0.01, *adj-p< 0.001. J) Pseudotime analysis (Monocle3) with starting node at the largest cluster in control, Cluster 0 (top) and at the cluster of proliferating cells, Cluster 4,9 (bottom). K) ISG Module Score by cluster. Module derived from macrophage response to IFNα (fold change> 2, p-value< 0.01) (Mostafavi et al, 2016) [30]. Data is compiled from two independent experiments (circle, triangle) with 3 conditions each for a total of 6 samples. https://doi.org/10.1371/journal.ppat.1011871.g003 in the relative size of this cluster between each of the three conditions (S8A Fig). However, there were a number of Differentially Expressed Genes (DEGs) between the groups, including decreases in expression of CD11b (Itgam) and Macrophage scavenger receptor (Msr1) for scBCG and coMtb macrophages compared to controls, increases in MHC-related genes (H2-Aa, Cd74) and iron-metabolism associated genes (Cd63, Fth1, Ftl1) for coMtb macro- phages compared to controls (S8B Fig). A previous study found IV BCG induced chromatin accessibility changes in AMs and IMs for some of these genes [31]. Additionally, we compared baseline changes to AMs following scBCG and coMtb expo- sures to AM changes following ivBCG vaccination (S9 Fig). Overall, we found that ivBCG exposure led to similar changes in AM populations to that of scBCG vaccination, with increased frequency of AMs clustering to “oxidative stress response” and “interferon stimu- lated genes (ISGs)” (S9C Fig). These baseline changes by scRNAseq mirror what is observed for the response of Mtb-infected AMs from ivBCG mice 24 hours after infection by bulk RNA- seq (S9A and S9B Fig). Profiles of Mtb-infected AMs from ivBCG vaccinated mice most closely match those of Mtb-infected AMs from scBCG vaccinated mice, with robust up-regula- tion of Interferon Stimulated Genes. These results demonstrate that both SC and IV BCG vac- cination lead to similar remodeling of AMs, with profiles distinct from that of coMtb exposure. In summary, scRNAseq analysis of macrophages isolated by BAL demonstrate that Myco- bacterium exposure leads to subtle changes in a small minority of AM subsets in the airway, including ones associated with interferon responses and an interstitial macrophage phenotype, while leaving the most abundant subsets of AMs unchanged in frequency or gene expression. We hypothesize that these small changes in baseline profiles may be sufficient to drive the more substantial changes observed in the AM Mtb response in vivo, as described in Fig 2. Mycobacterium exposure has minimal impact on T cell populations in the airway While AMs are the dominant immune cell type in the airway, other cell populations make up an average of 18.4% of the cells within the BAL in controls (range: 10.4–26.3%) and 31.3% in exposed groups (range: 14.0–48.8%). To examine how Mycobacterium exposure influenced other cells in the airway, we focused on T cells and dendritic cells (DCs) which have two of the highest relative frequencies after AMs (Fig 4A and 4B). T cells and DCs were each combined from two original clusters each. Neither population showed a statistically significant difference in relative frequency (Fig 4B). To examine qualitative changes in the T cell population in greater detail, we next reclustered the T cells, resulting in 7 T cell clusters. We manually anno- tated each of the clusters based on the most closely matched Immgen profiles and the expres- sion of key lineage specific markers (Figs 4A–4C and S10). We focused on the 5 most abundant T cell subclusters (Clusters 0–4). While we observed subtle shifts in the relative fre- quency of each group, none reached statistical significance. Cluster 0, the most abundant clus- ter, had an expression profile most consistent with γδ T cells, including expression of Cd3e with low to nil Cd4 and Cd8a and some expression of Zbtb16 (PLZF) and Tmem176a, an ion channel regulated by RORγt and reported to be expressed by lung γδ T cells [35,36] (Figs 4D– PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 10 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 4. Airway T cell and dendritic cell profiles following Mycobacterium exposure. Single-cell RNA-sequencing of BAL samples from control, scBCG, and coMtb mice pre-aerosol challenge. A) Compiled scRNAseq data for all BAL samples, with T cell and dendritic cell clusters highlighted. B) Relative frequency of T cells and DCs. C-F) T cell subcluster analysis. C) T cell subclusters compiled and split by condition. Annotations made following Immgen profile matches and manual marker inspection. D) Relative frequency of Clusters 0–4 for each condition. E) UMAP gene expression plot for general T cell markers. F) UMAP gene expression plot cluster-specific markers split by condition. G-J) Dendritic cell subcluster analysis. G) Dendritic cell subcluster, colored by each of 3 different clusters. H) Relative frequency of Clusters 0–2 for each condition. I) Violin plots for cluster-specific markers and genes of interest. J) Differentially expressed genes in Cluster 0 split by condition. adj-p< 0.05, adj-p< 0 .01, adj-p< 0.001, Wilcoxon Rank Sum Test, Bonferroni adjusted p-values. Data is compiled from two independent experiments with 3 conditions each for a total of 6 samples. https://doi.org/10.1371/journal.ppat.1011871.g004 4F and S10). Cluster 1 matched a profile for effector CD4+ T cells (Figs 4D–4F and S10), and Cluster 2 matched a profile for naïve CD8+ T cells (Figs 4D–4F and S10). Cluster 3 had a pro- file consistent with effector memory/resident memory CD8+ T cells (TEM/RM) (Figs 4D–4F and S10) and Cluster 4 had a profile consistent with NK cells. Overall, there were no PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 11 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium significant changes in the relative frequency of T cell or NK subclusters, despite detection of a number of different lymphocyte subsets in the airway. Mycobacterium exposure modifies the dendritic cell airway landscape Re-clustering of DCs yielded 2 major clusters (Cluster 0, 1) and 1 minor cluster (Cluster 2), which had a mixed phenotype with expression of genes from both major clusters (Fig 4G). Cells in Cluster 0 had high expression of Clec9a, Itgae (CD103), and MHC II genes (H2-Ab1, H2-DMa) consistent with an expression profile of lung CD103+ cDCs [37] (Fig 4I), while cells in Cluster 1 had higher expression of Batf3, Ccr7, and Fscn1. All three of the clusters had high Irf8 expression and lower expression of Xcr1, Irf4, and Itgam (CD11b) (Fig 4I). While the coMtb samples trended higher in relative frequency for Cluster 0 and low for Cluster 1, com- pared to the control or scBCG samples, these differences did not meet statistical significance (One-way ANOVA with Tukey post-test, p = 0.16, p = 0.11) (Fig 4H). This was likely due to the limit in statistical power with only 2 replicates. However, it was notable that for cells within Cluster 0, there was a significantly higher expression level for MHC II genes (H2-Aa, H2-DMb1, and Cd74) for coMtb cells compared to control or scBCG cells (Fig 4J). This sug- gests that coMtb might be able to elicit more mature or activated DCs in the airway. Overall, scRNAseq analysis shows that Mycobacterium exposure leads to minimal changes in T cell and dendritic cell populations in the airway, although we hypothesize that small changes in DC maturation/activation could have important impacts on adaptive immune priming dynamics after aerosol infection. Cell-intrinsic remodeling of alveolar macrophages following Mycobacterium exposure licenses an interferon response in vitro Analysis of the AM response to Mtb in vivo demonstrates that the very earliest immune response to Mtb is altered by previous Mycobacterium exposure. However, one limitation to this approach is the inability to discern whether changes to AMs are cell-intrinsic or dependent on the altered tissue environment, especially the presence of Mtb-specific T cells. Therefore, to determine whether Mycobacterium exposure induces cell-intrinsic changes to AMs, we iso- lated AMs from control, scBCG, and coMtb mice, stimulated them ex vivo with LPS, Pam3Cys, or H37Rv, and measured their transcriptional profiles 6 hours later (Fig 5A). First, PAMP-spe- cific trends were notable. AMs from coMtb and scBCG mice showed distinct responses com- pared to AMs from control mice following LPS and H37Rv stimulation, but only minimal changes following Pam3Cys stimulation(Fig 5B and S3 Table). No obvious changes in innate receptor or adaptor expression explain the PAMP-specific differences (S11 Fig). Second, as we have previously reported, Mtb-infected AMs did not strongly up-regulate Nrf2-associated genes ex vivo (Fig 5C). Third, when we examined the gene sets that distinguished the in vivo AM response between scBCG and coMtb mice, “Interferon Alpha/Gamma Response” and “IL6 JAK STAT3” (Fig 2E), we found that the differences between exposure modalities were diminished ex vivo, suggesting contribution of the lung environment to the quality of the response (Fig 5C). Using Gene Set Enrichment Analysis, we identified “Interferon Gamma Response”, “Interferon Alpha Response”, “TNFa signaling via NF-kB”, and”Inflammatory Response” pathways as the most strongly enriched for LPS and H37Rv responses from scBCG and coMtb AMs (Fig 5D). To assess whether the cell-intrinsic changes observed were long- lasting, we compared the responses of AMs at 8 or 23 weeks following scBCG vaccination by RT-qPCR. Increases in gene expression were as robust or even enhanced 23 weeks following exposure compared to 8 weeks, suggesting that exposure-induced changes to AMs are rela- tively long-lived (S12 Fig). To validate whether changes in gene expression were reflected at PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 12 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 5. Cell-intrinsic remodeling of alveolar macrophages following Mycobacterium exposure. A) AM isolation 8 weeks following scBCG or coMtb exposure. AMs were stimulated with Pam3Cys (10 ng/ml), LPS (10 ng/ml), and H37Rv (effective MOI ~2:1) for 6 hours (RNA-seq) or 20 hours (flow cytometry). B-D) Gene expression changes measured by bulk RNA-seq for stimulated AMs compared to respective unstimulated AMs (i.e., LPS-stim control AM vs unstim control AM; LPS-stim scBCG AM vs unstim scBCG AM; LPS-stim coMtb AM vs unstim coMtb AM). B) Scatterplots, log2 fold change gene expression for stimulated to unstimulated AMs for each condition (control, scBCG, coMtb). Differentially expressed genes (DEG) are highlighted for one or both conditions (\|Fold change\| > 2, FDR< 0.05 for Pam3Cys and LPS; \|Fold change\| > 2, FDR< 0.2 for H37Rv). C) Scatterplots, log2 fold change gene expression for H37Rv-stimulated to unstimulated scBCG versus coMtb AMs. Genes highlighted derived from gene sets in Fig 2E. Nrf2-associated genes (56 genes, purple), Interferon Alpha/ Gamma Response (61 genes, orange), and IL6 JAK STAT3 (23 genes, green). D) Gene Set Enrichment Analysis results for 50 HALLMARK pathways. Pathways shown have NES> 1.5 and FDR< 0.05 for at least one of the conditions. FDR< 0.05, FDR< 0.01, FDR< 0.001. E) Gating strategy and MHC II and TNF histograms for coMtb AMs, no stimulation versus LPS. F-G) MHC II and TNF MFI in control, scBCG, and coMtb AMs after 20 hours of LPS stimulation. p< 0.05, p< 0.01, p< 0.001, One-way ANOVA with Tukey post-test. https://doi.org/10.1371/journal.ppat.1011871.g005 the protein level, we sought to develop a flow cytometry-based assay to assess AM-specific responses. Primary AMs were stimulated with LPS for 20 hours and both MHC II and TNF expression were measured by flow cytometry (Fig 5E). We found that AM from coMtb mice PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 13 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium had significantly higher MHC II expression than controls and a similar pattern was seen for scBCG AM in 1 of 2 experiments (Fig 5F). AMs from coMtb mice also showed a significant increase in TNF expression in 1 of 2 experiments (Fig 5G). Because the “Interferon Alpha Response” and “Interferon Gamma Response” pathways were most highly enriched for the H37Rv stimulation following Mycobacterium exposure, we decided to further investigate the Interferon-associated response [30]. We specifically sought out a dataset that would identify ISGs specific to Mtb-infected macrophages. To generate an IFNγ-derived signature, we would need a macrophage-T cell co-culture system and to sort out the Mtb-infected macrophages, because murine macrophages do not produce IFNγ during Mtb infection in vitro. Therefore, we decided to examine an IFNα/β-derived signature from a data set of Mtb-infected IFNAR-/- bone marrow derived macrophages (BMDMs). We catego- rized the macrophage response to H37Rv stimulation as “IFN-dependent” or “IFN-indepen- dent” based on gene expression of WT versus IFNAR-/- BMDMs following H37Rv infection (see methods section) (S4 Table) [38]. Expression of IFN-dependent genes was minimally induced in control AMs but strongly up-regulated in AMs from Mycobacterium exposed mice, as measured by the GSEA normalized enrichment score (NES) (Fig 6A, left). In contrast, expression of IFN-independent genes was modestly upregulated in control AMs and only slightly altered by Mycobacterium exposure (Fig 6A, right). When we applied these two gene sets to the in vivo response profiles described in Fig 2 generated for Mtb-infected AMs follow- ing high dose infection with mEmerald-H37Rv, we observe that Mtb-infected AMs from scBCG mice up-regulate the IFN-dependent response in vivo, suggesting that the licensing of the IFN-dependent response plays a role in vivo following BCG vaccination (Fig 6B). The dif- ference between the in vitro and in vivo response for AMs from coMtb mice points to an addi- tional contribution of the lung environment. These results demonstrate that prior Mycobacterium exposure leads to cell-intrinsic changes in AMs that license an enhancement of IFN-dependent responses to Mtb that are retained in vitro, while qualitative differences in the response between scBCG and coMtb in vivo are dependent on signals from the lung environment. Discussion Here we describe remodeling of AMs, long-lived airway-resident innate cells, following two modalities of Mycobacterium exposure, scBCG vaccination and coMtb, a model of contained Mtb infection. AMs are the first cells to be productively infected in the lung following aerosol Mtb infection [10,11]. We previously showed that AMs initially respond to Mtb infection with a cell-protective, Nrf2-driven program that is detrimental to early host control [10], suggesting that the lack of a robust response by AMs prevents effective host control early on. In line with this model, others have shown that depletion of AMs or strategies that “bypass” AMs including directly injecting antigen-primed DCs or activating DCs accelerate the immune response and reduce bacterial burden [17,39,40]. However, how vaccination or prior exposures impact the initial response of AMs and whether there are therapeutic strategies that would enhance their initial response to infection have not been well studied [41]. Most studies examining the impacts of prior exposure to either Mtb or other species of mycobacteria, including BCG vaccination, have focused on the durable antigen-specific changes to the adaptive immune response. In contrast, we focused on changes to tissue-resi- dent innate cells and their responses at the earliest stages of infection (� 14 days). Along with early changes to the T cell response, both scBCG and coMtb accelerate innate cell activation and immune cell recruitment in the first 10–14 days following Mtb aerosol infection, and even the very initial AM response to Mtb, within the first 24 hours of infection, is remodeled PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 14 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 6. Mycobacterium exposure licenses an interferon-dependent response to H37Rv by alveolar macrophages. Gene expression changes measured by bulk RNA-seq for Mtb-infected AMs compared to respective unstimulated AMs (i.e., Mtb-inf control AM vs unstim control AM; Mtb-inf scBCG AM vs unstim scBCG AM; Mtb-inf coMtb AM vs unstim coMtb AM). A) Gene expression for control, scBCG, and coMtb AMs, 6 hour H37Rv infection ex vivo, log2 fold change (Mtb-infected/uninfected). IFN-dependent genes (339 total) and IFN-independent genes (352 total) based on WT vs IFNAR-/- BMDM bulk RNA-seq dataset (Olson et al, 2021) (see Methods section). B) Gene expression for control, scBCG, and coMtb AMs, 24 hour in vivo H37Rv infection, Mtb-infected sorted, log2 fold change (Mtb-infected/uninfected) for the same IFN-dependent and IFN-independent gene sets in (A). Grey bars indicate N.D. Normalized Enrichment Score (NES) calculated by GSEA for two data sets alongside Hallmark Pathways. +FDR< 0.05, ++FDR< 0.01, +++FDR< 0.001. https://doi.org/10.1371/journal.ppat.1011871.g006 following exposure to Mycobacterium. The durable changes observed fit with a number of recent studies which have uncovered either enhanced AM antimicrobial phenotypes [7–9] or impaired responses [42,43] following viral infection. Other studies have identified long-lasting changes to AMs following intranasal immunization of either adenoviral-based or inactivated whole cell vaccines [18,44,45]. We observe that the most robust cell-intrinsic changes to AM responses following scBCG or coMtb are found in IFN-dependent genes (Fig 6) and ISGs (Fig 2D), suggesting a critical role for interferon signaling in the changes to the early innate response in the lung during infection. Notably, this finding is not limited to the murine model. BAL from NHPs following IV, ID, or aerosol BCG vaccination similarly show AMs enriched for Interferon Gamma Response pathway genes [46]. AMs can respond to both Type I (IFNα/β) and Type II Interfer- ons (IFNγ) and it is not possible to distinguish between responses to IFNα/β and IFNγ based on transcriptional analysis alone. The presence of live bacteria within both scBCG and coMtb models limits system-wide perturbations, such as T cell depletion or anti-IFNγ blockade, which would reverse containment [24]. For this reason, we have not been able to directly test how interferon signals derived from scBCG or coMtb remodel AMs in a cell-autonomous manner, but we envision future studies to examine the specific effects of individual cytokines on AM remodeling. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 15 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Even though IFNAR-/- macrophages were used to generate the ISG signature identified in Fig 6, IFNγ is the more likely candidate to contribute to AM remodeling following Mycobacte- rium exposure. IFNγ is required for the generation of trained immunity in bone marrow- derived myeloid cells following IV BCG vaccination [15,47]. While IV and aerosol H37Rv infection was found to induce Type I IFNs and reduce myelopoiesis [47], we previously found that coMtb, in which Mtb is contained within the ear-draining lymph node, leads to low-level systemic cytokinemia, including IFNγ production. Using WT:Ifngr1-/- mixed bone marrow chimeras, we showed that IFNγ signaling was responsible for monocyte and AM activation fol- lowing establishment of coMtb [25]. Additionally, several reports have identified T cell-derived IFNγ as critical for altering AM function, although the immunological outcome varies sub- stantially based on the context. In one study, T cell-derived IFNγ following adenoviral infec- tion leads to AM activation, innate training and protection from S. pneumoniae [8], while in another study influenza-induced T cell-derived IFNγ leads to AM dysfunction and impaired clearance of S. pneumoniae [43]. A study of 88 SARS-CoV-2 patients identified AMs and T cell-derived IFNγ as part of a positive feedback loop in the airway [48]. In contrast, type I IFN signatures are associated with active TB or TB disease progression in both humans and non- human primates [49–51]. Host perturbations such as treatment with poly I:C or viral co-infec- tion that induce type I IFN lead to worsened disease [52,53], type I IFN has been shown to block production of IL-1β in myeloid cells during Mtb infection [54], and type I IFN drives mitochondrial stress and metabolic dysfunction in Mtb infected macrophages [38]. We note that the two modalities tested here consist of different mycobacterial species, dif- ferent doses, and different routes. We expect that all three of these factors likely contribute to the quality of AM remodeling. For example, they could be important for the location, timing, and amount of IFNγ that AMs are exposed to. While an in-depth examination of each of these factors is beyond the scope of this study, the side-by-side comparison of the two different exposure models, scBCG and coMtb, allows us to examine the plasticity of AM phenotypes and the impact of the local and/or systemic environments leading to different responses. It is notable that scBCG is quickly cleared from WT mice, while coMtb replication is sustained in the superficial cervical lymph node for up to a year or longer [25]. Protection from H37Rv challenge mediated by coMtb is abrogated following antibiotic treatment but not completely lost [25]. This suggests that there may be different contributions to AM remodeling from active bacterial replication and from long-term microenvironment changes following bacterial clearance, which will be addressed in follow-up studies. In particular, it is notable that the modality-specific signatures identified through in vivo transcriptional analysis disappeared following ex vivo isolation, along with the Nrf2 signature. The difference between in vivo and ex vivo signatures suggests a critical contribution of the altered lung microenvironments in AM remodeling, which deserves additional follow-up studies. One additional limitation of our approach is that the ex vivo samples were collected in bulk, in the absence of cell sorting, and so, unlike the in vivo studies, a very small number of bystander AMs were likely collected alongside the Mtb-infected AMs, which could have had minor impacts on the transcriptional signatures. The fact that AMs can be remodeled into more than one state suggests additional complexity in innate immune features that has not yet been fully explored. Heterogeneity in myeloid reprogramming is not limited to the murine model and has also been observed in human monocytes [55]. Several studies have recently described innate-adaptive interactions within the airway that are thought to impact infection dynamics [46,48]. We note that in these models we observe innate cell activation and recruitment occurring at the same time as T cell activation and recruitment, and that these events are likely promoting one another. We are particularly PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 16 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium intrigued by the changes in AM MHC class II expression that we observed in vivo during the first two weeks of infection (Fig 1A) and following ex vivo stimulation (Fig 5F). AMs are considered to be poor antigen presenters, relative to other myeloid subsets, yet the faster Mtb-specific T cells are recruited to the lung, the more likely it is that AMs will serve as pri- mary T cell targets [37,56–60]. Our results suggest that enhancement of AM antigen presenta- tion could be one innate mechanism that could be targeted to complement and synergize with the adaptive immune response during infection. Other potential mechanisms by which AM remodeling may contribute to enhanced bacterial control after Mtb aerosol challenge include enhanced phagocytic activity or increased direct antimycobacterial activity, as previously dem- onstrated by Jeyanathan et al [19]. Future studies are needed to further interrogate the contri- bution of these innate mechanisms. There are many other remaining questions. While we identify both cell-intrinsic changes and changes dependent on the lung environment, we do not yet know whether the cell-intrin- sic changes are retained long-term in the absence of environmental cues. We do not know the durability of the changes, both cell-intrinsic and environment-dependent, and whether they are mediated by epigenetic effects. Our longest experiment showed retention of cell-intrinsic changes to AMs after 23 weeks. In Nemeth et al, we showed that antibiotic treatment lessened the protection mediated by coMtb, suggesting that ongoing replication is a key part of host protection [25]. AM remodeling is retained 8 weeks or longer after the initial exposures, a timepoint when there is little to no detectable mycobacteria in the lung, ruling out a require- ment for local ongoing bacterial replication in AM remodeling, although systemic signals derived from remote bacterial replication may still play a role. We also performed several of these studies with intravenous BCG vaccination (ivBCG), which in the mouse model leads to more sustained bacterial replication than scBCG [61]. While we observed similar remodeling to AMs in the ivBCG model, these were not different in quality to those of scBCG vaccination, despite the major differences in bacterial replication and far greater T cell recruitment to the airway, suggesting that these changes are not required for AM remodeling (S9 Fig). There is still much unknown about the signals that drive reprogramming of tissue-resident innate cells. Ideally, vaccines would be designed to leverage these signals in order to promote the most effective interactions between innate and adaptive responses. Identifying the ways that AMs are reprogrammed by inflammatory signals and the effects of their changed pheno- types on the early stages of infection will help to improve future vaccines or host-directed therapies. Materials and methods Ethics statement Animal studies performed at Seattle Children’s Research Institute were performed in compli- ance with and approval by the Seattle Children’s Research Institute’s Institutional Animal Care and Use Committee. Animal studies performed at University of Massachusetts Amherst were performed in compliance with and approval by the University of Massachusetts Amherst’s Institutional Animal Care and Use Committee. All mice were housed and maintained in spe- cific pathogen-free conditions. Mice C57BL/6 mice were purchased from Jackson Laboratories (Bar Harbor, ME). 6–12 week old male and female mice were used for all experiments, except for RNA-sequencing, which used only female mice for uniformity. Mice infected with Mtb were housed in Biosafety Level 3 facilities in Animal Biohazard Containment Suites. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 17 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Mycobacteria exposure models: BCG immunization and establishment of coMtb BCG-Pasteur was cultured in Middlebrook 7H9 broth at 37˚C to an OD of 0.1–0.3. Bacteria was diluted in PBS and 1 x 106 CFU in 200 ml was injected subcutaneous (SC) or intravenous (IV). Intradermal infections to establish coMtb were performed as formerly described [24], with some modifications as detailed previously [25]. Briefly, 10,000 CFU of Mtb (H37Rv) in logarithmic phase growth were injected intradermally into the ear in 10 μL PBS using a 10 μL Hamilton Syringe, following anesthesia with ketamine/xylazine. M. tuberculosis aerosol infections and lung mononuclear cell isolation Aerosol infections were performed with wildtype H37Rv, including some transformed with an mEmerald reporter pMV261 plasmid, generously provided by Dr. Chris Sassetti and Christina Baer (University of Massachusetts Medical School, Worcester, MA). For both standard (~100 CFU) and high dose (1,473–4,800 CFU) infections, mice were enclosed in an aerosol infection chamber (Glas-Col) and frozen stocks of bacteria were thawed and placed inside the associated nebulizer. To determine the infectious dose, three mice in each infection were sacrificed one day later and lung homogenates were plated onto 7H10 plates for CFU enumeration. High dose challenge and sorting of Mtb-infected AM was performed 4 weeks following scBCG vac- cination and 2 weeks following coMtb vaccination as previously described [62]. All other anal- ysis was performed 8 weeks following Mycobacterium exposures. Lung single cell suspensions At each time point, lungs were removed, and single-cell suspensions of lung mononuclear cells were prepared by Liberase Blendzyme 3 (70 ug/ml, Roche) digestion containing DNaseI (30 μg/ml; Sigma-Aldrich) for 30 mins at 37˚C and mechanical disruption using a gentle- MACS dissociator (Miltenyi Biotec), followed by filtering through a 70 μM cell strainer. Cells were resuspended in FACS buffer (PBS, 1% FBS, and 0.1% NaN3) prior to staining for flow cytometry. For bacterial enumeration, lungs were processed in 0.05% Tween-80 in PBS using a gentleMACS dissociator (Miltenyi Biotec) and were plated onto 7H10 plates for CFU enumer- ation. ΔCFU (log) was calculated as follows: ΔCFU = log((sample CFU)/(average control CFU). For respective experiment, timepoint, and organ. A ΔCFU value of -1 corresponds to a 10-fold reduction in CFU for the sample, compared to the control. Similarly, a ΔCFU value of 1 corresponds to a 10-fold increase in CFU. Alveolar macrophage isolation AMs for cell sorting, bulk RNA-sequencing, single cell RNA-sequencing, and ex vivo stimula- tion were collected by bronchoalveolar lavage (BAL). BAL was performed by exposing the tra- chea of euthanized mice, puncturing the trachea with Vannas Micro Scissors (VWR) and injecting 1 mL PBS using a 20G-1” IV catheter (McKesson) connected to a 1 mL syringe. The PBS was flushed into the lung and then aspirated three times and the recovered fluid was placed in a 15mL tube on ice. The wash was repeated 3 additional times. Cells were filtered and spun down. For antibody staining, cells were suspended in FACS buffer. For cell culture, cells were plated at a density of 5 x 104 cells/well (96-well plate) in complete RPMI (RPMI plus FBS (10%, VWR), L-glutamine (2mM, Invitrogen), and Penicillin-Streptomycin (100 U/ml; Invitrogen) and allowed to adhere overnight in a 37˚C humidified incubator (5% CO2). Media with antibiotics were washed out prior to infection with Mtb. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 18 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Cell sorting and flow cytometry Fc receptors were blocked with anti-CD16/32 (2.4G2, BD Pharmingen). Cell viability was assessed using Zombie Violet dye (Biolegend). Cells were suspended in 1X PBS (pH 7.4) con- taining 0.01% NaN3 and 1% fetal bovine serum (i.e., FACS buffer). Surface staining, performed at 4 degrees for 20 minutes, included antibodies specific for murine: Siglec F (E50-2440, BD Pharmingen), CD11b (M1/70), CD64 (X54-5/7.1), CD45 (104), CD3 (17A2, eBiosciences), CD19 (1D3, eBiosciences), CD11c (N418), I-A/I-E (M5/114.15.2), Ly6G (1A8), Ly6C (HK1.4), TNF (MP6-XT22). For ICS, Brefeldin A was added for duration of LPS stimulation. Cyto-Fast Fix/Perm and Cyto-Fast Perm Wash reagents were used for intracellular staining. Reagents are from Biolegend unless otherwise noted. MHC class II tetramers ESAT-6 (I-A(b) 4–17, sequence: QQWNFAGIEAAASA) and MHC class I tetramers TB10.4 (H-2K(b) 4–11, sequence: IMYNYPAM) were obtained from the National Institutes of Health Tetramer Core Facility. Cell sorting was performed on a FACS Aria (BD Biosciences). Sorted cells were col- lected in complete media, spun down, resuspended in TRIzol, and frozen at -80˚C overnight prior to RNA isolation. Samples for flow cytometry were fixed in 2% paraformaldehyde solu- tion in PBS and analyzed using a LSRII flow cytometer (BD Biosciences) and FlowJo software (Tree Star, Inc.). Bulk RNA-sequencing and analysis All high dose infections and sorting for bulk RNA-sequencing of Mtb-infected AMs (control, scBCG, and coMtb) were performed in the ABSL-3 facility at Seattle Children’s Research Insti- tute. All infections used the same Mtb strain, mEmerald-H37Rv, and the TRIzol-based RNA isolation protocol was performed by the same individual (D.M.). RNA isolation was performed using TRIzol (Invitrogen), two sequential chloroform extractions, Glycoblue carrier (Thermo Fisher), isopropanol precipitation, and washes with 75% ethanol. RNA was quantified with the Bioanalyzer RNA 6000 Pico Kit (Agilent). cDNA libraries were constructed using the SMAR- Ter Stranded Total RNA-Seq Kit (v2)–Pico Input Mammalian (Clontech) following the manu- facturer’s instructions. Libraries were amplified and then sequenced on an Illumina NextSeq (2 x 76, paired-end (sorted BAL cells) or 2 x 151, paired-end (ex vivo stimulation samples)). Stranded paired-end reads were preprocessed: The first three nucleotides of R2 were removed as described in the SMARTer Stranded Total RNA-Seq Kit–Pico Input Mammalian User Man- ual (v2: 063017) and read ends consisting of more than 66% of the same nucleotide were removed). The remaining read pairs were aligned to the mouse genome (mm10) + Mtb H37Rv genome using the gsnap aligner [63] (v. 2018-07-04) allowing for novel splicing. Con- cordantly mapping read pairs (~20 million / sample) that aligned uniquely were assigned to exons using the iocond program and gene definitions from Ensembl Mus_Musculus GRCm38.78 coding and non-coding genes. Genes with low expression were filtered using the “filterByExpr” function in the edgeR package [64]. Differential expression was calculated using the “edgeR” package [64] from ioconductor.org. False discovery rate was computed with the Benjamini-Hochberg algorithm. Hierarchical clusterings were performed in R using ‘Tsclust’ and ‘hclust’ libraries. Heat map and scatterplot visualizations were generated in R using the ‘heatmap.2’ and ‘ggplot2’ libraries, respectively. Gene Set Enrichment Analysis (GSEA) Input data for GSEA consisted of lists, ranked by -log(p-value), comparing RNAseq expression measures of target samples and naïve controls including directionality of fold-change. Mouse orthologs of human Hallmark genes were defined using a list provided by Molecular Signa- tures Database (MsigDB) [65]. GSEA software was used to calculate enrichment of ranked lists PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 19 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium in each of the respective hallmark gene lists, as described previously [66]. A nominal p-value for each ES is calculated based on the null distribution of 1,000 random permutations. To cor- rect for multiple hypothesis testing, a normalized enrichment score (NES) is calculated that corrects the ES based on the null distribution. A false-discovery rate (FDR) is calculated for each NES. Leading edge subsets are defined as the genes in a particular gene set that are part of the ranked list at or before the running sum reaches its maximum value. Ingenuity Pathway Analysis (IPA) IPA (QIAGEN) was used to identify enriched pathways for differentially expressed genes between naïve and Mtb-infected AMs (cut-off values: FDR < 0.01, \|FC\| > 2). The top 20 canonical pathways with enrichment score p-value < 0.05 with greater than 10 gene members are reported. Single cell RNA-sequencing BAL from 10 mice per condition was pooled for each sample, with two independent replicates per condition. Samples were prepared for methanol fixation following protocol “CG000136 Rev. D” from 10X Genomics [67]. Briefly, samples were filtered with 70 μm filters and red blood cells were lysed with ACK lysis buffer. Samples were resuspended in 1 mL ice-cold DPBS using a wide-bore tip and transferred to a 1.5 mL low-bind Eppendorf tube. Samples were centrifuged at 700 × g for 5 minutes at 4˚C. Supernatant was carefully removed with a p1000 pipette, and the cell pellet was washed two more times with DPBS, counted, and resus- pended in 200 μL ice-cold DPBS/1 × 106 cells. 800 μL of ice-cold methanol was added drop- wise for a final concentration of 80% methanol. Samples were incubated at -20˚C for 30 min- utes and then stored at -80˚C for up to 6 weeks prior to rehydration. For rehydration, frozen samples were equilibrated to 4˚C, centrifuged at 1,000 × g for 10 minutes at 4˚C, and resus- pended in 50 μL of Wash-Resuspension Buffer (0.04% BSA + 1mM DTT + 0.2U/μL Protector RNAase Inhibitor in 3× SSC buffer) to achieve ~1,000 cells/μL (assuming 75% sample loss). Single cell RNA-sequencing analysis Libraries were prepared using the Next GEM Single Cell 30 Reagent Kits v3.1 (Dual Index) (10X Genomics) following the manufacturer’s instructions. Raw sequencing data were aligned to the mouse genome (mm10) and UMI counts determined using the Cell Ranger pipeline (10X Genomics). Data processing, integration, and analysis was performed with Seurat v.3 [68]. Droplets containing less than 200 detected genes, more than 4000 detected genes (doublet discrimination), or more than 5% mitochondrial were discarded. Genes expressed by less than 3 cells across all samples were removed. Unbiased annotation of clusters using the Immgen database [69] as a reference was performed with “SingleR” package [70]. Pseudotime analysis was performed using the “SeuratWrappers” and “Monocle3” R packages [71]. Data visualiza- tion was performed with the “Seurat”, “tidyverse”, “cowplot”, and “viridis” R packages. Alveolar macrophage Ex Vivo stimulation AMs were isolated by bronchoalveolar lavage and pooled from 5 mice per group. Cells were plated at a density of 5 x 104 cells/well (96-well plate) in complete RPMI (RPMI plus FBS (10%, VWR), L-glutamine (2mM, Invitrogen), and Penicillin-Streptomycin (100 U/ml; Invitrogen) and allowed to adhere overnight in a 37˚C humidified incubator (5% CO2). Media with antibi- otics and non-adherent cells were washed out prior to stimulation. AM were stimulated with LPS (LPS from Salmonella Minnesota, List Biologicals, #R595, 10 ng/ml), Pam3Cys PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 20 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium (Pam3CSK4, EMC Microcollections, GmbH, 10 ng/ml), or H37Rv (effective MOI ~2:1). H37Rv was prepared by culturing from frozen stock in 7H9 media at 37˚C for 48 hours to O. D. of 0.1–0.3. The final concentration was calculated based on strain titer and bacteria was added to macrophages for two hours. Cultures were then washed three times to remove extra- cellular bacteria. Cell cultures were washed once in PBS after 6 hours to remove dead cells and collected in TRIzol for RNA isolation via chloroform/isopropanol extraction or collected after 20 hours for flow cytometry and ICS. Filtering for IFN dependent and independent gene sets “IFN dependent” and “IFN independent” gene sets were generated from data from Olson et al [38], using the following filters starting from a total of 1,233 genes up-regulated in H37Rv- stimulated WT BMDM with average CPM >1, log2 fold change > 1 and FDR < 0.01: “IFN dependent” = H37Rv-stimulated IFNAR-/- BMDM: log2 fold change < 1 AND H37Rv-stimulated WT vs IFNAR-/-: log2 fold change > 2 = 339 genes “IFN independent” = H37Rv-stimulated IFNAR-/- BMDM: log2 fold change > 1, FDR < 0.01 AND H37Rv-stimulated WT vs IFNAR-/-: log2 fold change < 2 = 352 genes qRT-PCR Quantitative PCR reactions were carried out using TaqMan primer probes (ABI) and TaqMan Fast Universal PCR Master Mix (ThermoFisher) in a CFX384 Touch Real-Time PCR Detec- tion System (BioRad). Data were normalized by the level of Ef1a expression in individual samples. Statistical analyses RNA-sequencing was analyzed using the edgeR package from Bioconductor.org and the false discovery rate was computed using the Benjamini-Hochberg algorithm. All other data are pre- sented as mean ± SEM and analyzed by one-way ANOVA (95% confidence interval) with Tukey post-test (for comparison of multiple conditions). Statistical analysis and graphical representation of data was performed using either GraphPad Prism v6.0 software or R. PCA plots generated using “Prcomp” and “Biplot” packages. Venn diagrams and gene set intersec- tion analysis was performed using Intervene [72]. p-values, p < 0.05, p < 0.01, * p < 0.001. Supporting information S1 Fig. (related to Fig 1). Flow cytometry gating schemes. Gating strategies for myeloid (A) and T cell (B) analysis. (TIF) S2 Fig. (related to Fig 1). Mycobacterium exposure provides protection against standard low-dose H37Rv aerosol challenge. A) Lung, spleen, and lung-draining lymph node (LN) CFU in control mice at deposition, days 10, 12, 14, and 28. B-E) Summary plots of ΔCFU (log) in lung, spleen, and LN following low-dose infection with H37Rv at day 10 (B), day 12 (C), day 14 (D), and day 28 (E). p < 0.05, p < 0.01, *p < 0.001. One-way ANOVA with Tukey post-test. Data compiled from 2–3 independent experiments per condition, with 5 mice per group for each experiment. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 21 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S3 Fig. (related to Fig 2). Top 20 Canonical Pathways by Ingenuity Pathway Analysis for up-regulated genes by Mtb-infected alveolar macrophages. IPA analysis for Mtb-infected AMs from control, scBCG, and coMtb mice 24 hours following high dose mEmerald-H37Rv infection. Data representative of 3 independent experiments per condition. (TIF) S4 Fig. (related to Fig 2). Transcriptional changes to naive alveolar macrophages following Mycobacterium exposure by bulk RNA-sequencing. Bulk RNA-seq profiles of naive AMs (isolated alongside Mtb-infected AMs). Gene expression changes within naïve AMs are com- pared to AMs from control mice: naïve scBCG AM vs naïve control AM; naïve coMtb AM vs naïve control AM. A-B) Volcano plots depicting changes in baseline gene expression of naive AMs from scBCG (A) and coMtb(B) mice compared to naive AMs from control mice. Signifi- cantly changed genes (FDR < 0.05, \|FC\| > 2) highlighted and labeled. C) Gene expression for innate receptors and adaptors of interest, log2 fold change, unstimulated AMs from scBCG and coMtb mice compared to unstimulated AMs from control mice. FDR < 0.01. Compiled from 2 independent experiments for each condition. (TIF) S5 Fig. (related to Fig 3). Flow analysis of BAL samples prepared for 10X single-cell RNA- sequencing. Percentage of each population (AM, lymphocytes, eosinophils, MDM, other CD45+) out of CD45+ ZV-. AM = Siglec F+ CD64+, Eosinophils = Siglec F+ CD64-, lymphocytes = CD3/CD19+, MDM = Siglec F- CD64+, other CD45+ = CD3- CD19- Siglec F- CD64-. Note: One of the two coMtb samples analyzed by flow cytometry did not have an accompanying 10X sample. The second coMtb 10X sample was processed separately without flow analysis. (TIF) S6 Fig. (related to Fig 3). Top 10 genes differentially expressed for each of 11 macrophage subclusters. Heatmap of genes that are most differentially expressed for each of 11 clusters with all other clusters. Genes filtered with log fold change threshold of > 0.25 and minimum percentage expression of 25% of cells. All genes but one (Gsto1) had an adjusted p-value of < 1.0x10-5. Five genes (Fabp4, Fabp5, Stmn1, Mki67, Cbr2) met this criterion for more than one cluster, grouped with the more abundant cluster. Data is compiled from two inde- pendent experiments, 3 conditions each, for a total of 6 samples. (TIF) S7 Fig. (related to Fig 3). UMAP gene expression plots for genes associated with macro- phage subcluster 3 and found in AM_2 (Pisu et al) (31). Genes associated with mitochon- drial oxidative phosphorylation (mt-Co1, mt-Cytb, mt-Nd2), chromatin remodeling (Ankrd11, Baz1a), macrophage-associated transcription factors (Cebpb, Zeb2, Bhlhe40, Hif1a), and CARD9 signaling (Malt1, Bcl10). Data is compiled from two independent experiments with 3 conditions each, for a total of 6 samples. (TIF) S8 Fig. (related to Fig 3). Frequency and gene expression of Cluster 6 macrophages across exposure conditions. A) Single-cell RNA-sequencing from BAL samples from control, scBCG, and coMtb mice. Subcluster of macrophages with each cluster annotated. Relative fre- quency of Cluster 6 for each replicate. B) Differentially expressed genes within Cluster 6 between control vs scBCG vs coMtb samples. Wilcoxon Rank Sum Test, Bonferroni adjusted p-value, **adj-p < 0.001. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 22 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S9 Fig. (related to Fig 3). IV BCG vaccination leads to similar remodeling of alveolar mac- rophages as SC BCG vaccination. A-B) Bulk RNA-seq gene expression analysis between naive and Mtb-infected AMs 24 hours following high-dose mEmerald-H37Rv infection in mice previously exposed to scBCG, ivBCG, and coMtb, compared to controls. (controls- reported in Rothchild et al, 2019(10); CMTB- reported in Nemeth et al, 2020(25)). A) Principal Component Analysis using DEG (fold change > \|2\|, FDR < 0.05) in Mtb-infected AMs at 24 hours. B) Heatmap of 131 DEG at 24 hours in Mtb-infected AM (left), Interferon Stimulated Genes, derived from macrophage response to IFNα (fold change >2, p-value < 0.01) Mosta- favi et al, 2016 (30)(middle), IL6 JAK STAT3 hallmark pathway (right). C) Relative frequency of 3 key clusters for macrophage subset from control, scBCG, ivBCG, and coMtb scRNAseq BAL samples. (A-B) Compiled from 3+ independent experiments per condition for control, 2 independent experiments per condition for scBCG, ivBCG, and coMtb. (C) Data is compiled from two independent experiments (circle, triangle) with 3 conditions each for a total of 6 samples. (TIF) S10 Fig. (related to Fig 4). UMAP gene expression plots of cluster and lineage marker genes of interest for T cell subclusters. Data is compiled from two independent experiments with 3 conditions each for a total of 6 samples. (TIF) S11 Fig. (related to Fig 5). Gene expression of alveolar macrophages from ex vivo stimula- tions. A) Gene expression changes measured by bulk RNA-seq for stimulated AMs compared to respective unstimulated AMs (i.e., LPS-stim control AM vs unstim control AM; LPS-stim scBCG AM vs unstim scBCG AM; LPS-stim coMtb AM vs unstim coMtb AM). AMs were stimulated for 6 hours with Pam3Cys (10 ng/ml), LPS (10 ng/ml), or H37Rv (effective MOI ~2:1). Volcano plots depict fold change (log2) and P-value (-log10) for each stimulation condi- tion for each of the three groups (control scBCG, coMtb) compared to the respective unstimu- lated controls. DEG (p-value < 0.001; \|fold change\| > 2) highlighted and labeled, space permitting. B) Baseline gene expression for innate receptors and adaptors of interest from scBCG and coMtb AM compared to control AM, log2 fold change, unstim scBCG AM vs unstim control AM; unstim coMtb AM vs unstim control AM. Compiled from 3 independent experiments. (TIF) S12 Fig. (related to Fig 5). Cell-intrinsic changes in alveolar macrophage response is retained 23 weeks following vaccination. Gene expression of Mx1, Cxcl10, Il1b, Cxcl2, Irf7, and Il6 as measured by qPCR in AMs isolated by BAL from mice 8 and 23 weeks following scBCG vaccination and from age-matched controls, with and without LPS (10 ng/ml) stimula- tion. Data is representative of technical AM duplicates from a single experiment. (TIF) S1 Table. RNA-Sequencing data for alveolar macrophages 24 hours following high dose H37Rv-mEmerald challenge from scBCG mice. (XLSX) S2 Table. Top differentially expressed genes for individual clusters for macrophage, T cell, and dendritic cell sub-cluster analysis. (XLSX) S3 Table. RNA-Sequencing data for ex vivo stimulated alveolar macrophages. (XLSX) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 23 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S4 Table. IFN-independent and IFN-dependent genes based on WT and IFNAR-/- BMDM RNA-seq data. (XLSX) Acknowledgments We thank the Animal Care staff at Seattle Children’s Research Institute and University of Mas- sachusetts Amherst, Pamela Troisch and the Next Gen Sequencing core at the Institute for Sys- tems Biology. The authors acknowledge Research Scientific Computing at Seattle Children’s Research Institute for providing HPC resources that have contributed to the research results reported within this paper. We thank members of the Aderem, Urdahl, and Rothchild labs for helpful discussions. Author Contributions Conceptualization: Dat Mai, Johannes Nemeth, Kevin Urdahl, Alan H. Diercks, Alan Aderem, Alissa C. Rothchild. Data curation: Michael Morikubo, Alan H. Diercks, Alissa C. Rothchild. Formal analysis: Dat Mai, Michael Morikubo, Alan H. Diercks, Alissa C. Rothchild. Funding acquisition: Kevin Urdahl, Alan H. Diercks, Alan Aderem, Alissa C. Rothchild. 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10.1371_journal.pwat.0000227	A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t HHS Public Access Author manuscript PLOS Water. Author manuscript; available in PMC 2024 August 22. Published in final edited form as: PLOS Water. 2024 ; 3(3): . doi:10.1371/journal.pwat.0000227. Spatial and seasonal variation in disinfection byproducts concentrations in a rural public drinking water system: A case study of Martin County, Kentucky, USA Jason M. Unrine1,2,, Nina McCoy3, W. Jay Christian4, Yogesh Gautam5, Lindell Ormsbee5, Wayne Sanderson6, Ricki Draper7,8, Madison Mooney3,7, Mary Cromer8, Kelly Pennell5, Anna G. Hoover4 1Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky, United States of America, 2Kentucky Water Research Institute, University of Kentucky, Lexington, Kentucky, United States of America, 3Martin County Concerned Citizens, Inc., Inez, Kentucky, United States of America, 4Department of Epidemiology and Environmental Health, University of Kentucky, Lexington, Kentucky, United States of America, 5Department of Civil Engineering, University of Kentucky, Lexington, Kentucky, United States of America, 6Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, Kentucky, United States of America, 7Livelihoods Knowledge Exchange Network, Lexington, Kentucky, United States of America, 8Appalachian Citizens’ Law Center, Inc., Whitesburg, Kentucky, United States of America Abstract To increase our understanding of the factors that influence formation of disinfection byproducts (DBPs) in rural drinking systems, we investigated the spatial and seasonal variation in This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Jason.Unrine@uky.edu . Author Contributions Conceptualization: Jason M. Unrine, Nina McCoy, Lindell Ormsbee, Wayne Sanderson, Mary Cromer, Kelly Pennell. Data curation: Jason M. Unrine. Formal analysis: Jason M. Unrine, W. Jay Christian, Yogesh Gautam. Funding acquisition: Jason M. Unrine, Lindell Ormsbee, Wayne Sanderson, Kelly Pennell. Investigation: Jason M. Unrine, Nina McCoy, Ricki Draper, Madison Mooney. Methodology: Jason M. Unrine. Project administration: Jason M. Unrine. Resources: Jason M. Unrine. Supervision: Jason M. Unrine. Visualization: Jason M. Unrine, W. Jay Christian. Writing – original draft: Jason M. Unrine. Writing – review & editing: Nina McCoy, W. Jay Christian, Yogesh Gautam, Lindell Ormsbee, Wayne Sanderson, Ricki Draper, Madison Mooney, Mary Cromer, Kelly Pennell, Anna G. Hoover. Competing interests: The authors have declared that no competing interests exist. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 2 trihalomethane (THM) and haloacetic acid (HAA) concentrations in relation to various chemical and physical variables in a rural public drinking water system in Martin County, Kentucky, USA. We collected drinking water samples from 97 individual homes over the course of one year and analyzed them for temperature, electrical conductivity, pH, free chlorine, total chlorine, THMs (chloroform, bromodichloromethane, dibromochloromethane, dichlorobromomethane, and bromoform) and HAAs (monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, bromoacetic acid, and dibromoacetic acid). Spatial autocorrelation analysis showed only weak overall clustering for HAA concentrations and none for THMs. The relationship between modeled water age and TTHM or HAA5 concentrations varied seasonally. In contrast, there was strong variation for both HAA and THMs, with concentrations of HAA peaking in mid-summer and THMs peaking in early fall. Multiple regression analysis revealed that THM concentrations were strongly correlated with conductivity, while HAA concentrations were more strongly correlated with water temperature. Individual DBP species that only contained chlorine halogen groups were strongly correlated with temperature, while compounds containing bromine were more strongly correlated with conductivity. Further investigation revealed that increased drinking water conductivity associated with low discharge of the Tug Fork River, the source water, is highly correlated with increased concentrations of bromide. Discharge and conductivity of the Tug Fork River changed dramatically through the year contributing to a seasonal peak in bromide concentrations in the late summer and early fall and appeared to be a driver of brominated THM concentrations. Brominated DBPs tend to have higher toxicity than DBPs containing only chlorine, therefore this study provides important insight into the seasonal factors driving risk from exposure to DBPs in rural drinking water systems impacted by bromide. Introduction Drinking water infrastructure is degraded and in severe need of upgrades and repairs in many parts of the United States [1]. This is particularly true in rural communities which have fewer customers per kilometer of pipe and thus large per capita infrastructure costs. Infrastructure degradation can lead to decreases in water reliability and quality, including the presence of contaminants [2]. Among the issues faced by rural public drinking water systems is formation of disinfection byproducts (DBPs) during drinking water treatment and distribution. After total coliform bacteria violations, DBP violations are the second most common violation of drinking water regulations in the United States and most violations occur in rural drinking water systems, particularly in low-income rural areas [3, 4]. Drinking water disinfection byproducts (DBPs) are formed when disinfectants (e.g. chlorine, bromine, chloramine, UV radiation, ozone) react with natural and anthropogenic organic matter and inorganic ions (I− and Br−) during treatment of drinking water [5]. Of the many compounds that are formed, two classes of organic compounds, trihalomethanes (THMs) and haloacetic acids (HAAs), as well as the inorganic ions bromate and chlorite, are currently regulated in the United States under the Safe Drinking Water Act based on their prevalence and toxicity [6]. These compounds have been associated with a range of adverse health effects, including urinary tract cancers and adverse birth outcomes, depending on exposure level and duration [7–13], although there are many other DBP compounds of potentially higher toxicity which are unregulated [14]. Reducing concentrations of PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 3 DBPs can be challenging for drinking water systems, particularly for those with degraded infrastructure and inadequate financial and technical resources, and those that rely on chlorination as a disinfection method and surface water as the source water. Further, rural drinking water systems often have large distribution networks with low demand and long service lines contributing to increased water age at the tap, which could lead to increased DBP formation [15]. Previous modeling studies have shown that smaller systems may be more susceptible to variation in DBPs due to routine variation in system hydraulic operating parameters [16]. Additionally, intrusion of contaminants into pipes that occurs during intermittent operation, as could be the case in leaky systems with frequent service outages, can lead to increased formation of DBPs [17]. Spatial variation in DBP concentrations has been well studied; however, complex interrelated factors may influence the spatial patterns of DBP formation and the composition of DBP mixtures, making it hard to generalize among water systems [18]. Changes in concentrations with distance and water age are also not necessarily linear. For example, Rodriguez et al. found that THM concentrations initially increase and then level off further from the drinking water treatment plant while HAAs initially increase with distance from the treatment plant and then decrease, presumably due to microbial degradation [19]. Villanueva et al., found that within subject variability in THM exposure over time was far greater than between subject variability [20], suggesting a limited role for spatial variability in overall exposure patterns and a greater role of seasonal variation. Seasonal variation in DBP concentrations is often observed in drinking water systems, possibly due to factors such changes in water temperature and precursor (NOM and inorganic ion) concentrations, NOM reactivity, as well as chlorine dose [15, 19, 21–23]. As a result of large seasonal variation, quarterly samplings used in regulatory monitoring, and by extension many epidemiological studies that rely on these data, likely do not adequately capture temporal dynamics in exposure [19, 24]. While some of the health effects associated with DBPs, such as urinary tract cancers, typically require chronic exposure to develop, other effects such as cardiac birth defects have a very short window of susceptibility, even just a few weeks making short term exposures important [25]. Many authors have noted the importance of understanding seasonal variation in exposure to better understand and minimize the risk of reproductive effects of DBPs [18, 19, 21, 26]. Eastern Kentucky, the location of Martin County, has been identified as a relative hotspot for drinking water violations in the United States [3]. Prior to 2019, the drinking water system in Martin County, Kentucky had an 11-year history of violations of DBP regulations, specifically for total trihalomethanes (TTHMs; defined as the sum of the concentrations of chloroform, bromoform, chlorodibromomethane and dichlorobromomethane) and total haloacetic acids (HAA5; defined as the sum of monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, bromoacetic acid, and dibromoacetic acid) [27]. For example, according to the U.S. EPA Safe Drinking Water Information System, the Martin County Water District (Public Water System ID KY0800273) received 34 maximum contaminant level violations for TTHM and HAA5. between 2006 and 2017. Thus, it can serve as a case study for investigating the formation of DBPs in rural drinking water systems. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 4 The Martin County Water District public drinking water system was built in Martin County in the 1960s to serve around 600 households in the town of Inez, the county seat. Over the years the system was extended to serve around 4,300 households across the county without any significant upgrades to the treatment plant [27]. Because of the dissected nature of the landscape, a complex network of water lines, pumps, pressure regulating valves, and storage tanks is necessary to maintain proper pressure and flow across the service area. As much as ~69% of the produced water has been unaccounted for at service meters at times, a large proportion of which is likely due to leakage [27]. This leakage can create difficulties maintaining proper pressure and flow rates within the system, which is critical for regulating water age and possibly formation of DBPs within a distribution network [15]. For example, the leakage could make it difficult to flush the distribution lines, leading to greater biofilm formation and possibly formation of DBPs in the network due to the presence of organic matter precursors [28, 29]. Furthermore, long service lines with low demand could increase water age and potentially influence DBP formation [30]. While there have been many studies conducted on seasonal and spatial variation of DBPs in drinking water systems as highlighted above, there is limited information available about spatial and seasonal variation in small rural systems and how this potentially influences short term exposure. Given the complexity of the distribution network and the number of leaks, a key question is whether DBP exposure in certain parts of the distribution system was higher than others. It’s possible that DBP formation within the distribution network would lead to higher exposures in locations more distant from the treatment plant due to increased water age and DBP concentrations tend to increase while chlorine residuals decrease during distribution [31]. It is also possible that more complex spatial patterns could emerge given the complex network of storage tanks and pressure regulating valves present within the network coupled with the presence of long service lines with low water demand in remote areas, leading to a lack of correlation between distance from the treatment plant and water age. Sampling in this system is conducted quarterly at two locations. This small number of sampling locations and time points may fail to adequately characterize exposure in a complex network with strong seasonal variation in DBP concentrations. Further, large seasonal changes in source water chemistry due to the small size of the reservoir and river that serve as the source water may lead to large fluctuations in DBP formation. To better characterize the spatial variation of DBP concentrations, we collected samples from 97 individual homes, randomly selected from the drinking water distribution network. We hypothesized that locations more distant from the treatment plant would have higher concentrations of THMs and HAAs based on the potential for chlorine to continue reacting with unreacted precursors present in the water or with biofilms or other deposits within the pipes. Data from the 97 individual homes were also analyzed for seasonal trends. We hypothesized that DBP concentrations would peak during the summer and reach a minimum during the winter, and that these changes would be correlated with tap water temperature, conductivity, pH, and total chlorine concentrations. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Methods Ethics statement Page 5 This study was approved by the University of Kentucky Institutional Review Board (UK IRB protocol 44991). Informed consent was obtained in writing from all study participants between December 2018 and December 2019. Eligibility and participant enrollment procedures We used a stratified random sampling design to select households for the study. The study population included households served by the Martin County Municipal Water District (MCMWD). Individuals who were over the age of 18, residents of Martin County, customers of the MCMWD, and could speak English were eligible to participate in the study. Some authors had access to information that could identify individual study participants during and after data collection. We obtained a list of the service addresses of all customers within the MCMWD. The list was then divided into households within four discrete categories of distance from the water treatment plant (Fig 1). For each day of home visits, we focused on one distance category and rotated the distance categories. If the randomly selected resident was not home when we visited, we went to the next home on the street until we found a resident at home. We also left letters with contact information allowing the residents who were not at home to schedule an appointment at their convenience. We also collected latitude and longitude of the residence using the Global Positioning System (GPS). Sample collection Sample collection was conducted according to U.S. EPA protocols for the analytical methods noted in the sample analysis section. Prior to sample collection, we opened the cold water tap until the flowing water reached a constant temperature, thus purging the premises plumbing and obtaining water from the distribution network. We collected samples for TTHM and HAA5 in pre-cleaned, pre-preserved (containing dechlorinating agent) 40 mL amber glass vials with fluoropolymer septa (TTHM) or 500 mL pre-cleaned, pre-preserved amber glass jars (HAA5). Finally, we collected samples for conductivity measurement in 50 mL polypropylene vials. We determined pH and chlorine residuals at the home as described below. All DBP samples were extracted within 14 days and extracts were analyzed within 14 days of extraction according to U.S. EPA methods. Field blanks for TTHMs and HAA5 were periodically collected and analyzed with each analytical batch. We also collected latitude and longitude of the residence using the Global Positioning System (GPS). We collected source water samples at the Curtis Crum Reservoir and the Tug Fork River from September 2021-April 2022 to investigate the relationship between conductivity and bromide concentrations in the source water. Since we collected samples from public access boat ramps, no permission was required. Samples were collected in polypropylene vials and kept at 4°C until analysis. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Sample analysis Page 6 Analysis of DBPs, including TTHMs and HAA5 was performed by Pace Analytical Services (Madisonville, KY, USA) by purge and trap gas chromatography mass spectrometry (TTHMs) or derivatization and gas chromatography with electron capture detection (HAA5), following U.S. EPA methods 524.2 revision 4.1[32] and 552.2 revision 1 [33], respectively. Pace Analytical is accredited by the Kentucky Department of Environmental Protection for analysis of drinking water samples for regulatory compliance. We determined conductivity using a 5-ring conductivity cell with integrated temperature sensor and conductivity module (model 865, Metrohm, Herisau, Switzerland), which was calibrated to a certified reference conductivity standard. We determined pH, total chlorine, and free chlorine content during the home visits. We determined pH using phenol red as a pH indicator and a hand-held colorimeter (model DR300, Hach, Loveland, CO, USA). We determined free and total chlorine concentrations using the N,N-diethyl-p-phenylenediamine (DPD) method and the Hach DR300 pocket colorimeter (U.S. EPA method 330.5) [34]. Accuracy of the free and total chlorine method was verified by analyzing ultra-pure water as a blank and certified reference materials for chlorine residuals (Inorganic ventures, Christiansburg, VA, USA). We analyzed surface water samples for bromide using a ion chromatography coupled to inductively coupled plasma mass spectrometry (IC-ICP-MS) using previously described methods [35]. Samples were analyzed using an Agilent 1200 series chromatography system using a Dionex AS-11HC column. An Agilent 7900 ICP-MS was used to quantify bromide using m/z = 80. NIST traceable standards were obtained from Inorganic Ventures (Christiansburg, VA, USA). External data sources We obtained data on discharge of the Tug Fork River from the U.S. Geologic Survey (USGS), National Water Information System from December 15, 2018-January 15, 2020 from USGS station 03213700 at Williamson, WV (https://waterdata.usgs.gov/ nwis). Air temperature and precipitation data were obtained over the same period from the National Climate Data Center for the Inez, KY, USA, Global Historical Climatology Network ID USC00154138 (https://www.ncdc.noaa.gov). Data for regulatory monitoring samples for DBPs were obtained from the Kentucky Division of Water (https:// dep.gateway.ky.gov/DWW/). Data analysis and mapping We performed multiple linear regression analyses, pearsons correlations, linear regression and calculated descriptive statistics using SPSS version 26 (IBM, Armonk, NY) or with the R statistical package and generating plots with package ggplot2 (http://had.co.nz/ ggplot2/book). For multiple linear regression analyses we used backward model selection and initially entered water temperature, free chlorine, total chlorine, conductivity, pH, network distance from and water age. The selection criterion for removal was p > 0.10. Regression was performed separately for TTHM, HAA5, and each of the component PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 7 species. Additionally, we divided the distribution network into six distinct branches or sections based on water consumption zones (S1 Fig) and performed multiple regression analysis separately for each section individually as well as a combined analysis after coding section as a dummy variable. In all statistical analyses the level of statistical significance was considered to α = 0.05. We calculated the global Moran’s I in GeoDa (https://geodacenter.github.io) software to assess spatial autocorrelation, or spatial clustering of similar values, for TTHMs and HAA5, using spatial weights based on each participating household’s four nearest neighbors. The global Moran’s I ranges from −1 to 1, with a positive I indicating clustering of similar values, and negative I indicating dispersion of similar values. This analysis thus reveals whether there is a geographic pattern in contaminant concentrations, which would be expected if DBP concentrations increased with distance from the water treatment plant. We analyzed all TTHM and HAA5 values regardless of season, as well as values for each season separately, though there were too few Spring samples (n = 6) for meaningful analysis. For mapping, we obtained GIS shapefiles comprising the county boundary polygons from the Kentucky Geography Network (https://kygeonet.ky.gov), the spatial data clearinghouse for Kentucky. A ZIP file containing this publicly available shapefile can be downloaded from https://ky.app.box.com/v/kymartian-KyBnds-County/folder/137608414025. We used state cartographic boundary files from the U.S. Census for an inset U.S. map (https:// www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.2015.html), and the National Hydrography Data (NHD) from the United States Geological Survey (USGS; https://www.usgs.gov/national-hydrography/national-hydrography-dataset) to add the Tug Fork River. Physical and topological data used for use in building a computer model of the Martin County water distribution system was obtained from the Kentucky Water Resource Info System (WRIS) at https://kia.ky.gov/WRIS/Pages/WRIS-Portal.aspx. This information was verified in the field and adjusted when necessary after consultation with officials with the Martin County Water District (MCWD). Additional information on the spatial and temporal distribution of water demands, including estimated water loss, were also obtained from MCWD officials. A commercial software package for modeling water distribution hydraulics (KYPIPE) was used to estimate water age [36]. KYPIPE calculates water age using algorithms originally proposed and used in EPANET [37] a similar software package for use in modeling water quality parameters. EPANET calculates water age at various points in the distribution system using a Lagrangian time-based approach to track discrete parcels of water as they move through the distribution system. The average expected water age at any point in the system can then be approximated by running the model over an extended number of days until the water ages reach an equilibrium value. This is necessary to balance out the contributions of water ages from different sources to the system (e.g. water storage tanks). In the current study, the model for the Martin County system was run for 200 hours to establish equilibrium and then water ages associated with each sample point were estimated based on their proximity to the nearest node in the model. To ensure the accuracy of the PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 8 model estimates, the hydraulic parameters of the model (i.e., pipe roughness and spatial and temporal water demand) were first calibrated to match observed flows and pressures collected from fire hydrant flow tests in the field. The accuracy of the spatial demands was improved by partitioning the model into 23 water consumption zones (S1 Fig) which were separated by master water meters. Demands within each zone were then obtained by performing a mass balance of each zone. Estimates of water loss were then calculated by subtracting the sum of the individual customer meter readings within a zone from the total estimated demand for that zone. This process helped ensure a more accurate distribution of demands and helped minimize the impact of the aggregate water loss across the system. Field measurements of chlorine residuals and THMs were then to validate the relative water ages for the final model. Results Disinfection byproducts Descriptive statistics for TTHMs and HAA5 concentrations are summarized in Table 1. Mean concentrations of TTHMs (0.065 mg/L) and HAA5 (0.035 mg/L) were below the U.S. EPA maximum contaminant levels (MCLs) of 0.08 and 0.06 mg/L, respectively. We measured concentrations in 28 individual samples that exceeded the EPA MCL for TTHMs (0.08 mg/L) and 10 that exceeded the MCL for HAA5 (0.06 mg/L). Since our observations are heavily weighted towards the summer and fall, our mean concentrations may differ from the true annual mean concentrations. Average detected concentrations for individual DBP compounds as compared to the MCLs and maximum contaminant level goals (MCLGs) are presented in Table 2. Temperature, pH, conductivity, and chlorine residuals Table 1 shows the descriptive statistics for chlorine residuals, pH, conductivity, temperature, and total dissolved solids estimated from conductivity measurements. Generally, these values were within the normal expected range for potable water, except for a few isolated observations of inadequate chlorine residuals during the summer (<0.2 mg/L free chlorine). Spatial variation and water age Spatial autocorrelation analysis revealed significant, but weak, clustering of similar concentration values during the summer, but not in the winter or fall, for TTHMs (Table 3). Spatial autocorrelation analysis revealed stronger spatial clustering across all seasons for HAA5 than for TTHMs (Table 3). Note that we did not have enough samples in the spring for spatial autocorrelation analysis. Identification of local hot spots from the map for TTHM concentrations (Fig 2) is difficult. Although in the fall, there appeared to be a group of samples near the treatment plant with high TTHM concentrations, there was no statistically significant clustering of values during that period. For HAA5, the highest concentrations tended to occur at locations that were further from the treatment plant, although there were also observations with low HAA5 concentrations distant from the treatment plant, even during the summer (Fig 3). Multiple regression analysis revealed a significant positive effect of distance through the pipe network from the treatment plant on HAA5 concentrations when including physicochemical properties of the water in the model that were significant PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 9 (conductivity, pH, temperature) ((F3,92 = 23.08, p <0.001, R2 = 0.57; standardized (std) β for distance = 0.298, p<0.000; S1 Table) but no effect on TTHM concentrations (p = 0.916 for distance, F3,92 = 135.3, p <0.001, R2 = 0.82; S2 Table). Multiple regression analyses conducted for both TTHM and HAA5 using the six sections defined in S1 Fig revealed that section had no significant effect (p values ranging from 0.395–0.977) and was thus dropped from the models during backward selection. Models run on sections individually also revealed no change in the overall conclusions although this approach resulted in reduced statistical power in the individual models as opposed to a model including all sampling locations. For example, section 5 did not have enough degrees of freedom to run the full model. Distance and water age were not significant for any section for TTHMs and were significant for sections 2 and 3 for HAA5. Further examination of the individual species of HAA5 that had most observations above the method detection limit (dibromoacetic acid (DBA), dichloroacetic acid (DCA), and trichloroacetic acid (TCA)), showed statistically significant positive relationships between concentrations and distance for DCA and TCA but not DBA. The std β values for DCA and TCA were 0.171 (p = 0.045) and 0.165 (p < 0.022), respectively (See S3–S5 Tables). Overall, water age modeled using KYPIPE was not significantly correlated with distance from the water treatment plant (r = 0.174, p = 0.115). This stems from the complexity of the distribution network which includes many long service lines with low demand as well as multiple branches with differing water demands. Overall, water age had no statistically significant relationship with TTHM or HAA5; however, when examining this relationship month by month, there was a marginally significant relationship between water age and TTHM concentrations in January 2019 (S2 Fig; p<0.06, r2 = 0.23), and December 2019 (S2 Fig; p = 0.07, r2 = 0.51). The relationship between estimated water age and HAA5 was marginally significant for July 2019 (S3 Fig; p = 0.06, r2 = 0.15) and significant for August 2019 (S3 Fig; p = 0.01, r2 = 0.74). Temporal variation and relationship to water chemistry and temperature Visual inspection of the temporal distribution of TTHMs and HAA5 (Figs 4 and 5; Table 1) suggested significant seasonal variation in concentrations. The highest values for TTHMs and HAA5 in individual samples occurred exclusively in the summer and fall from June to November. There were differences in the temporal variation between TTHMs and HAA5 and among individual chemical species within those two classes. These differences were analyzed by relating them to physicochemical properties of the tap water. Multiple regression analysis (S2 Table) revealed that free chlorine, temperature, and conductivity were significantly associated with TTHMs but distance and pH were not. Overall, the model was highly significant and explanatory of TTHM concentrations (F3,92 = 135.3, p <0.001, R2 = 0.82). Conductivity and temperature had a positive correlation with TTHMs and total chlorine had a negative correlation (S2 Table). Substituting modeled water age for distance did not change the R2 value and water age was not significant (not shown). Concentrations peaked in late summer and early fall. The relationship between TTHM concentrations, temperature and conductivity are shown in Fig 6. Peak conductivity of drinking water occurred at the minimum discharge of the Tug Fork River (S4 Fig). This PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 10 coincided with the peak in TTHM concentrations (Fig 4), and a period of drought during the month of September 2019 (S4 and S5 Figs). The peak water temperature coincided with peak mean observed air temperature in July (S5 Fig). Tap water temperature was strongly correlated with air temperature (Pearson’s correlation; r = 0.901, p<0.001). Further regression analysis on individual THM compounds detected in individual samples showed a pattern dependent upon the halogen groups present. As the degree of bromine substitution (i.e. number of bromine atoms per molecule) for a compound increased, conductivity became more strongly correlated with its concentrations. Concentrations of chloroform were more strongly related to water temperature, while compounds containing bromine (bromodichloromethane and dibromochloromethane) were more strongly related to conductivity (Fig 6; S2–S4 Tables). For example, conductivity was not significant for the multiple regression model with only temperature (std β = 0.621) and free chlorine (std β = −0.362) being significant (S6 Table). Conversely, the std β for dibromochloromethane for conductivity was 1.084 and the std β for temperature was −0.069 (S7 Table). For bromodichloromethane, the std β values were 0.710 and 0.406 for conductivity and temperature, respectively (S8 Table). Bromoform was not detected in most of the samples, so we did not perform multiple regression for this species; however, except for one sample, it was only detected in the fall when conductivity was at its highest. In contrast, the peak concentrations of HAA5 (Fig 5) coincided with the peak air temperature observed in Martin County (S5 Fig). It was during this period that high concentrations of HAA5 occurred in individual samples, primarily in locations remote from the treatment plant (Fig 3). Concentrations of HAA5 showed temporal variation that differed from TTHMs, with the peak concentrations occurring in the summer rather than the early fall (Fig 5). Multiple regression analysis revealed that the most important factors associated with HAA5 were temperature, conductivity, and distance. The pH was not statistically significant (S1 Table). The absolute value of the coefficient for temperature was more than double the absolute values of the other coefficients. It is notable that the coefficient for conductivity for HAA5 was negative while it was positive for TTHMs. Overall, the model for HAA5 was less predictive than the TTHM model, but still statistically significant (F3,92 = 23.08, p <0.001, R2 = 0.57). When modeled water age was substituted for distance, water age was not significant (in contrast to distance which was), but the overall R2 value was unchanged. When analyzing individual species of HAA5 with a significant number of observations above the method detection limit (dibromoacetic acid (DBA), dichloroacetic acid (DCA), and trichloroacetic acid (TCA)), we observed that temperature had a stronger relationship with concentrations than conductivity on the chlorinated compounds (DCA and TCA), and conductivity while the opposite was true for DBA (S3–S5 Tables Fig 7). The std β value for conductivity for DBA was 0.850 while temperature was insignificant. For DCA and TCA, there was a positive effect of temperature (β values of 0.652 and 0.672) and a negative effect of conductivity (β values of −0.577 and −0.956). This again showed that chlorine substitution increased with temperature and bromine substitution increased with conductivity. Because monochloroacetic acid was only detected in two samples and PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 11 bromoacetic acid was only detected in six samples, they were not included in our multiple regression analysis. To follow up on the correlation between conductivity and increasing concentrations of brominated trihalomethane and haloacetic acid concentrations, we collected samples from the Tug Fork River and the Crum reservoir between September 2021 and May 2022 (S6 Fig). There was a significant positive relationship between conductivity and bromide concentrations in both bodies of water (r2 = 0.77 and 0.95, respectively). Discussion This study adds to our understanding of seasonal and spatial variation of DBPs in small rural drinking water systems in temperate climates. While this study was initially designed primarily to capture spatial variation within the system by visiting many locations, the data analysis revealed strong seasonal variation which appeared to be the source of much greater variation. Thus, in addition to average concentrations, we investigated whether there was significant variation in DBP concentrations throughout the drinking water distribution network, in time, and in relation to select water properties. We found widespread occurrence of TTHMs and HAA5, with concentrations for HAA5 peaking in the middle of the summer and TTHM concentrations peaking in the fall. The seasonal patterns observed here are similar to those found in previous studies. For example, Rodriguez et al. also observed higher concentrations of THMs in the summer and fall; however, HAA concentrations were highest in the spring as opposed to the summer in the present study [19]. After sampling in a single location repeatedly for a year, Wang et al., found that both THMs and HAAs had their highest concentrations in the summer; however, there seemed to be less variation in brominated THMs than in our study, perhaps indicating less seasonal change in bromide concentrations [38]. Baytak et al., observed a different pattern than most studies where DBP concentrations were at their highest in the winter [21]. They attributed this to higher non-purgeable organic carbon (NPOC) concentrations observed in surface source water winter relative to other seasons. It is also important to note that this study was conducted in Turkey, which has a Mediterranean climate, contrasting with the humid subtropical climate of Kentucky, so there are large differences in rainfall patterns. Some previous studies have observed spatial patterns of TTHM concentrations that differ from what we observed. For example, research on a system in Cyprus found that household TTHM levels increase with increasing distance from the chlorination point [15]. Increased residence time has also been shown to be positively correlated with TTHM concentrations [15, 39]. These conclusions are consistent with our HAA5 findings, but differ from our TTHM findings, where distance from the chlorination point was not a key factor overall. Since water age, rather than the distance travelled, is likely to be a better predictor of DBP concentrations, we also investigated the relationship between modeled water age and DBP concentrations and only found significant relationships during certain months. While it is known that both TTHM and HAA5 concentrations increase with water age, it is likely that the strong temporal variation in our data set make it difficult to discern this PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 12 pattern as the temporal variation appears to be much greater than the variation due to water age. For TTHMs, water age was significantly related to DBP concentrations only during the winter months when seasonal factors (changes in temperature and conductivity) were changing more slowly than during the summer months. It has been observed previously that spatial variation in DBP is not temporally consistent [18]. Rodriguez et al found water residence time, as measured by fluoride tracer, to be an important factor determining DBP concentrations [19]. However, our system had longer predicted residence times which could be as long as 200 hours compared to only up to 36 hours in the Rodriguez et al. In future studies in small systems with such large temporal variation, it is probably necessary to sample at many locations simultaneously or within a very short time to characterize the water age effect. Previous work has also demonstrated seasonal variation is closely linked with water temperature, organic matter concentrations, and organic matter reactivity [15, 21–23, 40, 41]. Often, drinking water utilities use higher doses of chlorine during the summer when microbial loads are higher [5]; however, we did not observe a positive correlation between total chlorine and DBP concentrations. Our finding that conductivity is correlated with brominated THM and HAA concentrations is explained by the correlation between conductivity and bromide concentrations in source water in our follow-up sampling. Previous studies of effects of seasonal changes in source water hydrology and watershed process on DBP precursor concentrations have largely focused on dissolved organic matter rather than bromide [14, 42–44]. Chlorination of bromide-containing waters forms HOBr, which is more reactive to natural organic matter than HOCl by an order of magnitude resulting formation of brominated DBPs [45]. Previous studies have observed that hydrophilic organic matter of lower molecular weight is more susceptible to bromination, while more aromatic NOM of higher molecular weight is more susceptible to chlorination [46], which could also contribute to seasonal changes in bromine substitution. Correlation between bromide concentrations and high conductivity has been observed in periods of low discharge of the Allegheny river in Pennsylvania, USA, in a stretch of the river receiving effluent from treated oil- and gas-produced water discharges [47]. This river system is also located within the Appalachian Mountain region of the USA. Indeed, the majority of predictive models for THM formation include the Br− concentration as a key term [5]. It is important to note that our study did not investigate all the brominated HAAs. In addition to dibromoacetic acid, there are four unregulated brominated HAAs. Future studies should include these compounds to determine if the trends observed between bromide concentration and degree of bromine substitution are consistent when all brominated HAAs are included. Formation of DBPs is also known to increase at higher temperatures during chlorination [48]. increased reaction rates simply due to temperature is a simple explanation; however, seasonal changes that are important and are merely correlated with increased temperature, such as in the concentrations and reactivity of the NOM cannot be ruled out as a contributing factor [41]. It is also important to note that although we found a significant effect of pH on HAA5 and not THMs, the pH value of the finished drinking water may differ from the pH during disinfection. Previous studies have shown that DBP formation is influenced by pH during water treatment [46, 49]. Note that the pH we measured at the tap may differ from the pH during the water treatment process. Unfortunately, sampling of source water PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 13 contemporaneously with the tap water sampling was beyond the scope of this study, so it is left to future studies to examine the relationship between source water characteristics and finished tap water characteristics in detail. Understanding the factors that determine the relative formation of brominated and chlorinated DBPs is a priority given the higher toxicity of brominated compounds in general. Bromine is ubiquitous in sea water; however, concentrations in inland water are general low [50]. Potential sources of Br- in the region include natural weathering of the abundant organic-rich shales and coal, as well as accelerated release of Br from these materials from coal mining activity or fracking [51]. Much of the land area of Martin County has been surface mined for coal. Coal-combustion waste from power plants is another potential source [52]. In addition to the natural presence of Br in coal, Br is added to coal, or brominated activated carbon is used to help in the removal of Hg from flue gasses in the United States [53]. Bromine is present within the coal combustion wastes and is elevated far above background in coal-fired power plant wastewaters and can be as high as bromide concentrations in sea water, concentrations of bromide in fracking produced waters can be even higher [54]. Concerns have been raised about formation of brominated DBPs in drinking water utilities which are downstream from coal-fired power plants in recent years [52, 55]. The present study also raises the question of the impact of periods of drought or low flow conditions in source water on brominated DBP concentrations particularly since increased frequency and severity of drought is expected in the southeastern United States due to climate change in the coming decades [56]. The highest concentrations of both THMs and HAA5 observed in this study were more than double the annual averages (0.155 vs 0.065 mg/L for TTHM; 0.073 vs 0.035 mg/L for HAA5). The LRAA based on the regulatory monitoring samples collected at the Meathouse Road pumping station, which typically has the highest values of the two regulatory monitoring stations, was only 0.056 and 0.040 mg/L for TTHMs and HAA5, respectively, during the quarter in which we observed peak TTHM and HAA5 concentrations (https:// dep.gateway.ky.gov/DWW/). Mean concentrations of TTHMs in Fall were more than triple the mean concentrations in winter (0.099 vs 0.030 mg/L). Mean concentrations of HAA5 were more than double in summer compared to the winter (0.020 vs 0.049 mg/L). This is similar to what was observed by Rodriguez et al., who observed TTHM concentrations averaged five times higher in summer and fall than in winter and HAA concentrations averaged four times higher in spring than winter [19]. While long-term exposures relevant to chronic health effects such as cancer may be well characterized by long term averages, such as the LRAA, the utility of the LRAA in characterizing short-term fluctuations in exposures, as observed in this study, and their impact on non-cancer endpoints such as birth defects warrants further study. For example, health effects which have been linked to THMs, particularly brominated THMs, include cardiac birth defects [9]. The heart has a critical window of susceptibility to teratogenesis during development within the first trimester. For example, conotruncal defects, which are associated with THMs, have a window of susceptibility between the 3rd and 7th weeks post-conception [9, 25]. Previous studies have also demonstrated that short-term exposures typically exceed the LRAA [26]. However, because most epidemiological studies rely on quarterly exposure data of TTHM PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 14 and HAA5 from regulatory data, the relationship between short-term exposures and adverse birth outcomes is not well characterized [9, 26, 57]. The average concentrations of DBPs observed in this study did not exceed the U.S EPA MCLs; however, several MCLGs were exceeded. The EPA MCLs consider multi-route exposure from drinking water (ingestion, dermal, inhalation) as well as exposure from other sources. Several considerations including animal studies, epidemiological data, economic feasibility, and economic analyses are used to determine the MCLs. The MCLs are a balance between economic and technical considerations and the likelihood of adverse health outcomes. The maximum contaminant level goals are the concentrations at which adverse health effects are not expected to occur and are the goals for protection of public health. Concentrations in our study exceeded these MCLGs for several compounds, particularly the more toxic brominated compounds (Table 2). Based on the U.S. EPA MCLGs, bromodichloromethane, bromoform, and dichloroacetic acid were the main drivers of risk of the measured compounds if only the individual effects of the compounds are considered. It is important to note that hundreds of DBPs have been identified and that THMs and HAAs are not necessarily a good indicator of exposure to potentially more toxic DBP compounds which are present [58, 59]. Other sources of DBPs exposure such as swimming in chlorinated water are also important [20]. Conclusions This study identified factors that are associated with DPB concentrations in a small rural drinking water system and demonstrated that dry summer conditions where source waters are at low flow conditions, increases in more toxic brominated DBPs are expected. It also demonstrated that spatial variation is complex and is season and DBP species-specific. This adds to the body of knowledge on sources of spatial seasonal variation in DBP concentrations in small rural drinking water systems in temperate climates. While this study is limited in that only one year of data were collected, there were gaps in sampling of the time series, and limited information on source water properties was obtained, it does provide important insights that can inform future research. Factors in small drinking water systems such as reliance on small rivers and streams and small reservoirs could exacerbate fluctuations in water temperature chemistry during periods of drought, which was related to increased DBP formation. The presence of long service lines with low demand also likely contribute to DBP formation. Future efforts at reducing DBP exposure could address seasonal changes in source water chemistry and how adjustments to operations might be made to reduce the formation of DBP compounds. Some measures, particularly those related to source water quality, might be system specific. In the case of Martin County, pumping from the reservoir during the spring and winter months and avoiding pumping from the river during periods of low river discharge during the late summer and early fall could reduce bromide concentrations in the source water which could then lead to a decrease in brominated DBP formation. Increasing the capacity of the reservoir could also lessen the need to pump water from the river during the late summer and early fall. Other measures might be more generalizable. For example, the utility could consider conducting more frequent (both temporal and spatial) sampling during the more critical summer months to monitor and detect increased DBP concentrations and then conduct targeted main flushing PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 15 in those segments. Altered storage tank management during the summer could also be considered. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Acknowledgments The authors acknowledge the advice and assistance of N. Kussainov, B. Tayor, S. Shrestha, T. Smith, D. Ma, W. Mitch, S. McSpirit, A. Fugate, E. Hahn, Martin County Concerned Citizens, and the study participants. Funding: This project was supported by University of Kentucky Center for Appalachian Research in Environmental Sciences (UK-CARES) through National Institute of Environmental Health Sciences (NIEHS) Grant P30ES026529 (JU) and through NIEHS grant R01ES032396 (JU). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS. This work was also partially supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Program under KY006133 (JU). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability Statement: Data are available from the University of Kentucky Center for Clinical and Translational Science RedCAP data repository (https://www.ccts.uky.edu/services-resources-researchers/ redcap) for researchers who meet the criteria for access to human subjects data. 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Villanueva CM, Castaño-Vinyals G, Moreno V, Carrasco-Turigas G, Aragonés N, Boldo E, et al. Concentrations and correlations of disinfection by-products in municipal drinking water from an exposure assessment perspective. Environmental Research. 2012; 114:1–11. 10.1016/ j.envres.2012.02.002 [PubMed: 22436294] 59. Furst KE, Bolorinos J, Mitch WA. Use of trihalomethanes as a surrogate for haloacetonitrile exposure introduces misclassification bias. Water Research X. 2021; 11:100089. 10.1016/ j.wroa.2021.100089 [PubMed: 33554102] PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 19 Fig 1. Map of Martin County, KY showing distance categories from the drinking water treatment plant that were used for distance-stratified sampling. We obtained the GIS data comprising the county boundary polygon data for Fig 1 and, thus, the base layer for these maps—from the Kentucky Geography Network (https:// kygeonet.ky.gov), the spatial data clearinghouse for Kentucky. A ZIP file containing this publicly available shapefile can be downloaded from https://ky.app.box.com/v/kymartian- KyBnds-County/folder/137608414025. We used state cartographic boundary files from the U.S. Census for the inset U.S. map (https://www.census.gov/geographies/mapping- files/time-series/geo/carto-boundary-file.2015.html), and the National Hydrography Data (NHD) from the United States Geological Survey (USGS; https://www.usgs.gov/national- hydrography/national-hydrography-dataset) for the Tug Fork River. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 20 Fig 2. Distribution of total trihalomethane (TTHM) concentrations in Martin County, Kentucky from Winter 2018-Winter 2019. We obtained the GIS data comprising the county boundary polygon data for Fig 2 and, thus, the base layer for these maps—from the Kentucky Geography Network (https:// kygeonet.ky.gov), the spatial data clearinghouse for Kentucky. A ZIP file containing this publicly available shapefile can be downloaded from https://ky.app.box.com/v/kymartian- KyBnds-County/folder/137608414025. We used state cartographic boundary files from the U.S. Census for the inset U.S. map (https://www.census.gov/geographies/mapping- files/time-series/geo/carto-boundary-file.2015.html), and the National Hydrography Data (NHD) from the United States Geological Survey (USGS; https://www.usgs.gov/national- hydrography/national-hydrography-dataset) for the Tug Fork River. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 21 Fig 3. Distribution of total haloacetic acid concentrations (HAA5) in Martin County, Kentucky from Winter 2018-Winter 2019. We obtained the GIS data comprising the county boundary polygon data for Fig 3 and, thus, the base layer for these maps—from the Kentucky Geography Network (https:// kygeonet.ky.gov), the spatial data clearinghouse for Kentucky. A ZIP file containing this publicly available shapefile can be downloaded from https://ky.app.box.com/v/kymartian- KyBnds-County/folder/137608414025. We used state cartographic boundary files from the U.S. Census for the inset U.S. map (https://www.census.gov/geographies/mapping- files/time-series/geo/carto-boundary-file.2015.html), and the National Hydrography Data (NHD) from the United States Geological Survey (USGS; https://www.usgs.gov/national- hydrography/national-hydrography-dataset) for the Tug Fork River. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 22 Fig 4. Total trihalomethane concentrations in drinking water from households in Martin County Kentucky as a function of date. Lack of a data point for a date indicates that the concentration was below the method detection limit. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 23 Fig 5. Haloacetic acid concentrations in drinking water from households in Martin County Kentucky as a function of date. Lack of a data point for a date indicates that the concentration was below the method detection limit. PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 24 Fig 6. Total trihalomethane (TTHM) concentrations as a function of tap water conductivity and temperature (top) along with concentrations of individual THM species (bottom). PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 25 Fig 7. Total haloacetic acid (HAA5) concentrations as a function of tap water conductivity and temperature (top) along with concentrations of individual haloacetic acid species (bottom). PLOS Water. Author manuscript; available in PMC 2024 August 22. Unrine et al. Page 26 Temperature (C), pH, Conductivity (mS/cm); total and free chlorine (mg/L) from municipal drinking water samples collected from homes in Martin County Kentucky between December 2018 and December 2019 (N = number of observations; SD = standard deviation). Table 1. Winter (Dec-Feb) Spring (Mar- May) Summer (Jun-Aug) Fall (Sep-Nov) Season SD Mean N Mean N 45 44 SD Mean 0.021 0.099 0.013 0.027 N 21 21 SD Mean 0.027 0.065 0.010 0.035 0.072 0.049 All N 96 95 SD 0.034 0.017 TTHMs (mg/L) HAA5 (mg/L) conductivity mS/cm free Cl (mg/L) total Cl (mg/L) pH Temperature °C Distance km Water age (hours) Mean 0.030 0.020 N 29 29 0.020 0.007 0.047 0.030 0.338 29 0.195 0.311 1.7 2.0 7.2 8.7 12.6 61.0 29 29 29 29 29 28 0.4 0.6 0.5 3.3 5.8 96.6 2.0 1.7 7.2 20.0 7.5 26.6 1 1 1 1 1 1 1 1 1 0.353 45 0.060 0.755 21 0.101 0.436 96 0.210 1.3 1.4 7.2 24.0 14.0 47.0 44 45 45 45 45 35 0.5 0.5 0.2 2.1 8.4 42.6 1.1 1.3 7.8 17.9 12.9 60.0 20 20 20 20 20 20 0.5 0.5 0.3 4.8 5.3 1.4 1.6 7.4 18.0 13.36 108.5 54.5 94 95 95 95 95 84 0.5 0.6 0.4 7.3 6.9 80.8 PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 27 Comparison of U.S. EPA maximum contaminant level goals (MCLG) with average concentrations detected in Martin County, KY, USA. Table 2. Note that only observations above the method reporting limits were used to compute means. MCLG (mg/L) Martin County mean (n above reporting limit) Compound Chloroform 0.07 Dibromochloromethane 0.046 Bromodichloromethane Bromoform Dichloroacetic acid Trichloroacetic acid Monochloroacetic acid Bromoacetic acid Dibromoacetic acid 0 0 0 0.02 0.07 NA NA 0.035 (95) 0.010 (90) 0.019 (95) 0.005 (32) 0.017 (95) 0.012 (94) 0.003 (2) 0.003 (6) 0.018 (50) PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t Unrine et al. Page 28 Spatial autocorrelation of total trihalomethanes and haloacetic acids in water samples from Martin County, KY (Dec 2018- Dec 2019). Table 3. Higher Moran’s I value indicates more spatial clustering of high values. Pesudo p-value < 0.05 indicates statistical significance. Total Trihalomethanes (TTHM) Haloacetic Acids (HAA5) All seasons (n = 97) I = −0.002; No clustering of similar values (pseudo p-value = 0.435) I = 0.177; Some clusteringof similar values (pseudo p-value = 0.005) Summer (n = 42) I = 0.189; Some clustering of similar values (pseudo p-value = 0.015) I = 0.193; Some clustering of similar values (pseudo p-value = 0.023) Fall (n = 30) Winter (n = 18) I = −0.104; No clustering of similar values (pseudo p-value = 0.298) I = 0.050; No clustering of similar values (pseudo p- value = 0.177) I = 0.084; No clustering of similar values (pseudo p-value = 0.140) I = 0.233; Some clustering of similar values (pseudo p-value = 0.036) Spring (n = 6) Not enough observations Not enough observations PLOS Water. Author manuscript; available in PMC 2024 August 22. A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t A u t h o r M a n u s c r i p t
10.1371_journal.ppat.1012128	RESEARCH ARTICLE A clinically attenuated double-mutant of porcine reproductive and respiratory syndrome virus-2 that does not prompt overexpression of proinflammatory cytokines during co-infection with a secondary pathogen Chia-Ming Su1¤a, Jineui Kim1¤b, Junyu Tang1, Yu Fan Hung1, Federico A. Zuckermann1, Robert Husmann1, Patrick Roady2, Jiyoun Kim3, Young-Min Lee3, Dongwan YooID 1* 1 Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America, 2 Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America, 3 Department of Animal, Dairy, and Veterinary Sciences, Utah State University, Logan, Utah, United States of America ¤a Current address: Department of Biochemistry and Cell Biology, School of Medicine, Boston University, Boston, Massachusetts, United States of America ¤b Current address: Department of Microbiology, Institute for Viral Diseases, Vaccine Innovation Center, College of Medicine, Korea University, Seoul, Korea * dyoo@illinois.edu Abstract Porcine reproductive and respiratory syndrome virus (PRRSV) is known to suppress the type I interferon (IFNs-α/β) response during infection. PRRSV also activates the NF-κB sig- naling pathway, leading to the production of proinflammatory cytokines during infection. In swine farms, co-infections of PRRSV and other secondary bacterial pathogens are common and exacerbate the production of proinflammatory cytokines, contributing to the porcine respiratory disease complex (PRDC) which is clinically a severe disease. Previous studies identified the non-structural protein 1β (nsp1β) of PRRSV-2 as an IFN antagonist and the nucleocapsid (N) protein as the NF-κB activator. Further studies showed the leucine at posi- tion 126 (L126) of nsp1β as the essential residue for IFN suppression and the region span- ning the nuclear localization signal (NLS) of N as the NF-κB activation domain. In the present study, we generated a double-mutant PRRSV-2 that contained the L126A mutation in the nsp1β gene and the NLS mutation (ΔNLS) in the N gene using reverse genetics. The immunological phenotype of this mutant PRRSV-2 was examined in porcine alveolar macro- phages (PAMs) in vitro and in young pigs in vivo. In PAMs, the double-mutant virus did not suppress IFN-β expression but decreased the NF-κB-dependent inflammatory cytokine pro- ductions compared to those for wild-type PRRSV-2. Co-infection of PAMs with the mutant PRRSV-2 and Streptococcus suis (S. suis) also reduced the production of NF-κB-directed inflammatory cytokines. To further examine the cytokine profiles and the disease severity by the mutant virus in natural host animals, 6 groups of pigs, 7 animals per group, were used a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Su C-M, Kim J, Tang J, Hung YF, Zuckermann FA, Husmann R, et al. (2024) A clinically attenuated double-mutant of porcine reproductive and respiratory syndrome virus-2 that does not prompt overexpression of proinflammatory cytokines during co-infection with a secondary pathogen. PLoS Pathog 20(3): e1012128. https://doi.org/10.1371/journal. ppat.1012128 Editor: Marjolein Kikkert, Leiden University Medical Center, NETHERLANDS Received: June 27, 2023 Accepted: March 15, 2024 Published: March 28, 2024 Copyright: © 2024 Su et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Some of the data underlying the results presented in the study are parts of the degree theses for Jineui Kim and Chia- Ming Su, and their theses have been deposited to the University of Illinois Urbana-Champaign. The thesis for JK is fully accessible at https://www. ideals.illinois.edu/items/116355. The thesis for CMS contains some additional data unrelated to the current study, which be published in peer- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 1 / 30 PLOS PATHOGENSreviewed journals. Once all chapters of the thesis are published, the entire thesis will fully be available without restriction. The University will release the entire thesis after August 2025 regardless of its publication. The CMS thesis is accessible at https:// www.ideals.illinois.edu/items/128551. Funding: This project was supported by Agriculture and Food Research Initiative (AFRI) Competitive Grants nos. 2018-67015-28287 and 2023-67015- 39710 from the U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) to DY and no. 2018-67016-28296 to YML. Part of this material is based upon work supported by the Cooperative State Research Service, USDA, under Project Number ILLU-888-944 awarded to DY. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. None of the authors received a salary from the funders. Competing interests: The authors have declared that no competing interests exist. Type I interferon suppression-negative and NF-κB activation-negative PRRSV for co-infection with the mutant PRRSV-2 and S. suis. The double-mutant PRRSV-2 was clinically attenuated, and the expressions of proinflammatory cytokines and chemokines were significantly reduced in pigs after bacterial co-infection. Compared to the wild-type PRRSV-2 and S. suis co-infection control, pigs coinfected with the double-mutant PRRSV-2 exhibited milder clinical signs, lower titers and shorter duration of viremia, and lower expres- sion of proinflammatory cytokines. In conclusion, our study demonstrates that genetic modi- fication of the type I IFN suppression and NF-κB activation functions of PRRSV-2 may allow us to design a novel vaccine candidate to alleviate the clinical severity of PRRS-2 and PRDC during bacterial co-infection. Author summary Porcine reproductive and respiratory syndrome virus (PRRSV) is a significant swine path- ogen worldwide. PRRSV suppresses the type I interferon (IFN) response and activates the NF-κB signaling, leading to the production of proinflammatory cytokines. Co-infection of PRRSV and a secondary pathogen is common in farms and exacerbates the production of proinflammatory cytokines, contributing to the porcine respiratory disease complex (PRDC) which is a clinically severe disease. Here, we generated a double-mutant PRRSV- 2 in the nsp1β and N genes by reverse genetics. In cells, the mutant virus did not suppress IFN expression and reduced the NF-κB-mediated inflammatory cytokines compared to wild-type PRRSV-2. Co-infection of macrophages with the mutant virus and Streptococcus suis reduced the production of inflammatory cytokines. The immunological phenotypes of the mutant PRRSV-2 were examined in pigs, 6 groups of pigs, 7 animals per group, after co-infection with S. suis. The double-mutant PRRSV-2 was clinically attenuated, and the expressions of proinflammatory cytokines were significantly reduced in pigs after bac- terial co-infection. Our study provides insights that the reprogramming of viral immune antagonism may allow to design of a novel vaccine candidate to alleviate the clinical sever- ity of PRRS and PRDC during co-infection. Introduction The host innate immunity contributes to the first line of defense against viral infections, and type I interferons (IFNs-α/β) are the most potent antiviral cytokines produced in cells against invading viruses. Type I IFNs are considered principle antiviral cytokines, but a large body of evidence indicates that they also play a pleiotropic role in regulating the adaptive immunity. IFNs can enhance the adaptive immunity by targeting dendritic cells, natural killer cells, T cells, and B cells [1]. In virus-infected mice, type I IFNs stimulate CD4+ T cells undergoing clonal expansion [2]. T cells primed by type I IFNs also show an increased ability to help B cells to enhance antibody secretions [3]. In other studies, type I IFNs have been shown to upre- gulate the survival, maturation, cytotoxicity, and clonal expansion of CD8+ T cells [4–14]. In addition, type I IFNs promote the differentiation of memory CD8+ T cells by affecting the ini- tial clonal expansion during virus infection [8,15,16]. Type I IFNs can also regulate B cell acti- vation, antibody secretion, and isotype switching during viral infections [17–19]. By increasing the level of B-cell survival factors, type I IFNs can promote B-cell survival and acti- vation and enhance autoantibody production [20]. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 2 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Porcine reproductive and respiratory syndrome (PRRS) is a swine disease, causing signifi- cant economic losses in most pork-producing countries worldwide. As the name suggests, the typical clinical signs of PRRS include reproductive failures in pregnant animals and respiratory disease and pneumonia in all age of pigs with more pronounced symptoms in young pigs. Immunologically, the hallmarks of PRRS are represented by unusually poor production of type I IFNs early in infection, accompanied by low and delayed neutralizing antibody response and viral persistence, suggesting that the adaptive response to invading virus is perturbed [21– 23]. Conversely, the downregulation of IFN production by PRRSV seems to be an important viral strategy to evade host antiviral defense to facilitate its own replication. The etiological agent PRRSV belongs to the family Arteriviridae of the order Nidovirales (http://www.ictv.global/report/arteriviridae). The PRRSV genome is a single-strand positive- sense RNA of ~15 kb in length, and based on their profound genomic differences, the viruses are assigned to two distinct species Betaarterivirus suid 1 (PRRSV-1; European genotype) and Betaarterivirus suid 2 (PRRSV-2; North American genotype) [24]. PRRSV-1 and PRRSV-2 are often collectively referred to as PRRSV in the literature and in the present article. The genome organization of PRRSV-1 and PRRSV-2 resembles those of coronaviruses in the order Nidovir- ales, but the genome length of PRRSV is approximately half of coronaviruses. The 5’ ~12 kb of the genome codes for two large polyproteins, pp1a and pp1ab, of which the latter is produced by the ribosomal frame-shifting mechanism. The two polyproteins are further processed to generate 14 nonstructural proteins (nsps). The remaining ~3 kb of the 3’ proximity of the genome codes for eight structural proteins: envelope (E), glycoprotein (GP) 2, GP3, GP4, GP5, open reading frame (ORF) 5a, membrane (M), and nucleocapsid (N) proteins [25,26]. Of PRRSV-2 proteins reported to downregulate IFN suppression, nsp1β is the most potent IFN antagonist [27–31]. The nsp1β protein blocks the host mRNAs export from the nucleus to the cytoplasm and allows PRRSV-2 to utilize the cellular translational machinery exclusively for viral protein synthesis and thus promotes progeny production [32]. This function has been correlated with nsp1β-mediated IFNs suppression. A specific motif for SAP [Scaffold attach- ment factor-A/B, Acinus, and Protein inhibitor of activated STAT (signal transducer and acti- vator of transcription)] has been identified in the nsp1β protein with the consensus sequence of 124-KxLQxxLxxxGL-135 within the papain-like proteinase domain [33]. Mutational analyses in the SAP motif revealed that L126A conferred the loss of host mRNA nuclear retention and nsp1β-mediated type I IFNs suppression. A mutant PRRSV-2 containing L126A was gener- ated, and the phenotype of the mutant PRRSV-2 was host mRNA nuclear retention-negative and type I IFN suppression-negative. In addition to IFN modulation, PRRSV-2 utilizes NF-κB signaling for its own benefit. Con- tradictory but complementary data are available for PRRSV-mediated NF-κB regulation. In cells expressing nsp1α, nsp1β, nsp2, nsp4, or nsp11, the NF-κB activity was downregulated, and this downregulation was linked to the inhibition of type I IFNs production pathway [34,35]. In contrast, cells expressing the N protein upregulated the NF-κB activity [36,37]. This finding was supported by the demonstration of N protein-mediated expression of proinflam- matory cytokines such as interleukin (IL)-1β, IL-6, IL-8, and TNFα. The molecular basis for NF-κB activation by PRRSV-2 N has been determined [37]. N is specifically distributed in the nucleus and the nucleolus in addition to the cytoplasm. N contains the nuclear localization sig- nal (NLS), and its nuclear localization is NLS-dependent through the binding of N to impor- tins-α/β. We have previously identified protein inhibitor of STAT1 (PIAS1) as the cellular partner of N [37]. PIAS is a negative regulator for the JAK-STAT signaling for antiviral protein expressions and functions as a repressor for NF-κB by binding to the NF-κB subunit p65. PRRSV N binding to PIAS1 overlaps the p65 binding domain. The binding of N to PIAS1 results in the release of p65 from PIAS1, leading to the NF-κB activation [37]. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 3 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV PRRSV-1 and PRRSV-2 are predominant pathogens in many commercial operations. PRRSV increases the susceptibility of infected hosts to secondary pathogens. Co-infection with other pathogens is frequent and causes porcine respiratory disease complex (PRDC), resulting in more severe clinical disease [38–40]. Co-infection increases the secretion of proinflamma- tory cytokines and exacerbates tissue damages and pulmonary infiltration [40]. Cytokine storm-like overproduction of inflammatory cytokines have been reported for PRRS, and the clinical outcome is more severe in the PRDC animals [41–45]. Since PRRSV N protein upregu- lates NF-κB, co-infection with PRRSV and a secondary bacterial infection can trigger synergis- tic activation of NF-κB and overproduction of inflammatory cytokines, which is likely the mechanistic basis for severe pathogenesis and higher mortality. In the present study, we hypothesized that a mutation in the SAP motif of the nsp1β protein would reverse the viral IFN antagonism and could induce better adaptive immune response, while the PIAS1-binding motif-mutation in N would reserve the NF-κB activation and attenu- ate the production of proinflammatory cytokines. We generated a double-mutant PRRSV-2 to harbor both mutations and examined for their IFN and NF-κB phenotypes in porcine alveolar macrophages after co-infection with Streptococcus suis (S. suis). Subsequently, the clinical and immunological profiles of the mutant virus were examined in the natural host animals upon co-infection with S. suis. Our results showed indeed that the double mutant PRRSV-2 exhib- ited elevated IFN responses and reduced inflammatory cytokines and chemokines in pigs. Fur- thermore, the double-mutant virus was clinically attenuated in pigs, as shown by the reduced clinical severity, lower viral titers, and shorter duration of viremia, compared to those in pigs co-infected with wild-type PRRSV-2 and S. suis. Our study highlights that the reprogramming of viral immune evasion is possible, which can be developed as a new strategy for a design of next-generation vaccines. Results Mutations in the SAP motif of PRRSV-2 nsp1β and PIAS1-binding motif of N confer IFN suppression-negative and NF-κB activation-negative The PRRSV-2 nsp1β protein contains a highly conserved sequence at positions 124–135 among all strains, and this sequence of 124-KxLQxxLxxxGL-135 (where x for any amino acid) resembles the eukaryotic SAP consensus motif (Fig 1A) [46]. Previously, we have shown in monkey kidney epithelial cells that the SAP motif of nsp1β is associated with type I IFN sup- pression [32,33]. To confirm and validate the IFN regulation by the SAP motif of nsp1β, por- cine pulmonary alveolar macrophage (PAM)-originated 3D4/21 cells or Cl3 cells, which are natural target cells of PRRSV, were used to express the L126A nsp1β mutant. IFN productions were then examined using IFN-β-luciferase reporter assay. In this assay, the reporter expres- sion reflects the transcriptional activity of the IFN-β promoter and represents the levels of IFN production. Previously, various IFN assays were compared to each other, including RT-qPCR for IFN mRNAs, vesicular stomatitis virus (VSV)-based bioassay for IFN proteins, and lucifer- ase reporter assay for IFN expressions. VSV is extremely sensitive to type I IFNs, and in the presence of IFNs, VSV replication is inhibited, which can quantitatively be measured [47]. All three assays were comparable and reliable to replace the ELISA-based immunoassay, and thus RT-qPCR and luciferase reporter assays were employed. HeLa cells were co-transfected with wild-type nsp1β (nsp1β-WT) or SAP-motif mutant nsp1β (nsp1β-L126A) along with pIFN-β- luc reporter, followed by quantification of luciferase expressions. Poly(I:C) stimulation increased the luciferase activity by 11.54 folds in empty vector (pXJ41)-transfected cells, whereas the reporter was not increased in nsp1β-WT expressing cells even after stimulation. In contrast, nsp1β-L126A mutant elicited the reporter activity 4.9-folds higher than nsp1β-WT PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 4 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 1. Mutations in the SAP motif of PRRSV nsp1β (A) and the PIAS1-binding motif of N (F) result in the loss of IFN suppression and NF-κB activation, respectively. (A), The functional domains of PRRSV-2 nsp1β. Grey box indicates the papain-like proteinase (PLP) domain, and blue box indicates the SAP motif. Leucine at 126 in the SAP motif of nsp1β was substituted to alanine to generate nsp1β-L126A. HeLa (B) or PAM 3D4/21 (D) cells were cotransfected with pRL-TK and IFN-β-luc along with a respective viral gene and stimulated with poly(I:C) for 12 h. The firefly and Renilla luciferase activities were determined. For IFN-β gene expressions, HeLa (C) or PAM 3D4/2 (E) cells were grown in 6-well plates and transfected with a target gene followed by RT- qPCR at 24 h post-transfection. Swine β-actin mRNA served as a loading control. (F), The nuclear localization signal (NLS) is located at positions 41–47 of the N protein. N-ΔNLS was generated by deleting two lysine residues at 43 and 44 and substituting asparagine at 49 with serine residue in NLS of N to result in PG —SKKKS. (G) HeLa or (H) PAM 3D4/21 cells were co-transfected with the pRL-TK and NF-κB-luc plasmids along with a respective gene and stimulated with TNF-α for 6 h. The firefly and Renilla luciferase activities were determined. Error bars, mean ± standard deviation (s.d.). , P<0.05; , P<0.01; , P<0.001. https://doi.org/10.1371/journal.ppat.1012128.g001 upon poly(I:C) stimulation (Fig 1B), indicating the loss of nsp1β-mediated IFN suppression. To further assess the IFN regulation by nsp1β, total RNA was isolated from wild-type nsp1β- or nsp1β-L126A-expressing cells, and RT-qPCR was conducted. As shown in Fig 1C, nsp1β- WT induced a significantly lower level of IFN-β gene expression than empty vector, while nsp1β-L126A did not suppress IFN-β gene expression after poly(I:C) stimulation (Fig 1C). Similar results were obtained in PAM 3D4/21 cells that the nsp1β-WT protein downregulated both the luciferase reporter and IFN-β gene expressions, whereas nsp1β-L126A was unable to PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 5 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV suppress the IFN-β and reporter expressions (Fig 1D and 1E). These results show that the L126 mutation in nsp1β causes the loss of IFN suppression and reverses the IFN antagonism. During PRRSV-2 infection, the NF-κB signaling is activated, which has been shown in cells and pigs. For the NF-κB activation, the viral N protein has been identified as the main effector [37]. The N protein binds to PIAS1 which is the repressor for NF-κB, and the PIAS1-binding region in N has been mapped to amino acid positions 37–72 which includes the NLS at 41–47 as PGKKNKK. Thus, the PIAS1-binding motif in N was mutated such that two lysine (K) resi- dues at 43 and 44 were substituted with glycine (G) residues. This mutation conferred the loss of NF-kB activation, and the NF-κB activity induced by this mutant was only slightly higher than or not different from that of the empty vector, indicating that the PIAS1-binding motif of N contributes to the NF-κB activity [37]. To further modify this construct, which first two lysine residues at 43 and 44 were deleted and the fourth lysine at 47 was substituted to serine (S), to make PG—NKS. This construct was designated N-ΔNLS. N-ΔNLS was then expressed in cells and examined for the NF-kB activation using the NF-kB promoter-based reporter plas- mid pNF-κB-luciferase (Fig 1G and 1H). Cells were co-transfected with the pNF-κB-luciferase reporter, pRL-TK as an internal control, and individual viral genes, and their relative luciferase activities were obtained after normalizing the firefly luciferase to Renilla luciferase activities. The pXJ41 plasmid was included as an empty vector control without TNF-α treatment, and this value set the baseline (value = 1). HeLa cells were treated with TNF-α for 6 h before lysis, which was then used as a positive control. As shown in Fig 1G, the TNF-α treatment stimu- lated the reporter activity by 2.53 folds, while N-WT induced the reporter activity by 2.17 folds. In contrast, N-ΔNLS was unable to elicit a higher reporter activity (1.17 folds) (Fig 1G). Similarly, N-WT stimulated the reporter activity by 2.34 folds, while N-ΔNLS did not increase the reporter expression in PAM 3D4/21 cells (Fig 1H). Together, our data validate that the PIAS1-binding-motif mutation in N confers the loss of NF-κB activation and suggest that the N gene can be modified to generate a new virus whose NF-κB activation function is removed. Generation of SAP motif- and NLS motif-double mutant virus Since both the type I IFN suppression and NF-κB activation functions can be modified by mutating the nsp1β and N genes, respectively, as shown in the ectopic gene expression system (Fig 1), it was hypothesized that a mutant PRRSV-2, whose genes for the SAP motif and the NLS motif were individually altered, might lose immunomodulatory functions with its antici- pated phenotypes of type I IFN suppression-negative and NF-κB activation-negative. To test this hypothesis, mutant viruses were generated using the infectious cDNA clone of PRRSV-2 strain P129. The vL126A mutant virus was generated by substituting the leucine at 126 to ala- nine in the SAP motif of the nsp1β. For vΔNLS virus, the mutation described above was intro- duced to the N gene of the infectious clone. Then, a double-mutant virus was generated and designated vL126A/ΔNLS (Fig 2A). The PRRSV-2 mutants were rescued and amplified in MARC-145 cells by four subsequent passages, and their genetic stability for mutated sequences were confirmed by sequencing. The viral infectivity was determined by cytopathic effects (CPE) (Fig 2B) and immunofluorescence assays (IFA) for the nsp1β and N protein expressions (Fig 2C). The plaques of the vL126A and vL126A/ΔNLS mutants were significantly smaller than those of wild-type virus vWT and vΔNLS at five days, indicating the slower growth of vL126A and vL126A/ΔNLS than vWT and vΔNLS. The multi-step growth kinetics further revealed that vL126A and vL126A/ΔNLS exhibited decreased growth rates with peak titers approximately 1 log lower than that of vWT in MARC-145 cells (Fig 2D). The multi-step growth kinetics were determined for mutant viruses in PAM Cl3 cells. vL126A/ΔNLS dis- played reduced growth rates with 1 log lower than that of vWT at 24 hpi through 48 hpi PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 6 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 2. Generation and growth kinetics of PRRSV-2 mutants in MARC-145 cells. (A), Genome organization and constructions of SAP motif nsp1β mutant virus (vL126A), PIAS1-binding motif N mutant virus (vΔNLS), and double-mutant virus (vL126A/ΔNLS) using the P129 infectious clone. Alphabets in red color indicate mutated amino acids. Hyphens in red color indicate amino acid deletions. (B), Plaque morphologies of mutant PRRSVs. (C), Subcellular localization of PRRSV-2 nsp1β (red) and PRRSV-2 N (green) proteins. MARC-145 cells were infected with the respective PRRSV-2 at 1 MOI for 48 h. Cells were stained with α-PRRSV-nsp1β rabbit serum (red) and α-PRRSV-N Mab (green). The nuclei were stained with DAPI (blue). Images were taken by confocal microscopy (Nikon A1R). (D, E), Multistep growth curves for mutant viruses. Cells were infected at 1 MOI in MARC-145 (D) or PAM Cl3 cells, and virus titers were determined as a 50% tissue culture infectious dose (TCID50) in MARC-145 cells at indicated times. (F), JAK-inhibitor assay. MARC-145 cells were seeded in 6-well plates and incubated with 1,000 units of human IFN-β for 2 h for stimulation of the JAK-STAT pathway. Then, cells were treated with Ruxolitinib (STEMCELL Technologies, Cambridge, MA) at indicated concentrations. 24 h later, total cellular RNA was extracted, and RT-qPCR was conducted for ISG15, ISG54, and ISG56. The relative fold changes of ISG transcripts were normalized to that of β-actin and statistically analyzed. , p<0.001. (G), MARC-145 cells were treated at 1 μM concentration for 24 h and infected with vWT or vL126A/ΔNLS for 48 h. Culture supernatants were collected and titrated by TCID50 in a 96-well plate format by the Reed-Muench method. The experiment was conducted twice in duplicate each. (H), PAM-CL3 cells were treated with Ruxolitinib for 24 h at a concentration of 1 μM and infected with vWT and vL126A/4NLS. At 48 h post-infection, cell lysates were prepared, and Western blot was performed using rabbit anti-N pAb (Novus Biologicals, Centennial, CO) and β-actin mouse mAb. https://doi.org/10.1371/journal.ppat.1012128.g002 (Fig 2E). The IFN suppression by nsp1β is non-essential for virus replication, but the removal of this function negatively affects the growth of the virus, presumably due to increased produc- tion of type I IFN antiviral cytokines (Also see Fig 3). To address this hypothesis, a JAK-STAT inhibitor experiment was conducted. Ruxolitinib is a potent JAK inhibitor, and we first examined the expression of three representative ISGs at various concentrations of the inhibitor. At 1 μM concentration of Ruxolitinib, the expressions of ISG15, ISG54 and ISG56 were all inhibited compared to the control (Fig 2F). We then examined the replication of the double-mutant virus vL126A/ΔNLS in the presence of Ruxoli- tinib. By 48 h post-infection, the titer of vL126A/ΔNLS reached a similar level to the wild-type PRRSV-2 (Fig 2G). The titers of both vWT and vL126A/ΔNLS were 1 x 10^4 /ml, and these titers were comparable to each other. We also determined the relative productions of two viral proteins nsp1β and N in virus-infected cells by Western blot (Fig 2H). Taken together, our data indicate that the reduced titers of vL126A/ΔNLS observed in Fig 2D and 2E were indeed due to the alleviation of innate immune suppression and induction of inflammation, not due PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 7 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 3. Regulation of type I IFN production by nsp1β SAP mutant PRRSV-2. (A), PAM Cl3 cells were transfected with the pIFN-β-luc reporter, infected with a respective mutant virus 24 h post-transfection (hpi), and subjected to luciferase assays to determine their IFN-β induction. (B), PAM Cl3 cells were infected with a mutant virus and stimulated with poly(I:C) at 24 hpi for 6 h, and then IFN-β mRNA was examined by RT-qPCR. (C), PAM Cl3 cells were infected with a mutant virus, and ISG15 mRNA was examined by RT-qPCR after IFN stimulation. Error bars, mean ± standard deviation (s.d.). ns: no significant difference, , P<0.01; , P<0.001. https://doi.org/10.1371/journal.ppat.1012128.g003 to the intrinsic property of the mutant virus derived from the viral manipulation. These find- ings also suggest that IFN suppression is a significant benefit for PRRSV replication. Type I IFN suppression of the nsp1β SAP mutant PRRSV in vitro To examine the IFN suppression by mutant PRRSVs, PAM Cl3 cells were infected with vWT, vL126A, or vL126A/ΔNLS, followed by stimulation with poly(I:C) or porcine-specific recom- binant IFN-β. IFN activities were examined using IFN-β-luciferase reporter assays in virus- infected cells. PAM Cl3 cells were first transfected with the pIFN-β-luc reporter and after 24 h later, infected with a mutant virus followed by luciferase determination. In mock-infected cells, poly(I:C) stimulation resulted in a 3-fold increase of reporter, whereas vWT-infected cells showed a lower level IFN-β expression even after stimulation. In contrast, vL126A and vL126A/ΔNLS were able to elicit 4.2-fold and 4.4-fold higher levels of IFN-β expression upon poly(I:C) stimulation, respectively (Fig 3A). To further assess the IFN production and JAK- STAT signaling response in mutant virus-infected cells, total RNA was extracted at 24 hpi and analyzed by RT-qPCR for the expression of IFN-β and ISG15 transcripts. In mock-infected cells, poly(I:C) stimulation resulted in an increase of IFN-β mRNA, whereas in vWT-infected cells, IFN-β transcription was significantly lower than that of mock-infected group, indicating the suppression of IFN-β gene expression by vWT as expected. In contrast, both vL126A and vL126A/ΔNLS mutants elicited higher levels of IFN-β expression than vWT, and the expres- sion levels were even higher when compared with mock-infection with stimulation (Fig 3B). The modulation of the JAK-STAT signaling pathway was also assessed by examining ISG15 transcripts in IFN-β-stimulated cells. Incubation of cells with IFN-β enhanced the ISG15 tran- scription more than 145 folds in mock-infected cells. While vWT triggered the suppression of PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 8 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV ISG15 transcription, both vL126A and vL126A/ΔNLS induced the ISG15 transcription to simi- lar levels in mock-infected cells after the IFN-β stimulation (Fig 3C). These results demonstrate that the nsp1β SAP mutant PRRSV does not further suppress the IFN production and JAK- STAT signaling pathways. These data indicate that the L126A mutation in nsp1β results in the loss of type I IFN antagonism, and their immunological phenotype is IFN suppression- negative. NF-κB and cytokine responses to the N mutant PRRSV-2 in PAMs The PIAS1-binding motif in the N gene of PRRSV-2 has previously been demonstrated to play a crucial role in activating NF-κB and promoting proinflammatory cytokine productions [37]. The activation of NF-κB is dependent on the translocation of p65 (RelA) to the nucleus, and therefore, it was deemed necessary to confirm the NF-κB activation by examining the subcellu- lar distribution of p65 in PIAS1-binding motif-knockout mutant virus vL126A/ΔNLS-infected cells (Fig 4). The results confirmed that p65 was predominantly localized in the nucleus in vWT-infected cells, similar to that observed with TNF-α stimulation. Conversely, in vΔNLS and vL126A/ΔNLS-infected cells, p65 was mostly distributed in the cytoplasm (Fig 4A), indi- cating the absence of NF-κB activation. To validate this function in the context of virus infec- tion in natural target cells, NF-κB-mediated proinflammatory cytokine gene expressions were quantified in PAM Cl3 cells by RT-qPCR at 24 hpi (Fig 4C). PRRSV N mRNA was first exam- ined in virus-infected cells, and the results showed 1 log lower expression of the viral gene for vL126A/ΔNLS than for vWT and vΔNLS (Fig 4B). When NF-κB-directed proinflammatory cytokine expressions were examined (Fig 4C), vWT significantly upregulated the IL-6, IL-8, and TNF-α transcriptions in PAM Cl3 cells. In contrast, the transcription levels of IL-6 (P < 0.001, ), IL-8 (P < 0.001, ), and TNF-α (P < 0.001, ) were significantly lower in PAM Cl3 cells infected with vL126A/ΔNLS compared to vWT infection (Figs 4C–4E). We also determined the expression of other inflammatory cytokines in virus-infected cells (Fig 4F and 4G). The results showed that vL126A/ΔNLS infection lowered the transcriptions for IL-1α (P < 0.001, ) and GM-CSF (P < 0.01, ) compared to vWT infection. For the chemokine response to virus infection, MCP1 and MCP2 expressions were significantly reduced (P < 0.05, , and P< 0.01, , respectively) in vL126A/ΔNLS-infected cells, compared to those of vWT infection. These findings indicate that vL126A/ΔNLS downregulates NF-κB activation and accordingly reduces the expression of proinflammatory cytokines and chemokines during infection. Co-infection of macrophages with PRRSV-2 mutants and Streptococcus suis and proinflammatory cytokine responses in vitro Co-infection of pigs with multiple pathogens is common in swine farms causing PRDC, and PRRSV is the crucial agent. Other pathogens contributing to PRDC include porcine circovirus, swine influenza virus, Mycoplasma hyopneumonia, Actinobacillus pleuropneumonia, Pasteur- ella multocida, Streptococcus suis, and Hemophillus parasuis [48,49]. Previous studies showed that co-infection of PRRSV and S. suis induced severe clinical symptoms accompanied by NF- κB activation and enhanced the production of proinflammatory cytokines in cells and pigs [38,50,51]. Since the vL126A/ΔNLS mutant induced decreased levels of proinflammatory cyto- kines in PAM Cl3 cells compared to those of vWT (Fig 4), this mutant virus was expected to relieve the hyperexpression of proinflammatory cytokines during co-infection of PRRSV-2 and a secondary pathogen. To examine this hypothesis, PAM Cl3 cells were infected with vWT or vL126A/ΔNLS and, at 24 hpi, inoculated with S. suis, followed by quantification of specific transcripts at 36 hpi (Fig 5A). As shown in Figs 5B–5E, significant increases of IL-6, IL-8, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 9 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 4. Attenuated expression of immunomodulatory cytokines in PAMs by PIAS1-binding motif mutant PRRSV-2. (A), PAM Cl3 cells were infected with the respective PRRSV at 1 MOI for 24 h. For positive control group, cells were treated with TNF-α (20 ng/ml) for 6 h. Cells were stained with α-PRRSV-nsp1β rabbit serum (red) and α-p65 PAb (rabbit) (green). The nuclei were stained with DAPI (blue). Images were taken by confocal microscopy (Nikon A1R). White arrows indicate the virus-infected cells, and yellow arrows head indicate uninfected cells. (B through I), PAM Cl3 cells were infected with PRRSV-2 at 1 MOI for 24 h or treated with TNF-α (20 ng/ml) for 6 h, and total RNAs were isolated. RT-qPCR was performed to detect PRRSV-N mRNA (B) and transcripts for IL-6 (C), IL-8 (D), TNF-α (E), IL-1α (F), GM-CSF (G), MCP1 (H), and MCP2 (I). The relative amounts of transcripts were calculated using the 2−ΔΔCT method by normalizing the values to that of β-actin. Error bars, mean ± standard deviation (s.d.). ns: no significant difference. , P<0.05; , P<0.01; , P<0.001. https://doi.org/10.1371/journal.ppat.1012128.g004 TNFα, and IL-1α were observed in the vWT and S. suis co-infection group, compared to the vWT single-infection group, indicating that co-infection of PRRSV-2 and S. suis enhanced the proinflammatory cytokine expressions. In contrast, the co-infection of vL126A/ΔNLS with S. suis induced significantly lower-level expressions for IL-6, IL-8, TNFα, and IL-1α in compari- son with the vWT and S. suis co-infection. These results demonstrate that vWT had the ability PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 10 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 5. Attenuated production of proinflammatory cytokines by PIAS1-binding-motif N mutant PRRSV-2 in PAMs coinfected with Streptococcus suis (S. suis). (A), Co-infection model of PRRSV-2 and S. suis in PAM Cl3 cells. PAM Cl3 cells were infected with 1 MOI of PRRSV-2 and, at 24 hpi, inoculated with 10 MOI of S. suis, followed by quantification of specific transcripts by RT-qPCR at 36 hpi. (B through E), The transcripts for IL-6 (B), IL-8 (C), TNF-α (D), and IL-1α (E) were determined in coinfected cells. The relative levels were calculated using the 2−ΔΔCT method by normalizing the values to that of β-actin. Error bars, mean ± standard deviation (s.d.). , P<0.05; **, P<0.001. Panel (A) was created with BioRender.com. https://doi.org/10.1371/journal.ppat.1012128.g005 of NF-κB activation and, during co-infection with S. suis, produced synergistic expression of cytokines. On the contrary, the vL126A/ΔNLS mutant induced only a marginal elevation of cytokine productions during co-infection with a secondary bacterium. Co-infection of natural host animals with PRRSV-2 mutants and Streptococcus suis and their clinical, pathological, and virological properties To investigate the immunological and pathogenetic characteristics in the natural host animals after infection with the vL126A/ΔNLS mutant virus, a swine experiment was conducted. A total of 39, 5-week-old piglets were randomly allotted to 6 groups. The mock-control group consisted of 4 animals, while the remaining groups had 7 animals per group. The animals were acclimatized for 1 week and tested negative for PRRSV, porcine circovirus, and swine influ- enza virus. On day 0, all animals were inoculated intranasally with 4×104 TCID50/2 ml of PRRSV per pig, and on day 7, pigs were inoculated intranasally with 2×107 CFU of S. suis per pig. On day 14, all piglets were euthanized, lung pathology was examined, and tissues were col- lected for further analysis. Blood samples were periodically collected as indicated (Fig 6A). Body weights were measured on day 0 and day 14, and weight gain changes were recorded. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 11 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 6. Clinical features of pigs coinfected with PRRSV-2 mutants and S. suis. (A), Experimental design of the co-infection of PRRSV-2 and S. suis in pigs. (B), Body weights of pigs were measured at day 0 and day 14, and weight gains of individual pigs are shown. (C), representative gross lung pathology of each infection trial. (D), Gross lung lesion scores were calculated by determining the mean percentage value of each lobe that showed visible signs of pneumonia. Gross lung lesion scores provide estimates of the overall proportion of lung tissues affected by gross visible pneumonia. Each dot represents an individual pig. Error bars, mean ± standard deviation (s.d.). ns: no significant difference. , P<0.05; **, P<0.001. Panel (A) was created with BioRender.com. https://doi.org/10.1371/journal.ppat.1012128.g006 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 12 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV The pigs that were inoculated with only S. suis showed lower body weight gains than ani- mals in the mock-infection group. However, no significant differences were found in body weight gains between other infection groups and the mock-control group (Fig 6B). Three of the pigs infected with vWT and S. suis (vWT+Strep) exhibited swollen rear hock joints 5 days after S. suis challenge. S. suis was isolated from swabs taken at necropsy from two of these joints. In addition, this group exhibited the highest extent of gross lung pathology (37.7±5.8%) (Fig 6C), which was characterized by a multifocal to diffuse, lobular pattern of reddening that corresponded with a rubbery feel on palpation, consistent with interstitial pneumonia (Fig 6D, second row-right). Conversely, pigs coinfected with vL126A/ΔNLS and S. suis (vL126A/ΔNLS +Strep) showed no clinical signs of a systemic S. suis infection (Fig 6D, third row-right), and exhibited a much lower extent/severity of pneumonic changes (2.3±5.8). The extent of gross lung pathology exhibited by the lungs of pigs in all other treatment groups was not statistically different from the strict control group (Fig 6C). Histologically, specimens taken from pneu- monic lungs demonstrated varying severities of histiocytic interstitial pneumonia, character- ized by expansion of the alveolar septa by mononuclear inflammatory cells (histiocytes/ macrophages), alveolar exudate consisting of necrotic debris and macrophages, and atelectasis with resultant tissue consolidation. All pigs in the vWT and vWT+Strep groups showed viremia by 4 dpi, with an average viral titer of 4.17 and 4.19 log TCID50/mL, respectively (Fig 7A). In the vL126A/ΔNLS group, 4 out of 7 pigs became viremic at 4 dpi with a mean titer of 2.26 log TCID50/ml. Simi- larly, in the vL126A/ΔNLS+S. suis group, 4 out of 7 pigs became viremic at 4 dpi, with a mean virus titer of 2.76 log TCID50/ml. The viral titers of all groups decreased gradually from 7 dpi. With a viral titer of 2.87 log TCID50/ml for the vWT group and 2.90 log TCID50/ml for the vWT+Strep group on 7 dpi, only 2 out of 7 pigs remained viremic (1.92 log TCID50/ml) for the vL126A/ΔNLS group. All pigs in the vL126A/ΔNLS+Strep group became negative for viremia. On day 9, which was 2 days after S. suis infection, the vWT/ ΔNLS+Strep group showed higher titers of viremia (2.68 log TCID50/mL) than the vWT Fig 7. Virological characteristics of pigs coinfected with PRRSV-2 mutants and S. suis. (A), Viral titers were determined in the sera of pigs at the indicated times. 50% tissue culture infectious dose (TCID50) was determined in triplicate using MARC-145 cells, and the viral titers was calculated as TCID50/mL of serum. (B), PRRSV-specific antibody was determined using the IDEXX HerdCheck PRRSV ELISA kit. S/P ratio = 0.4 was set as the cutoff value (dotted line) for positive/negative. S/P ratios greater than 0.4 were considered positive. An individual pig is represented by each dot, and different shapes of symbols in different colors represent different groups. Error bars, mean ± standard deviation (s.d.). ns: no significant difference. , P<0.05. https://doi.org/10.1371/journal.ppat.1012128.g007 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 13 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV group of 2.30 log TCID50/ml. The viremia became negative by day 14 for the vWT and vWT/ΔNLS+Strep groups with 1 pig in the vWT group and 2 pigs in the vWT/ΔNLS+Strep group continued to be viremic. This observation on the clearance of viremia by 2 weeks post-infection was similar to the previous report [52], which led us to ignore the immuno- histochemistry and H&E staining for viral antigens in the lungs. Overall, the duration of viremia for the vWT and vWT/ΔNLS+Strep groups was longer with higher titers than for the vL126A/ΔNLS and vL126A/ΔNLS+Strep groups (Fig 7A). The PRRSV-specific antibody response was also assessed by ELISA (Fig 7B). Throughout the study, both the mock and S. suis groups remained seronegative, indicating no cross-infec- tion between groups. For the vWT and vWT+Strep groups, all pigs seroconverted by 9 dpi and remained seropositive throughout the study. For the vL126A/ΔNLS group, only 2 pigs out of 7 seroconverted at 9 dpi, and for the vL126A/ΔNLS+Strep group, only 1 out of 7 pigs serocon- verted at 9 dpi. By 11 dpi, however, all remaining pigs (5 pigs in the vL126A/ΔNLS group and 4 pigs in the vL126A/ΔNLS+Strep group) became seropositive. Significant differences (P < 0.05) were observed between the average titers (S/P ratio) of antibodies for pigs in the vWT and vL126A/ΔNLS infection groups. All pigs in the PRRSV-infected groups serocon- verted by 14 dpi. No statistical differences were identified in antibody titers between all four PRRSV-infection groups (Fig 7B). Pigs infected with vL126A/ΔNLS exhibited lower lung lesions and lower viral titers with a shorter duration of viremia compared to those infected with vWT. No significant differences, however, were identified for PRRSV-specific antibody titers between two groups of pigs. Inflammatory cytokines and chemokine responses in co-infected pigs PRRSV-2 plays a major role in the development of PRDC when coinfected with a secondary pathogen [48], and because NF-κB activation is one of the key features in pigs with PRDC [41–45], the NF-κB activation-negative PRRSV is hypothesized to relieve hyperproduction of cytokines and thus, to attenuate the clinical severity of PRDC in pigs. To examine this hypoth- esis, we harvested bronchoalveolar lavages (BAL) from individual pigs at the time of sacrifice, and collected cells, most of which would represent alveolar macrophages and some neutro- phils. We then determined the relative mRNA transcripts by RT-qPCR and the proteins by immunoassays to represent the expression of inflammatory cytokines and chemokines (Figs 8 and 9). The viral N protein was also determined as the representative viral gene product to ensure the infection (Fig 8A). The PRRSV-2 N gene expression was detectable in all infection groups except the mock-con- trol group (Fig 8A), confirming that PAMs in all infection groups were infected and responsive. The vL126A/ΔNLS+Strep group exhibited a significantly lower-level expression of IL-6 com- pared to the vWT+Strep group (Fig 8C) when NF-κB-directed proinflammatory cytokines were examined. However, no significant differences were identified for IL-1β, IL-8, and TNFα (Fig 8B, 8D and 8E) between the vWT+Strep and vL126A/ΔNLS+Strep groups. With regards to immunoregulatory cytokine responses, the vWT+Strep group induced statistically higher-level expressions of IL-10, GM-CSF, IFN-β, and IFN-γ (Fig 8) compared to those of the vL126A/ ΔNLS+Strep group, while no statistical difference was found for IL-1α (Fig 8F). The IL-17 tran- scription also showed an increase in the vWT+Strep group compared to the vL126A/ΔNLS +Strep group although it was statistically insignificant (Fig 8H). Previously, the immunoregula- tory chemokines were reported to be upregulated in PRRSV-infected PAMS and were suggested as a major contributor to the pathogenesis of PRRSV-2 infection [53]. In our study, immuno- regulatory chemokines were rather decreased for RANTES, IP-10, MCP1, and MCP2 in the vL126A/ΔNLS+Strep group compared to the vWT+Strep group (Fig 8J–8M). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 14 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 8. Gene expression profiles for mRNA transcripts of immunoregulatory cytokines and chemokines in cells isolated from bronchoalveolar lavages (BALs) from pigs determined by RT-qPCR. BALs from individual pigs at the time of sacrifice were collected, and cells were isolated which would mostly represent alveolar macrophages. Total RNAs were extracted, and RT-qPCR was conducted to measure specific mRNAs for various genes, including PRRSV-N gene (A) and transcripts for IL-1β (B), IL-6 (C), IL-8 (D), TNF-α (E), IL-1α (F), IL-10 (G), IL-17 (H), GM-CSF (I), RANTES (J), IP-10 (K), MCP1 (L), MCP2 (M), IFN-β (N), and IFN-γ (O). The 2−ΔΔCT method was used to calculate the relative expressions by normalizing the values to that of β-actin. The results were presented as mean ± standard deviation (s.d.) with error bars. , P<0.05; , P<0.01; , P<0.001. https://doi.org/10.1371/journal.ppat.1012128.g008 Besides the mRNAs for cytokines, a protein-based assay was conducted to confirm the RT- qPCR data. Due to the numbers of pigs representing different treatment groups and the com- parative quantifications of cytokines and chemokines from each pig, Western blots were con- sidered inappropriate for this study. Instead, Luminex porcine-specific cytokine immunoassays were chosen to analyze comparative expressions of inflammatory mediators from lung lavages collected from pigs of coinfection groups (Fig 9). The multiplex immunoas- say is analogous to ELISA and can simultaneously detect multiple cytokines. The lower levels of IL-1β, IL-1Ra, IL-6, IL-12, and IL-18 were detected in the lung lavages from pigs of the vL126A/ΔNLS+Strep groups compared to those of the vWT+Strep group (Fig 9B, 9C, 9D, 9E and 9H). In severe and fatal COVID-19 patients, those cytokines were identified as markers for a hypercytokinemia profile according to the meta-analysis [54,55], supporting our data and hypothesis that vL126A/ΔNLS PRRSV reduces the NF-kB-mediated, cytokine storm-like response during coinfection with Strep. suis. The decreased expressions of IL-1Ra and IL-12 in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 15 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Fig 9. Expression profiles of immunoregulatory cytokines in bronchoalveolar lavages (BALs) collected from pigs coinfected with vWT or vL126A/ΔNLS and Strep. suis. A total of 9 indicated cytokines were determined using the PCYTMG-23K-13PX porcine cytokine immunoassay plate in the MILLIPLEX Multiplex for Luminex Immunoassay system as described in the Materials and Methods. Each sample was analyzed in duplicate. Quality controls and standard controls were established for the porcine cytokine-antibody immobilized magnetic bead panel. Signals were detected PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 16 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV using Streptavidin-Phycoerythrin, and the plate was read in the multiplex analyzer (Luminex 200 System, DiaSorin; Stillwater, MN). The data were analyzed using the accompanied software (Luminex SD xMAP Technology LX100/200). , <0.01. https://doi.org/10.1371/journal.ppat.1012128.g009 particular were notable and significant. IL-1Ra (interleukin-1 receptor antagonist) is secreted from macrophages, monocytes, and neutrophils, and its expression is stimulated by bacterial or viral components [56]. In mice, the tissue distribution of IL-1Ra is found predominantly in peripheral blood cells, lungs, spleen, and liver. It is a key regulator of the inflammatory responses in a host-specific manner [57]. Although the expression levels of some of inflamma- tory cytokines were statistically insignificant between the vWT+Strep and vL126A/ΔNLS +Strep groups, their expression levels still showed a tendency of reduction in the vL126A/ ΔNLS+Strep. The immunoassay data were largely consistent with the mRNA data, and all together, our data demonstrate that the vL126A/ΔNLS double mutant PRRSV-2 attenuates the proinflammatory cytokine expressions during coinfection with a bacterial pathogen in pigs. Genetic stability of the SAP motif in nsp1β and NF-kB regulating domain in N of the mutant viruses in vivo The smaller plaque sizes and lower viral titers of the vL126A/ΔNLS and vL126 mutants impli- cate the possible presence of strong selection pressure on the IFN-suppression function of PRRSV-2. We postulated that our mutant viruses might have undergone compensatory muta- tions or sequence changes to recover their evasion function during the infection of pigs. We passaged the mutant viruses in cell culture to examine their genetic stability and sequenced the nsp1β and nucleocapsid protein genes to which two mutations were introduced. Both vL126A/ ΔNLS and vL126 were stable in cell culture at least for up to 10 passages. For the N protein gene, reversion was not anticipated and did not occur because a deletion of two amino acids was introduced (Table 1). For nsp1β, a single amino acid substitution was introduced with 2 Table 1. Reversion of the SAP motif in the nsp1β sequence and the NLS motif in the N sequence. Group Pig no. nsp1β SAP motif N NLS vL126A/ΔNLS vL126A/ΔNLS+Strep nucleotide sequence AAGTACCTACAGCGGAGG AAGTACGCTCAGCGGAGG AAGTACGCTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGCTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGCTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACGTTCAGCGGAGG AAGTACATTCAGCGGAGG AAGTACGTTCAGCGGAGG reversion n/a n/a 126A!V 126A!V 126A!V 126A!V n/a 126A!V 126A!V n/a 126A!V 126A!V 126A!I 126A!V nucleotide sequence CCGGGCAAGAAAAGTAAGAAGAAAAAC CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) CCGGGC(cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC (cid:0) TCTAAGAAGAAATCC reversion n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 9 10 24 25 31 34 36 5 14 16 17 19 29 40 A-V, alanine to valine mutation; A-I, alanine to isoleucine mutation. n/a, not applicable. https://doi.org/10.1371/journal.ppat.1012128.t001 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 17 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV nucleotide changes to alter the lysine codon (Table 1), and no reversion was observed in cell culture. The stability of the nsp1β mutation in cell culture was thought to be due to the absence of selective pressure in vitro. To examine the evolution of mutant PRRSVs in vivo, the viral RNAs isolated from PAMs of BAL were subjected to RT-PCR and sequencing for the nsp1β and N genes. No single mutation or changes to the wild-type sequence was identified in the N gene for both the vL126A/ΔNLS and vL126A/ΔNLS+Strep groups (Table 1). For the nsp1β gene, however, 4 out of 7 pigs in the vL126A/ΔNLS group and 5 out of 7 pigs in the vL126A/ ΔNLS+Strep group undergone mutations from GCU for Ala 126 to GUU for Val 126. In addi- tion, 1 pig in the vL126A/ΔNLS+Strep group acquired a mutation from GCU for Ala 126 to AUU for Ile 126. 3 pigs out of 7 in the vL126A/ΔNLS group and 1 pig in the vL126A/ΔNLS +Strep group did not undergo any sequence changes for the nsp1β gene. The mutation of L126V in the SAP motif of nsp1β was shown to regain nsp1β-mediated type I IFN suppression function in cells, suggesting that vL126A/ΔNLS may have lost the type I IFN suppression by 14 dpi. These results demonstrate the presence of strong selection pressure on the IFN antago- nism for PRRSV. Although we had a particular interest in type I IFN response of pigs in the mutant PRRSV-infected groups, PRRSV-2 suppresses IFN response at an early stage of infec- tion, and shortly after, IFNs are bounced back to 20, normal and increased by 3–4 days of infection, which led us to ignore the type I IFN measurements in the present study. Discussion Porcine respiratory disease complex (PRDC) is a global challenge to the pig industry, and PRRSV is one of the major responsible pathogens [39]. However, development of effective prevention strategies against PRRSV and PRRSV-mediated PRDC has been hindered by the viral antagonism to impair the host immune system and to modulate adequate host response to infections [58,59]. While type I IFNs are potent antiviral cytokines, NF-κB activation and enhanced production of proinflammatory cytokines are considered major effectors for viral pathogenesis both in cells and pigs [44,60,61].These studies suggest that higher levels of type I IFNs and lower levels of proin- flammatory cytokines may render protection against PRRSV-mediated PRDC. PRRSV-2 nsp1β is a highly potent viral protein that disrupts the type I IFN production and signaling [27–30]. The conserved SAP motif within nsp1β interacts with Nup62, which is cru- cial for nucleocytoplasmic trafficking of cellular molecules and inhibits type I IFNs by imped- ing the cytoplasmic translation of host mRNAs [32,62]. In a previous study, a series of SAP mutants that showed the negative phenotype of host mRNA nuclear retention [32]. Among these SAP mutants, replacing the leucine at position 126 to alanine in nsp1β showed the pre- dominant effect on IFN suppression [32]. In the present study, we generated an infectious PRRSV-2 of which phenotype was type I IFN suppression-negative. The plaque size and growth rate of the SAP mutant PRRSV-2 displayed significantly different from those of wild- type PRRSV-2 (Fig 2), indicating that mutation in the SAP motif of nsp1β affects negative effects on the viral replication. The decreased growth of the mutant viruses may be attributed to the increased production of IFNs. A previous study has shown that nsp1β also facilitates the translational frameshifting in the nsp2 region of PRRSV, and the leucine at 126 in the SAP motif resides in the close proximity to key residues that mediate the frameshift expression of nsp2TF and nsp2N [63]. Consequently, it is plausible that the SAP mutant PRRSV-2 may not process nsp2TF and nsp2N normally and further influence the viral replication. In the current study, we demonstrated that the SAP mutant PRRSV-2 lost the suppression function for IFN production and JAK-STAT signaling in PAMs, the natural target cells for PRRSV (Fig 3). Besides PRRSV, SAP motif mutation and IFN upregulation have been demonstrated for foot-and-mouse disease virus (FMDV). The leader protein Lpro of FMDV contains the SAP PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 18 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV motif, and the SAP mutant FMDV was attenuated in pigs, as evidenced by the absence of clini- cal signs and viremia [64]. Additionally, pigs inoculated with the SAP mutant FMDV exhibited a robust immune response, characterized by high levels of neutralizing antibodies. Such an enhanced immune response provided sufficient protection to animals against subsequent chal- lenge with virulent FMDV. Given the pleiotropic effects of type I IFNs on the adaptive immu- nity, it is plausible that the increased levels of IFNs observed in SAP mutant FMDV-infected pigs can be linked to the elevated antibody response. After removing the IFN suppression function from FMDV, the IFN antagonism-negative virus induced a strong neutralizing anti- body response in vaccinated animals, and these animals were completely protected from the high dose virulent challenges. Swine influenza virus (SIV) is another example with a similar outcome. For SIV, NS1 protein is the IFN antagonist, and the mutation in NS1 of SIV caused the loss of viral IFN suppression. The NS1 mutant SIV was clinically attenuated in pigs and stimulated type I IFN production [65]. Furthermore, the pigs immunized with the IFN-sup- pression-negative SIV conferred the protection against both homologous and heterologous SIV challenges. In the present study, we showed that SAP mutant PRRSV-2 induced lower viral titers and shorter duration of viremia in pigs compared to wild-type PRRSV-2 (Fig 7A). Nevertheless, the pigs infected with SAP mutant PRRSV-2 developed similar levels of PRRSV- specific antibody titers to those of wild-type PRRSV-2 (Fig 7B). Yet, the role of PRRSV-specific antibodies in protection remains unknown. Previously, the passive transfer of serum antibod- ies conferred partial protection against homologous challenge [66,67]. The development of neutralizing antibodies typically requires multiple exposures to PRRSV over an extended period of time to reach high titers [66–68], limiting us to examine neutralizing antibodies at 14 dpi in the current study. Based on the previous findings, however, it is tempting to speculate that immunization of pigs with the SAP mutant PRRSV may result in an improved protection. Viruses can exploit the NF-κB signaling pathways to facilitate their own replication [69,70]. The replication of PRRSV in porcine macrophages produces endoplasmic reticulum stress triggering an unfolded protein response resulting in the activation of NF-κB and the produc- tion of TNF-α [71]. PRRSV infection in pigs results in an increased expression of proinflam- matory cytokines and leads to the recruitment of monocytes and neutrophils to the lungs, thereby contributing to the pathogenesis of PRRSV [72]. PRRSV activates NF-κB in infected pigs, which in turn triggers the production of NF-κB-dependent proinflammatory cytokines [73,74]. Studies have shown that highly pathogenic (HP)-PRRSV enhanced NF-κB activation more than typical PRRSV, emphasizing the crucial role of NF-κB in the viral pathogenesis for PRRSV [36]. The viral N protein is so far the only known PRRSV-2 protein that activates NF- κB [37]. Specifically, the region between residues 37 and 72 of N is known to interact with PIAS1, which releases p65 and frees up NF-κB from the repressor. The mutation of PIAS1- binding region from N abrogates its ability for NF-κB activation [37]. The PIAS1-binding region in N includes NLS motif, and previously, a series of NLS-mutant PRRSVs was gener- ated and examined for their clinical outcome in pigs. These NLS mutant PRRSVs were clini- cally attenuated in pigs, indicating a correlation between the nuclear localization of N and the clinical severity of PRRSV [75,76]. In the current study, we rescued a PIAS1-binding motif- mutant PRRSV-2, and this mutant virus showed reduced expressions of NF-κB-mediated cytokines, especially for IL-6, IL-8, and TNF-α in PAM Cl3 cells compared to wild-type PRRSV-2 (Fig 4). This observation indicates that the mutation in the PIAS1-binding domain of N suppresses the production of proinflammatory cytokines in the context of PRRSV infec- tion in natural target cells. Activation of NF-κB and subsequent increase of proinflammatory cytokines are major effectors on pathogenesis during co-infection of PRRSV-2 with secondary microbial pathogens such as S. suis and H. parasuis [43,44]. S. suis is a pathogen commonly observed with PRRSV PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 19 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV in clinical cases, and co-infection of PRRSV-2 with S. suis can lead to exacerbated clinical dis- ease, resulting in increased morbidity and mortality in pigs [61]. The findings from our study support these observations. Specifically, wild-type PRRSV-2 increased activation of NF-κB and enhanced induction of IL-6, IL-8, and TNF-α in coinfected cells compared to single infec- tion with S. suis (Fig 5). In contrast, the expression levels of IL-6, IL-8, and TNF-α were decreased in cells coinfected with vL126A/ΔNLS and S. suis compared to wild-type PRRSV-2 and S. suis co-infection. These results suggest that PIAS1-binding motif-mutant PRRSV lacks the ability to induce NF-κB and proinflammatory cytokines. Multiple studies have shown enhanced cytokine expressions in cells and pigs when coin- fected with PRRSV and various bacterial pathogens. Co-infection of monocytes with PRRSV-2 strain IAF-Klop and S. suis led to synergistic effects on the expressions of IL-6, CCL5, and TNF-α, and additive effects on productions of CCL4, CCL14, CCL20, IL-15, and PTGS2 (COX-2) [43]. Additionally, co-infection of pigs with NADC30-like PRRSV-2 strain SDlz1601 and S. suis upregulated IL-1β, IL-6, IL-8, TNF-α, CCL4, IL-10, and INF-β in PAMs [60]. In other studies using H. pararsuis and PRRSV-1 strain CAPM V-490, IL-1β, IL-8, TNF-α, CD80, and IL-10 expressions were increased in PAMs [77]. IL-1β and TNF-α were upregulated in PAMs of pigs coinfected with H. pararsuis and HP-PRRSV-2 strain HuN4 [44]. Co-infection of PAMs with HP-PRRSV-2 strain NJGC and M. hyopneumonia increased IL-1β expression by more than 10 folds [78]. We also identified in the current study that co-infection of pigs with vL126A/ΔNLS and S. suis decreased expression of immunomodulatory cytokines and chemo- kines (Fig 8). Notably, our study is the first to use a mutant PRRSV-2 for co-infection in PAMs and pigs and to show the deletion of PIAS1-binding motif from N attenuate proinflammatory cytokine productions in pigs. In summary, the double-mutant PRRSV-2 lacking both SAP in nsp1β and PIAS1-binding motifs in N was successfully generated. This virus exhibited type I IFN suppression-negative and NF-κB activation-negative and resulted in attenuated production of proinflammatory cytokine in cells. Furthermore, this double-mutant PRRSV-2 reduced clinical severity during co-infection with a secondary bacterial pathogen. Our study paves a way to the development of a new vaccine candidate aiming to reduce the clinical severity of PRDC. Materials and methods Ethics statement The animal study protocol was approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Illinois at Urbana-Champaign. Cells, viruses, bacteria HeLa (NIH HIV Reagents Program, Germantown, MD) and MARC-145 cells were main- tained in Dulbecco’s modified Eagle’s medium (DMEM; Corning Inc., Corning, NY) supple- mented with 10% heat-inactivated fetal bovine serum (FBS; Gibco, Grand Island, NY). 3D4/21 (ATCC CRL-2843) cells that are SV40 large T-transformed porcine alveolar macrophages were cultivated in RPMI 1640 medium (Gibco, Grand Island, NY) supplemented with 10% heat-inactivated FBS in a humidified incubator with 5% CO2 at 37˚C. PRRSV-2 (North Amer- ican type) strain PA8 was propagated in MARC-145 cells and used as a virus stock. PRRSV-2 strain P129 was reconstituted from the P129 infectious clone (51). The reconstituted virus was designated vWT and used as a wild-type virus control for co-infection studies in cells and pigs, which implicates that the current findings may be limited to PRRSV-2. PAM Cl3 is an immor- talized porcine alveolar macrophage cell line developed by Y. Lee, Utah State University (Logan, UT) and was cultivated in RPMI 1640 medium (Gibco) supplemented with 10% heat- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 20 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV inactivated FBS, 1X MEM Non-Essential Amino Acids Solution (Gibco), and 250 μg/mL of G418 sulfate (Corning Inc.) in a humidified incubator with 5% CO2 at 37˚C. Streptococcus (S.) suis was originally isolated from the porcine brain submitted as clinical specimens to the Vet- erinary Diagnostic Laboratory (VDL), University of Illinois at Urbana-Champaign (UIUC; Urbana, IL), and was kindly provided by Chien-Che Hung at VDL. S. suis was grown on Tryp- tic Soy Agar (TSA, Difco Laboratories) containing 5% FBS for 12 h. Then, the bacteria were washed with PBS using centrifugation, and the final concentration was adjusted to an optical density of OD 0.5 at 595 nm before use. Antibodies and chemicals Antibodies and chemicals used in the present study are listed as follows. Anti (α)-PRRSV- nsp1β PAb (rabbit polyclonal antibody) specific for PRRSV-2 nsp1β was generated at the Immunological Research Center, University of Illinois at Urbana-Champaign (Urbana, IL). α- N protein MAb (MR40; mouse monoclonal antibody) was obtained from E. Nelson (South Dakota State University, Brookings, SD). α-p65 Mab (mouse) (F-6, sc-8008) was purchased from Santa Cruz Biotechnologies Inc. (Santa Cruz, CA). Alexa-Flour 488-conjugated and Alexa-Flour 568-conjugated secondary antibodies were obtained from ThermoFisher (Rock- ford, IL). Human tumor necrosis factor-α (TNF-α) (8902) was purchased from Cell Signaling (Danvers, MA). DAPI (40, 60-diamidino-2-phenylindol) and Polyinosinic:polycytidylic [poly (I:C)] were obtained from Sigma (St. Louis, MO). Human and porcine recombinant IFN-β were purchased from Calbiochem (San Diego, CA), and for stimulation, 1000 unit/ml was added to cells for 6 h. Genes and plasmids The genes for nsp1β and N were cloned from of PRRSV-2 strain VR2332 and inserted into the pXJ41 expression vector as described previously [33]. The mutant plasmids nsp1β-L126A and N-ΔNLS were constructed by PCR-based site-directed mutagenesis using specific primer pairs as follows; for nsp1β-L126A, forward 5’-TGCAGCCTCCGTTGTGCGTACTTGCCAGCG AC-3’, reverse 5’-GTCGCTGGCAAGTACGCACAACGGAGGCTGCA-3’; for N-ΔNLS, forward 5’- GGCAAGGGACCGGGAAATAAGAAGAAATCC-3’, reverse 5’- GGATTT CTTCTTATTTCCCGGTCCCTTGCC-3’. PCR-based mutagenesis was performed using the QuikChange II XL Site-Directed Mutagenesis kit (Agilent, Santa Clara, CA) according to the manufacturer’s instruction. The pIFN-β-luciferase reporter plasmid was kindly provided by Stephan Ludwig (Institute of Molecular Medicine, Heinrich Heine Universta¨t, Du¨sseldorf, Germany). The pNF-κB-luciferase reporter plasmid was purchased from Stratagene Inc (La Jolla, CA). The pRL-TK Renilla luciferase reporter plasmid was purchased from Promega (Madison, WI). Viral RNA isolation and RT-qPCR Viral RNA from sera was extracted using the QIAamp Viral RNA mini kit according to the manufacturer’s instruction (QIAGEN). Viral RNA extraction from cells was carried out using TRIzol (Invitrogen). Briefly, one ml of TRIzol was added to cells, and the mixture was incu- bated for 5 min at room temperature (RT). Next, 0.2 ml of chloroform was added, and the mix- ture was shaken vigorously for 20 seconds and incubated for 3 minutes. After centrifugation at 12,000 rpm in a microcentrifuge for 15 minutes at 4˚C, the aqueous phase was transferred to a fresh tube. Then, 0.6 ml of isopropyl alcohol was added, and the mixture was centrifuged again at 12,000 rpm for 10 minutes at 4˚C. The RNA pellet was washed with 1 ml of 75% ethanol, air-dried, and dissolved in 30 μl of RNase-free water. The extracted RNA was stored at -80˚C PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 21 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV until use. To detect the viral sequences in the sera and PAMs, RT-PCR was carried out for the nsp1β and N genes using M-MLV reverse transcriptase (Invitrogen) and Taq DNA polymerase (Invitrogen) following the manufacturer’s instructions. Specific primers for the nsp1β and N gene amplifications were as follow; for nsp1β, forward 5’-TACAGGTTTATGAACGGGGTT G-3’, reverse 5’-GCGGGGAATAGTACTTGAGATG-3’; for N, forward 5’-GATAACCACG CATTTGTCGTC-3’, reverse 5’-TTGAACAAATTAAAACAAAAAGGTG-3’. The PCR prod- ucts were sequenced at the Roy Carver Biotechnology Center of the UIUC. Construction of mutant PRRSV-2 infectious clones and generation of mutant viruses The mutant virus PRRSV-2 vL126A was created by substituting the CTA codon for leucine at 126 to GCT for alanine in nsp1β using the P129 infectious clone [32]. vΔNLS was generated by deleting two lysine residues at 43 and 44 and substituting the asparagine at 49 with serine in N of the PRRSV infectious clone [75]. To create the vL126A/ΔNLS mutant, a long-range inverse PCR-based mutagenesis was performed using the vΔNLS infectious clone as a template, using the QuikChange II XL Site-Directed Mutagenesis Kit according to the manufacturer’s instruc- tions (Agilent, Santa Clara, CA). To rescue mutant PRRSV-2 from infectious clones, MARC- 145 cells were transfected with 2 μg of full-length DNA clone using Lipofectamine 2000 (Invi- trogen, Carlsbad, CA). The culture supernatants were harvested at 6-day post-transfection and designated “passage-1.” The passage-1 virus was propagated in MARC-145 cells, and the 6-day harvest was designated “passage-2” virus. The “passage-3” and “passage-4” viruses were pre- pared in the same way as passage-2, and each passage was aliquoted and stored at -80˚C until use. Viral infectivity was confirmed by the appearance of CPE and by IFA with PRRSV-2 nsp1β and N protein antibodies. For viremia, standard plaque assay was performed in MARC- 145 cells. Briefly, cells were grown in 6-well plates as a monolayer and infected with 0.1 ml of 10-fold serial dilutions of sera collected at indicated days. Virus-infected cells were then over- laid with 0.8% agarose in DMEM and incubated at 37˚C. Between 3 to 4 dpi, cell monolayers were stained with 5% crystal violet in 20% ethanol for 10 min and washed with water several times. The size of plaques was recorded by taking pictures. Each assay was conducted in dupli- cate and repeated twice. Viral titers were calculated and expressed as TCID50/ml. To confirm the successful generation of desired mutant viruses, viral genomic RNA was extracted using the QIAamp Viral RNA mini kit (QIAGEN, Hilden, Germany), and RT-PCR amplification was performed for the full nsp1β and N genes followed by sequencing to verify the desired mutations. JAK-inhibitor assay To examine the replication efficiency of the double-mutant virus vL126A/ΔNLS, the JAK- inhibitor assay was conducted. (F), To determine relative expressions of ISGs, MARC-145 cells were seeded in 6-well plates and incubated with 1,000 units of human IFN-β for 2 h for activa- tion of the JAK-STAT pathway. Then, the cells were treated with ruxolitinib at indicated con- centrations (STEMCELL Technologies, Cambridge, MA). 24 h later, total cellular RNA was extracted for RT-qPCR for ISG15, ISG54, and ISG56. The relative fold changes of ISG tran- scripts were normalized to that of β-actin and statistically analyzed. ***, p<0.001. (G), MARC- 145 cells were treated for 24 h at 1 μM concentration, followed by infection with vWT or vL126A/ΔNLS for 48 h. Culture supernatants were collected and titrated by tissue culture infectious dose 50 (TCID50) in 96-well plate using the Reed-Muench method. The experiment was conducted twice in duplicate each. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 22 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Immunofluorescence analysis (IFA) Virus-infected MARC-145 cells were grown on microscope coverslips and fixed with 4% para- formaldehyde in PBS for 30 min at RT. After three washes with PBS, cells were permeabilized with 0.1% Triton X-100 for 15 minutes at RT and then washed with PBS three more times. To block non-specific binding, cells on coverslip were incubated with 1% BSA in PBS for 30 min at RT. Next, primary antibody of 1:200 dilution in PBS containing 1% BSA was added to cells, and the cells were incubated for 2 h. After three washes with PBS, the cells were incubated with secondary antibody (1:200 dilution) for 1 h. The nuclei were stained with DAPI for 5 min at RT, and the coverslips were washed with PBS. Finally, coverslips were mounted onto micro- scope slides using Fluoromount-G mounting medium (Southern Biotech, Birmingham, AL), and signals were examined using the Nikon A1R confocal microscope. Dual luciferase reporter assay To assess gene expressions driven by the IFN and NF-kB promoters, dual-luciferase reporter assays were conducted using the Dual-Glo Luciferase assay system (Promega) after DNA transfections using Lipofectamine 2000 (Invitrogen). Cells were grown in 12-well plates and transfected with 0.5 μg of viral gene, 0.5 μg of reporter plasmid, and 0.05 μg of pRL-TK in 1:1:0.1 ratio. To detect virus-induced reporter activities, cells were grown in 12-well plates and transfected with 0.5 μg of reporter plasmid and 0.05 μg of pRL-TK. At 6 hours post-transfec- tion, the cells were infected with 1 MOI of respective virus. At 24 h post-transfection or post- infection with virus, 0.5 μg of poly(I:C) was transfected for 6 h for IFN induction. For induc- tion of NF-κB, TNF-α (20 ng/ml) was added for 6 h, and cell lysates were prepared for lucifer- ase assays. Luciferase activity was measured the Wallac 1420 VICTOR multi-label counter (Perkin Elmer, Waltham, MA). Renilla expression was used as an internal control for normali- zation, and the results were expressed as relative luciferase activity. Each assay was conducted in triplicate and repeated three times. Real time quantitative RT-PCR (RT-qPCR) Total cellular RNA was extracted using the TRIzol reagent according to the manufacturer’s instruction (Invitrogen). RT-qPCR reactions were performed using the final volume of 25 μl containing 2.5 μl of cDNA, 12.5 μl of SYBR Green Master mix (Applied Biosystems), 2.5 μl of primer pairs [1.25 μl each of forward and reverse primers (10 mM)], and 7.5 μl of water in the ABI sequence detector system (ABI Prism 7000 and software; Applied Biosystems). The ampli- fication parameters were 40 cycles of two steps each cycle comprised of heating to 95˚C and 60˚C. The primer sets used in assays were described in Table 2. The mRNA levels for target genes were calculated based on 2-ΔΔCt method [79] and normalized using GAPDH as an inter- nal control. Swine-specific oligonucleotides for RT-qPCR were synthesized at the Eurofins Genomics (Louisville, KY). Experimental infection of pigs The animal infection and necropsies were conducted in the biosafety level 2 (BSL2) biocon- tainment facility of UIUC. A total of 39 piglets of 5-week-old, mixed-breed pigs were brought to the facility and pre-screened for the evidence of exposure to PRRSV, porcine circovirus, S. suis, and Mycoplasma hyopneumoniae. The animals were randomly allotted to 6 groups; 7 ani- mals per infection group and 4 animals for mock infection group. The animals were housed in separate environmentally isolated pens within the facility. Throughout the study, pigs were handled according to the IACUC protocol and were fed an age-appropriate non-medicated PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 23 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Table 2. Primer sets used for RT-qPCR in this study. Name Forward primer (50 ! 30) Reverse primer (50 ! 30) IL-1α IL-1β IL-6 IL-8 IL-10 IL-17 GM-CSF RANTES TNFα IP-10 MCP1 MCP2 IFNβ IFNγ β-actin GTGCTCAAAACGAAGACGAACC AACGTGCAGTCTATGGAGT CTGGCAGAAAACAACCTGAACC CCGTGTCAACATGACTTCCAA CGGCGCTGTCATCAATTTCTG AATGCTGAAGGGAAGGAAGA GCAGAACCTGCTTCTCCTG AGCATCAGCCTCCCCATATG AACCTCAGATAAGCCCGTCG CTGCATCAAGATCAGTGACAGAC GCAGCAAGTGTCCTAAAGAAGCA AAGACCAAAGCCGACAAGGA AGTGCATCCTCCAAATCGCT AATGGTAGCTCTGGGAAACTG GTGCGGGACATCAAGGAGAAG CATATTGCCATGCTTTTCCCAGAA GAACACCACTTCTCTCTTCA TGATTCTCATCAAGCAGGTCTCC GCCTCACAGAGAGCTGCAGAA CCCCTCTCTTGGAGCTTGCTA CCCACTGTCACCATCACTTT GGCTCAGGGCTTCTTTGAT TTGCTGCTGGTGTAGAAATATTCC ACCACCAGCTGGTTGTCTTT TTGTGGCAATGATCTCAACAT GCTTGGGTTCTGCACAGATCT TCATGGAATTCTGGACCCACTT GCTCATGGAAAGAGCTGTGGT ACTTCTCTTCCGCTTTCTTAGG CAGGAAGGAGGGCTGGAAGAG https://doi.org/10.1371/journal.ppat.1012128.t002 diet. Feed and water were provided ad libitum, and pigs in the same group were allowed to freely mingle. On the day of infection, PRRSV-2 stocks were prepared to make 2×104 TCID50/ mL, and each pig was infected with a total of 4×104 TCID50 by a single intranasal administra- tion with 2 ml of virus. After infection, an aliquot of each inoculum was retitrated by TCID50 assay to confirm the dose of infection. The bacterial inoculation was conducted with 2 ml of 4×107 colony-forming unit (CFU)/mL of S. suis through intranasal administration. Pigs were monitored daily for any symptoms of illness in consultation of the attending veterinarian. Blood samples were taken on 0, 4, 7, 9, 11, and 14 dpi for virus isolation and serology, and serum were aliquoted and stored immediately at −80˚C until use. Pigs were weighed upon arrival at the site and on days collecting blood samples. The animals were euthanized on 14 dpi to end the study. Necropsies were performed by a board-certified veterinary anatomic patholo- gist that was blinded to the treatment of the animals. The extent of gross lung pathology was assessed by calculating a mean percentage value of the lung exhibiting gross visible pneumonia based on the percentage of each lobe exhibiting pneumonic changes. The overall percent of lung pathology for each lung was calculated using a standard scoring method [80,81]. Bronch- oalveolar lavage (BAL) fluid was collected from the lungs, and PAMs were isolated from BAL as described elsewhere [82]. The lung tissue samples were taken and immersed in formalin for histopathological examination. Viremia and PRRSV-specific serum antibodies Viremia was determined as a 50% tissue culture infectious dose (TCID50) in MARC-145 cells. Briefly, cells were grown in 96-well plates as a monolayer and infected with 0.05 ml of 10-fold serial dilutions of the serum collected at indicated days. At 3–4 dpi, cells were moni- tored for the development of cytopathic effects, and viral titers were calculated using the method of Muench and Reed [83]. Each sample was examined in triplicate. PRRSV-specific serum antibody was determined at the VDL of UIUC using the HerdCheck PRRS ELISA kit according to the manufacturer’s instruction (Westbrook, ME). The S/P ratio value of 0.4 was set as the cutoff for positive/ negative. S/P ratios greater than 0.4 were considered positive. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 24 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV Profiling porcine cytokine expressions Porcine cytokine expressions in the lung lavages were determined using the Multiplex for Luminex Immunoassays according to the manufacture instructions (PCYTMG-23K-13PX, Millipore Sigma, St. Louis, MO). Quality controls and standard controls were first established for the porcine cytokine/chemokine-antibody immobilized magnetic bead panel. On day 1, 200 μl of assay buffer was added to each well of the plate and mixed on a shaker for 10 min at RT. The buffer was discarded and 25 μl each of the standard buffer, assay buffers, and matrix solution was added to appropriate wells. An equal volume of samples was added, and the plate was incubated overnight in the dark at 4 C in a plate shaker. On day 2, the plate was screwed tightly on the handheld magnet and the content was discarded. The plate was washed three times in the same way, and 50 μl of detection antibodies was added to each well for 2 h incuba- tion, followed by adding 50 μL of Streptavidin-Phycoerythrin to each well. The plate was incu- bated for 30 min at RT and washed three times with washing buffer. After the final wash, the beads were resuspended with 100 μl of washing buffer for 5 min. The plate was read in the multiplex analyzer (Luminex 200 System, DiaSorin; Stillwater, MN), and the data were ana- lyzed using the accompanied software (Luminex SD xMAP Technology LX100/200). Statistical analysis Statistical significance was determined by a two-tailed Student’s t-test. Data analyses were per- formed using GraphPad Prism version 9.00 (San Diego California USA). Acknowledgments Figs 5 and 6 were created with BioRender.com Author Contributions Conceptualization: Federico A. Zuckermann, Dongwan Yoo. Data curation: Young-Min Lee, Dongwan Yoo. Formal analysis: Chia-Ming Su, Jineui Kim, Junyu Tang, Yu Fan Hung, Federico A. Zucker- mann, Jiyoun Kim. Funding acquisition: Young-Min Lee, Dongwan Yoo. Investigation: Chia-Ming Su, Jineui Kim, Junyu Tang, Yu Fan Hung, Federico A. Zucker- mann, Robert Husmann, Patrick Roady, Jiyoun Kim. Methodology: Chia-Ming Su, Jineui Kim, Junyu Tang, Yu Fan Hung, Federico A. Zucker- mann, Robert Husmann, Patrick Roady, Jiyoun Kim, Young-Min Lee. Project administration: Federico A. Zuckermann, Dongwan Yoo. Resources: Chia-Ming Su, Jineui Kim, Junyu Tang, Yu Fan Hung, Federico A. Zuckermann, Robert Husmann, Patrick Roady, Jiyoun Kim, Young-Min Lee. Supervision: Dongwan Yoo. Validation: Chia-Ming Su, Jineui Kim, Jiyoun Kim, Dongwan Yoo. Visualization: Patrick Roady. Writing – original draft: Chia-Ming Su, Jineui Kim. Writing – review & editing: Young-Min Lee, Dongwan Yoo. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012128 March 28, 2024 25 / 30 PLOS PATHOGENSType I interferon suppression-negative and NF-κB activation-negative PRRSV References 1. Schneider WM, Chevillotte MD, Rice CM. Interferon-stimulated genes: a complex web of host defenses. Annu Rev Immunol. 2014; 32:513–45. Epub 20140206. https://doi.org/10.1146/annurev-immunol- 032713-120231 PMID: 24555472; PubMed Central PMCID: PMC4313732. 2. Havenar-Daughton C, Kolumam GA, Murali-Krishna K. Cutting Edge: The direct action of type I IFN on CD4 T cells is critical for sustaining clonal expansion in response to a viral but not a bacterial infection. 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10.1371_journal.pone.0300534	RESEARCH ARTICLE Neural flip-flops I: Short-term memory Lane YoderID* Department of Science and Mathematics, University of Hawaii, Honolulu, Hawaii, United States of America * LYoder@hawaii.edu Abstract a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Yoder L (2024) Neural flip-flops I: Short- term memory. PLoS ONE 19(3): e0300534. https:// doi.org/10.1371/journal.pone.0300534 Editor: Sunder Ali Khowaja, University of Sindh, PAKISTAN Received: December 4, 2022 Accepted: February 27, 2024 Published: March 15, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0300534 Copyright: © 2024 Lane Yoder. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The novel networks proposed in the manuscript are theoretical. There are no data. Funding: The author(s) received no specific funding for this work. The networks proposed here show how neurons can be connected to form flip-flops, the basic building blocks in sequential logic systems. The novel neural flip-flops (NFFs) are explicit, dynamic, and can generate known phenomena of short-term memory. For each net- work design, all neurons, connections, and types of synapses are shown explicitly. The neu- rons’ operation depends only on explicitly stated, minimal properties of excitement and inhibition. This operation is dynamic in the sense that the level of neuron activity is the only cellular change, making the NFFs’ operation consistent with the speed of most brain func- tions. Memory tests have shown that certain neurons fire continuously at a high frequency while information is held in short-term memory. These neurons exhibit seven characteristics associated with memory formation, retention, retrieval, termination, and errors. One of the neurons in each of the NFFs produces all of the characteristics. This neuron and a second neighboring neuron together predict eight unknown phenomena. These predictions can be tested by the same methods that led to the discovery of the first seven phenomena. NFFs, together with a decoder from a previous paper, suggest a resolution to the longstanding con- troversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts. Two novel NFFs are composed of two and four neurons. Their designs follow directly from a standard electronic flip-flop design by moving each negation symbol from one end of the connection to the other. This does not affect the logic of the network, but it changes the logic of each component to a logic function that can be implemented by a sin- gle neuron. This transformation is reversible and is apparently new to engineering as well as neuroscience. 1. Introduction This article is the fourth in a series of articles that show how neurons can be connected to pro- cess information. The first three articles [1–3] explored the analog properties of neuron signals in combinational logic operations, whose outputs depend only on the current state of the inputs. A fuzzy logic decoder was shown to generate the major phenomena of both olfaction and color vision (such as color mixing, mutually exclusive colors, and the shape of perceived color space), including the brain’s shortcomings (such as the Bezold-Bru¨cke hue shift) [1, 2]. The decoder’s design is radically different from a standard electronic digital (Boolean logic) decoder [2, 4, 5]. If implemented with electronic components and given digital inputs, the decoder performs the same Boolean function as the standard digital design more efficiently. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 1 / 18 PLOS ONECompeting interests: The authors have declared that no competing interests exist. Neural flip-flops I: Short-term memory It was shown that a single neuron with one excitatory input and one inhibitory input, with signal strengths X and Y, respectively, can function as a logic primitive, X AND NOT Y [1, 2]. In simplest terms, this is because the neuron is active when it has excitatory input and does not have inhibitory input. It was also shown that an AND-NOT gate can be configured to function as an inverter (i.e., a NOT X logic primitive). The AND-NOT gate together with a NOT gate make up a functionally complete set, meaning any logic function can be performed by a net- work of such components. The neuron AND-NOT gate will be reviewed here and used in the proposed networks. The present article considers the Boolean logic properties of neuron signals in sequential logic operations, whose outputs are functions of both the current inputs and the past sequence of inputs. That a neuron can operate as a functionally complete logic gate, analog or digital, provides a framework for the brain’s processing of information—analog and digital, combina- tional and sequential. Flip-flops are the basic building blocks of sequential logic systems. A flip-flop is a mecha- nism that can be set repeatedly to either one of two stable states, commonly labeled 0 and 1. A flip-flop can be used as a memory mechanism to store one bit of information. It is shown here that a few AND-NOT gates can be connected to perform the same function as two standard electronic flip-flops, an active low and an active high Set-Reset (SR) flip-flop. These are not the only flip-flops that can be constructed with AND-NOT gates, but they may be the simplest. The network designs are modifications of standard electronic logic circuit designs. It is shown here that the NFF designs are derived directly from the standard electronic designs simply by moving each negation circle from one end of the connection to the other. This changes the logic of each component, but it does not materially affect the logic of the network. The modifi- cations are necessary to implement the circuits with neurons because the AND-NOT gate is virtually never used as a building block in electronic computational systems. The NFFs produce both known and testable, unknown phenomena of short-term memory. With inputs from the outputs of NFFs, neural decoders proposed in [2] can retrieve encoded information that is held in NFFs. That is, a memory can be recalled. The NFFs’ robust opera- tion in the presence of noise is demonstrated here by simulation, but the properties can be proven directly from the explicit network connections and minimal neuron properties of exci- tation and inhibition. In [6] it was shown that NFFs, together with a network that can produce the oscillations commonly known as brainwaves, suggest a resolution to the longstanding con- troversy of whether short-term memory depends on neurons firing persistently or in brief, coordinated bursts [7, 8]. The NFFs’ operation is dynamic, meaning the only changes are the levels of neuron activity. No structural change is required, such as neurogenesis, synaptogenesis, or pruning, nor is any change required in the way neurons function, such as a change in synaptic strength or the strength of action potentials. This makes the networks’ speed consistent with the “real time” of most brain functions (a few milliseconds). The NFFs’ architectures are explicit, meaning all neurons, connections, and types of synapses are shown explicitly, and all assumptions of neu- ron capabilities are stated explicitly. Only minimal neuron capabilities are assumed, and no network capabilities are assumed. It was shown in [9] that designing a simple logic circuit that can perform a single, biologi- cally advantageous task can lead to a discovery of how neurons are connected to process infor- mation. This is the method that was used to find the networks proposed here and in [1–6]. Besides performing a biologically useful task, the networks are dynamic, explicit, and able to generate phenomena that are central to a particular brain function. These four properties are characteristics that networks in the brain must have. The neuron properties used to achieve PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 2 / 18 PLOS ONENeural flip-flops I: Short-term memory the results for these networks—excitation, inhibition, and sigmoid neuron responses—have been known a long time. 2. Unexplained phenomena and previous models 2.1. Single neuron logic capability McCulloch and Pitts’ seminal paper [10] proposed that the brain is made up of logic gates. The idea of Boolean neurons had a tremendous effect on artificial neural networks and machine learning, but it had a limited impact on neuroscience [11]. More than 70 years later, the brain’s computational capabilities are still unclear [12]. In that time span, many theoretical models have been proposed for neuron responses as mathematical or logic functions, but the modern view follows “the adage that all models are wrong, but some are useful” [13]. The neuron response model proposed in [1, 2] demonstrated that a neuron with one inhibi- tory input that can suppress one excitatory input can function as an AND-NOT gate, and that this logic primitive is sufficient for all logic operations. This demonstration was apparently the first claim that a single neuron can function as a specific logic primitive based on minimal neu- ron capabilities of excitation and inhibition. This neuron response model will be reviewed and used here. 2.2. Short-term memory Memory tests have shown that certain neurons fire continuously while information is held in short-term memory. This activity was found in neurons in the visual, auditory, and sensorimo- tor cortexes of monkeys while corresponding sensory information is held in memory [14, 15]. Similar activity has been found more recently in humans [16]. In the first experiments [14, 15], seven characteristics of neural activity were associated with memory formation, retention, retrieval, termination, and errors: 1) Before the stimulus was presented, the sampled neuron discharged at a low, baseline level. 2) When the stimulus was presented, or shortly after, the neuron began to fire at a high frequency. 3) The high frequency firing continued after the stimulus was removed. 4) The response was still high when the mem- ory was demonstrated to be correct. 5) The response returned to the background level shortly after the test. 6) In the trials where the subject failed the memory test, the high level firing had stopped or 7) had never begun. It will be shown that the memory bank of NFFs presented here produces all of these phenomena. 2.3. Previous models Considerable progress has been achieved for long-term memory, notably with models based on synaptic strength changes [e.g., 17–20]. Models of single neuron logic gates [e.g., 21] and short-term memory mechanisms composed of neurons [e.g., 21–26] have multiple problems. Much of the literature on possible mechanisms for memory in the brain concentrates on observed changes in the nervous systems of various organisms and says little about the charac- teristics necessary for these changes to serve as memory. Since changes occur continually throughout the body for many reasons, change by itself is weak evidence of memory formation. If a change is to serve as memory, it must be capable of representing information and there must be a means of retrieving that information. If the mechanism is to be more flexible than a permanent storage space, there must be a way to replace the information stored there. In addi- tion, a robust and practical memory device should be capable of storing different kinds of PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 3 / 18 PLOS ONENeural flip-flops I: Short-term memory information; it should be inexpensive in resource requirements; storing, retrieving, and chang- ing information should be simple, reliable, and fast; information should remain stored reliably and unambiguously until it is no longer needed or until new information replaces it; and errors should be minimal and correctable. The absence of one or more of these properties is the reason such changes as muscle growth after exercise are not plausible memory devices. These characteristics are not obvious in most memory models that have been proposed. On the contrary, most models appear to be incapable of many of them. Yet even the most basic requirements for a memory mechanism are routinely ignored in the literature. Most models of neuron response functions and short-term memory mechanisms composed of neurons are speculative, needlessly complex, and do not include evidence or even a plausible argument that the proposed mechanisms would operate as claimed. Some network models are simply “black boxes” with no evidence that they can actually be implemented with neurons. Neurons and connections are seldom shown explicitly. Some models make tacit assumptions of powerful neuron capabilities. When assumptions of neuron capabilities are stated, support- ing evidence is not included. Except for the models presented here, networks that are dynamic, explicit, and can produce known phenomena of short-term memory are virtually nonexistent. At a minimum, these properties are necessary for a realistic model of short-term memory. As two of the most plausi- ble examples of other models, claims in [21] of a single neuron logic gate and an explicit net- work that can function as a flip-flop are discussed here in some detail. Reviewing more models that are not dynamic and not explicit and do not produce known phenomena would not serve any useful purpose. 2.3.1. Threshold oscillator neuron response model. The neuron response model in [21] is a “threshold oscillator.” This means that for an excitatory input strength below a certain threshold, the neuron has little or no response, and for inputs at or above the threshold, the neuron spikes at a high rate. 2.3.2. AND gate. The authors of [21] claim that with the threshold oscillator model for neuron signals, a single neuron can function as a logic AND gate. The AND gate neuron has two (or presumably more) excitatory inputs representing logic values TRUE or FALSE. The claim for the AND gate neuron is based on two assumptions of finely tuned input strengths. If all of the inputs represent the logic value TRUE, they are 1) assumed to be suffi- ciently high for the combined input to reach or surpass the neuron’s threshold, producing a high output representing the logic AND value TRUE. The TRUE inputs are also 2) assumed to be sufficiently low so that if one of the inputs represents the truth value FALSE, the combined input is below the gate’s threshold, producing a low output representing the AND value FALSE. This logic gate model has at least two problems. Although the paper’s neuron response model is said to be the threshold model, the AND gate’s input neurons do not produce the high signals of the threshold model. The high input values representing the logic value TRUE are each necessarily below the AND gate neuron’s threshold. Second, no evidence or argument is given for how the input neurons can maintain the signals of intermediate strengths repre- senting TRUE with enough precision (high enough to surpass the threshold together, but not high enough to surpass the threshold if one is low) to produce the claimed outputs. In contrast, the design of the AND gate implemented with AND-NOT gates follows from straightforward logic because the AND-NOT gate is functionally complete: X AND Y ¼ X AND NOT NOT Y ð Þ: PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 4 / 18 PLOS ONENeural flip-flops I: Short-term memory The AND gate can be implemented with two AND-NOT gates: a first AND-NOT gate con- figured as an inverter (Fig 4B) that provides input to a second AND-NOT gate. 2.3.3. Fitzhugh Nagumo set reset flip-flop. This simple flip-flop model [21, Fig 10] con- sists of two neurons with reciprocal inhibitory input and continuously high excitatory input to each cell. Each cell has an additional excitatory input (Set and Reset) that is variable and nor- mally low. The model’s operation is based on four assumptions. The flip-flop is 1) assumed to be ini- tially in a stable state, with the inhibitory input from one cell 2) assumed to inhibit the continu- ous high input to the other cell, leaving the first cell with no inhibitory input and a high output representing one bit of stored information. The flip-flop state is inverted with a brief, high excitatory input to the second, inhibited cell. The combined two high inputs are 3) assumed to be sufficiently high to override the inhibitory input and produce an inhibitory signal to the first cell. This inhibition is 4) assumed to be sufficiently high to suppress the first cell’s excit- atory input, thus switching the outputs. When the brief high excitatory input to the second cell ends, the flip-flop is in a stable state with the second cell inhibiting the first. This flip-flop model has several problems. How the flip-flop is initialized in a stable state without producing a race condition is not discussed. The model’s operation is demonstrated by simulation with electronic components, but no evidence or argument is given that indicates neurons are capable of the four assumptions of somewhat complex behavior. 2.4. Testable predictions of unknown phenomena This article ends with several testable predictions that are implied by the models, briefly out- lined here. Since the proposed networks are explicit, any of them can be constructed with actual neurons and tested for specific predicted behaviors. As noted above, one of an NFF’s two outputs produces all seven characteristics of neuron activity while information is held in short-term memory. NFFs predict eight additional phe- nomena for the pair of outputs. These predictions can be tested by the same methods that led to the discovery of the first seven phenomena. The two NFF output neurons are predicted to have 1) close proximity; 2) reciprocal, 3) inhibitory inputs; 4) complementary outputs; and 5) noise-reducing responses to the inputs. When the memory state is changed, 6) the neuron with high output changes first with 7) the other changing a few milliseconds later. 8) After the memory test, the outputs of both neurons are low. 3. Simulation methods A neuron’s response to an excitatory input of strength X and an inhibitory input of strength Y is represented by the function F(X, Y). The response function’s minimal noise reducing prop- erties that can produce the network properties claimed here are given in inequalities 1 and 2, section 4.2.2.1 below. These conditions generalize the noise-reducing properties of a sigmoid function. (A sigmoid response reduces moderate levels of additive noise in a binary input sig- nal by producing an output that decreases a low input and increases a high input.) An example of a neuron response function that satisfies these conditions is given in section 4.2.2.2. The graphs of this function and the associated plane in Fig 2B were created in MS Excel and MS Paint. The graph in Fig 2A was created with Converge 10.0. This example neuron response function F(X, Y) was used to simulate the NFF shown in Fig 4F. The simulation was done in MS Excel as follows. The number ti represents the time after i neuron delay times, i = 0, 1, 2,. . .. At time t0, the NFF’s neurons are initialized in a stable state. Simulated inputs to the NFF are given. At time ti for i > 0, the output Zi of each NFF neuron PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 5 / 18 PLOS ONENeural flip-flops I: Short-term memory that has excitatory and inhibitory inputs Xi-1 and Yi-1 at time ti-1 is Zi = F(Xi-1, Yi-1). The graphs of the simulated NFF inputs and outputs are shown in Fig 5. A simulation using a specific neuron response model can support network claims only for that model. This study goes substantially further. As stated above, the inequalities 1 and 2 in section 4.2.2.1 are the minimum neuron requirements to produce the NFF results in the pres- ence of noise. All of the claims for the NFF’s sustained binary outputs in the presence of noise can be proven (somewhat tediously) from the two properties and the network architecture in Fig 4F. Therefore the network results are verified for any neuron response function that satis- fies these two inequalities. A single-transistor AND-NOT gate is shown in section 4.2.2.3, Fig 3A, to demonstrate that the two noise-reducing properties do not indicate capabilities of sophisticated mathematics. The figure was created and simulated in CircuitLab. The graphs of its response function and related plane in Fig 3B were created in MS Excel and MS Paint. 4. Analysis 4.1. Figure symbols For several reasons, the neural networks in the figures are illustrated with standard (ANSI/ IEEE) logic symbols rather than symbols commonly used in neuroscience schematic diagrams. A comparison is shown in Fig 1. The symbols in Fig 1A can be interpreted in two ways. As a logic symbol, the rectangle with one rounded side represents the AND logic function, and a circle represents negation. So the networks in the figures can be constructed with ordinary electronic components or simulated with electronic circuit software. Second, it will be shown that the logic gate represented by an AND symbol and a circle can be implemented by a single neuron, with a circle representing inhibitory input and no circle representing excitatory input. As shown in Fig 1B, neurons are often represented by a circle, inhibition by a small closed circle, and excitation by a closed tri- angle, but there does not seem to be an accepted standard of symbols for networks of neurons. The standard logic symbols normally represent Boolean logic, which for most electronic computational systems means digital signal processing. Neurons can convey analog signals, either with signals of graded strength or with the strength of signals consisting of spikes mea- sured by spike frequency. It will be shown that the neural networks in the figures can generate robust digital signals, i.e., signals with only high and low strengths (except during transition from one state to the other). The similarities and differences between the novel diagrams of networks that can be imple- mented with neurons, and diagrams of standard logic circuits for the same functions imple- mented electronically, are easier to see if they are both illustrated with the same symbols. The single, branching output channels in Fig 1A are more realistic depictions of most axons than the multiple output channels of Fig 1B. Finally, diagrams in standard engineering form clarify the connectivity, the type of each connection, the logic function of each component, the distinction between feedback (right to left) and feed-forward (left to right) signals, and the overall direction of a network’s signal pro- cessing from input to output (left to right). 4.2. Neuron signals All results for the networks presented here follow from the neuron response to binary (high and low) input signals, given in the next section, and the algebra of Boolean logic applied to the networks’ connections. Although binary signals are common in modeling neuron response, how neural networks are capable of maintaining binary outputs in the presence of PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 6 / 18 PLOS ONENeural flip-flops I: Short-term memory Fig 1. Network symbols. A. A logic circuit illustrated with standard logic symbols. Each of the four components represents a logic function that can be implemented with electronic hardware or with a single neuron. B. The same logic circuit illustrated with symbols commonly used in neuroscience schematic diagrams. https://doi.org/10.1371/journal.pone.0300534.g001 additive noise in binary inputs has apparently not been demonstrated. Analog signals (inter- mediate strengths between high and low) are considered here only to show how the networks in the figures can generate robust binary signals in the presence of moderate levels of additive noise. 4.2.1. Binary neuron signals. Neuron signals can be graded or they can consist of all-or- nothing action potentials, or spikes. As noted above, the strength, or intensity, of a signal con- sisting of spikes can be measured by the spike frequency. Neuron signal strength is normalized here by dividing it by the maximum strength for the given level of adaptation. This puts inten- sities in the interval from 0 to 1, with 0 meaning no signal and 1 meaning the maximum inten- sity. The normalized number is called the response intensity or simply the response of the neuron. Normalization is only for convenience. Non-normalized signal strengths, with the highest and lowest values labeled Max & Min rather than 1 and 0, would do as well. The responses 1 and 0 are collectively referred to as binary signals and separately as high and low signals. For a signal consisting of spikes, a high signal consists of a burst of spikes at a high frequency. If 1 and 0 stand for the truth values TRUE and FALSE, neurons can process information contained in neural signals by functioning as logic operators. For binary signals, the response of a neuron with one excitatory and one inhibitory input is assumed to be as shown in Table 1. Of the 16 possible binary functions of two variables, this table represents the only one that is consistent with the customary meanings of "excitation" and "inhibition." The table essentially says that a low excitatory input produces a low output signal (rows 1 and 2), a high excitatory input produces a high output (row 3), and a high inhib- itory input suppresses a high excitatory input (row 4). Some of the components in the figures require continuous, high input. This input is repre- sented by the logic value "TRUE." For an electronic logic circuit, the high input is normally Table 1. Neuron response to binary inputs. The table is also a logic truth table, with the last column representing the truth values of the statement X AND NOT Y. Excitatory X Inhibitory Y Response 0 0 1 1 0 1 0 1 0 0 1 0 https://doi.org/10.1371/journal.pone.0300534.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 7 / 18 PLOS ONENeural flip-flops I: Short-term memory provided by the power supply. If the components represent neurons, the high input can be achieved by neurons in at least four ways. 1) A continuously high signal could be provided by a neuron that has excitatory inputs from many neurons that fire independently [27]. 2) Neu- rons that are active spontaneously and continuously without excitatory input are known to exist [28, 29]. A network neuron that requires a high excitatory input could receive it from a spontaneously active neuron, or 3) the neuron itself could be spontaneously active. 4) It will be seen that the high input could be provided by one of a flip-flop’s outputs that is continuously high. 4.2.2. Additive noise in binary neuron signals. This section covers a potential problem for neural processing of digital (Boolean) information: Additive noise in binary inputs may affect the intended binary outputs of Table 1. The section includes three main points: Evidence indicates that some neurons have at least some rudimentary noise-reducing capabilities. For the NFF properties obtained here, noise can be sufficiently reduced by neurons that have two simple properties that generalize the noise-reducing properties of sigmoid functions. These properties do not indicate sophisticated capabilities. 4.2.2.1. Noise reduction. Two lines of evidence indicate that neurons have at least a minimal capability of reducing moderate levels of additive noise in binary inputs. The persistent high and low firing frequency associated with short-term memory [14–16] and discussed above is itself evidence of a noise-reducing property. Without some noise-reduc- ing capability, it would be difficult if not impossible for a network to maintain a variable output that can be either high or low. The cumulative effect of additive noise would quickly attenuate the output strength to a random walk through intermediate levels. This is the reason that sim- ple noise-reducing nonlinearities are intentionally built into the materials in electronic compo- nents for digital signal processing, as demonstrated below by a single transistor’s response. Second, many neurons have sigmoid responses to single inputs, including inhibitory inputs [30–32]. In fact, “. . .the vast majority of neurons show sigmoid nonlinearities” [33]. A sigmoid response reduces moderate levels of additive noise in a binary input signal by producing an output that decreases a low input and increases a high input. It will be demonstrated by simu- lation that a neuron response that is sigmoid in both excitatory and inhibitory inputs is suffi- cient for the noise-reducing requirements of the NFFs presented here. But such a response is not necessary; a simpler, more general property is sufficient. Reduction of noise in both excitatory and inhibitory inputs can be achieved by a response function of two variables that generalizes a sigmoid function’s features. The noise reduction need only be slight for the proposed NFFs because they have feedback loops that continuously reduce the effect of noise. Let F(X, Y) represent a neuron’s response to an excitatory input X and an inhibitory input Y. The function must be bounded by 0 and 1, the minimum and maximum possible neuron responses, and must satisfy the values in Table 1 for binary inputs. For other points (X, Y) in the unit square, suppose F satisfies: 1. F(X, Y) > X − Y for inputs (X, Y) near (1, 0) and 2. F(X, Y) < X − Y or F(X, Y) = 0 for inputs (X, Y) near the other three vertices of the unit square. The neuron responses of Table 1 are max{0, X-Y} (the greater of 0 and X-Y). For binary inputs with moderate levels of additive noise that makes them non-binary, conditions 1 and 2 make the output either closer to, or equal to, the intended output of Table 1 than max{0, X-Y}. Neurons that make up the networks proposed here are assumed to have these minimal noise- reducing properties. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 8 / 18 PLOS ONENeural flip-flops I: Short-term memory Conditions 1 and 2 are sufficient to suppress moderate levels of additive noise in binary inputs and produce the NFF results found here. The level of noise that can be tolerated by the NFFs depends on the regions in the unit square where conditions 1 and 2 hold. If a binary input (X, Y) has additive noise that is large enough to change the region in which it lies, an error can occur. 4.2.2.2. Example of a neuron response that satisfies conditions 1 and 2. For any sigmoid func- tion f from f(0) = 0 to f(1) = 1, the following function has the noise-reducing properties 1 and 2 and also satisfies Table 1: F X; Yð Þ ¼ f Xð Þ (cid:0) f Yð Þ; bounded below by 0: This function is plausible as an approximation of a neuron response because it is sigmoid in each variable and some neurons are known to have sigmoid responses to single inputs, as men- tioned above. The same sigmoid function applied to X and Y is not necessary to satisfy condi- tions 1 and 2. The function F could be the difference of two different sigmoid functions. The function F is illustrated in Fig 2 for a specific sigmoid function f. The sine function of Fig 2A was chosen for f rather than any of the more common examples of sigmoid functions to demonstrate by simulation that a highly nonlinear function is not necessary for robust maintenance of binary signals. On half of the unit square, where Y � X, Fig 2B shows that F has the value 0. This reflects the property that a large inhibitory input generally suppresses a smaller excitatory input. 4.2.2.3. A primitive noise-reducing AND-NOT gate. A response that satisfies conditions 1 and 2 in section 4.2.2.1 does not indicate capabilities of sophisticated logic or mathematics. An AND-NOT response with properties 1 and 2 can be produced by mechanisms that are quite simple. Fig 3 shows that a single transistor and three resistors can be configured to accomplish this. The network output was simulated in CircuitLab, and the graph was created in MS Excel and MS Paint. The inputs X and Y vary from 0V to 5V in steps of 0.05V. A 5V signal com- monly stands for logic value 1, and ground stands for logic value 0. Fig 2. Noise-reducing AND-NOT function. The graphs show an example of a neuron response to analog inputs that reduces moderate levels of additive noise in binary inputs. A. A sigmoid function f(x) = (1/2)sin(π(x—1/2)) + 1/2. B. Graph of a function that has the noise-reducing properties 1 and 2. The function is F(X, Y) = f(X)—f(Y), bounded by 0. Wireframe: Graph of the response function Z = F(X, Y). Green and red: A triangle in the plane Z = X—Y. Red: Approximate intersection of the plane and the graph of F. Purple: Approximate region in the unit square where F(X, Y) > X—Y (condition 1). Blue: Approximate region in the unit square where F(X, Y) < X—Y or F(X, Y) = 0 (condition 2). https://doi.org/10.1371/journal.pone.0300534.g002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 9 / 18 PLOS ONENeural flip-flops I: Short-term memory Fig 3. Single transistor AND-NOT gate that reduces noise. This minimal logic circuit satisfies the noise-reducing conditions 1 and 2. A. A logic circuit consisting of one transistor and three resistors. B. Engineering software simulation. Wireframe: Graph of the transistor response function Z = F(X, Y). Green and red: A triangle in the plane Z = X—Y. Red: Intersection of the plane and the graph of F. Purple: Region in the unit square where F(X, Y) > X—Y (condition 1). Blue: Region in the unit square where F(X, Y) < X—Y or F(X, Y) = 0 (condition 2). https://doi.org/10.1371/journal.pone.0300534.g003 4.3. Neural logic gates and flip-flops Fig 4 shows three logic primitives and three flip-flops. 4.3.1. Neural logic gates. As discussed above, Fig 4A, consisting of an AND symbol and a NOT symbol, represents the logic function X AND NOT Y. The figure can also represent a neuron with one excitatory input and one inhibitory input, whose response to binary inputs is X AND NOT Y by Table 1. The logic outputs shown for Fig 4B and 4C also follow from the AND and NOT symbols. The AND-NOT logic primitive has simplicity, efficiency, and power that have been under- appreciated. It is in the minority of logic primitives that are functionally complete. (As a tech- nicality of logic, the AND-NOT operation is not functionally complete by itself because it requires access to the input TRUE to produce the NOT operation. Only the NAND and NOR operations are functionally complete by themselves. As a practical matter, NAND and NOR also require a high input for implementation.) Analogously to the single-neuron AND-NOT gate, the function can be implemented electronically with a single transistor and one resistor [5]. Any mechanism that can activate and inhibit like mechanisms and has access to a high activating input is a functionally complete AND-NOT gate. It may not be coincidence that the components of disparate natural signaling systems have these capabilities, e.g., immune system cells [34–37] and regulatory DNA [38, 39], in addition to transistors and neurons. As noted in the introduction, AND-NOT gates with analog signals can make up a powerful fuzzy logic decoder whose architecture is radically different from, and more efficient than, standard elec- tronic decoder architectures [2, 4, 5]. Implemented with neural AND-NOT gates, these fuzzy decoders generate detailed neural correlates of the major phenomena of color vision and olfac- tion [1, 2]. 4.3.2. Neural flip-flops. Fig 4 also shows three flip-flops. A flip-flop, or latch, is a common type of memory element used to store one bit of information in electronic computational sys- tems. The more formal name is bistable multivibrator, meaning it has two stable states that can alternate repeatedly. A distinction is sometimes made between a "flip-flop" and a "latch," with the latter term reserved for asynchronous memory mechanisms that are not controlled by an oscillator. The more familiar "flip-flop" will be used here for all cases. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 10 / 18 PLOS ONENeural flip-flops I: Short-term memory Fig 4. Neural logic gates and flip-flops. A. A symbol for an AND-NOT logic gate, with output X AND NOT Y. The symbol can also represent a neuron with one excitatory input X and one inhibitory input Y. B. An AND-NOT gate configured as a NOT gate, or inverter. C. A NAND gate (NOT AND). The output is NOT (X AND Y). There is no obvious way to implement this gate with a single neuron. D. A standard design for an electronic active low Set-Reset (SR) flip-flop composed of two NAND gates. E. An active low Set-Reset (SR) flip-flop composed of two AND-NOT gates. This design is derived from the design in D by moving each negation circle from one end of the connection to the other. This inverts the outputs. F. An active high SR flip-flop. https://doi.org/10.1371/journal.pone.0300534.g004 A flip-flop stores a discrete bit of information in an output with low and high values usually labeled 0 and 1. This output variable is labeled M in Fig 4. The value of M is the flip-flop state or memory bit. The information is stored by means of a brief input signal that activates or inac- tivates the memory bit. Input S sets the state to M = 1, and R resets it to M = 0. Continuous feedback maintains a stable state. A change in the state inverts the state. Fig 4D shows a standard design for an electronic active low SR flip-flop. The S and R inputs are normally high. A brief low input S sets the memory bit M to 1, and a brief low input R resets it to 0. Fig 4E can be derived from Fig 4D simply by moving each negation circle from one end of the connection to the other. Importantly, this changes the logic of each component from NAND to AND-NOT, which can be implemented with a single neuron. The change only has one small effect on the network logic: If a circle is moved past an output, the output is inverted, as shown in Fig 4D and 4E. Adding inverters to the inputs of Fig 4E produces the active high SR flip-flop of Fig 4F. The S and R inputs are normally low. A brief high input S sets the memory bit M to 1, and a brief high input R resets it to 0. 4.3.3. Neural flip-flop simulation. The simulation in Fig 5 demonstrates the robust oper- ation of the NFF in Fig 4F in the presence of additive noise, using the neuron response func- tion of Fig 2B in section 4.2.2.2. The simulation was done in MS Excel. The slow rise and fall of Set and Reset, over several delay times, is exaggerated to make the robust operation of the net- work clear. Low level additive noise and baseline activity in the inputs are simulated by a computer- generated random number uniformly distributed between 0.01 and 0.1. The noise is offset by 0.01 so it does not obscure the high and low outputs in the graphs. The high Enabling input TRUE is simulated by 1 minus noise. Each of the medium bursts in Set and Reset is simulated by the sum of two sine functions and the computer-generated noise. These signals could represent either noise bursts that are PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 11 / 18 PLOS ONENeural flip-flops I: Short-term memory not high enough to cause an error, or high input signals intended to invert the memory state but sufficiently reduced by noise to cause an error. The two higher Set and Reset signals that invert the memory state are simulated by a sine function plus noise. These signals could represent either high input signals intended to invert the memory state, substantially reduced by noise but not enough to cause an error, or noise bursts with enough amplitude to cause an error. The function F(X, Y) in Fig 2 was used for the simulated response of each NFF neuron as follows. The number ti represents the time after i neuron delay times, i = 0, 1, 2,. . .. At time t0, the outputs are initialized at M0 = 0 and �M0 ¼ 1. (If both are initialized at 0, they will oscillate until either Set or Reset is high.) At time ti for i > 0, the output Zi of each neuron that has excit- atory and inhibitory inputs Xi-1 and Yi-1 at time ti-1 is: ð Zi ¼ F Xi(cid:0) 1; Yi(cid:0) 1 ¼ max 0; Þ 1=2 f ð ½ Þsin p Xi(cid:0) 1 (cid:0) 1=2 ð ð Þ Þ þ 1=2 � (cid:0) ½ ð 1=2 Þsin p Yi(cid:0) 1 (cid:0) 1=2 ð ð Þ Þ þ 1=2 � g: 4.3.4. Neural memory bank. If information stored in short-term memory is no longer needed, active neurons consume energy without serving any useful purpose. An energy-saving function can be achieved with NFFs. Fig 6 shows a memory bank of three NFFs of Fig 4F, with a fourth serving as a switch to turn the memory bank on and off. The memory elements are enabled by excitatory input from the switch. A large memory bank could be organized as a tree, with switches at the branch potionints and memory elements at the leaves, so that at any time only the necessary memory elements are enabled. 5. Results 5.1. Plausibility of NFFs as short-term memory mechanisms In all of the characteristics that are necessary for a mechanism to function as memory, as out- lined in section 2.3, NFFs are plausible memory devices. Flip-flops are well understood and work well as memory devices in electronic computational systems. They are capable of storing different kinds of information. It is unlikely that any short-term memory mechanism in the brain could be simpler than the NFFs in Fig 4. The simulation shown in Fig 5 illustrates that NFFs can be robust in storing information. The decoders proposed in [2] can retrieve informa- tion held in NFFs. NFFs are efficient in material requirements (two or four neurons), Fig 5. Simulation of an NFF operation with noise in the inputs. This simulation of the NFF in Fig 4F shows the NFF’s operation is robust in the presence of moderate levels of additive noise in binary inputs. The effect of baseline noise on the memory bit is negligible, and temporary bursts of larger noise have no lasting effect. https://doi.org/10.1371/journal.pone.0300534.g005 PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 12 / 18 PLOS ONENeural flip-flops I: Short-term memory Fig 6. Neural memory bank. Three NFFs (Fig 4F) are enabled by a fourth NFF serving as an on-off switch. https://doi.org/10.1371/journal.pone.0300534.g006 operating requirements (no physical changes besides the level of neuron activity), and compo- nent capability requirements (excitation and inhibition). Because NFFs function dynamically, information can be stored quickly. The time required to set or reset an NFF is the time a signal takes to pass through two or three neurons, roughly 10–15 ms. The speed makes NFFs plausi- ble models for short-term memory. NFFs consume energy continuously while they are holding information. This is consistent with the brain’s high energy consumption, and it may be one of the selective pressures that resulted in static mechanisms for long-term memory. 5.2. Known memory phenomena generated by NFFs NFF memory banks (Fig 6) can generate the seven characteristics of neuron firing that were listed in the section on unexplained memory phenomena. For all of the characteristics, one of the two outputs of an NFF in a memory bank is identical to the sampled neuron’s response. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 13 / 18 PLOS ONENeural flip-flops I: Short-term memory Since each NFF can store one bit of information, the number of NFFs that are required would depend on the amount of information to be recorded. To record the information conveyed by the stimulus, the visual, auditory, and sensorimotor cortexes would need to have neural struc- tures to send the Set and Reset signals to the corresponding memory banks. 1. Before the stimulus was presented, the sampled neuron discharged at a low, baseline level. This describes one of the two NFF output neurons before the NFF state is inverted to record information. For convenience, label the output M before the NFF is set. 2. When the stimulus was presented, or shortly after, the neuron began to fire at a high fre- quency. This is the output M after the NFF is set by the input S. 3. The high frequency firing continued after the stimulus was removed. This is the stored mem- ory bit M after the brief NFF input S returns to its normal state. 4. The response was still high when the memory was demonstrated to be correct. This is the high value of M holding information in memory as it is recalled. 5. The response returned to the background level shortly after the test. The memory bank (Fig 6) is turned off when the stored information is no longer needed, disabling all of the outputs. 6. In the trials where the subject failed the memory test, the high level firing had stopped or 7) had never begun. In instances where the high level firing had stopped, the memory bank was turned off before the memory was tested, or a distraction caused the NFF to be overwritten with new information, or noise or other errors inverted the NFF. In instances where the high level firing had never begun, the NFF was not set to record the information or the NFF recorded it incor- rectly (for one of many possible reasons, e.g., the subject was not paying attention or was not motivated to remember). For each of these possibilities, the NFF would correctly predict both the failed memory test and the corresponding observed neuron behavior. 5.3. Limited capacity and duration of short-term memory Compared to long-term memory, short-term memory can store only a small amount of infor- mation and only for a short time. The NFF model may provide at least a partial explanation for these limitations. Short-term memory is short not only in duration but also in formation (a few milliseconds). Fast memory formation is an obvious biological advantage, even necessary for many mundane functions. That advantage could well have been the selective pressure that led to short-term memory. Speed means the mechanism must be dynamic in the sense that the only changes are the strengths of the neurons’ signals. Long-term memory is static. Memory models typically involve structural changes, such as neurogenesis, synaptogenesis, or pruning, or changes in synaptic strength or the strength of action potentials. Such changes may provide the large capacity and robust durability of long- term memory, but the changes are too slow for the fast formation of short-term memory. Dynamic operation has the advantage of speed, but there are tradeoffs. First, NFFs’ dynamic operation is expensive in energy use because at least one neuron in each NFF is highly active continuously while information is held in memory. This NFF activity is consistent with empir- ical evidence in short-term memory [14–16]. Second, the dynamic operation makes NFFs’ stored information volatile. The state of an NFF can be inverted by temporary high additive noise in a set or reset input, a temporary loss of continuous energy input, or a temporary loss of a continuous high signal input. Neurons are notoriously unreliable in these aspects, which PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 14 / 18 PLOS ONENeural flip-flops I: Short-term memory would have a detrimental effect on the duration of memory implemented with NFFs. The ces- sation of the memory neuron activity before an error is made in a memory test is consistent with empirical evidence in short-term memory [14–16]. The memory bank in Fig 6 shows that many NFFs can be deactivated when information stored in short-term memory is no longer needed. This energy-saving function shortens the duration of short-term memory. The cessation of the memory neuron activity when informa- tion is no longer held in memory after a memory test is consistent with empirical evidence in short-term memory [14–16]. The capacity of short-term memory could be affected by its limited duration. The number of neurons devoted to short-term memory may be limited by the brain’s ability to use informa- tion before the memory decays or becomes useless. If the prefrontal cortex cannot process large amounts of information conveyed by the senses at a sufficiently fast rate, there would be no reason to store that much information. The capacity and duration of short-term memory could be affected by the difficulty in forming links. Long-term memory evidently relies on links for memory formation and retrieval. A name can be recalled from information associated with it. An extraordinary exam- ple of linkage is the number of songs that can be learned and recalled for decades, with seem- ingly effortless memory formation and recollection. A song’s lyrics and melody are each connected linearly, and the two are connected to each other in parallel. Short-term memory may not involve information that has such connections. Short-term memory often involves pseudorandom information (e.g., a phone number) that has no appar- ent possibility for a link. Arduous mnemonic techniques for linking unfamiliar items to famil- iar objects and places, such as “memory palaces,” can increase the capacity of short-term memory somewhat, but such techniques fall far short of the capacity of long-term memory and have little or no effect on duration. Other, unknown differences between long- and short-term memory may affect capacity or duration, such as how information is encoded in memory and how it is decoded. 5.4. Testable predictions 5.4.1. Unknown memory phenomena generated by NFFs. An NFF’s outputs M and �M together predict eight unknown phenomena that could further test whether short-term mem- ory is produced by NFFs. These predictions can be tested by the same methods that were used in discovering the first seven phenomena since either M or �M is predicted to be the output that produces those phenomena, and the other is predicted to be nearby. 1. Along with the persistently active neuron associated with short-term memory [14, 15], another neuron has complementary output; i.e., when one is high the other is low. This is pre- dicted by M and �M in the NFFs in Fig 4 and demonstrated in the simulation of Fig 5. 2. The two neurons have reciprocal inputs. This is shown in the NFFs in Fig 4. 3. The two neurons are in close proximity. This is because the neurons have reciprocal inputs and are part of a small network. 4. The reciprocal inputs are inhibitory. This is shown in the NFFs in Fig 4. 5. The two neurons have some noise-reducing capability, such as responses that satisfy the inequalities 1 and 2. Some noise-reducing capability is necessary to maintain robust binary outputs in the presence of additive noise. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 15 / 18 PLOS ONENeural flip-flops I: Short-term memory 6. When the neurons change states, the high state changes first. This is because the change in the neuron with the high output causes the change in the neuron with the low output. This can be seen in the NFFs in Fig 4, and it is demonstrated in the simulation of Fig 5. The change order is difficult to see in Fig 5 because of the time scale and the slow rise time of the Set and Reset inputs, but the simulation does have one neuron delay time between the completions of the two outputs’ state changes. 7. The other neuron’s output then changes from low to high within a few milliseconds. This hap- pens quickly because reciprocal input from the first change causes the second within approx- imately one neuron delay time, regardless of how long information is held in memory. 8. After the memory test, the outputs of both neurons are low. The memory bank (Fig 6) is turned off when the stored information is no longer needed, disabling all of the outputs. 5.4.2. Predicted behavior of constructed neural networks. Any of the networks in Fig 4 or the memory bank of Fig 6 could be constructed with neurons and tested for predicted behavior. If the single neuron in Fig 4A produces the outputs of Table 1, then the predicted operations of all of the networks should follow. The NFFs are predicted to have stable outputs that are inverted by a brief input from S or R. (Recall the NFF of 4E is active low.) The outputs should also exhibit the properties predicted for NFFs in the preceding section. Acknowledgments Simulations were done with MS Excel and CircuitLab. Network diagrams were created with CircuitLab and MS Paint. Graphs were created with Converge 10.0, MS Excel, and MS Paint. The author would like to thank Arturo Tozzi, David Garmire, Robert Barfield, Paul Higashi, Anna Yoder Higashi, Sheila Yoder, and especially Ernest Greene and David Burress for their support and many helpful comments and suggestions. Author Contributions Conceptualization: Lane Yoder. Data curation: Lane Yoder. Formal analysis: Lane Yoder. Funding acquisition: Lane Yoder. Investigation: Lane Yoder. Methodology: Lane Yoder. Project administration: Lane Yoder. Resources: Lane Yoder. Software: Lane Yoder. Supervision: Lane Yoder. Validation: Lane Yoder. Visualization: Lane Yoder. Writing – original draft: Lane Yoder. Writing – review & editing: Lane Yoder. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300534 March 15, 2024 16 / 18 PLOS ONENeural flip-flops I: Short-term memory References 1. Yoder L. Relative absorption model of color vision. 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10.1371_journal.ppat.1009441	RESEARCH ARTICLE Viral infection of human neurons triggers strain-specific differences in host neuronal and viral transcriptomes Colleen A. MangoldID L. SzparaID 1* 1,2, Molly M. Rathbun1, Daniel W. RennerID 1, Chad V. KunyID 1, Moriah a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mangold CA, Rathbun MM, Renner DW, Kuny CV, Szpara ML (2021) Viral infection of human neurons triggers strain-specific differences in host neuronal and viral transcriptomes. PLoS Pathog 17(3): e1009441. https://doi.org/10.1371/ journal.ppat.1009441 Editor: Clinton Jones, Oklahoma State Univeristy, UNITED STATES Received: December 14, 2020 Accepted: March 1, 2021 Published: March 22, 2021 Copyright: © 2021 Mangold et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Raw RNAseq data have been deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive as Bioproject number PRJNA593260. All other data are within the manuscript and its Supporting Information files. Funding: This research was supported by a postdoctoral fellowship award from the American Heart Association to C.A.M. (16POST29920001), a Commonwealth Universal Research Enhancement Program (CURE) grant from the Pennsylvania 1 Departments of Biology, Biochemistry and Molecular Biology, Center for Infectious Disease Dynamics, and the Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America, 2 Department of Entomology, College of Agricultural Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America * moriah@psu.edu Abstract Infection with herpes simplex virus 1 (HSV-1) occurs in over half the global population, caus- ing recurrent orofacial and/or genital lesions. Individual strains of HSV-1 demonstrate differ- ences in neurovirulence in vivo, suggesting that viral genetic differences may impact phenotype. Here differentiated SH-SY5Y human neuronal cells were infected with one of three HSV-1 strains known to differ in neurovirulence in vivo. Host and viral RNA were sequenced simultaneously, revealing strain-specific differences in both viral and host tran- scription in infected neurons. Neuronal morphology and immunofluorescence data highlight the pathological changes in neuronal cytoarchitecture induced by HSV-1 infection, which may reflect host transcriptional changes in pathways associated with adherens junctions, integrin signaling, and others. Comparison of viral protein levels in neurons and epithelial cells demonstrated that a number of differences were neuron-specific, suggesting that strain-to-strain variations in host and virus transcription are cell type-dependent. Together, these data demonstrate the importance of studying virus strain- and cell-type-specific fac- tors that may contribute to neurovirulence in vivo, and highlight the specificity of HSV-1– host interactions. Author summary Infection with herpes simplex virus 1 (HSV-1) affects a significant portion of the global population, and recent research has implicated persistent HSV-1 infection with the devel- opment of disease later in life, including neurodegenerative disease and cardiovascular disease. It is clear that individual strains of HSV-1 that exist within the circulating popula- tion exhibit specific genetic differences that affect their phenotypes in experimental set- tings. These differences in turn may contribute to the wide range of clinical outcomes observed between infected individuals. In this study, we sought to understand virus strain- and host-specific transcriptional changes during HSV-1 infection using an in vitro model PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 1 / 32 Department of Health (M.L.S.), an Academic Computing Fellowship from the Graduate School of the Pennsylvania State University (M.M.R.), and startup funds from the Pennsylvania State University (M.L.S.). Additional support was provided by NIH R01 AI132692 (M.L.S.) and NIH 5 T32 GM 102057-5 (M.M.R.). This work was initiated in the lab of Dr. Lynn W. Enquist (Princeton University, L.W.E.), with funding from a Driving Biological Project award of the NIH-NIAID Virus Pathogen Resource (ViPR) Bioinformatics Resource Center (M.L.S.), NIH-NIGMS Center (grant P50 GM071508, L.W.E.), and NIH (grant P40 RR 018604 [M.L.S.]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Strain-specific differences in HSV-1 infection of human neuronal cells of human neurons, a key cell type involved in HSV-1 persistence in humans. We show that different strains of HSV-1 demonstrate differences in viral gene expression and pro- tein levels in infected human neuronal cells. These differences are not as pronounced in epithelial cells, suggesting that dissimilarities in viral gene expression and protein levels between strains may be cell-type specific. Infected neurons also exhibit unique transcrip- tional changes in response to specific HSV-1 strains, in pathways such as integrin signal- ing and remodeling of adherens junctions. Together, these data highlight the specificity of HSV-1 strain- and host-interactions, and the need to study the virus strain- and cell type- specific factors that contribute to HSV-1 pathogenesis. Introduction Herpes simplex virus type 1 (HSV-1) is a human pathogen that affects over half the global pop- ulation and causes recurrent epithelial lesions throughout an individual’s lifetime [1]. The HSV-1 lifecycle begins upon contact with mucosal surfaces, and it is in this niche where it actively replicates and can induce local lesion formation. The virus then enters local sensory nerve endings and traffics in a retrograde direction back to neuronal cell bodies in the periph- eral nervous system (PNS). It is in this location where the virus enters into a latent, nonreplica- tive stage until later reactivation [2]. The ability of HSV-1 to infect and establish latency in neurons allows for lifelong infection, and can provide the virus with access to other sites such as the central nervous system (CNS). Recent research has implicated HSV-1 infection with the development of disease later in life, including Alzheimer’s disease [3–9]. It has been hypothe- sized that reactivating HSV-1 may travel from the site of latency in the trigeminal ganglia to areas of the brain known to be impacted by Alzheimer’s disease, resulting in subclinical inflammation and the formation of neuronal lesions [3, 10]. Similarly, reactivation of HSV-1 in autonomic nerves that innervate coronary arteries may introduce lytic virus to vascular endothelial cells, causing local injury and thrombosis [11] as well as potentially contributing to other cardiovascular disorders [11–15]. Despite these hypothesized connections between HSV-1 infection and disease later in life, the molecular mechanisms underlying neuronal responses to HSV-1 and the variability of these neuropathological effects due to differences between HSV-1 strains remains limited [9]. The study of both host and virus responses to infection in neurons is therefore critical to address these prevalent health concerns, and to elu- cidate host- and virus-specific factors that contribute to neurovirulence in vivo. Previous work has sought to understand the effects of HSV-1 infection on neuronal tran- scription using a number of in vitro and in vivo neuronal models combined with microarray analysis of transcript expression, as reviewed in [16]. The neuronal models employed for these studies include primary rodent cells [17–22], immortalized murine neuroblastoma cell lines [23], and human teratocarcinoma cells [24]. These studies have provided a strong foundation of knowledge about common neuronal responses to HSV-1 infection across an array of cellular models and virus strains [16]. By applying RNA-sequencing methods to HSV-1 studies and using a human in vitro neuronal model, we can identify species-specific changes in host gene expression and simultaneously characterize viral gene expression, in a synchronized time course of infection. Several recent studies have used non-neuronal cell models (e.g., fibroblasts and other epithelial-like cells) with RNA-sequencing to study changes in host and virus tran- scription during HSV-1 infection [25–34]. These latter studies have significantly advanced our understanding of the effects of HSV-1 infection on host transcriptional processes during pro- ductive and quiescent infection of epithelial and fibroblast cells. However, in addition to their PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 2 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells ongoing cell cycle, these cellular models for HSV infection lack the elaborate cellular architec- ture of mature neurons, and the expression of neuron-specific components such as synaptic proteins. The ability of HSV-1 to travel from its site of latency in the peripheral ganglia to the CNS is hypothesized to be a crucial step in the development of disease later in life. Specific strains of HSV-1 exhibit differing abilities to reach the CNS from inoculation at peripheral sites in ani- mal models (termed “neuroinvasion”) [35], and this phenotype is thought to contribute to their observed differences in overall neurovirulence, or ability to cause disease in the nervous system [36–38]. Evidence of disparities in neuroinvasion comes from a number of studies that combine the use of in vivo and in vitro models to assess the ability of different strains of HSV-1 to enter the nervous system and replicate in neurons [35, 39, 40]. Multiple aspects of the virus, host, and environment contribute to neurovirulence in vivo. These factors include the ability of the virus to replicate at epithelial sites of entry, to undergo axonal transport and replication in host neurons, and to evade the host immune system [35, 39, 40]. Three well-characterized strains of HSV-1 include F, KOS, and McKrae. Strains F and KOS were originally isolated from facial lesions whereas strain McKrae was isolated from a patient with herpes simplex keratitis [41–43]. Footpad inoculation of either strain F or KOS yielded lower mortality in comparison to clinical strains of HSV-1, suggesting that these clinical iso- lates were more neuroinvasive than either F or KOS [35]. Following ocular inoculation of either KOS or McKrae, McKrae demonstrated higher titers in the nervous system than KOS in two mouse strains [40]. This is true despite the fact that KOS replicated equally well if not bet- ter than McKrae over time in the murine corneal epithelium [40]. Strain KOS has a known point mutation in the Us9 gene, whose protein product plays key roles in neuronal infection [44–50]. Together these in vivo studies highlight strain-specific differences in the ability of HSV-1 to move within the nervous system as well as to cause pathology in neurons. Differ- ences in the ability of any virus strain to reach the CNS may be due to host- or virus-specific factors, or a combination of the two. Strains F, KOS, and McKrae are all able to establish latency and spontaneously reactivate following high dose ocular inoculation in rabbits [51]. However, only McKrae can spontaneously reactivate via endogenous mechanisms in the rab- bit, as well as via exogenous mechanisms such as adrenergic induction. This suggests that there may also be strain-specific differences in viral genes that play a role in reactivation [51]. Additionally, differences in amino acid sequences within HSV-1 glycoproteins involved in cell binding and entry have been proposed to contribute to the enhanced neuroinvasiveness observed in McKrae [52]. Comparative genomic and transcriptomic approaches have been used to identify factors associated with disease pathogenesis in several viruses, and have helped elucidate virus strain- dependent differences in host responses that may impact disease outcome [53–56]. To date, few studies have simultaneously analyzed both the host and virus transcriptomes in an effort to identify potential virus strain-dependent differences in gene transcription that may differen- tially impact host cell processes. In the study presented here, we infected mature neuronal cells derived by differentiation of human SH-SY5Y neuroblastoma cells [57] with one of three well- known laboratory strains of HSV-1 (F, KOS, and McKrae) [35, 40, 51] (S1 Fig). We then assessed neuronal and viral transcriptional responses to this productive infection at 12 and 24 hours post infection (hpi). Using RNA-sequencing and bioinformatics analyses, we compared the host and virus transcriptomes between the three viral strains and over time. We found that the virus strain used for infection had a significant impact on both viral- and host-gene expres- sion. Additionally, we observed strain-dependent differences in viral protein levels in neurons that were less pronounced in non-neuronal cells (e.g. primate epithelial cells and human fibro- blasts), suggesting that at least a portion of inter-strain differences in viral protein levels may PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 3 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells be neuron-specific. Analysis of host-pathways differentially impacted by HSV-1 strain revealed strain-specific differences in adherens junction structure, integrin signaling, and others. The range of neuronal host responses to HSV-1 infection seen here highlight the need to examine virus-neuron interactions on a per-strain basis, rather than using epithelial cells as a universal model, or using a single virus strain to encompass all responses to a given viral species. Results Transcriptome analysis of HSV-1-infected human neuronal cells allows for the simultaneous assessment of both the virus and host The present study sought to identify both host- and virus-specific factors that may contribute to previously observed strain-specific differences in HSV-1 neurovirulence in vivo [35, 40, 51]. To achieve this, we used a human neuronal cell model combined with RNA-sequencing to characterize strain-dependent differences in host cell responses as well as differences in viral gene transcription between HSV-1 strains and over time (S1 Fig). Immunofluorescence assays were performed to determine the number of purified virions of HSV-1 required to achieve synchronous infection in this neuronal model. Neuronal cultures were infected with a range of infectious doses and probed for HSV-1 protein at 14 hpi (S2 Fig). At lower concentrations, absence of anti-HSV-1 immunoreactivity was evident, particularly in strains F and KOS. How- ever, at the highest infectious dose, all neuronal cell bodies for each strain had detectable viral protein consistent with productive replication (S2 Fig). This finding is in accordance with prior studies demonstrating that high multiplicities of infection are necessary to uniformly infect neurons with alphaherpesviruses [58–61]. For samples used in the RNA-sequencing analysis, differentiated SH-SY5Y human neuronal cells were infected with purified virions of HSV-1 strain KOS, F, or McKrae at 1.6e7 PFU/dish to ensure synchronous infection. Infected neuronal cells were harvested and total RNA extracted at 12 and 24 hpi. These time points have been shown previously to represent the midpoint and peak times of virus production by these neuronal cells [62]. After RNA-sequencing, differential gene expression of both the host and virus transcriptomes were compared relative to mock-infected neurons, between virus strains, and across time. Mock infections consistently showed little to no reads mapping to the HSV-1 transcriptome (average 94% host), and virus infections showed similar counts of reads mapping to each strain and at each time point (average 46% host, 43% HSV-1) (see S1 Table for sequence read statistics, and S2 Table for logCPM values of host transcripts). Neuronal responses to productive HSV-1 infection To determine whether there are any overt dissimilarities in host neuronal responses to produc- tive infection with different strains of HSV-1, a principal components analysis (PCA) was per- formed to visualize patterns in gene expression between groups. With this approach, we can reduce the multitude of variability coming from each gene transcript count into two axes rep- resenting the most impactful composite effects that distinguish each test group. In doing so we detected the largest effect of group separation, or highest proportion of variance, between Prin- cipal Component 1 (PC1, 24.3%) and Principal Component 2 (PC2, 7.4%). Differences in host transcription in response to infection were dependent on virus strain in addition to the dura- tion of infection (Fig 1A). The transcriptional profiles of neurons infected with KOS at 12 and 24 hpi clustered closely to each other (Fig 1A), and were separated from the profiles of neurons infected with F or McKrae at either timepoint. In contrast, host transcriptional changes due to infection with F and McKrae clustered together at both 12 and 24 hpi (Fig 1A). Each of these clusters was manually highlighted in Fig 1A to depict the observed differences. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 4 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 1. The host neuronal transcriptome demonstrates strain- and time-dependent differences in response to infection. (A) A plot of the principal component analysis (PCA) on the neuronal transcriptome at 12 and 24 hpi reveals three different groupings of samples. The host transcriptomes of neurons infected with either F or McKrae at 12 hpi and 24 hpi demonstrate similar patterns of transcript expression, while neurons infected with KOS cluster mostly together. Gray clouds have been added to illustrate these focal areas. (B) Host transcripts that were differentially expressed in neurons infected with either F, KOS, or McKrae versus Mock at 12 hpi (S2 Table) were used as input for three independent core analyses in Ingenuity Pathway Analysis, and the resulting pathways were then compared across strains. Shown here are the top 22 host pathways that were most significantly regulated in these comparisons (activation z-score = a score that predicts whether an upstream regulator in the pathway is activated or inactivated). These data suggest differential impacts of virus strain on multiple neuronal signaling pathways. Pathways discussed in the Results section are indicated with a black circle. All core pathways impacted by F, KOS, and McKrae infection are listed in S3 Table along with gene names. P-values and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 5 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells z-scores associated with the 22 pathways shown here are also included in S3 Table. (C) Transcripts of interest identified from the pathway analysis (B) were plotted individually to assess differential gene expression of specific transcripts. Transcripts were chosen based on their association with different pathways, including remodeling of epithelial adherens junctions (nectin-1, beta catenin [CTNNB1], and delta-like ligand [DLL1]) and endocytosis-related pathways (clathrin light chain A [CLTA]). Box plots show median, quartile ranges, and individual data points. A two-way ANOVA with post-hoc pairwise comparisons and a Bonferroni multiple testing correction was used to assess statistical significance. Comparisons versus mock-infected neurons are indicated in red font. �p < 0.05, ��p < 0.001, ��p < 0.0001. https://doi.org/10.1371/journal.ppat.1009441.g001 In order to identify host pathways that were impacted by each strain, a core pathway analy- sis was performed on all host transcripts identified as differentially expressed in virus-infected versus mock-infected neurons at 12 hpi for HSV-1 F, KOS, and McKrae respectively (see S2 Table for list of all differentially expressed host transcripts). All host pathways impacted by F, KOS, and McKrae infection are listed in S3 Table along with the specific genes that were dif- ferentially expressed in each pathway. Pathways that were identified as activated (positive z- score) or deactivated (negative z-score) following infection (p < 0.05 in at least one compari- son) are shown in Fig 1B and listed in S3 Table, along with respective -log(p-values) and z- scores. Many host pathways and genes identified as differentially expressed in the current anal- ysis corroborate previous findings [16]. Host processes that were most highly regulated (increased or decreased) by infection regardless of strain included the role of breast cancer type 1 susceptibility protein (BRCA1) in DNA damage response, vitamin D receptor/retinoid X receptor (VDR/RXR) activation, and retinoic acid mediated apoptosis signaling (Fig 1B). Other pathways that demonstrated differential regulation by infection included integrin- linked kinase (ILK), integrin-, ephrin B-, and ephrin receptor-signaling as well as remodeling of epithelial adherens junctions. Specifically, KOS-infected neurons demonstrated higher acti- vation of the latter pathways in comparison to neuronal cells infected with either HSV-1 F or McKrae. Several of these pathways, such as integrin-, ephrin B-, and ephrin receptor-signaling as well as adherens junction structure are involved in synaptic and cytoskeletal structure [63]. These data suggest that KOS infection induces differential responses in synaptic and cytoskele- tal morphology that may impact its intracellular transport, cell-to-cell transmission, and potentially its neurovirulence in vivo. Genes identified within pathways of interest were individually plotted (Fig 1C). In particu- lar, nectin-1, beta-catenin (CTNNB1, or catenin beta 1), and delta-like canonical Notch ligand 1 (DLL1) were chosen from the remodeling of epithelial adherens junctions pathway, and cla- thrin light chain A (CLTA) was chosen from endocytosis-related pathways. Specific transcripts were chosen based on raw expression levels (i.e., the gene transcript that demonstrated the highest level of expression was chosen as the ‘main’ transcript and plotted in Fig 1C) (see S2 Table for list of all differentially expressed host transcripts). Some transcripts of interest showed differential expression in all viral-infected groups versus mock-infected neurons only (e.g. beta-catenin and clathrin light chain), while others also displayed significant differences between viral strains (e.g. nectin-1 and delta-like DLL1). Of note, alterations in host gene expression did not occur in one direction only; for example, nectin-1 decreased in expression in all infected neurons as compared to mock, while delta-like DLL1 expression increased in all infected neurons (Fig 1C). HSV-1 strains KOS, F, and McKrae demonstrate differential viral gene expression patterns in neurons The HSV-1 reference genome contains at least 76 canonical open reading frames, of which 49 are 3’ co-terminal [34, 64, 65]. Each set of overlapping, co-terminal genes was grouped together and counted as one transcriptional unit (TU) [30] to avoid ambiguity in mapping of reads to these areas of transcriptional overlap (Fig 2, S4 Table). Comparison of viral gene PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 6 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 2. HSV-1 transcriptome map. The HSV-1 transcriptome includes both single-gene transcripts and 3’ co-terminal, overlapping transcriptional units (TUs), which are assigned to shared TU’s in order to analyze differences in viral gene expression between HSV-1 strains F, KOS, and McKrae. Red boxes indicate transcripts encoded on the forward strand while blue boxes indicate transcripts encoded on the reverse strand. Grey arrows indicate individual canonical genes encoded by the HSV-1 reference genome (JN555585, strain 17). Where there is overlap, genes are grouped together into a single TU (red/blue-filled annotations) while in areas without overlap, genes are assigned as individual transcripts (red/blue-outlined annotations). HSV-1 gene assignments within each TU are noted. Spatial coordinates of the 152 kb HSV-1 genome indicate the classic nomenclature of unique long (green bars) and unique short (yellow bar) regions, which are flanked by internal and terminal inverted repeats (aqua bars). Terminal repeats (fading blue bars) are not included in full in order to devote more space to unique annotations. https://doi.org/10.1371/journal.ppat.1009441.g002 expression between infected groups and over time indicated that each virus strain exhibited distinct expression patterns of its TUs (Fig 3). Additionally, it was evident through our PCA (principal components PC1 = 35.5%, PC2 = 23.5%) that virus strain had a greater impact on viral gene expression than time post infection (Fig 3A). Differences in viral gene expression observed between strains were maintained across time, as evidenced by grouping of the 12 and 24 hpi time points within strain (Fig 3A). With the exception of KOS-infected neurons, the 12 and 24 hpi data formed sub-groups suggesting that there are changes in virus TU expression that are specific to these time points. These observations were manually highlighted in Fig 3A. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 7 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 3. Comparison of viral transcriptomes reveals distinct differences between strains in viral gene expression during neuronal infection. (A) A plot of the principal components analysis (PCA) of viral transcriptional unit (TU) expression in infected SH-SY5Y neurons demonstrates the separation of samples based on virus strain. A colored cloud has been added to highlight the focal areas of specific virus strains (KOS = green, F = red, McKrae = blue). Differences between HSV-1 strains account for most of the viral transcriptome variance. Minor separation based on time (hpi) is evident in strains F and McKrae. In contrast, the transcriptomes of neurons infected with strain KOS for either 12 or 24 hpi cluster together, suggesting a slower progression of change in this strain. (B) Heatmap analysis of virus TU expression shows five clusters of TUs (red numbers on dendrogram at left). Data are normalized by row by z-score with hierarchical clustering (Pearson correlation). The two largest clusters (labelled 1 and 3) depict TUs with either lowest, or highest, expression by HSV-1 KOS strain. Another two clusters show F (cluster 4) or McKrae (cluster 2) as having the highest or lowest expression. In cluster 5, the highest and lowest TU expression depends on both strain and time post-infection. As seen in the PCA plot at left, overall patterns in KOS cluster together regardless of time (vertical dendrogram label X), whereas F and McKrae are interleaved based on time point (vertical dendrogram, Y, Z). TU’s with statistically significant differences in expression between groups are marked with a red asterisk (see Fig 4 for details). https://doi.org/10.1371/journal.ppat.1009441.g003 Given the differences in viral transcription that are evident between strains, we tested for differences in productive viral replication in this neuronal model. Viral titers were quantitated at 0, 6, 12, 24, and 48 hpi and compared between HSV-1 strains F, KOS, and McKrae (S3 Fig). A two-way analysis of variance (ANOVA) revealed a significant main effect of time post infec- tion on viral titer (p<0.0001) with no significant main effect of virus strain. However, a signifi- cant interaction was identified between strain and time that impacts viral titer (p<0.0001). Post hoc analysis with a Bonferroni multiple testing correction (MTC) revealed no significant differences in viral titer evident between strains at 0, 6, and 12 hpi. At 24 hpi, strain F PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 8 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells demonstrated a higher titer than McKrae (p = 0.009), and at 48 hpi strain F demonstrated a lower titer than both McKrae and KOS (p<0.001). Together, these results demonstrate that F, KOS, and McKrae exhibit similar growth kinetics at the early infection time points in our tran- scriptome analysis. To explore these groups further, we looked for broad trends across viral TUs using hierar- chical clustering of TU expression levels (logCPM) normalized by z-score (Fig 3B). Five major clusters resulted from this analysis. Clusters one and three (as labeled in Fig 3B) contained the majority of TUs and were distinguishable by TUs with the highest overall expression in F- or McKrae-infected (cluster 1) or KOS-infected cells (cluster 3), respectively. Clusters two and four were similar in that they showed relatively moderate KOS-derived TU expression, and alternated whether the highest or lowest TU expression came from F- or McKrae-infected cells. Finally, TUs in cluster five showed a more varied pattern of expression. Virus gene fami- lies (S4 Table) were distributed across these clusters, further supporting a non-uniform differ- ential expression pattern to distinguish each strain. Of note, KOS TU expression patterns at 12 and 24 hpi (vertical cluster X) were more similar to each other than to F and McKrae at either 12 (vertical cluster Y) or 24 hpi (vertical cluster Z) (Fig 3B, vertical dendrograms), in keeping with the patterns of host responses to these strains (see Fig 1A). Additionally, KOS TU expres- sion at 24 hpi was more similar to that exhibited by F and McKrae at 12 hpi than to F or McKrae at 24 hpi. These data highlight the viral transcriptional differences that exist between HSV-1 strains in neurons. To investigate the most pronounced differences in viral gene transcription between strains, we fit a generalized linear model to the viral TU expression data, and contrasted groups by strain and time using a quasi-likelihood F-test. Of the 43 TUs, 11 were statistically significant (p<0.05, FDR<0.05) in at least two comparisons, and revealed a log fold change (logFC) rang- ing from 0.4–1.6 logCPM (Fig 4, see also Fig 3B and S4 Table). Comparisons that contrasted KOS vs. F or McKrae showed the highest number of differentially expressed TUs, which became more pronounced at 24 hpi. Contrasts between time points of the same strain showed almost no differential expression of TUs (S4 Table). Notably, these differences in gene expres- sion consisted largely of TUs involved in DNA replication and DNA binding, as well as tran- scriptional regulation, and several glycoproteins (Fig 4, S4 Table). In particular, the TU containing the host and viral transcriptional regulator ICP22 (TU: US1, US12, start in IRS) showed less expression in KOS-infected cells than F- or McKrae-infections (p<0.00004, FDR<0.0009) (Fig 4B). Of the significant TUs, four out of five encoding a glycoprotein were expressed at a lower level in KOS-infected cells than in cells infected with F or McKrae. These include the axon-transport and viral egress associated TU: US8, US8A, US9, in which US8 encodes glycoprotein gE (p<0.001, FDR<0.007) (Fig 4B), and the viral fusion-associated gly- coproteins gJ, gD, and gI that are encoded by TU: US5, US6, US7 (p<0.004, FDR<0.02). A subset of viral TUs showed significantly higher expression in KOS-infected cells than in cells infected with F or McKrae (Fig 4B). Specifically, UL30, the virus-encoded DNA polymer- ase, demonstrated highest expression in KOS-infected neurons, and comparable expression between F- and McKrae-infected neurons (p<0.0002, FDR<0.002). Additionally, UL29, which encodes the integral DNA binding protein ICP8, was more highly expressed in KOS- infected neurons than in those infected with either F or McKrae (p<0.002, FDR<0.02). These differences either became significant at the 24-hour timepoint, or were already present at the 12-hour timepoint (S4 Table). Together, these data demonstrate that the viral transcriptome of each strain is slightly different, with a shared transcriptional program playing out with dif- ferent timing and/or levels of each viral transcript. The differential gene expression of specific TUs may underlie the previously-observed differences in neurovirulence in vivo between these virus strains. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 9 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 10 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 4. Visualization of individual viral transcripts and transcriptional units (TUs) highlights specific genes that exhibit differences in expression between strains. Scatterplots graphically depict the log2fold change (logFC) of HSV-1 strains F vs. KOS (x-axis) relative to strains McKrae vs. KOS (y-axis). These data are plotted separately for (A) 12 hpi or (B) 24 hpi. The upper right quadrant of each graph (shaded in red) depicts TUs that show higher expression in strains F or McKrae relative to KOS, while the bottom right quadrant (shaded in blue) shows those TUs that are lower in expression in F or McKrae compared to KOS. HSV-1 strains McKrae and F have more similar transcriptional profiles to each other than to strain KOS. Using a quasi-likelihood F-test, 11 TUs were found to be statistically significant (p<0.05, FDR<0.05) in two or more comparisons. These are highlighted as open triangles, squares, or circles (see key). (C) Examples of each class of TU are individually plotted to highlight particular differences in transcription between strains. The top row shows TUs from the red quadrant, which have higher expression in strains McKrae and/or F than KOS. The bottom left plots show TUs from the blue quadrant (UL30, UL29), where expression in KOS is higher than in F and/or McKrae strains. Finally the bottom right plots illustrate TUs for which no statistical significance in expression levels between strains was detected. Box plots show median, quartile ranges, and individual data points. https://doi.org/10.1371/journal.ppat.1009441.g004 Morphological impacts of HSV-1 infection on human neurons The host pathway analysis above revealed potential virus strain-specific differences in host-cell synaptic remodeling, neuronal connectivity, and cell cytoarchitecture during productive infec- tion. We thus sought to confirm broad changes in neuronal morphology following HSV-1 infection using microscopy. To observe whether alterations in neuronal morphology reflected the host processes identified in the pathway analysis (Fig 1B), we performed time-lapse microscopy and scanning electron microscopy (SEM) of these human neuronal cells over the course of infection. Regardless of the virus strain used, neurons infected with HSV-1 F, KOS, or McKrae began to round up and clustered together by 12 hpi (Fig 5A; see S1–S4 Movies for full image series, and S4 Fig for inverted images). Additionally, the multiple long neurites emerging from each neuronal cell body began to fasciculate together after infection, and neu- ron-to-neuron contacts were also visibly impacted. These data correlate well with the host transcriptional pathway changes observed above (Fig 1B). To obtain a higher resolution view of changes in cell-to-cell contacts and morphology in this setting, we performed SEM on HSV-1-infected neurons. Under normal conditions, differ- entiated SH-SY5Y neurons formed small clusters of neuronal cell bodies with well-defined cell-to-cell contacts (Fig 5B). Neurites projecting from cell bodies were long and distinct, and appeared to contact neighboring neurons. Additionally, extracellular matrix material was apparent in areas underlying neurites and neuronal cell bodies. In contrast, neurons infected with HSV-1 McKrae for just 6 hours showed a distinct increase in clustering of neuronal cell bodies (Fig 5B). In addition, cell borders changed in infected neurons, as neurons rounded up and points of contact between cell bodies became smaller. Neurites fasciculated together, while areas of extracellular matrix became less evident. Additionally, small filopodia-like projections formed diffusely across neuronal cell bodies (Fig 5B, white arrowheads). This observation agrees with previously published data, which demonstrated that increased formation of F- actin-based dendritic filopodia may aid in early infection of neurons [66]. Together, these morphological data supported the findings from the host pathway analysis, in that both analy- ses indicated alterations in cell-to-cell contacts and neuronal cytoarchitecture as a consequence of HSV-1 infection. Assessment of viral protein levels In order to explore how the observed changes in host and viral transcription are translated into infection-related changes in neuronal morphology, we performed targeted Western blots to explore several viral proteins in greater detail. At 12 hpi, neurons infected with HSV-1 KOS exhibited lower total viral protein levels than neurons infected with either F or McKrae (Fig 6A). In contrast, in Vero cells infected with the same three strains for 6 hours, equivalent levels of total viral protein were observed (Fig 6B). The levels of total viral protein in primary human foreskin fibroblasts (HFFs) at 6 hpi were not as equivalent across HSV-1 strains as observed in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 11 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 12 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 5. Brightfield and scanning electron microscopic (SEM) analysis of differentiated SH-SY5Y neuroblastoma cells reveals morphological and phenotypic changes that occur in neurons following infection with HSV-1. A). Brightfield microscopy illuminates mock-infected neurons as small clusters of individual cell bodies, with extensive networks of neurites projecting outward to surrounding cell clusters. Minimal movement of cell bodies and neurites over the course of 24 hours was evident. All infected neurons regardless of strain demonstrated cell body rounding, clustering of cell bodies, and detachment and fasciculation of neurites. Images shown here are representative of a time series shown in S1–S4 Movies. Scale bars = 100 μm. Images were acquired at 20X magnification once every hour for 24 hours (see S4 Fig for inverted images, which highlight the reduced adhesion and increased fasciculation of neurites). B) SEM analysis of infected SH-SY5Y cells reveals more detail of the changes in neuronal morphology following HSV-1 infection. Mock-infected neurons possess numerous long and thin neurites that appear to contact neighboring cells (white arrows). The neuronal cell bodies are oblong and cell-to-cell contacts are evident (red arrowheads), as is extracellular matrix (red arrows). Scale bar = 20 μm. The right-hand panel shows a 5,770X magnification of the inset marked in the left-hand panel. Scale bar = 2 μm. Differentiated SH-SY5Y neuroblastoma cells were infected with McKrae at an MOI of 10. At 6 hpi, neuronal processes fasciculate together (white arrows), extracellular matrix is less evident, cell bodies round up, and cell boundaries are more apparent (red arrowheads). In addition, numerous short, thin filipodia extend from cell bodies (white arrowheads). Scale bar = 20 μm. Right-hand panel shows a 3,320X magnification of inset marked in the left-hand panel. Scale bar = 10 μm. https://doi.org/10.1371/journal.ppat.1009441.g005 Vero cells (Figs 6C and S5A), and viral protein levels in KOS-infected HFFs were marginally lower than in cells infected with either F or McKrae. At an RNA level, the TU encoding US8, US8A, US9 demonstrated significantly less expression in KOS-infected neurons than neurons infected with either F (1.6 log2-fold lower) or McKrae (1.1 log2-fold lower) (Fig 4B). To exam- ine these differences at the protein level, US8 (gE) and US9 were analyzed by Western blot. KOS-infected neurons, HFF, and Vero cells demonstrated no discernable US9 protein (Fig 6), which is consistent with a known point mutation in the KOS gene encoding US9 [44–46]. Consistent with what was observed at the RNA level (Fig 4B), the level of US9 protein was higher in strain F than strain McKrae. While all infected neurons showed gE protein at 12 hpi regardless of strain, neurons infected with KOS reproducibly exhibited less overall gE immu- noreactivity, as well as reduced expression of the lower band in particular (Figs 6A and S5A). The upper, 75–80 kDa band has been hypothesized to represent mature fully glycosylated gE, while the lower 65 kDa band is thought to represent the cleaved, partially-glycosylated precur- sor of gE [67, 68]. This suggests that in neurons, KOS may produce less precursor gE than the other two strains, and/or these may be polar effects of the US9 mutation in KOS. Once again, strain-specific differences in total gE protein levels (both the upper and lower bands) were not observed in infected Vero cells (Fig 6B). In human fibroblasts (HFFs), marginal differences in the levels of gE were also observed between strains (Figs 6C and S5A); however, in contrast to neurons, the lower band appeared to demonstrate similar levels across all three strains. As pre- dicted by the transcriptional analysis (Fig 4B), the viral glycoprotein gD (US6) also demon- strated lower protein levels in KOS-infected neurons than those infected with strains F or McKrae (Figs 6A and S5A). The virus strain-specific difference observed in neurons was not equally detected in Vero cells, as KOS-infected Vero cells had equivalent levels of gD protein relative to F and McKrae. However, McKrae-infected Vero cells demonstrated higher levels of the lower molecular weight band (Figs 6B and S5A). In human fibroblasts, the strain-specific patterns of gD protein levels mirrored those observed in neurons (Figs 6B and S5). These data emphasize the value of examining HSV-1 strain-specific differences in different cell types. Non-neuronal cells such as Vero and HFF cells are not necessarily predictive of differences in viral protein levels seen in neurons. Several TUs did not show significant differences in gene expression between strains and over time in the neuronal transcriptome analysis. RNA expression of the TU containing the immediate early gene ICP0 (TU: LAT, RL1, RL2) was relatively consistent between strains and over time (Fig 4B), which was also reflected at the protein level in neurons, Vero, and HFF cells (Fig 6). Likewise, the TU encoding VP22 (TU: UL49A, UL49) was not statistically signifi- cantly different at the neuronal transcription level, and no differences in VP22 protein levels were observed between strains in infected neurons (Fig 6A). This was also true in infected Vero and HFF cells (Fig 6B and 6C). Together these data demonstrate our ability to detect strain-specific differences in gene transcription that correlate with differences in protein levels. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 13 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 6. Different viral protein levels between strains demonstrates cell type specificity. (A) In neurons, immunoblot analysis of total HSV-1 protein, gE, gD, and US9 demonstrates different viral protein levels between strains at 12 hpi. HSV-1 strain KOS exhibited overall lower levels of total viral protein as well as less gD (see also S5 Fig). Additionally, marginally lower gE levels and a complete lack of US9 expression were observed in KOS-infected neurons. In contrast, VP22 protein levels were relatively consistent expression between strains. Subtle differences in total protein, ICP0, gE, gD, and US9 levels between strains were particularly evident in the corresponding intensity spectra. In contrast to the neuronal infections depicted in (A), Vero epithelial cell lysates did not reveal lower levels of total viral protein, gE, or gD in KOS-infected cells (B). Due to the known point mutation in US9 [44–46], this protein is not detected in Vero cells either. Consistent with neurons, no differences in VP22 levels between strains were evident in Vero cells and subtle differences such as higher levels of US9 and ICP0 in strain F persisted in both cell types. (C) Western blots in primary human fibroblasts (HFFs) demonstrate similar trends in viral protein levels as observed in neurons, although to a marginally lesser degree. In HFFs the immunoreactivity of a high molecular weight band at roughly 150 kDa was detected in all lanes, including mock-infected cells, and likely indicates non-specific binding of anti-ICP0 antibody to an HFF-specific cellular protein. Blots shown are representative of 3–4 biological replicates, each prepared and immunoblotted independently. All blots depicted in A–C come from a single one of these biological replicates. https://doi.org/10.1371/journal.ppat.1009441.g006 These and the data above highlight the cell-type dependent differences in protein levels exhib- ited by different strains of HSV-1. Effects of HSV-1 F, KOS, and McKrae on adherens junction components Remodeling of epithelial adherens junctions was one of the host neuronal pathways identified as differentially impacted by each strain of HSV-1 (Fig 1B), suggesting that disruption of cell- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 14 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells to-cell contacts may differ between virus strains. We therefore hypothesized that altered expression of gD (US6), gE (US8), and US9 between strains may differentially impact host adherens junction structure, and therefore virus spread. This hypothesis was supported by changes observed in neuronal morphology following infection (Figs 5 and S4, S1–S4 Movies), as well as transcriptional and protein level changes in gE (US8), and gD (US6) expression (Figs 4C and 6A). These viral glycoproteins have been shown to play key roles in interacting with adherens junctions during viral spread from cell-to-cell, and host nectin-1 and beta-cate- nin have both been implicated in these interactions with HSV-1 [69–75]. While all viral- infected groups demonstrated significantly elevated beta-catenin transcript expression versus mock-infected neurons, no differences in transcript expression were observed between specific HSV-1 strains (Fig 1C). To examine beta-catenin further, immunofluorescence assays were performed on neurons at 6 hpi with either HSV-1 F, KOS, or McKrae. In mock-infected neu- rons, beta-catenin protein was diffuse, with areas of concentration at cell soma borders, and beta-catenin immunofluorescence was evident along neuronal processes (Fig 7A). Infection with all HSV-1 strains resulted in relatively similar beta-catenin immunofluorescence in neu- rons (Fig 7A), although quantitation revealed higher beta-catenin levels for strain F infection than strain McKrae (S6A Fig). No differences in total beta-catenin protein levels were evident at 12 hpi when assessed by Western blot (S6B Fig). While beta-catenin showed no differential transcript expression between strains, another adherens junction component and known HSV-1 entry mediator, nectin-1, was differentially expressed between virus strains in infected neurons (Fig 1C). To investigate potential strain- dependent differences in nectin-1 localization and production, neurons were infected with either F, KOS, or McKrae for 6 hours and then immunolabeled for nectin-1. In mock-infected neurons, nectin-1 protein was diffuse, as evidenced by dense immunostaining in the cell body and immunofluorescence throughout neuronal processes (Fig 7B). While the immunofluores- cence of nectin-1 appeared to decrease in all infected neurons and particularly in the neurites (Fig 7B), the overall quantitation of immunofluorescence revealed no changes in total fluores- cence levels as compared with mock-infected neurons (S6A Fig). Western blot analysis of nec- tin-1 levels in infected neurons were inconclusive due to high background in neuronal cells (S6B Fig). Together, these immunofluorescence data (at 6 hpi) and changes in neuronal mor- phology (at 12 and 24 hpi; see Fig 5) reflect the host transcriptional changes in pathways asso- ciated with neuronal adherens junctions, integrin signaling, and others (Fig 1). Discussion It is clear that disease pathogenesis and prognosis are dependent on a combination of host- and virus-specific factors, especially in the case of HSV-1 infection. While many studies have elucidated host-cell-specific factors and viral factors that may contribute to HSV-1 disease and persistence, no studies have sought to simultaneously analyze both the host and viral transcrip- tomes during productive infection in different cell types and using different HSV-1 strains. Using this approach, the present study sought to reveal host- and virus-specific factors that may contribute to previously observed strain-dependent differences in HSV-1 neurovirulence in vivo [35, 40, 51]. To achieve this, we utilized an experimental model consisting of differenti- ated human SH-SY5Y neuroblastoma cells infected with one of three different strains of HSV- 1 (F, KOS, and McKrae). Differences in virus and host transcription were analyzed in infected neurons over time and between virus strains (S1 Fig). This experimental approach allowed for the simultaneous assessment of both host and viral transcriptional changes during productive HSV-1 infection, providing important data on virus-host interactions in the specialized cell type of fully differentiated human neurons. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 15 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Fig 7. Neuronal adherens junction components reveal changes in levels following infection with HSV-1. (A) In mock-infected neurons, beta catenin is localized at cell borders, and along neurites. At 6 hours post infection with HSV-1, regardless of strain, beta-catenin displayed relatively similar beta-catenin immunofluorescence in neurons, although quantitation revealed higher beta-catenin levels for strain F than strain McKrae (S6A Fig). No differences in total beta catenin protein levels were observed by Western blot at 12 hpi (S6B Fig). (B) In mock-infected neurons, nectin-1 exhibits diffuse expression in neuronal cell bodies and along neurites. While the immunofluorescence of nectin-1 appeared to decrease after infection for all strains, particularly in the neurites, the overall quantitation of immunofluorescence revealed no changes in total fluorescence levels as compared with mock-infected neurons (S6A Fig). Western blot analysis of nectin-1 levels in infected neurons were inconclusive due to high background in neuronal cells (S6B Fig). For each overlay panel, a single-channel image of the neuronal marker (beta-catenin or nectin-1) is also included, where the fluorescence has been inverted for easier visualization of protein localization. (HST, Hoechst nuclear stain) Scale bar = 10 μm. https://doi.org/10.1371/journal.ppat.1009441.g007 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 16 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells For both the viral and host transcriptomes, we found that virus strain used for infection had a significant impact on overall gene expression patterns. Both viral and host neuronal gene expression following infection with HSV-1 KOS were distinct from the patterns observed fol- lowing infection with either F or McKrae (Figs 1A and 3A). This separation is corroborated by our observation of lower total viral protein levels in KOS-infected neurons (Fig 6A). Regardless of the HSV-1 strain used for infection, we saw changes in the transcriptional activa- tion of a number of host pathways that regulate neuronal cell adhesion, migration, and cyto- skeletal rearrangement (e.g., integrin, ILK, ephrin B, ephrin receptor signaling, and the regulation of epithelial adherens junctions; Fig 1B). Additionally, we observed the activation of host immune responses, including genes involved in pathways such as death receptor signal- ing and retinoic acid-mediated apoptosis signaling (Fig 1B). Broadly, these findings are in agreement with previous findings indicating the induction of host immune pathways with neuronal HSV-1 infection and latency, and the altered activation of neuronal cell adhesion and migration pathways [17, 19–21, 23, 24, 61]. Our data indicate that the three different strains of HSV-1 used in the current investigation cause host neurons to exhibit varying degrees of transcriptional responses for a number of these pathways. For example, many host pathways involved in signaling associated with cell adhesion, cytoskeletal reorganization, and cell migration demonstrate higher activation scores in KOS-infected neurons (Fig 1B). Differential activation of these pathways may alter cell adhesion and the number of cell contacts in a strain-specific manner, thereby impacting cell- to-cell spread of HSV-1. Analysis of neuronal morphology following infection (Fig 5, S1–S4 Movies), combined with these pathway data, suggest that there are strain-specific differences in the regulation of host adherens junction structure and cell-to-cell contacts. The HSV-1 pro- tein gE localizes at adherens junctions, specifically with the host protein beta-catenin, and is hypothesized to utilize adherens junctions for cell-to-cell spread [69]. While the adherens junction protein beta-catenin demonstrated no overt strain-specific differences in protein lev- els, there appeared to be a subtle reduction in distal neurites after infection (Figs 7 and S6). This observation echoes recent data from non-neuronal cells that demonstrated relocalization of beta-catenin to the nucleus and viral replication compartments after infection with HSV-1 strain 17, leading to altered host gene transcription and facilitating late viral gene expression [31]. We were unable to detect beta-catenin localization in the nucleus due to the inherent autofluorescence of these neurons. However, future investigations could use other methods, such as the expression of fluorescently-tagged beta catenin, to test whether nuclear relocaliza- tion of beta-catenin also occurs in HSV-1-infected neurons and if this process exhibits any strain-specific differences. In addition to its interactions with beta-catenin, HSV-1 protein gD has been shown to bind to nectin-1, mediating viral entry, and potentially impacting adherens junction stability [70, 72, 73, 76]. Nectin-1 serves as an important entry receptor for HSV-1 [74, 77, 78]. Prior studies have demonstrated internalization and decreased expression of nectin-1 at cell junctions in non-neuronal cells following HSV-1 infection and/or after co-culture with gD expressing cells [72, 79]. It has been hypothesized that the viral protein gD disrupts nectin-1 homophilic inter- cellular trans-interactions at cell junctions, and may replace nectin-1 at the cell surface, poten- tially facilitating HSV-1 entry and cell-to-cell spread [72, 76]. Previous studies used different strains of HSV-1 KOS in their model systems [45, 46, 80]. We observed strain-dependent dif- ferences in nectin-1 transcript expression in infected neurons at 12 and 24 hpi (Fig 1C). At 6 hpi, we did not observe overt differences in nectin-1 protein levels between strains, although there appeared to be a relocalization of nectin-1 out of neurites (Figs 7 and S6). Due to the loss of adhesion to the substrate at later time points in infection (Fig 5), it was not possible to examine nectin-1 immunofluorescence at later timepoints in this neuronal model. Given the PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 17 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells previously described interactions between gD and nectin-1, and the differential gene expres- sion and protein levels of gD as observed here (Figs 4 and 6A), there may be additional effects on nectin-1 protein at later timepoints in infected neurons. This hypothesis remains to be tested. The current data in combination with prior works suggest that the lower transcript and protein levels of gE and gD in neurons may contribute to the observed attenuation of HSV-1 KOS in vivo, and also contribute to its slower rate of neuronal cell-to-cell spread both in vitro and in vivo [35, 40, 62]. These data may inform current studies using this popular laboratory model strain of HSV-1 [40, 44–46]. Viral strain divergence in the activation of host-neuron pathways, such as adherens junc- tion remodeling and signaling, may be due to differences in the expression of viral genes between strains. Viral glycoproteins in particular (Figs 3, 4 and 6) may differentially impact the ability of the virus to enter and spread, and thus the magnitude or speed of host-cell responses. Observed differences in viral protein levels between strains were evident in both neurons and primary human fibroblasts to varying degrees, but were lacking in Vero cells (Figs 6 and S5). It is well-known that Vero cells are defective in the production of interferon [81, 82]. This suggests that different strains of HSV-1 may have varying impacts on host cell immune responses, which could differ between cell types (e.g., neurons versus epithelial cells or fibroblasts). This hypothesis is supported by our data indicating that death receptor and ret- inoic acid-mediated apoptosis signaling demonstrates variable activation in neurons in a virus strain-specific manner, with KOS-infected neurons exhibiting the highest activation (Fig 1B). At present, it is unclear whether the differences in viral gene expression observed in this study (Figs 3 and 4) are more driven by viral genetics or by the strain-specific host response. How- ever, it is likely that a complex interaction between virus strain-specific gene expression and host cell type-specific transcriptional responses contribute to the overall differences in patho- genesis and neurovirulence that have been previously observed for these strains in vivo [35, 40, 51]. Future studies should aim to investigate kinetic differences in viral gene expression and protein production between strains, in both epithelial and neuronal cell models. HSV-1 strain comparisons using in vivo models have revealed that KOS is less neuroviru- lent when compared to other strains of HSV-1 [35, 40]. In prior analyses in human SH-SY5Y neurons, KOS exhibited slower replication than McKrae in a low-multiplicity of infection (MOI) assay of cell-to-cell spread (i.e. a multi-step growth curve) [62]. Our data using synchro- nous infection at a high dose (i.e. a single-step growth curve) demonstrate similar replication in differentiated SH-SY5Y neurons between strains F, KOS, and McKrae at early timepoints (0 to 12 hpi; S3 Fig). At 24 hpi and 48 hpi, strain F demonstrated statistically significant differ- ences in titer. The similarity in replication at early timepoints suggests that inherent differ- ences in viral replication are not driving the large transcriptomic differences observed between strains at 12 hpi (Fig 1). Rather it suggests that the early differences in viral and host transcrip- tomes which are evident at 12 hpi, lead to the later divergence in maximal titer achieved by each viral strain. The transcriptional differences observed here, in combination with additional inter-cellular signaling and immune responses that would normally occur in parallel during in vivo infection, may contribute to the previously observed divergence in overall neurovirulence of these strains in animal models [35, 40]. These and other circulating HSV-1 strains have numerous genetic differences [83–85], which have been proposed to impact observed phenotypic differences in HSV pathogenesis [86–88] and spread [89]. Many TUs containing HSV-1 glycoprotein transcripts (e.g., US5, US6, US7, US8, and UL44) demonstrated higher expression in F- and McKrae-infected neu- rons versus KOS-infected neurons (Figs 3B and 4). Conversely, the TU containing glycopro- tein B (TU: UL27, UL28) exhibited higher expression in KOS-infected neurons at 24 hpi. Amino acid differences in HSV-1 glycoproteins can impact viral entry by modifying a strain’s PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 18 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells ability to bind to cell entry receptors [52], an effect which can then be amplified over multiple rounds of cell to cell spread. Given the variable expression profiles of entry receptors between cell types [90], the observed differences in viral glycoprotein sequence, transcript expression, and protein levels between HSV-1 strains have the potential to impact virus spread in a cell- type-dependent manner. Alphaherpesviruses also exhibit genetic variability in short sequence repeats, which may impact transcriptional differences between strains [85, 91, 92], including of viral glycoproteins [91]. Future studies comparing these strains and their tandem repeat regions may provide key insights on strain-specific transcriptional regulation, and how the strain-specific expression level of key viral factors (e.g., glycoproteins) impacts viral spread and virulence. The data presented here highlight the importance of using diverse strains of a given virus to probe neuron-virus interactions. Our study demonstrated that strain-specific differences in viral protein levels may become more pronounced in terminally differentiated neurons versus rapidly dividing cells such as Vero cells, which also lack an intact interferon response [81, 82] (Fig 6). Neuron-specific differential expression of gE and gD, compounded with cell-type inde- pendent differences in US9 expression between HSV-1 strains, likely influence host adherens junction structure and signaling [44, 45, 47–50]. This may result in alterations in neuron-to- neuron spread capabilities between strains, which would ultimately contribute to differences in virus spread within the nervous system in vivo. Previous analysis of HSV-1 transcript expression in MRC5 epithelial cells and primary trigeminal ganglion neurons demonstrated differences in the accumulation of viral immediate early transcripts between these cell types [25]. Differences in viral transcript expression between cell types may be further exacerbated by the selection of virus strain(s) used for infection. These results highlight the need for future comparative studies that investigate inter-strain differences in gene expression and protein levels in different cell types, in order to understand how these differences impact host-cell responses. Since SH-SY5Y neurons are derived from a neuroblastoma cell line, future studies may seek to include the comparison of strain-specific differences in viral transcription and host responses in different human neuronal cell types, such as iPSCs and LUHMES cells [60, 93, 94]. Characteristics of HSV-1 infection observed in epithelial cells (e.g., readthrough of host genes [26], or relocalization of beta-catenin and the requirement for late viral gene expression [31]) should be further investigated using neuronal cell models. A larger panel of HSV-1 strains could also be included in future studies, including diverse clinical strains. Our Western blot data indicate potential differences in the glycosylation of the viral glycoproteins gD and gE between HSV-1 strains, and the extent of this phenotype differs between cell types (Fig 6). Glycosylation of envelope proteins has been indicated as a determinant of virulence in flavivi- ruses [95, 96]. As such, future studies may also aim to characterize potential differences in HSV-1 virion protein glycosylation between strains and examine how this may differ by cell type as well. It is also important to note that many new and non-canonical ORFs have recently been detected in models of productive HSV-1 infection of non-neuronal cells [29, 34]. It would be beneficial for future studies utilizing long-read sequencing [30, 97] and/or mass spec- trometry-based approaches [34] to explore whether or not these novel transcripts and/or translated products are also detected during neuronal infection, and whether differential expression is evident between HSV-1 strains. Methods Cell culture, infections, and virus strain characterization Maintenance and differentiation of the human SH-SY5Y neuroblastoma cell line (ATCC, CLR-2266) was performed as previously described [57, 62], in an approach that is based on PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 19 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells prior protocols [98, 99]. Briefly, undifferentiated SH-SY5Y cells cultured in 35 mm2 dishes were gradually serum-deprived over the course of 10 days by treating with Eagle’s minimal essential medium (EMEM, Sigma) supplemented with decreasing concentrations of heat-inac- tivated fetal bovine serum albumin (FBS) (Hyclone), 1X penicillin-streptomycin (Life Tech- nologies-Gibco), 2 mM L-glutamine (Thermo Fischer Scientific-Hyclone) and 10 μM retinoic acid (Sigma). On Day 10, cells were passaged onto extracellular matrix-coated plates or cover- slips (MaxGel, Sigma) and cultured in neuronal terminal differentiation media, containing Neurobasal (Life Technologies-Gibco), 1X B-27 (Thermo Fischer Scientific), 2 mM Glutamax (Life Technologies-Gibco), 1X penicillin/streptomycin, 1 M KCl, 2 mM dibutyryl cyclic AMP (Sigma), 50 ng/mL brain-derived neurotrophic factor (BDNF, Sigma), and 10 μM retinoic acid [57]. Cells were considered to be terminally differentiated on day 18 post initial plating. Vero African green monkey kidney cells (ATCC, CCL-81) and primary human foreskin fibroblasts (HFFs) were maintained in Dulbecco’s minimal essential medium (DMEM) supplemented with 10% FBS, 1X penicillin/streptomycin, and 2 mM L-glutamine. HFFs were derived from newborn male foreskin, and were kindly provided by Dr. Todd Ridky, University of Pennsylvania. For all transcriptomic studies, terminally differentiated neurons were infected with either HSV-1 strain F [41], KOS63D (provided by Richard Dix, referred to as KOS throughout, [35, 42]), or McKrae (obtained from James Hill, [43]). Strains F and KOS were originally isolated from the lips of patients with herpes labialis infection [35, 41, 42], while McKrae was isolated from the eye of a patient with HSV-1-associated keratitis [43]. All strains were passaged numerous times following isolation, and the exact genome sequences of each viral stock has been identified and published previously (F [84], KOS [45], and McKrae [100]). To ensure syn- chronous infection in light of the wide spacing of neurons relative to more densely plated fibroblast or epithelial cell lines, neurons were infected with 1.6e7 PFU per 35-mm2 dish (mock, n = 2; infected, n = 3-4/strain; each replicate was generated on the same day using the same passage neuronal culture). Purified virion preparations of each strain were used to infect neurons for the transcriptomic analysis. The virion purification protocol was based on prior protocols [101–103] and utilized a Nycodenz gradient instead of sucrose to improve virion integrity. To determine the optimal infectious dose for synchronous infection of neuron cultures using purified virions, anti-HSV-1 immunofluorescence assays were performed. Fully differ- entiated neurons were infected with 1.2e6, 6e6, or 1.2e7 PFU of purified virions per 35-mm2 dish for 14 hours (n = 2 dishes per virus strain per dose). At 14 hpi, cells were rinsed with PBS, fixed with 3.2% paraformaldehyde/PBS for 10 minutes at room temperature, rinsed with PBS, and then permeabilized with 0.5% Triton X-100/3% BSA/PBS for 5 minutes. Once permeabi- lized, cells were blocked 0.1% Triton X-100/3% BSA/PBS for 30 minutes at room temperature, and then incubated with anti-HSV-1 primary antibody (DAKO, 1:100) in blocking buffer for 1 hour. Cells were washed twice with 3% BSA/PBS and then incubated with fluorescently-tagged anti-rabbit secondary antibody and Hoechst nuclear stain (anti-rabbit Alexa Fluor 633, 1:1000; Hoechst 1:1000). Following incubation with secondary antibody, cells were washed once with 3% BSA/PBS, twice with 0.1% Triton X-100/PBS, and then mounted with Aqua-Polymount (Polysciences) and stored at 4˚C. Images were acquired using a Nikon Eclipse inverted epi- fluorescence microscope. In order to test whether any differences in replication exist between F, KOS, and McKrae in neurons, a single-step growth curve was performed. Fully differentiated SH-SY5Y neurons grown in 35-mm2 dishes were infected with either F, KOS, or McKrae at a concentration of 1.6e7 PFU/dish (n = 4 dishes per strain per timepoint) and incubated for 1 hour at 37˚C with rocking every 15 minutes. Following incubation, the inoculum was removed and replaced PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 20 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells with fresh terminal differentiation media. Cells and media were then scraped and collected at 0, 6, 12, 24, and 48 hpi. Replicates from each timepoint were then titered on Vero cells. To determine strain- and time-dependent differences in replication, a 2-way ANOVA with inter- action effects and post-hoc testing with Bonferroni multiple testing correction was performed. All other experiments, including generation of SH-SY5Y, Vero, and HFF cell protein lysates, as well as fixed cells used for imaging, used an MOI of 10 for infection. Dilutions of viral inocula were prepared using neuronal terminal differentiation media for SH-SY5Y cells or DMEM supplemented with 2% FBS, 1X penicillin/streptomycin, and 2 mM L-glutamine for Vero and HFF cells. Mock-infected samples were treated with respective virus-free media alone. During infection, cells were incubated at 37˚C for 1 hour with gentle rocking every 15 minutes. Media was then removed and replaced with fresh media. RNA isolation and sequencing RNA was isolated from differentiated SH-SY5Y neuronal cells at 12 and 24 hpi using the man- ufacturer’s protocol for Trizol (Thermo Fisher Scientific), with minor adaptations for low input samples. To harvest neuronal samples, media was gently removed and Trizol added directly to each sample dish. Pipette-facilitated fluid motion was sufficient to dissolve the neu- ronal network and enable sample transfer to an Eppendorf tube. Chloroform (0.2 volumes) was added to the Trizol-sample mixture and vigorously mixed, before phase separation via centrifugation on a phase-lock gel tube. Linear polyacrylamide was added to the aqueous layer, along with isopropanol (0.5 volumes), for an overnight precipitation. After centrifugation, the pellet was washed with 70–75% ethanol twice, then dried, and resuspended in 10 mM Tris-Cl, pH 8.5. Total RNA quality was assessed using a 2100 Bioanalyzer (Agilent Technologies), and RNA concentration was quantified by QuBit (average yield = ~1.1 μg per sample; Thermo Fisher Scientific). Library preparation was performed according to manufacturer’s instructions using 500 ng total RNA input (TruSeq RNA kit, Illumina, Protocol Part# 15026495 Rev. D). Quality and quantity of cDNA libraries was then assessed by QuBit and Bioanalyzer. Samples were normalized to a molarity of 10 nM and pooled for sequencing. Three independent sequencing runs were performed using 100 base-pair sequencing on Illumina HiSeq platforms at Prince- ton University (paired-end), or Penn State University (single-end). Raw RNAseq data have been deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) as BioProject number PRJNA593260. Host and virus transcriptomic analysis RNA-sequencing data was assessed for quality using FastX-Toolkit and FASTQC to measure base quality scores and other relevant metrics. Reads from run one (paired-end sequencing) were trimmed of any adapters via FastX-Toolkit and low-quality bases (lower than Q30) were trimmed using a sliding window of 15 in Trimmomatic. Length based filtering was then applied, with any reads under thirty base pairs in length being discarded. Reads from runs two and three (single-end sequencing) were of higher quality, and did not require trimming. To analyze host transcriptional changes in response to infection, reads were mapped to the host genome (Homo sapiens (release 37) reference sequence (GRCh37/hg19) using the HiSat aligner with default settings. Transcripts were assembled with StringTie using the GRCh37 ref- erence to guide the assembly process and read counts were generated using an accompanying Python script (prepDE.py). Read counts were normalized in EdgeR [104] by sequencing depth and batch effect removal, and low expression transcripts (< -1 median log counts per million (CPM)) were removed. Normalized counts were then fitted to a negative binomial generalized PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 21 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells log-linear model (GLM), using empirical Bayes tagwise dispersions to estimate the dispersion parameter for each transcript. Differentially expressed genes were identified using GLM likeli- hood ratio tests. Statistical significance was determined using a Benjimini Hochberg MTC (α = 0.05 threshold, false discovery rate (FDR) < 0.05). Assessment of differential HSV-1 gene expression is challenging due to the limitations of short sequencing reads in resolving splicing and 3’ co-terminal transcripts [30]. We used bow- tie2 [105] to align reads to viral transcripts to avoid spurious detection of splicing due to areas of high sequence similarity in the HSV-1 genome, and the nature of Illumina short read sequencing. Virus transcripts were counted once per transcriptional unit (TU), such that non- overlapping gene transcripts were counted uniquely, and overlapping transcripts were counted as one unit (Fig 2, S4 Table). Virus TU data were normalized in EdgeR [104] for sequencing library size, sequencing run batch effects, and low expression transcripts. Normalized data was analyzed for differential gene expression using a GLM and quasi-likelihood F-tests. Statistical significance was determined using a Benjimini Hochberg MTC at an α = 0.05 threshold, and FDR < 0.05. Plots for both human and viral transcriptome data were generated using the ggplot2, ggfortify, ggbiplot, ggrepel, pheatmap, and EnhancedVolcano packages [106–111]. To determine pathways of host genes that are differentially regulated following HSV-1 infection, host-specific genes identified as differentially expressed following infection with either F, KOS, or McKrae at 12 hpi (p<0.05, Benjimini Hochberg MTC) were analyzed using Ingenuity Pathway Analysis (IPA) software (Qiagen). We then performed a Comparison Anal- ysis within IPA to determine host pathways that may be differentially impacted by each HSV-1 strain. Protein isolation and Western blotting Neuronal protein lysates were isolated at 12 hpi as previously described (n = 4 independent neuronal cultures) [62]. Briefly, neurons were rinsed twice with 1X PBS, lysed in Radio Immu- noprecipitation Assay (RIPA) buffer (Sigma Aldrich) supplemented with Pierce protease and phosphatase inhibitor (PPi) mini tablets (Thermo Scientific), scraped, and pooled into 1.5 mL centrifuge tubes. To ensure minimal loss of protein during media removal and washes, all media and PBS were removed, combined, and centrifuged at 1,000 x g for 2 minutes to pellet any cells that had lifted off of dishes during processing. Pellets were then washed twice with PBS, resuspended in RIPA + PPi, and combined with scraped cells. Vero protein lysates at 6 hpi were prepared by rinsing twice with 1X PBS, lysing in RIPA buffer supplemented with pro- tease and phosphatase inhibitor tablets, and then scraping into 1.5 mL centrifuge tubes. All lysates were sonicated (80% amplitude, 10 seconds on/off, Q500 ultrasonic processor), rocked for 15 minutes at 4˚C, and then centrifuged at 4˚C at 12,500 x g for 10 minutes. Soluble protein present in the supernatant was removed and quantified using a Bicinchoninic acid (BCA) assay (Thermo Scientific) and a Nanodrop 2000c spectrophotometer. Host and viral protein levels were assessed by Western blot as previously described [62]. Equal concentrations of protein were loaded and separated by sodium dodecyl sulfate poly- acrylamide gel electrophoresis (SDS-PAGE) (Miniprotean; Bio-Rad), and then transferred to nitrocellulose membranes (Amersham GE Healthcare) using a Trans Blot SD semi-dry electro- phoresis transfer cell (Bio-Rad). Membranes were blocked with 5% non-fat dry milk in wash buffer (1 M Tris (pH 7.4), 154 mM NaCl, 0.2% Tween 20) overnight at 4˚C with gentle rock- ing. Blocked membranes were then incubated with primary antibody (see S5 Table) diluted in blocking buffer for 2 hours at room temperature. Membranes were then washed 3 times, incu- bated with species-specific secondary antibody diluted in blocking buffer for 1 hour at room temperature, washed again, and developed using either enhanced chemiluminescence PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 22 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells substrate or SuperSignal West Dura substrate (Thermo Scientific). Generation of pixel inten- sity profiles of representative lanes and band volume quantitation were performed using Ima- geQuant 8.1 (GE Healthcare), with image rectangle background subtraction applied to all images. Immunofluorescence of fixed cells Immunofluorescence assays were performed as previously described (n = 3 independent neu- ronal cultures, 2 coverslips per condition) [62, 112]. Partially differentiated SH-SY5Y cells were plated onto MaxGel (Sigma) coated coverslips on day 10 of differentiation and main- tained until terminal differentiation on day 18 [57]. Following terminal differentiation, neu- rons were counted and then infected with KOS, F, or McKrae at an MOI of 10 for 6 hours. We were unable to perform immunofluorescence assays on fully differentiated SH-SY5Y cells at 12 hpi, since most infected neurons lose their contact adhesion at this stage of infection. At 6 hpi, coverslips were rinsed twice with 1X PBS, fixed with 4% paraformaldehyde/PBS, washed with PBS, and then permeabilized with 0.1% Triton X-100/PBS for 10 minutes. Cells were then blocked with 10% goat serum diluted in PBS for 1 hour at room temperature and incubated with primary antibody (see S5 Table) diluted in the same blocking buffer overnight at 4˚C in a humid chamber. Coverslips were washed in PBS, and incubated in species-specific fluores- cence-labeled F(ab’)2 fragment secondary antibody (Jackson ImmunoResearch) and Hoechst nuclear stain (HST; 1:10,000) for 1.5 hours at room temperature in a light-protected humid chamber. Cells were then washed in PBS and mounted onto glass slides using ProLong Gold antifade mounting medium (Thermo Fischer Scientific) and allowed to dry overnight. Images were acquired using a Nikon Ti Eclipse inverted epifluorescence microscope with equivalent laser settings applied to all coverslips within a comparison. Image z-stacks were imported into Fiji (ImageJ) where each sample was visualized as a maximum projection and background sub- traction was applied (rolling ball radius = 100 pixels). Image quantitation was performed in Fiji (ImageJ), where each image was visualized as a sum projection, background subtracted, and the mean gray value was assessed. For each group, 5 to 14 fields of view were analyzed. Processed images were then imported into Adobe PhotoShop where brightness and contrast was adjusted equally across images within a comparison. Live-cell imaging Acquisition of time-lapse videos was performed as previously described [62]. Briefly, fully dif- ferentiated SH-SY5Y neuroblastoma cells were infected with viral inocula diluted in neuronal terminal differentiation media for 1 hour with gentle rocking every 15 minutes. Mock-infected neurons were treated with neuronal terminal differentiation media only. Following the 1 hour incubation, inocula was removed and 1 mL fresh warmed neuronal terminal differentiation media was added to each 35 mm2 dish. Brightfield images were acquired of mock-, F-, KOS-, and McKrae-infected neurons at 20X magnification every hour for 24 hours using a Nikon Ti Eclipse microscope equipped with a stage-top incubator (Tokai Hit). For each sample, three image series per well were acquired (Mock, n = 2 wells; F, KOS, McKrae, n = 3 wells). Time- lapse data were imported into Fiji, brightness and contrast of brightfield images were adjusted, and movies were exported at 1 frame/second. Scanning electron microscopy (SEM) Neurons were differentiated and passaged onto MaxGel-coated coverslips as described above (n = 2 independent neuronal cultures, 2 coverslips each). Following terminal differentiation, neurons were either mock-infected with media containing no virus or infected with HSV-1 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 23 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells McKrae at an MOI of 10 for 6 hours. Due to insufficient cell adhesion to withstand the SEM sample preparation and imaging, it was not possible to collect SEM data at 12 hpi. Coverslips were rinsed twice with 1XPBS and then fixed with 2.5% glutaraldehyde in 0.1M sodium phos- phate buffer at room temperature for 30 minutes. Neurons were washed 3 times with 0.1M sodium phosphate buffer, and then dehydrated in a series of ethanol washes (25%, 50%, 70%, 85%, and 95% one time each for 5 minutes, and then 100% ethanol 3 times). Dehydrated cells were prepared for EM by critical point drying (Leica EM CPD300, Leica, Wetzlar, Germany) and sputter-coating with gold (Denton). Neuronal cells were imaged using a Zeiss Sigma VP-FESEM (Zeiss, Thornwood, NY). Supporting information S1 Fig. Experimental design to interrogate host and viral transcription in HSV-1-infected neurons. The goal of the present study was to detect both the virus- and host-specific differ- ences in response to infection, which may contribute to previously observed differences between HSV-1 strains in terms of their neurovirulence in vivo [35, 40, 51]. To achieve this, terminally differentiated human SH-SY5Y neuroblastoma cells were mock-infected or infected with either F, KOS, or McKrae. Relative neurovirulence of HSV-1 strains in vivo is indicated by the red bar, increasing from strains KOS and F to the highly virulent strain McKrae. While Dix et al found KOS and F to be similar in neurovirulence in vivo, strain KOS has since been sequenced and revealed to lack US9, which plays a role in neuronal transport [35, 44–50]. Total RNA was isolated at 12 hpi (infection midpoint) and 24 hpi (peak virus production). RNA was sequenced using Illumina technology to detect differences in viral and host gene transcription between viral strains and over time. Following identification of potential proteins and pathways of interest, targeted Western blots and immunofluorescence assays were per- formed to confirm transcriptomic findings. (TIF) S2 Fig. Determination of infectious dose required for synchronous neuronal HSV-1 infec- tion. Immunofluorescence assays were used to determine the optimal infectious dose of puri- fied virions of HSV-1 F, KOS, and McKrae that is required to produce synchronous infection in differentiated SH-SY5Y neurons. Neurons were infected at an infectious dose of 1.2e6, 6e6, or 1.2e7 PFU/dish and incubated for 14 hours. Neurons were then fixed, permeabilized, and probed with anti-HSV-1 antibody (S5 Table), and the presence of HSV-1 in neurons was assessed by immunofluorescence. At the lower doses used here, neuronal cell bodies lacking HSV-1 immunoreactivity were evident in F- and KOS-infected dishes. At an infectious dose of 1.2e7, all neuronal cell bodies, regardless of HSV-1 strain used, were infected. HSV-1, red; nuclei, blue; n = 2 per strain. Scale bar represents 100 μm. (TIF) S3 Fig. Single-step growth curve analysis demonstrates few differences in viral replication between strains in differentiated SH-SY5Y neurons. To determine whether HSV-1 strains F, KOS, and McKrae differ in their ability to replicate in neurons, a single-step growth curve was performed. Differentiated SH-SY5Y neurons were infected with 1.6e7 PFU/dish of F, KOS, or McKrae. Neurons were harvested at 0, 6, 12, 24, and 48 hpi and titered on Vero cells. While there was no significant main effect of virus strain on titer, time post-infection did impact viral titer (p<0.0001) and a significant interaction exists between HSV-1 strain and time post-infec- tion (p<0.0001). Data are shown as average titer ± standard deviation (n = 4 dishes per strain per timepoint). A two-way ANOVA with interaction effects and post-hoc testing was per- formed followed by a Bonferroni multiple testing correction. �p = 0.009 F vs McKrae, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 24 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells ��p<0.001 F vs McKrae, ##p<0.001 F vs KOS. (TIF) S4 Fig. Inverted brightfield images of mock- and HSV-1-infected neurons. Different HSV-1 strains are depicted in columns, and 12 vs 24 hpi in rows. Images from Fig 5A were inverted and brightness and contrast were equally adjusted to better visualize changes in neurite adhe- sion and fasciculation following infection. The neurons appear to have decreased attachment or adhesion to the substrate at both 12 and 24 hpi, with increased bundling or fasciculation of neurites. Scale bars = 100 μm. (TIF) S5 Fig. Quantitation of Western blot data demonstrating cell type- and strain-specificity of viral protein levels. Replicate Western blots probed for gD, gE, and total viral protein (HSV-1) were quantitated. These include the representative Western blot images shown in Fig 6, as well as additional replicate blots (n = 2–4 experiments per strain per cell type, each pre- pared and immunoblotted independently). In neurons and primary human fibroblasts (HFFs), the levels of gD, gE, and total HSV-1 protein were lower in KOS-infected cells than for either of the other two strains. This effect was most noticeable for gD. In Vero cells, the levels of these viral proteins were roughly equivalent across all three HSV-1 strains. Data were nor- malized to the average band volume for F-infected cells and are shown as the average band volume ± standard error. (TIF) S6 Fig. Immunofluorescence quantitation and Western blots of neuronal adherens junc- tion components following infection with HSV-1. A). The total fluorescence of beta-catenin and nectin-1 immunostaining of mock-infected or HSV-1-infected SH-SY5Y neurons was quantitated in Fiji. Beta-catenin immunofluorescence was significantly different after infection by strain F vs McKrae by one-way ANOVA (�p < 0.05). These data are plotted as the mean gray value ± standard error (n = 5–14 fields of view per group). These data include the repre- sentative immunofluorescence images shown in Fig 7, as well as additional fields of view. B) At 12 hpi, mock-infected SH-SY5Y neurons and neurons infected with either HSV-1 F, KOS, or McKrae were probed for levels of beta-catenin and nectin-1 protein. Consistent with immu- nofluorescence assays, no differences in beta-catenin levels were observable between groups. Nectin-1 levels appeared marginally higher in neurons infected with KOS versus mock-, F-, or McKrae-infected neurons across multiple replicates, but in all cases the level of background was too high for quantitation. Neuronal lysates shown here are from the same biological repli- cate as shown in the panel of Western blots in Fig 6A, and GAPDH (depicted in Fig 6A) was used as the loading control. The GAPDH loading control is not repeated here, to avoid image duplication. (TIF) S1 Table. Number of reads mapping to neuronal and HSV-1 transcriptomes for RNA- sequencing data. (XLSX) S2 Table. This file lists the logCPM values for all host transcripts (spreadsheet tab #1), and those host transcripts identified as differentially expressed (versus mock-infected neurons) at 12 hpi with HSV-1 strain F, KOS, or McKrae (spreadsheet tabs #2–4). The data in tabs #2–4 served as the input for pathway analysis (Ingenuity Pathway Analysis; see Methods for details). (ZIP) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 25 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells S3 Table. This file lists the canonical host pathways identified as differentially impacted by HSV-1 strain (spreadsheet tab #1), along with three tables of strain-specific data on the host neuronal pathways significantly regulated by HSV-1 infection at 12 hpi with strain F, KOS, or McKrae (spreadsheet tabs #2–4). Table abbreviations: FDR, false discovery rate- adjusted P value; LR, likelihood ratio; logCPM, log counts per million; logFC, log fold-change. (XLSX) S4 Table. Virus transcriptional units (TUs) and gene functions (spreadsheet tab #1) and the log2 fold-change (LogFC) of statistically significant viral TU’s; (p-value<0.05, FDR<0.05) (spreadsheet tab #2). (XLSX) S5 Table. List of antibodies used in this manuscript along with dilutions, catalog numbers, and sources. (XLSX) S1 Movie. Mock-infected neurons. One image was acquired every hour for 24 hours. (AVI) S2 Movie. F-infected neurons. One image was acquired every hour for 24 hours. (AVI) S3 Movie. KOS-infected neurons. One image was acquired every hour for 24 hours. (AVI) S4 Movie. McKrae-infected neurons. One image was acquired every hour for 24 hours. (AVI) Acknowledgments We appreciate the mentorship of Dr. Lynn Enquist for early phases of this research, and cur- rent and former members of the Szpara lab for their contributions to its later development. We also thank Yolanda Tafuri for her exceptional work in the initiation of these studies. We thank the faculty and staff of the Huck Microscopy Facility at Penn State University for their support and guidance during imaging experiments, as well as Dr. Harvey Friedman for gE antibody and Dr. Bill Freeman for software access. Human fibroblasts were kindly provided by Dr. Todd Ridky, University of Pennsylvania. Author Contributions Conceptualization: Colleen A. Mangold, Molly M. Rathbun, Daniel W. Renner, Chad V. Kuny, Moriah L. Szpara. Data curation: Molly M. Rathbun, Daniel W. Renner. Formal analysis: Molly M. Rathbun, Daniel W. Renner. Funding acquisition: Colleen A. Mangold, Molly M. Rathbun, Moriah L. Szpara. Investigation: Colleen A. Mangold, Molly M. Rathbun, Daniel W. Renner, Chad V. Kuny. Supervision: Moriah L. Szpara. Visualization: Colleen A. Mangold, Molly M. Rathbun, Daniel W. Renner. Writing – original draft: Colleen A. Mangold, Molly M. Rathbun, Daniel W. Renner. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1009441 March 22, 2021 26 / 32 PLOS PATHOGENSStrain-specific differences in HSV-1 infection of human neuronal cells Writing – review & editing: Colleen A. Mangold, Molly M. Rathbun, Daniel W. Renner, Chad V. Kuny, Moriah L. Szpara. References 1. Looker KJ, Magaret AS, May MT, Turner KME, Vickerman P, Gottlieb SL, et al. Global and regional estimates of prevalent and incident herpes simplex virus type 1 infections in 2012. PLoS ONE. 2015; 10: e0140765. https://doi.org/10.1371/journal.pone.0140765 PMID: 26510007 2. Roizman B, Knipe DM, Whitley R. Herpes Simplex Viruses. 6th ed. In: Knipe DM, Howley PM, editors. Fields Virology. 6th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2013. pp. 1823–1897. 3. Harris SA, Harris EA. 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10.1371_journal.pstr.0000077	RESEARCH ARTICLE Urban demand for cooking fuels in two major African cities and implications for policy Ipsita DasID Marc JeulandID 1,5 1, Leonard le Roux2, Richard Mulwa3, Remidius Ruhinduka4, 1 Sanford School of Public Policy, Duke University, Durham, North Carolina, United States of America, 2 Sciences Po Department of Economics, Paris, France, 3 Department of Economics and Development Studies, University of Nairobi, Nairobi, Kenya, 4 Department of Economics, University of Dar es Salaam, Dar es Salaam, Tanzania, 5 Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America ipsita.das@duke.edu Abstract Nearly 2.3 billion people lack access to clean cooking fuels and technologies worldwide, rep- resenting a critical failure to achieve SDG7’s cooking energy access goal. In Sub-Saharan Africa, dependence on polluting cooking fuels is particularly high, resulting in considerable environmental, health, and time-related costs. Progress in the region has been greatest in urban areas, partly because incomes are higher and alternative fuels more widely available than in rural areas, but understanding of the dynamics of urban cooking energy transitions remains limited, and reasons for the divergent paths of different cities are unclear. Our pri- mary objective is, therefore, to understand differences in the demand for several fuels among low-income households in two contrasting cities–Nairobi, where the transition is well advanced (N = 354), and Dar es Salaam, where progress has been slower (N = 1,100). We conducted a double-bounded, dichotomous choice contingent valuation experiment to eluci- date how urban households would respond to changes in cooking fuels’ prices. Our analysis shows that fuel price responses vary across the income distribution and across these cities. Willingness to pay for the most commonly used cooking fuel in Nairobi–liquefied petroleum gas–is nearly twice that in Dar es Salaam, where more households prefer charcoal. In Dar es Salaam, low-income charcoal users appear especially entrenched in their cooking fuel choice. Our results have important implications for the effectiveness of different policy tools (e.g., bans, taxes, or clean fuel subsidies), since responses to pricing policies will depend on these varying price sensitivities, as well as targeting and the readiness of the supply chain (including policy enablers of supply) to meet increased demand. In conclusion, though policies are commonly designed at the national-level, policy-makers need to understand nuances in the local demand context very well when choosing instruments that best support energy transition among their most vulnerable citizens. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Das I, le Roux L, Mulwa R, Ruhinduka R, Jeuland M (2024) Urban demand for cooking fuels in two major African cities and implications for policy. PLOS Sustain Transform 3(2): e0000077. https://doi.org/10.1371/journal.pstr.0000077 Editor: Paola D’Orazio, Chemnitz University of Technology: Technische Universitat Chemnitz, GERMANY Received: July 31, 2023 Accepted: January 29, 2024 Published: February 28, 2024 Copyright: © 2024 Das et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data may be found here: https://urldefense.com/v3/__https:// doi.org/10.7924/r4qf8x909__;!!OToaGQ!oUvItyVL C6NVRttkwI0Z8a1Y3GENeXatmIOtoVlI8ZDWdK W05-OzWFbqyHjpSkwfVrNIyaPTQKR9vzhQFw$. Funding: MJ acknowledges financial support from the Clean Cooking Alliance (https://cleancooking. org/) for the Nairobi survey (PR-18-41711). RR and MJ received funding for the Dar es Salaam survey from The Swedish International Development Cooperation Agency, Sida (https:// PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 1 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONwww.sida.se/en) through the Environment for Development network, School of Business, Economics and Law, University of Gothenburg, Sweden (https://www.efdinitiative.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Demand for cooking fuels in two African cities and policy implications Author summary Though populations in urban areas are more rapidly progressing towards SDG7’s univer- sal clean cooking access goals, there is limited understanding of cooking energy transi- tions in cities in low- and middle- income countries. The impacts of policy instruments in fostering urban energy transition remain particularly unclear. This paper considers the demand for several cooking fuels among low-income households in two such contrasting cities–Nairobi (where the clean cooking energy transition is well advanced) and Dar es Salaam (where progress has been slower). We show that the willingness to pay for the most commonly used clean cooking fuel–liquefied petroleum gas–among the poor in Nai- robi is nearly twice that in Dar es Salaam, where households prefer charcoal. In Dar es Salaam, low-income charcoal users appear more entrenched in their cooking fuel choice and less likely to switch to LPG. LPG subsidies targeted to low-income households appear especially crucial for fostering LPG uptake and regular use. The extent to which policy tools (e.g., taxes, fees) can be effective also depends crucially on the readiness of the supply side to meet increased demand, and complementary mechanisms (e.g., reducing upfront clean stove investments, efficient supply networks for fuel refills, information and behav- ior change campaigns). Introduction Globally, nearly 2.3 billion people lack access to clean cooking fuels and technologies [1]. High reliance on polluting fuels such as biomass and kerosene persists especially in Sub-Saharan Africa (SSA), generating time and drudgery costs, high exposures to health-damaging emis- sions, and substantial environmental damages [2]. Among the 20 countries with the smallest population share with clean cooking access, 19 are least-developed countries in SSA [1]. More- over, between 2010 and 2020, the region experienced the lowest annualized increase in access (+0.48 percentage points per year) [1]. Progress towards clean cooking goals lags other energy- related objectives such as ensuring access to electricity. Prior literature on drivers of improved and clean cooking energy access has largely focused on rural settings in low- and middle-income countries (LMICs), where clean energy access is generally lowest [3–12], with much lesser evidence from urban areas [13–17]. However, clean cooking fuel use remains far from universal in many urban LMIC settings, and use of polluting fuels persists alongside high rates of electricity access and ample availability of a variety of alternative cooking fuels [1]. Typical explanations revolve around the widespread belief that clean fuels like liquefied petroleum gas (LPG) and electricity are too expensive to use for cook- ing purposes [18–21], or issues related to the unreliability of LPG and electricity supply in many LMIC cities [22,23]. Small-scale production of fuel-efficient (relative to traditional cook- ing technologies) improved cookstoves (ICS), their limited marketing and distribution net- works, and challenges with product quality, also mean that relatively affordable and high efficiency ICS can be difficult to procure reliably in many urban centers of LMICs [24–27]. As such, household use of polluting fuels continues to be a major contributor to the cocktail of sources that are increasingly making ambient air in urban areas unbreathable [28]. Understanding household preferences and demand for cooking fuels and technologies can facilitate informed policymaking to stimulate adoption and use of clean cooking alternatives, thereby contributing to meeting SDG7. However, careful demand studies on cooking choices are relatively rare, especially from urban locations of LMICs. For example, evidence from rural India indicates that the households who most value reduced smoke emissions are also most PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 2 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications likely to opt for a clean alternative (in one case, an electric stove that was low in cost and emit- ted little smoke [29], and in another, biogas [30]). In rural Ethiopia, meanwhile, willingness to pay (WTP) for new cooking technology has been shown to be lower than the market price of such technology, but stove attributes such as emissions reduction and stove durability increase demand [31]. Several demand studies also increasingly emphasize that subsidies and financing may be needed to overcome affordability challenges arising from poor households’ tight liquidity constraints, contradicting the idea that low WTP indicates that households do not value improved technology [32]. Consistent with this idea, households in rural Senegal contin- ued to use ICS intensively even six years after they were first distributed free of charge, and recipients’ WTP was no lower than that of other households [33]. Similarly, in one of the few urban demand studies for improved cooking technology, in Nairobi, Kenya, a recent study found that households’ WTP for an energy-efficient charcoal ICS ($12) was less than half of that stove’s market price (which ranges between $27-$41), but loan provision significantly increased WTP [34]. Activating levers other than subsidy and finance, meanwhile, have been found to have mixed effects on WTP for cleaner cooking technologies: in rural Uganda, for example, neither health nor time savings messaging increased WTP for ICS, while in rural India, health messag- ing had a modest positive effect on households’ reported WTP for LPG fuel [35,36]. In other evidence from rural India, significant predictors of exclusive LPG use (i.e., no fuel stacking) were knowledge about LPG’s health benefits and community-level LPG diffusion [37]. Limited evidence on electric induction stoves shows that in urban Nepal, monthly expenditures (a proxy for socio-economic status) and electricity supply are significant determinants of electric cooking, and information on electric cooking benefits increases induction stoves’ WTP by a modest amount (~10%) (22). Very few studies have examined the relationship between fuel stacking and demand for LPG, however [38]. To address some of the gaps in the literature, this paper focuses on urban cooking fuel demand in two of East Africa’s largest and fastest growing cities–Nairobi (population of 4.4 million), Kenya and Dar es Salaam (population of 5.4 million), Tanzania [39,40]. Our dou- ble-bounded, dichotomous choice contingent valuation (CV) experiment helps us to under- stand how urban households would respond to changes in the price of their preferred (main) cooking fuels [41]. It acknowledges that households typically stack fuels, and that households may react to higher prices by either maintaining their current cooking energy portfolio, cooking less with their preferred fuel, or switching entirely away from it. In the latter two cases, the surveys also provide information on households’ preferred back-up fuel, which helps to reveal the transitions that might occur if policy actions were to change relative fuel costs. In the Materials and Methods section, we provide details of the CV exper- iment, including how it addresses some of the issues that typically arise in administering such surveys in LMICs [42,43]. In addition to the basic analysis, we explore the correlates of households’ willingness to maintain their current fuel use under increased prices, focusing especially on the heterogeneity in responses across cities and across the income distribution. The analysis adds nuance and policy relevance to the conventional finding that low income and affordability are key factors that slow the transition away from polluting cooking fuels [13,44–48]. Judicious application of price instruments can facilitate substitution into socially beneficial solutions, but those responding to policy instruments such as taxes and subsidies are not always the most intensive users of polluting fuels [49,50]. Better understanding variation in both price and income responses of demand for cooking energy across locations is essential to informing more effec- tive policy design and targeting. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 3 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications Context of cooking energy use in Nairobi and Dar es Salaam There are key differences between Nairobi and Dar es Salaam that inform interpretation of the comparative analysis of cooking fuel use in these two large and important East African capitals. In 2019, average gross annual income per capita in Nairobi County was 5,497 USD, compared to 1,941 USD in the Dar es Salaam Region (assuming 1 USD = 2,333.3 TZS, and 1 USD = 108.5 KS) [51,52]. Despite having similar urban use of clean fuels in 2000 (4% in urban Kenya and 2% in urban Tanzania), adoption in urban Kenya has far outpaced that in urban Tanzania over the past twenty years (24% in urban Kenya and 7% in urban Tanzania) (S1 Fig) with apparent rapid changes in the primary cooking fuel identified by households in both cities between 2015 and 2020 [53]. Over the same period, the LPG market has also been expanding in both cities (in Nairobi, reported primary LPG use was 40% in 2015 and 65% in 2020; in Dar es Salaam, reported primary LPG use was 12% in 2015 and 36% in 2020), with the proportion of households using LPG more than doubling in Dar es Salaam even as charcoal dependence remains high (in Nairobi, reported primary charcoal use was 5% in 2015 and 3% in 2020; in Dar es Salaam, reported primary charcoal use was 76% in 2015 and 60% in 2020) (S2 Fig) [54– 56]. In both locations and in other similar settings, most households perceive that electricity is too expensive to use for cooking purposes [57]. Policies that affect cooking fuel prices have likely played some role in influencing these trends. The pro-clean cooking policy stance of the Kenyan government, and the dynamism of the private sector response to rising demand for clean fuels, for example, are well known [58]. Throughout the 2000’s, Kenya eliminated excise duties on kerosene, facilitating adoption of that fuel at the expense of solid fuels [59]. In 2016, Kenya zero-rated the value-added tax (VAT) on LPG, and introduced subsidized access to electricity for low-income households [60]. The Government-led Mwananchi gas project launched in 2017 had an ambitious target of increasing nation-wide LPG penetration from 10–70% in three years, by subsidizing pur- chases of LPG canisters and stoves by low-income households [61]. That project was sus- pended in 2018 owing to many issues, however, including high rates of LPG cylinder defects, poor targeting of beneficiaries, and low preparedness of the project implementer, the National Oil Corporation of Kenya, for the distribution and refilling of cylinders [62]. In keeping with its 2030 target of 35% LPG cooking fuel adoption [58], though, the Kenyan government’s LN 121 reforms of 2019 included better enforcement against illegal refilling of LPG cylinders, enhanced safety protocols, and creation of a structure of single LPG cylinder brand ownership that unified and overcame the opposition of majority members of the Energy Dealers Associa- tion (EDA, a trade group comprising small-scale LPG marketers) [62]. As a result of these reforms, new market entrants like Proto Energy have been able to drive LPG prices down in the Nairobi market, by about 25%, and these effects have begun to spread to other parts of Kenya as well. Finally, though it was criticized as regressive and ineffective, a ban on the pro- duction and transportation of charcoal into Nairobi was introduced in 2018, which increased the effective price of charcoal [63–65]. In contrast, while the Tanzanian government has long embraced similar goals for house- holds transitioning to modern fuels (in its national energy policy of 1992, amended in 2003 and 2015) [66], it has taken fewer specific policy actions to support those goals, and most urban households continue to use charcoal as their main cooking fuel. In 2008, the govern- ment did exempt LPG stoves and fuel from VAT and excise duties in order to encourage uptake [67]. In both Nairobi and Dar es Salaam, the VAT is not collected on charcoal transac- tions given the informality of this industry, though various other royalties and license fees are collected from producers and transporters in Tanzania [68]. Kenya and Tanzania also differ substantially in their LPG distribution and storage infrastructure investments. The capacity of PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 4 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications Kenya’s existing supply chain infrastructure is adequate to meet its 2030 demand: 92% of Ken- ya’s LPG comes through two terminals at the Mombasa port; the publicly-owned terminal’s storage capacity is 3,000 metric tons and the privately-owned terminal’s bulk storage capacity is ~26,000 metric tons and the temporary floating facility’s capacity is 14,000 metric tons [62]. Tanzania, on the other hand, has historically had lower storage capacity (8,050 metric tons as of 2016, though two new facilities constructed and commissioned as of 2019 added about 9,000 metric tons) [58]. Energy use characteristics of sampled households In 2019, we interviewed 354 households in four informal settlements in Nairobi (we focused on lower income areas in Nairobi since these are the areas where the city’s population contin- ues to rely on polluting fuels [69]), and in 2020 we interviewed a representative sample of 1,100 households (largely residing in informal settlements) across Dar es Salaam (S3 Fig). In the Materials and Methods section, we elaborate on our sampling strategy. A detailed sum- mary of the characteristics of sampled households in both locations is presented in S1 Table. Here, we specifically describe the energy profile of the sampled households. In the Nairobi sample, LPG is the most common cooking fuel (54%), followed by kerosene (29%) and char- coal (11%). In Dar es Salaam, charcoal is the most common cooking fuel (61%), followed by LPG (32%) and kerosene (4%). In Nairobi, fuel procurement times for all major cooking fuels are similar, ranging from 10–14 minutes per purchase. Average reported daily fuel collection times in Dar es Salaam are much higher than in Nairobi, with firewood collection time being the highest (44 minutes per trip) and kerosene collection time being the lowest (24 minutes). All households in the Nairobi sample have electricity, and electricity access is slightly lower in the Dar es Salaam sample (85%). Households in the Nairobi sample are of slightly higher socio-economic status than those in the Dar es Salaam sample. Per capita monthly expenses are 94 USD in Nairobi, compared to 78 USD in the Dar es Salaam sample, and roughly 51% of the Nairobi sample had completed secondary schooling, compared to 33% in Dar es Salaam. Primary cooking fuel choices in response to price increases For each of the three main cooking fuels in Nairobi (charcoal, kerosene and LPG) and Dar es Salaam (firewood, charcoal and LPG), we assessed demand over randomly-assigned price increases that ranged from 25% to 200% (S2 Table presents the full range of prices offered for the three fuels in both cities). The derived demand curves from responses to initial bids show a mostly linear relationship between WTP for cooking fuel and price (Fig 1). Among respondents in Nairobi that use charcoal as their primary cooking fuel, nearly half the respondents were willing to maintain their primary fuel use under the lowest price increase of 25%, but only 10% were willing to pay 200% more for the fuel. The WTP probabilities among primary kerosene-using respondents were similar: ranging from 62% to 16% for these initial lowest and highest bids, respectively. For primary LPG fuel respondents, the range of WTP probabilities was somewhat higher, dropping from 93% to 30%. Regression analyses fur- ther show that the price elasticities of maintaining use of each of these primary fuels are some- what similar, ranging from -0.5 to -0.7 (Table 1, columns 1, 4, and 7). Yet, controlling for fuel stacking (i.e., use of multiple cooking fuels), which represents an important strategy for coping with high fuel costs or unreliable fuel alternatives [70,71], adds important nuance to these find- ings. Specifically, we find that accounting for stacking leads to higher estimates of the price elasticity for charcoal and LPG (Table 1, columns 2 and 8), while kerosene use appears less price-sensitive (Table 1, column 5). Primary LPG-using households are 13 percentage points more likely to switch away from that fuel when they already use other cooking fuels (Table 1, PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 5 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications Fig 1. Demand graph for cooking fuels in Nairobi and Dar es Salaam (initial bids only). Note: This figure shows the percentage of households in the Dar es Salaam and Nairobi samples reporting that they would continue using their primary cooking fuel when faced with a given initial price increase. Price increases are randomly assigned across survey respondents and baseline prices are converted into USD for comparison in this figure. The initial bids in the price increases and willingness to maintain use responses are presented here. https://doi.org/10.1371/journal.pstr.0000077.g001 column 8). Moreover, the higher the proportion of LPG consumed in total cooking fuel use, the more likely a household is to maintain use of this option (Table 1, column 9). In S3 Table, we further explore the determinants of cooking fuel stacking. For primary charcoal users in Dar es Salaam, 70% were willing to pay the lowest increase, and 30% the highest price (Fig 1). For primary firewood users, half of the respondents facing the lowest bid level were willing to pay, and only 14% were willing to pay the highest price. Finally, for LPG 72% of primary LPG users were willing to continue using LPG at the lowest price increase, which dropped to 27% willing to continue at the highest price. Unlike in Nai- robi, the regression analyses indicate very different price elasticities for maintaining use of charcoal (-1.1) vs. LPG (-0.3) in Dar es Salaam (Table 1, columns 10 and 13), and controlling for fuel stacking does not substantively alter these estimates (Table 1, columns 11, 12, 14 and 15). However, primary charcoal and LPG users are more likely to maintain their primary use when they rely more heavily on these as their primary fuels, as measured by proportion of total cooking fuel use (Table 1, columns 12 and 15). In models that pool across all fuels in each location, based on conversions of quantities in kg or L to energy equivalents (S4 Table, Columns 1–6), we find similar negative fuel price PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 6 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications ) 5 1 ( G P L ) 4 1 ( m a a l a S s e r a D l a o c r a h C ) 3 1 ( ) 2 1 ( ) 1 1 ( ) 0 1 ( ) 9 ( * * * 4 3 1 1 - . * * * 4 3 1 1 - . * * * 7 1 1 1 - . ) 0 1 1 0 ( . ) 0 1 1 0 ( . ) 5 1 1 0 ( . G P L ) 8 ( i b o r i a N e n e s o r e K l a o c r a h C ) 7 ( ) 6 ( ) 5 ( ) 4 ( ) 3 ( ) 2 ( ) 1 ( * * * 2 4 8 0 - . * * * 6 2 7 0 - . * * * 0 4 5 0 - . l a i t i n I f o g o L . s t c e f f e l a n i g r a m e g a r e v a : m a a l a S s e r a D d n a i b o r i a N n i e s u l e u f g n i k o o c y r a m i r p g n i n i a t n i a m f o y t i l i b a b o r P . 1 e l b a T * * * 0 2 3 0 - . * * * 1 4 3 0 - . * * * 6 9 4 0 - . ) 5 0 1 0 ( . ) 6 0 1 0 ( . ) 4 1 1 0 ( . ) 2 5 1 0 ( . ) 2 4 1 0 ( . ) 0 9 1 0 ( . * * * 3 6 3 0 - . * * * 7 4 3 0 - . * * * 7 4 3 0 - . ) 9 5 0 0 ( . ) 9 5 0 0 ( . ) 7 5 0 0 ( . * * * 9 2 7 0 - . * * * 8 3 7 0 - . * * * 2 6 6 0 - . ) 7 5 0 0 ( . ) 3 5 0 0 ( . ) 0 5 0 0 ( . * 1 9 0 0 . ) 6 4 0 0 ( . * 0 1 1 0 . ) 9 5 0 0 ( . * 0 7 0 0 - . ) 7 3 0 0 ( . * * 0 3 1 0 - . ) 4 6 0 0 ( . 5 6 0 0 . ) 8 9 0 0 ( . 1 6 6 0 - . ) 2 3 4 0 ( . 0 2 0 0 . ) 0 4 1 0 ( . * 9 5 1 0 . ) 3 8 0 0 ( . 1 2 3 3 4 1 0 . * * * 7 3 5 0 . ) 7 3 1 0 ( . * 4 4 2 0 - . ) 5 4 1 0 ( . 1 2 3 3 4 1 0 . 1 2 3 5 6 0 0 . 3 8 5 2 8 1 0 . 3 8 5 2 8 1 0 . 3 8 5 4 8 0 0 . 2 9 1 7 4 4 0 . 2 9 1 2 1 4 0 . 2 9 1 9 1 2 0 . 2 0 1 3 4 3 0 . 2 0 1 7 2 3 0 . 4 0 1 8 9 0 0 . 9 3 8 1 5 0 . 8 3 5 8 4 0 . 0 4 4 2 1 0 . f o r e b m u n e h t r o f e l p m a s e h t s A . G P L d n a e n e s o r e k , l a o c r a h c , d o o w e r i f e r i u q c a o t n e k a t e m i t , y t i c i r t c e l e o t d e t c e n n o c l s i d o h e s u o h r e h t e h w , r a e y t s a p e h t n i e r e h w y n a y e n o m d e v a s l s a h d o h e s u o h r e h t e h w , o i t a r y c n e d n e p e d , l d a e h d o h e s u o h f o r e d n e g d n a n o i t a c u d e , e g a l , e z i s d o h e s u o h , ) D S U n i ( s e r u t i d n e p x e d o h e s u o h l l a t o t a t i p a c r e p y l h t n o m f o g o l e r a d e d u l c n i s l o r t n o c l l e v e l - d o h e s u o H . e l p m a s - b u s t a h t r o f s t l u s e r w o h s t o n o d e w l , ) s d o h e s u o h 2 2 ( l l a m s s i m a a l a S s e r a D n i s r e s u d o o w e r i f y r a m i r p 1 0 0 t . 7 7 0 0 0 0 0 . r t s p . l a n r u o j / 1 7 3 1 . 0 1 / g r o . i o d / / : s p t t h . s e s e h t n e r a p n i s r o r r e d r a d n a t s t s u b o R . 1 0 0 < p * * * . 5 0 0 < p * * . 1 0 < p * f o e c i r P : d i b r e p l a o c r a h c ) D S U n i ( g k l a i t i n I f o g o L f o e c i r P : d i b r e p e n e s o r e k n i ( r e t i l ) D S U l a i t i n I f o g o L f o e c i r P : d i b g k r e p G P L ) D S U n i ( l e u f y r a n B i g n i k c a t s s u o u n i t n o C l a o c r a h c g n i k c a t s e l b a i r a v s u o u n i t n o C e n e s o r e k g n i k c a t s e l b a i r a v g n i k c a t s G P L s u o u n i t n o C e l b a i r a v s n o i t a v r e s b O d e r a u q s - R PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 7 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications elasticities in the two cities (ranging from -0.5 to -0.6). The relationship between the stacking variables and the WTP probability is markedly different in the two cities, however. Stacking is associated with a greater likelihood of switching away from one’s primary fuel in Nairobi. In Dar es Salaam, stacking has the opposite relationship with switching away from one’s primary fuel. This may be due to the differing stage of the energy transition in these cities and how it relates to fuel preferences. That is, given the relative early stage of LPG adoption in Dar es Salaam compared to Nairobi, LPG users (who tend to be stackers) there may perceive charcoal as their primary fuel despite reporting, aspirationally perhaps, that LPG is their primary fuel. Percentage decreases in cooking Rather than responding on the extensive margin of primary fuel use (that is, switching away from it entirely), households may choose only to reduce their use of a more expensive primary cooking fuel when its price increases. In the restricted sample of households that accepted the first proposed price increase and maintained primary use of their preferred fuel, we analyze the extent of the cooking reduction they predicted they would make. In Nairobi (S5 Table), we find that a 1 USD increase in LPG price per kg would reduce cooking with LPG by 9–12 per- centage points. In Dar es Salaam (S6 Table), we find that a 1 USD increase in charcoal per kg and LPG price per kg faced by primary users of those fuels would reduce cooking with those fuels by 25–30 percentage points, and 8 percentage points, respectively. Willingness to pay for the various primary cooking fuels We provide three different measures of WTP for each of the primary fuels considered: non- parametric a) Turnbull lower-bound estimates and b) Kristrom mid-point estimates, as well as c) estimates obtained from application of maximum likelihood regression estimation to the double-bounded dichotomous choice responses to the CV questions (see the Materials and Methods section for details on the differences in these methods). In Nairobi, the measures range from 0.14–0.2 USD/kg charcoal, 1.1–1.4 USD/liter kero- sene, and 2.3–2.9 USD/kg LPG (Table 2). These measures are generally not sensitive to Table 2. Willingness to pay estimates (in USD) for cooking fuels in Nairobi and Dar es Salaam. Nairobi Dar es Salaam Charcoal (per kg) Kerosene (per L) LPG (per kg) 1.38 2.60 Pooled (per MJ useful energy) Firewood Charcoal LPG Pooled (per MJ useful energy) 0.09 0.09 0.13 0.21 0.32 0.66 1.01 0.40 1.21 0.89 1.08 1.41 2.90 0.14 0.14 0.20 0.20 1.10 2.67 0.07 0.05 0.43 1.20 0.06 0.20 1.10 2.67 0.07 0.05 0.42 1.22 0.06 0.20 1.18 2.28 0.07 0.13 0.41 1.10 0.06 40 102 192 334 22 583 321 926 Market price Turnbull lower bound Kristrom mid-point estimates Double bound With household covariates (no stacking variables) Binary stacking (with household covariates) Continuous stacking (with household covariates) Observations https://doi.org/10.1371/journal.pstr.0000077.t002 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 8 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications controlling for stacking behavior in the regression models, though the estimate declines slightly for LPG in Nairobi, from 2.7 to 2.3 USD/kg. In Dar es Salaam, the measures range from 0.05–0.13 USD/kg firewood, 0.3–0.4 USD/kg charcoal, and 1.0–1.2 USD/kg LPG. Con- trolling for stacking measures slightly increases WTP for firewood and LPG. In the pooled analysis, despite the substantial difference in the fuel mix relative to that in the Nairobi sample, WTP for 1 MJ of cooking fuel in Dar es Salaam is similar to that in Nairobi, at 0.1 USD. These estimates provide valuable insight on the nature of demand for cooking fuels in these two cities. First, in both cities, the WTP for users’ primary fuels is somewhat higher than cur- rently observed prices. For LPG, WTP is much higher (by roughly over 65%) than current prices, reflecting a strong preference among users of this clean fuel for the benefits that it pro- vides. Policy may need to focus on better targeting of incentives and other approaches to foster uptake and increase access among households who persist in their use of polluting cooking alternatives. Second, WTP for LPG in Nairobi is roughly double that in Dar es Salaam, despite the low- income Nairobi sample only being slightly richer on average than the representative sample that was drawn in Dar es Salaam. Third, WTP for charcoal among primary charcoal users is substantially lower and different in Nairobi (where it is less than 1.5 times the prevailing mar- ket price) and Dar es Salaam (where it is twice the prevailing market price). Thus, households clearly have higher demand for charcoal in Dar es Salaam, which may be related to the less vig- orous clean cooking policy agenda there relative to that in Nairobi. From a clean cooking tran- sition perspective, it is important to know how much relative charcoal prices would need to change to induce more substantial switching towards clean fuels. Distributional aspects of price change policies It is critical to also understand the distributional impacts that fuel pricing policies might have across the income distribution. Low-income households are likely to respond differently to price increases than high-income households, especially when the switching costs are high. There is also a risk that price increases on some fuels could be regressive. To explore such aspects, we restrict the Dar es Salaam sample to households who cook mainly with charcoal, given that over 60% households (n = 672) use charcoal as their primary cooking fuel. In Nai- robi, we restrict the sample to households who cook mainly with kerosene, who constitute 30% of the sample (n = 104). These two subsamples represent households that policy makers might target for transitioning to cleaner fuels. We then divide the income distribution into rel- atively low- and high-income households, based on whether households fall below or above the median of per capita monthly expenditures. Finally, we group the low price (25–50%) and high price increases (100–200%) together. In Dar es Salaam, high-income charcoal users are more likely than low-income charcoal users to switch up the energy ladder to LPG for any given price increase, especially when the price increase is large (Fig 2). Lower-income households are less likely to switch away from charcoal, but those switching more often move to kerosene and firewood. Thus, large charcoal price increases in Dar es Salaam at this time could be regressive, in the sense that low-income house- holds either maintain the use of charcoal, or switch down the energy ladder. In Nairobi, in con- trast, where policies are more supportive of clean options, low-income households appear more likely to switch to LPG than high-income households. High-income kerosene-using households are instead more likely to switch to charcoal, suggesting that these households’ current preference for polluting fuel may reflect a resistance to using LPG that should be further explored. Similar results are obtained from regression analyses (S4 Table). In the Dar es Salaam sam- ple, higher expenditure households are more likely to switch fuels, while the income elasticity PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 9 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications Fig 2. Stated responses to hypothetical price increases for low- and high-income households in Dar es Salaam and Nairobi. Note: This figure restricts the samples to households who mainly cook with charcoal in Dar es Salaam (n = 672), and households who mainly cook with kerosene in Nairobi (n = 104), as these are the most commonly used polluting primary cooking fuels in our study sample. These respondents are divided into relatively low-income and relatively high-income households, based on whether they fall below or above the median of per capita monthly expenditure in the sample. The hypothetical price increases are then categorized into low increases (25–50%) and high price increases (100–200%). The figures present stated responses to the question of whether households continue to use their primary cooking fuel, or switch to another cooking fuel. https://doi.org/10.1371/journal.pstr.0000077.g002 of fuel switching is much lower in the Nairobi sample. The Nairobi results may be driven by vertical differentiation in the quality of kerosene stoves (between wick and kerosene stoves); in Dar es Salaam, charcoal stoves are comparatively homogenous. Thus, low-income kerosene users in Nairobi may switch up the energy ladder because the low utility of using a low-quality kerosene stove constitutes an additional cost for the poor. Alternatively, these results may be driven by differences in the market for alternative fuels like LPG and charcoal in these two contexts, as influenced by historical and existing policy actions. Discussion Our comparative study in two of the fastest growing cities in East Africa contributes to a sparse empirical literature on WTP for cooking fuels in urban LMICs, particularly in SSA where access to clean cooking fuels remains the lowest worldwide. We analyzed demand for the vari- ous primary cooking fuels used in each city, and account for fuel stacking. As fuel pricing poli- cies are likely to affect low- versus high-income households differently, we also analyzed distributional impacts of price changes, thereby filling an important gap in the literature that relates to the affordability challenges impeding adoption of clean cooking technology. Over the last decade, there have been rapid changes in cooking fuel use in Dar es Salaam and Nairobi, with LPG emerging as a key competitor to charcoal and kerosene. However, much of this transition has been driven by high-income households who can afford the higher upfront and running costs of LPG. Given the challenge of financing clean fuel subsidies, a PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 10 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications critical question that policy makers face is whether and to what extent taxes, fees, charges, and other policies should be used to reduce demand for polluting fuels and encourage fuel switch- ing. The effectiveness and viability of each of these depends on the elasticities of demand for different fuels, as well as targeting and policy enforcement aspects. Our findings show that fuel switching patterns in response to price changes vary across the income distribution and depending on the specific context, i.e., across these two East African cities. In both locations, the price elasticity of demand for cooking energy overall is similar, and LPG demand appears to be somewhat less price elastic than charcoal demand. WTP for LPG among respondents cooking primarily with that fuel is also significantly higher than prevailing market prices, consistent with evidence from India [37]. In relative terms, however, in our Nai- robi sample, the ratio of the market price to WTP is low for LPG compared to charcoal and ker- osene, while it is only low for LPG compared to firewood (and not compared to charcoal) in the Dar es Salaam sample. In the latter sample, low-income charcoal users thus appear more entrenched in their choice of cooking fuel and less likely to switch to cleaner LPG, indicating the need for policies to subsidize clean cooking there. LPG subsidies, while critical for fostering uptake [49], can also be regressive unless they are well targeted to reach the poor; evidence from many national LPG subsidy programs, globally, has found that the beneficiaries of these pro- grams are mainly upper-income households [32,72]. Therefore, LPG subsidies targeted to low- income households appear especially crucial for fostering LPG uptake and its regular use among lower-income segments of the population. Learning from other environmental health domains, there could be complementary approaches to targeting of clean fuel subsidies to low- income households such as volumetric targeting (relating subsidy amounts to consumption vol- ume), categorical targeting (subsidy provision basis geographic location or observed character- istics [73]), and means-testing on the basis of income, assets, or consumption [74]. The extent to which different policy tools can be effective also depends crucially on the readiness of the supply side to meet increased demand [23]. Non-price instruments such as bans on charcoal that effectively increase the price of charcoal in low enforcement contexts, or taxes, may be successful in urban areas where a market for alternative fuels exists, but can be regressive or backfire when households have limited access to affordable clean fuel alternatives, inducing back-sliding to even dirtier fuels [64]. As such, other complementary mechanisms are essential, such as supporting access to clean fuels by reducing upfront stove and canister investments [32], aiding the private sector in developing efficient supply networks for fuel refills [75], and shifting preferences away from polluting fuels with information and behavior change efforts [76]. Impact evaluations on LPG and ICS provision and other empirical work on national policies around cooking energy have emphasized that single interventions reliant on a singular policy lever are often insufficient to advance wide-reaching household energy transition [32]. Our study has important limitations. First, our purposive sampling in Nairobi affects the generalizability of the study findings to the entire city’s population. Our rationale for focusing on Nairobi’s informal settlements was to obtain a mix of various cooking fuels among sampled households, but this came at the cost of representativeness over the entire city. Had we not focused on only low-income areas of the city, our sample would have included far fewer kero- sene users, affecting the precision of our estimates of WTP for kerosene. Second, in the two cit- ies, our samples do not include primary users of bio-ethanol, a clean cooking fuel that is now actively being promoted by the private sector in Nairobi and Dar es Salaam [77,78], but that has thus far achieved only limited reach. We are, therefore, unable to compare WTP for LPG with that for alternative clean cooking fuels. Third, we control for only two measures of fuel stacking, but literature has argued convincingly that stacking behavior is highly complex and dynamic over time [70]. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 11 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications Despite these limitations, this comparative CV study provides a set of valuable insights that should help guide similar research on the persistent cooking energy poverty problem facing many LMICs today. In particular, our research highlights that cooking fuels demand estima- tion must be conducted on relatively large sample sizes that are representative of the popula- tions and cooking fuel behaviors being targeted by interventions. Moreover, as fuel stacking is widely prevalent in LMICs, it is imperative to understand household preferences and motiva- tions for continued use of polluting fuels, using a mix of quantitative and qualitative (including semi-structured in-depth interviews, focus group discussions and participant observations) approaches [16]. This combination of methods provides a more comprehensive understanding of contextual cost barriers to the clean cooking energy transition. Finally, by providing a rich characterization of the demand for alternative cooking fuels in two rapidly growing cities in SSA, our study can be valuable for enhanced policymaking. Our findings especially highlight the need for various policy instruments to discourage use of pol- luting cooking fuels and stimulate sustained demand for cleaner cooking options. More specif- ically, policies must address affordability constraints, particularly among low-income households (e.g., taxing polluting cooking fuels, while increasing subsidies for improved and clean cooking technologies and fuels, and improving their targeting to low-income consum- ers). Such subsidies must also be complemented with improving clean technology and fuel dis- tribution infrastructure, developing and streamlining the market, information and education campaigns, and efforts to empower women as primary consumers and suppliers of clean cook- ing technology and fuels [32]. Holistic, multi-faceted approaches are sorely needed to tackle such a major challenge as the global cooking energy poverty problem. Materials and methods Sampling strategy We draw on data collected in mid-2019 in Nairobi, Kenya and early-2020 in Dar es Salaam. The two surveys were part of distinct energy access studies conducted less than a year apart in the two locations. Their respective budget constraints determined the final sample size in each location. All data were gathered before the COVID-19 pandemic began in these countries. In Nairobi, the sample comprises 354 households living in four informal settlements, with sam- pling in each area following a probability proportional to size (PPS) sampling methodology (S3 Fig, Panel A). In each informal settlement, households were randomly selected using a field-based counting method to fulfil the study sample requirement. In Dar es Salaam, the fieldwork was conducted in January and February 2020, and a total of 1,100 households were interviewed (S3 Fig, Panel B). A similar multi-stage stratified random sampling design was applied for selection of final wards, streets and households, also using a PPS sampling methodology. To meet our research goal of obtaining a distribution of cooking fuel users in each city, par- ticularly polluting cooking fuel users, we used somewhat different sampling approaches in the two cities. In Dar es Salaam, the sampling strategy aimed for a representative sample of cook- ing fuel use in the city. Our Dar es Salaam study data are comparable with the Household Bud- get Survey (HBS) (2017–2018) for the Dar es Salaam Region. For example, the average age in the HBS 2017–2018 is 26 years (N = 3,272 household members in Dar es Salaam Region) and 28 years in our survey (N = 4,393 household members). In addition, 52.4% of HBS 2017–2018 household members in Dar es Salaam Region are female, while this figure is 55.2% in our sur- vey. The proportion of household heads whose highest level of education was primary school is 47.8% in the HBS 2017–2018 and 48.4% in our survey. In terms of fuel use, 61.5% of respon- dents in our survey cook mainly with charcoal, 32.2% with LPG, 3.7% with kerosene and 2.4% PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 12 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications with firewood in our survey; in the 2017–2018 HBS these figures for Dar es Salaam region are 62% of households cook mainly with charcoal, 10.4% with LPG, 6.9% with kerosene and 4.5% with firewood. On the other hand, in Nairobi’s primary cooking fuel mix, LPG has the highest share (65%, as of 2020) and use of polluting fuels is relatively low (compared to Dar es Salaam). Based on our extensive interviews with public and private sector stakeholders in the energy landscape in Nairobi, discussions with the local field partner and prior empirical literature on energy use in Nairobi, we targeted informal settlements where polluting cooking fuel use remains much higher. Therefore, the Nairobi sample was explicitly designed to cover lower- income households in the city residing in informal settlements. While the share of LPG in the primary cooking fuel mix in our sample is lower (54%) compared to that for Nairobi in the Kenya Continuous Household Survey Programme 2020 (65%) [54], it is comparable with that for Nairobi in the Kenya Household Cooking Sector Study 2019, where LPG was the most common primary cooking fuel (56%), followed by kerosene (27%) and charcoal (6%) [79]. Informed consent The Campus Institutional Review Board (IRB) at Duke University reviewed and approved the research protocol for the Nairobi survey (Campus IRB Protocol Number: 2019–0330). Research permits were obtained from the University of Dar es Salaam (UDSM) and study dis- trict officials for the Dar es Salaam survey (UDSM Reference Number: AB3/12(B)). In both Nairobi and Dar es Salaam, we obtained oral informed consent from the household head and the primary cook in our sampled households, prior to administering the questionnaire. Oral consent was deemed acceptable because the research took place in settings where requesting people to sign a document can cause distress and mistrust. Field officers read out the consent script to the respondents, and their response to the consent question was recorded in the sur- vey form. Survey Some of the key issues in administering CV experiments in LMICs are poor development of CV scenarios, poor survey implementation and oversight of not testing the effects of survey design variations on the CV experiments’ results [43]. As part of the survey elaboration pro- cess, we reviewed pertinent reports and documents on the cooking sector in both countries. For the survey design and implementation, we worked with local partners (research institu- tions and survey firms), conducted scoping field visits, and prepared a sampling framework (using maps in both locations). The survey instruments in Nairobi and Dar es Salaam were similar, save for minor adjust- ments to ensure suitability to the local context. Comprehensive data were collected on house- hold demographics, cooking practices and fuel preferences, household consumption and wealth and access to credit. To assess households’ WTP for cooking fuels (namely, firewood, charcoal, kerosene and LPG), each CV experiment included a double-bounded, dichotomous choice design, thereby avoiding incentive-incompatibility problems [43]. Testing of the CV experiment was especially important given the dearth of prior work in resource-constrained settings that aimed to value cooking fuels. In addition to helping frame the CV scenario, pilot testing helped determine the percentage price increases that would be most relevant to selected wards in Nairobi and Dar es Salaam. Following the empirical literature [43], we randomly assigned four bidding games or four price increases to different respondents in each study location. As expected, these different starting points brought out different responses. Experienced field partners implemented the field work, where enumerators underwent rig- orous training on the survey instrument. Two co-authors of this study extensively trained PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 13 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications enumerators on the CV experiment module of the household survey. Local field teams’ thor- ough pilot testing of the household survey and the CV experiment informed the final question- naire. Surveys were completed using tablet-based, in-person enumeration. In both the Nairobi and Dar es Salaam household surveys, in the CV experiment module, the hypothetical situation was described to the respondent in detail. S1 Text and S2 Text include the CV experiment modules administered in Nairobi and Dar es Salaam, respectively. Enumerators first described how different cooking fuels affect households in a multitude of ways. They then asked respondents if they would consider switching their primary cooking fuel should there be an increase in its price. In both settings, respondents received randomized initial bids for cooking fuel price increases (in percentage terms) from a set of four different prices increases (25%, 50%, 100% and 200%). S2 Table shows the randomized price increases offered to households in both locations. If respondents responded positively to the initial bid, they received a follow up question with a payment option that was double the initial bid; if respondents replied in the negative to the initial bid, they received a follow up question of a payment option that was half the initial bid. In addition, respondents who declined to switch their main cooking fuels were asked whether, and to what extent, they would reduce their cooking in response to the price increase. This design allows us to assess both intensive and extensive margin responses to hypothetical fuel price changes. Relationship between cooking fuel preferences and price We examine households’ propensity to maintain their primary fuel use in the face of the ran- domly-assigned price increases. In the basic analysis, we examine the role of price alone (Model 1); more sophisticated regression analysis then controls for fuel stacking behavior and a range of household characteristics (Models 2 and 3). Fuel stacking behavior is operationa- lized as both binary and continuous (proportion energy use) variables. (In S3 Text, we describe the construction of these variables and in S3 Table, we examine the determinants of cooking fuel stacking among surveyed households in Nairobi and Dar es Salaam). In determining the relationship between cooking fuel preference and stacking, WTP is a binary variable that indi- cates whether the respondent agreed to the first price increase randomly offered. Using a probit specification, we estimate a household’s demand for cooking fuel, wherein the func- tional form assumes that: PðEi ¼ 1jP; S; ZÞ ¼ Fðb1S þ b2Z þ gPÞ ð1Þ where F is the cumulative distribution function of the standard normal distribution. In Eq 1, the outcome variable is the binary answer of whether (1) or not (0) the household is willing to pay the first randomly allocated price increase, P is the price variable of the ran- domized increase (categorical), S is the fuel stacking variable (run separately for binary and the continuous variables), and Z is the vector of all other variables included in the model. The Z variables included in the regressions are: household size, dependency ratio (defined as the ratio of the younger (ages 14 and under) and older (ages 65 and above) population in the household to the working age population (between 15–64 years) in the household), the age of the household head (in years), the education level of the household head (categorical), an indicator for whether the household head is female, the log of monthly per capita total expen- ditures (in USD), whether the household has saved money anywhere in the past year, whether the household has an electricity connection, and weekly time taken (in minutes) to acquire various cooking fuels (firewood, charcoal, kerosene, LPG). We also analyze whether households that said they would continue with the same primary fuel use, would change their amount of cooking given the price increase. For this analysis, we PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 14 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications run a conditional regression on those that responded positively to the initial bid (in other words, they would continue using their primary fuel even if its unit price increased), wherein the outcome variable is the percentage of normal cooking that would be reduced, and the explanatory variables are the same right-hand side variables as in Eq 1. Estimating WTP for cooking energy We adopt a double-bounded, dichotomous choice CV experiment to elicit respondents’ will- ingness to maintain both primary reliance on their main cooking fuel (namely, charcoal, kero- sene and LPG in Nairobi, and charcoal, LPG and firewood in Dar es Salaam) [41,80]. We examine the sensitivity of that main fuel use to price increases that were randomly assigned to survey respondents. We estimate average WTP in the sample of primary users of each fuel, both in terms of units purchased, and their useful energy content. Taking into account the double-bounded design of the CV experiment, we use a maximum likelihood estimator that includes both the initial and second bids to estimate the WTP for each cooking fuel–charcoal, kerosene and LPG in Nairobi, and firewood, charcoal and LPG in Dar es Salaam. The user-generated STATA command ‘doubleb’ is used for this calculation [81]. The independent variables controlled for are the same as those used in Eq 1. For compari- son, we also derive non-parametric WTP estimates, namely the conservative Turnbull lower- bound estimates of WTP and the Kristrom mid-point estimates [82,83]. These alternative esti- mates only leverage the data on response to the first experimentally assigned price increase, which maximizes the incentive compatibility of the CV design, and do not control for house- hold-specific factors that might influence demand [84]. We also pool responses across the three cooking fuel categories in each city to examine the links between household characteristics and cooking fuel valuation, in addition to eliciting WTP for cooking fuels. We use a similar probit regression approach as in Eq 1 to estimate this association, where the left-hand side variable is the probability that a household responds posi- tively to the CV questionnaire. We normalize across fuels in the pooled models to account for the different useful energy content of each. Specifically, prices were pooled and normalized by dividing by calorific value for each fuel (MJ/kg) and again by fuel efficiency (%); the final unit is in KES/MJ in Nairobi and TZS/MJ in Dar es Salaam, which have further been converted into USD/MJ. Supporting information S1 Fig. Access to clean fuels and technologies for cooking in urban areas in selected East African countries. Note: Clean cooking fuels are defined as electricity, LPG, natural gas. Source: WHO. 2023. Household Energy Database. (DOCX) S2 Fig. Main household cooking fuels in Nairobi and Dar es Salaam in 2015 and 2020. Source: Nairobi: Kenya Integrated Household Budget Survey 2015–2016 for Nairobi County (N = 554). Kenya Continuous Household Survey Programme (KCHSP) - 2020 Annual data for Nairobi County (N = 795). Dar es Salaam: 2014–2015 Tanzanian National Panel Survey data for Dar es Salaam Region (N = 552). 2020 EfD Household Energy Survey (N = 1,098). (DOCX) S3 Fig. Wards selected in Nairobi and Dar es Salaam study sites. Note: OpenStreetMap is used to create the base map layers for both figures. Ward shapefiles, showing the wards visited in the surveys, are overlaid on these base maps. Dar Es Salaam base layer of map: https://www. openstreetmap.org/#map=12/-6.8243/39.2239. Terms of use: https://operations. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000077 February 28, 2024 15 / 21 PLOS SUSTAINABILITY AND TRANSFORMATIONDemand for cooking fuels in two African cities and policy implications osmfoundation.org/policies/tiles/. Copyright and License Terms: https://www.openstreetmap. org/copyright. Dar Es Salaam ward shapefiles: https://data.humdata.org/dataset/2012-census- tanzania-wards-shapefiles. License: (CC BY-IGO) Creative Commons Attribution for Inter- governmental Organisations. Nairobi base layer of map: https://www.openstreetmap.org/ #map=13/-1.2828/36.8020. Terms of use: https://operations.osmfoundation.org/policies/tiles/. Copyright and License Terms: https://www.openstreetmap.org/copyright. Nairobi ward sha- pefiles: https://data.humdata.org/dataset/administrative-wards-in-kenya-1450. License: (CC BY) Creative Commons Attribution International. (DOCX) S1 Text. Contingent Valuation (CV) experiment in Nairobi. (DOCX) S2 Text. Contingent Valuation (CV) experiment in Dar es Salaam. (DOCX) S3 Text. Fuel stacking. (DOCX) S1 Table. Household characteristics of study sample in Nairobi and Dar es Salaam. (DOCX) S2 Table. Price (in USD) of cooking fuels in Nairobi and Dar es Salaam. (DOCX) S3 Table. Correlates of cooking fuel stacking. (DOCX) S4 Table. Probability of maintaining primary cooking fuel use (pooled): average marginal effects. (DOCX) S5 Table. Percentage change in cooking among those that continue using primary cooking fuel after hypothetical price increase in Nairobi. (DOCX) S6 Table. Percentage change in cooking among those that continue using primary cooking fuel after hypothetical price increase in Dar es Salaam. (DOCX) Acknowledgments We are grateful to EED Advisory for leading the data collection in Nairobi, and to the Environ- ment for Development-Tanzania center for leading data collection in Dar es Salaam. We thank the Clean Cooking Alliance for useful comments on the survey instruments and data interpretation, particularly from the Nairobi study. We are grateful to seminar participants at Duke University, at the Environment for Development annual meetings, and at the annual Sustainable Energy Transitions Initiative conference, for their valuable comments that helped improve the analysis and work. All errors are our own. Author Contributions Conceptualization: Ipsita Das, Leonard le Roux, Remidius Ruhinduka, Marc Jeuland. Data curation: Ipsita Das, Leonard le Roux. 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10.1371_journal.pwat.0000181	RESEARCH ARTICLE Microbial and physicochemical water quality changes within distribution and premise plumbing systems during a chlorine conversion Helen Y. BuseID 1, Jatin H. Mistry2 1 Office of Research and Development, United States Environmental Protection Agency, Cincinnati, Ohio, United States of America, 2 Drinking Water Section, United States Environmental Protection Agency Region 6, Dallas, Texas, United States of America buse.helen@epa.gov Abstract A strategy for nitrification control within chloraminated drinking water systems (CDWSs) is to temporarily switch from chloramine secondary disinfection to free chlorine, also known as a free chlorine conversion (FCC). However, the long-term and beneficial effects of FCCs are unclear, especially regarding opportunistic pathogen occurrence. In this study, the impacts to microbial and physicochemical parameters were monitored throughout a CDWS implementing a FCC. Water samples were collected weekly for 4–6 weeks before, during, and after a FCC at eight locations: four distribution system and four residential sites. Mono- chloramine residual (mean±standard deviation) before and after the FCC averaged 1.8±0.9 and 1.6±1.0 parts per million (ppm) for all sites, respectively. Free chlorine levels averaged 2.3±0.9 ppm. There were no significant differences in turbidity and hardness at each loca- tion during the three time periods, but some were noted for pH, temperature, and orthophos- phate levels across various sites and sampling periods. For all locations, heterotrophic plate count levels were lower during the FCC compared to the periods before and after. All sam- ples from one residence were culture positive for P. aeruginosa which exhibited high levels before the FCC, decreasing levels during, and steadily increasing levels after. Additionally, one week prior to the FCC, sediment samples from two elevated storage tanks, ET-1 and ET-2, were analyzed with ET-1 displaying higher levels of culturable heterotrophic bacteria and molecularly detected total bacteria, Legionella spp., and nontuberculous mycobacteria (NTM), as well as presence of culturable P. aeruginosa and total coliforms compared to ET- 2. Fourteen P. aeruginosa and total coliform isolates were whole genome sequenced with genetic differences observed depending on the sampling location and timepoint. Collec- tively, the observed differences in chemical and microbial parameters advocates for a better understanding of the effects associated with implementing FCCs to determine both their effectiveness and potential risks/rewards to water quality. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Buse HY, Mistry JH (2024) Microbial and physicochemical water quality changes within distribution and premise plumbing systems during a chlorine conversion. PLOS Water 3(2): e0000181. https://doi.org/10.1371/journal. pwat.0000181 Editor: Xueming Chen, Fuzhou University, CHINA Received: July 21, 2023 Accepted: December 1, 2023 Published: February 8, 2024 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: Raw data is publicly deposited on the USEPA’s ScienceHub website (https://catalog.data.gov/harvest/epa-sciencehub). The Illumina raw sequence reads are deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the BioProject accession number PRJNA871216 (Temporary Submission ID: SUB11952375 Release date: 2023-10-31). https:// www.ncbi.nlm.nih.gov/sra/. Funding: This work was supported by the U.S. Environmental Protection Agency (EPA), through PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 1 / 23 its Office of Research and Development and Region 6 (Regional Applied Research Effort Program Project 2165 to HB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Drinking water quality changes resulting from a chlorine conversion 1. Introduction Chlorine, chloramines, chlorine dioxide, and ozone are commonly used as oxidizing disinfec- tants for microbiological control in drinking water, whose importance, applications, and effi- cacy assessments have been studied and discussed extensively [1, 2]. Primary disinfection occurs within the drinking water treatment process while secondary disinfection is applied to maintain a disinfectant residual, control microbial regrowth, and reduce risks from microbial pathogens throughout the drinking water distribution system. The advantages of using chlora- mines as a secondary disinfectant include stability and maintenance of disinfectant residual [3], lower total trihalomethane (TTHM) and haloacetic acid (HAA) disinfection byproduct (DBP) formation potential [4], greater biofilm penetration [5], and reduction in the occur- rence of Legionella within premise plumbing systems [6]. However, more recent studies have reported that the greater biofilm penetration of chloramines did not correlate with loss of microbial viability and decreased biofilm material [7]. Moreover, the advantages of chlora- mines may be offset because they have been shown to: decay as quickly as chlorine [8], result in increased formation of iodinated- and nitrogen-containing DBPs [9], inactivate biofilm- associated L. pneumophila differently depending on the underlying pipe material type [10], and select for chloramine tolerant Mycobacterium species [11]. A recent 2017 survey of 375 drinking water systems in the United States indicated that 65% of systems utilized chlorine as a secondary disinfectant and 25% used chloramines, with the latter showing a decrease from 30% in the previous 2007 survey of 312 systems [12, 13]. These chloraminated drinking water system (CDWS) survey respondents indicated a concern about balancing DBPs and simultaneous compliance, public perception issues regarding chloramine usage, and nitrification [13]. In CDWSs, nitrification is indeed a major concern and is caused by microbially mediated oxidation of ammonia to nitrite, and then nitrite to nitrate. Nitrification can also occur with complete oxidation of ammonia to nitrate [14]. Chemical, microbial, and aesthetic water qual- ity is negatively impacted by nitrification as it leads to loss of disinfectant residual; dissolved oxygen depletion; reduction in pH and alkalinity; DBP formation due to implementation of control strategies; production of nitrite and/or nitrate; increases in heterotrophic growth, coli- form occurrences, and nitrifying microorganisms; along with taste and odor, color, and tur- bidity concerns [15]. Within premise plumbing systems, degradation of water quality due to nitrification can be more pronounced than that observed in the overall distribution system because of intermittent water usage, longer water stagnation times, elevated temperatures, and presence of copper and polyvinyl chloride (PVC) piping materials [16, 17]. CDWSs typically have a nitrification monitoring program or a nitrification control/action plan in place. Water quality parameters, such as pH, temperature, free chlorine, monochlora- mine, total or combined chlorine, free ammonia, nitrite/nitrate, and heterotrophic plate counts (HPC) can be used for nitrification monitoring and detection [13, 15]. Nitrification control can include operational (e.g., manual or automated flushing to minimize water age and maintaining disinfectant residual in the distribution system) and treatment practices (e.g., removing, or controlling for, excess ammonia). CDWSs typically undergo a free chlorine con- version (FCC), or chlorine burn, which is a temporary secondary disinfectant change from chloramines to free chlorine resulting in the oxidation of ammonia and thus, removal of this substrate for nitrifying bacteria. It is not unusual for a water system to conduct their FCC out- side of their regular DBP compliance monitoring period since formation and elevated levels of DBPs are expected to occur during the FCC [18]. The frequency and duration of FCCs can vary by utility. Most drinking water systems in EPA R6 conduct a FCC once a year, can be ini- tiated as early as Winter to Fall, and can last between 30–60 days. However, most drinking PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 2 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion water systems do not conduct a FCC more than once a year due to the extensive preparation required, public notifications, and consumer complaints (e.g., inconveniences to homeowners with fish tanks, dialysis centers, and other customers, such as industries, that demand a certain water quality). Despite the frequent use of FCCs, the effectiveness and benefits of this practice along with changes to drinking water chemistry, microbiology, and overall water aesthetics, before, dur- ing, and after an FCC are not well studied, especially within both the distribution and premise plumbing systems. In this study, microbial and physicochemical water quality parameters were monitored throughout a surface water CDWS implementing a FCC to understand the impacts and assess changes in water quality. 2. Materials and methods 2.1 Description of the drinking water treatment plant and processes Samples used in this study were collected throughout a CDWS supplied by a surface water treatment plant utilizing conventional treatment processes (coagulation, sedimentation, filtra- tion, and disinfection). No permits were required for this work as authorized water treatment plant personnel conducted the field sampling and performed on-site water quality measure- ments. For corrosion control and sequestration of iron and manganese in the system, the treat- ment plant utilizes an approximately 1:3 orthophosphate to polyphosphate blend, applied at a 1.25 parts per million (ppm) concentration, after filtration. In this study, the average ± standard deviation (SD) level of orthophosphate leaving the treatment plant was 0.27 ± 0.03 ppm, which was within the utility’s targeted range of 0.25–0.28 ppm. While chlo- rine is the primary plant disinfectant, chloramine is added prior to water entering the distribu- tion system. This system undergoes a free chlorine conversion (FCC) for approximately 30 to 60 days every year. During this study, the FCC lasted 42 days. 2.2 Description of sampling locations Water samples were collected at eight locations: the entry point (EP) of treated water into the dis- tribution system, from a storage tank inlet (STa) and outlet (STb), at a maximum hydraulic resi- dence time (MRT) location, and four premise plumbing residential sites, designated RC, RG, RT, and RW (Fig 1A). EP and MRT are regulatory compliance monitoring locations for the CDWS used in this study. Locations STa and STb were chosen to evaluate the impacts of a stor- age tank on water quality. Residential locations were incorporated in this study to evaluate water quality at the point of use and thus, would have more public health implications. Sediment sam- ples from ET-1 and ET-2 were only collected once (one week prior to the FCC) when the storage tanks were drained and cleaned in preparation for the FCC. EP, STa and STb, and MRT are established compliance monitoring site locations in the distribution system. Fig 1B illustrates the sampling timeline periods. Prior to the FCC, when chloramine was the disinfectant residual, abbreviated here as Mono (Pre), samples were collected weekly for four weeks (wk-4 to wk-1). During the FCC period, samples were collected weekly for 5 weeks (wk0 to wk5). When the sys- tem converted back to using a chloramine residual, abbreviated here as Mono (Post), samples were collected weekly for another 5 weeks (wk6 to wk10). In this study, the kitchen faucet served as the sampling outlet for the premise plumbing residential sites (S1 Fig). 2.3 Sample collection and processing 2.3.1 Bulk water. At each distribution system sampling location (EP, STa, STb, and MRT), bulk water samples were collected after a 1–3 min flush. For residential locations (RG, PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 3 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Fig 1. Drinking water distribution system sampling locations and timeline. (A) Distribution system sites: finished water entry point (EP), storage tank inlet (STa) and outlet (STb); and elevated storage tanks, ET-1 and ET-2. Residential (R), premise plumbing sampling sites: RG, RT, RW, and RC. Relative distance of each site from the EP, below each graphic. (B) Sampling timeline: weeks (black vertical lines), days (short grey vertical lines), microbial sampling days (vertical blue lines). Mono(Pre), chloramine disinfectant residual period prior to the Free Chlorine Conversion (FCC). FCC, free chlorine disinfectant residual period. Mono(Post), period after the FCC and return to chloramine disinfectant residual. https://doi.org/10.1371/journal.pwat.0000181.g001 RC, RT, and RW), cold bulk water samples were collected from the kitchen after an overnight stagnation period of at least 6 h. 100 mL were collected for immediate onsite water quality analysis described in Section 2.4, followed by 2 L from the EP, STa, STb, and MRT locations and from the kitchen faucet at the residential sites, RG, RC, RT, and RW. Cold bulk water sam- ples were collected in sterile 1L plastic bottles containing 1 mL of 10% w/v sodium thiosulfate to neutralize any disinfectant residual. Sample bottles were shipped overnight on wet ice (� 4˚C) and were processed within less than 24 h after collection. 1 L of each sample was filtered through a 0.2 μm polyethersulfone membrane (Supor Mem- brane, Pall Life Sciences, Nassau, NY, USA). Filters were placed into 10 mL of dechlorinated, 0.22 μm filtered drinking water (dfH2O), and vortexed at maximum speed for 1 min to resus- pend the concentrated bulk water material. Approximately 1 mL of the concentrated bulk water suspension was analyzed for Legionella spp. colony forming unit (CFU), as described in Section 2.5.2. For wk1 samples, collected during the FCC period, 750 mL of the bulk water samples were filtered through a 0.4 μm polycarbonate membrane (Pall Life Sciences). Mem- branes were placed in Lysing Matrix A tubes (MP Biomedicals, Solon, OH, USA) for nucleic acid extraction as described in Section 2.6. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 4 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion 2.3.2 Storage tank sediment. One week prior to the FCC period, two elevated storage tanks in the distribution system were emptied and cleaned, ET-1 and ET-2 (Fig 1A). Storage tank sediment samples were collected into four sterile 1L bottles from ET-1 and two 1L bottles from ET-2 (S2 Fig). The liquid phase from each bottle, which contained particulate sediment matter (S2E and S2F Fig), was decanted into separate sterile containers, and processed for microbial analyses as described in Section 2.5. Five hundred microliters of the sediment sam- ples were placed in a Lysing Matrix A tube (MP Biomedicals, Solon, OH, USA) for nucleic acid extraction as described in Section 2.6. Mass of each aliquot was recorded to express results as units per gram (g-1). 2.4 Water quality analysis Bulk water samples were analyzed for physicochemical parameters (pH, temperature, turbid- ity) and chemical parameters (monochloramine, free and total chlorine, free ammonia, ortho- phosphate, nitrite, and hardness). pH, temperature, monochloramine, free and total chlorine, free ammonia, orthophosphate, and nitrite were analyzed onsite using the Hach SL1000 Porta- ble Parallel Analyzer and Chemkey reagents following manufacturer’s instructions (Hach, Loveland, CO, USA). After laboratory receipt of the shipped samples, turbidity and hardness were measured using a Hach 2100Q Portable Turbidimeter and EDTA titration kit, respec- tively (Hach, Loveland, CO, USA). 2.5 Microbial culture analysis 2.5.1 Heterotrophic plate count (HPC). HPCs in unconcentrated bulk water and storage tank sediment samples were enumerated using the spread plate method on Reasoner’s 2A (R2A) and Plate Count (PC) agar (Difco Laboratories, Detroit, MI, USA). R2A plates were incubated at 28˚C for 7 d and PC plates at 35˚C for 48 h [19]. 2.5.2 Legionella spp.. Legionella enumeration and presumptive colony analysis was per- formed as previously described [20] and following ISO 11731 [21]. Briefly, undiluted and seri- ally diluted suspensions, of bulk water and sediment samples, were spread plated on buffered charcoal yeast extract (BCYE) agar plates (BD Diagnostics, Franklin Lakes, NJ, USA) and incu- bated for 4–6 days at 36˚C. A portion of the sample was also heat treated, by incubating in a 50˚C water bath for 30 min, before plating on BCYE agar plates. A 100 mL portion of uncon- centrated bulk water samples collected from RG, RT, RW, and RC at wk4 (Fig 1) was analyzed using Legiolert (IDEXX, Westbrook, ME, USA) following manufacturer’s instructions. One milliliter of the storage tank sediment sample was added to 99 mL of Butterfield’s phosphate buffer (Hardy Diagnostics, Santa Maria, CA, USA) and analyzed using Legiolert, not in accor- dance with manufacturer’s protocols since tank sediment is neither a potable nor non-potable water sample. Presumptive Legionella colonies and Legiolert positive wells were isolated and confirmed as Legionella spp. or L. pneumophila via polymerase chain reaction (PCR) using the 16S rRNA gene assays described in Section 2.7. 2.5.3 Pseudomonas aeruginosa, Total Coliform (TC), and Escherichia coli. Pseudalert and Colilert (IDEXX) were used to analyze 100 mL of the unconcentrated bulk water samples according to the manufacturer’s instructions. One milliliter of the storage tank sediment sam- ples was added to 99 mL of Butterfield’s phosphate buffer and analyzed, which was not in accordance with manufacturer’s protocols. Presumptive positive wells from the Pseudalert trays were extracted and confirmed as P. aeruginosa via polymerase chain reaction (PCR) using the ecfX gene assay described in Section 2.7. Well extracts from the Pseudalert and Coli- lert positive wells were streaked onto a Tryptic Soy Agar (TSA; BD Company) plates and incu- bated for 1–3 days at 36˚C. TSA plates were checked for purity and a single colony was used to PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 5 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion inoculate 10 mL of Tryptic Soy Broth (TSB; BD Company) and incubated for 15–18 h with shaking at 36˚C. One milliliter of the stationary phase culture was (i) pelleted and processed for total DNA extraction as described in Section 2.6 and (ii) washed, resuspended in 1mL TSB with 10% glycerol, and stored at -80˚C. 2.5.4 Limit of detection (LOD). To account for zero values, 1 was added to all data points before conversion to the log10 scale (e.g., log10 (CFU + 1)). For HPCs, the LOD for bulk water samples was 10, or 1 log10, CFU mL−1. For Legionella spp., the LOD for bulk water samples was 1, or 0 log10, CFU mL-1, and for sediment samples 10, or 1 log10, CFU g-1. For Legiolert, the LOD was 10 MPN 100mL-1 with a quantification limit of 22,726 MPN 100mL-1 for bulk water samples. The LOD for the Quanti-Tray/2000, used for Pseudalert and Colilert, was 1 MPN 100mL-1 with a quantification limit of 2,419.6 MPN 100mL-1. For sediment samples, the LOD and quantification limit for Legiolert was 100 and 227,260 MPN g-1, respectively; and for Pseudalert and Colilert, LOD was 100, or 2 log10, MPN g-1 and the quantification limit 241,960, or 5.4 log10, MPN g-1. 2.6 Isolation and preparation of total DNA DNA was extracted using the MasterPure Complete DNA purification kit (Epicentre Biotech- nologies Inc., Madison, WI, USA) according to manufacturer’s protocol. Samples were homogenized using the FastPrep-24 bead beating and lysing system (MP Biomedicals, Solon, OH, USA) and processed twice for 30 s and oscillated at a speed of 4 meters s-1. The DNA pel- let was resuspended in 100 μL of molecular grade water (Corning, Manassas, VA, USA). 2.7 Quantitative polymerase chain reaction (qPCR) Legionella spp., L. pneumophila, and Vermamoeba vermiformis qPCR was performed as previ- ously described [20]. TaqMan qPCR assays for detection of total bacteria targeted the 16S rRNA gene [22]; for P. aeruginosa, the extracytoplasmic function sigma factor, ecfX, gene [23]; for nontuberculous mycobacteria (NTM), the heat shock protein 65, hsp65, gene [24]; and for Acanthamoeba spp. and Naegleria fowleri, their respective, 18S rRNA gene [25]. DNA samples were analyzed in duplicate using the Applied Biosystems QuantStudio 6 Flex Fast Real-Time PCR system (ThermoFisher, Waltham, MA, USA). A 10-fold dilution of each sample was also analyzed in duplicate to test for presence of environmental qPCR inhibitors. For all microbial targets, standard curves were generated, on each plate, using a plasmid vector (pUCIDT-AMP; Integrated DNA Technologies, Inc., Coralville, IA, USA) containing a cloned region of the gene target. Standards ranging from 1 to 107 gene copies (GC) for each target were generated and analyzed in triplicate along with duplicate no-template controls for each 96-well plate. The limits of detection for bulk water and storage tank sediment supernatant samples were 1.3 log10 GC mL−1 and 1.3 log10 GC g-1, respectively. 2.8 Whole genome sequencing and analysis Fourteen presumptive P. aeruginosa, total coliform, and E. coli isolates were chosen for whole genome sequencing. Total genomic DNA from each isolate was prepared as described above. DNA extracts were quantified using an Invitrogen Qubit 4 Fluorometer and 1x dsDNA High Sensitivity Assay Kit (ThermoFisher Scientific, Waltham, MA, USA). Metagenomic libraries were prepared using the DNA extracts and the Nextera XT DNA Library Preparation kit (Illu- mina, San Diego, CA, USA). Libraries were quality checked with an Agilent 2100 Bioanalyzer and DNA High Sensitivity kit and pooled in an equimolar ratio. The pool was gel purified using a 2% agarose gel and the Qiagen QIAquick gel extraction kit (Qiagen, Germantown, MD, USA). Following purification, the pool was sequenced by Wright Labs (Huntingdon, PA, PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 6 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion USA) using an Illumina NextSeq 550 to produce 2x150 bp reads. The Illumina reads are depos- ited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the BioProject accession number PRJNA871216 (Temporary Submis- sion ID: SUB11952375 Release date: 2023-10-31). Prior to assembly, raw sequences were quality checked with FastQC v0.11.9 (Babraham Institute, UK, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Libraries were (i) cleaned from contaminants, adapters, and other Illumina-specific sequences from the reads; (ii) removed of low coverage reads; (iii) filtered to a minimum length read of 100 nt; and (iv) assembled using Trimmomatic v0.36 [26], PRINSEQ v0.20.4 [27], and SPAdes v3.15.3 [28]. Taxonomic identification on assembled genomic sequences were assigned using GTDB-Tk v1.7.0 [29] and verified with the average nucleotide identity (ANI) tool [30]. Assembled genomes were annotated with Prokka v1.14.5 [31] and quality checked with CheckM v1.0.18 [32]. Phylogenetic trees were constructed using FastTree2 [33]. Genomes were also annotated with rapid annotations using subsystems technology (RAST) and the comparative genomics environment, SEED, was used to examine the general functional gene distribution, specific functional gene families, and protein domains within genome sets [34, 35]. 2.9 Statistical analysis The Shapiro–Wilk normality test was conducted on the datasets collected during the Mono (Pre), FCC, and Mono (Post) sampling periods for each analyte at each sampling location to determine distribution of the data throughout that sampling period. A one-way analysis of var- iance (ANOVA) using the Tukey multiple comparisons test was conducted between analytes, with P-values of < 0.05 considered statistically significant. Two-tailed paired t-tests (paramet- ric) were used to compare HPC levels using the R2A and PC method. All statistical analyses were performed using Prism v8.4.3 (GraphPad Software, San Diego, CA, USA). 3. Results 3.1 Physiochemical water quality during the study period Two 1L water samples were collected weekly from eight locations throughout a chloraminated drinking water system (CDWS) (Fig 1A). Sampling occurred for 15 weeks and spanned three distinct time periods: before, during, and after a 42-day free chlorine conversion (FCC) (Fig 1B). S3 Fig shows the pH, temperature, turbidity, and hardness levels observed throughout the study. Average±SD pH levels at the RT site before, during, and after the FCC were 7.4±0.1, 6.9± 0.3, and 6.7±0.04, respectively, with levels before the FCC statistically higher than the lev- els during and after the FCC (P < 0.05). For all locations, there were no statistically significant differences in their respective pH, turbidity, and hardness levels across the three sampling peri- ods, except for pH at the RT site (S3D Fig). Within the distribution system, temperatures were statistically lower before the FCC com- pared to after: 28±2 v 32±2˚C at EP (P = 0.0004), 26±1 v 30±0.1˚C at STa (P = 0.001), 26±1 v 29±1˚C at STb (P = 0.0017), and 25±1 v 28±0.5˚C at MRT (P = 0.0054). The different tempera- tures observed before and after the FCC at these sites most likely reflected the progression of sampling into the warmer summer months. At the residential locations, statistically different temperatures were observed at the RT site before and during the FCC (25±1 and 28±1˚C, P = 0.0185), at the RW site before and after the FCC (24±1 and 28±1˚C, P = 0.0012), and at the RG site both before and after the FCC (26±1 v 23±0.4˚C, P < 0.05) as well as during and after the FCC (27±2 v 23±0.4˚C, P < 0.0001). Temperature differences at the residential locations most likely reflected a combination of seasonal effects and variations in indoor ambient condi- tions (S3B and S3E Fig). PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 7 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Fig 2. Temporal disinfectant residual summary for distribution (A-C) and residential (D-F) sites. Sampling occurred weekly before (wk-4 to wk-1), during (wk0 to wk5), and after (wk6 to wk10) the FCC period. Monochloramine (A, D), free chlorine (B, E), and total chlorine (C, F) levels are shown in triangle, circle, and square symbols, respectively. Each sampling location is represented by different colors (EP, black; MRT, grey; STa, light green; STb, light blue; RG, pink; RT, orange; RW, dark green; RC, dark blue). nd, no data, for STa and STb during week 5, for RC during week 4, and for RW during week 7. https://doi.org/10.1371/journal.pwat.0000181.g002 The monochloramine, free chlorine, and total chlorine residual levels observed throughout the study are shown in Fig 2. At each location, their respective monochloramine and total chlorine levels, before (wk-4 to wk-1), during (wk0 to wk5), and after (wk6 to wk10) the FCC, were not statistically different (P > 0.11). The average ± standard deviation (SD) in parts per million (ppm) of monochlora- mine residual was 3.6±0.2 at the EP location, 1.8±0.7 at STa, 1.6±0.3 at STb, and 0.5±0.2 at MRT for the distribution system locations (Fig 2A) and 1.0±0.4 at RG, 2.2±0.2 at RT, 1.7±0.2 at RW, and 1.0±0.2 at RC for the residential locations (Fig 2D). Due to the hydraulic distance of the MRT location from the treatment plant, the monochloramine residual at MRT was PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 8 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion 0.71 ppm at wk0, 48 hr after the conversion to free chlorine (Fig 2A, grey circle at wk0). Simi- larly, at wk6, after the conversion back to monochloramine, the free chlorine residual at MRT was 1.86 ppm, (Fig 2B, grey circle at wk6). Free chlorine levels for all sites before and after the FCC were 0.1±0.04 and 0.1±0.3 (Fig 2B and 2E). During the FCC, free chlorine levels (average ± SD ppm) at the EP site (3.8±0.2) were statistically higher than those at the STa (2.8±0.1), STb (2.5±0.1), MRT (1.7±0.9), RG (1.7 ±0.5), RT (2.7±0.5), RW (2.0±0.4), and RC (1.5±0.1) locations (P < 0.0001, Fig 1B and 1E). The RT site had the highest residential free chlorine level and was statistically higher than those at the RG and RC locations (P < 0.05). Free ammonia, nitrite, and orthophosphate levels were also monitored throughout the study (S4 Fig). Free ammonia levels at the RG site were higher than levels during the FCC (0.38±0.04 v 0.10±0.09 ppm, P = 0.0047). Nitrite levels at the MRT site, before the FCC (70.6 ±14.7 ppb), were higher than levels during (16.0±17.3 ppb) and after (7.4±0.5 ppb) the FCC (P < 0.0001). For all locations, there were no statistically significant differences in their respec- tive nitrite and free ammonia levels across the three sampling periods, except for nitrite levels at MRT (S4B Fig) and free ammonia levels at RG (S4D Fig). Orthophosphate levels were statistically the lowest at EP (0.27±0.03 ppm, P < 0.001) and highest at MRT (0.61±0.08 ppm, P < 0.0001) amongst the distribution system locations. For the residential sites, orthophosphate levels at RT (0.38±0.07 ppm, P < 0.05) and RG (0.64 ±0.08 ppm, P < 0.01) were statistically the lowest and highest, respectively (S4C and S4F Fig) even though RG is closer to the EP location compared to the other residential sites. Moreover, there were no statistical differences between the orthophosphate levels at the MRT and RG locations (P = 0.7350). 3.2 Analysis of sediment samples from elevated storage tanks In preparation for the FCC, two elevated storage tanks, ET-1 and ET-1 (Fig 1A) were emptied and sediment samples from the bottom of the tank were collected and processed for culture and molecular analysis (Fig 3). No culturable Legionella spp. was detected in these samples using the ISO 11731:2017 method. Using Legiolert, ET-2 samples were found to be negative for L. pneumophila; how- ever, each of the four ET-1 samples displayed presumptive L. pneumophila positivity (turbidity and brown color). From those Legiolert trays, presumptive positive wells (25% of the large wells and 10% of the small wells) were extracted for confirmation tests. The wells were negative for Legionella spp. and L. pneumophila via PCR and after plating for bacterial isolates, colonies exhibited non-Legionella-like morphology on BCYE agar plates. Thus, the Legiolert presump- tive positive wells were determined to be L. pneumophila false positives. Culturable levels of R2A and PC HPC, total coliforms, and P. aeruginosa measured from the sediment samples are shown in Fig 3A. Average ± SD log10 CFU g-1 of PC and R2A HPC levels in ET-1 samples were 5.9±0.5 and 6.4±0.4; and in ET-2 samples, 3.3±0.6 and 5.3±0.2, respectively (Fig 3A, brown squares and circles). Unlike R2A HPC levels, PC HPC levels between ET-1 and ET-2 were statistically different (P < 0.0001) and most likely due to the abil- ity of P. aeruginosa to be detected using the PC HPC method. P. aeruginosa and total coliforms were only detected in ET-1 samples at 3.0±0.9 and 4.4±0.8 log10 MPN g-1, respectively (Fig 3A, pink and blue triangles). Sediment samples were all qPCR negative for L. pneumophila and three different free-living amoeba populations: Acanthamoeba spp., Vermamoeba vermiformis, and Naegleria fowleri. However, total bacteria, Legionella spp., nontuberculous mycobacteria (NTM), and P. aerugi- nosa were all detectable via qPCR in ET-1 and ET-2 sediment samples, except for P. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 9 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Fig 3. Culture (A) and qPCR (B) analysis of sediment samples from storage tanks ET-1 and ET-2. Culture results are expressed as mean log10 CFU g-1 for heterotrophic plate counts (using the R2A and PC methods) or MPN g-1 for P. aeruginosa and total coliform (TC). qPCR results are expressed as mean log10 gene copies (GC) g-1. https://doi.org/10.1371/journal.pwat.0000181.g003 aeruginosa, which was only detected in ET-1 (Fig 3B). In ET-1 sediment samples, average ± SD log10 gene copies (GC) g-1 levels of total bacteria were 7.6±0.3, 4.8±0.3 for Legio- nella spp., 4.9±0.5 for NTM, and 2.6±0.3 for P. aeruginosa. Statistically lower levels of total bac- teria (6.4±0.02 log10 GC g-1, P = 0.003), Legionella spp. (4.1±0.2 log10 GC g-1, P = 0.03); and NTM (2.2±0.3 log10 GC g-1, P < 0.0001) were detected in ET-2 sediment samples, while no P. aeruginosa was detected in ET-2 sediments via qPCR (Fig 3B). 3.3 Microbiological quality of drinking water samples 3.3.1 Bacterial enumeration and isolation. Bulk water samples were analyzed for cultur- able HPCs, Legionella spp., Pseudomonas aeruginosa, total coliforms, and Escherichia coli (Fig 4). Although paired t-tests indicated HPC levels were statistically different between the R2A and PC methods, except for site EP (S1 Table), similar trends were observed for both at each location across the three sampling periods (Fig 4, brown circles and brown squares, respectively). For all locations, HPC levels were generally lower during the FCC compared to the periods before and after. Average±SD log10 CFU mL-1 R2A HPC levels for distribution system and res- idential locations were 2.2±1.3 and 3.6±1.3 before, 1.5±0.8 and 2.8±0.9 during, and 2.2±1.0 and 3.8±1.0 after the FCC, respectively (Fig 4, brown circles). Similarly, average±SD log10 CFU mL-1 PC HPC levels for distribution system and residential locations were 1.6±1.2 and 2.4±1.2 before, 1.3±0.7 and 2.3±1.1 during, and 1.5±0.7 and 3.3±1.3 after the FCC, respectively (Fig 4, brown squares). There were no statistical differences in average HPC levels before, during, and after the FCC for each sampling location (P > 0.2), except for PC HPC levels at the RW loca- tion before and after the FCC (Fig 4G, 1.7±0.6 and 5.0±0.4 log10 CFU mL-1, respectively, P < 0.001). PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 10 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Fig 4. Culture analysis of water samples collected from distribution (A-D) and residential (E-H) sites. Heterotrophic plate counts (using the R2A and PC methods) are plotted on the left axis and expressed as log10 CFU mL-1. For the P. aeruginosa, total coliform (TC), and E. coli positive sites, STa (C), RG (E), and RW (G), culture results for are plotted on the right axis and expressed as MPN 100 mL-1. The grey dotted line indicates 500 CFU mL-1 or 2.7 log10 CFU mL-1. n. d., no data. *, levels were greater than the P. aeruginosa assay limit of 2,419.6 MPN 100mL-1. https://doi.org/10.1371/journal.pwat.0000181.g004 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 11 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion No culturable Legionella spp. was detected throughout the study period using the ISO 11731:2017 method. Additionally, a subset of residential bulk water samples, at wk4 during the FCC, also tested negative for L. pneumophila using Legiolert. P. aeruginosa was only detected in bulk water samples from the RG location (Fig 4E, pink triangles). Levels ranged from 7.4 to >2,419.6 MPN 100mL-1 with the four highest levels of P. aeruginosa observed at wk-2 through wk1. Excluding those four highest P. aeruginosa levels, average ± SD MPN 100mL-1 levels before, during, and after the FCC were 57±7, 53±34, and 51±45, respectively. Total coliforms were detected in four bulk water samples during this study: wk5 at STa (1 MPN 100mL-1), wk4 and wk7 at RG (both 1 MPN 100mL-1), and wk8 at RW (219 MPN 100mL-1) with the latter sample also being E. coli positive at a concentration of 2 MPN 100mL-1 (Fig 4C, 4E, and 4G: blue triangles and black diamond). 3.3.2 Bacterial isolate analysis via whole genome sequencing. Presumptive P. aerugi- nosa, total coliform, and E. coli culture isolates were processed for whole-genome sequence analysis to identity and confirm their genetic lineage and to better understand their molecular diversity and occurrence within the distribution system. Sequenced isolates included a subset of seven P. aeruginosa strains, six cultured from the RG water samples and one from ET-1 sed- iments; six total coliform strains; and one E. coli strain. All were taxonomically identified as shown in Table 1. The seven P. aeruginosa isolates were confirmed as P. aeruginosa with the % average nucleo- tide identity (ANI) to reference P. aeruginosa genomes ranging from 99.4 to 99.5% (Table 1). A phylogenetic tree was constructed and showed that the RG Pa isolates were all located on the same branch except for the one isolated during the last week of the study and a month after the switch back to chloramine disinfection (RG, wk10, Mono(Post), Fig 5A). The ET-1 storage tank sediment sample, collected one week prior to the free chlorine conver- sion, was found to be total coliform positive (Fig 3A, blue triangle). This ET-1 total coliform iso- late, wk-1 Mono (Pre), was identified as Enterobacter ludwigii with an average nucleotide identity (ANI) of 99.0% to reference genomes (Table 1). Another Enterobacter total coliform species, Enterobacter hormaechei, was isolated nine weeks later from the RW residential bulk water sample (Fig 4G, blue triangle; Table 1, RW #1, 99.0% ANI). RW #1 was isolated at wk8 Mono (Post), which was the period after the FCC and two weeks after the return to chloramine disinfection (Fig 1). Phylogenetic analysis indicated relatedness between the ET-1 E. ludwigii and the RW E. hormaechei isolates (Fig 5B and S5A Fig). The RW wk8 sample was also E. coli positive (Fig 4G, black diamond) and genetically confirmed with an ANI of 97.0% (Table 1, RW #2; S5B Fig). The wk5 STa water sample, collected during the free chlorine conversion, was also positive for total coliforms (Fig 4C, blue triangle). However, during the isolation for pure bacterial col- onies, two different colony morphologies were observed on the TSA plates with STa colony type #1, identified as Stenotrophomonas maltophilia with 97.8% ANI, and STa colony type #2, as Raoultella planticola with 99.3% ANI (Table 1, S5C and S5D Fig). Notably, R. planticola was also isolated from the RG water sample, collected during the free chlorine conversion at wk4 (Fig 3E, blue triangle; Table 1, 99.3% ANI). Phylogenetic analyses indicated that both wk4 RG and wk5 STa R. planticola isolates were closely related (Fig 5B and S5D Fig). Another RG water sample was also total coliform positive, at wk7 collected two weeks after the water system switched back to applying a chloramine residual (Fig 4E) but was identified as Acinetobacter johnsonii with 95.9% ANI (Table 1; Fig 5B and S5E Fig). 4. Discussion In chloraminated drinking water systems (CDWSs), activity of nitrifying bacteria and their microbial products results in both unstable, and loss of, chloramine residuals throughout PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 12 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Table 1. Summary statistics of whole-genome assemblies for the drinking water and storage tank isolates. Isolate Origin Taxonomic Identification Genome size (bp) No. of contigs Contig N50 (bp) G+C content (%) No. predicted genes No. protein coding genes NCBI BioSample Accession No.a Sampling location ET-1 Time point wk-1 Period Predicted organism ANI (%) RG RG RG RG RG RG Mono (Pre) Mono (Pre) Mono (Pre) Mono (Pre) FCC Mono (Post) wk-4 wk-1 wk0 wk5 wk6 wk10 Mono (Post) Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudomonas aeruginosa 99.4 6,321,307 266 48,402 99.5 6,994,078 112 221,566 99.5 6,993,886 111 221,566 99.5 6,994,629 109 222,231 99.5 6,994,701 107 259,311 99.5 6,994,677 110 183,537 99.5 6,995,519 264 58,435 ET-1 wk-1 STa #1 STa #2 RG RG RW #1 RW #2 wk5 wk5 wk4 wk7 wk8 wk8 Mono (Pre) FCC FCC FCC Mono (Post) Mono (Post) Mono (Post) Enterobacter ludwigii 99.0 4,936,203 120 81,037 Stenotrophomonas maltophilia Raoultella planticola Raoultella planticola Acinetobacter johnsoniia Enterobacter hormaechei 97.8 4,762,406 99.3 99.3 95.9 5,483,668 5,483,859 3,419,695 64 32 30 70 145,667 492,012 497,847 84,111 99.0 4,654,922 113 79,059 Escherichia coli 97.0 4,956,538 1,323 97,970 66 66 66 66 66 66 66 54 66 56 56 41 56 48 5,837 6,496 6,492 6,483 6,489 6,500 6,493 4,627 4,430 5,131 5,128 3,357 4,399 7,198 5,767 SAMN29983815 6,423 SAMN29983816 6,423 SAMN29983817 6,413 SAMN29983818 6,416 SAMN29983819 6,430 SAMN29983820 6,422 SAMN29983821 4,558 SAMN29983828 4,357 SAMN29983823 5,062 5,060 3,305 SAMN29983824 SAMN29983822 SAMN29983825 4,325 SAMN29983826 7,099 SAMN29983827 Abbreviations: ANI, average nucleotide identify; bp, base pair; FCC, free chlorine conversion; G+C, guanine-cytosine content; Mono (Pre), monochloramine residual period prior to FCC; Mono (Post), monochloramine residual period after the FCC; NCBI, National Center for Biotechnology Information; No., number. aSequences are deposited under NCBI BioProject PRJNA871216. https://doi.org/10.1371/journal.pwat.0000181.t001 portions of the distribution system [36, 37]. For the surface water CDWS evaluated in this study, the State Primacy Agency requires water systems to implement a nitrification control plan that describes target levels along with monitoring and follow up actions to control nitrifi- cation. When the onset of nitrification occurs, a FCC is recommended for remediation. More- over, CDWSs in this State typically implement a FCC either following the detection or suspected occurrence of Naegleria fowleri in the system; addressing nitrification events (e.g., elevated ammonia, nitrite, and/or nitrate levels; and/or when disinfection residuals are not within the minimum requirements for CDWSs in the State (e.g., 0.5ppm). During the start of this study, the CDWS suspected N. fowleri occurrence in their system due to the heavy and sustained rainfall events in the Spring of 2021. Previous studies have demonstrated the occurrence of N. fowleri in roof harvested rainwater and that surface waters can become contaminated with N. fowleri due to soil runoff after rainfall events [38, 39]. More- over, recreational usages of treated water (e.g., residential lawn water slide, water park/splash pad) have been previously reported as sources of N. fowleri infections and deaths in young chil- dren [40, 41]. Thus, the CDWS planned for a 60-day FCC and water samples were collected from the distribution system and sent to a commercial N. fowleri analytical lab by the State PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 13 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Fig 5. Phylogenetic trees illustrating isolate relatedness to reference genomes. Representative P. aeruginosa (A), total coliform and E. coli (B) isolates from each sampling location and time were chosen for construction of these phylogenetic trees. Isolate genomes (blue-colored text) are described by the sampling location, sampling week, presumptive isolate type, and disinfectant residual period during isolation. Branch support values (red-colored text) are shown for each node and represent confidence values (bootstrapping) used by FastTree2 to estimate maximum likelihood. The scale bar represents the phylogenetic distance of 0.03 (A) or 0.10 (B) nucleotide substitutions per site. https://doi.org/10.1371/journal.pwat.0000181.g005 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 14 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion during the first week of the FCC. Additionally, 1 L of water samples collected at wk1 from all 8 study locations (Fig 1A) were concentrated and analyzed by the USEPA for N. fowleri using qPCR targeting the 18S rRNA gene [25]. Both State and USEPA samples were culture- and qPCR-negative for N. fowleri; thus, the CDWS reduced duration of the FCC to 42 days. Previous studies have reported nitrification reoccurring within 10 to 16 weeks after systems have implemented a 4 to 6 weeklong FCC suggesting benefits of this practice may be tempo- rary [42–44]. Absence of chloramine residual and increased nitrate in the system was observed as early as 12 weeks after the FCC [42]. Ammonia-oxidizing bacteria (AOB) were detected within pipe wall biofilms during, and at the end of, the FCC [42]; bacterial cell counts increased within days after the FCC [45]; continuous rapid regrowth of nitrifying bacteria was observed 16 weeks after the FCC [43]; and bacterial cell counts and nitrite levels increased sig- nificantly 10 weeks after the FCC [44]. Collectively, these studies demonstrate that FCCs may not completely and effectively remove nitrifying bacteria and may not confer long-term nitrifi- cation control after the return to chloramine secondary disinfection. Thus, CDWSs may need to customize and/or manage their implementation of a FCCs differently to maximize its bene- fits for their system. The heterotrophic plate count (HPC) method is used for enumeration of culturable hetero- trophic bacteria in water and serves as a general assessment of drinking water quality [46]. Reasoner’s 2A (R2A) and Plate Count (PC) agar methods were evaluated in this study to quan- tify slow-growing, water-based bacteria versus fast growing, more fastidious, higher tempera- ture tolerant heterotrophic bacteria, respectively [19, 46]. Although R2A HPCs were statistically higher than PC levels, similar trends were observed for both at each location across the three sampling periods (Fig 4). Average HPC levels at the MRT and residential sites, gradu- ally decreased from the first to sixth week during the FCC (4.2 to 2.3 log10 CFU mL-1, respec- tively), but then quickly increased to similar or higher levels from the first to fifth week after the FCC (3.2 to 4.3 log10 CFU mL-1, respectively) indicating that free chlorine was able to bet- ter control and limit HPC growth compared to chloramine. Moreover, the return to high HPC levels shortly after the FCC observed in this study, may also suggest the short-term benefits of the FCC (Fig 4). However, other factors can contribute to high HPC levels (e.g., temperature, availability of oxygen and nutrients, pH, etc.); thus, increases in HPC levels alone are not indic- ative of nitrification [15]. Opportunistic pathogens such as Legionella pneumophila and Pseudomonas aeruginosa are significant public health concerns especially given their frequent occurrence in, and persistent colonization of, premise plumbing systems [47, 48]. Thus, both pathogens were monitored during this study to evaluate their occurrence in both the distribution and premise plumbing residential sites. While no culturable Legionella spp. was detected for the entire study period, all RG samples were culture positive for P. aeruginosa (Fig 4E). The four highest levels of cul- turable P. aeruginosa, (� 2,419 MPN 100mL-1) were observed during each of the two weeks before and two weeks after initiation of the FCC. The lowest level (7 MPN 100mL-1) occurred during the last week of the FCC and the second highest (120 MPN 100 mL-1) occurred two days after the switch back from free chlorine to chloramine (Fig 4E). These observations are supported by Xue et al. [49] which reported the high reactivity of P. aeruginosa extracellular polymeric substances (EPS) to chlorine compared to low reactivity with monochloramine. The mechanism was attributed to EPS shielding of the P. aeruginosa cell surface preventing monochloramine access to disinfection reactive sites on the bacterial cell membrane [49]. Notably, the plumbing under the RG kitchen sink is comprised of flexible plastic and copper pipe material while RT is comprised of braided polymer, plastic and copper piping, RW shows a mixture of copper, plastic, and braided polymer piping, and RC plumbing consists of copper, braided polymer, and cross-linked polyethylene (PEX) materials (S1 Fig). PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 15 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Monochloramine disinfection of biofilms has been shown to be less effective on PVC surfaces compared to copper, and conversely, free chlorine disinfection was shown to be more effective on PVC compared to copper biofilms [10]. However, it is unclear how plumbing configura- tions composed of mixed plastic and metal piping materials would impact disinfection effi- cacy. Although no single chemical and physiochemical water quality parameter could explain the levels of P. aeruginosa observed at each corresponding time point, it is likely a combination of environmental factors, water quality parameters, and bacterial traits contributed to the growth patterns of P. aeruginosa observed in the RG samples. To genetically characterize the P. aeruginosa detected in the RG and ET-1 samples, total genomic DNA from six RG P. aeruginosa isolates obtained before, during, and after the FCC, and one isolate from ET-1 was processed for whole-genome sequencing. The P. aeruginosa genome sizes were between 6.3 to 7 million base pairs (Mbp) (Table 1) which is within the range of 5.5 to 7 Mbp observed in previously sequenced isolates [50]. The RG strains were genetically similar except for the RG wk10 isolate obtained 5 weeks after the FCC, as indicated by the separation onto another branch in the phylogenetic tree (Fig 5A). Based on RAST and SEED analyses, these genetic differences between RG wk10 and the other RG isolates included a higher number of protein domains associated with cell wall synthesis and DNA repair; and less domains associated with motility and phenazine biosynthesis, which has been shown to play an important role in gene expression and antibiotic tolerance [51]. For the ET-1 P. aeruginosa isolate, separation from the RG isolates was due to the higher amount of protein coding domains associated with amino acid, carbohydrate, DNA, protein, and RNA metabolism; heat shock and oxidative stress responses; iron acquisition and metabo- lism; membrane transport; cellular respiration; and denitrification. Under anaerobic condi- tions and in the presence of nitrate, P. aeruginosa can perform complete denitrification by reducing nitrate to molecular nitrogen via nitrite, nitric oxide, and nitrous oxide utilizing the enzymes: nitrate reductase, nitrite reductase, nitric oxide reductase, and nitrous oxide reduc- tase [52]. The nitrogen oxides are utilized by P. aeruginosa as alternative electron acceptors that enable their growth under anaerobic conditions [53]. The ET-1 P. aeruginosa isolate, com- pared to the residential isolates, contained higher numbers of protein domains associated with nitrite reductase as well as two enzymes, formate dehydrogenase and nicotinamide adenine dinucleotide hydrogen (NADH)-quinone oxidoreductase, which are involved with cellular res- piration [53]. Utilizing drinking water system sediment deposits, Liu et al. [54] showed that nitrification occurs within the oxic, or oxygen containing, zone; while denitrification occurs in the anoxic, or oxygen depleted, zone. Thus, the genetic traits unique to ET-1 P. aeruginosa most likely plays an important role in supporting and enabling their growth in storage tank sediment environment. Molecular detection of several bacterial and eukaryotic opportunistic pathogens was also performed on the ET-1 and ET-2 sediment samples. L. pneumophila and the free-living amoe- bae, Acanthamoeba spp., Vermamoeba vermiformis, and Naegleria fowleri were not detected in these samples by qPCR. However, total bacteria, Legionella spp, and nontuberculous mycobac- teria (NTM), a group that includes opportunistic pathogens, were detected in ET-1 and ET-2; while P. aeruginosa was only detected in ET-1 (Fig 3B). Combining ET-1 and ET-2 levels, aver- age±SD log10 GC g-1 levels of total bacteria were 7.2±0.6 log10 GC g-1; for Legionella spp., 4.6 ±0.5 log10 GC g-1; for NTM, 4.0±1.5 log10 GC g-1; and for P. aeruginosa, 2.6±0.3 log10 GC g-1. Except for P. aeruginosa, the opportunistic pathogen levels observed in this study were lower than those reported previously during a large survey of 87 sediment samples collected from 18 locations across 10 US states [55], which was most likely due to differences in sample number, sediment collection and processing methodology, and qPCR assay targets between studies. However, the reproducible detection of opportunistic pathogens in storage tank sediment PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 16 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion samples further highlights their role as potential reservoirs of human pathogens. Thus, these results advocate for better operations and management of water storage structures (e.g., more frequent inspections, regular cleaning/removal of debris, biofilm build-up, and sediment, along with addressing identified sanitary defects such as missing/damaged screens and gaskets, unprotected holes, and assessment of the structural integrity) to reduce the risk of widespread contamination in distributed bulk water and to ensure public health protection from these appurtenances. Total coliforms were also isolated from the ET-1 sediment samples as well as from RG at wk4 and STa at wk5, during the FCC; and from RG at wk7 and RW at wk8, which was also E. coli positive, after the FCC (Fig 4). Table 1 shows the taxonomic identification of the six total coliform isolates which were identified as Stenotrophomonas maltophilia (from STa), Raoul- tella planticola (from STa and RG), two members of the Enterobacter cloacae complex, E. lud- wigii and E. hormaechei (from ET-1 and RW, respectively), and Acinetobacter johnsonii (from RG), all of which have been associated with human infections and are considered opportunis- tic pathogens [56–60]. Due to the sporadic detection of total coliforms and E. coli at the various locations during this study, the positives detected may have been a result of their temporal and spatial proximity to boil advisory issuing events and not related to the FCC. The time span, proximity, and reason for the boil advisories related to the TC and E. coli positive locations were: for the RG wk4 detect, a main repair that took place 2.3 mi south of the RG site 10 days prior to sampling; for the STa wk5 detect, a location experienced a pressure loss requiring a hydrant repair 10 days prior to sampling and 7.5 mi north of STa site; for the RG wk7 detect, an equipment failure occurred at a pumping station 11.2 mi southwest of the RG location four days prior to sampling; and for the RW wk8 detect, a transmission line was repaired due to pressure loss at a location 10 days prior to sampling and 6 mi north of the RW site. However, correlations between total coliform and E. coli detection and water quality changes because of the FCC cannot be ruled out, especially since total coliforms and E. coli were not detected before the FCC. Statistical relationships between levels of total coliforms and disinfectant residual in treated drinking water is a current knowledge gap that needs to be addressed to ensure microbial regrowth is controlled and microbial water quality is maintained throughout the water distri- bution network. Sporadic detection of total coliforms adds to the challenge of establishing those statistical correlations on top of the delay due to incubation times needed for total coli- form culture analyses. Rapid detection of coliform bacteria and the ability to quickly identify potential contamination sources will help maximize public health protection from microbial regrowth and contamination within the distribution system. Thus, future studies should explore monitoring total coliforms throughout distribution and premise plumbing systems using molecular techniques and evaluate their use as a rapid and specific test for coliform bac- teria while monitoring for disinfectant residuals. Several PCR assays have been developed for total coliform detection and tested with over 150 total coliform strains representing 76 species using potable ground and surface water samples and confirmed with standard TC culture methods [61–64]. In addition to the chemical and microbial parameters collected during this study, future FCC studies should also incorporate molecular monitoring of ammonia oxidizing bacteria (AOB), ammonia oxidizing archaea (AOA), nitrite oxidizing bacteria (NOB), and complete ammonia oxidation (commamox) groups and species to identify significant changes in, and responses of, nitrifying populations during a FCC. The differences in physio- and chemical water quality and genetic diversity of culturable opportunistic and nosocomial pathogens, observed in this study, highlights the complexity of water quality changes that can occur dur- ing implementation of FCCs. Accordingly, FCC practices should be evaluated to ensure PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 17 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion regulatory compliance while maximizing long-term water quality benefits and public health protection. 5. Summary • Based on the microbial culture results, the beneficial impacts of the FCC conducted in this study may have been temporary. • Monitoring for genetic diversity of pathogens throughout a distribution system can provide mechanistic insights into their occurrence and reveal specific niches/habitats and their genetic adaptations to those environments such as with the ET-1 P. aeruginosa isolate ana- lyzed in this study. The conducive growth environments could then be removed or managed with targeted treatment or operational practices to control and prevent their growth in those environments. • Utilizing molecular methods for total coliform detection could reveal correlative links and insights between their occurrence and disinfectant residual concentrations. • Due to the frequent use of FCCs, similar monitoring studies should be conducted to aid water treatment operators and managers in optimizing their standard practice or perhaps replace or supplement with other practices (e.g., flushing) that can maximize benefits of the FCC. Supporting information S1 Fig. Kitchen faucet type, plumbing materials, and configuration for residential sites. Images were taken by homeowners for the RG (A), RT (B), RW (C), and RC (D) kitchen sam- pling location used in this study. The under sink plumbing consists of: plastic hoses from the valve to the fixture with the valve connected to copper plumbing at RG (A); braided polymer tubing from the valve to fixture with the valve is connected to copper plumbing at RT (B); braided polymer and plastic tubing from the valve to fixture with the valve connected to cop- per plumbing at RW (C); and cross-linked polyethylene (PEX) and copper materials from the valve to fixture with the valve is connected to copper plumbing at RC (D). Note that drain line materials and components are not included in this plumbing materials list. Published with per- mission from the USEPA Region 6 participating drinking water utility. (TIF) S2 Fig. Processing of storage tank sediment samples. Sediment samples from ET-1 were col- lected into four bottles (A) and from ET-2 into two bottles (B). The liquid phase from each bot- tle (C, D) was decanted into separate sterile containers (e.g., glass 1L bottle shown on the left side of panels C and E). Small aliquots of the liquid phase were placed in 50mL conical tubes. Settled and resuspended particles in the sediment liquid phase are shown for ET-1 (E) and ET- 2 (F). (TIF) S3 Fig. Temporal physiochemical parameters summary for distribution (A-C) and residential (D-F) sites. Sampling occurred weekly before (wk-4 to -1), during (wk0 to 5), and after (wk6 to 10) the FCC period. pH (A, E), temperature (B, F), turbidity (C, G), and hardness (D, H) levels are shown in the down triangle, hexagon, star symbols, and cross-hatched circle symbols, respectively. Each sampling location is represented by different colors (EP, black; MRT, grey; STa, light green; STb, light blue; RG, pink; RT, orange; RW, dark green; RC, dark blue). nd, no PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 18 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion data, for STa and STb during week 5, for RC during week 4, and for RW during week 7. (TIF) S4 Fig. Temporal free ammonia, nitrite, and orthophosphate level summary for distribution (A-C) and residential (D-F) sites. Sampling occurred weekly before (wk-4 to -1), during (wk0 to 5), and after (wk6 to 10) the FCC period. Free ammonia (A, D), nitrite (B, E), and ortho- phosphate (C, F) levels are shown in the diamond, cross-hatched squares, and X, respectively. Each sampling location is represented by different colors (EP, black; MRT, grey; STa, light green; STb, light blue; RG, pink; RT, orange; RW, dark green; RC, dark blue). nd, no data, for STa and STb during week 5, for RC during week 4, and for RW during week 7. (TIF) S5 Fig. Phylogenetic trees based on total coliform classifications. Phylogenetic trees based on Enterobacter (A), E. coli (B), Stenotrophomonas (C), Raoultella (D), and Acinetobacter (E) classifications of study isolates are shown. Genomes (blue-colored text) are described by the sampling location, sampling week, presumptive isolate type, and disinfectant residual period during isolation. Branch support values (red-colored text) are shown for each node and repre- sent confidence values (bootstrapping) used by FastTree2 to estimate maximum likelihood. The scale bar represents the phylogenetic distance of either 0.01 (A, B, D), 0.02 (E), or 0.04 (C) nucleotide substitutions per site. (TIF) S1 Table. Two-tailed paired t-test summary for culturable HPC levels using the R2A and PC methods. (PDF) Acknowledgments Authors would like to sincerely thank the EPA Region 6 drinking water utility and staff that anonymously participated in this study. This project would not have been possible without their interest, sampling and onsite analysis efforts, and logistical support. The authors would also like to thank our EPA colleagues and project team collaborators, whose research publica- tions from this large study are forthcoming: Mr. Matthew Alexander, Dr. Maura Donohue, Ms. Dawn King, Ms. Christy Muhlen, Dr. Jingrang Lu, Dr. Darren Lytle, Dr. Jonathan Press- man, Mr. Nathan Sienkiewicz, Mr. Ian Struewing, Dr. David Wahman, and Ms. Peyton Woodruff. The authors would like to thank Chelsea Hintz, Sharon Kidney, Brian Morris, Brindha Murugesan, Nicole Sojda, and Sue Witt for their technical support in sampling preparation, shipping and receiving, and sample processing; Katherine Loizos for her graphical design of Fig 1; and Dr. Jim Goodrich and Dr. Matthew Magnuson for their EPA technical review of this manuscript. This manuscript has been subjected to the Agency’s review and has been approved for pub- lication. The views expressed in this article are those of the authors and do not necessarily rep- resent the views or policies of the Agency. Mention of trade names, commercial products, and/ or services does not imply an endorsement or recommendation for use by the U.S. Govern- ment or EPA. Author Contributions Conceptualization: Helen Y. Buse, Jatin H. Mistry. Data curation: Helen Y. Buse. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000181 February 8, 2024 19 / 23 PLOS WATERDrinking water quality changes resulting from a chlorine conversion Formal analysis: Helen Y. Buse. Funding acquisition: Helen Y. Buse, Jatin H. Mistry. Investigation: Helen Y. Buse. Methodology: Helen Y. Buse, Jatin H. Mistry. Project administration: Helen Y. Buse, Jatin H. Mistry. Resources: Helen Y. Buse, Jatin H. Mistry. Supervision: Helen Y. Buse. Validation: Helen Y. Buse. Visualization: Helen Y. Buse. Writing – original draft: Helen Y. Buse. Writing – review & editing: Helen Y. Buse, Jatin H. Mistry. References 1. NRC. National Research Council (US) Safe Drinking Water Committee. Drinking Water and Health: Vol- ume 2. The Disinfection of Drinking Water. Washington, D. C.: National Academies Press; 1980. 2. WHO. Guidelines for Drinking-water Quality, Fourth Edition, Incorporating the First Addendum. Geneva, Switzerland: World Health Organization; 2017. 631 p. 3. 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10.1371_journal.pwat.0000184	RESEARCH ARTICLE An invisible water surcharge: Climate warming increases crop water demand in the San Joaquin Valley’s groundwater-dependent irrigated agriculture Kelley MoyersID Joshua H. ViersID 1* 1, John T. Abatzoglou2, Alvar Escriva-Bou3, Josue´ Medellı´n-Azuara1, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Civil and Environmental Engineering, School of Engineering, University of California, Merced, Merced, California, United States of America, 2 Department of Management of Complex Systems, School of Engineering, University of California, Merced, Merced, California, United States of America, 3 Samueli School of Engineering and Institute of the Environment and Sustainability, University of California, Los Angeles; Los Angeles, California, United States of America OPEN ACCESS Citation: Moyers K, Abatzoglou JT, Escriva-Bou A, Medellı´n-Azuara J, Viers JH (2024) An invisible water surcharge: Climate warming increases crop water demand in the San Joaquin Valley’s groundwater-dependent irrigated agriculture. PLOS Water 3(3): e0000184. https://doi.org/10.1371/ journal.pwat.0000184 Editor: Majid Shafiee-Jood, University of Virginia, UNITED STATES Received: June 6, 2023 Accepted: January 24, 2024 Published: March 13, 2024 Copyright: © 2024 Moyers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The gridMET datasets are cataloged at https://thredds. northwestknowledge.net/thredds/reacch_climate_ MET_catalog.html. Land use data are available online through the California Natural Resources Agency available at https://data.cnra.ca.gov/ dataset/statewide-crop-mapping. Crop coefficients are available online California Natural Resources Agency available at https://data.cnra.ca.gov/ dataset/cal-simetaw-unit-values. Code for calculations used in this manuscript is available on * jviers@ucmerced.edu Abstract California’s bountiful San Joaquin Valley (SJV), a critical region for global fruit and nut pro- duction, has withstood two severe, multi-year droughts in the past decade, exacerbated by record-breaking high temperature and evaporative demand. We employed climate data and crop coefficients to estimate the crop water demand in the SJV over the past forty years. Our approach, using crop coefficients for Penman-Montieth modeled evapotranspiration, focused on the climate effects on crop water demand, avoiding the confounding factors of changing land use and management practices that are present in actual evapotranspiration. We demonstrate that increases in crop water demand explain half of the cumulative deficits of the agricultural water balance since 1980, exacerbating water reliance on depleting groundwater supplies and fluctuating surface water imports. We call this phenomenon of cli- mate-induced increased crop water demand an invisible water surcharge. We found that in the past decade, this invisible water surcharge on agriculture has increased the crop water demand in the SJV by 4.4% with respect to the 1980–2011 timeframe—more than 800 GL per year, a volume as large as a major reservoir in the SJV. Despite potential agronomic adaptation and crop response to climate warming, increased crop water demand adds a stressor to the sustainability of the global fruit and nut supply and calls for changes in man- agement and policies to consider the shifting hydroclimate. 1. Introduction California is a key contributor to the global supply of fruits, nuts, and vegetables, producing 81% of the world’s almonds, 42% of the world’s pistachios, and 26% of the world’s processing tomatoes [1–3]. California’s fruit and nut production is concentrated in the San Joaquin PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 1 / 17 Github (https://github.com/kdrechsler2/Ag-Tax) and can be run in Octave (open access) or MATLAB. Spatial analyses were restricted to California Department of Water Resources sub- basin boundaries denominated as Planning Areas 606, 607, 608, 609, 702, 703, 704, 705, 706, 708, 709, and 710 described here https://data.cnra.ca. gov/dataset/ca-gw-basin-boundary-descriptions. These largely comport with the 15 groundwater basins used by Escriva-Bou et al. 2023. Map data sources for Fig 1 are cropping from CNRA as above; California State Outline from TIGER 2016 United State Census Bureau; and Global base map from Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community. Funding: This work was supported by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Agriculture and Food Research Initiative (Award No. 2021- 69012-35916 to KM, JTA, AEB, JMA, and JHV) and by NSF and USDA-NIFA under the AI Research Institutes program for the AgAID Institute (Agricultural AI for Transforming Workforce and Decision Support) (Award No. 2021-67021-3534 to JTA, JMA, and JHV). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Climate warming increases the San Joaquin Valley’s crop water demand Valley, which has a Mediterranean climate ideal for fruit and nut production. These crops require significant irrigation provided by a combination of surface and groundwater sources, the latter of which has been overdrafted for over a century in parts of the state [4]. The amount of cumulative groundwater loss in California’s Central Valley during 2003–2021 was compara- ble to the total surface storage of major reservoirs in the state (roughly 62,000 GL) [5]. Overal- located surface water supplies and groundwater overreliance to meet agricultural water demands have resulted in many negative impacts such as land subsidence [6], dry wells [7, 8], and land fallowing [9, 10], which highlight the unsustainable nature of current agricultural practices [11, 12]. To address groundwater overdraft impacts and increase resilience to future droughts, the California legislature enacted the Sustainable Groundwater Management Act in 2014 [13], which is meant to bring groundwater basins into balance. While groundwater levels in the southern portion of the San Joaquin Valley (SJV) have been declining for more than a century [14], the pace of decline has accelerated in the past two decades [5]. This acceleration of groundwater depletion coincided with multi-year to multi- decadal droughts that have been among the most extreme in a millennium [15–17]. These recent droughts have been characterized as hot droughts where below-normal precipitation is accompanied by also higher evaporative demand that exacerbates drought impacts [18]. While there are no significant long-term trends in precipitation in California, the state has warmed by 1.6˚C since 1900 and annual evaporative demand has increased by approximately 100 mm during the past four decades [19] contributing to increased aridity and impacts on water resources. A critical, yet understudied, aspect of the water budget is the role of increased evap- orative demand on crop water demand and consumptive use in the Central Valley’s irrigated agriculture. Sustainable water management is of particular importance in the southwestern US given the dual stressors of climate change and overallocation of water resources [20]. Prior studies have examined climate-induced changes in water supplies from precipitation [21] and snow- pack [22], and their collective influence on the seasonal availability of water for irrigated agri- culture [23]. Likewise, the demand side of the water budget through changing evaporative demand with climate change will impact ecosystem disturbance [24, 25], water resources [26], and drought [27, 28]. Earlier studies at various scales have examined projected changes in irri- gation requirements due to climate change [29–34], yet their applicability to California charac- terized by rather diverse set of crops including specialty perennial crops is limited. Similar studies for California [35] overlay projected cropping patterns, climate projections, supple- mental pumping to fully offset interannual shortage, soil salinization, and potential phenologi- cal response in the western SJV, which merits revisiting in light of new groundwater regulation and better understanding of the water balance elements for planning. Other studies have coupled this increased demand with other anthropogenic drivers such as increasing human population [36]. More often, however, climate warming-induced increases in crop water demand and evapotranspiration loss are embedded within larger hydroclimatic model- ing exercises that couple climate projection-perturbed crop water demands and cropping fac- tors [37]. Plant physiological responses to increased air temperature and elevated carbon dioxide as related to water use efficiency have been studied [38], but fewer studies have exam- ined the climate change-induced increases in crop water demand. Given the complexities of agronomic and ecological feedbacks, including but not limited to phenology, disease, and agri- cultural practice, this area of research remains understudied and carries high uncertainty [39]. In this study, we quantify the role of observed changes in climate on crop water demand in California’s SJV, independent of changes in land use and management practices. We present the climate change-induced change in crop water demand in the context of the agricultural water budget during the past four decades. We conclude by characterizing these results as an PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 2 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand invisible water surcharge on our global food system, joining other pervasive human-induced impacts to the Earth where many effects are pervasive but not immediately visible, including groundwater depletion [40], ocean acidification [41], soil degradation [42], and biodiversity loss [43]. 2. Materials and methods 2.1 Study area The SJV study area is bounded by the San Joaquin River and Tulare Lake Basin hydrologic regions, which have the subject of research on water balance studies [14, 44, 45]. The SJV region in this study covers 3.6 million hectares of land and around 1.8 million ha of irrigated cropland [44] and is home to seven of California’s top ten producing counties in a state gener- ating over $54.5B in gross agricultural receipts, the highest in the US and accounting for 11% of the national share [46]. Within the SJV, Fresno, Kern, and Tulare Counties are three of the US’s leading counties in agricultural sales [47]. As of 2018, the SJV’s farmland was 28% almonds (437,516 ha), 11% pistachios (169,163 ha), 10% vineyards (164,914 ha), 6% citrus (98,906 ha), 6% cotton (97,691 ha), 5% alfalfa (84,840 ha), 4% processing tomatoes (60,545 ha), and 30% (466,419 ha) all other crops, respectively (Fig 1). The SJV is an important focus region for several reasons, not least of which is its importance to the global food supply, where Fig 1. Map of the crop area of the San Joaquin Valley, showing the areas of almonds (blue), citrus (orange), pistachios (pink), cotton (red), alfalfa (green), vineyards (purple), and all other crops (yellow). Map data sources are Cropping from California Natural Resources Agency; California State Outline from TIGER 2016 United State Census Bureau; and Global base map from Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community. https://doi.org/10.1371/journal.pwat.0000184.g001 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 3 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand California’s export crop trade accounts for $6.3B to Asia and $3.5B to Europe [48]. None of this agricultural productivity would be possible without the irrigation supply provided by a vast and intricate statewide water supply capture, storage, and delivery system network. Agri- culture in the SJV consumes an annual average of 17,880 GL, with water imports to the SJV averaging 4000 GL and an additional 2200 GL in groundwater overdraft [14]. Given this water imbalance, climate warming is expected to affect both water availability and demand by agri- culture in this region [44]. 2.2 Estimation of crop evapotranspiration We followed the crop coefficient approach described by the FAO-56 irrigation and drainage paper [49] for calculating the potential crop evapotranspiration (ETc) which we will summa- rize in this section. First, it is important to clarify that we define ETc as the evapotranspiration that would occur under standard optimal growing conditions and full production [49]. ETc differs from actual evapotranspiration (ETa) which is modulated by crop management (e.g., irrigation practices). We acknowledge that ETc differs from ETa, and we are not suggesting that they are equal, but rather that ETc represents potential ET under well-watered conditions. By focusing on ETc, we isolate the climate change effects on the agricultural water budget with- out concern about variations in crop management over the study period from 1980 to 2023. We chose not to look at ETa for two reasons: (1) lack of long-term ETa data from the last four decades in the SJV, and (2) irrigation management practices have changed in the last four decades, making it difficult to differentiate the climate change effects from changes in manage- ment practices effects on ETa. While SJV agricultural landscapes have changed substantially in cropping mix in recent decades (e.g., perennial crop acreage expansion over annual crops [50]), we focus on climate contributors using a static 2018 agricultural land use for our ETc baseline calculations. Here, we go into detail about the approach for calculating ETc. The first step to using this method is to calculate the grass-based reference evapotranspiration (ETo) using the American Society of Civil Engineers (ASCE) standardized Penman-Monteith (PM) equation [51] (shown by Eq 1). ETo ¼ 0:408D Rn (cid:0) G ð ð Þ þ g 900 Tþ273 ð D þ g 1 þ 0:34u2 ð Þ u2 es (cid:0) ea Þ Þ ð1Þ where ETo is the reference evapotranspiration [mm day-1], Rn is the net radiation at the crop surface [MJ m-2 day-1], G is the soil heat flux density [MJ m-2 day-1], T is the mean daily air temperature at 2 m height [˚C], u2 is the wind speed at 2 m [m s-1], es is the saturation vapor pressure [kPa], ea is the actual vapor pressure [kPa], es − ea is the saturation vapor pressure def- icit [kPa], Δ is the slope of the vapor pressure curve [kPa ˚C-1], and γ is the psychometric con- stant [kPa ˚C-1]. This version of the Penman-Monteith equation defines the reference crop as a hypothetical crop with a height of 0.12 m, a surface resistance of 70 s m-1, and an albedo of 0.23, which resembles a well-watered smooth green grass of uniform height [49]. The data for calculating ETo using Eq 1 was from gridMET. The gridMET gridded dataset includes daily surface meteorological data at ~4 km resolution (1/24th degree) across the con- tiguous United States, starting from 1979 [52]. The primary climate variables include maxi- mum daily air temperature, minimum daily air temperature, precipitation accumulation, downward surface shortwave radiation, wind velocity, specific humidity, maximum daily rela- tive humidity, and minimum daily relative humidity. These climate variables are used to derive the grass reference evapotranspiration (ETo) using [49] Eq 1, also included in the gridMET PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 4 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand dataset. While gridMET is one of several datasets for estimating gridded ETo, others have shown largely similar variability and trends in the SJV region [19]. The next step of the crop coefficient method for determining ETc is to employ a five-point crop coefficient model (Kc), which simulates the four major crop development stages, initial (constant Kc), rapid growth (increasing Kc), mid-season (constant Kc), and late season (decreasing Kc), for 17 major crop categories in the San Joaquin Valley, namely grain, rice, cot- ton, corn, dry beans, safflower, alfalfa, processing tomatoes, cucurbits, onions and garlic, pota- toes, truck crops, other field crops, almonds, and pistachios, other deciduous trees, citrus and subtropical, and vineyard. The Kc values and typical dates of development stages were obtained from the California Department of Water Resources [53]. Static typical dates of development stages were used instead of dates based on year-to-year variability. We note that changing crop phenology associated with climate variability and change may complicate this assumption. Warming may in some cases hasten crop maturation [54] and reduce the number of days irri- gation is needed [55]. Likewise, warmer and longer growing periods may enable management strategies such as double cropping that may increase irrigation needs [56], or increasing cropped acreage and subsequent water demand [57]. Herein, we constrain our focus to static crop phenology to avoid confounding phenological and management influences on changing crop water demand. The daily Kc values for each crop development stage of each crop category are reported by California Detailed Analysis Units (DAU), which are sub-boundaries within the SJV. The Kc values for rice, alfalfa, processing tomatoes, corn, onions, almonds and pista- chios, vineyard, and citrus and subtropical were developed by measuring ET using eddy covariance or surface renewal methods at sites in California (personal communication with DWR staff). The Kc values for all other crops were obtained from FAO-56 [49]. We employed main season summer crop land use data from the 2018 Land IQ land use sur- vey [58], which claims a 95% accuracy by combining remote sensing, statistical, and temporal analysis techniques. Land use from 2018 was used instead of dynamic land use as other spatial data (e.g., Cropland Data Layer [59]) are unreliable for California because of high proportions of specialty crops [60], and the 2018 snapshot is a conservative estimate [9]. By keeping land use constant, we controlled for the effects of the land use changes on ETc and focused on the climate change effects on ETc. Crop evapotranspiration (ETc) under well-watered, optimal management conditions were simulated using the FAO-56 single crop coefficient equation, by multiplying total ETo (in mm) from gridMET and the single crop coefficient Kc [49]. To obtain a single Kc coefficient, we first calculated a monthly Kc, by averaging the daily Kc values by month for each crop category, to account for crop phenology. We then calculated a weighted mean Kc based on weights of the area of each crop category i (wi) in each DAU, using Eq (2): wi ¼ Crop Areai DAU Area For every DAU and each month, a wi weighted mean Kcw was calculated and the Kc,i for each crop category i over the total of crops n: Kcw ¼ � (cid:0) Xn i¼1 Xn Kc;i � wi wi i¼1 ð2Þ ð3Þ Gridded monthly totals of ETo from gridMET were clipped and averaged by the study area boundary. Monthly total ETc was calculated using average SJV monthly Kcw, average SJV PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 5 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand monthly total ETo, and the single crop coefficient approach using Eq (4) [49]: ETc ¼ Kcw � ETo ð4Þ The total ETc was summed on an annual water-year basis (running from October 1 of the previous calendar year through September 30). For example, water-year 1980 is from October 1, 1979, to September 30, 1980. Gridded monthly totals of precipitation (P) from gridMET were clipped by the study area boundary and then summed on an annual water-year basis to produce annual water-year total P. 2.3 Selection of the baseline period We identified 2012 as the starting point for recent observations using a breakpoint analysis of ETc timeseries [61]. This date also corresponded with the start of the first multi-year drought of the past decade. The period preceding 2012 (water-years 1980–2011) was used to define the baseline. The mean annual water-year total ETc and P between 1980 and 2011 were calculated to produce the baselines. The baselines averaged across the extent of the SJV were 1,014 mm/ year for ETc and 262 mm/year for P. 2.4 Wilcoxon rank sum test A Wilcoxon rank-sum test was conducted for determining whether the ETc was significantly higher in 2012–2023 compared to the baseline period. The same test was also applied for P and vapor pressure deficit (VPD). The ETc, P, and VPD were separated into the baseline period (1980–2011) and the 2012–2023 period. The non-parametric Wilcoxon rank-sum statistic tests the null hypothesis that two samples belong to the same distribution (i.e., no significant differ- ence in ETc between the baseline period and the 2012–2023 period). The alternate hypothesis is that the values in one sample group have a higher probability to be greater than the values in the other sample (i.e., there is a significant difference in ETc between the baseline period and the 2012–2023 period). The p-value was calculated for each Wilcoxon rank-sum statistic and was considered statistically significant if less than 0.05 (5% significance level). 2.5 Linear regression analysis A linear regression analysis was performed to determine the interannual relationship between ETc and P in the SJV. The motivation behind this analysis was to quantify any shift in the rela- tionship between ETc and P between the two periods. The land-surface coupling between sur- face temperatures–a key driver of ETc−and P is widely recognized. We examined this relationship over the two periods (1980–2011 and 2012–2023) to assess whether changes in P alone account for changes in ETc. The regression model is shown in Eq 5. ETc tð Þ ¼ b0 þ b1P tð Þ þ b2BPWY2011 þ εi ð5Þ where β0 and β1 are the parameters estimated through the least-squares method, representing the baseline relationship between ETc and P. The term t denotes the water-year. The coefficient β2 captures the shift in ETc associated with the post-2011 period, as indicated by the breakpoint variable BPWY2011. This term factors for changes in ETc attributable to the transition across the breakpoint. The random error term is represented by εi. Notably, tests for variable interaction within this model indicated no significant differences in the slope coefficients (i.e., β1) between these periods, suggesting a consistent effect of P over time. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 6 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand 2.7. Anomalies calculations Anomalies in the annual water-year total (P—ETc) were calculated as the difference between annual water-year total (P—ETc) and the mean annual water-year total (P—ETc) between 1980 and 2011. The cumulative annual anomalies in climate variables can be helpful to describe the chronic spells of anomalous moisture deficits and surpluses [62]. The cumulative annual anomalies of (P—ETc) were calculated for the water-years of the analysis period with the summation starting at water-year 1980. 3. Results Fig 2 shows time series of water-year total ETc and mean VPD during the last four decades. As expected by the Penman-Monteith equation, the annual total ETc mimicked the same tempo- ral pattern as the annual mean VPD. Drier conditions and warmer temperatures have occurred since the turn of the 21st century. This climate data shows that the aridification pro- cess that happened in the last four decades corresponded to a trend toward increasing ETc. Water-year ETc increased by 44 mm, P decreased by 31 mm, and VPD increased by 0.20 kPa between the baseline and the 2012–2023 period. We additionally found that both ETc and VPD were significantly higher in the 2012–2023 period compared to the baseline (p-value of 0.0061 and 0.0004, respectively). By contrast, water year P was not significantly different in the last decade compared to the baseline (p-value = 0.2). Fig 2. (a) Time series of the annual total crop evapotranspiration (ETc) in mm, (b) time series of the mean vapor pressure deficit (VPD) in kPa in the San Joaquin Valley during water-years 1980 to 2023. https://doi.org/10.1371/journal.pwat.0000184.g002 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 7 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand The increasing ETc in the last four decades without a significant increase in P has implica- tions on the overall agricultural water budget. Although annual total ETc always exceeded P in the SJV during 1980–2023 (Fig 3), this climate shifted during the 1980–2023 study period to higher ETc. Specifically, the annual total ETc was on average 3.9 times higher than on-farm P in water-years 1980–2011 but shifted to 4.6 times higher in water-years 2012–2023. This increase in ETc relative to P in the past decade coincided with two historical warm droughts in 2012–2016, and 2020–2022, further amplifying climate driven pressures on the SJV agricul- tural water balance. These observations hold despite the very wet conditions of the 2023 water- year. Aggregated across the agricultural area of the SJV, the increase in ETc translates to an annual increase of 717.9 GL (582 thousand-acre-feet, TAF) in crop evaporative demand during 2012–2023 compared to the 1980–2011 baseline. We call this increase in climate change induced ETc an invisible water surcharge. We want to emphasize that this “surcharge” is not an economic phenomenon, but rather a climate change phenomenon and demonstrates both the hidden nature and the additional burden of the increased water demand due to climate change. The invisible water surcharge is equivalent to a 4.4% increase in ETc with respect to precedent decades, and it is equivalent to more than two thirds (67%) of the annual urban water use of the 4.3 million human population of the SJV, assuming 681 L (180 gal) per capita Fig 3. (a) Time series bar chart of the average San Joaquin Valley water-year annual total precipitation (P) and the baseline for a 1980–2011 baseline shown by horizontal dashed line, and (b) time series bar chart of the average San Joaquin Valley water-year annual total potential crop evapotranspiration (ETc) and the baseline for a 1980– 2011 baseline shown by horizontal dashed line. https://doi.org/10.1371/journal.pwat.0000184.g003 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 8 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand per day [63, 64]). The average annual increase in ETc not only exceeds the capacity of the SJV’s Millerton Lake Reservoir (690 GL or 520 TAF), but when integrated over the 12-year study period, it exceeds the combined storage capacity of SJV’s five largest reservoirs (8,560 GL or 6,938 TAF [65]). The extremely dry water-year 2021 featured a 12.3% increase in ETc relative to the baseline —equivalent to 2,036 GL (1,651 TAF), which represents almost twice the total annual urban water use, and three times the capacity of SJV’s Millerton Lake Reservoir (641 GL or 520 TAF), which diverts most of the San Joaquin River for irrigation. There is a strong negative interannual correlation between water-year P and ETc that com- pounds drought impacts in the SJV (Fig 4). The linear regression analysis resulted in a constant relationship (ETc = −0.368 × P), but statistically different ETc intercepts between baseline (1110.7 mm) and the 2012–2023 period (1143.8 mm), suggesting a potential shift in the hydro- climate. The divergence between the regression lines for these two periods likely suggests that the low precipitation years during 2012–2022 cannot fully explain the notably upward shift in ETc. Notably, 8 of the 10 warmest summers in the state since 1895 occurred during 2012–2023 [66] highlighting a shift to hot-dry compound extremes. Fig 4. A scattergram of the average San Joaquin Valley total water-year (October 1 to September 30) annual potential crop evapotranspiration (ETc) in mm versus average San Joaquin Valley total annual precipitation (P) in mm with linear regression lines for 1980–2011 (blue) (n = 32) and 2012–2023 (red) (n = 12). Bands around the regression lines are 95% confidence intervals. The colors of the scatterplot points indicate the average San Joaquin Valley annual water-year mean air temperature ranging from 16.3 to 19.0˚C using the blue-red color bar. Labels of the scatterplot points are water-years. https://doi.org/10.1371/journal.pwat.0000184.g004 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 9 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand Fig 5. Annual water-year anomalies of ETc relative to the from the mean ETc of the period 1980–2011. https://doi.org/10.1371/journal.pwat.0000184.g005 The anomalies in ETc from the baseline tended to increase during the last four decades (Fig 5). In the 2012–2023 period, the ETc was higher than the baseline in nine out of twelve of those years, indicating that the crop water demand was usually higher than average in recent years. The difference between P and ETc is a simplified metric of the crop irrigation demand that must be supplied from reservoir storage, imports from the upper watersheds of the Sierra Nevada, and groundwater reserves. We calculated the cumulative crop water balance depar- tures in (P—ETc) from baseline conditions to quantify changes in agricultural water deficits. Anomalies or differences between each water-year annual total (P—ETc) from the baseline indicate whether each water-year had excess (P—ETc) or a deficit in (P—ETc) compared to the baseline (Fig 6). The cumulative anomalies show a net increased deficit in (P—ETc) since the turn of the 21st century. We additionally demonstrate that increased ETc explains half of the cumulative departure in P—ETc in the last decade. This result means that ETc should be con- sidered alongside interannual variations in P to comprehensively assess the agricultural water budget. 4. Discussion The key finding in this study was that the invisible water surcharge was equivalent to a 4.4% increase in crop water demand in the last decade compared to precedent decades. Agricultural droughts are not only caused by precipitation deficits, but more and more by increased crop demands. We want to emphasize that crop water demand normally exceeds precipitation in the SJV, requiring surface water imports and groundwater supplies to fulfill the deficit, with greater reliance on groundwater extraction during drought water-years with diminished snow- pack [14]. Thus, it is important to understand that the 4.4% increase in crop water demand in the last decade due to the invisible water surcharge is on top of the challenges of irrigation- dependent agriculture in the SJV due to the normal Mediterranean climate. The magnitude of the invisible water surcharge calculated here is similar to the acceleration of groundwater PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 10 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand Fig 6. Annual water-year anomalies of P—ETc (the difference between precipitation and crop evapotranspiration on the San Joaquin Valley floor) from the mean P—ETc of the period 1980–2011, shown by the bar chart. Annual water-year cumulative anomalies of P—ETc from the mean P—ETc of the period 1980–2011, starting in 1980, are shown by the black line. The green and blue bars isolate the ETc and P portions of the (P—ETc) anomaly, respectively. The California major drought years [9, 67] are shown in grey. https://doi.org/10.1371/journal.pwat.0000184.g006 overdraft reported for the Central Valley [5] emphasizes the importance of groundwater regu- lations such as SGMA [13]. Further research should focus on confirming whether the invisible water surcharge has contributed to the acceleration of groundwater overdraft that was reported in recent years [5]. Any contribution of the invisible water surcharge to the acceleration of groundwater overdraft should be factored into long-term groundwater sustainability manage- ment plans. The increasing crop water demand should be considered alongside increasing inter-annual P variability [21] in water planning and management to comprehensively account for the effects of climate change. As stated earlier, the annual total ETc was on average 3.9 times higher than on-farm P in water-years 1980–2011 but shifted to 4.6 times higher in water-years 2012–2023. Furthermore, previous work showed that evaporative demand will continue increasing across the broader region of California due to climate change [24], which will further the gap between water availability and demand. Other recent studies have focused on the increasing range of extreme hydroclimate events and thus shifting baseline conditions for water budgets that may obscure near term anomalies [68]. This observed shift in the hydroclimate in the SJV calls for policy changes, such as improved water demand management (e.g., establishing groundwater pumping allocations and re-evaluating the mix of annual and perennial crops) and a reconsid- eration of water storage options such as the expansion of groundwater banks [69]. The invisible water surcharge jeopardizes the sustainability of fruit, nuts, and vegetable pro- duction in the SJV by adding an increased reliance on groundwater for irrigation under PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 11 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand declining snowpack storage and unreliable imports [70, 71]. While this study focused on the invisible water surcharge in the SJV, the same analysis could be replicated in other regions with a Mediterranean climate to obtain a broader picture of the role of climate change on surcharg- ing the sustainability of fruit and nut production. Specifically, the “Old World” Mediterranean Basin is anticipated to undergo both increased human population growth and accelerated cli- mate change impacting both water and food security, exacerbating existing environmental problems caused by a combination of changes in land use, increasing pollution and declining biodiversity [72]. While improving irrigation efficiency [73] is seen as a potential adaptation strategy to the invisible water surcharge, it is unlikely to overcome the severity of climate change induced droughts in the Mediterranean Basin [74] and points to a need for more com- prehensive water budget accounting [75]. In the “New World” Mediterranean, which includes Australia [76], California, Chile [77], and South Africa [78], similar evidence of increasing crop water demand due to climate change has been found. Many fruit and nut crops can only be grown in a Mediterranean climate; thus, the invisible water surcharge has important impli- cations on the types of crops within our global food supply. California’s historic drought policies have focused on the multi-year major droughts in 1987–1992, 2007–2009, 2012–2016, and 2020–2022 shown in Fig 5 [9, 67]. While California’s multi-year major droughts are important, they should be considered along with the long-term effects of the invisible water surcharge on the agricultural water balance shown by the anoma- lies in (P-ETc) in Fig 5. The percentage of P and ETc contributing to the anomalies of (P-ETc) during major droughts shows that the P portion of (P-ETc) has decreased while the ETc por- tion of (P-ETc) have increased during the last four decades. Specifically, the P portion of (P-ETc) during the major droughts in California (1987–1992, 2007–2009, 2012–2016, and 2020–2022) tended to decrease (89%, 64%, 58%, and 50%, respectively) while the ETc portion of (P-ETc) tended to increase (11%, 36%, 42%, and 50%, respectively). Increased drought risk [28, 68] coupled with climate change induced increased ETc suggests that water policy should treat warm and dry conditions as a long-term and worsening phenomenon rather than tempo- rary multi-year droughts. Even after a major drought is over, the effects of climate change on the agricultural water balance continues and, thus, groundwater policy should always factor in shifts in the hydroclimate. This conclusion is reinforced by the inclusion of the historical pre- cipitation rebound in 2023. It is worth noting that the use of the crop coefficient method for estimating ETc was essen- tial for this analysis. The long-term ETo data provided by the gridMET repository made it pos- sible to analyze changes in the crop water demand over the last four decades using the crop coefficient approach [49]. While ETa data exist for the San Joaquin Valley, such as OpenET [79], these data do not span a sufficiently long time series for assessing long-term climate- induced increases in crop water demand and are subject to a host of uncertainties—such as the potential shortening of the growing season due to more rapid phenological development with warming for some crops—that are beyond the scope of this study. Furthermore, ETa is affected by management practices and changing crop mixes [49], preventing the isolation of climate- induced changes in crop water demand. Ultimately, this defends the usefulness of the crop coefficient approach for estimating crop water demand in climate change studies, despite its limitations. Lastly, the extent that crop response to increased carbon dioxide concentrations [80] might offset increased crop water demand remains unknown for the SJV. Assuming static land use was essential for this analysis. We explicitly isolate the climate fac- tors associated with increased crop water demand by using static 2018 cropland for the analysis period. While we show that a warmer climate has contributed to a change in crop water requirements, projecting future change in crop water demands merits assumptions on future cropping patterns including the increased share of perennials [81] and varieties with higher PLOS Water \| https://doi.org/10.1371/journal.pwat.0000184 March 13, 2024 12 / 17 PLOS WATERClimate warming increases the San Joaquin Valley’s crop water demand economic return per unit area or applied water [82]. Further research using finding from this approach may characterize the relative contributions of changing crops patterns, groundwater regulation and climate change in the invisible water surcharge in the SJV. Likewise, while our study held crop phenology, physiology, and management constant through time, additional work is needed to elucidate the roles these factors have on crop water demands with a warming climate. 5. Conclusions California’s recent warm droughts call for the investigation into the effects of climate change on the agricultural water balance. Although decreased precipitation is usually the focus of droughts, in this study, we demonstrate that increased crop water demand is also a flagship of California’s warm droughts. Here, we quantified the increased crop water demand using a gridded meteorological dataset using a crop coefficient approach which does not depend on changing land use and management practices. The results showed that the 2012–2023 period had significantly higher crop water demand than the baseline 1980–2011 period, indicating that climate-induced changes in crop water demand should be considered with precipitation in water resources planning and management. Through an analysis of cumulative anomalies, we showed that the chronic increases in crop water demand over the last four decades explain half of the cumulative deficits in the agricultural water budget, and this trend is expected to worsen in the future. From these results, we can conclude that ignoring climate-induced increased crop water demand will impact the agricultural water balance, calling for further research to focus on the implications of this phenomenon on groundwater depletion in groundwater-dependent irrigated agriculture. Acknowledgments We are grateful to Sarah Naumes and Nick Santos for Secure Water Future (https:// securewaterfuture.net) coordination, and Anna Rallings for assistance with early portions of this study. Author Contributions Conceptualization: Kelley Moyers, John T. Abatzoglou, Alvar Escriva-Bou, Josue´ Medellı´n- Azuara, Joshua H. Viers. Data curation: Kelley Moyers, John T. Abatzoglou. Formal analysis: Kelley Moyers, John T. Abatzoglou, Alvar Escriva-Bou, Joshua H. Viers. Funding acquisition: Joshua H. Viers. Methodology: Kelley Moyers, John T. Abatzoglou, Alvar Escriva-Bou, Josue´ Medellı´n-Azuara, Joshua H. Viers. Project administration: Joshua H. Viers. Visualization: Kelley Moyers, Alvar Escriva-Bou, Joshua H. Viers. Writing – original draft: Kelley Moyers. Writing – review & editing: Kelley Moyers, John T. Abatzoglou, Alvar Escriva-Bou, Josue´ Medellı´n-Azuara, Joshua H. Viers. 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10.1371_journal.pwat.0000226	RESEARCH ARTICLE Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment Francisco Rodrigues do AmaralID Nicolas Gratiot1,2 1, Thierry Pellarin1, Tin Nguyen Trung2, Tran Anh Tu2,3, 1 Universite´ Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, Grenoble, France, 2 CARE, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Viet Nam, 3 Vietnam National University-Ho Chi Minh City (VNU-HCM), Thu Duc City, Ho Chi Minh City, Viet Nam francisco.amaral@univ-grenoble-alpes.fr Abstract The Surface Water and Ocean Topography (SWOT) mission aims to provide essential data on river width, height and slope in order to estimate worldwide river discharge accurately. This mission offers a powerful tool for monitoring river discharge in dynamic coastal areas, like the Saigon-Dongnai estuary in Southern Vietnam. However, estimating discharge of tid- ally-influenced rivers using SWOT measurements can be challenging when hydraulic vari- ables have the same order of magnitude as SWOT measurement errors. In this paper we present a methodology to enhance discharge estimation accuracy from SWOT measure- ments based on simulated SWOT products at the 200 meter node resolution and varying river reach size. We assess measurement error variability and its impact on discharge esti- mation by employing a Monte Carlo analysis. Our approach significantly improved discharge estimation in the Saigon tidal river, reducing RMSE from 1400 m3/s to 180 m3/s and increas- ing R2 from 0.31 to 0.95. Notably, the percentage of Monte Carlo particles meeting the 30% rRMSE threshold rose from 0% to 79%. This study underscores the feasibility of obtaining reliable discharge estimates from SWOT data in complex coastal areas where hydraulic var- iables are of the same order of magnitude as SWOT errors. Additionally, the proposed meth- odology to improve discharge estimation from SWOT measurements is widely adaptable as it can be applied to similar regions and can be combined with any discharge estimation method. 1. Introduction Geophysical processes occurring at the surface of the Earth in low elevation coastal zones (LECZs) are primarily regulated by the interplay of river discharge and the intricate dynamics imposed by the coastal ocean. Within this transitional zone, there exists a region often referred to as “the tidal river”. This area is profoundly influenced by tidal movements, surges, and the variations in mean sea level but remains devoid of significant salinity. It is a stretch of the river that is impacted by these marine forcing processes, yet it has historically received relatively a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Rodrigues do Amaral F, Pellarin T, Trung TN, Anh Tu T, Gratiot N (2024) Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment. PLOS Water 3(2): e0000226. https://doi.org/10.1371/journal. pwat.0000226 Editor: Daniel Reddythota, Faculty of Water Supply & Environmental Engineering, ArbaMinch Water Technology Institute (AWTI), ETHIOPIA Received: October 11, 2023 Accepted: January 10, 2024 Published: February 12, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pwat.0000226 Copyright: © 2024 Rodrigues do Amaral et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The in-situ data used in this study was acquired from the following open- PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 1 / 25 access publication: https://www.sciencedirect.com/ science/article/pii/S2352340923002664?via% 3Dihub This data can be downloaded directly from the following public repository: https://dataverse. ird.fr/dataset.xhtml?persistentId=doi:10.23708/ NKQDNB. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Enhancing discharge estimation from satellite data in a tidal river environment little attention from the geophysical research community [1]. Notably, these tidal river reaches can extend inland for hundreds of kilometers, and in the case of larger rivers, they often rival or surpass the estuary in terms of both length and sometimes even area. Predicting water levels in tidal rivers or estuaries is a challenge due to the highly variable flow conditions that charac- terize these environments. The hydrological dynamics within a tidal river are inherently dis- tinct, marked by a constant flux in water levels driven by the interplay of hydro-meteo-marine phenomena. Consequently, the hydrodynamic processes governing tidal rivers defy simplifica- tion, showcasing traits such as complexity, non-stationarity, and nonlinear behavior [2]. Remote sensing observations play a pivotal role in providing information on the spatial var- iations of water surface elevations across diverse hydrodynamic scenarios. In the last two decades, satellite radar altimeters, which excel at gauging sea level fluctuations, have intro- duced significant advancements in our understanding of ocean dynamics [3, 4]. However, their utility has been notably impeded when applied to coastal environments, where data accu- racy decreases as these instruments approach coastal regions. This limitation is further exacer- bated by the inherent constraints of nadir altimetry missions, exemplified by missions like Topography Experiment/POSEIDON (TOPEX/POSEIDON) and the Jason series [5]. These missions grapple with challenges posed by inter-track spacing and temporal resolution, which curtail their ability to effectively observe smaller-scale coastal phenomena, including shelf tides, coastal tides, wind-induced effects, and storm surges [6]. A transformative shift in the landscape of coastal remote sensing is to be expected with the National Aeronautics and Space Administration/Centre National d’E´tudes Spatiales (NASA/CNES) Surface Water and Ocean Topography (SWOT) satellite mission, launched on December 15th, 2022. The SWOT satellite signifies a leap forward in our capacity to estimate global river dis- charge, substantially enriching our observational foundation for understanding worldwide hydrological phenomena [7]. River discharge measurements offer a comprehensive synthesis of upstream water cycle processes, rendering them indispensable data resources for gaining insights into hydrology across scales, from watershed dynamics to continental-scale patterns. Nonetheless, a substantial portion of the world’s rivers remains unmonitored, owing to vari- ous constraints encompassing resource limitations and data sharing challenges [8, 9] espe- cially in the inter-tropical zone [10]. Remote sensing techniques aimed at gauging river discharge hold the potential to extend global observation capabilities, even in regions lacking conventional gauging infrastructure. However, this expanded reach comes with trade-offs. Remote sensing methods generally entail compromises, encompassing reduced measure- ment precision, accuracy, and temporal sampling frequency when compared to in-situ dis- charge monitoring practices [11]. SWOT’s capability of capturing parameters such as river water surface elevation (WSE) (also referred to simply as river water level), top width, and longitudinal water surface slope (also referred simply as slope) [12] enables estimations of river discharge. The SWOT satellite, equipped with advanced radar altimetry technology and unprece- dented spatial resolution, offers an innovative opportunity to quantify river discharge across the globe with unparalleled accuracy. However, harnessing SWOT data for discharge estima- tion requires comprehensive evaluation and optimization, particularly in complex hydrologi- cal settings such as tidal rivers. The SWOT team’s objective for the coastal ocean is to “quantify the water exchanges between coastal regions, estuaries, deltas, and wetlands” [13]. It is in accordance with this objective that we propose an evaluation of the capability of future SWOT satellite measurements in estimating water discharge in the Saigon river situated in Southern Vietnam. The Saigon river is a tidal river located in a densely populated, highly com- plex LECZ. The study of the hydrodynamics and hydrology of the Saigon River is important in water resource management, flood control, and navigation in the Ho Chi Minh City (HCMC) PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 2 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment megalopolis. Nonetheless, this case study has the potential to be applied to other megalopolis sharing comparable human and geophysical environments. Another reason for the interest in how SWOT satellite measurements can be used to esti- mate tidal river discharge is the alarming flood vulnerability in coastal and other low-lying cit- ies [14]. In particular, HCMC is one of the most vulnerable cities in the world with respect to climate change [15]. Some of these vulnerabilities are water-related issues such as lack of urban services like drinking-water management, sanitation, rainwater drainage and com- pound flooding. Specifically, flooding vulnerability is linked to sea level rise, rainfall intensifi- cation, storm surges and ground subsidence while 65% of the city is located at less than 1.5 m above sea level. In addition, HCMC is home to almost 10 million inhabitants and its popula- tion grows at about 3% per year with these risks posing a threat to many livelihoods [15]. Sev- eral studies consider HCMC as a hotspot of vulnerability to climate change [16–21]. However, there has been little comprehensive studies regarding the potential of SWOT in an estuary set- ting such as the one in this study area. Furthermore, we suggest an innovative approach to sim- ulate SWOT data and enhance its quality. This method is adaptable and can be applied to other intricate tidal river systems within dynamic coastal areas. Given the accessibility of hydrological variables derived from SWOT, characterized by spe- cific resolutions and accuracy thresholds, the scientific community has put forward multiple methodologies for estimating river discharge. These methodologies are constructed upon dis- tinct assumptions and simplifications. For instance, Durand et al. 2016 [22] conducted a com- parative analysis, evaluating the efficacy of five different algorithms that leverage SWOT-like observations in assessing flow rates across 19 major rivers. While this examination revealed that, in nearly every case (specifically, 14 out of 19 instances) at least one approach yielded sat- isfactory performance as indicated by a root mean square error below the 35% threshold, it underscores that achieving precise and dependable discharge estimation remains a topic of concern. However, SWOT capabilities for estuaries and tidal rivers have received compara- tively little attention [5, 23, 24]. This calls for continued research efforts in the field to enhance the accuracy and reliability of these estimations, particularly in LECZs, a focus that this paper seeks to address. In this paper we propose an original methodology to obtain high spatial resolution water level time-series directly from in-situ measurements. Then, we re-create SWOT-like measure- ments of water level and slope along the Saigon river, coupled with innovative reach size selec- tion to estimate river discharge. Through an extensive analysis, we evaluate the performance of this methodology under various conditions, including reach sizes, discharge values, and error considerations. Our investigation explores the limitations of the expected SWOT-like dis- charge estimation errors and proposes an enhanced approach that leverages SWOT data more effectively. We assess the impact of reach size, a critical parameter in SWOT-based discharge estimation, on accuracy and computational efficiency. By conducting a Monte Carlo analysis, we scrutinize the sensitivity of discharge estimates to errors inherent in SWOT-like measure- ments [25, 26]. Ultimately, this research advances the broader goal of harnessing the potential of SWOT measurements to enhance our understanding of tidally forced riverine systems, con- tributing to improved water resource management and environmental conservation efforts in vulnerable LECZ. 2. Materials and methods 2.1. Study area description The Saigon-Dongnai River system is located in Southern Vietnam (Fig 1). The Saigon river branch is a complex river system, subject to several human and environmental interactions PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 3 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Fig 1. Map of the study area. The Saigon-Dongnai river system and the two SWOT satellite swaths (hatch pattern) and nadir lines (dotted line) that cover the area. The river is covered by both swaths in areas where the red and blue hatch patterns intersect and form squares. The water level gauges namely, Cu Chi (CC), Hobo 1 (H1), Hobo 2 (H2), Hobo 3 (H3), La Garden (LG) and Dongnai 2 (DN2) are shown as well as the ADCP campaign locations (triangles). The spline spatial interpolation nodes at every kilometer is illustrated as white dots. The area shown in the larger map is represented by the green box in the overview map. Basemap used in main map can be downloaded from www.igismap.com/vietnam-shapefile- download-country-boundaryline-polygon (under CC BY 3.0 IGO licence. For terms of use please see: https://map.igismap.com/terms-of-services). Basemap used in the overview map downloaded from www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries (public domain data see www.naturalearthdata.com/about/terms-of-use/ for the terms of use). https://doi.org/10.1371/journal.pwat.0000226.g001 before flowing into the Dongnai River and finally, into coastal waters. It flows from its source in Cambodia to the Dau Tieng Reservoir (270 km2 and 1,580 � 106 m3) before passing through the HCMC megalopolis. In total, it is 225 km long and its catchment area has a surface of about 4,800 km2. The Dau Tieng Reservoir was designed for flood control, water supply, and preventing saltwater intrusion [27, 28]. The hydrodynamics of the Saigon River are influenced PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 4 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment by various factors including tidal processes, freshwater inflows, and sediment transport. Tidal dynamics play the most significant role in shaping the river’s behavior, with tidal amplitudes ranging from 1 (neap tide) to 4 meters (spring tide) [29] and water discharge ranging between -1500 m3s−1 and 2000 m3s−1[30]. Tidal fluctuations dominate the totality of the river Saigon from the confluence with the Dongnai river to the outlet of the Dau Tieng reservoir. In fact, at the seasonal scale the river water levels are controlled by the coastal water levels [20]. The HCMC megalopolis, with a population of approximately 10 million inhabitants, is located along the banks of the Saigon River and is the largest and most densely populated city in Vietnam. The population is concentrated in the heart of the city, with the urban districts housing about 6.7 million inhabitants [31]. The city’s rapid urbanization, economic develop- ment, and population growth have posed challenges including river pollution, flood risk, and wastewater management [32, 33]. 2.2. Measurement campaign A high-resolution dataset comprising water level measurements and Acoustic Doppler Cur- rent Profiler (ADCP) campaigns was collected during a 2-month field campaign conducted in the study region, as part of a collaborative effort between the Centre Asiatique de Recherche sur L’Eau (CARE, Vietnam) and the Institut des Ge´osciences de l’Environnement (IGE, France). The campaign was specifically designed to support the hydraulic study aimed at assessing the applicability of the SWOT satellite to the Saigon-Dongnai estuary system. The dataset includes water level measurements at six selected locations along the Saigon River, namely Cu Chi (CC), Hobo 1 (H1), Hobo 2 (H2), Hobo 3 (H3), La Garden (LG) and Dongnai 2 (DN2) (triangles in Fig 1). The selection of these locations was bound by on-site constraints (inaccessibility to the riverbank, challenges in sensor installation, among others) and aimed to align with two primary criteria: i. comprehensively covering key sections of the Saigon River, particularly within Ho Chi Minh City and immediately upstream, areas of significant societal interest and impact; and ii. maintaining a distance of approximately 10 km between sensors to emulate the spatial resolution of SWOT reach products. These measurements were obtained by a submersed brick-and-pipe installation of Onset HOBO U20L-01 water level loggers measuring absolute pressure at a time resolution of 15 minutes. To ensure accuracy, the water level measurements were barometrically compensated using air pressure measurements. The vertical reference point of the pressure sensors is differ- ent and unknown. In order to be able to compare the signals at different locations, all signals are normalized [34]. Furthermore, we use a slope correction parameter in our discharge esti- mation method that is used to compensate for the fact that the reference points of each loca- tion are different and unknown (explained in Sect. 2.6). Additionally, discharge measurements were obtained through three 24-hour ADCP campaigns during both symmetric and asymmet- ric tidal regimes at two locations (yellow triangles in Fig 1). Despite its relatively short dura- tion, the dataset provides unprecedented spatial and temporal resolution for water level and discharge measurements. The synchronous recording of multiple water level sensors along the river enables the study of water surface slope profiles. Furthermore, the concurrent discharge measurements facilitate the calibration of the discharge estimation law proposed in this paper. More detailed information on this dataset can be found in [34]. 2.3. The SWOT mission The SWOT mission, led by the National Aeronautics and Space Administration (NASA) and the French space agency (Centre National d’E´tudes Spatiales, CNES), in collaboration with the Canadian and UK space agencies (CSA and UKSA, respectively), aims to contribute to the PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 5 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment fundamental understanding of the Earth system by providing high spatial resolution and global measurements for ocean and inland water. The launch of SWOT took place on Decem- ber 16, 2022, and the mission is currently in the science phase. The first validated river prod- ucts of water surface elevation (WSE), width, and slope are expected to be available in April 2024. The mission focuses on ocean and terrestrial water bodies including lakes, reservoirs, wetlands, and rivers, with specific criteria such as surface area exceeding 62,500 m2 and rivers wider than 100 m, with the goal of potentially reaching 50 m [12, 35, 36]. The SWOT satellite’s core payload includes a Ka-band radar interferometer (KaRIn) operating at a frequency of 35.75 GHz (wavelength of 8.6 mm) and with a near-nadir incidence angle [37]. In terms of spatial coverage and revisit time, the SWOT satellite aims to achieve near-global coverage between 78˚S and 78˚N with a revisit time of approximately 21 days. The satellite will employ radar observations with pixels of about 6 m in the azimuth direction and varying from 10 to 60 m in the direction perpendicular to the azimuth. To meet mission requirements for observation accuracy, averaging procedures will be applied to the radar data [7, 12, 38]. The resulting SWOT products will be available within two swaths of 50 km width, separated by a gap of 20 km known as the “nadir gap”. The SWOT mission’s capabilities enable the measurement of water extent, water surface elevation, and slope, facilitating the estimation of river and global storage variation at sub- monthly, seasonal, and annual time scales [12]. The SWOT observations, with their finer spa- tial resolution, provide a global inventory of terrestrial water bodies, offering insights into lakes, reservoirs, wetlands, and rivers. The relative error for water-surface-area measurements is targeted to be less than 15% [9]. Additionally, SWOT products combined with hydraulic models or flow laws show potential to estimate instantaneous river discharge within 35% root- mean-square error [22]. The Saigon-Dongnai river system is monitored by two SWOT orbits: 90 and 271 (Fig 1 red and blue, respectively). This system will therefore receive two observations within the period of 21 days (i.e., satellite revisit time) namely, on day 4 (orbit 90) and day 10 (orbit 271). Together both orbits cover the entirety of the system at least once per 21 day period. The Sai- gon portion of the system is observed in an heterogeneous way in both space and time due to the nadir gaps on both orbits intercepting the river. Starting from the upstream Dau Tieng res- ervoir, the first 56 kms are observed on day 10; the section from kilometers 56 to 94 is observed on day 4 and the remaining section from kilometer 94 to the confluence with the Dongnai river is monitored on day 4 and 10. On the other hand, the Dongnai river is observed in its entirety by both orbits’ right swaths. The section of the Saigon river under scope extends from the CC water level gauge to the DN2 gauge downstream of the Saigon confluence with the Dongnai river (Fig 1, triangles). This section of about 90 km in length will be monitored once per orbit from CC to a few kilo- meters downstream of H1 and twice per orbit on the remaining river reaches up to DN2. The main interest of this study is to understand the potential of SWOT observations to observe instantaneous discharge in a low elevation tidal river given SWOT’s expected errors. Hence, the temporal resolution of the SWOT observations is not taken into account. 2.4. Spatial interpolation of in-situ measurements of water level In order to provide spatial continuity to the data, we chose to employ a quadratic spline inter- polation technique. We explored alternative interpolation techniques, including higher-order polynomials and Kriging, but these methods did not yield results as favorable as the second- degree polynomial interpolation (results not presented). The alternative techniques frequently introduced unrealistic characteristics into the water surface due to their increased degrees of PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 6 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment freedom. To define the spatial framework for our interpolation, we used the SWOT River Database (SWORD) [39]. The SWORD database contains the SWOT mission’s river vector products for each SWOT overpass, as detailed in the Jet Propulsion Laboratory (JPL) internal document [25]. These vector data products are the definition of SWOT river reaches and nodes for all observable rivers (width > 100 m) in the world. The database provides high-reso- lution river nodes (200 m) and reaches (� 10 km). The 200 meter nodes defined in SWORD for the Saigon river serve as the basis for our spatial interpolation. The quadratic spline interpolation technique was employed to estimate water levels at unmeasured locations between the in-situ measurement points. This method utilizes a piece- wise continuous quadratic polynomial to fit the available data points and provide a smooth representation of the water level distribution along the river. The quadratic spline interpola- tion relies on the assumption that the river water level between two measurement points can be approximated by a quadratic function. Let’s consider a set of in-situ measurements at dis- tinct locations along the river, denoted as (xi, yi), where xi represents the spatial coordinate and yi represents the measured water level at that point. The quadratic spline interpolant, I(x), between two consecutive data points (xi, yi) and (xi+1, yi+1), can be expressed as follows [40]: IðxÞ ¼ aiðx (cid:0) xiÞ2 þ biðx (cid:0) xiÞ þ ci; for xi � x � xiþ1 ð1Þ where ai, bi, and ci are the coefficients of the quadratic polynomial determined by solving a sys- tem of equations based on the boundary conditions and smoothness constraints. These con- straints ensure that the interpolant is continuous and has continuous first derivatives at the data points. Boundary conditions define the behavior of the interpolant at the endpoints of the data points. In our case, we used the natural boundary condition, where the second derivative at the boundary points is set to zero. This condition ensures a smooth and continuous curve throughout the interpolation range. Smoothness constraints impose constraints on the first derivatives of the interpolant at the internal points. These constraints guarantee a smooth tran- sition between adjacent quadratic polynomials and prevent any sudden changes or discontinu- ities. The quadratic spline interpolation method enforces these constraints, resulting in a physically sound representation of the water level variation along the river’s surface. To com- pute the quadratic spline interpolation, we utilized Python’s Scipy library [41]. The library pro- vides a convenient function that automatically determines the parameters for smoothness based on the data distribution. By using these parameters, the spline interpolation adapts to the characteristics of the in-situ measurements, ensuring an optimal balance between smooth- ness and fidelity to the observed data. The extensive amount of interpolant functions, I(x), results in a considerable number of parameters (ai, bi, and ci) as interpolant functions are com- puted piece-wise at each 15-minute timestep throughout the 1.5-month period of in-situ mea- surements (not shown). To assess the accuracy of the spline interpolation, we employ a framework encompassing three steps: firstly, we evaluate the RMSE and R2 between the measured and spline time-series, then we compute and compare different tidal metrics, also called datums, that are important for understanding the tidal behavior at each gauge location and its evolution as the tidal wave progresses upstream [42]. Finally, we use wave celerity as a reliable proxy for validation. In a tidal river such as the Saigon River, the tidal wave propagates upstream and is captured by all sensors along the river. The wave celerity is independent of a vertical reference for water level measurements and can serve as a reliable validation parameter for the spline interpolation results. The process of calculating wave celerity involves identifying points where the first derivative of water level measurements is zero at each sensor location. These points, which cor- respond to the maxima and minima of water levels, are tracked from the downstream to the PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 7 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment upstream sensors to determine travel times. Subsequently, wave celerity in the Saigon river is derived by computing the velocity of these maxima and minima, a procedure that can also be applied to the spline interpolation using the corresponding water level data at sensor locations. A close agreement between the measured and interpolated wave celerities provides confidence in the accuracy of the spline interpolation method to capture the tidal propagation dynamics that govern the water level surface. To contextualize the wave celerity results, we considered several river geomorphological metrics for each river section. Along-river length (La), straight- line length (Ls), and sinuosity were quantified to characterize the shape and meandering nature of each river section. Sinuosity is calculated as the ratio of La to Ls: Sinuosity ¼ La Ls ð2Þ This ratio provides a measure of the river’s curvature and its deviation from a straight-line course. Rivers can have sinuosity ranging from 1 to 3 (i.e., the river length is three times longer than the valley). For the period of water level measurements the following metrics were computed according to the general tidal datum computation procedures as described by the US National Oceanic and Atmospheric Administration [43]: MHHW (Mean Higher-High Water): MHHW is defined as the arithmetic mean of the higher high water heights of the tide observed over a specific period of measurement. Solely, the higher high water of each pair of high waters of a tidal day is included in the mean. It is a reference point for the highest high tides, which occur during spring tides. MHW (Mean High Water): is defined as the arithmetic mean of all of the high water heights observed over a specific period. It is a reference for the typical height of high tides and is used for navigation, coastal construction, and environmental monitoring. MRL (Mean River Level): MRL is the average water level over a time period. MLW (Mean Low Water): is defined as the arithmetic mean of all of the low water heights observed over a specified period and serves as a reference for the typical height of low tides. It is used for navigation, especially in shallow waters. MLLW (Mean Lower-Low Water): MLLW is defined as the arithmetic mean of the lower low water heights of the tide observed over a specific period. Only the lower low water of each pair of low waters of a tidal day is included in the mean. It serves as a reference point for extremely low tides and is used for coastal planning and environmental monitoring. Gt (Great Diurnal Range): Gt represents the difference between the mean higher high water (MHHW) and the mean lower low water (MLLW). A larger GT implies a greater differ- ence between high and low tides. Mn (Mean Range of Tide): Mn is the difference between MHW and MLW. It provides a general measure of the typical tidal variation in a given location. DHQ (Mean Diurnal High Water Inequality): DHQ is the difference in elevation between MHHW and MHW. It helps assess variations in daily high tide levels. DLQ (Mean Diurnal Low Water Inequality): DLQ is the difference in elevation between MLLW and MLW. Similar to DHQ, it assesses variations in daily low tide levels. 2.5. Simulation of SWOT satellite products: Water level and slope In order to conduct an extensive study of error analysis on water level and slope and their impact on discharge estimation using SWOT satellite data we need to simulate these variables. This section outlines the methodology we employed to simulate SWOT water level and slope products. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 8 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment 2.5.1. “True” water level and slope. From the previous section’s spline spatial interpola- tion, we consider the water level obtained as the “true” water level of the river. For the purpose of this study, we focus on the river section around our two sites where ADCP campaigns were done, specifically location H1 and H3 (yellow triangles in Fig 1). However, we will only pro- vide the results for section H1 due to its lower hydraulic complexity compared to location H3, which is located downstream in the center of Ho Chi Minh City. At this location the Saigon river is connected to a complex canal network making the estimation of discharge more chal- lenging. A recent 1D numerical modelling effort has showed that canals influence the dynam- ics of the river Saigon locally but have negligible influence on the river as a whole [44]. Additionally, at H1 we conducted two ADCP campaigns during both symmetric and asym- metric tidal regimes, allowing us to calibrate our model (to be explained in the next section) based on different tidal types whereas at H3 we only conducted one ADCP campaign. At the exact location of the ADCP campaign (H1), we consider the spline water level as the true water level, ztrue. To compute the slope, we select two water level nodes, n0 and nN, around H1 with a distance of 1 km between them, as illustrated in green in Fig 2. The true slope, Strue, is then calculated using the following equation: Strue ¼ nN (cid:0) n0 x ð3Þ with x the along-river distance between the two nodes. This approach allows us to determine the true slope value for our discharge estimation. To simulate SWOT water level and slope products, we employ two different approaches. Firstly, we aim to obtain the SWOT variables as they are expected to be outputted by the satel- lite’s measurements. Secondly, we propose a new methodology to compute water levels and slope from the SWOT water level products at 200 m resolution. 2.5.2. Approach 1: Expected SWOT products. In this approach, we compute the average water level from the 200 m nodes for the 10 km reach between nodes ni and nN as illustrated in red in Fig 2, where N is the total number of water level nodes in the reach and ni the ith water Fig 2. Flowchart of methodology. Flowchart of data used for estimating discharge based on the spline water level (green) to obtain the “True” discharge; based on water level as expected to be provided directly by SWOT (red) to obtain the “SWOT-like” discharge; and based on water level using improved reach selection methodology (blue) to obtain an improved SWOT-like discharge. Discharge is obtained from a modified Manning-Strickler law referred to as the “MODEL” in this flowchart. https://doi.org/10.1371/journal.pwat.0000226.g002 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 9 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment level node. The SWOT-like water level, zswot, is thus given by: zswot ¼ 1 N XN i¼0 ni ð4Þ The obtained value for zswot mimics the water level product as directly provided by SWOT at the reach scale. We then add the expected noise of 10 cm [12, 45–47] directly to this value. To study the variability of the error, we employ a Monte Carlo white noise approach with stan- dard deviation equal to the expected error and mean equal to zero as has been done in previous studies [22, 36]. For the computation of SWOT-like slope, we follow the methodology pro- posed by the SWOT team [48] at reach level where the first, n0, and last node, nN, of a given reach are used to compute the slope as follows: Sswot ¼ nN (cid:0) n0 x ð5Þ where x is the distance spanned by the reach. Similar to the SWOT-like water level, �Z swot, we directly add the expected SWOT error of 1.7 cm/km [12, 45–47] to the computed slope using a Monte Carlo white noise approach. Using this methodology we obtain 1000 samples of SWOT-like water level and slope with associated errors that correspond to the 10 km reach products that will be provided by the SWOT mission. 2.5.3. Approach 2: Improved methodology for water level and slope estimation from SWOT observations. The order of magnitude of the Saigon river’s slope (cm/km) is the same as the expected SWOT error’s magnitude. Thus, we anticipate that SWOT slope products may introduce significant errors in discharge estimation when used as is. To mitigate this, we pro- pose a new methodology to compute water levels and slope from the SWOT water level prod- uct at 200 m resolution, which are expected to have an error of 35 cm [12, 45]. To incorporate this error, we add it to the 200 m nodes of the spline using the Monte Carlo white noise approach as previously described. The water level, z2, is taken as the average water level between points n0 and nN (Fig 2, blue illustration): z2 ¼ 1 N XN i¼0 ni ð6Þ For the slope computation we take a reach length of x km and compute two supplemental water levels: the first, P1, is the average of the nodes in the section between point n0 and the ADCP point (yellow triangle in illustration, Fig 2) with a length of x/2 km, and the second is the average of the nodes in the section between the ADCP location and nN, also with a length of x/2 (point P2). The slope is then computed as follows: S2 ¼ P1 (cid:0) P2 x ð7Þ We conduct experiments for reach lengths equal to 10, 15, 20 and 25 km. These lengths translate to using 50, 75, 100 and 125 nodes for the water level and slope computations, respec- tively. By utilizing more information from the SWOT measurements and increasing the num- ber of river nodes used to compute the hydraulic variables we expect a reduction in the error associated with discharge estimation. The simulated water levels and slopes, obtained through the aforementioned approaches, are utilized to estimate the discharge. The details of the discharge estimation process are explained in the subsequent section. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 10 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment 2.6. River discharge estimation The instantaneous river discharge was estimated by applying a stage-fall-discharge (SFD) rat- ing curve adapted from the general Manning-Strickler law (Eq 8), previously tested and vali- dated by [49] and used to predict the total discharge of the Saigon river in [20, 30]. The discharge is estimated as follows: QðtÞ ¼ signðSÞ � K � AwðzðtÞÞ � RhðzðtÞÞ2=3 � p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi jSðt þ DtÞj ð8Þ with Q the water discharge [m3s−1], K the Manning-Strickler coefficient [m1/3s−1], Rh = Aw/Pw the hydraulic radius [m], Aw the wet section [m2], Pw the wet perimeter [m]. Note that Aw and Pw are both a function of the water level and thus, of time. Furthermore, in Eq 8 above z(t) = ztrue(t) for computing the “True” discharge, z(t) = zswot(t) for approach 1 and z(t) = z2(t) for approach 2. The term sign(S) is equal to the sign of the slope, S, taking the values of +1 or -1. The energy slope, S [-], is assumed equal to the water slope and is computed as in sub-sections 2.5.2 and 2.5.3 for approaches 1 and 2, respectively. Since there is no fixed datum between river gauges, we normalize all signals by mean removal, as previously mentioned. This makes the tidal harmonics oscillate about zero for all gauge locations thus, making them comparable. However, datum errors still persist and a vertical adjustment is required for discharge compu- tation. This adjustment is done via an additional term, dz, which serves as an extra tuning parameter of our model and allows it to better capture the surface slope dynamics of the Saigon river. This parameter is introduced in the slope computation as follows: SðtÞ ¼ s þ dz x ð9Þ where s = Strue for computing the “True” discharge, s = Sswot for approach 1 and s = S2 for approach 2. In addition, a time lag, Δt, is required to account for the propagation of the tidal wave between the two points used to compute the slope. This method allows the computation of three time-series of discharge namely: the “True” discharge which is used as the benchmark for the discharge computed using approach 1, the SWOT-like discharge, and using approach 2, the improved SWOT-like discharge. 2.7. Performance evaluation indices The evaluation of the applied approaches was carried out using two statistical indicators: root mean square error (RMSE)and the coefficient of determination (R2). The RMSE has been eval- uated both in terms of absolute (m3/s) and relative (rRMSE expressed as a percentage) values. These indicators have been widely used in literature for this purpose [36]. Those performance indices have been evaluated referring to the discharge time-series obtained via the Monte Carlo approach for each of the methods described ind section 2.6. The equation, range, and optimal value of each index are presented in Table 1. Table 1. Equations and optimal values of statistical indices. Qi and Qi iables, respectively, at time i. true denote the predicted discharge from SWOT-like variables and “True” discharge from spline var- Indices Root Mean Square Error Coefficient of determination RMSE ¼ q Equation ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PN trueÞ2 1 i¼1 ðQi (cid:0) Qi N P P ðQi i ðQi true (cid:0) true (cid:0) Qi Þ2 � Þ2 Qi true R2 ¼ 1 (cid:0) Range 0 to 1 0 to 1 Optimal values 0 1 https://doi.org/10.1371/journal.pwat.0000226.t001 i PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 11 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment 3. Results 3.1. Validation of the spline spatial interpolation In Fig 3, the time-series from the in-situ water level gauges are shown in the top panel. Six curves are shown for each river gauge from the most downstream (bottom, darker green) to the most upstream (lighter green, top) locations DN2 and CC, respectively. As can be seen, tidal fluctuations govern the water level dynamics at all locations. In fact, the Saigon river’s lev- els are influenced by tides up until the Dau Tieng reservoir (Fig 1) [20]. The panels 0 to 3 illus- trate the spatial spline interpolation (black points) and in-situ measurements (triangles) for the four SWOT passes that would occur during the time period of in-situ measurements. For simplicity, we assume that the first pass over the Saigon river (Orbit 1, Day 4, Pass 90) happens on the first day of in-situ measurements. Additionally, the SWOT spatial coverage of the river is indicated in red (pass 90) and blue (pass 271). Fig 3. Illustration of spline interpolation and SWOT satellite passes. Time-series from in-situ water level gauges at all locations (top panel). Water levels have been displaced by 3 meters for easier reading. Measurements are shown in increasingly darker shades of green from closest to source to further away from source namely, CC, H1, H2, H3, LG and DN2. Additionally, 4 time stamps corresponding to what would be the SWOT satellite temporal resolution during the period of in-situ measurements are indicated by black, vertical lines. Panels 0 to 3 correspond to the timestamps in the top panel. The results of the spatial interpolation using a second degree spline are illustrated for the SWOT passes over the region namely, pass 90 and 271. Additionally, the SWOT spatial coverage of the river is shown in red (pass 90) and in blue (pass 271). Triangles in panels 0 to 3 indicate in-situ measurements whereas the black dots are the interpolated spline values at the SWORD nodes location. https://doi.org/10.1371/journal.pwat.0000226.g003 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 12 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Table 2. Water level Root Mean Square Error (RMSE) and coefficient of determination R2 at each measurement location. Comparison between the spline fit and in- situ data. River Location RMSE [m] R2 CC 0.02 0.99 H1 0.09 0.98 H2 0.10 0.96 H3 -0.08 0.99 LG -0.05 0.99 DN2 0.04 0.99 https://doi.org/10.1371/journal.pwat.0000226.t002 At the DN2 gauge different tidal regimes can be seen propagating upstream. At the begin- ning of the time-series, a symmetric regime can be seen where different cycles of high and low tide present similar amplitude (Fig 3, from timestamps 0 until November 8th, 2022 in top panel). Then, the tide fluctuations transition to an increasingly asymmetric semi-diurnal regime (timestamp 1) that passes through a diurnal phase (around November 17th, 2022) before cycling back towards a symmetric regime. From panels 0 to 3 in Fig 3, it can be seen that the quadratic spline fits well to the measured values. From the depicted examples the H3 location seems to be consistently below the spline curve whereas H2 is consistently above. In Table 2, the RMSE and R2 values for all locations are presented. The results reveal that river measurements at the H2 and H3 locations differ the most from the spline values with, respectively, RMSE of 10 cm and -8 cm. At the other loca- tions RMSE values are below ±10 cm agreeing to the measured values. Additionally, the R2 val- ues at all locations are above 0.96 which indicates that the spline replicates well the tidal fluctuations along the river. The tidal metrics are reference water levels describing the characteristic periodic variations in sea surface elevation relative to a vertical datum at a particular location. Table 3 presents tidal metrics for the six gauge locations in meters. For each location, two values are provided in the format “measured, spline.” The first value represents the metric computed from mea- surements (in-situ data), while the second value represents the metric computed from the spline interpolation. The results show consistency in the measured MHHW and MHW values across all locations with values ranging from 0.56 to 1.14 meters and 0.50 to 1.03 meters, respectively. Note that both metrics increase from upstream to downstream within the Saigon river (from CC to LG) and then decrease between the Saigon river (LG) and the Dongnai river (DN2). The spline-based estimates closely align with the measured values, demonstrating the reliability of this method for capturing the tidal dynamics. However, the aforementioned behaviour between the Saigon and Dongnai rivers is not captured by the spline-derived met- rics as they monotonically increase from upstream to downstream. Table 3. Tidal metrics for each gauge location in meters. Two values are shown in the form “measured, spline”: the first is the value computed from measurements and the second is the value computed from the spline. MHHW MHW MRL MLLW MLW Gt Mn DHQ DLQ CC 0.56, 0.56 0.50, 0.51 -0.11, -0.11 -1.28, -1.28 -0.81, -0.81 1.84, 1.84 1.31, 1.31 0.06, 0.05 H1 0.78, 0.79 0.72, 0.72 0.00, 0.02 -1.26, -1.25 -0.79, -0.76 2.04, 2.04 1.51, 1.47 0.06, 0.07 H2 0.84, 0.88 0.77, 0.81 0.00, 0.05 -1.37, -1.32 -0.84, -0.78 2.21, 2.20 1.61, 1.59 0.07, 0.07 H3 0.89, 0.85 0.81, 0.76 0.00, -0.04 -1.46, -1.49 -0.89, -0.91 2.35, 2.35 1.69, 1.67 0.09, 0.09 LG 1.14, 0.98 1.03, 0.88 0.15, 0.00 -1.49, -1.65 -0.83, -0.99 2.63, 2.63 1.86, 1.87 0.10, 0.10 DN2 1.07, 1.06 0.95, 0.95 0.00, 0.00 -1.80, -1.81 -1.04, -1.03 2.87, 2.87 1.99, 1.98 0.12, 0.12 -0.47, -0.47 -0.47, -0.49 -0.53, -0.54 -0.57, -0.58 -0.66, -0.66 -0.77, -0.77 https://doi.org/10.1371/journal.pwat.0000226.t003 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 13 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Table 4. Along-river length, La, straight-line length, Ls, and sinuosity of each river section between two sensors. Wave celerity is computed for each river section. The “Measured” column refers to the wave celerity computed from measurements and the “Spline” column to wave celerity computed from the spline interpolant. The last row shows the wave celerity for the whole domain. River section CC-H1 H1-H2 H2-H3 H3-LG LG-DN2 CC-DN2 La [km] 27.8 10.5 8.2 15.9 23.5 85.9 Ls [km] 16.8 9.2 3.8 5.3 13.8 48.5 Sinuosity Measured [m/s] Spline [m/s] 1.65 1.14 2.56 3.00 1.70 1.77 6.0 5.6 6.3 7.5 6.8 6.4 5.3 6.0 7.3 6.9 6.8 6.8 https://doi.org/10.1371/journal.pwat.0000226.t004 MRL values exhibited some variability among locations. The expected measured values for this metric should be around zero which is the case for most stations. However, at the CC sta- tion MRL is negative (-0.11 m) and at LG it is positive (0.15 m) potentially due to a measure- ment bias in the sensors. Spline interpolation provided MRL estimates generally close to zero for all locations. It matched the MRL at the CC station but not at the LG station. This could indicated that the spatial interpolation removed the bias from the measured data at LG as it is located between two other measurement points whereas at CC it could not as it was the last point in the studied river reach. The results show consistency in the measured MLLW and MLW values across all locations with values ranging from -1.28 to -1.8 meters and -0.81 to -1.04 meters, respectively. As for MHHW and MHW, these metrics increase (in modulus) as we travel downstream but do not show the decrease in value from LG to DN2. The spline-based estimates closely follow the measured values, reinforcing the robustness of the interpolation technique. Other metrics such as Gt, Mn, DHQ and DLQ are computed from MHHW, MHW, MLLW and MLW. As expected these derived metrics show similar characteristics as those discussed and a close agreement between the measured and spline-derived values. In the Saigon river the tidal wave progresses upstream and is detected by all sensors distrib- uted along the river’s course. Wave celerity, a parameter unaffected by the vertical reference of water level measurements, emerges as a dependable means of accessing the capacity of the spline interpolation to propagate the tidal wave. By calculating wave celerity through the use of available in-situ measurements, we can compare it with the wave celerity estimated from the spline-generated water level data. Table 4 presents the wave celerity computed from measured values and from the spline interpolation for river sections between sensor locations. The results show an average wave celerity along the whole domain of 6.4 m/s based on mea- sured data and 6.8 m/s for the spline interpolation across the domain. Wave celerity values show variability between river sections as these are of different lengths and sinuosity. The most sinuous section is H3-LG with the river being 3 times longer than the straight line course. Overall, the spline is capable of simulating the propagation speed of the tidal wave over the whole domain. However, it seems to have problems in the parts of the river that are most sinu- ous namely, H2-H3 and H3-LG. This proxy highlights that variations in wave propagation are influenced by the interpolation method and its capacity to capture the timing of high and low waters. 3.2. Calibration of the discharge model using ADCP measurements Two ADCP (Acoustic Doppler Current Profiler) campaigns were conducted in November 2022 and December 2022, with the utilization of a Rio Grande 600 kHz instrument [50]. Over PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 14 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment a 24-hour period, hourly gauging activities were carried out, encompassing two transects, at locations H1 and H3 (indicated by yellow triangles in Fig 1) using a boat equipped with a geor- eferenced ADCP device. Nevertheless, the subsequent discussion will focus solely on the H1 location and results for the H3 location can be found in the Figs A-D in S1 Appendix. These ADCP campaigns aimed to calibrate the estimation of water discharge, calculated according to Eq 8 [49]. The methodology followed for calibration was a one-at-a-time procedure that mini- mizes the RMSE between predicted and observed values. The parameters K, Δt, and dz were adjusted to ensure that the model-derived discharge matched the observed data. Calibration was performed under both symmetric (November 2022) and asymmetric (December 2022) tidal conditions to ensure the robustness of the discharge estimation law. Once calibrated, the parameters are kept constant. In addition, these ADCP campaigns were employed to deter- mine the river’s cross-section characteristics and compute Aw and Rh as functions of water level. Considering adverse conditions, ADCP measurement errors were assessed at 10%, with a minimum error threshold set at 100 m3/s [51]. The optimal results were obtained with a Manning-Strickler coefficient of K = 25 m1/3/s, a time step of Δt = -75 minutes, and a vertical resolution of dz = 0.05 meters. Results are depicted in Fig 4. The November 2022 campaign exhibited notably accurate outcomes. In contrast, the December 2022 campaign yielded slightly less precise results but remained in good agreement with the data. The discernible influence of the asymmetric semi-diurnal tidal signal accounted for some of the discrepancies [30]. The discharge time-series for the full period under scope is presented in S1 Fig. The com- bined RMSE for both campaigns was 450 m3/s, equivalent to approximately 22% of the maxi- mum observed discharge. Error sensitivity to the calibration parameters was performed and is presented in S2 Fig. Fig 4. Comparison of river water discharge. Water discharge Q comparison between model results (dashed, green), ADCP measurements (black points), SWOT-like discharge (red) and improved SWOT-like discharge (shades of blue) at the H1 location: (A) symmetric tide during campaigns of November 2022 and (B) asymmetric tide during campaign of December 2022. The curves for SWOT-like discharge and improved SWOT-like discharge are the average of the 1000 Monte Carlo simulations of discharge. All curves were obtained using the optimal parameters namely, K = 25m1/3/ s, dz = 0.05 meters and dt = −75 minutes. https://doi.org/10.1371/journal.pwat.0000226.g004 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 15 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment 3.3. Discharge estimation using SWOT-like measurements Fig 5 provides a comprehensive view of the performance and accuracy of SWOT-like discharge estimation techniques based on satellite-derived measurements. In Fig 5A SWOT-like dis- charge is plotted against the “True” discharge. SWOT-like discharge (red in the figure) is obtained from measurements of water level and slope as SWOT would directly provide and including its expected errors. This estimation of discharge has an R2 value of 0.31. This signi- fies a weak correlation between the estimated and “true” discharge values. This discrepancy is particularly noticeable as SWOT-like discharge consistently underestimates true discharge, primarily for higher discharge values (in modulus). Notably, this underestimation is most apparent within the range of positive discharge values spanning from 0 to 1000 m3/s. A notable improvement in discharge estimation is introduced through the enhanced SWOT-like discharge estimation methodology, applied to a 10 km reach size (lightest blue in Fig 5A). The resulting R2 value rises to 0.72, signifying a more robust correlation with true dis- charge values in comparison to the prior SWOT-like approach. The enhancement is especially evident in cases of negative discharges and positive values exceeding 1000 m3/s. Nevertheless, we note a persisting underestimation of discharge within the range of 0 to 1000 m3/s, highlighting ongoing challenges in accurately assessing moderate positive discharge values. In Fig 5A the effect of varying reach sizes on SWOT-like discharge estimation can be seen with reaches ranging from 15 km to 30 km (increasing shade of blue in figure). As reach size increases, the R2 value progressively improves, with an improvement from an R2 of 0.88 at 15 km to 0.94 at 30 km. However, it is essential to acknowledge that this increase in reach size introduces error, leading to a deterioration in discharge estimation accuracy for values between 0 m3/s and 1000 m3/s, often yielding negative discharge estimates within this range. Furthermore, the figure shows that beyond a 20 km reach size the improvement in the R2 Fig 5. R2 and RMSE. A) comparison between model discharge taken as “True” discharge, SWOT-like discharge at 10 km reach size and improved SWOT-like discharge at 10, 15, 20, 25 and 30 km reach sizes. Each particle corresponds to the one timestamp in the discharge time-series from each Monte Carlo run. B) RMSE boxplots from Monte Carlo runs between model discharge take as “True” discharge, and SWOT-like discharge at 10 km reach size and improved SWOT-like discharge at 10, 15, 20, 25 and 30 km reach sizes. https://doi.org/10.1371/journal.pwat.0000226.g005 PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 16 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment value becomes less significant. This suggests a diminishing return in accuracy associated with larger reach sizes. Fig 5B provides a visual representation of the Root Mean Square Error (RMSE) values between the “True” discharge and SWOT-like discharge estimates for a range of Monte Carlo runs. The x-axis comprises six entries, denoting the different methods: SWOT-like (red), 10, 15, 20, 25 and 30 km reaches (shown as increasingly darker shades of blue). Each entry corre- sponds to a boxplot in the figure, derived from the analysis of time-series from 1000 Monte Carlo runs. The first box corresponding to SWOT-like discharge estimation exhibits substantial errors across all Monte Carlo runs. The median RMSE for SWOT-like discharge is around 1500 m3/s (red box in Fig 5B), indicative of a significant discrepancy between the estimated values and the “True” discharge. Moreover, the presence of relatively high outlier RMSEs, reaching values around 800 m3/s, underscores the variability and extent of errors associated with discharge estimated directly from SWOT measurements in this region. These findings emphasize the limitations of the SWOT satellite and the challenges it presents in accurately approximating “True” discharge values. Conversely, the introduction of the improved SWOT-like methodol- ogy with a 10 km reach size results in a enhancement in discharge estimation accuracy. The median RMSE decreases to approximately 700 m3/s, signifying a considerable improvement in accuracy compared to the SWOT-like approach. Furthermore, the maximum RMSE values remain below 1200 m3/s, demonstrating a reduced spread in error magnitudes. This marked improvement suggests that the presented methodology for a 10 km reach size is critical in enhancing the precision of discharge estimation from SWOT measurements. As the reach size increases beyond 10 km, the boxplots in Fig 5B illustrate a trend of dimin- ishing RMSE values. The median RMSE values progressively decrease with larger reach sizes, seemingly reaching a plateau at approximately 180 m3/s. This plateau suggests that further increases in reach size do not significantly improve the accuracy of discharge estimation, and the methodology approaches a stable level of error. However, it is noteworthy that even at reach sizes between 20 km and 30 km, certain outlier maximum RMSE values remain high at around 800 m3/s, indicating that challenges persist in accurately estimating discharge for spe- cific Monte Carlo runs. Consequently, a 20 km reach size appears to be an optimal choice for discharge estimation from SWOT measurements, as improvements in median RMSE are not significant with increasing reach size. Fig 6A presents the relative Root Mean Square Error (rRMSE) values expressed as a per- centage (%) between “True” discharge and SWOT-like discharge estimates. These values are shown across the different reach sizes under scope (10 km, 15 km, 20 km, 25 km, and 30 km). Each data point in the figure corresponds to the rRMSE value for a timestamp in the 1000 time-series obtained from the Monte Carlo method, capturing the variability of discharge esti- mation under different conditions. As can be seen for all estimation methodologies under investigation, the rRMSE values exhibit a trend of increasing error as discharge approaches zero. This trend implies that discharge estimation accuracy diminishes as discharge values approach the lower end of the spectrum. SWOT-like discharge estimation emerges as the least accurate method, with the highest rRMSE values (red, Fig 6A). The minimum error in discharge is of 50% rRMSE observed at the highest discharge values. This suggests that the SWOT-like methodology struggles to accu- rately estimate discharge along the full spectrum of discharge values of the Saigon river. Even at the highest discharge values, the rRMSE remains relatively high. Conversely, when imple- menting the improved SWOT-like methodology with a 10 km reach size (lighter blue particles in Fig 6A), a substantial reduction in rRMSE values is observed. The minimum rRMSE drops to approximately 20% for the largest discharge values, reflecting a considerable enhancement PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 17 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Fig 6. rRMSE. A) rRMSE between model discharge take as “True” discharge, and SWOT-like discharge at 10 km reach size and improved SWOT-like discharge at 10, 15, 20, 25 and 30 km reach sizes. Each particle corresponds to the rRMSE value for a given timestamp in a given Monte Carlo run. The horizontal dashed line represents the 30% rRMSE threshold. B) Bar plot of percentage of Monte Carlo particles below or equal to 30% rRMSE. https://doi.org/10.1371/journal.pwat.0000226.g006 in accuracy compared to the SWOT-like approach. However, rRMSE values greater than 30% persist for discharge values ranging from -1500 m3/s to +1500 m3/s, indicating that challenges in accurate estimation remain within this range. As in previously seen, the figure demonstrates that increasing the reach size leads to a decrease in rRMSE values. For instance, at a 20 km reach size (indicated by the intermediate blue data points), the minimum rRMSE approaches 10% for the largest discharge values, representing a substantial improvement. Nevertheless, for discharge values between -800 m3/s and +1200 m3/s, rRMSE values exceeding 30% are still observed, highlighting the per- sistence of estimation challenges. Additionally, beyond the 20 km reach size, further increases in reach size do not substantially improve the minimum rRMSE at the largest dis- charge values. However, these larger reach sizes do result in an increase in the quantity of particles falling below the 30% error threshold. For instance, at a 30 km reach size, discharge values between -600 m3/s and 1000 m3/s are still above this threshold. This suggests that while the reach size increase may not substantially enhance accuracy at the highest discharge values, it does contribute to reducing the number of instances where the error exceeds 30% rRMSE. In Fig 6B the bar plot of the percentage of Monte Carlo particles that are below 30% is shown. As can be seen, we increase the number of particles below this threshold from 0% to 25.6% when applying the improved discharge estimation method for the same reach size. Fur- thermore, as reach size increases so does the number of particles below the threshold. Increas- ing reach size from 20 to 30 km increases the number of particles below the 30% rRMSE threshold by 9.3%. Consequently, selecting an appropriate reach size is a crucial consideration for achieving a balance between minimizing error and effectively capturing discharge variability. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 18 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment In order to study the influence of lower SWOT measurement errors on our discharge esti- mation methodology, we conducted a sensitivity analysis. The first, publicly available observa- tions of SWOT were remarkably promising, surpassing the SWOT mission team’s expectations, and revealing the satellite’s impressive ability to discern water nodes in river sys- tems and bodies of water with widths less than 100 meters [13]. Hence, we proceeded to explore the ramifications of reduced SWOT measurement errors. Specifically, we assessed the implications of reducing errors to 30 cm (a 5 cm reduction from the original error) and 25 cm (a 10 cm reduction from the original error) at 200-meter nodes, followed by the application of our methodology under these revised error scenarios. The outcomes of this sensitivity analysis are presented in S3 Fig. We provide comprehensive insights into the rRMSE and the propor- tion of Monte Carlo particles that fall below the critical 30% rRMSE threshold. Notably, the reduction of SWOT measurement errors to 30 cm yielded a subtle improvement. These improvements were reflected in the percentages of Monte Carlo particles associated with improved SWOT discharge estimates, which ranged from 29.4% to 82% for reaches of 10 to 30 km in length. Similarly, the further reduction to 25 cm in measurement error yielded improved results, demonstrating percentages between 33% and 83.6% of Monte Carlo particles below the 30% rRMSE threshold for various reach sizes spanning from 10 to 30 km. It is important to note that, despite the favorable outcomes observed with the reduced SWOT error scenarios, SWOT-like discharge Monte Carlo particles consistently remained above the speci- fied threshold. However, the overall improvements, albeit notable, remained somewhat limited in magnitude. This restricted enhancement can be attributed to the inherent sensitivity of our simplified discharge estimation model, particularly concerning flow transitions during the shift from positive to negative flow regimes. Consequently, the marginal increment in the number of particles falling below the 30% rRMSE threshold underscores the inherent con- straints of our methodology in capturing the intricate dynamics governing tidal flows. 4. Discussion 4.1. Validation of the spline spatial interpolation It was found that spline interpolation generally fits well with the measured values however, it is noteworthy that the H3 gauge measurements consistently fall below the spline, while H2 gauge values consistently exceed it. This pattern is also reflected in the root mean square error (RMSE) values, which are notably higher at these two locations compared to other gauge loca- tions. This divergence can be attributed to the fact that H3 and H2 stations are situated in an area characterized by high sinuosity (as indicated in Table 4) and an intricate artificial canal network. These two factors exert a significant influence on the propagation of the tidal wave and the hydraulic behavior of the river. Consequently, the spline interpolation encounters challenges in accurately fitting the measurement values at these locations. We also found that the behaviour between the Saigon and Dongnai rivers is not captured by the spline. The spline method is not adapted for simulating adequately discontinuities, such as the confluence between the two rivers. In contrast, we observed that the spline interpolation aligns well with measurements when considering metrics such as Mean Higher High Water (MHHW), Mean High Water (MHW), Mean Lower Low Water (MLLW), and Mean Low Water (MLW). This consistency suggests that the spatial interpolation method simulates average high and low tides in a coherently over the studied time frame. As anticipated, these metrics, as derived from measurements, exhibit a decline as one moves upstream along the Saigon River, progressing from LG to CC. However, it’s worth noting an exception with the DN2 gauge, which shows a decrease in these metrics compared to the LG gauge, even though DN2 is the most downstream measurement location. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 19 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment This discrepancy arises because DN2 is situated in the Dongnai River, which is considerably wider (by a factor of 3, sometimes more in some regions) and shallower (also by a factor of two) compared to the Saigon River. The spatial interpolation method does not capture this behavior, as it is primarily a mathematical construct. The use of wave celerity as a proxy to evaluate the spline’s ability to propagate the tidal wave yielded promising results across the entire domain. This methodology proved effective in sim- ulating wave celerity. A crucial aspect for discussion pertains to the spatial variability of wave celerity. The spline interpolation performs well in river sections characterized by lower sinuos- ity (below a value of 2). However, it is evident that the spline-derived wave celerity diverges from measurements within the city center area, where the river exhibits the highest sinuosity, notably between H2 and LG. In the lower reaches of tidal rivers, wave celerity is primarily influenced by friction [1]. Since the spline method represents a mathematical interpolation rather than the physical properties of the water surface, it encounters difficulties in accurately representing wave celerity in highly sinuous sections of the river, leading to either overestima- tion or underestimation. Moreover, these sinuous areas are often characterized by hydraulic infrastructure and frequent dredging activities, which further contribute to the discrepancies between spline-derived and measured wave celerity values. Dredging, in particular, has a sig- nificant impact on this parameter [52]. 4.2. Discharge estimation using SWOT-like measurements SWOT-like discharge obtained from measurements of water level and slope, mirroring the data SWOT satellite would directly provide, demonstrates a relatively weak correlation with “True” values. This observation holds true for both negative and positive discharge values. Notably, an immediate improvement in correlation becomes evident when we implement our new methodology to compute these variables at the same reach size. In fact, the correlation coefficient (R2) rises to 0.95 for a reach size of 30 km. However, it is noteworthy that as the reach size is increased, the estimation of discharge values within the 0 to 1000 m3/s range tends to be underestimated compared to SWOT-like discharge (as shown in Fig 5A). This behavior is indicative of the limitations inherent in our discharge estimation method, which relies on the Manning-Strickler law and assumes uniform flow conditions. In situations where the discharge is about to transition from positive to negative or vice versa, uniform flow condi- tions do not apply, leading to discrepancies. This also elucidates why larger positive and nega- tive values of discharge exhibit a better correlation with “True” values, as flow conditions tend to be closer to uniform when the discharge sign remains constant. Furthermore, as depicted in Fig 4, the mean Monte Carlo profiles for SWOT-like and improved SWOT-like discharge exhibit no temporal discrepancy or time shift. This observation reinforces the notion that the consistent errors mentioned earlier are primarily attributed to the limitations of our discharge estimation model. Our analysis also revealed that increasing the reach size results in a decrease in discharge RMSE values, eventually reaching as low as 180 m3/s (as demonstrated in Fig 5B). Neverthe- less, it’s worth noting that even for larger reach sizes, several outliers persist, with RMSE values reaching up to 800 m3/s. The presence of these outliers can be attributed to the challenges asso- ciated with accurately estimating discharge around zero values, as discussed previously. Furthermore, our examination revealed that within the range of -500 to +500 m3/s, the rela- tive RMSE (rRMSE) values surge to more than 200 percent. This phenomenon can be attrib- uted to the same factors discussed earlier. However, it is encouraging to note that our method substantially improves the proportion of Monte Carlo particles that fall below the 30 percent rRMSE threshold, widely considered as satisfactory [9, 53, 54]. The reduction in rRMSE values PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 20 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment can be achieved for discharge values outside the aforementioned range, primarily because we leverage a greater number of SWOT data points for water level. Despite their larger associated errors, the abundance of data points provides more information, which, when averaged, effec- tively enhances discharge estimation. This aspect is particularly relevant in the context of the Saigon River, given its tidal nature where slope values are of the same order of magnitude as the slope measurement errors provided by SWOT [26]. 5. Conclusion SWOT satellite-based river discharge estimates are poised to offer global discharge informa- tion for rivers with widths exceeding 100 meters, encompassing some of the world’s largest unmonitored basins. These discharge datasets hold the promise of catalyzing a transformative shift in global hydrological research, contingent on the acceptance of their space-time sam- pling and associated uncertainties by the global scientific community. The computation of SWOT discharge estimates will rely on relatively straightforward flow laws, which involve the amalgamation of SWOT measurements encompassing water surface elevation (WSE), river width, slope, and parameter estimates related to flow laws. This study aimed to explore the potential role of the upcoming SWOT satellite data in esti- mating discharge, a vital variable in various hydraulic and hydrology-related applications. In particular, tidal rivers and their associated floodplains represent critical yet understudied envi- ronments that serve as focal points for various human activities and provide habitats for diverse ecosystems. Managing flood risk in these areas is of paramount importance, necessitat- ing comprehensive monitoring and a profound understanding of their hydrological dynamics. In this context, we employed a stage-fall-discharge rating curve derived from the widely recog- nized Manning-Strickler law [49] to evaluate the potential of SWOT satellite measurements to estimate in real-time water discharge levels in the Saigon-Dongnai River system. Our analysis focused on a 86-kilometer segment of the Saigon-Dongnai River system in Southern Vietnam, leveraging in-situ water level measurements to obtain a second-degree spline spatial interpolation of the river mimicking that of SWOT (every 200 meters). As the main purpose of this study was to evaluate SWOT-error propagation to discharge estimations as a function of reach size we did not consider the temporal resolution of SWOT data. Further- more, for low latitude regions the greatest advantage of SWOT is its spatial resolution (river variables at the scale of 10-km reaches or 200-m nodes) rather than its temporal resolution (2 to 3 measurements per month). These simulated measurements were intentionally corrupted with random errors adhering to mission requirements [25]. Especially interesting are the effect of SWOT-derived slope errors on discharge estimates as these are within the same order of magnitude as the Saigon river’s slope variations. Subsequently, we estimated discharge values by employing five different river stretch resolutions (equal to 10, 15, 20, 25 and 30 km), follow- ing the methodology of [35, 36]. The comparison between synthetic true and SWOT-based discharge at the gauging stations underscores the satellite’s potential. The discharge records anticipated from the satellite mis- sion appear capable of providing reliable assessments of the flow regime at various locations. Among the tested discretizations, 20 km stretches exhibited the best performance to computa- tion time ratio, with mean root mean square error (RMSE) values lower than 200 m3/s and 70.6% of Monte Carlo particles below 30% relative Root Mean Square Error (rRMSE). How- ever, it’s imperative to acknowledge that the proposed methodology encounters challenges when estimating discharge values during the transitions between high and low tides, given the non-uniform flow conditions in these situations. Consequently, the estimates are most reliable for high, consistently uniform discharges, whether positive or negative. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 21 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Future applications of this research will delve into the potential of spatio-temporal interpo- lation criteria, such as data assimilation or statistical approaches, which are expected to enhance spatial and temporal coverage of the river network within the mission’s constraints (i.e., low revisit time). It’s crucial to note that the reliability and completeness of the acquired hydrological information will also hinge on the specific characteristics of the rivers, including discharge variability, seasonality, and more. Rivers with relatively stable discharge patterns are likely to yield more accurate discharge estimations through satellite monitoring. In contrast, rivers characterized by rapid flood waves and high discharge variability may pose challenges for capturing all hydraulic conditions through satellite observation. Subsequent analyses will further investigate these aspects, emphasizing discharge sensitivity to the satellite overpass period and the significance of the river’s hydrologic regime. Additionally, since the proposed methodology is independent of the discharge estimation model it can be applied using any other approach for discharge estimation, such as hydraulic numerical models. An effort towards an accurate modelling of the Saigon-Dongnai system is already under way [44]. In conclusion, the methodology put forth to enhance SWOT output and, consequently, improve discharge estimation can be extended to other regions facing data scarcity, hindering continuous monitoring of hydraulic variables. It is crucial to emphasize that in the case of the suggested discharge estimation law it the availability of at least one ADCP campaign at the spe- cific location of interest, and adherence to the assumptions outlined in the proposed model is required. Supporting information S1 Appendix. Study at the H3 location. (PDF) S1 Fig. Full time-series of discharge at H1 location. Water discharge time-series (green) at the H1 location, residual water discharge time-series obtained from a moving average with a 15 day window size (black) and ADCP campaign (black dots with error bars). (TIFF) S2 Fig. Sensitivity analysis of discharge model at the H1 location. Sensitivity analysis of parameters K, dz and dt of the discharge estimation model used in this study at the H1 loca- tion. (TIFF) S3 Fig. Sensitivity to SWOT error at H1 location. SWOT error of 30 cm (A and B) and 25 cm (C and D) at the 200 meter node level. A) and C): rRMSE between model discharge take as “True” discharge, and SWOT-like discharge at 10 km reach size and improved SWOT-like dis- charge at 10, 15, 20, 25 and 30 km reach sizes. Each particle corresponds to the rRMSE value for a given timestamp in a given Monte Carlo run. The horizontal dashed line represents the 30% rRMSE threshold. B) and D): Bar plot of percentage of Monte Carlo particles below or equal to 30% rRMSE. (TIFF) Acknowledgments This research was conducted thanks to the financial, technical and human support of the CARE-RESCIF initiative (http://carerescif.hcmut.edu.vn/) within the International Joint Labo- ratory LECZ-CARE. In addition, we would like to thank Dr. Benoit Camenen for his meaning- ful input on the first draft of this manuscript. PLOS Water \| https://doi.org/10.1371/journal.pwat.0000226 February 12, 2024 22 / 25 PLOS WATEREnhancing discharge estimation from satellite data in a tidal river environment Author Contributions Conceptualization: Francisco Rodrigues do Amaral, Tin Nguyen Trung, Nicolas Gratiot. Data curation: Francisco Rodrigues do Amaral, Tin Nguyen Trung. Funding acquisition: Thierry Pellarin. Investigation: Francisco Rodrigues do Amaral, Thierry Pellarin, Tin Nguyen Trung, Tran Anh Tu. Methodology: Francisco Rodrigues do Amaral. Project administration: Thierry Pellarin, Tran Anh Tu, Nicolas Gratiot. Resources: Tin Nguyen Trung, Tran Anh Tu, Nicolas Gratiot. Supervision: Thierry Pellarin, Tran Anh Tu, Nicolas Gratiot. Validation: Thierry Pellarin, Nicolas Gratiot. Visualization: Tin Nguyen Trung. Writing – original draft: Francisco Rodrigues do Amaral. Writing – review & editing: Francisco Rodrigues do Amaral. References 1. Hoitink AJF, Jay DA. Tidal river dynamics: Implications for deltas. 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10.1371_journal.ppat.1012129	RESEARCH ARTICLE A novel sORF gene mutant strain of Yersinia pestis vaccine EV76 offers enhanced safety and improved protection against plague Xiao Guo2☯, Youquan Xin3☯, Zehui Tong1☯, Shiyang Cao2, Yuan Zhang2, Gengshan Wu2, Hongyan Chen2, Tong Wang2, Yajun Song2, Qingwen Zhang3, Ruifu Yang2, Zongmin DuID 1,3 1 School of Basic Medical Sciences, Anhui Medical University Hefei, China, 2 State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China, 3 Key Laboratory for Plague Prevention and Control of Qinghai Province, Qinghai Institute for Endemic Disease Prevention and Control, Xining, China ☯ These authors contributed equally to this work. * ruifuyang@gmail.com (RY); zmduams@163.com (ZD) Abstract We recently identified two virulence-associated small open reading frames (sORF) of Yersi- nia pestis, named yp1 and yp2, and null mutants of each individual genes were highly atten- uated in virulence. Plague vaccine strain EV76 is known for strong reactogenicity, making it not suitable for use in humans. To improve the immune safety of EV76, three mutant strains of EV76, Δyp1, Δyp2, and Δyp1&yp2 were constructed and their virulence attenuation, immunogenicity, and protective efficacy in mice were evaluated. All mutant strains were attenuated by the subcutaneous (s.c.) route and exhibited more rapid clearance in tissues than the parental strain EV76. Under iron overload conditions, only the mice infected with EV76Δyp1 survived, accompanied by less draining lymph nodes damage than those infected by EV76. Analysis of cytokines secreted by splenocytes of immunized mice found that EV76Δyp2 induced higher secretion of multiple cytokines including TNF-α, IL-2, and IL- 12p70 than EV76. On day 42, EV76Δyp2 or EV76Δyp1&yp2 immunized mice exhibited sim- ilar protective efficacy as EV76 when exposed to Y. pestis 201, both via s.c. or intranasal (i.n.) routes of administration. Moreover, when exposed to 200–400 LD50 Y. pestis strain 201Δcaf1 (non-encapsulated Y. pestis), EV76Δyp2 or EV76Δyp1&yp2 are able to afford about 50% protection to i.n. challenges, significantly better than the protection afforded by EV76. On 120 day, mice immunized with EV76Δyp2 or EV76Δyp1&yp2 cleared the i.n. chal- lenge of Y. pestis 201-lux as quickly as those immunized with EV76, demonstrating 90– 100% protection. Our results demonstrated that deletion of the yp2 gene is an effective strat- egy to attenuate virulence of Y. pestis EV76 while improving immunogenicity. Furthermore, EV76Δyp2 is a promising candidate for conferring protection against the pneumonic and bubonic forms of plague. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Guo X, Xin Y, Tong Z, Cao S, Zhang Y, Wu G, et al. (2024) A novel sORF gene mutant strain of Yersinia pestis vaccine EV76 offers enhanced safety and improved protection against plague. PLoS Pathog 20(3): e1012129. https://doi.org/ 10.1371/journal.ppat.1012129 Editor: Gregory P. Priebe, Children’s Hospital Boston, UNITED STATES Received: August 28, 2023 Accepted: March 15, 2024 Published: March 28, 2024 Copyright: © 2024 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting information files. Funding: This work was supported by the National Key Research and Development Program of China (2022YFC2604204 to Y. Z., 2022YFC2303503 to Z. D.), Open Project of State Key Laboratory of Pathogen and Biosecurity (SKLPBS2135, to Y. X.) and National Natural Science Foundation of China (No.32070136, to Z.D.). The funder played no role in study design, data collection, analysis and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 1 / 25 PLOS PATHOGENSinterpretation of data, or the writing of this manuscript. Competing interests: The authors have declared that no competing interests exist. Evaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Author summary Y. pestis, the causative agent of the deadly plague, has caused three global pandemics throughout history and continues to pose a significant threat in various regions of the world. Unfortunately, there are currently no approved vaccines available to safeguard humans against Y. pestis infection. While the live-attenuated Y. pestis EV76 vaccine strain has been utilized in high-risk populations in China, Mongolia, and former Soviet coun- tries, its usage is restricted due to potential severe adverse effects. In a recent study, we identified two small open reading frames (sORF)-encoded peptides of Y. pestis, namely SEP-yp1 and SEP-yp2, which are associated with the bacterium’s virulence. To improve the safety and efficacy of the EV76 vaccine, we constructed three mutant strains (Δyp1, Δyp2, and Δyp1&yp2) and evaluated their virulence attenuation, immunogenicity, and ability to protect against Y. pestis infection in mice. Our findings demonstrated that delet- ing the yp2 gene was a successful approach to diminish the virulence of Y. pestis EV76 while simultaneously enhancing its ability to provoke an immune response. Furthermore, our study revealed that EV76Δyp2 exhibited superior protection against different forms of plague, including pneumonic and bubonic, making it a promising candidate for further development as a plague vaccine. Introduction Plague caused by Yersinia pestis is a fulminant infectious disease, resulting in nearly 200 mil- lion deaths in human history [1,2]. Plague presents itself in various clinical forms, namely bubonic, pneumonic, and septicemic plague. It is commonly transmitted among rodents through flea bites, usually leading to bubonic plague, which can progress to septicemic plague as the disease advances. Pneumonic plague is the most lethal form, and it can be transmitted among humans through the inhalation of infectious aerosols. The incubation period (1–3 days) of pneumonic plague is short, followed by a rapid disease progression that is often associ- ated with high mortality [3]. Although plague endemics have been successfully controlled over the last half century, the 2017 plague epidemic in Madagascar was a reminder that Y. pestis remains a real threat in many parts of the world [4]. There are still several hundred human cases annually, scattered in different countries in Africa, the Americas, and Asia [5,6]. Unfor- tunately, antibiotic-resistant Y. pestis strains have already emerged in different countries in the meanwhile [7–10]. Due to its extreme lethality and ability to be transmitted via aerosol, Y. pes- tis is categorized as a Tier 1 select agent, or one of the few agents most likely to be utilized as a biological weapon [11]. Vaccination is the most effective and economical method to prevent plague, but there are currently no World Health Organization-recommended vaccines to pro- tect human population against plague [12]. Thus, developing a safe and effective vaccine against plague remains of high interest. Those currently under clinical trials are all subunit vaccines based on two antigens: F1 (cap- sular antigen) and LcrV (low calcium response V antigen, a type 3 secretion system [T3SS] component) [13–16]. Non-encapsulated (F1-negative) Y. pestis strains are widespread in nature, about 10–16% in a field sampling study [17], and this type of strain remains fully viru- lent [18–20]. Likewise, LcrV exists in at least five clades in the Yersinia species, and antibody responses to these LcrV variants are not cross-protective [21]. Therefore, there is always a con- cern that F1 and LcrV-based subunit vaccines may not provide complete protection against all Y. pestis strains [22]. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 2 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Compared to other types of plague vaccines, live-attenuated plague vaccines possess a dis- tinct advantage in rapidly activating both humoral and cellular immunity, [23,24], eliciting immunity against numerous antigens [25], and the immunoprotective effect can last for 10–12 months [26,27]. The live-attenuated Y. pestis EV76 vaccine strain has been used in populations at high-risk exposure in China, Mongolia, and former Soviet countries. EV76 is highly attenu- ated due to the absence of the pigmentation locus (pgm-) that is responsible for acquisition of host iron [28,29], and it can induce protection against both bubonic plague and pneumonic plague [30]. However, EV76 vaccine potentially causes major side effects, such as severe head- aches, pyrexia, and general malaise [30]. It has been shown that pgm- vaccine strains can restore virulence under conditions of iron overload [31,32]. There is also a case report of death in a patient with hereditary hemochromatosis caused by infection with an attenuated strain of pgm- Y. pestis [31,33,34]. Thus, EV76 vaccine has not been approved to be used worldwide due to its strong reactogenicity. Our previous study has identified two small open reading frames (sORF)-encoded peptides (SEPs) of Y. pestis, namely SEP-yp1 and SEP-yp2. Deleting either of them in Y. pestis 201 strain results in a significant reduction in the intracellular survival capability and virulence in mice [35]. Specifically, s.c. challenge with approximate 100 CFU did not results in the death of mice challenged with the yp2 mutant and only caused death of 25% of mice challenged with the yp1 mutant. These findings suggest that yp1 and yp2 mutants exhibit significantly reduced viru- lence in mice. To determine whether the side effects of EV76 vaccine can be lowered while maintaining its immune protections by introducing these mutations, we construct mutants of EV76 by deleting one of SEP-yp1 and SEP-yp2 encoding genes, yp1 or yp2, or both of them and evaluate the residual virulence of these mutant strains, including their safety under iron overload condition. We further compared different sORF genes mutant strains and EV76 vac- cine in a short-term study and a long-term study for their protective efficacies. Our results show that the deletion of sORF genes further lowers the residual virulence of EV76. EV76Δyp2 induces a stronger cellular immune response than EV76 and EV76Δyp1&yp2. Moreover, EV76Δyp2 and EV76Δyp1&yp2 afford better protection against i.n. challenge of non-encapsu- lated Y. pestis than EV76 after two doses of immunization. Results Construction of sORF genes deletion mutants of Y. pestis To further reduce the residual virulence of EV76 and improve the vaccine safety, we deleted the yp1 and (or) yp2 genes from EV76, generating different mutants named EV76Δyp1, EV76Δyp2, and EV76Δyp1&yp2 (S1 Table). The in-frame deletion of the yp1 and (or) yp2 genes from EV76 was confirmed by PCR using specific primers (S2 Table) as well as by DNA sequencing of the PCR products flanking the yp1 and yp2 genes (S1A and S1B Fig). To deter- mine the expression of the major protective antigen in these mutants of EV76, the expression of F1 and LcrV was detected by Western blot (S1C and S1D Fig). No obvious difference was found between the three mutants and EV76. Characterization of the residual virulence, immunogenicity, and safety of the sORF genes mutant strains of EV76 To further confirm the contributions of yp1 and yp2 to the pathogenicity of Y. pestis, we deter- mined the median lethal dose (LD50) of 201 mutants with deletion of either yp1, yp2 or both genes. Table 1 displays the LD50 values of these Y. pestis 201 mutants in BALB/c mice (6–8 weeks old, n = 5 or 10 per group) challenged subcutaneously. All the Y. pestis 201 mutants PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 3 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Table 1. Virulence attenuation in BALB/c mice of Y. pestis 201 sORF mutants. Strain 201 201Δyp1 201Δyp2 201Δyp1&yp2 Characteristics wild type in-frame deletion of yp1 in-frame deletion of yp2 in-frame deletion of yp1 and yp2 LD50 by s.c. Route (CFU)a 3.1 4.8×104 9.0×104 5.2×104 References [36] [36] This studyb This studyb a The confidence interval was unbounded. b Survival curves of mice s.c challenged with different doses of Y. pestis 201 sORF mutant strains were shown in S2 Fig. https://doi.org/10.1371/journal.ppat.1012129.t001 exhibited a significant attenuation in virulence, with as increase in LD50 more than 104-folds, suggesting that yp1 and yp2 are critical for the virulence of Y. pestis (S2 Fig). Previous studies showed that LD50 of EV76 in mice challenged subcutaneously was deter- mined to be 6.3×107 CFU [37]. Therefore, we administered a subcutaneous inoculation of 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2, respectively, to groups of BALB/c mice, and monitored their survival and weight for 14 days to assess their residual viru- lence. All the mice survived after inoculation with the four strains (Fig 1A). The body weight of mice inoculated with EV76Δyp1 or EV76Δyp2 strain reached its lowest level at 2 days post- inoculation (dpi.), displaying average levels of 94.85% and 88.83% of their body weight before inoculation, respectively. Mice inoculated with the EV76 or EV76Δyp1&yp2 strain experienced the lowest body weight at 3 dpi., with an average of 87.92% and 88.91% of their initial body weight, respectively. At 3 to 4 dpi., the body weight of mice began to recover, and the subse- quent trend of body weight increase in the four immunized groups became similar after 6 dpi. (Fig 1B). In summary, inoculation with EV76Δyp1 had significantly lower impact on the body weight of mice, whereas EV76Δyp2 and EV76Δyp1&yp2 strains exhibited comparable effects to EV76. It has been reported that attenuated Y. pestis pgm- strains have the ability to regain virulence under conditions of iron overload [31,32]. Thus, we further assessed the virulence of the mutant strains in mice with iron overload. BALB/c mice (n = 5 per group) were administered 100 μg of FeCl2 and subsequently s.c. inoculated with ~107 CFU of EV76, EV76Δyp2, EV76Δyp1 or EV76Δyp1&yp2. After inoculation, the mice received daily injections of 100 μg of FeCl2 and were consecutively monitored for 14 days. As shown in the survival curves, all mice inoculated with EV76Δyp1 survived, while those inoculated with EV76Δyp2, EV76Δyp1&yp2, and EV76 succumbed between 2 and 6 dpi. under iron overload conditions (Fig 2A). With the exception of one mouse that died after being inoculated with EV76, all the mice in the non-iron overload groups survived challenges with EV76 and the 3 mutants (Fig 2B). This indicates that EV76Δyp1 exhibited significantly better safety compared to EV76 in iron overload mice. However, no improvement was observed for EV76Δyp2 and EV76Δyp1&yp2. To further evaluate the residual virulence of mutant strains, we analyzed the bacterial loads in the lymph nodes, spleens, and liver of mice that were inoculated s.c. with approximately 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2 at bilateral groins, using equal amounts of bacterial suspensions. At 1, 3, and 6 dpi., animals (n = 10 per group) were sacrificed, and their inguinal lymph nodes, spleen, and liver were harvested for bacterial load determination. The results showed that there was no significant difference in the bacterial load in the inguinal lymph nodes and liver among the four groups across all the time points (Fig 3A and 3C). Interestingly, the bacterial loads in the spleen of mice inoculated with the three mutant strains were significantly lower than those in mice inoculated with EV76 at 1 and 3 dpi. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 4 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 1. The survival curves and body weight loss of mice s.c inoculated with the EV76-derived candidate vaccines. (A) BALB/c mice (n = 10, female) were inoculated s.c. with 1×107 CFU of Y. pestis EV76, EV76Δyp1, EV76Δyp2 or EV76Δyp1&yp2, respectively; (B) Body weight was monitored continuously for 14 days. The weight changes on days 2 and 3 were shown in detail in the right panel. One-way ANOVA with Tukey post hoc test was used to analyze the significance of differences in body weight losses among the infected groups. ns indicates no statistical significance. **P<0.0001. https://doi.org/10.1371/journal.ppat.1012129.g001 (Fig 3B), suggesting a faster clearance of the mutant strains. These results indicate that all three mutants exhibited similar efficiency as EV76 in traveling to draining lymph nodes, but they were cleared more rapidly from the spleen. Moreover, there was no significant difference in the distribution of the three mutant strains to the liver compared to EV76. We also performed histopathological analysis on various tissues from mice inoculated s. c. with 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2. At 3 or 6 dpi., the mice were sacrificed, and the inguinal lymph nodes, spleens, lungs, and livers were har- vested for examination of pathological alterations (Fig 4A and 4B). At 3 dpi., inflammatory lesions were observed in lymph nodes, spleen, liver, and lungs of all the inoculated groups. The pathological scores in the inguinal lymph nodes of the EV76Δyp1-inoculated group were lower compared to the other groups, while no statistically significant difference was observed in the spleen, liver, and lungs among the groups (Fig 4C). At 6 dpi., the pathologi- cal score of inguinal lymph nodes in the EV76Δyp1- and EV76Δyp1&yp2-inoculated group was lower than that of EV76- and EV76Δyp2-inoculated group. The lymph nodes in the for- mer two groups returned to normal size, while those in the latter two groups still exhibited pathological changes, such as inflammatory cell infiltration and connective tissue hyperpla- sia (Fig 4D). The deletion of the yp1 gene from EV76 significantly reduces damage to drain- ing lymph nodes. Taken together, these results demonstrate that EV76Δyp2, EV76Δyp1, and EV76Δyp1&yp2 exhibit varying degrees of reduced virulence compared to EV76, thus they are promising candidate for further evaluation as attenuated living vaccines against plague. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 5 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 2. The survival curves of mice inoculated with the EV76-derived candidate vaccines under iron overload conditions. BALB/c mice (n = 5 per group, females) were inoculated s.c. with 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1 or EV76Δyp1&yp2, respectively, and subsequently intraperitoneally (i.p.) injected daily with 100 μg of FeCl2 (A) or sterile PBS (B). Kaplan–Meier analysis with log-rank (Mantel-Cox) test was used to calculate P values, comparing the results to the EV76-inoculated groups. P<0.01. https://doi.org/10.1371/journal.ppat.1012129.g002 Protection efficacy of EV76-derived candidate vaccines in short-term study Mice were immunized s.c. twice at a 21-day interval with a dose of 5×106 CFU of EV76, EV76Δyp2, EV76Δyp1 or EV76Δyp1&yp2. The evaluation of humoral and cellular immune responses, as well as the protective effect of immunization against the virulent Y. pestis strain, was performed six weeks after initial immunization. 1. Evaluation of the specific humoral immune response in mice immunized with candi- date EV76-derived vaccines. Sera were collected from vaccinated mice 41 days after initial immunization. The levels of serum IgG titers against antigens F1 and LcrV were measured using ELISA. Among all the three EV76 mutants, all of them induced significant levels of IgG titer against F1 antigen in s.c. immunized mice (Fig 5A). However, only EV76Δyp2, but not EV76Δyp1 and EV76Δyp1&yp2, elicited IgG titers against F1 comparable to those induced by EV76 in mice. None of the tested attenuated strains induced high IgG titer against LcrV anti- gen (Fig 5B), although EV76Δyp2- and EV76-immunized groups showed slightly higher IgG titers against LcrV compared to the control group. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 6 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 3. Bacterial loads in organs of mice inoculated with the EV76-derived candidate vaccines. Mice (n = 10 per time point in each group) were inoculated s.c. with 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2. At different time points, mice were sacrificed, and bilateral inguinal lymph nodes (A), spleens (B) and liver (C) were collected, and the number of living bacteria was determined. Two-way ANOVA with Tukey’s post hoc test was used to calculate significant differences in bacterial loads. ns indicates no statistical significance, P<0.05, P<0.01, P<0.001, *P<0.0001. https://doi.org/10.1371/journal.ppat.1012129.g003 To assess the IgG titer against antigens other than F1, the serum IgG titer against the whole cell lysate of Y. pestis 201Δcaf1 (201Δcaf1-WCL) was measured. The results showed that all three EV76 mutants induced significantly higher levels of IgG against 201Δcaf1-WCL in mice compared to the group treated with PBS (phosphate buffer saline) alone (Fig 5C). Consistently, Western blot analysis confirmed that besides F1 antigen, immune sera also recognized multi- ple antigens in 201Δcaf1-WCL (Fig 5D). Immunoblotting analysis of serum samples from the EV76Δyp2- and EV76-immunized mice revealed similar protein patterns, while some bands were missing for serum samples from the EV76Δyp1 and EV76Δyp1&yp2-immunized groups, suggesting that the deletion of the yp1 gene might impact the expression or immunoactivities of certain antigens. The IgG1 and IgG2a subclasses of serum IgG against 201-WCL were analyzed to provide further insight. We observed significantly higher titers of IgG1 and IgG2a against 201-WCL in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 7 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 4. Histopathological analysis on mouse tissues following inoculation with the EV76-derived candidate vaccines. Mice (n = 5 per time point in each group) were inoculated s.c. with 1×107 CFU EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2. Representative hematoxylin-eosin (HE) staining results for the different tissues at 3 dpi. (A) and 6 dpi. (B) are shown as indicated. HE staining of the lymph nodes tissue sections revealed various degrees of inflammatory cell infiltration (red arrows), necrosis (black arrows), connective tissue hyperplasia (yellow arrows), and congestion (blue arrows). The spleen tissue sections showed different degrees of inflammatory cell infiltration (red arrows), massive extramedullary hematopoiesis (black arrows), and a mild increase of multinucleated giant cells (yellow arrows). The liver tissue sections showed degeneration of hepatocytes (black arrows), few foci of extramedullary hematopoiesis (red arrows), and few venous congestion and vasodilatation (blue arrows), inflammatory cell infiltration (yellow arrows). Pathological examination of lung tissues from mice revealed mild inflammatory cell infiltration (black arrows), with limited thickening of alveolar walls observed. The histopathological scores of various organs at (C) 3 dpi. and (D) 6 dpi. were determined using Kruskal-Wallis with Dunn’s post hoc test to assess statistical significance. P<0.05. https://doi.org/10.1371/journal.ppat.1012129.g004 all the immunized mice compared to the control group (Fig 5E). Among the strains tested, all except EV76Δyp1&yp2 induced comparable IgG2a titers against 201-WCL as observed in the EV76-immunized group. Regarding the IgG1 subclasses of anti-201-WCL IgG, no significant differences were observed between the different groups. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 8 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 5. Humoral immune responses in mice administrated s.c. with the EV76-derived candidate vaccines in short-term study. Mice (n = 10 per group) were immunized s.c. with two doses of 5×106 CFU of EV76, EV76Δyp2, EV76Δyp1 or EV76Δyp1&yp2 at a 21-day interval. Sera were collected on day 41 after the initial immunization. Titers of IgG specific to F1 (A), LcrV (B), and 201Δcaf1-WCL (C) were measured using ELISA. (D) For Western blotting analysis, blotted antigens were obtained from the whole cell lysates of 201 or 201Δcaf1, and pooled sera from immunized mice were used as primary antibodies. (E) IgG subclasses to 201-WCL antigen were analyzed by ELISA. (F) The serum IgG2a/IgG1 ratios in the immunized mouse groups were determined. Statistical analysis was conducted using One-way ANOVA with Tukey post hoc to determine the significance of differences. P<0.05, P<0.01, P<0.001 and *P<0.0001. https://doi.org/10.1371/journal.ppat.1012129.g005 The anti-201-WCL IgG2a/IgG1 ratios in the EV76Δyp1-, EV76Δyp2-, EV76-immunized groups were approximately to 1.0 (Fig 5F), suggesting the induction of a balanced Th1/Th2 immune response by these three strains. In contrast, the EV76Δyp1&yp2-immunized group PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 9 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague displayed an anti-201-WCL IgG2a/IgG1 ratio of 0.69 (Fig 5F), pointing to a Th2-skewed response. 2. Characterizing specific cellular immune response in mice immunized with EV76-der- ived candidate vaccines. To assess cell-mediated immune responses in vaccinated mice, splenocytes were isolated and stimulated in vitro with the whole cell lysate of Y. pestis 201 (201-WCL) acquired by sonication. The cytokine levels in the cell culture supernatants were measured using a cytokines determination by Luminex assay (n = 5 per group). The EV76Δyp1-immunized group was excluded from the analysis due to its inferior performance in humoral immunity. When stimulated with 201-WCL, splenocytes from mice immunized with EV76Δyp2 secreted significantly higher levels of cytokines, including IL-12p70, IL-13, IL-1β, IL-2, IL-6, TNF-α, and GM-CSF, compared to splenocytes from mice immunized with EV76 (Fig 6). In contrast, no discernible difference was observed in cytokine secretions between the EV76Δy- p1&yp2-immunized and the EV76-immunized groups. Similar results were observed when Fig 6. Evaluation of cellular immune responses in mice inoculated s.c. with EV76-derived candidate vaccines. Splenocytes were harvested and stimulated with 201-WCL and the supernatants were evaluated for cytokines levels by Luminex assay (n = 5 for each group). One-way ANOVA with Tukey post hoc was used to determine the significance of differences. P<0.05, P<0.01, P<0.001, **P<0.0001. https://doi.org/10.1371/journal.ppat.1012129.g006 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 10 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague splenocytes were stimulated with 201Δcaf1-WCL (S3 Fig), suggesting that vaccination with EV76 and the mutants elicited similar cell-mediated immune responses against Y. pestis 201Δcaf1 and 201. These results indicated that EV76Δyp2 elicits a stronger cell-mediated immune compared to EV76, indicating its potential to provide enhanced protection against plague. In addition, this increased response induced by EV76Δyp2 was observed when the splenocytes were stimu- lated with both 201-WCL and 201Δcaf1-WCL (S3 Fig), suggesting the involvement of multiple antigens beyond F1 in the cell-mediated immune responses against plague following immunizations. 3. Protective efficacy of candidate vaccines derived from EV76 in immunized mice. To enable further investigation into the protective efficacy, we assessed the susceptibility of BALB/ c mice to 201, 201Δcaf1 or 201-lux. The result was shown in S3 Table. On day 42 after the initial immunization, vaccinated mice were subjected to s.c. or i.n. chal- lenges with Y. pestis 201. All mice in the immunized group survived when exposed to a low dose of 1000 CFU (322 LD50) of Y. pestis 201 (Fig 7A) and no symptoms such as ruffled fur, hunch back or lethargy were observed. In contrast, all the unvaccinated mice in the control group succumbed to death following the same challenges. When challenged with a high dose Fig 7. Efficacy of EV76-derived candidate vaccines in protecting immunized mice against exposure to Y. pestis 201 or 201Δcaf1. Mice were immunized s. c. twice at 21-day interval with 5×106 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2, respectively. Vaccinated mice were subjected to s.c. or i.n. challenge 42 days after the initial immunization. Groups of mice injected with PBS instead of the bacterial suspension served as a control. Vaccinated mice (n = 9~10 per group) were challenged s.c. with 1000 CFU (322 LD50) (A), 1×107 CFU (3.22×106 LD50) (B) of Y. pestis 201, 1000 CFU (403 LD50) of Y. pestis 201Δcaf1 (C), or i.n. challenged with 5000 CFU (11.4 LD50) (D), 2×104 CFU (45.6 LD50) (E) of Y. pestis 201, or challenged i.n. with 7×104 CFU (207 LD50) of Y. pestis 201Δcaf1 (F). Kaplan–Meier analysis with log-rank (Mantel-Cox) test was used to determine the significance of differences between the survival curves. ns, no significance, P<0.05, P<0.01, P<0.001. https://doi.org/10.1371/journal.ppat.1012129.g007 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 11 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague of 1×107 CFU (3.22×106 LD50) of Y. pestis 201, 90% of the mice in both the EV76- and EV76Δyp2-immunized group survived, and 80% of the mice in the EV76Δyp1&yp2-immu- nized group survived. In contrast, only 10% of the mice in EV76Δyp1-immunized group sur- vived this challenge (Fig 7B). The immunization with EV76, EV76Δyp2, or EV76Δyp1&yp2 provided mice with complete protection against i.n. challenge with 5000 CFU (11.4 LD50) of Y. pestis 201. In contrast, immu- nization with EV76Δyp1 conferred only 60% protection to the mice (Fig 7D). After being chal- lenged i.n. with 2×104 CFU (45.6 LD50) of Y. pestis 201, all the mice in the EV76- and EV76Δyp1&yp2-immunized group survived, 90% of the mice in the EV76Δyp2-immunized group survived, and only 20% of the mice in EV76Δyp1-immunized group survived (Fig 7E). To evaluate the protective efficacy of these EV76-derived candidate vaccines against non- encapsulated Y. pestis, mice were immunized s.c. twice at a 21-day interval with 5×106 CFU of EV76, EV76Δyp2, EV76Δyp1 or EV76Δyp1&yp2. On day 42 after the initial immunization, the vaccinated mice were subjected to s.c or i.n. challenges with virulent Y. pestis 201Δcaf1. The immunization with EV76, EV76Δyp2, or EV76Δyp1&yp2 provided mice with complete protec- tion against s.c. challenge with 1000 CFU (403 LD50) of Y. pestis 201Δcaf1, whereas immuniza- tion with EV76Δyp1 conferred only 80% protection to the mice (Fig 7C). After being challenged i.n. with 7×104 CFU (207 LD50) of Y. pestis 201Δcaf1, 55.6% of the EV76Δyp2- immunized mice and 44.4% of the EV76Δyp1&yp2-immunized mice survived. By contrast, none of mice in the EV76Δyp1- and EV76-immunized group survived (Fig 7F), suggesting that EV76Δyp2 provides superior protective efficacy against non-encapsulated Y. pestis com- pared to EV76. Rapid protection provided by a single-dose immunization of EV76-derived candidate vaccines As previously reported, live-attenuated plague vaccines have been shown to provide rapid pro- tection against Y. pestis with a single-dose immunization [23,38], making them particularly suitable for emergency use in the face of imminent risk of exposure. To assess the ability of dif- ferent EV76-derived candidate vaccines to confer rapid protection through a single-dose immunization, mice were immunized s.c. with 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2. After 21 days, the mice were subjected to i.n. challenge with Y. pestis 201 to evaluate the protective efficacy of the vaccines. The immunization with EV76Δyp2, or EV76Δy- p1&yp2 provided the mice with complete protection against i.n. challenge of 3×104 CFU (68 LD50) of Y. pestis 201. However, the immunization with EV76Δyp1 and EV76 only resulted in a 60% and 70% of mice survival rate, respectively (Fig 8). Protection efficacy of EV76-derived candidate vaccines in long-term study To evaluate the long-term protection efficacy of these attenuated mutants, mice were immu- nized s.c. twice at a 21-day interval with 5×106 CFU of EV76, EV76Δyp2, or EV76Δyp1&yp2. Serum samples were collected from the mice at days 20, 41, 62, 83, and 112 after the initial immunization, and specific IgG titers were determined by ELISA analysis. At day 20 post-ini- tial immunization, the immunized mice in all three groups demonstrated elevated IgG titers against F1 antigen. Specifically, the EV76Δyp2-immunized group exhibited a significant increase in F1 IgG titers following the booster immunization compared to the initial immuni- zation (Fig 9A). However, no significant difference was found between the initial and the booster immunization in mice vaccinated with EV76Δyp1&yp2 or EV76. Following two doses of s.c. inoculations, the IgG titers in all three groups reached their highest levels at day 41 post- initial immunization. Subsequently, the titers gradually declined but remained consistently PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 12 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 8. Evaluation of rapid protection efficacy in mice immunized with a single-dose of EV76-derived candidate vaccines. Mice (n = 10 per group) were immunized s.c. with 1×107 CFU of EV76, EV76Δyp2, EV76Δyp1, or EV76Δyp1&yp2. After 21 days, the vaccinated mice were subjected to i.n. challenge with 3×104 CFU (68LD50) of Y. pestis 201. Kaplan–Meier analysis with log-rank (Mantel-Cox) test was used to determine the significance of differences. ns, no significance, P<0.05. https://doi.org/10.1371/journal.ppat.1012129.g008 high, ranging from 35% to 37% of the maximum IgG titers, during a period of 112 days post- initial immunization (Fig 9B). Among the mice immunized with the two-dose inoculation, a group of mice (n = 3–4 for each candidate vaccines) were left untreated, and their sera were collected one year after the initial immunization to measure the IgG titers against 201-WCL or F1 antibody. As depicted in Fig 9D and 9E, immunized mice in all three groups exhibited high IgG titers against 201-WCL or F1 antigen one year after the initial immunization. Interest- ingly, the mean titers against F1 antibody in mice immunized with EV76Δyp2 were found to be higher than those in mice immunized with EV76, although the difference was not statisti- cally significant (p = 0.0889). This observation suggests that the presence of F1 antibody may persist for a longer duration in EV76Δyp2-immunized mice compared to that in EV76-immu- nized mice. At day 120 after initial immunization, mice were challenged i.n with 1.7×104 CFU (19.7 LD50) of Y. pestis 201-lux, which express luciferase and its substrates [39]. The immunization with EV76Δyp2 and EV76Δyp1&yp2 provided complete protection to mice against i.n. chal- lenge with Y. pestis 201-lux, whereas 10% of EV76-immunized group succumbed to the infec- tion (Fig 9C). Mice were imaged by IVIS (In Vivo Imaging System) to track the dissemination of Y. pestis 201-lux during the infection. At 3 dpi., out of the 10 mice in the control group injected with PBS, 7 exhibited persistent luminescence signals in the lungs, whereas no lumi- nescence signal was detected in all the vaccinated mice. In the control group, 90% of mice (9 out of 10) succumbed to the infection prior to 12 dpi. The only surviving mouse exhibited luminescence around the trachea, along with symptoms such as hair shrugging and significant weight loss. The mouse expired shortly after undergoing imaging. In the EV76-immunized group, one mouse succumbed to the infection before imaging at 12 dpi., and no luminescence signal was detected in the remaining mice. In the EV76Δyp2- and EV76Δyp1&yp2-immunized group, all mice survived, with only one mouse in each group exhibiting weak and limited lumi- nescence signals (Fig 9F). These results suggest that protection provided by EV76Δyp2 or EV76Δyp1&yp2 is comparable to EV76 at 120 days post-vaccination. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 13 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Fig 9. Efficacy of EV76-derived candidate vaccines in protecting mice from i.n. challenge with Y. pestis 201-lux. Mice (n = 10 each group) were immunized s.c. twice at a 21-day interval with 5×106 CFU either of EV76, EV76Δyp2, EV76Δyp1&yp2, or PBS. The IgG titers against F1 antigen in the sera of mice were then determined by ELISA. (A) The IgG titers against F1 after initial and booster immunization. (B) The kinetics of IgG titers against F1 were monitored over 112 days. Two-way ANOVA with Tukey post hoc was used to determine the significance of differences in IgG titers between the different groups (C) On day 120 post initial-immunization, the vaccinated mice were challenged i.n. with 1.7×104 CFU (19.7 LD50) of Y. pestis 201-lux. Mouse survival was monitored daily for 14 days and the survival curves were plotted using GraphPad 8.0.1 Kaplan–Meier analysis with log-rank (Mantel-Cox) test was used to determine the significance of differences. One-year post initial immunization, IgG titers against 201-WCL (D) or F1 antigen (E) were measured in the sera of mice (n = 3 or 4 per group) immunized with the indicated Y. pestis strains. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 14 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague One-way ANOVA with Dunnett was used to determine the significance of differences. (F) In vivo luminescence imaging of mice i.n. challenged with Y. pestis 201-lux. On 3 and 12 dpi, surviving mice were imaged, and those exhibiting luminescence signals were denoted with red asterisks. The luminescent intensity ranged from 1.5×e6 (red) to 1.5×e5 (violet) as indicated. ns, no significance, P<0.05, *** P<0.0001. https://doi.org/10.1371/journal.ppat.1012129.g009 Discussion Vaccine candidate should provide significant protection with minimal side effects. The live- attenuated vaccine EV76 has been used in tens of millions of people since 1936 and provides protection against both bubonic and pneumonic plague. However, its variable lethality in some animal models [40,41] and reactogenicity in humans have hindered its global acceptance [42]. Nevertheless, modification of different virulence associated genes, i.e., lpxM or pla [37,43], has improved safety while retaining its immunogenicity. In this study, we investigated whether deletion of the sORF gene yp1 and yp2, based on the EV76 vaccine strain, hold the promise of creating a more potent and safer live attenuated plague vaccine. The significant attenuation of virulence in the EV76Δyp1 strain was evidenced by a number of experimental observations. These included a markedly reduced loss of body weight in mice (Fig 1B), avirulence under iron overload conditions (Fig 2A), a significantly lower bacterial burden in the spleen (Fig 3B) and reduced pathological damage to the local lymph nodes when compared to EV76. The bacterial loads of EV76Δyp2 and EV76Δyp1&yp2 in the spleen were significantly reduced compared to EV76 at both 1 or 3 dpi. (Fig 3B). Although no difference was found between them and EV76 in other animal experiments, these results suggest that the two strains exhibit lower virulence than EV76 in terms of dissemination. Intriguingly, the dou- ble gene mutant strain EV76Δyp1&yp2 did not exhibit the virulence attenuation observed in EV76Δyp1. This suggests a complex interaction between yp1 and yp2 genes that may underlie this phenomenon, which needs further exploration. Previous studies have demonstrated that live-attenuated vaccines against Y. pestis can effec- tively trigger both humoral and cell-mediated immune responses [43–46]. It has also been established that F1 and LcrV are the most important protective antigens [30,47]. Our findings demonstrated that immunization with the various EV76-derived candidate vaccines resulted in remarkably elevated serum IgG titers against F1 in mice (Fig 5A). However, the induction on IgG titers against LcrV was negligible, with a significantly higher level observed only in mice immunized with EV76Δyp2 or EV76, but not in the other groups compared to the con- trol group (Fig 5B). Our observations are consistent with previous studies indicating that live- attenuated vaccines against Y. pestis have a limited capacity to stimulate an anti-LcrV antibody response. [26,46,48,49]. This finding raises concerns regarding the protective efficacy of live- attenuated vaccines against non-encapsulated Y. pestis strains. Therefore, we further detected the serum IgG titers and antibody profile against 201Δcaf1-WCL (F1 negative). As shown in Fig 5C and 5D, all the vaccinated mice displayed high IgG titers against 201Δcaf1-WCL. More- over, Western blotting analysis revealed that the immune sera recognized a variety of antigens, in addition to F1, indicating a broad antibody response. Considering the potential loss of con- formational epitopes during the detection processes, it is plausible that the actual number of antigens successfully recognized by the immune sera was even higher [50]. Previous studies have suggested that s.c. immunization of attenuated living vaccines against plague tend to favor Th2-biased humoral immune responses [44,51], while intramuscular immunization tend to promote a Th1/Th2 balanced humoral immune response. Our results revealed that s.c. immunization of EV76Δyp1, EV76Δyp2 or EV76 elicited a balanced Th1/Th2 immune response against 201-WCL with IgG2a/IgG1 ratios approximately to 1.0 (Fig 5F), whereas mice immunized with EV76Δyp1&yp2 displayed an anti-201-WCL IgG2a/IgG1 ratio PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 15 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague of 0.69 (Fig 5F), pointing to a Th2-skewed response. The discrepancy between our observa- tions and the previous finding may be due to the intrinsic characteristics of the specific strains and further experiments are needed to verify this issue. The bacterial cell lysates of both 201 and 201Δcaf1 were capable of stimulating splenocytes from immunized mice to secret multiple cytokines including IFN-γ, TNF-α, IL-4, IL-13, IL-6, GM-CSF and IL-18 (S3 Fig). These findings suggest that in addition to F1 antigen, a diverse range of other antigens contribute to the humoral and cellular immune response elicited by live-attenuated vaccines against Y. pestis. Notably, compared to the EV76-immunized group, EV76Δyp2-immunized mice exhibited a significantly higher secretion level of Th1-related cytokines, including TNF-α, IL-2, and IL-12p70 (Fig 6), which have been recognized as crucial factors in conferring protection against plague [52–56]. These results suggested the potential of EV76Δyp2 as a promising candidate vaccine for eliciting enhanced cellular immunity against plague. The evaluation of immune protection in short-term study revealed that EV76Δyp2 and EV76Δyp1&yp2 showed similar levels of protection efficacy to EV76, whereas EV76Δyp1 was relatively less effective, consisting to the facts that F1 antibody titer was lowest in the group immunized with EV76Δyp1. This observation can be attributed to the rapid clearance of EV76Δyp1 in mice, which hindered the stimulation of sufficient adaptive immunity [44,57,58]. Almost all mice immunized with candidate vaccines achieved complete protection against s.c. challenge of non-encapsulated Y. pestis (Fig 7C). In contrast, only EV76Δyp2 or EV76Δy- p1&yp2 provided partial protection against i.n. challenge of the same strain, while EV76 and EV76Δyp1 immunizations did not confer significant protection (Fig 7F). It has been reported that the non-encapsulated Y. pestis can evade immune responses elicited by pgm- Y. pestis strains or F1 subunit vaccines in models of pneumonic plague in mice [19,57,59]. In certain potential application scenarios, EV76Δyp2 and EV76Δyp1&yp2 demonstrate superior protec- tion efficacy than EV76. The discovery that deleting the yp2 gene in EV76 led to a significant enhancement in pro- tection against non-encapsulated Y. pestis strains is truly exciting. Moreover, both EV76Δyp2 and EV76Δyp1&yp2 provided superior protection against pneumonic plague compared to EV76 after a single-dose of immunization. We hypothesized this was partially due to that dele- tion of yp2 enhanced the presentation of T-cells epitopes by host cells, resulting in an elevated cellular immunity. Taken together, these findings suggest that in specific application scenarios, such as when encountering F1-negative Y. pestis strains, EV76Δyp2 and EV76Δyp1&yp2 pro- vide superior protection compared to EV76. In regards of long-term protective efficacy, we observed no significant difference between protective efficacy of EV76 and EV76Δyp2. However, it is worth noting that all the mice in EV76Δyp2 immunized group survived, while one mouse in the EV76 immunized group died. Furthermore, the IgG titer for F1 antigen of serum samples from EV76Δyp2 immunized group was slightly higher than that of the EV76 immunized group, with a p-value of 0.089. We specu- late that this difference may become statistically significant when the number of the animal (currently n = 3) is increased. The development of vaccines against various mutant strains is a significant challenge in the field of plague vaccine research. Not only do natural non-encapsulated Y. pestis strains pose a challenge, but strains expressing LcrV variants are also able to evade the immune protection by LcrV subunit vaccine. These mutants possess the ability to bypass plague vaccines that rely solely on F1 and LcrV for protection [60]. Live-attenuated plague vaccines, with their complex antigenic composition, show potential in providing protection against various virulent Y. pestis strains. One example is the yscN mutant of Y. pestis C12, a strain lacking the ability to produce the F1 antigen. Although this strain is unable to elicit anti-LcrV antibodies, it still provides PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 16 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague partial protection against bubonic plague in 40% of animals s.c. challenged [46]. This observa- tion suggests that live attenuated vaccines can offer partial protection against bubonic plague, even without the protection provided by F1 and LcrV antigens. Several studies have established the crucial role of LcrV antibodies in protecting against non-encapsulated Y. pestis [19,61–63]. Unfortunately, live-attenuated vaccines have not been effective in inducing significant produc- tion of LcrV antibodies in mice, resulting in their suboptimal performance in protection against non-encapsulated Y. pestis. On the other hand, cellular immunity is also paramount for protection against plague infection [55,64] since a significant number of Y. pestis antigens other than F1 and LcrV can also be recognized by T cells [65]. Enhancing the cellular immu- nity elicited by live-attenuated vaccines towards these antigens other than F1 and LcrV, rather than solely relying on humoral immunity triggered by these antigens, may represent a feasible approach to improving the protective effect of live-attenuated vaccines. Our study highlights that deletion of the yp2 gene from EV76 did not increase LcrV antibody production, but signif- icantly enhanced cellular immune responses, resulting in better protection against non-encap- sulated Y. pestis. Our results present a new strategy for improving the protective effect of live- attenuated Y. pestis vaccines. In conclusion, we propose that EV76Δyp2 is a promising live- attenuated plague vaccine candidate that offers improved safety and efficacy when compared to EV76. Materials and methods Bacterial strains and growth conditions Bacterial strains used in this study were shown in S1 Table. Y. pestis strains, 201 and EV76 vac- cine, were routinely grown in Luria Bertani (LB) broth medium at 26˚C. Bacteria were cul- tured at 26˚C until an optical density (OD) of approximately 0.6 at 620 nm, followed by an incubation at 37˚C for additional 3 hours prior to Western blotting analysis or preparation of bacterial lysates (201-WCL or 201Δcaf1-WCL). LB plates containing 7% sucrose and Yersinia selective agar (BD, Voigt Global Distribution Inc., Lawrence, KS) were used for sacB gene- based counter-selection in allelic exchange experiments for mutant constructions. Escherichia coli DH5α and E. coli S17-1 λpir were usually cultivated at 37˚C in LB or on LB agar plates as plasmid donor strains. Kanamycin at 50 g/ml, ampicillin at 100 g/ml, and chloramphenicol at 25 g/ml were added as antibiotic supplements to the culture medium when necessary. Construction of the mutant strains of EV76 Y. pestis mutants were constructed using the λRed-based recombinant system as previously reported [66]. Briefly stated, kanamycin resistant cassettes from the plasmid pKD4 were ampli- fied using primer sets yp1-P1/yp1-P1 and yp2-P1/yp2-P2 (S2 Table), respectively, to create EV76Δyp1 and EV76Δyp2. In order to replace the yp1 and yp2 with the kanamycin resistance cassette, the PCR products were purified and electroporated into the competent bacterial cells. The cassette was later eliminated by introducing pCP20 [66], generating EV76Δyp1 and EV76Δyp2. The primer sets Δyp1-F/Δyp1-R and Δyp2-F/Δyp2-R were utilized to further verify the strains. A double mutant of EV76 was constructed using a suicide vector as previously reported [67]. Using the primer sets pre-yp1-F/ pre-yp1-R and post-yp1-F/ post-yp1-R, the homologous arm DNA segments from Y. pestis were amplified and cloned into SphI and SacI sites of pDS132, generating pDS132-yp1. The suicide plasmid pDS132-yp1 was then introduced to EV76Δyp2 from E. coli S17-1 λpir by conjugation. On LB agar plates containing chloramphen- icol, clones with the correct insert were identified, and the suicide vector was then removed PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 17 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague through the second recombination by sacB counter-selection. PCR was used to confirm the clones with correct mutation using the primers Δyp1-F/Δyp1-R. Animal experiments All the animal experiments were reviewed and approved by The Institute of Animal Care and Use Committee of the Academy of Military Medical Sciences (IACUC-DWZX-2020-071). 6- to 8-week-old female BALB/c mice were purchased from Vital River Laboratories (Beijing, China). 1. Survival analysis of mice inoculated with different mutant strains of EV76. Groups of mice (n = 10 per group for individual strain) were s.c. inoculated with 1×107 CFU of EV76 or different mutant strains. The mice were monitored for 14 days and their weight was mea- sured daily after infection. 2. Analysis of bacterial virulence to mice under iron overload conditions. Groups of mice (n = 10 per group for individual strain) were s.c. inoculated with 1×107 CFU of EV76 and different mutant strains. Each group of mice was randomly divided into two subgroups. A daily intraperitoneal (i.p.) injection of 100 μl 0.1% FeCl2 (100μg) buffer or sterile deionized water was administered to each of the subgroups [32]. The survival rate of the mice was evalu- ated after 14 days of observation. 3. Determination of bacterial loads in organs. Groups of mice (n = 30 per group for individual strain) were inoculated s.c. with 1×107 CFU of EV76 and different mutant strains. 10 mice from each group were humanly euthanized at 1, 3, and 6 dpi. Inguinal lymph nodes and spleen were then aseptically extracted for further analysis. Using the MagNA Lyser (Roche, Germany), tissue samples were homogenized in 800 μl PBS. The homogenate was seri- ally diluted in PBS and plated on Y. pestis Hettinger’s agar media plates (Hope Bio-Technology Co., Qingdao, China) to count bacterial numbers in different organs of mice administrated with different strains post 3 days’ incubation at 26˚C [43]. 4. Animal immunization. The overnight cultures of EV76 and different mutant strains were inoculated into fresh LB broth and allowed to grow at 26˚C for 12 h. Bacterial cultures were then re-inoculated into fresh LB broth and cultivated at 26˚C with shaking at 200 rpm until reaching an OD of approximately 1.0 at 620 nm. Bacterial cells were harvested, washed, and diluted in PBS to OD620�1.0. The bilateral groins of mice were inoculated s.c. with 100 μl with bacterial suspensions. The actual bacterial number was calculated by plating serial dilu- tions on Y. pestis Hettinger’s Agar plates. 5. Evaluation of protection efficacy. For the s.c. challenge with Y. pestis 201, 201Δcaf1, or 201-lux. the bacteria strains were inoculated into LB and cultivated at 26˚C until reaching an OD620 of approximately 1.0. For the i.n. challenge with the aforementioned strains, the bacteria were inoculated into LB and cultivated at 26˚C until an OD620 of approximately 0.6, followed by an additional 2 h incubation at 37˚C. Bacterial cells were harvested by centrifugation and the pellets were resuspended in sterile PBS. Groups of immunized mice were s.c. challenged. with 100μl suspensions of Y. pestis 201, 201Δcaf1, or 201-lux at appropriate concentrations, respectively. For i.n. challenge, immunized mice were anesthetized with iso pentobarbital (1.4mg/each) and then infected via nostril with 10 μl suspensions of Y. pestis 201, 201Δcaf1, or 201-lux suspension at appropriate concentrations. 5.1 Evaluation of short-term protection efficacy. Groups of mice (n = 10 per group for indi- vidual strain) were s.c. immunized with 1×107 CFU of EV76 and different mutant strains, whereas mice injected with sterile PBS served as the control group. On day 21 post-immuniza- tion, immunized mice were subjected to i.n. challenged with Y. pestis 201 as described above. To evaluate the effectiveness of a single dose immunization in providing protection against PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 18 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague pneumonic plague, mortality and morbidity of infected mice were monitored daily for the fol- lowing 14 days. Mice in various groups were immunized s.c. twice at a 21-day interval with 5×106 CFU/ dose of EV76 and different mutant strains (n = 20 per group for each strain, n = 10 in the con- trol group). Immunized mice were subjected to s.c. or i.n. challenged with various dosages of Y. pestis 201 as above on day 42 following their initial immunization. Only the low dose of Y. pestis 201 was administered by s.c. or i.n. to the unvaccinated control group. The mortality and morbidity of infected mice were recorded daily for the following 14 days to evaluate the short- term protective effects of different vaccine candidates on mice. In order to assess the potential effectiveness of vaccine candidates against Y. pestis 201Δcaf1 in mice, the two-dose immunization strategy was also adopted. Immunized mice were sub- jected to i.n. challenged with 7×104 CFU of Y. pestis 201. Mortality and morbidity of infected mice were monitored daily for the following 14 days. 5.2. Evaluation of long-term protection efficacy. Groups of mice (n = 15 per group for each strain or the control group) were s.c. vaccinated twice with 5×106 CFU/dose of EV76 and dif- ferent mutant strains at a 21-day interval. On day 120 post-initial immunization,10 mice from each immunized group were subjected to s.c. or i.n. challenge with Y. pestis 201-lux. Mortality and morbidity of the infected mice were consciously observed daily for the next 14 days. On days 123 and 130 post-immunization (day 3 and 12 post-infection), the animals were imaged by using IVIS (Spectrum, PerkinElmer, USA) to examine the dissemination and progress of infection. Histopathology Group of mice (n = 10 per group for individual strain) were s.c. infected with 1×107 CFU of EV76 and different mutants. The inguinal lymph nodes, lungs, spleens, and livers of five mice from each group were collected at 3 and 6 dpi. The organ tissues were fixed in 4% paraformal- dehyde and then sliced, mounted on slides, and stained with hematoxylin-eosin (HE). Patho- logical alterations in the tissue slices were observed using light microscopy. A pathologist with specialized training performed blinded evaluation and assigned pathology scores to tissue sec- tions according to the "International Harmonization of Nomenclature and Diagnostic Criteria for Lesions in Rats and Mice (INHAND)”: 0, no pathological lesions; 1, minimal; 2, mild; 3, moderate; 4, severe. [43]. The abnormal scores in the inguinal lymph nodes comprised conges- tion, necroptosis, inflammatory cell infiltration, and hyperplasia. Scores for spleens included extramedullary hematopoiesis, inflammatory cell infiltration, expansion of germinal centers, an increase of multinucleated giant cells, and congestion. The liver scores comprised hyperpla- sia, extramedullary hematopoiesis, inflammation cell infiltration, congestion, and degenera- tion of hepatocytes. Lastly, the lung scores were based on the degree of pulmonary alveolar wall thickening, inflammatory cell infiltration, congestion, and hemorrhage. Detection of antibody responses by ELISA and immunoblotting On day 41 post first immunization, sera were collected from 10 immunized mice per group (2 doses, 5×106 CFU/dose, 21 days apart). The levels of IgG that recognize F1, LcrV, or antigens in sonicated Y. pestis 201Δcaf1 were evaluated by ELISA as previously described [43]. The soni- cate of the Y. pestis 201Δcaf1 strain was obtained by sonication of bacteria cells grown at 37˚C in LB broth. Briefly, 96 well enzyme-linked plates (Costar, Corning, NY) were individually coated overnight with 2 μg/ml of rF1, 1μg/ml of rLcrV, or 10μg/ml of sonicated Y. pestis 201Δcaf1. After that, 2% bovine serum albumin (BSA) was used to block the 96 well plates. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 19 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague Serial 2-fold dilutions of serum samples were performed. The diluted serum was next indi- vidually applied to wells, and the wells were then incubated at 37˚C for 30 min with the appro- priate antigen. The plates were then washed 5 times with PBS containing 0.1% Tween-20 (PBST), 100 μl of HRP-conjugated sheep anti-mouse IgG, or anti-mouse IgG1, IgG2a, IgG2b (Thermofisher, Vienna, Austria) were then added, followed by incubation at 37˚C for 20 min. After an additional 5 washes, antibody titers were detected by a TMB substrate kit and ana- lyzed with an iMark plate reader (Bia-Rad) at 450/630 nm. Background values were obtained from samples collected from the untreated mice. The titers of specific antibodies were calcu- lated as the reciprocal of the OD620 value of the lowest sample dilution that produced a signal 2.1 times higher than that of background. For immunoblotting analysis, the bacterial cell pellets were weighed to ensure equal amounts of Y. pestis 201 and 201Δcaf1 bacterial cells were used to prepare the WCLs. Identical volumes of 201-WCL and 201Δcaf1-WCL were loaded onto 12% sodium dodecyl sulfate–poly- acrylamide gel electrophoresis (SDS-PAGE). Separated proteins were transferred onto polyvi- nylidene difluoride (PVDF) membranes (Cytiva, Germany). Before being used as a primary antibody, serum samples collected from each group 41 days post-immunization (comprising equal volumes of serum from 10 mice) were diluted to a 1:100 ratio with TBST. The membrane was then incubated with the IRDye 800CW-conjugated goat-anti mouse secondary antibody and the immunoblotting results were imaged by an Odyssey SA imaging system (LI-COR Bio- sciences). as described previously [68]. Detection of Antigen-specific T-cell responses On day 41 after first immunization, 5 mice per group had their spleens as single cells, which were plated into 24-wells plates (Corning, Corning, NY) with RPMI 1640 medium (Gibco, Grand Island, NY) containing 10% FCS at a concentration of 5×106 cells per well. The T cells in each well were then stimulated with 16 μg of sonicated Y. pestis 201-WCL or 201Δcaf1- WCL at 37˚C in a 5% CO2 incubator. After 48 hours, culture supernatant from each well was collected to detect cytokines using Th1/Th2/Th9/Th17/Th22/Treg Cytokine 17-Plex Mouse Panel kits (Thermofisher, Vienna, Austria). Statistical analyses All statistical analyses were conducted using GraphPad Prism version 8.0.1. Differences in bac- terial load, antibody levels, and cytokine levels among various immunization groups were eval- uated using one-way or two-way ANOVA, with subsequent Tukey’s or Dunnett’s post hoc tests. The Kruskal-Walli’s test, followed by Dunn’s post hoc test, was utilized to analyze pathological impairment scores. The animal survival rate was analyzed using Kaplan–Meier survival esti- mates. P < 0.05 was considered significantly different for all statistical analyses. Supporting information S1 Table. Strains and plasmids used in this study. (DOCX) S2 Table. Primers used in this study. (DOCX) S3 Table. The LD50 of Y. pestis 201, 201Δcaf1 or 201-lux in BALB/c mice exposed via whole body aerosol or subcutaneous challenge. (DOCX) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 20 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague S1 Fig. Construction of mutant strains and expression of their major protective antigens. (A) yp2 gene of the mutant strains was identified through PCR using outer primers (including yp2-F and yp2-R). (B) yp1 gene of the mutant strains was identified through PCR using outer primers (including yp1-F and yp1-R). The amplification length of gene yp1 in EV76Δyp1 and EV76Δyp1&yp2 is different because different gene editing techniques were used. (C) Expres- sion of F1 antigen. (D) Expression of LcrV antigen. (TIF) S2 Fig. Survival curves of mice s.c challenged with Y. pestis 201 mutant strains. (TIF) S3 Fig. Cellular immune responses to 201Δcaf1 in mice administrated s.c. with EV76-der- ived candidate vaccines. Splenocytes were harvested and stimulated with 201Δcaf1-WCL and the supernatants were evaluated for cytokine secretion by Luminex assay (n = 5 for each group). One-way ANOVA with Tukey post hoc was used to determine the significance of dif- ferences. P<0.05, P<0.01, P<0.001, **P<0.0001 (TIF) S1 Data. Raw data for figures. (XLSX) Author Contributions Conceptualization: Zongmin Du. Data curation: Xiao Guo, Youquan Xin, Zehui Tong, Gengshan Wu, Hongyan Chen. Formal analysis: Xiao Guo, Zehui Tong, Hongyan Chen, Tong Wang, Yajun Song, Zongmin Du. Funding acquisition: Youquan Xin, Yuan Zhang, Zongmin Du. Investigation: Xiao Guo, Zehui Tong. Methodology: Xiao Guo, Youquan Xin, Zehui Tong, Shiyang Cao, Yuan Zhang, Gengshan Wu, Hongyan Chen, Tong Wang, Yajun Song, Ruifu Yang, Zongmin Du. Project administration: Zongmin Du. Resources: Shiyang Cao, Yajun Song, Qingwen Zhang, Ruifu Yang, Zongmin Du. Supervision: Ruifu Yang, Zongmin Du. Validation: Zehui Tong. Visualization: Xiao Guo. Writing – original draft: Xiao Guo. Writing – review & editing: Zongmin Du. References 1. Perry RD, Fetherston JD. Yersinia pestis—etiologic agent of plague. Clin Microbiol Rev. 1997; 10 (1):35–66. Epub 1997/01/01. https://doi.org/10.1128/CMR.10.1.35 PMID: 8993858; PubMed Central PMCID: PMC172914. 2. Barbieri R, Signoli M, Cheve D, Costedoat C, Tzortzis S, Aboudharam G, et al. Yersinia pestis: the Nat- ural History of Plague. Clin Microbiol Rev. 2020;34(1). Epub 2020/12/11. https://doi.org/10.1128/CMR. 00044-19 PMID: 33298527; PubMed Central PMCID: PMC7920731. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012129 March 28, 2024 21 / 25 PLOS PATHOGENSEvaluation of safety and protection efficacy of a novel sORF mutant of EV76 against plague 3. Ansari I, Grier G, Byers M. Deliberate release: Plague—A review. 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10.7554_elife.86852	Reviewed Preprint Published from the original preprint after peer review and assessment by eLife. About eLife's process Reviewed preprint posted July 12, 2023 (this version) Posted to bioRxiv February 8, 2023 Sent for peer review February 8, 2023 Genetics and Genomics, Microbiology and Infectious Disease Influenza virus transcription and progeny production are poorly correlated in single cells David J. Bacsik, Bernadeta Dadonaite, Andrew Butler, Allison J. Greaney, Nicholas S. Heaton, Jesse D. Bloom Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America • Department of Genome Sciences & Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America • Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, United States of America • Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, United States of America • Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America (https://en.wikipedia.org/wiki/Open_access) (https://creativecommons.org/licenses/by/4.0/) Abstract The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza-virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny, and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population. eLife assessment This important paper reports a novel, compelling method, based on barcoding viral genes and next-generation sequencing, to quantify both viral transcription levels and progeny virus production in influenza virus-infected cells at the single-cell level. The authors show that viral transcription and progeny virus production are unexpectedly poorly correlated, and that cells in which viral RNAs are transcribed at high levels are not necessarily those producing the most progeny virions. Because of its novelty, the study will be of interest to the broader virology community. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 1 of 36 Introduction Many aspects of viral infection are heterogeneous when measured across single cells. Individual infected cells vary widely in transcription of viral genes [1–4], expression of viral proteins [4–6], presence of viral mutations [7], replication of viral genomes [8], and production of viral progeny [6,8–10]. However, it is unclear how variation in these different aspects of infection are related within the same infected cells. For instance, to what degree does the extent of viral transcription in an infected cell determine the number of progeny virions the cell produces? The answer to this question remains elusive because the most common single-cell techniques (flow cytometry and single-cell RNA sequencing) measure the levels of proteins and transcripts, rather than the number of viral progeny produced. Here, we develop a novel approach to simultaneously measure viral transcription, viral mutations, and viral progeny production in single cells infected with influenza virus. We find that progeny production is even more heterogeneous than viral transcription in single cells. The cells that express the most viral transcripts often do not generate any detectable viral progeny. Instead, cells with extremely high viral transcription often fail to express the NS gene and represent non-productive infections. Our findings emphasize that different aspects of viral infection are not always correlated at the single cell level, and that many of the cells contributing large amounts of viral mRNA to bulk RNA sequencing studies do not appreciably contribute virions to the progeny population. Results Viral barcoding to measure transcription, progeny production, and viral genotype in single cells To quantify the progeny virions released from single infected cells, we inserted random nucleotide barcodes [11–13] into the influenza virus genome so that they are positioned near the 3’ end of the viral mRNAs (Fig 1A). Standard 3’-end single-cell sequencing of the mRNA in infected cells [1,2,7,14–16] captures the viral barcode sequence along with host and viral transcripts, enabling determination of which barcoded virion(s) infected each cell (Fig 1A). We can sequence the viral barcodes on progeny virions released into the supernatant to quantify the relative number of physical progeny produced by each cell, and sequence the viral barcodes in cells secondarily infected with an aliquot of the supernatant to quantify the relative number of infectious progeny produced by each cell. Additionally, we can reconstruct the genome of the virion that infected each cell by selectively amplifying viral genes from the single-cell cDNA library and performing long-read sequencing as described previously [7]. This strategy enables simultaneous measurement of transcription, progeny production, and viral genotype in single cells. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 2 of 36 Fig. 1. Strategy to measure transcription, progeny production, and viral genotype in single cells. (A) Insertion of barcodes in the vi- ral genome makes it possible to quantify the progeny released from single cells, and relate prog- eny production to viral transcrip- tion and viral genotype. (B) Barcodes were inserted near the 3’ end of the mRNA sequence be- tween the stop codon and the polyA site, using a duplicated packaging signal scheme to avoid disrupting viral genome packag- ing. (C) Viruses with one or two barcoded segments grew to simi- lar titers as viruses with unmodi- fied genomes. The titers shown were measured after generating the viruses by transfection. Creation of dual-barcoded virus library To insert barcodes into influenza virus genes, we used a previously described approach to duplicate the packaging signals of the HA and NA genes to create sites where exogenous sequence can be added without disrupting viral genome packaging [17, 18]. This approach allowed us to insert 16-nucleotide random barcodes near the 3’ end of the genes, downstream of the stop codon but upstream of the polyadenylation signal (Fig 1B). These barcodes were therefore present in both viral mRNAs and genomic RNAs (vRNAs), but did not modify the amino acid sequence of the viral protein. We refer to these viruses as “dual- barcoded” as they have barcodes on two different genes. Dual barcodes provided duplicate measurements of progeny production from the same infected cell, which were averaged and normalized (see Methods). We engineered barcodes into the A/California/04/2009 (pdmH1N1) strain of influenza virus with the G155E cell-culture adaptation mutation [19]. Viruses with barcoded HA and NA segments could be generated by reverse genetics, and in cell culture grew to titers comparable to unmodified viruses (Fig 1C). To confirm that the sequence of individual barcodes did not affect viral growth in cell culture, we generated virus libraries carrying a small pool of barcodes and verified that the virus titers and barcode frequencies were stable across three passages (Fig S1). David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 3 of 36 For our single-cell experiments, we generated libraries of virions with a high diversity of barcodes on the HA and NA genes. Our experiments utilized between 1,500 and 4,000 infecting virions. It was important that nearly every virion have a unique barcode when randomly sampled from the library. We used deep sequencing to verify that for both HA and NA, in a sample of 1,500 barcodes from our virus library, >96% of barcodes were unique (Fig S2). In a sample of 4,000 barcodes, >92% were unique (Fig S2). We recapitulate prior findings that viral transcription is extremely heterogeneous across single infected cells We implemented the experiment in Fig 1A at two different MOIs. For the low MOI condition, we infected ∼104 MDCK-SIAT1-TMPRSS2 cells [20] with the dual-barcoded virus library at a MOI of ∼0.15; under these conditions, most cells are uninfected, and most infected cells are infected by a single virion. For the high MOI conditions, we infected ∼7x103 cells at a MOI of ∼0.6; under these conditions, fewer cells are uninfected and a substantial fraction of the infected cells are infected by multiple virions. To ensure a single round of relatively synchronized infection, we replaced the virus inoculum with fresh medium after one hour and added ammonium chloride, which prevents secondary infection by blocking the endosomal acidification necessary for viral fusion [21, 22]. We collected the cells for single- cell RNA sequencing at a single timepoint 12 hours after infection. Before loading onto a 10X Chromium device, we added a control sample that allowed us to quantify the rate of cell multiplets and PCR strand exchange. This control sample contained cells infected with a second virus carrying synonymous mutations that could be distinguished in sequencing data (see Methods). For the low MOI sample, we obtained single-cell RNA sequencing data for 254 cells infected with our barcoded virus library, resulting in an empirical MOI of 0.17 (since we recovered data for ∼1,600 total cells from this sample). For the high MOI sample, we obtained single-cell RNA sequencing data for 357 cells infected with our barcoded virus library, resulting in an empirical MOI of ∼0.59 (since we recovered data for ∼800 total cells from this sample). Note that many cells are lost during preparation for single-cell RNA sequencing, so an important caveat of our study is that some infected cells that produced viral progeny are absent in the transcriptome data [23, 24]. However, because the number of barcodes in our virus library greatly exceeds the number of infected cells, these undetected infections should not substantially affect our measurements of relative viral progeny among the cells that were captured. Under both infection conditions, there was extremely wide variation in the amount of viral transcription among infected cells (Fig 2A), similar to that observed in prior single-cell studies of influenza infection [1,2,7,14,15]. In most infected cells, viral transcripts accounted for <10% of all transcripts, but in a small number of cells, over half of the transcripts were derived from virus (Fig 2A). On average, cells infected at high MOI expressed higher levels of viral transcripts than cells infected at low MOI, but there was extensive variation under both conditions (Fig 2A). David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 4 of 36 Fig. 2. Viral transcription is extremely heterogeneous across single infected cells, and some cells fail to express some viral genes. This plot shows single-cell RNA-sequencing data for the 254 cells that were infected at low MOI and 357 cells that were infected at high MOI. (A) Viral transcription in infected cells is extremely heterogeneous, with viral mRNA composing <1% of total mRNA in some cells, but >80% in others. (B) The number of viral genes detected in each infected cell. More than half of infected cells express mRNA from all 8 viral segments at both low MOI and high MOI. (C) The fraction of infected cells express- ing each viral gene. Under low MOI conditions, a substantial fraction (∼40%) of the infected cells also failed to express all eight viral genes (Fig 2B), a phenomenon that has been extensively described in prior studies [1,2,5]. Under high MOI conditions, fewer cells failed to express all eight viral genes (Fig 2B), likely because of complementation by co-infection [25–27]. At a per gene level, each viral gene was not expressed in some infected cells, with variation between the rates of absence for each specific gene (Fig 2C). Note that our ability to determine whether a viral gene is absent depends on the total level of viral transcription in a cell (Fig S3 and Methods), which could reduce our ability to detect the absence of the four viral genes involved in transcription (PB2, PB1, PA, NP). Full genome sequences of the influenza virions infecting individual cells at low MOI Virions can be defective in two ways: they can fail to express a viral gene, or they can encode mutated viral proteins. To identify cells infected by mutated virions, we used long-read sequencing to reconstruct the genome of virions infecting single cells under low MOI conditions [7]. Under these conditions, we expect most cells that are infected to be infected by only one virion. We amplified the viral transcripts from our single-cell RNA sequencing library and subjected them to PacBio sequencing. Because each transcript carries a cell barcode, we could link the sequence of each viral transcript to the cell that produced it. We obtained complete sequences of all expressed viral genes for 131 of the 254 infected cells in our low MOI dataset (Fig S4A). About a third of the infected cells expressed all eight viral genes without any non-synonymous mutations (Fig 3A, Fig S5). The remainder of infected cells failed to express a viral gene, expressed a gene with a non-synonymous mutation, or both (Fig 3A). Mutated virions most commonly had just one non-synonymous mutation in their genome, but some virions had two or three mutations (Fig 3B, Fig S5). Note that some David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 5 of 36 virions had large internal deletions in a gene (Fig S5) as has been previously described [28, 29]; here we have classified deletions as non-synonymous mutations. Fig 3. Consensus viral genome sequences from single infected cells with long-read viral sequencing data. Viral genomes were reconstructed for cells infected at low MOI. (A) The number of single in- fected cells expressing all eight viral genes without non-synonymous mutations, expressing all eight viral genes with one or more non-synonymous mutation(s), missing one or more viral gene(s), or with both mutated and missing genes. (B) The number of non-synonymous muta- tions in each viral genome. Deletions are classified as a non-synonymous mutation for these counts. This plot shows only the 131 of 254 single infected cells for which we could determine the sequence of all genes expressed by the infecting virion. See Fig S3A for details on proper- ties of infected cells for which we could obtain full viral sequences, and Fig S4 for the full set of viral mutations in each infected cell. Progeny production from single influenza-infected cells is more heterogeneous than viral transcription We measured the amount of physical and infectious progeny virions produced by single infected cells. We quantified physical progeny virions by sequencing viral barcodes from vRNA molecules in the supernatant at 12 hours post-infection (Fig 1A). For the low MOI sample, we also quantified infectious progeny virions by infecting a second set of cells with some of the viral supernatant and sequencing viral barcodes from vRNA expressed in these newly infected cells (Fig 1A). We analyzed progeny production measurements for the 91 infected cells from the low MOI sample that met the following criteria: both barcoded genes were expressed (allowing us to identify both viral barcodes), and the sequences of all expressed viral genes were obtained with long-read sequencing (providing a complete viral genome) (Fig S4). For the high MOI sample, we analyzed progeny production measurements for the 290 infected cells that expressed both barcoded genes, since we did not have sequences of the viral genes. The number of progeny virions produced per cell was extremely heterogeneous at both low MOI and high MOI (Fig 4A,B). Under low MOI conditions, nearly half of infected cells failed to produce any detectable physical or infectious progeny. At the extreme high end of viral transcription, a few cells each generated >10% of all the virions detected in the progeny David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 6 of 36 population (Fig 4A,B). A similar trend was seen at high MOI, although under these conditions the most productive cells generated a smaller fraction of progeny (Fig 4A). Fig. 4. Viral progeny production is more heterogeneous than viral transcription across single infected cells. Heterogeneity across single infected cells in (A) physical progeny production, (B) infectious progeny production, and (C) viral transcription. The Gini coefficient [30] quantifying the extent of cell-to-cell variability is indicated on each panel; a larger Gini coefficient indicates a more uneven distribution. For (A) and (B) the x-axis is the fraction of viral barcodes associated with each cell among all barcodes assignable to any infected cell; for (C) the x-axis is the fraction of mRNA in each cell that is derived from virus. The outset bar on the left shows the number of cells that produced no detectable progeny. This plot shows only single infected cells with com- plete measurements (see Fig S4). For cells infected at low MOI, this is cells that express both barcoded genes and for which we could determine the sequence of all genes expressed by the infecting virion. For cells infected at high MOI, this is cells that express both barcoded genes. Progeny production was much more heterogeneous across single cells than viral transcription (Fig 4). While just 6 of 91 infected cells were responsible for generating half of the physical progeny at low MOI, 23 cells were required to account for half of the viral transcripts (Fig S6). We can quantify the heterogeneity in these distributions formally by calculating a Gini coefficient, which ranges from zero to one with larger values indicating more uneven distributions [30]. Under low MOI conditions, the Gini coefficients were 0.78 and 0.88 for physical and infectious progeny production (Fig 4A,B). At high MOI, heterogeneity was reduced slightly and the Gini coefficient for physical progeny production was 0.66 (Fig 4A). The Gini coefficients for viral transcription were 0.46 at low MOI and 0.40 at high MOI (Fig 4C), both of which are lower than the corresponding coefficients for progeny production. Cells that transcribe more viral mRNA do not produce more progeny, and many high-transcribing cells represent aberrant infections that fail to express the NS gene The correlation between viral transcription and progeny production in single cells is surprisingly poor at both low MOI and high MOI (Fig 5A,B). Under low MOI conditions, none of the cells with >25% of their mRNA transcripts derived from virus produce any detectable progeny (Fig 5A). Instead, most progeny come from cells with moderate viral transcription. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 7 of 36 At high MOI, viral progeny are produced by cells throughout the range of viral transcription (Fig 5B), but there is no trend for cells with high transcription to produce more progeny even at high MOI. Fig. 5. Relationship between viral transcription and progeny production in single infected cells. Relationship between viral tran- scription and progeny virion pro- duction. Each point is a different cell. (A) For cells infected at low MOI, both physical and infectious progeny were quantified and cells are colored according to whether the cell expresses unmutated copies of all eight genes, all genes with one or more non-synony- mous mutations, or fewer than all genes (with or without mutations). (B) For cells infected at high MOI, only physical progeny were quanti- fied and cells are colored accord- ing to whether they express all eight viral genes or are missing one or more viral genes. Circular points indicate cells that express the NS gene and triangular points indicate cells that do not express the NS gene.. An interactive ver- sion of this figure that enables mouse-overs of points with details about individual cells is at https://jbloomlab.github.io/barcoded_flu_pdmH1N1. (C) Total viral transcription is plotted for each cell. The mean for each group is shown as a blue line. Cells that do not express the NS gene transcribe significantly more viral mRNA than cells expressing all viral genes (statistical significance deter- mined by permutation test with 5000 random simulations; p=0.0002 for cells infected at low MOI and p=0.004 for cells in- fected at high MOI). (D) Like panel (C), but for physical progeny production. Cells failing to express any viral gene–includ- ing NS–produce little or no physical progeny. At both low and high MOI, the viral gene expression information provided by single-cell RNA sequencing offers a straightforward explanation for why some cells with very high viral transcription fail to produce progeny. Cells that fail to express any viral gene produce little or no detectable progeny virions, regardless of their total viral transcription activity. The lack of physical progeny produced by cells that fail to express even a single viral gene presumably occurs because the absence of the encoded protein impairs virion formation (Fig 5D; note that our analysis is limited to cells that express HA and NA since those are the barcoded genes). But although cells that fail to express a viral gene produce little or no David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 8 of 36 progeny, the converse is not true: cells that express the full complement of viral genes often still fail to produce detectable progeny (Fig 5D). Strikingly, absence of the influenza NS gene not only precludes progeny production but is specifically associated with an aberrant state of high viral transcription (Fig 5C). At both low MOI and high MOI, many of the highest transcribing cells fail to express NS, and the mean level of viral transcription is significantly higher (p < 0.01) in cells that do not express NS compared to cells that express all viral genes (Fig 5C). These data suggest that NS acts as a negative regulator of viral transcription. This observation is consistent with the known functional roles of the NEP protein expressed from the NS gene, which is to export viral ribonucleoprotein complexes from the nucleus [31, 32] and possibly to switch the viral polymerase from transcription to genome replication through direct protein-protein interactions [33, 34]. Overall, this result suggests that the cells that contribute the most to the signal observed in transcriptomic studies often represent aberrant non-productive infections that do not contribute viral progeny. For cells infected at low MOI, we can also study the effect of viral mutations on progeny production. At low MOI, physical viral progeny are produced both by cells that express viral genes with mutations and by cells that express unmutated viral genes; however, more of the infectious progeny virions come from cells with unmutated viral genomes (Fig 5A, Fig S7)— probably because some non-synonymous mutations interfere with protein functions that are required for infection of new cells. Nonetheless, physical and infectious progeny production are much more correlated among single cells than are transcription and progeny production (Fig 5A versus Fig S7; Pearson’s R values of -0.14 for the correlation of transcription with infectious progeny at low MOI versus 0.39 for the correlation of physical progeny with infectious progeny at low MOI). However, none of the viral factors we measure (viral transcription, expression of each viral gene, or mutated viral proteins) fully explain the extreme variation we observe in progeny production. Progeny production remains highly variable across cells without any viral deficit (e.g. cells that express unmutated copies of all viral genes), although it is less variable across this population than across all cells (Fig 6A,B). This unexplained variation suggests that cellular or uncharacterized viral factors must also contribute to cell-to-cell variation in progeny production. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 9 of 36 Fig. 6. Viral gene absence and viral mutations only explain a fraction of heterogeneity observed in progeny production. (A) Distribution of physical progeny virions. For cells infected at low MOI, compare hetero- geneity in all infected cells (left, dark grey) with cells that express all eight viral genes without non-synonymous mutations (right, blue). For cells infected at high MOI, compare heterogene- ity in all infected cells (left, dark grey) with cells that express all eight viral genes (right, light grey). The outset bar on the left shows the number of cells that produced no detectable prog- eny. (B) Like panel (C), but for infectious progeny rather than physical progeny at low MOI. The plots showing all infected cells are duplicated from Fig 4 to facilitate direct comparison of all cells to those with complete unmutated genomes. For cells infected at low MOI, this figure shows the 91 infected cells for which we could identify the viral barcode on both barcoded genes and determine the sequence of all genes expressed by the infecting virion. For cells in- fected at high MOI, this figure shows the 290 infected cells for which we could identify the vi- ral barcode on both barcoded genes. Discussion Most prior single-cell studies of infection have examined intracellular viral products, like mRNA transcripts or proteins [1–7]. However, the most important outcome of infection for multi-cycle viral growth is how many progeny virions are produced by an infected cell. Prior studies of progeny production from single cells have relied on isolating individual infected cells in small volumes [6,8–10]. These studies have shown progeny production is highly heterogeneous, but have not provided measurements of most other properties of the infected cells—and have therefore lacked explanatory power to understand the basis for the variation in progeny production. Here, we have overcome these limitations with a new method to simultaneously measure physical and infectious progeny production alongside transcription of all viral and host genes, as well as sequencing of the genome of the virion that infected each cell. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 10 of 36 The most striking finding from our work is that while viral transcription and progeny production are both highly heterogeneous across influenza-infected cells, they are not well correlated at either low MOI or high MOI. In particular, the cells that transcribe the most viral mRNA often generate no detectable progeny virions. Part of the discrepancy between these two single-cell measurements is due to the fact that cells that fail to express the influenza NS gene tend to transcribe very high levels of mRNA but produce no progeny. This result makes biological sense: one of the proteins encoded by the NS gene is NEP, which exports viral ribonucleoproteins from the nucleus [31], terminating their transcriptional activity [32]. NEP may also mediate a switch from transcription to genome replication by the viral polymerase [33, 34]. However, absence of NS and other defects in the viral genome only explain part of the discordance between viral transcription and progeny production in single influenza-infected cells. These two properties are often discordant even in cells expressing unmutated copies of all influenza genes. We suggest that further study of both viral and cellular factors that promote transcription versus progeny production is an interesting area for future work. Our study has several limitations. Our experiments used a cell line rather than the differentiated airway cells that are the actual target cells during human influenza infections. We performed experiments using a single strain of H1N1 influenza at a single timepoint. We were also able to profile all relevant single-cell properties for a relatively modest number of infected cells, since single-cell RNA sequencing captures only a fraction of the input cells. However, the method we have described should be extensible to other cell-culture systems and larger numbers of cells in future work. Despite these limitations, our results suggest several implications for broader thinking about viral infections. First, recent studies have examined the distribution of influenza virus transcription or protein expression across differentiated airway cells ex vivo [24,35,36] or in vivo [15,16,37]. Our results suggest it is also important to measure progeny production across airway cells, as the cell types expressing the most viral transcripts or proteins may not be the ones producing the most viral progeny. Second, our results suggest failure of some cells to express specific viral genes contributes to the discordance between viral transcription and progeny production. Failure to express the influenza NS gene is strongly correlated with high viral transcription, even under high MOI conditions that promote genetic complementation [25–27]. Third, recent work on the transmission of influenza virus [38, 39] and SARS-CoV-2 [40–42] in humans has emphasized the narrow genetic bottleneck, with only a small fraction of viral diversity in the donor transmitted to the recipient. Our results suggest physical bottlenecks in how many virions reach the recipient may be further narrowed by the fact that only a small fraction of the initially infected cells will produce most of the progeny that continue the infection. Acknowledgements Thanks to Jason Underwood for providing valuable guidance related to performing long- read sequencing on single-cell cDNA libraries. We thank Will Hannon for assistance with interactive plots. BioRender was used to generate Figs 1A and 1B. This work was funded in part by the NIH/NIAID under grant R01AI165821 and contract No. 75N93021C00015, as well as using a Burroughs Wellcome Fund Young Investigator in the Pathogenesis of Infectious Diseases grant to JDB. JDB is an Investigator of the Howard Hughes Medical Institute. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 11 of 36 Competing interests JDB consults or has recently consulted with Apriori Bio, Merck, Moderna, or Oncorus on topics related to viruses and their evolution. JDB and AJG are inventors on Fred Hutch licensed patents related to viral deep mutational scanning. The other authors declare no competing interests. Methods Engineering barcodes in the influenza virus HA and NA genes The HA segment of the A/California/04/2009 (pdmH1N1) strain of influenza virus with the G155E cell-culture adaptation mutation was engineered to carry exogenous sequence by duplicating the packaging signals at each end [17, 18], as schematized in Fig 1B. A complete plasmid map of the barcoded HA plasmid is at https://github.com/jbloomlab/barcoded_flu _pdmH1N1/blob/main/data/flu_sequences/plasmid_maps/pHH_bcHA_G155E_DropSeqR1.gb. We included the G155E mutation as it greatly enhances viral growth in cell culture [19]. Packaging signal length and location was informed by previous studies [43–45]. The terminal 105 nucleotides of the HA coding sequence were duplicated to provide an authentic packaging signal at the 5’ end of the vRNA. The corresponding 105 nucleotides of the HA protein coding sequence were synonymously recoded to remove competing RNA-RNA interactions. A second stop codon (TGA) was added at the end of the coding sequence to reduce the chance of translation read-through. The stop codons were followed by an exogenous sequence containing a priming site, a 16-nucleotide random barcode, a second priming site, and a HindIII restriction site. The 3’ end of the vRNA was treated similarly. The first 67 nt of the HA coding sequence were duplicated and the corresponding region of the coding sequence was synonymous recoded. All potential start codons were removed from the duplicated packaging signal using single nucleotide substitutions. A BamHI restriction site was added between the duplicated packaging signal and the start codon. The NA segment of the A/California/04/2009 strain was engineered using the same strategy except we duplicated 99 nucleotides at the 5’ end of the vRNA and 93 nucleotides at the 3’ end of the vRNA. A complete map of the barcoded NA plasmid is at https://github.com /jbloomlab/barcoded_flu_pdmH1N1/blob/main/data/flu_sequences/plasmid_maps/pHH _bcNA_DropSeqR1.gb. Cloning barcoded plasmid libraries To facilitate cloning highly-diverse barcoded plasmid libraries, a recipient vector was created for each segment. The recipient vectors contained an eGFP insert flanked by the duplicated packaging signals described above. Recipient vector maps are at https://github .com/jbloomlab/barcoded_flu_pdmH1N1/blob/main/data/experiment_resources/plasmid _maps/2548_pHH_Haflankpdm-eGFP-DropSeqR1.txt and https://github.com/jbloomlab /barcoded_flu_pdmH1N1/blob/main/data/experiment_resources/plasmid_maps/2549_pHH _Naflankpdm-eGFP-DropSeqR1.txt. Inserts were prepared by amplifying the HA and NA genes from templates with synonymously-recoded terminal regions. Random barcodes were added as a string of 16 nucleotides in the primer that binds near the 3’ end of the viral mRNA. PCR was performed David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 12 of 36 using KOD Hot Start Master Mix with 1 ng of plasmid template for 17 cycles. Reactions were treated with DpnI for 1 hour to remove the template plasmid. Barcoded products were gel purified and cleaned with 1X AmpureXP beads. The recipient vectors were prepared by digestion with BamHI and XbaI for 1 hour to remove the eGFP insert and linearize the backbone. Linear backbones were gel purified and cleaned with 1X AmpureXP beads. Plasmids were assembled from linear vector and barcoded insert using NEBuilder HiFi Assembly Master Mix. A 2:1 molar ratio of insert to vector was used. 25 µl of NEBuilder Master Mix was combined with 0.27 pmol of barcoded insert and 0.13 pmol of linearized vector in a total volume of 50 µl. Assembly was allowed to proceed for 1 hour. Reactions were cleaned with 0.6X AmpureXP beads and eluted in 26 µl of EB. A small portion of the assembled product (1 µl) was used to transform 20 µl of NEB 10-Beta electrocompetent E. coli cells. Transformation was performed at 1.8 kV for > 5 ms per sample. Cells were grown in SOC media for 1 hr at 37C with shaking. After shaking, transformed E. coli were plated on large LB-ampicillin agar plates and grown at 37C overnight to produce a “lawn” of bacterial colonies. Liquid medium was pipetted onto the plate and a sterile plastic scraper was used to collect all of the bacterial colonies. Bacteria were grown in 200 ml of liquid medium in a 1 liter flask for 4 hours at 37C with shaking. Bacteria were pelleted by centrifugation and frozen at -20C. Plasmid libraries were collected using Qiagen HiSpeed Maxi Prep kit. Generating a dual-barcoded virus library We generated a dual-barcoded virus library with all non-HA/NA genes derived from the A/California/04/2009 (pdmH1N1) strain of influenza virus. Virus was generated by reverse genetics in 39 independent transfection reactions. For each transfection reaction, 4e5 293T cells (ATCC #CRL-3216) were seeded in a well of a 6-well dish. Cells were grown in D10 medium (DMEM supplemented with 10% heat-inactivated fetal bovine serum, 2 mM L- glutamine, 100 U per mL penicillin, and 100 µg per mL streptomycin). After ∼16 hours, we transfected each well with bidirectional reverse-genetics plasmids based on the pHW2000 vector [46] carrying the six unmodified segments: (PB2, PB1, PA, NP, M, and NS), unidirectional reverse-genetics plasmids based on the pHH21 vector [47] carrying the two barcoded segments (HA and NA), and a plasmid constitutively expressing the TMPRSS protease (which proteolytically activates HA) [20]. Maps of all plasmids are available at https://github.com/jbloomlab/barcoded_flu_pdmH1N1/tree/main/data/flu_sequences /plasmid_maps. We used 250 ng of each plasmid and 3.4 µl BioT transfection reagent per reaction. Twenty-four hours after transfection, the medium was replaced with Influenza Growth Medium (Opti-MEM supplemented with 0.1% heat-inactivated FBS, 0.3% bovine serum albumin, 100 µg per mL of calcium chloride, 100 U per mL penicillin, and 100 µg per mL streptomycin) and 3e5 MDCK-SIAT1-TMPRSS2 cells [20] were added to each well. Viral supernatants were collected at 65 hours post-transfection and centrifuged at 500 RCF for 5 min to remove any cellular material. Aliquots were frozen at -80°C and titered by TCID50 assay. To ensure a genotype-phenotype link between the viral genome and the proteins displayed on the surface of each virion, the virus library was passaged at low MOI. Infections were done at large scale to maintain library diversity. Four five-layer flasks (Falcon #353144) were seeded with 50 million MDCK-SIAT1-TMPRSS2 cells each [20] in D10 medium, for a total of approximately 200 million cells. After 4 hours, the medium was removed and two million TCID50 units of virus library in IGM were used to infect the cells. Viral supernatants were David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 13 of 36 collected at ∼38 hours after infection and centrifuged at 500 RCF for 10 min to remove cellular material. Aliquots were frozen at -80°C and titered by TCID50 assay. We obtained titers of ∼1e4 TCID50/µl (Fig 1C). Estimating the rate of infected cell multiplets and chimeric PCR products using a second control virus library Immediately prior to performing single-cell RNA sequencing on our sample of interest, we mixed the infected cells with a second control sample of cells. The control cells were infected with an otherwise isogenic influenza virus that carried identifying synonymous mutations on all eight viral genes. The synonymous mutations are detectable by sequencing. They mark each mRNA transcript and genome segment derived from the virus library with a distinct “genetic tag.” These synonymous genetic tags allow us to distinguish between viral transcripts from our sample of interest and the control sample, thereby enabling us to quantify two important sources of technical error. First, in the single-cell RNA sequencing data, these tags provide a means to detect transcriptomes that are derived from droplets that encapsulated multiple infected cells (multiplets) [48]. Such transcriptomes are marked by high frequencies of both tags among the viral transcripts. The overall rate of multiplets among all cells was calculated, and multiplets bearing both tags (which will be about half of multiplets) were excluded to remove them from the dataset. Second, the genetic tags are also detectable in the long-read viral sequencing data [7] we used to reconstruct the genotype of infecting virions. In the course of preparing long-read sequencing libraries, a polymerase can move from one template molecule to another in the midst of synthesizing its product–a phenomenon known as “strand exchange” [49]. This phenomenon can be detected in long-read viral sequences that contain discordant genetic tags (see Fig S10 of [7]). We estimated the rate at which this type of error occurs, and sequences bearing both tags were excluded from contributing to the results. The second dual-barcoded virus library was prepared identically to the first viral library as described above. The second library contains synonymous variants near the 5’ and 3’ ends of each viral segment. Plasmid maps are available at https://github.com/jbloomlab/barcoded _flu_pdmH1N1/tree/main/data/flu_sequences/plasmid_maps. Infecting cells with a dual-barcoded virus library at low MOI To infect cells at low MOI, 1x104 MDCK-SIAT1-TMPRSS2 [20] cells were suspended in D10 medium and plated in a well of 24-well plate. After 5 hours, cells were observed by microscopy and were confirmed to be well-attached. The medium was aspirated and 1500 transcriptionally active units (measured by single-cell RNA sequencing) of dual-barcoded virus library in 100 µl of Influenza Growth Medium was added to the well. The cells were incubated with virus for 1 hour, and the plate was rocked by hand every 15 minutes. After 1 hour, the inoculum was removed and the cells were washed once with 250 µl of phosphate- buffered saline. 500 µl of Influenza Growth Medium supplemented with 20 mM ammonium chloride (to prevent further entry of virions into cells [21, 22]) was added to the well. At 12 hours post-infection, the supernatant was collected and cells and debris were removed by centrifugation at 300 RCF for 3 min. The supernatant was split into 2 aliquots of 220 µl each and frozen at -80°C. The cells were collected by addition of 100 µl trypsin and a single- cell suspension was generated. The trypsin digestion was stopped by addition of 400 µl of David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 14 of 36 D10 medium. The cells were washed 3 times with phosphate-buffered saline supplemented with 0.8% by volume non-acetylated bovine serum albumin. The cells were counted to confirm that approximately 10,000 cells were present per well. Infecting cells with a dual-barcoded virus library at high MOI High MOI infections were performed similarly to the low MOI infections described above. The following changes were employed: 6.7x103 MDCK-SIAT1-TMPRSS2 cells [20] were plated and the cells were infected with 4000 transcriptionally active units (measured by single-cell RNA sequencing) of dual-barcoded virus library. Single-cell RNA sequencing Infected cells were prepared and mixed with a second control sample of infected cells to control for technical sources of error (see “Estimating the rate of infected cell multiplets and chimeric PCR products using a second control virus library” above). Approximately 20,000 cells (low MOI) or 13,000 cells (high MOI) were loaded into the 10X Chromium device. Single- cell RNA sequencing was performed with the 10X Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1. The manufacturer’s standard protocol [50] was used with the following modifications. The template-switching oligo was replaced with a modified single- stranded DNA oligo with the sequence 5’- AGAGTGTTTGGGTAGAGCAGCGTGTTGGCATGTrGrGrG-3’ at a final concentration of 45 µM in the reaction mix. This change was made to accommodate some of the barcoded influenza segments’ exogenous sequence which shares homology with the standard 10X template- switching oligo. The cDNA amplification primer mix was replaced with a pair of primers with the sequences 5’-AGAGTGTTTGGGTAGAGCAGCG-3’ (binding to the custom template- switch oligo mentioned above) and 5’-CTACACGACGCTCTTCCGATCT-3’ (binding to the standard 10X adapter sequence) at a final concentration of 1 µM in the reaction mix. The cDNA amplification PCR reaction extension time was increased to 20 seconds to encourage the formation of full-length cDNA products. The amplified cDNA product was split in half. One half was used for fragmentation and preparation of the transcriptome sequencing library while, for the low MOI sample, the other half was used as template for long-read sequencing of viral transcripts. Viral long-read sequencing to reconstruct infecting viral genomes We determined the sequence of the virion that infected each cell for the low MOI sample. Because the cells were infected at a low MOI, infection was initiated by one virion in the large majority of infected cells. To capture these sequences, we selectively enriched viral cDNA molecules using a method described previously [7]. In brief, cDNA derived from the 10x Genomics protocol was first amplified in a semi-specific PCR reaction. Each segment was amplified with a primer annealing to the universal TruSeq primer site that is added to all cDNA molecules during the reverse transcription step of 10x Genomics protocol and a segment-specific primer annealing to 5’ end of the viral mRNA, which also contains a flanking sequence that is complementary to the TrueSeq primer site (Table S1). Semi-specific PCR reaction conditions were as follows: 12 ng cDNA, 0.5 µM of forward and reverse primer, 10 µl of KOD (EMD Millipore, 71842), 0.1 mg/ml BSA, and final volume adjusted to 20 µl with water. PCR was incubated for 120 s at 95°C followed by 10 cycles of 120 s at 95°C, 20 s at 55°C, 90 s at 70°C, and the final extension step at 70°C for 120 s. Semi-specific PCR reactions were purified using AMPure XP beads at 1.8x beads to sample ratio and eluted in 12 µl of water. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 15 of 36 Following purification, PCR products were circularized via complementary TrueSeq sequence. For circularization, 10 µl of purified PCR product was used in a 20 µl HiFi assembly reaction (NEB, E2621S). HiFi assembly was performed at 50°C for 1 hour. Next, HiFi products were used in segment-specific PCR reactions. To amplify viral products of all lengths, primers that anneal to the ends of viral mRNA were used; to preferentially amplify full-length viral segments, primers that anneal to the middle of each viral segment were used (Table S2). Segment-specific PCR conditions were as follows: 9 µl of Hifi reaction, 0.5 µM of forward and reverse primer, 25 µl of KOD, and the final PCR reaction volume adjusted to 50 µl with water. PCR was incubated for 120 s at 95°C followed by cycling 120 s at 95°C, 20 s at 55°C and 90 s at 70°C with a final extension step of at 70°C for 120 s. Cycles were kept to a minimum to reduce strand exchange; since different segments required different yield, different numbers of cycles were employed each segment-specific reaction. For the polymerase segments, 14 cycles of segment-specific PCR were performed;for the HA, NA and NP segments, 10 cycles were performed;, for the M and NS segments, 7 cycles were performed. PCR reactions were purified using AMPure XP beads at 1.8x beads to sample ratio and eluted in 12 µl of water. All purified PCR products were pooled together and long-read sequencing was performed on a PacBio Sequel II. We generated CCS sequences of each viral transcript using PacBio long-read sequencing. We measured the rate of strand exchange that occurred during sequencing library preparation (see Fig S10 of [7]), and found that fewer than 1% of sequences were affected, providing high confidence that the sequences we obtained could be assigned to their cell of origin. We generated a consensus sequence for each viral genome (see “Computational analysis of single-cell RNA sequencing, long-read virus sequencing, and progeny production viral barcode data” below) . We counted the number of non-synonymous mutations found in each consensus genome; deletions were considered non-synonymous mutations for this purpose. Quantifying progeny production The amount of progeny produced by single infected cells was determined by sequencing the viral barcodes on vRNA molecules. To quantify physical progeny virions, we sequenced the vRNA in the viral supernatant at 12 hours post infection. For the low MOI sample, to quantify infectious progeny virions, we infected a second set of cells to select for virions that could perform viral entry and genome replication (Fig 1A) and sequenced the intracellular vRNA molecules at 13 hours post infection. In detail, we thawed frozen viral supernatants that were collected at 12 hours post infection and split them into four equal volumes. Two volumes were used to isolate supernatant RNA directly. For the low MOI sample, the other two volumes were used to infect MDCK-SIAT1- TMPRSS2 cells [20] at a moderate estimated MOI of ∼0.25 in two independent replicates. To infect the cells, 60,000 MDCK-SIAT1-TMPRSS2 cells [20] were suspended in D10 medium and plated in a well of 6-well plate. After 7 hours, cells were observed by microscopy and were confirmed to be well-attached. The medium was aspirated and an aliquot of supernatant with an estimated 15,000 TCID50 units was added to the well in 500 µl of Influenza Growth Medium. The cells were incubated with virus for 1 hour, and the plate was rocked by hand every 15 minutes. After 1 hour, the inoculum was removed and the cells were washed once with 500 µl of phosphate-buffered saline. 1600 µl of Influenza Growth Medium supplemented with 20 mM ammonium chloride (to prevent further entry of virions into cells [21, 22] was added to the well. At 13 hours post infection, the cells were collected by aspirating the growth medium and incubating with 300 µl trypsin to detach them from the plate. Trypsin digestion was stopped by the addition of 700 µl of D10 medium. The cells were David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 16 of 36 pelleted by centrifugation at 400 RCF for 3 min. The cell pellet was washed by resuspending in 1 ml of phosphate-buffered saline and pelleting at 400 RCF for 3 min again. The phosphate-buffered saline was aspirated and the cell pellet was flash-frozen on dry ice. RNA was isolated from the viral supernatant or infected cell pellets using the RNeasy Mini Kit (Qiagen, 74104). Lysis buffer was mixed with the viral supernatant sample and 70% ethanol was added . For the infected cell pellets, the sample was mixed with lysis buffer and homogenized by vortexing at high speed for 20 seconds. The homogenized sample was processed on a gDNA eliminator spin column to remove genomic DNA. The processed sample was combined with 70% ethanol. From this point, both the viral supernatant and infected cell pellets were treated identically and followed the standard RNA purification protocol specified by the manufacturer [51]. The RNA for each sample was eluted in 50 µl of RNase-free water. Reverse transcription was performed with a segment-specific primer targeted to the HA or NA vRNA (Table S3). Two replicate reactions were performed using RNA from the viral supernatant sample, and two independent reactions were performed using RNA from the two infected cell pellets; these replicates provide technical duplicate measurements of both the physical progeny in the supernatant and the infectious progeny in the cell pellets. Reverse transcription was performed using the SuperScript III First-Strand Synthesis SuperMix kit according to the manufacturer protocol [52]. For the viral supernatant samples, 12 µl of each RNA sample was used as template for each 40 µl reaction. For the infected cell pellet samples which contain much larger amounts of total RNA due to the host RNA present in the cell, 1000 ng of RNA was used as template for each 40 µl reaction. The low- concentration cDNA generated from the viral supernatant samples was purified and concentrated using 2X Ampure SPRI beads and eluted into 22 µl of elution buffer. Viral barcodes were amplified in 50 µl PCR reactions using KOD Hot-Start Master Mix (Sigma-Aldrich, 71842). For the viral supernatant samples, 22 µl of concentrated cDNA was used as template. For the high-concentration infected cell pellet samples, 10 µl of unpurified cDNA was used as template. Segment-specific primers (Table S3) were used and reactions were run for 20 cycles. Amplicons were size-selected and purified using a double-sided AmpureXP bead cleanup. Samples were first combined with 0.8X AmpureXP beads and the supernatant was collected. The supernatant was then combined with 1.8X AmpureXP beads and the bound DNA was collected. Sequencing indices and adapters were attached in a 50 µl PCR reaction using KOD Hot-Start Master Mix. For all samples, 2 ng of purified amplicon DNA was used as template. Sample- specific index primers (Table S3) were used and reactions were run for 20 cycles. The resulting amplicons were gel-purified and pooled for single-end sequencing on an Illumina MiSeq. The progeny contribution of each cell was calculated (see “Computational analysis of single-cell RNA sequencing, long-read virus sequencing, and progeny production viral barcode data” below). Computational analysis of single-cell RNA sequencing, long-read virus sequencing, and progeny production viral barcode data A reproducible pipeline that performs all analysis is at https://github.com/jbloomlab /barcoded_flu_pdmH1N1. The pipeline uses Snakemake [53]. The pipeline begins with raw sequencing data and ends by generating the figures shown in this manuscript. Most code in the pipeline is arranged in Jupyter notebooks (https://jupyter.org). David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 17 of 36 Briefly, the raw sequencing data from the single-cell RNA sequencing was aligned using STARsolo [54] against a composite reference made up of the canine genome CanFam3.1.98 concatenated to the A/California/04/2009 influenza virus genome. Alignment produced a cell- gene matrix containing the gene expression of every canine and virus gene for each single cell. Custom Python code was used to parse the “genetic tag” encoded on viral transcripts which differentiates our library of interest from a second control library. The multiplet rate was calculated and only transcriptomes from our library of interest were used for analysis. Transcriptomes from the second control library and transcriptomes composed of multiple infected cells were excluded. The total viral gene expression was calculated for each infected cell. Because the amount of viral transcripts in the ambient environment during single-cell RNA sequencing [55] varied by MOI, different thresholds were used to call cells as infected at each MOI. For the low MOI sample, cells were called as infected if at least 1% of their transcripts came from virus. For the high MOI sample, cells were called as infected if at least 2.5% of their transcripts came from virus. These thresholds provided clear separation of an infected and uninfected population. Individual viral genes were called as expressed if their frequency was greater than the 99th percentile observed in uninfected cells (see Fig S3). For the statistical test in Fig 5C, we classified each cell as expressing all viral genes, missing the NS gene, or missing another influenza gene. Because the data are not normally distributed, we performed a non-parametric permutation test, randomly labeling each observed value with a classification. We performed 5000 permutations and calculated the p- value as the frequency with which the randomly generated difference between classified groups matched or exceeded the observed difference between groups. The results indicated that cells missing the NS gene have a statistically significant difference in viral transcription compared to cells that express all viral genes (p < 0.01) under both low MOI and high MOI conditions. For the low MOI sample, the raw PacBio sequencing data was processed using PacBio’s ccs program (https://github.com/PacificBiosciences/ccs). Consensus sequences were generated from the subread files, requiring a minimum accuracy (‘rq’) of 0.99 for the consensus sequence. The chimera rate was estimated using the “genetic tags”. The cell barcode and UMI were parsed from each CCS using custom Python code that utilized the alignparse package [56]. A consensus sequence was called for each cell barcode-viral gene-UMI combination. A mutation was included in the consensus sequence if it was found in >50% of the CCS for the cell barcode-viral gene-UMI combination. A consensus sequence was then called for each cell barcode-viral gene combination. A mutation was included in the consensus sequence if it was found in >50% of the UMIs for the cell barcode-viral gene and was found in at least two UMIs. To parse the viral barcodes sequenced from the supernatant (representing physical progeny) and from the second infection (representing infectious progeny), we used custom Python code that utilized the dms_variants package (https://jbloomlab.github.io/dms_variants/). The viral barcodes were error-corrected using UMI-tools [57]. The technical replicates for each sample were plotted against each other and the limit of detection was set at 1e-5, where viral barcode frequencies fail to correlate (Fig S8), indicating bottlenecked subsampling of the molecules carrying the viral barcodes, and suggesting that frequency measurements below this threshold are not reliable; values below the limit of detection were set to the limit of detection. The mean frequency of both replicates was calculated. A subset of infected cells expressing both barcoded viral genes and with complete long-read sequencing data was used to calculate progeny contributions. To determine the fraction of progeny contributed by each infected cell in this set, we took the geometric mean of the HA and NA barcode frequencies associated with each cell. We normalized the progeny contributions by the total David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 18 of 36 frequencies assignable to any cell in this set. The data were visualized in Jupyter notebook (https://github.com/jbloomlab/barcoded_flu_pdmH1N1/blob/main/final_analysis.py.ipynb). We used custom Python code utilizing a combination of plotnine (https://github.com/has2k1 /plotnine) and altair (https://github.com/altair-viz/altair). An R script utilizing gggenes (https: //github.com/wilkox/gggenes) was used to plot the complete viral genomes of infected cells. The figures generated by this notebook are displayed in this manuscript. Data availability All data and code are available in the GitHub repository at https://github.com/jbloomlab /barcoded_flu_pdmH1N1. The analysis can be reproduced by running the Snakemake pipeline and final analysis notebook according to the instructions at https://github.com /jbloomlab/barcoded_flu_pdmH1N1/blob/main/README.md. Key output files are hosted at the following locations. All raw sequencing files are available on GEO under the accession number GSE214938. The single-cell RNA sequencing cell-gene matrix is also available on GEO under the accession number GSE214938.. An integrated CSV produced by the Snakemake pipeline with cell barcodes, viral gene expression, viral genome sequence, and viral barcode frequencies is available at https://github.com/jbloomlab /barcoded_flu_pdmH1N1/blob/main/results/viral_fastq10x/all_samples.csv. The final CSV file with progeny contribution measurements, viral gene expression, and viral mutations (when available) for the infected cells with complete measurements is available at https://github .com/jbloomlab/barcoded_flu_pdmH1N1/blob/revisions/results/viral_fastq10x/all_samples _complete_measurements_cells_data.csv. Fig. S1. Viral barcode sequences are selectively neutral. Influenza virus carrying a pool of HA and NA barcodes was generated by reverse genetics and passaged 3 times at low MOI. (A) The titers were measured after each growth step by TCID50. (B) The frequency of each barcode in the viral population was measured by deep sequencing after each passage. Each color represents a unique viral barcode. The fre- quencies of viral barcodes were fairly consis- tent across passages, indicating a lack of se- lection for any particular barcode sequence. The viral barcode frequencies were calculated using the code at https://github.com/dbacsik /barcode_neutrality. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 19 of 36 Fig. S2. Extremely diverse barcoded virus libraries. Rarefaction curves show the diversity of the viral barcodes. The x-axis represents the number of barcodes sampled. The y-axis represents the number of sampled barcodes that are unique. A hypothetical perfect library where every barcode is unique appears as a straight line with formula x=y and is shown here with a blue dashed line. Our low MOI experiments used ap- proximately 1500 virions per sample. The number of unique barcodes in a sample of 1500 is annotated in black in each facet. Our high MOI experiments used approximately 4000 virions per sample. The number of unique barcodes in a sample of 4000 is annotated in red in each facet. The rarefaction curves were calculated using https://jbloomlab.github.io/dms_variants /dms_variants.barcodes.html?highlight=rarefybarcodes#dms_variants.barcodes.rarefyBar- codes. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 20 of 36 Fig. S3. Expression of viral genes in infected cells. This plot shows single-cell RNA-sequencing data for the 254 infected cells infected at low MOI and the 357infected cells infected at high MOI. Total viral transcription and expression of each viral gene in single infected cells. Genes with low average transcript counts in the single-cell RNA sequencing data (PB2, PB1, and PA) are called as absent if there are zero transcripts de- tected in a cell. Genes with higher average transcript counts in this data (HA, NP, NA, M, and NS) are called as absent if their abundance falls at or below the 99th percentile observed in uninfected cells. Low, non-zero transcript counts for these genes most likely result from tran- scripts leaking from one oil droplet to another during single-cell RNA sequencing [55]. Fig. S4. Number of cells with progeny measurements and viral genome sequencing. Each point indicates a cell, and blue lines indicate the mean. (A) In the low MOI experiment, 254 infected cells were identified by single-cell RNA sequencing. Viral transcription was similar in cells that expressed both barcoded vi- ral genes and cells that were missing expression of one or both. Viral genomes in the low MOI experiment were analyzed using long-read viral genome se- quencing. 131 infected cells had complete PacBio long-read se- quencing data for every ex- pressed viral gene. On average, cells with complete sequencing coverage had higher viral transcription than cells without David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 21 of 36 complete sequencing coverage. 91 infected cells had long-read sequencing of all expressed viral genes and expressed both barcoded viral genes. On average, cells with all measurements had slightly higher viral transcription than cells with- out all measurements. (B) In the high MOI experiment, 357 infected cells were identified. 290 cells expressed both bar- coded genes, providing complete measurements for that sample. Cells with expressing both barcoded genes had higher viral transcription, on average. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 22 of 36 Fig. S5. Viral genotypes in cells infected at low MOI. The sequence of the infecting virion for the 131 infected cells infected at low MOI for which we could determine the sequence of all expressed viral genes. Each infected cell is represented as a row and each viral transcript is represented as an arrow. Missing viral genes, insertions, deletions, and mutations are annotated on the arrows. Viral transcription (as a fraction of UMIs in the cell), and viral progeny production (as a fraction of the physical progeny virions in the supernatant) are shown for each infected cell. Cells with one or more missing barcoded vi- ral genes have “NA” values listed for progeny production. A high-resolution version of this fig- ure is available at https://github.com/jbloomlab/barcoded_flu_pdmH1N1/blob/main/results /figures/viral_genomes_plot.pdf. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 23 of 36 Fig. S6. Cumulative fraction of viral products produced by single infected cells. For the viral mRNA values, the y-axis represents each cell’s contribution to the total viral mRNA transcripts across all cells. For the progeny values, the y-axis represents each cell’s con- tribution to the barcodes in the supernatant or second infection that are assignable to one of the infected cells. A horizontal line is drawn at y=0.5 to indicate the minimum number of cells that generated half of the total amount of each viral product. This plot shows the single infect- ed cells for which we obtained complete measurements. For the low MOI experiment, this was 91 infected cells for which we could identify the viral barcode on both barcoded genes and de- termine the sequence of all genes expressed by the infecting virion. For the high MOI experi- ment, this was 290 infected cells expressing both barcoded viral genes. Fig. S7. Frequency of physical progeny and infectious progeny from single infected cells infected at low MOI. Each point represents a single infected cell infected at low MOI. The x-axis represents the frac- tion of physical progeny generated by each cell. The y-axis represents the fraction of infec- tious progeny generated by each cell. This plot shows the 91 cells infected at low MOI for David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 24 of 36 which we could identify the viral barcode on both barcoded genes and determine the se- quence of all genes expressed by the infecting virion. Fig. S8. Technical replicates of progeny measurements. This plot shows the frequency of each viral barcode as measured in two technical replicates. Imperfect correlations indicate that bottlenecking in recovery of molecules contributes noise to the measurements. As described in Fig 1A, physical progeny were measured by sequencing viral barcodes in RNA extracted from viral supernatants, and infectious progeny were mea- sured by sequencing viral barcodes in RNA extracted from cells infected with progeny virus supernatants. For physical progeny, technical replicates represent independent reverse-tran- scription reactions. For infectious progeny, independent replicate infections were performed before reverse transcription. Each point represents the fraction of viral barcodes that could be associated with that cell. The limit of detection (dashed blue line) is set as the point below which replicate frequency correlations become much worse, suggesting severe bottlenecking. To calculate the progeny contribution, we averaged the two replicates and then took the geo- metric mean of the values for HA and NA for each cell. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 25 of 36 Table S1. Semi-specific primers for amplification of influenza transcripts from full-length cDNA library. These primers were used to amplify each influenza gene from the full-length single-cell RNA sequencing library. For each reaction, a segment-specific primer was paired with the “Read- 1_TruSeq” primer, which binds to the Illumina sequencing primer found on all transcripts in the library. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 26 of 36 Table S2. Segment-specific primers for amplification of influenza transcripts. These primers were used to amplify the influenza genes from circularized templates. For each gene, two reactions were performed. One set of reactions targeted templates without large deletions and bound near the middle of the open reading frame; these reactions utilized the primers with “mid” in their name. The other reactions targeted all templates (with and with- out deletions) and bound near the ends of the open reading frame; these reactions utilized the primers with “end” in their name. David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 27 of 36 Table S3. Primers for the reverse transcription, amplification, and indexing of viral barcode sequencing samples. Binding sequences are shown in uppercase and overhangs are shown in lowercase. 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Bacsik Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America, Department of Genome Sciences & Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America ORCID iD: 0000-0003-4912-0209 David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 33 of 36 Bernadeta Dadonaite Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America ORCID iD: 0000-0003-0908-6982 Andrew Butler Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America Allison J. Greaney Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America, Department of Genome Sciences & Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America ORCID iD: 0000-0001-7202-3349 Nicholas S. Heaton Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, United States of America, Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, United States of America ORCID iD: 0000-0002-5307-3428 Jesse D. Bloom Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America, Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America For correspondence: jbloom@fredhutch.org ORCID iD: 0000-0003-1267-3408 Editors Reviewing Editor Jos van der Meer Radboud University Medical Centre, Netherlands Senior Editor Detlef Weigel Max Planck Institute for Biology Tübingen, Germany Public Review: In this article, a novel technique allowing the linking of viral transcription levels and progeny virion production is presented. Barcoded libraries of an H1N1 influenza virus (two genes were barcoded near the 3'end) were used to infect cells using an experimental approach ensuring that, in the low multiplicity of infection condition, each cell is infected by one virion and that nearly every virion has a unique barcode. This allows then, upon single- cell RNA sequencing and sequencing of the supernatants, to infer back the cells that were producing certain barcoded viruses. Assessing detection frequencies of barcodes in the David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 34 of 36 single-cell sequencing and in the sequencing of the supernatants allows us to compare the levels of viral transcription and progeny virion production. Observations that viral transcription levels are very heterogenous at the single-cell level are not novel, but reinforce those from previous studies. The major findings of this study are (i) progeny virion production is also very heterogenous, i.e., a few cells produce most of the progeny virions and (ii) there is a poor correlation between viral transcription levels and progeny virion production at the single-cell level. Strengths: The article is very well written, the experimental choices are very well justified and the methods are very detailed, allowing the possibility of reproducing the work performed in this study. The conclusions are very well supported by the data and the limitations of the study and how those might influence the conclusions are also clearly explained. In addition, several experimental caveats, such as PCR cross-overs in next-generation sequencing and cell multiplets in single-cell sequencing, were well accounted for, which is not always the case in studies using these techniques. Weaknesses: It seems that the results presented here are from one single experiment. How reproducible are the results? As explained in the article, it is important that nearly every virion has a unique barcode. This was assessed by sequencing the barcodes in the virus libraries. Between 92% to 96% of the barcodes were unique. With this information, it should be possible to assess whether non-unique barcodes were detected in infected cells, and if yes, remove these from the downstream analysis. It seems like all the information available in this very rich dataset was not fully exploited. For instance, Figure 5C suggests that cells missing the expression of one viral gene might still be able to produce progeny viruses. It would be interesting to have the information regarding which gene was not expressed in these cells. The introduction and discussion are rather short and the article could benefit from expanding them. Additional speculations about viral or cellular factors (e.g. differences in innate immune responses, differences in cell division status) that might explain the observed heterogeneity, both at the viral transcription and viral progeny virus production levels, would be interesting. Author Response: We thank eLife and the reviewer for the nice summary of our manuscript. We largely agree with the summary and review, and just add a few small points. First, the review asks about the reproducibility of our findings, and suggests that they are only from a single experiment. In fact, our manuscript reports data from two independent single- cell experiments: one performed at low multiplicity of infection (MOI), and another at higher MOI. The broad trends, including the lack of strong correlations between viral mRNA transcription and progeny production, are consistent across both experiments. Second, the reviewer asks about what happens when two different virions bearing the same viral barcode infect two different cells, given that we estimate 4-8% of barcodes to be shared between multiple infecting virions. When two cells are infected by different virions with the same barcode, this breaks the one-to-one link between transcription in that cell and progeny in the supernatant, since it is not possible to determine which cell contributed the progeny David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 35 of 36 with that barcode. This means that between 4-8% of the points on our correlation plots could be affected by this factor, meaning that a few outliers should be expected. Another scenario, where a single cell is infected by two barcodes, is not problematic for our method because we can simply sum the progeny output for both barcodes from that cell. Finally, the reviewer notes that some cells appear to produce progeny virions despite failing to express one or more viral genes. Such cells can be explained in one of two ways. First, as noted immediately above, we expect a small fraction (4-8%) of the points to be erroneous due to a lack of a guaranteed one-to-one link between cell and progeny for non-unique barcodes. Second, in some cases the missing viral gene could be a technical artifact caused by a stochastic failure to capture modestly expressed transcripts from the gene; this phenomenon, known as gene dropout, occurs at a fairly high rate in single-cell experiments (see Qiu Nature Communications 2020 for a detailed discussion). Genes that are expressed at lower levels, like the Influenza virus polymerase genes, are more likely to be missed during single-cell RNA sequencing. The absent viral genes in each infected cell can be explored in detail using the interactive plots at https://jbloomlab.github.io/barcoded_flu_pdmH1N1/ David J. Bacsik et al., 2023. eLife https://doi.org/10.7554/eLife.86852.1 36 of 36
10.1371_journal.pstr.0000097	RESEARCH ARTICLE Epistemic outsiders: Unpacking and utilising the epistemic dimension of disruptive agency in sustainability transformations Sergiu SpatanID Franziska EhnertID 1, Daniel PeterID 2,3, Gundula ThieleID 4, Marc WolframID 2,3, 2, Stefan ScherbaumID 4 4, Moritz Schulz1, Caroline SurreyID a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Spatan S, Peter D, Thiele G, Wolfram M, Ehnert F, Scherbaum S, et al. (2024) Epistemic outsiders: Unpacking and utilising the epistemic dimension of disruptive agency in sustainability transformations. PLOS Sustain Transform 3(2): e0000097. https://doi.org/10.1371/journal. pstr.0000097 Editor: Ana Delicado, Universidade de Lisboa Instituto de Ciencias Sociais, PORTUGAL Received: May 16, 2023 Accepted: January 10, 2024 Published: February 14, 2024 Copyright: © 2024 Spatan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: We have no data to report. Funding: This paper is an outcome of the project "The Disruptivity of the Others in Urban Transformations" (DOUbT), which is part of the Excellence Measure "Disruption and Societal Change" at TU Dresden (TUDiSC) and is funded by the Federal Ministry of Education and Research (BMBF) and the Free State of Saxony under the Excellence Strategy of the Federal Government and 1 Department of Philosophy, Dresden University of Technology, Dresden, Germany, 2 Leibniz Institute of Ecological Urban and Regional Development, Dresden, Germany, 3 Faculty of Environmental Sciences, Dresden University of Technology, Dresden, Germany, 4 Department of Psychology, Dresden University of Technology, Dresden, Germany sergiu.spatan@tu-dresden.de Abstract Disruptions (systemic disturbances) are crucial to initiate and accelerate sustainability trans- formations of large-scale social systems (be they socio-ecological, socio-technical, or socio- institutional). Their emergence, characteristics and effects strongly relate to the role of agents who aim to disrupt and transform the status quo, and which thus possess what we call disruptive agency. In this paper, we highlight the epistemic dimension of disruptive agency in social transformations, first by conceptualizing disruptive agents as epistemic out- siders with respect to the social system that they intend to disrupt and transform, and sec- ond by connecting this conceptualization to notions of belief, social practices, social networks, discourses, or institutions. We identify five advantages of this approach. Firstly, it informs and conceptually enables various promising interdisciplinary avenues to explore and potentially influence transformative change towards sustainability. Secondly, an episte- mic conception of disruptive agency offers a key for an integrated analysis of the individual and collective levels of agency involved in sustainability transformations. Thirdly, the notion of epistemic outsiders conceptually connects agent positions across system boundaries that are understood to be of crucial importance for sustainability transformations respec- tively (e.g., “niche innovators” or “regime intermediaries”) but which lack an integrated understanding. Fourthly, an epistemic perspective additionally highlights the changing requirements and challenges resulting in two principal stages of transformations unfolding over time, namely before/after a new epistemic layout is shared by a majority of agents. Finally, the above features allow to derive and conceive of new intervention formats and strategies. Author summary What can I do to change society for the better? How can I contribute to a more sustainable world? It often seems like there is only so much that individual humans can do to change PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 1 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONthe La¨nder. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Epistemic outsiders entire societies. The question of how human agency relates to ample social transforma- tions lies at the heart of our paper. Our hope is that, by looking at the epistemic dimension of agency in societal transformations, we can learn more about how sustainability trans- formations can be developed and supported, both at an individual level and at a collective level. By ‘epistemic’ we mean the way people think about themselves and about the norms, rules, and standards that they are ready to follow. Our contention is that every agent- driven societal transformation is enabled by people who have a diverging set of beliefs from the system that they try to dislocate. They are epistemic outsiders to that system, as we call them. By looking closer at epistemic outsiders and at the epistemic dimension of social change, we hope to better understand how agents can drive sustainability transfor- mations. Thus, we note that one of the tacit aims of epistemic outsiders aiming at sustain- ability transformations is to change the minds of the people composing the status quo–the epistemic layout of the reference system, as we call it. We aim to better understand how this can happen by combining our epistemic reading of societal transformations with existing research on the topics of belief change, networks, discourses, institutions, and social practices. This allows us to cut across different levels (from individuals to collect- ives) at which transformative processes occur and connect different strands of research that otherwise are approached separately. 1 Introduction In an urgent call for sustainability transformations, societies across the globe face the challenge of shaping path-deviant and rapid systemic change. Ecological boundaries and tipping points force us to acknowledge that planetary justice and well-being require accelerating deep trans- formations in our current energy, transportation, housing, food, or health systems [1,2]. Cor- respondingly, we also witness the emergence of more and more examples of individuals and collectives actively striving to disrupt and change the way our societies operate. This can be schoolchildren skipping classes and demanding climate action (e.g., Fridays for Future move- ment [3,4]), scientists pushing for new modes of knowledge co-production [5], or activists blocking busy highways (e.g., the Last Generation movement [6]). While these are highly visi- ble forms of human agency aimed at sustainability transformations, they are however neither the only ones nor necessarily the most effective and efficient. Transformative change occurs through an intricate interplay of external pressures and opportunities, structural shifts and disruptions, as well as emerging novelties–all of which facil- itated by particular forms of agency. Understanding how human agents contribute to such complex transformation dynamics has thus formed an important focus of a large and diverse body of literature. This has provided conceptual and empirical insights regarding the distinc- tive role of various types of “change agents” and their agency in system transformations [7,8,9,10,11]. On the one hand, high importance has been attributed to actors outside the mainstream that create innovation niches in which alternative system configurations of limited scale and scope are trialled. In such contexts, the protagonists follow values, goals, rules and practices that differ substantially from those of the prevailing regime [12,13,14,15]. On the other hand, also agency exercised by certain incumbent actors within the mainstream has been recognized to be crucial if providing, e.g., opportunity spaces for niche actors or direct sup- port, as well as contributing to regime destabilization. Similarly, such action outside the rules of the established regime is seen to be essentially motivated by not sharing the prevailing world- views [16,17]. In all of these cases, change agents are therefore seemingly acting from a posi- tion of an “outsider” to the system they would like to see transformed. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 2 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders Against this backdrop, the goal of our paper is to offer an integrated perspective on disrup- tive agency in transformations that can provide new insights into their particular dynamics, but also suggest novel intervention strategies aimed at steering transformations towards sus- tainability. In order to bridge between various actor typologies that have been developed to understand and illustrate the relevance of particular agency forms for sustainability transfor- mations [7,18,19,20], which are usually analysed and interpreted separately, we propose the concept of “epistemic outsiders” as an overarching ontological category that characterizes all forms of disruptive agency directed towards transformations. By so doing, we aim to enable more agile analytical approaches and interventions that connect between the role of individu- als and collectives, thereby also addressing relations across agency levels, system boundaries and transformation phases, and the problem of scaling innovations [21,22]. In what follows, we will first lay down the ontology assumed throughout the paper (section 2). We will then present our perspective on the epistemic dimension of transformation and dis- ruptive agency (section 3). Finally, we will discuss ways in which this epistemic reading can enrich understandings of and intervention strategies for sustainability transformations by invoking four prevalent schools of thought in related scientific debates (social practice theory, network theory, discourse theory, and institutional theory) as well as the psychology of mental constructs (section 4). 2 Ontological assumptions In this section we set out the ontological assumptions we adopt regarding the nature of social systems, the transformation of social systems, and disruptive agency as a basic condition for social system transformations. We also reflect on the fundamental importance of normativity in such processes. These conceptual prerequisites will allow us to subsequently identify and elaborate on the role of the epistemic dimension in transformation dynamics. 2.1 Social systems For understanding social systems, we refer to Anthony Giddens’ structuration theory ([23]; see also [24] with a view to transformations). According to Giddens, a social system is a pat- terned spatiotemporal set of interrelationships existing between agents (individuals, groups, or organizations) acting in institutional, technical, and ecological contexts. Their interrelation- ships are governed by rules, norms and standards that constitute the structural properties of the social system. The structures inform individual and collective agency, stipulating, e.g., how to relate to each other and to the environment, what technology to use and how, or what social behaviours to accept and in which circumstances, etc. Following Giddens, structures imply a duality in the sense that while structural properties do enable and constrain agency, they simultaneously also depend on their continuous repro- duction through agents. For a social system to maintain a particular configuration most agents must therefore follow the rules, norms, and standards specific to that system. In turn, this entails that when the share of deviating agents rises above a critical threshold, social systems can start to destabilize and potentially become reconfigured or even transformed entirely. Deviant thinking and acting of individuals or collectives thus needs to be understood as a fun- damental precondition for any deeper change in social systems. 2.2 Transformations and disruptions While incremental changes happen all the time in all social systems, transformations refer to nonlinear change processes that fundamentally alter the structures and practices that charac- terize a given system [25,26]. This particular type of systemic change dynamic depends on PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 3 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders numerous coevolving factors (social, economic, ecological, cultural, institutional, technologi- cal, etc.) that together create disruptions of the system. According to a well-known definition from the socio-technical system literature [27, p.119], disruption is “[. . .] a high-intensity effect in the structure of the sociotechnical system(s), demonstrated as long-term change in more than one dimension or element, unlocking the stability and operation of: incumbent technology and infrastructure, markets and business models, regulations and policy, actors, networks and ownership structures, and/or practices, behaviour and cultural models”. To this understanding we need to add two important twists: Firstly, we acknowledge that disruptions do not always lead to a change in the structural properties of the system, even if they interfere with them. A system may just as well return to its baseline configuration after the disruption ends (depending on its resilience). Secondly, we add an epistemic dimension by recognizing that the interferences with the structural properties of the system cannot be generated by the disrupted system itself. Therefore, we consider an event (or a chain of events) E a disruption of the reference system R if and only if (i) E is a high-intensity interference with the structural properties of R and (ii) E is unanticipated and unplanned by R. Disruptions thus represent major windows of opportunity for leveraging transformations, but they require epistemic posi- tions from “outside” of the system, i.e., not derived from its rules, norms and standards—an important point that we will expand on in subsection 3.3. With a view to the temporality of change we have to note that transformations imply accel- eration and unfold rapidly compared to the established pathway. Social systems are dynami- cally stable, i.e., changes happen continuously either as a result of adaptation or simply because incremental shifts are stipulated by the rules of the system. Even structural properties can be changed over a very long period of time through incremental steps without this constituting a transformation: Those changes have been anticipated and planned, but not elicited by a dis- ruption. Consequently, in order to deal with the grand challenges of the Anthropocene, dis- ruptions can offer an important lens to explore options for purposively accelerating and scaling up transformations. 2.3 Disruptive agency We assume a basic conception of agency as “the ability to act with intention–as opposed to just reacting” [19, p.279]; cf. [28,29,30,31]. As noted above, we assume that social structures are continuously recreated by individual and collective action, while the intentions behind indi- vidual action are in turn influenced by social structures. Therefore, it is always uncertain how independent and deviant the agency of actors within a social system can be, given how much they are influenced by path dependencies, socialization, social pressures etc. With a view to transformations, this demands to specify and distinguish forms of agency that are explicitly driven by the motive to transform the reference social system, i.e., to create purposive disruptions. Admittedly, one may also imagine agents who aim to disrupt without pursuing any aspirations in terms of transformation, but although such cases could exist and also contribute to transformation dynamics, they lack plausibility and provide little justifica- tion for further theorising. Therefore, we define disruptive agency as the ability to act with the intention of disrupting a social system in order to transform it. Arguably, actors pursuing trans- formation strive to produce destabilization and deeper change in the system instead of tacitly following its rules as the majority of conformists does. Correspondingly, the literature on sus- tainability transformations has identified diverse types of actors who exercise such disruptive agency, labelled, e.g., “forerunners”, “niche innovators”, “institutional entrepreneurs” or “knowledge brokers”, and who significantly influence the transformation dynamics observed [19,32,10]. While differing in their respective role, these actors share an underlying motive of PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 4 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders system transformation informed by epistemic and normative orientations and their intertwin- ing. Hence, before expanding on the epistemic dimension in section 3, we need to also account for normativity in disruptive agency and social system transformations. 2.4 Normativity Transformations of social systems can happen in any direction. Hence, the pursuit of transfor- mations by design apparently raises fundamental ethical questions that require societal delib- eration. Also, a normative concept like “sustainability” that may seem to be supported by a broad (inter-) societal consensus in fact remains (and must remain) subject to contestation regarding its particular normative postulates when it comes to the grand challenges of the Anthropocene [33,34,35]. Nevertheless, the processes and dynamics we aim to unpack here in principle apply to any transformations of socio-technical, socio-ecological and/or socio-insti- tutional systems, independent from the value propositions they embrace. In this, we do acknowledge that actors’ compliance with or deviance from established rule systems for the sake of transformations (i.e., disruptive agency—not delinquents escaping the rule of law) is also driven by particular normative orientations. Therefore, we subsequently address the cru- cial role of normativity in two ways: First, we situate values and normative claims in the con- text of broader belief sets that underpin the structuration of social systems (section 3). Second, in the light of the grand challenges we adopt the normative stance of sustainability asking for new insights and strategies for intervention that our approach can offer (e.g., regarding the need to overcome the reluctance of incumbents to change, the need for building networks of change, etc.) to help accelerate deep and path-deviant change (section 4). 3 The epistemic dimension of transformations and disruptive agency As outlined above, we are interested in human agents who aim to disrupt and transform an unsustainable social system. Having recognised the important role such agents play in sustain- ability transformations, our aim is to further illuminate how their agency, responsibility and ethical concerns can be instrumental in fostering disruptions. In this paper, we do not aspire to present an exhaustive framework to capture how transformations occur, or of all the mecha- nisms through which agents contribute to transformations. More modestly, we want to high- light the existence of an epistemic dimension in this which is largely overlooked or only implicit in the common approaches used to study sustainability transformations. Acknowledg- ing for and analysing this dimension, however, can benefit new understandings of sustainabil- ity transformations, as well as different forms of intervention (see section 4). There are three claims that circumscribe our epistemic reading of transformations and disruptive agency: a. The transformation of a social system involves a modification of the epistemic layout of that system. b. The agents who attempt to disrupt and transform a social system are epistemic outsiders to that social system. In turn, agents reproducing and stabilizing the system can be considered epistemic insiders. c. Drawing on their perspective as epistemic outsiders, disruptive agents aiming for social sys- tem transformation always strive to alter the epistemic layout of that system. In what follows we explore each of these points. We will first introduce the concept of an epistemic layout of a social system (section 3.1), which will then help us define epistemic out- siders more sharply (section 3.2). We can then show that various types of disruptive agents can PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 5 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders be conceptualized as epistemic outsiders (section 3.3). This will allow us to revisit the basic mechanisms of social system transformations, this time having an epistemic reading in mind (section 3.4). 3.1 Epistemic layouts of social systems So far, we assumed a fairly traditional ontology of social systems that contains two categories (in the same vein as structuration theory; [23]): (i) agents and their agency and (ii) the struc- tural properties of social systems. In what follows, we want to highlight a third ontological cate- gory, which contributes to the structuration of social systems and thus helps explain how a social system is created and perpetuated: (iii) the epistemic layout of a social system. Consider any socio-technical system such as the energy or transport system as an example. Call this system X. The current configuration of X is shaped by a set of beliefs about the values, the aims, the hierarchy, the expectations, etc.–in other words, the rules, the norms, and the standards (or “grammar”; [36, p.340])–that underlie the working of the system. X functions the way it does because, presumably, its stakeholders accept these beliefs (expressed, e.g., in regulations, policies, markets, contracts, signs) and have confidence that other agents compos- ing the system also accept these beliefs and act based on them. On the one hand, these are structural beliefs about the rules, norms, values, and standards of the system. On the other hand, these are also relational beliefs regarding the behaviour of others (i.e., if I don’t do this, another agent will react in that way, etc.). All these beliefs together constitute the epistemic layout of X. Were the agents composing the system holding alternative beliefs, the social system would be very differently configured. How we interact with each other is based on our beliefs about rules of interaction and on our beliefs about what others believe about those rules of interaction. In this sense, our social sys- tems are “republics of beliefs” [37]–a notion also inspired by game-theoretical considerations about social conventions and law-abiding behaviour [38,39]. Of course, people don’t simply decide ex nihilo about an epistemic layout they want to sup- port. People are born and socialized in particular social systems (be it a family, a community, a religion, a nation or capitalism), such that their structural and relational beliefs regarding that system are passed on to them in the process of socialization. This reflects the relation between social structure and agency: The epistemic layout of a social system is part of the deep struc- turation process of that system (see Giddens [23]). This enables a social system to perpetuate itself. That being said, it is also possible for people to change their beliefs based on new experi- ences or evidence. If sufficiently many people do so, the epistemic layout of the system changes as well, making it possible for the system to become transformed. This leads us to further explore the possibility of particular agents deviating from a given epistemic layout. 3.2 Epistemic outsiders In this paper we introduce the conception of epistemic outsiders understood as those agents who disagree with some or all of the rules, norms and standards constituting the epistemic lay- out of a social system. In other words, epistemic outsiders “fail” to hold the structural beliefs corresponding to the reference system. Formally, we define an epistemic outsider in the fol- lowing way: Supposing that N = {p, q, r . . .} is the set of all structural propositions corresponding to the reference system R (describing the rules, norms and standards of R), an agent A is an epistemic outsider to the system R if A disagrees with at least one of the propositions from the set N. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 6 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders A few observations are in place. Firstly, for A to disagree with one of the propositions from N, say p, is for A to have doubts about p or to believe that not-p. This applies both to when A is merely an individual or when A is a group, as groups can presumably also hold beliefs [40]. It goes without saying that A can be an epistemic outsider to R while at the same time being an insider to another reference system, say S. Agents normally belong to several social systems simultaneously (A might be part of a family, of a company, of a political party, etc.). Secondly, epistemic outsiders can be differentiated by degrees, depending on how many normative propositions underlying R the agent disagrees with and how essential they are. Sup- posing that A1 disagrees merely with p, while A2 disagrees with all the propositions from set N, A2 is more of an epistemic outsider to R than A1. Also, if p represents a core value the degree of being an epistemic outsider is higher than if it refers to a behavioural rule, for instance. Thirdly, focusing on the concept of epistemic outsiders allows to draw parallels between agents occupying very different positions regarding the reference system. In particular, those who play an active role within the system, engaging in its institutions and practices and repro- ducing them, and those who are not part of this process but relate to it from the system’s envi- ronments. We therefore suggest acknowledging for endogenous and exogenous outsiders. Both are agents who disagree with at least some of the key tenets of the reference system, but whose distinct position regarding the system implies different options for taking action in order to address tensions between the epistemic layout and their own deviant belief sets. For instance, in the sustainability transformations literature endogenous outsiders are sometimes framed as “forerunning” incumbent actors or certain types of intermediaries ([10,16], see also section 3.3). Looking back at the socio-technical system example from above, an exogenous outsider to X can be environmental NGOs or civic initiatives who criticise the current energy/transport system without having any concrete influence on its development. Finally, we are of course aware that the term ‘outsider’ as such has also been used and defined in many different ways. One could speak about institutional outsiders as those individuals who do not formally belong to a reference institution. Or about marginalized outsiders as those who are discriminated against or refused access to resources and privileges. Nevertheless, we are focusing here on defining what it means to be an outsider strictly from an epistemic point of view, acknowl- edging that these different understandings often overlap and constitute one another. 3.3 Disruptive agents as epistemic outsiders Considering the notions introduced above it seems plausible to assume that all agents usually associated with disruptions and transformative action are also epistemic outsiders to the social system they intend to transform. There are two arguments we employ in order to substantiate this claim: (i) conceptually, based on the definitions of disruption, disruptive agency and epi- stemic outsiders presented above; (ii) interpretatively, drawing on sustainability transforma- tions literature and its findings regarding disruptive agents. The first argument recognises that, for an agent or a cluster of agents to non-arbitrarily (that is, on purpose, and not by accident) attempt to disrupt a social system R, it must be the case that these disrupting agents are epistemic outsiders to R. This can be reconstructed as follows: Premise 1. Agent-driven disruptions to a reference system R are planned by the agents who initiate them. Premise 2. In principle, such agents could be either epistemic insiders to R or epistemic outsid- ers to R. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 7 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders Premise 3. However, agent-driven disruptions cannot be planned by epistemic insiders to R. Therefore, agent-driven disruptions to a system R are planned by epistemic outsiders to R. As defined above, disruptions are unanticipated and unplanned interferences with the struc- tural properties of R. This means that planning such interference cannot be part of the shared set of beliefs of the epistemic insiders of R. The second argument is based on a scoping review of the literature on sustainability trans- formations, which suggests many different conceptions of transformative agents and their agency. Despite the fact that these different types of change agents are not informed by the concept of epistemic outsiders as we present it here, such an understanding is somewhat inher- ent to them. A prominent example forms the concept of niche, as proposed in the Multi-Level Perspective (MLP) [12,13,36], or the Strategic Niche Management (SNM) framework [36,41]. Niches are conceived as “protected spaces” that are kept free from the institutional constraints and path dependencies of the dominant socio-technical regime. They allow agents to develop their own rationalities, institutional structures and technologies with the goal and the potential to change or disrupt the “paradigmatic core” [42, p.1] of the status quo specific to a given sec- tor. In these frameworks the agency required for change has essentially been located inside niches and in their interactions with the regime. Furthermore, the niche concept has also been broadened to capture social innovation contexts in which civil society agents (such as civic ini- tiatives, activist groups, non-governmental organizations) try to initiate transformative change from below [14,43,44]. In this sense, niche actors can well be interpreted as epistemic outsiders to the systems they strive to transform. Also various conceptions of entrepreneurship have become quite influential in this litera- ture. For instance, “institutional entrepreneurs” try to transform institutions through identify- ing opportunities, mobilising stakeholders, and leveraging resources [45,46]. “Social entrepreneurs” focus on business undertakings with the goal of creating social and ecological value and innovations instead of mere economic profit in a strict sense [47,48]. Following this strand, more specific concepts such as “sustainable entrepreneurship” [49,50] or “ecopreneur- ship” [51,52] were developed. In the context of the MLP, Antadze & McGowan [53] propose to call agents who aim at disrupting the existing system through “questioning normative rules at the landscape level that support the regime in question” [53, p.2] “moral entrepreneurs”. All of these entrepreneurial agents try to initiate and facilitate change on the basis of their own per- spectives and practices which deviate from the norm. This characterises them as epistemic out- siders trying to shape a more desirable mainstream according to their views. Yet, the entrepreneurial metaphor focuses more on the specific activities agents do or the skills they require, less on their deviating belief systems or their general relation to the prevalent reference system (as an epistemic perspective would do). Similarly, the crucial role of intermediaries in transformation processes, acting at the vari- ous interfaces of reference systems and niches, has received increasing attention [54]. They are facilitators, networkers, mediators or brokers who establish links and translate between stake- holders, thus playing an interesting role regarding the epistemic insider/outsider dichotomy. While incumbent actors are usually depicted as inhibiting institutional change and thereby slowing down transformative processes [55,56,57], certain individual incumbents may also act as regime intermediaries who effectively foster emerging transformations by influencing niche-regime interactions. Niche intermediaries in turn support niche formation and amplifi- cation, connecting between niche agents but also towards the regime [58,59]. Intermediaries thus share an epistemic outsider position but can form part of both the reference system (endogenous) or niches (exogenous) in some way, which appears to be crucial for creating disruptions. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 8 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders It appears that all of the specific agent types so far identified as necessary driving forces behind system transformations can be understood as epistemic outsiders, questioning or rejecting the essential structural propositions of a given social system. From our perspective, it is this rejection that genuinely enables the disruptive momentum these agents can bring for- ward. Although not framed in such terms, this is widely recognised in the pertinent literature on the subject. The epistemic reading introduced above thus enables conceptual connections between diverse research strands and a more integrated approach for analysis and interpreta- tion–a perspective expanded on in section 4. 3.4 Changing roles of disruptive agents Finally, we want to highlight some overarching implications regarding the role(s) of disruptive agents in social system transformations. As noted in section 3.1, such transformations neces- sarily involve the alteration of the epistemic layout underpinning that system. This requires that sufficiently many agents composing that system abandon their current belief set and adopt an alternative one within a shorter period of time (acceleration). This is precisely where an understanding of disruptive agents as epistemic outsiders mat- ters. Regardless of their diversity, disruptive agents are characterised by a shared intention to transform a reference system because they hold alternative beliefs, including specific norma- tive orientations such as sustainability. Their actions thus implicitly or explicitly pursue the take-up, diffusion and institutionalisation of an alternative belief set in accordance with that normativity, e.g., by creating new imaginaries and narratives, confronting and/or coordinating with other individual or collective actors (both from inside and from outside the system), or enabling joint learnings in experimental settings. In other words, disruptive agents intervene and strive to change social systems in such a way that their epistemic position moves from out- sider to insider while maintaining their own belief sets. Therefore, we can distinguish two basic stages in the process of a social system transforma- tion that imply rather different roles and (epistemic) strategies for disruptive agents: (i) The outsider stage, when disruptive agents attempt to destabilize the reference system; (ii) The insider stage, when disruptive agents act upon successful disruptions and attempt to secure the prevalence of their own belief sets, thus transforming the epistemic layout of the system. Each stage suggests different strategies, tactics and approaches, operating at individual and collective levels–and pointing towards available insights and research frameworks from a variety of sci- entific disciplines. 4 Epistemic outsiders and sustainability transformations: Interdisciplinary avenues for future research and action While in section 3 we have referred to social system transformations in general, we will now discuss how an epistemic reading can potentially benefit the understanding and also shaping of transformations with a particular normative orientation, namely towards sustainability. Apparently, we make this choice with a view to the urgency of the grand challenges that demand purposive socio-ecological transformations. To this end we will first briefly outline the utility of an epistemic perspective for interdisciplinary analyses that cut across system boundaries, connect individual and collective agency and account for critical stages in such transformations (section 4.1). In the following five sections we will then sketch how the con- cept of epistemic outsiders can open up promising future research avenues for tackling the complex dynamics of sustainability transformations by providing a fundamental boundary object—not only between the specific research strands working on this topic so far (cf. section 3.3) but additionally inviting theoretical approaches to social change that resonate with PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 9 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders sustainability transformation research respectively, but are usually applied separately: psychol- ogy of mental constructs (section 4.2), practice theory (section 4.3), network theory (section 4.4), discourse theory (section 4.5), and institutional theory (section 4.6). Focusing on the emergence, roles and impacts of epistemic outsiders across these complementary epistemolo- gies of social change can thus build an interdisciplinary bridge to integrate methods, data and insights. Additionally, each section also sketches resulting options for novel intervention forms and strategies, even if space for a more in-depth elaboration is obviously limited here. 4.1 Mapping disruptive agency across system boundaries, levels, and time A basic advantage of an epistemic perspective resides in its ability to equally address change processes at individual, inter-personal and collective levels, including at various scales (e.g., household, organization, sector, society). As noted in section 3, the transformation of a social system involves the modification of the epistemic layout of that system, which is shared and reproduced by all individual and collective agents. Correspondingly, disruptive agents may develop actions tailored towards triggering a re-assessment of and change in individual and/or collective belief sets, thus ranging, e.g., from personal conversations, discussion groups or pub- lic happenings to media campaigns, large-scale demonstrators or policy pilots. For each of these levels (and partly also their relations), however, there are frameworks and concepts avail- able that can help to explain the particular dynamics of stability/change at play (e.g., in individ- ual behaviour, everyday practices, social networks, discursive or institutional settings), as well as corresponding success factors regarding a normative orientation at sustainability. Tracing the re-/configuration of an epistemic layout across these levels thus sheds light on the dis-/ alignment between very different change dynamics responsible for often neglected conflicts and synergies in system transformations. Additionally, the changing role of disruptive agents in the course of a transformation process must be taken into account, focusing especially on the critical transition between the two stages identified above (outsider and insider stage). For instance, moving from awareness-raising activism to co-developing regulation proposals does not happen automatically, but relies on social change processes occurring at different levels. Therefore, we conceive of sustainability transformation dynamics in epistemic terms by mapping out disruptive agency across system boundaries (endogenous/exogenous), levels (individual to society) and time (Fig 1). In this, the range of theoretical perspectives Fig 1. The epistemic dimension of sustainability transformations: Mapping disruptive agency across system boundaries, levels and time. https://doi.org/10.1371/journal.pstr.0000097.g001 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 10 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders represented here remains only indicative and may well be further expanded, even if it does in fact reflect current debates in sustainability transformations research. As a side note we acknowledge that the processes summarized in Fig 1 also apply to cases in which the disruptive agents do not themselves initiate the disruptions but harness external disruptions (e.g. pan- demics, earthquakes, etc.). In such cases one may equally identify the different positions, levels and stages at which attempts to modify the epistemic layout are made so as to bring about sus- tainability transformations. 4.2 Beliefs The psychology of belief change provides important insights and explanations regarding at least two key aspects of transformations and disruptive agency: (i) the reluctance of incum- bents to change [60,61]; and (ii) the importance of sense making and bridge building [62,63,64,65]. Especially combining Kelly’s [66] theory of personal constructs with a complex systems view on belief structures [67] can be very instructive here. The notion of personal constructs proposes that every person builds a unique representation of the world by extracting regularities, striving to anticipate events and actively exploring their envi- ronment to make sense of their experiences. Characteristically, this works through making dis- tinctions between so-called elements (mental representations of real-world objects or people) based on idiosyncratic dimensions, so-called constructs, which result in a personal map of the world [66]. These elements and constructs can be added and modified according to the experi- ences of an individual. Apart from its cognitive component, every element is also assumed to have an emotional component, according to the theory of emotional coherence [68,69]. In general, individuals strive to keep thematically self-contained parts of the construct system coherent to avoid suffering from cognitive or emotional dissonance [70,68,69].Cognitive and emotional coherence in a system of constructs could be conceptualized as holding as few contradictory beliefs as possible at a certain moment, with less contradictory beliefs indicating higher coherence. This leads the system to stabilize in a state of the best satisfaction of all constraints provided by the different constructs (see [68,69,71]for a view of parallel constraint satisfaction networks). These theoretical considerations straightforwardly suggest how to examine and interpret the two aspects mentioned above. i) Looking at the need for cognitive and emotional coher- ence is crucial for understanding reluctance to change. Avoiding cognitive and emotional dis- sonance can hinder an individual to integrate new beliefs (belonging to an alien, outsider position) into their belief system, even if those would be a more accurate representation of the world. The fact that “[i]ncumbent regime actors initially tend to downplay the need for trans- formation” [72, p.244], or even oppose it altogether [73,74] is partly explained by their reluc- tance to change their own structural beliefs. Doing so is mentally costly, while the epistemic layout of an existing social system offers both comfort and stability. ii) Disrupting structural beliefs by only doubting or deconstructing them is not enough as beliefs are embedded in a cognitive-emotional network striving for coherence. Presenting alternative views and narra- tives is important to fill the “gap” in the belief system. This explains why the creation of “new social imaginaries” [75, p.1], and the generation of a diversity of new ideas, alternative view- points and novel solutions is so crucial. In other words, the outsider perspective needs to be made palpable as a narrative that offers high positive returns and will soon become an insider one. It is also important to note that an overlap in beliefs can facilitate communication between individuals to allow for belief change [67]. Such overlaps can therefore be an entry point or mutual understanding that serves as the foundation of communication. This gives certain agents (endogenous outsiders) an important role in changing beliefs [16,17], as they already share some beliefs with insiders. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 11 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders Furthermore, the psychology of belief change can also help to operationalize the concept of epistemic outsiders at the individual and the social level. On the individual level, internal dynamics striving for cognitive and emotional coherence drive action and communication. On a social level, the forming of group beliefs can be described as a loose coupling of the agents’ individual networks of beliefs. This yields collectively coordinated (but individually implemented) belief networks and can cause individual beliefs to partly align with the groups view through shared constructs and valences [67]. It equally offers methods for observing belief systems and their dynamics, not only in Kelly’s [66] repertory grid method, but also in tools like cognitive-affective maps [76]. Having these concepts in mind also allows to think about new ideas for intervention. To overcome the mental cost of belief change, incumbents of unsustainable social systems must be persuaded of the benefits of changing their structural beliefs towards more sustainable ones. This refers not only to rational persuasion in order to maintain cognitive coherence, but also to ways of providing emotional coherence, given the perceived threats of potential transforma- tions [67]. For instance, socio-spatial intervention formats such as cooperatives or innovation districts that provide for novel social or human-nature experiences and can therefore create emotional responses are plausible options here that address this need. As we will see, other per- spectives on social change (like social practices, networks, discourses and institutions) are also instrumental in identifying relevant mechanisms to provide the comfort and stability needed for emotional and cognitive coherence. 4.3 Social practices Practice theory acknowledges how reality is continuously performed through multiple routin- ized actions people undertake in their daily lives [77,78,79]. It accounts for an important cor- nerstone of sustainability transformation processes that has become addressed increasingly in the literature, i.e., it was not recognized from the outset [80,81]. Notably, it also incorporates an epistemic dimension and therefore adds a complementary focus to an analysis of disruptive agency regarding the role of epistemic outsiders in both discontinuing unsustainable social practices and adopting novel and sustainable ones. Practice theory expands from the premise that social practices are the building blocks of society, and are deeply embedded in cultural, institutional and physical contexts. Shove et al.’s [78] well-known conceptualization of practices as routinized behaviours emerging from inter- dependent relations between meanings, materials and competences underlines their distinctive role in the formation, perpetuation but also alteration of an epistemic layout. Especially mean- ings largely correspond to the concept of belief sets as they refer to ideas or symbols reflecting social and cultural norms. But also competences, i.e., the knowledge and skills enabling partic- ular practices share an epistemic dimension. Routinized actions provide comfort and stability and therefore also cognitive and emotional coherence. However, changing them turns out to form a specific challenge regarding the implicit entanglement of meanings and competences with material settings, objects and tools, which strengthens their obduracy and resistance to change. All practice components are also closely linked to social networks, discourses and institutions since they can contribute signifi- cantly to enable or constrain their performance and proliferation. In order to escape an unjust or unsustainable status quo of a social system, the transforma- tion of practices thus forms another crucial lever. Epistemic outsiders are individuals who can potentially trigger such processes as they develop different meanings and competences com- pared to those shared in the practice performance of insiders and may also link these mean- ings/competences to existing materialities and their re-interpretation and re-use, or the design PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 12 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders of entirely new ones. Given their reliance on the prevailing epistemic layout, this task is unlikely to be realized by insiders who will rather prioritize change in the material dimension instead, thereby supporting the widely observed bias towards technological fixes [82]. Therefore, practice-oriented intervention approaches that attend to the epistemic dimen- sion of disruptive agency would seek to dis-/connect between the practice components involved in order to foreground and modify especially the underpinning belief sets. This may entail actions designed to question or disrupt established routines, creating space for a reinter- pretation of existing material realities (as in “pop-up” lanes or parks). But this needs to go hand-in-hand with the creation of novel physical-material settings or tools that enable the per- formance of sustainable practices, linked to corresponding meanings and competences. In this, the epistemic reading allows to also consider the direct influence of related social change dynamics involving social networks and discourses. 4.4 Social networks The driving role of social networks in sustainability transformations has been pointed out fre- quently in the literature [83,84,85]. Recent advancements have been made both conceptually [86,87,88] and empirically [89,90,91] that deepen the understanding of their particular role and relevance. Here we want to connect these contributions to the perspective on belief change outlined above to show how the structure of social networks can inform the analysis of an epi- stemic layout of collectives and social systems as well as efforts to change it towards a more sus- tainable configuration. Since humans strive for cognitive and emotional coherence (cf. section 4.2), changing a per- son’s core belief set while maintaining cognitive and especially emotional coherent appears to be a challenging task. In this regard, social networks can prove extremely important. Accord- ing to insights from the network modelling of social contagion [92,93] the network structure plays a crucial role for the quality and success of spreading beliefs, knowledge, behaviours or even practices. The underlying mechanism requires distinguishing between weak ties (loose relationships between people such as acquaintances) and strong ties (strong relationships between people such as close friends or family) in a network [94]. While weak ties are charac- terized by great reach, strong ties are characterized by redundancy. These features–reach or redundancy–are extremely relevant for what type of contagion these networks facilitate best. Weak-tie networks facilitate simple contagions. These are spreading processes that do not encounter resistance (like infection during the Covid-19 pandemic). Strong-tie networks, on the other hand, facilitate complex contagions. A complex contagion is a spreading process that needs to overcome substantial resistance (like the adoption of a new social behaviour). Considering these different configurations of social networks, the spreading of structural beliefs apparently requires strong-tie networks. Redundancy and social approval of values, norms, rules, etc. from an agents’ strong ties is key for them to accept the appropriateness of those beliefs. Since strong ties exist especially with people one depends on the most, changing one’s own structural beliefs is greatly facilitated if new beliefs, e.g., about how to live sustain- ably are socially approved by these people. This can be expected to most effectively support the persistence of a high level of emotional coherence. Indeed, numerous studies have shown that the influence of redundant strong ties is much more effective in spreading behaviour-deter- mining beliefs than merely weak ties [93,95]. Consequently, from the perspective of disruptive agents striving for sustainability transfor- mations, a suitable strategy for changing belief sets and epistemic layouts would thus be to focus attention and resources not on broad awareness-raising and a wide distribution of infor- mation (e.g., by influencers), but on rather tightly knit social networks. Research has also PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 13 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders shown that practices are directly transmitted through social networks [96]. Moreover, chang- ing belief sets in a well-connected trans-/local community or neighbourhood can provoke snowball effects that lead to systemic changes (see, e.g., [97,98,99] for the role of strong ties in the adoption of rooftop photovoltaic technologies). 4.5 Discourses The study of discourses forms another prominent strand of social change research that is of crucial importance for an epistemic perspective on transformations. Discourses are under- stood as sets of ideas, concepts, arguments or narratives that are continuously produced and reproduced by agents and through which meaning is given to reality [100]. Obviously, they incorporate beliefs about the values, norms, rules and standards that structure (inter)actions in this social system. Therefore, analysing discourses is very relevant for understanding (i) how the epistemic layout of the status quo is perpetuated in practice, (ii) how disruptions occur in processes of belief change, and (iii) how moving to a novel epistemic layout may work out. We will address these topics in turn: First, the epistemic layout of a social system is to a large extent produced and reproduced dis- cursively by the insiders of this social system. For instance, regulations as well as practices and routines are framed and argued for on the basis of the belief sets of the agents who form the system. Thereby discourses continuously shape agents’ behaviour in practice but also inform their expectations of what is generally believed in or perceived as normal, as well as how other agents should behave. Taking up Basu’s [37] notion of ‘republics of beliefs’ again, discourses thus form a linguistic and semiotic backbone for their constitution and stability. Second, in the literature on sustainability transformations the possible impacts of certain ‘outsider’ agents on a given discourse have been prominently highlighted by Pesch [18]. While using a different notion of outsiders (more as agents outside design and decision-making pro- cedures), he describes their unique discursive agency as their capability to bring new views in, stipulating “out-of-the-box patterns of thinking, thereby creating space for discursive change” (p. 386). In our term this means that they may be able to disrupt discourses perpetuating the status quo by questioning the established framework and bringing in new views. Similarly, also other scholars in this field have explored conceptually and empirically how particular agents are sometimes able to interfere in discourses and modify them [53,101]. In our reading, this refers to epistemic outsiders because they are not bound by the epistemic layout of the refer- ence social system and can therefore view things differently and articulate their perspectives correspondingly. Third, the importance of shifting discourses for sustainability transformations has already been shown for various empirical contexts such as, e.g., energy and water transitions [102,103,104,105], financial services [106] or urban mobility planning [107]. These studies illustrate how discourses are highly instrumental for the process of providing cognitive and emotional coherence, connecting rational choices and evidence-based orientations with narra- tives and imaginaries. In the process of transformative change, they can help to reframe the position of epistemic outsiders, placing them at the core of a desirable future system configura- tion. A transformation is thus complete when alternative discourses supporting a new episte- mic layout have become mainstream (i.e., “hegemonic” in discourse theoretical terms). From the perspective of disruptive agents pursuing sustainability transformations, discur- sive approaches are therefore highly instructive [75], e.g., for conceiving of frames, tropes and concepts, as well as communication and media strategies. Here, an epistemic perspective adds a crucial success criterion in that such discursive interventions need to consistently focus on the underlying belief sets and their coherence, striving, e.g., to exhibit and critique the PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 14 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders unsustainability of the current system (as in “extinction rebellion”) while simultaneously pre- senting liveable and attractive alternative futures (as in “nature-based solutions”). 4.6 Institutions Institutional analysis is concerned with the influence of social rule structures on processes of societal change and stagnation [108,109]. Studies of sustainability transformations have there- fore often recurred to an institutional perspective, analyzing the juridical, administrative, terri- torial and political fabrics of certain systems to illuminate how these affect system change dynamics, including the role of agency [110,111,112]. The focus often lies on institutional log- ics which presuppose and purport certain beliefs and behaviours [113,114,74]. In this regard, institutional approaches help to identify Gidden’s “duality of structure” in societal realities. For our perspective here, institutions represent perhaps the most change-resistant sedimen- tation of belief sets into social structures, compared to practices, networks and discourses. Their establishment requires large amounts of resources and societal coordination. The belief sets institutions incorporate also shape those of the agents acting within them, simultaneously enabling certain actions (in conformity) and constraining others (in deviation). Some authors in sustainability transformation studies also point towards the specific sets of beliefs required to follow and comply with institutions [115,116,42]. Due to this, it is very difficult for most agents to even consider a change in their beliefs or social practices since they are bound by the rewards and sanctions imposed by institutions, as has been shown extensively with a view to institutional lock-ins, i.e., complete stagnation [117,118,119]. With a view to sustainability transformations, it appears that disruptive agency may thus have to rely on two known mechanisms of institutional change: i) Instances of agency typically emerge in the context of institutional tensions since these offer opportunities for agents to intervene, thereby inducing change [42,112]. ii) Certain influential key agents who act as endogenous outsiders can use their resources to drive institutional change from within [120,121]. Hence, a belief change of these few agents can have a disproportionate effect on sys- tem disruptions. The literature around institutional entrepreneurship addresses such issues on a strategic level [122,123,57], but so far largely neglects their epistemic dimension. A disruptive agency perspective would thus entail to focus on the availability and targeted take up of sustainable belief sets in such (rare) instances of institutional change, considering the role and contribution of epistemic outsiders (including e.g. as advisers, intermediaries or via social networks). It would equally ask for and pursue the (disruptive) appropriation or con- ception of institutions to embrace and support deviating beliefs while in turn constraining the pursuit of established ones. With a view to the insider stage of transformations, such processes of transformative institutionalization have been discussed extensively in the literature [124,87,125], although without recognising their fundamental epistemic dimension. However, as Haslanger has pointed out, institutional changes most often follow the changes that occur in the “cultural techne¯” [126], referring to what we characterised as an epistemic layout. 5 Conclusion In this paper, we have proposed an epistemic reading of disruptive agency in social system transformations. It suggests that alterations in the belief sets that structure social systems require a particular type of agents that hold deviant beliefs, at least in part affirming values, rules, norms or practices that differ from the epistemic layout of the system. These epistemic outsiders play a fundamental role in enabling but also initiating disruptions, which form a pre- requisite for systemic change. By concentrating on this role, we have then unpacked what it implies for research on and interventions for sustainability transformations, identifying PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 15 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders notions of epistemic outsiderness across a selected range of highly pertinent theoretical approaches to social change dynamics. We justified our normative focus on sustainability and purposive disruption with a view to urgently required socio-ecological transformations while recognising the general applicability of the conceptual approach. In result, it appears that the notion of epistemic outsiders holds considerable potential both as a genuine conceptual framework enabling new understandings and interpretations of social system change, and as a boundary object for productively integrating existing approaches. More specifically, we recognise the following five advantages of this perspective: First of all, it generally informs and conceptually enables various promising interdisciplinary avenues to explore and potentially influence transformative change towards sustainability. An epistemic reading connects not only between strands within sustainability transformation studies dealing with different forms and conceptions of disruptive agency already. Most importantly, it also points to essential contributions from psychology regarding the under- standing of processes of belief change, and in turn relates these to key theoretical approaches to social change (social practices, networks, discourses, institutions). Tracing epistemic outsid- ers in sustainability transformations across these complementary perspectives thus enables to devise novel interdisciplinary lenses that can further illuminate the complex dynamics of whole system change. Second and more specifically, an epistemic conception of disruptive agency offers a key for an integrated analysis of the individual and collective levels of agency involved in sustainability transformations. From personal mental constructs to social networks or complex multi-level governance settings it allows to scrutinize social change dynamics more seamlessly and across scales by “zooming in/out”, recognising the role and relevance of specific epistemic relations between individuals and society at large. Third and similarly, the notion of epistemic outsiders conceptually connects agent positions across system boundaries that are understood to be of crucial importance for sustainability transformations respectively (e.g., “niche innovators” or “regime intermediaries”) but lack an integrated understanding. Conceiving of endogenous and exogenous outsiders focuses on their common epistemic grounds rather than on obvious distinctions, suggesting a potentially important role of their mutual awareness, direct interactions and coordinated actions. In par- ticular, this also applies with a view to multi-system transformations, i.e., the boundaries or interrelations between different social systems (e.g., energy, mobility, housing)–a crucial aspect considering the inter-sectoral character of sustainability challenges. Fourth, an epistemic perspective additionally highlights the changing requirements and challenges resulting in two principal stages of transformations unfolding over time, namely before/after a new epistemic layout is shared by a majority of agents. This adds to a deeper understanding of the different perspectives, needs and strategies of epistemic outsiders and insiders in the course of a transformation, as well as a focus on the critical momentum and movements when roles become inverted (regarding existing phase models, e.g., pre-develop- ment, take-off, acceleration, stabilisation [127]). Last but not least, the above features allow to derive and conceive of new intervention for- mats and strategies as discussed in section 4, tailored to their respective epistemic contribu- tions. It thereby acknowledges a fundamental condition for successful transformations and suggests ways of addressing it in sustainability oriented policy and practice. In particular, thinking of disruptive agency in epistemic terms may be helpful to explore the conflicts and synergies of novel policy mixes (e.g., linking behavioural, organisational and institutional change in the public, private and civic domains) for effective sustainability transformations. Some caveat seems in place though. We are of course aware that an epistemic perspective can and should not replace other valuable and necessary approaches for analysing and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000097 February 14, 2024 16 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONEpistemic outsiders navigating sustainability transformations, notably those including conceptions of power and capital. Rather, it provides a complementary conceptual canvas that enables novel cross-overs towards and between such approaches, considering for instance that each body of literature on social change invoked here already includes strands that explicitly account for both. Future research approaches in this direction will require suitable research policy frame- works and funding instruments that enable the kind of broader inter- and also transdisciplin- ary (given the need for stakeholder participation) research on complex sustainability challenges sketched here. While disciplinary piecemeal studies can certainly contribute, this would likely not yield the added value targeted. Correspondingly, both conceptual and empiri- cal studies should focus on gaining novel insights through the latitude of an epistemic approach (across boundaries, levels, stages) by priority. 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10.1371_journal.pone.0299479	RESEARCH ARTICLE Association between human papillomaviruses, metabolic syndrome, and all-cause death; analysis of the U.S. NHANES 2003–2004 to 2015–2016 Parmis Mirzadeh1, Akinkunle Oye-Somefun1, Chris I. ArdernID 1, Catriona J. BuickID 2,3* 1 School of Kinesiology and Health Science, Faculty of Health, York University, Toronto, Canada, 2 School of Nursing, Faculty of Health, York University, Toronto, Canada, 3 Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 * Cbuick@yorku.ca Abstract Introduction OPEN ACCESS Citation: Mirzadeh P, Oye-Somefun A, Ardern CI, Buick CJ (2024) Association between human papillomaviruses, metabolic syndrome, and all- cause death; analysis of the U.S. NHANES 2003– 2004 to 2015–2016. PLoS ONE 19(3): e0299479. https://doi.org/10.1371/journal.pone.0299479 Editor: Graciela Andrei, Katholieke Universiteit Leuven Rega Institute for Medical Research, BELGIUM Received: September 19, 2023 Accepted: February 9, 2024 Published: March 7, 2024 Copyright: © 2024 Mirzadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the results presented in the study are available from https://www.cdc.gov/nchs/nhanes/about_ nhanes.htm Human papillomavirus (HPV) is the most common sexually transmitted infection, attributed to 4.5% of all cancers worldwide. Co-infection with the metabolic syndrome (MetS), a com- mon cluster of cardiometabolic risk factors, has been shown to increase the persistence of HPV. The purpose of this study was to estimate the association between HPV and MetS on mortality risk. Methods Data for the current study was drawn from seven consecutive cycles (2003–2004 to 2015– 2016) of the U.S. NHANES. The final analytic sample consisted of 5,101 individuals aged 18-65y with HPV and MetS information with follow-up to Dec. 31st, 2019. Baseline HPV sta- tus was assessed by either vaginal swab, penile swab or oral rinse and used to classify par- ticipants as: no HPV (n = 1,619), low (n = 1,138), probable (n = 672), and high-risk (n = 1,672; 22% type 16, and 10% type 18) HPV using IARC criteria. MetS was assessed by the Harmonized criteria. Results The average follow-up was 9.4 y with 240 all-cause deaths (no HPV: n = 46 deaths; low-risk: n = 60 deaths; probable: n = 37 deaths, and; high-risk: n = 97 deaths). HPV status alone revealed no associations with mortality in fully adjusted models. Cross-classification into dis- crete MetS/HPV strata yielded an increased risk of mortality in females with high-risk HPV/ MetS relative to the no MetS/no HPV group. Funding: The author(s) received no specific funding for this work. Conclusions Competing interests: The authors have declared that no competing interests exist. In this study, low, probable, and high-risk HPV and MetS were differentially related to mortal- ity risk in men and women. Further work is necessary to separate the temporal, age, PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 1 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. vaccination, and sex effects of HPV diagnosis in these relationships using prospective stud- ies with detailed histories of HPV infection and persistence. Introduction Human Papilloma Virus (HPV) is a common sexually transmitted infection (STI), and the most prevalent STI in the United States, with over 20 million people living with HPV and 5.5 million new cases each year [1]. As of now, over 200 types of human papillomaviruses (HPVs) have been identified, with approximately 40 of them known to infect the genital tract [2]. These include both “low-risk” or “high-risk” HPV subtypes that have been classified based on their association with cancer [3–5]. Low-risk types of HPV can cause genital warts and low- grade intraepithelial neoplasia on the cells of the cervix [6]. High-risk HPV can cause low- grade and high-grade intraepithelial neoplasia and are implicated in cancer [6]. Persistent infection by high-risk or oncogenic HPV types is firmly established as the necessary cause of most premalignant and malignant epithelial lesions of the cervix, and a variable fraction of neoplastic lesions of the vulva, vagina, anus, penis, and oropharynx [1, 7]. Of note, high-risk HPV is the cause of 5% of all cancers worldwide [8], and two of most common oncogenic types (HPV 16 and 18) are responsible for ~70% of all cervical cancers [9]. To date, most research on HPV risk has focused on psychosocial predictors, preventive screening, and health system-related factors, with relatively few studies addressing issues of chronic disease comorbidity. Despite an increasing trend in the incidence and mortality asso- ciated with cervical cancer over time [10], non-communicable diseases account for the over- whelming burden of premature death worldwide [11], highlighting a need to jointly address factors that may contribute to augmented cervical cancer risk. One such factor is the metabolic syndrome (MetS), a cluster of cardiometabolic risk factors [12] that increases the risk of CVD and all-cause death [13] and is found in more than 40% of the U.S. population [14]. Of importance, MetS has been recently found to both co-occur with HPV, and increase risk of both HPV persistence [15, 16] and HPV-related cancers in the presence of MetS [17] or its individual components [18–25]. Whereas two prior studies have found an increased risk of mortality due to HPV-related cancers [26, 27], no studies to date have quantified the associa- tion between HPV and MetS on risk of death. The purpose of this study is to therefore explore the association between HPV and MetS on mortality risk, using a nationally representative sample from U.S. NHANES. Methods Database Data for the study were drawn from the U.S. National Health and Nutrition Examination Sur- vey (NHANES), which is a publicly available program of population-based studies on health and nutritional status of males and females of all ages and ethnicities in the United States [27]. The current analysis combines health history and sociodemographic information, dietary questionnaires, physical laboratory examination, and biospecimen (HPV subtype and cardio- metabolic biochemistry) components of NHANES. Ethics approval was obtained from the National Center for Health Statistics Research Ethics Review Board (ERB) for NHANES 1999– 2004 (Protocol #98–12), NHANES 2005–2010 (Protocol #2005–06), NHANES 2011–2016 (Protocol #2011–17), and written informed consent was obtained from all participants. This study is an analysis of NHANES publicly available anonymized data (Internet address: https:// PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 2 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. www.cdc.gov/nchs/nhanes/index.htm), and thus, does not require further ethical review from the York University institutional review board. Study sample The present analysis is based on a pooled sample of n = 71,058 participants across seven conse- cutive NHANES cycles, from 2003–2004 to 2015–2016. The sample was reduced to 36,567 individuals by excluding those under 18 years and above 64 years old, as alterations in body composition can alter assessment of MetS [28]. In total, a subset of n = 13,763 individuals aged 18–64 y had information on HPV and MetS status. After excluding those with missing covari- ate and follow-up data a final analytic sample of n = 5,101 was available for the current study (Fig 1). Variables Baseline HPV status was assessed through vaginal, penile, and oral swabs and classified as posi- tive or negative for each HPV subtype. HPV testing was done through vaginal swabs for females (18-59y, 2003–2004 to 2015–2016) and penile swabs for males (18-59y, 2013–2014 to 2015–2016) using Roche Linear Array Assays, and oral swabs for both males and females (18- 69y, 2009–2010 to 2015–2016). Further information on NHANES laboratory methodology is available in the data documentation, codebook, and frequencies files [29]. HPV related cancer risk was subsequently categorized into “no” HPV, “low”, “probable”, and “high” risk HPV groups based on IARC criteria [3]. Individuals with a negative HPV test for all sub-types were categorized into no HPV. Individuals who tested positive for one or more of sub-types 6, 11, 40, 42, 54, 55, 61, 62, 64, 71, 72, 81, 83, 84, or 89 were classified as low-risk; probable-risk was assigned to anyone testing positive for one or more of sub-types 26, 53, 66, 67, 68, 69, 70, 73, or 82, and; anyone testing positive for one or more of sub-types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, or 59 were classified as having high-risk HPV. As co-infections of different genotypes are common, the low-risk HPV category excluded positive tests for probable or high-risk sub- types, and the probable-risk HPV category excluded positive tests for high-risk subtypes. This was done to ensure that individuals who tested positive for a low-risk and probable or high- risk subtype were not categorized into the low-risk group, and that those with probable-risk and high-risk subtypes were not categorized into the probable-risk groups. In short, individu- als with co-infections were coded as the IACR classification with the highest cancer risk; as a result, low-risk sub-types are under-counted in the current analysis. MetS was defined as having three or more of the following five components: high waist cir- cumference (M: � 102 cm, F: � 88 cm), high blood pressure (systolic � 130 mmHg or dia- stolic � 85 mmHg, or taking hypertensive medications), high blood glucose (� 100 mg/dl or taking diabetes medication), high blood triglycerides (� 150 mg/dl), or low HDL-cholesterol (M: � 40 mg/dl, F: � 50 mg/dl) [12]. Age, sex, ethnicity, education, health insurance status, smoking status (nicotine), were cap- tured by self-report questionnaire. Height and weight were measured and used to classify body mass index (BMI: kg/m2) into categories of “underweight” (BMI < 18.5 kg/m2), “healthy weight” (BMI: 18.5–24.9 kg/m2), “overweight” (BMI: 25–29.9 kg/m2), and “obesity” (BMI � 30 kg/m2). Physical activity was assessed by self-reported minutes of activity and coded as “meet- ing” (150+ min / week) or “not meeting” (< 150 min / week) current recommendations. Statistical analysis Descriptive statistics were used to examine the prevalence of HPV sub-types, stratified by sex. Prevalence of HPV cancer risk groups, demographics, and health behaviors were stratified by PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 3 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Fig 1. Flow chart of NHANES cycles 2003–2004 to 2015–2016 full sample to final analytic sample. https://doi.org/10.1371/journal.pone.0299479.g001 sex. A series of multivariable analyses were then developed to account for key sociodemo- graphic and clinical factors. As an intermediate analysis, logistic regressions were performed to estimate the odds of MetS by HPV cancer risk groups using two sex-specific models: 1) unadjusted; 2) adjusted for smoking, age, health insurance, physical activity, and education to prevent confounding effects as these factors may be associated with mortality. Probability of survival across HPV cancer risk groups was subsequently assessed using sex-specific Kaplan PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 4 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Meir curve analysis. Finally, to assess the joint effect of MetS and HPV on mortality risk, these two independent variables were cross-classified into eight discrete groups: i) no HPV and no MetS (HR = 1.0, referent); ii) low-risk HPV and no MetS; iii) probable-risk HPV and no MetS; iv) high-risk HPV and no MetS; v) no HPV and MetS; vi) low-risk HPV and MetS; vii) proba- ble-risk HPV MetS, and; viii) high-risk HPV and MetS. Cox proportional hazard regression analysis was then used to examine the individual and combined effects of HPV and MetS on all-cause mortality in men and women separately (unadjusted; and adjusted for age, smoking status, health insurance, physical activity, and education). The proportional hazards assump- tion of the log-linear Cox regression model was assessed by a formal test of proportionality with time-dependent cancer-risk groups. This test revealed no violation of proportional hazard assumptions (Wald χ2 = 2.05, df = 2, p = 0.13). Data analysis was performed with SAS software version 9.4. For the analysis of HPV subtype we used unweighted frequencies to visualize the raw frequency distribution. In all other cases except the Kaplan Meier curves, analyses were weighted to be representative of the U.S. population using the svyweight procedure. Statistical significance was set at alpha = 0.05. Results Table 1 displays the demographic and health characteristics of the sample, stratified by sex (M: 36%; F: 64%) and category of HPV cancer risk (no HPV, low, probable, and high-risk). Young adult females, 18 to 24 years of age, represented half of the population with a high-risk HPV. By contrast, those in the no HPV group were more likely to be non-smokers, individuals with higher education, and young adult males 18 to 24 years of age. Fig 2 Panel A shows the prevalence of each cancer risk category by sex. Overall, a majority of the sample displayed high-risk HPV (35% males, 31% females) or no HPV (34% males, 34% females). Panel B shows the unweighted frequency of HPV subtypes in the NHANES sample. Within each subtype, females had a higher case-count than males. Within the high-risk catego- ries exclusively, ~22% of females and 22% of males displayed subtype 16, whereas 8% of males and 11% of females displayed subtype 18. Fig 3 displays the survival probability of the HPV risk status in men and women. Over an average 9.4 years of follow-up there were 240 all-cause deaths (no HPV: n = 46 deaths; low- risk: n = 60 deaths; probable: n = 37 deaths, and; high-risk: n = 97 deaths). Visual inspection of the survival probability curves is suggestive of lower survival probabilities among the proba- ble-risk and high-risk HPV groups for males (p<0.05), and no clear relationships observed in females (p = 0.97). Table 2 shows the cross-sectional association between HPV status and MetS, stratified by sex. Compared to females with no HPV (OR = 1.00, ref), the odds of MetS were lower in those with high-risk HPV (OR = 0.74, 95% CI: 0.57–0.97), however these results were no longer sig- nificant in models that covaried for age, smoking status, health insurance, physical activity, and education. There were no significant associations between HPV status and MetS observed in males. Table 3 shows the association between HPV cancer risk status on risk of mortality. Relative to males with no HPV, those with high-risk HPV (HR = 2.59, 95% CI: 1.22–5.49) were at increased risk of all-cause death, however these results were no longer significant after adjust- ment for covariates. No significant associations between HPV status on risk of mortality were found in females. Finally, Fig 4 displays the sex-specific mortality risk across each HPV/MetS strata. In males, there was over a three-fold higher risk of mortality in the high-risk HPV/MetS groups com- pared to those with no HPV and no MetS; however, fully adjusted models revealed no PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 5 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Table 1. Characteristics of 5101 U.S. adults aged 18–64 years old, NHANES 2003–2004 to 2015–2016. Male 36% (n = 1827) Female 64% (n = 3274) n No HPV Low risk Probable risk High risk n n = 599 n = 392 n = 209 n = 627 No HPV n = 1020 Low risk Probable risk High risk n = 476 n = 463 n = 1045 n = 4003301 n = 2259244 n = 1331405 n = 4063397 n = 7777914 n = 4675956 n = 3120279 n = 6868309 Age 18 to 24 years old 25 to 44 years old 45 years and older Race/Ethnicity Mexican American Other Hispanic Non-Hispanic White Non-Hispanic Black Other Education Level Highschool and less Some College/AA degree College graduate or above Health Insurance Covered Not covered BMI Category Underweight Normal weight Overweight Obesity Physical Activity Does not meet guidelines Meets guidelines Smoking (Nicotine) Status Non-smoker Smoker Metabolic Syndrome Yes No Mortality Status Alive Dead 168 834 825 248 188 757 407 227 1031 537 259 1187 640 92 458 675 602 489 1338 756 1071 415 1412 1739 88 41.88 37.67 29.17 44.98 37.97 33.60 19.67 46.81 31.95 36.12 37.77 36.30 29.45 36.89 34.19 32.39 36.18 35.27 34.01 41.14 28.32 34.37 34.33 35.26 15.14 16.41 18.23 21.25 18.71 24.33 18.06 26.25 16.53 20.34 17.63 19.87 19.00 20.33 21.30 17.87 19.37 20.13 21.16 18.75 16.37 22.05 20.34 19.06 19.38 19.49 14.44 9.29 12.95 8.98 10.23 12.68 11.58 4.63 11.04 11.40 12.51 11.88 10.27 5.82 10.95 11.73 12.07 9.97 11.94 12.68 10.31 11.90 11.26 11.42 11.54 27.27 34.82 36.62 27.33 27.48 35.66 42.50 32.03 36.67 34.84 29.84 32.82 39.96 35.99 36.99 36.51 31.61 33.61 35.30 29.81 39.32 33.39 35.35 33.95 53.83 367 1543 1364 363 258 1750 688 215 1631 1147 496 2378 896 255 783 848 1388 1422 1852 1264 2010 853 2421 3122 152 20.54 33.07 39.87 36.31 34.11 37.03 19.69 32.40 27.62 35.77 47.70 36.45 28.75 26.07 35.15 34.22 36.46 30.51 37.52 45.94 26.47 37.58 33.74 35.13 24.12 14.24 20.85 22.45 20.36 22.19 18.89 30.95 25.71 21.62 21.22 18.45 20.02 23.51 21.99 20.11 19.56 22.01 23.25 19.16 19.34 21.91 22.88 20.20 20.72 23.28 15.62 13.41 14.01 15.57 14.14 13.80 16.05 8.74 15.98 12.41 12.18 14.22 12.86 17.96 11.64 15.08 13.81 14.19 13.71 11.79 15.44 13.16 14.13 13.77 16.88 49.60 32.66 23.67 27.76 29.55 30.28 33.31 33.15 34.78 30.60 21.67 29.31 34.87 33.98 33.10 31.14 27.73 32.05 29.61 22.93 36.18 26.37 31.92 30.37 35.72 This table contains sex stratified weighted frequencies across cancer risk groups to better understand sample characteristics. Sample sizes (n) across cancer risk groups represent unweighted and weighted values. Numbers represent percentages across rows. Sex stratified chi-squared analyses were performed using case counts to assess overall differences across groups and revealed significant differences (p<0.05) across all variables except BMI categories and physical activity in males, and BMI categories and mortality status in females. https://doi.org/10.1371/journal.pone.0299479.t001 significant associations between HPV/MetS groups and mortality in males. In females, mortal- ity risk was four-fold higher in those with high-risk HPV/MetS compared to those with no PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 6 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Fig 2. Sex-specific prevalence of cancer risk groups and frequency of HPV subtypes according to cancer risk groups. Panel B) Individuals with multiple infections of a specific cancer group appear as multiple counts if they have multiple HPV infections. For example, if an individual has an HPV type 6 and 11 infection, they will appear as a count for both bars 6 and 11. HPV testing was done through vaginal swabs for females (18-59y, 2003–2004 to 2015–2016) and penile swabs for males (18-59y, 2013–2014 to 2015–16) using Roche Linear Array Assays, and oral swabs for both males and females (18-69y, 2009–2010 to 2015–2016). https://doi.org/10.1371/journal.pone.0299479.g002 HPV and no MetS, an effect that was moderately attenuated with further adjustment (HR = 2.60, 1.09–6.19). Discussion The current study extends previous research on the risk of cancer morbidity and mortality with high-risk HPV by examining the joint effect of MetS, a common cluster of pre-clinical risk factors, and HPV sub-type on all-cause death. Using a nationally representative sample of US adults (2003–2016) with an average of 9.4 y of follow-up we observed that the co-occur- rence of MetS and high-risk HPV notably elevated risk of mortality in females. PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 7 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Fig 3. Kaplan-Meier curves across cancer risk groups, stratified by sex. Survival differences across cancer risk groups was assessed by global chi-squared analysis. https://doi.org/10.1371/journal.pone.0299479.g003 In the pooled NHANES sample, HPV types 16 and 18 accounted for approximately 22% and 10% of high-risk HPV, respectively. Consistent with previous literature [30, 31], HPV type 16 was the most common high-risk HPV subtype in both men and women. In this sam- ple, high-risk HPV was highest among females aged 18 to 24 years old, and subsequently decreased with age. By contrast, high-risk HPV tended to increase with age in males. These findings are generally consistent with global systematic reviews that report a decrease in PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 8 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Table 2. Odds of MetS across cancer risk groups, stratified by sex. Male Female Unadjusted HPV status No HPV Low risk HPV Probable risk HPV High risk HPV Adjusted HPV status No HPV Low risk HPV Probable risk HPV High risk HPV OR 1.00 1.07 1.06 0.94 1.00 1.06 1.04 0.94 95% CI ref 0.66–1.68 0.64–1.75 0.63–1.41 ref 0.67–1.67 0.64–1.69 0.61–1.44 OR 1.00 1.02 0.84 0.74* 1.00 0.91 0.80 0.77 95% CI ref 0.79–1.31 0.60–1.17 0.57–0.97 ref 0.69–1.20 0.59–1.10 0.58–1.03 This table provides results of logistic regressions; assessing the odds of MetS across cancer risk groups in unadjusted and fully adjusted models, stratified by sex. OR: odds ratio. The adjusted model accounted for age, smoking status, health insurance, physical activity, and education. Indicates significance, p<0.05. https://doi.org/10.1371/journal.pone.0299479.t002 HPV prevalence with age in females [32], and lower prevalence of HPV subtypes of concern in younger males [33]. Our results diverge slightly from existing literature on the distribution of HPV sub-types in that we assigned HPV group risk (“none”, “low”, “probable”, and “high”) as the “highest” HPV sub-type observed within an individual. In our study, high-risk HPV sub-types were the most common; however, low-risk genotypes tended to be the most prevalent HPV sub-type [34]. Screening characteristics for co-infections within the low-risk category or multiple positive Table 3. Effects of HPV on risk of mortality, stratified by sex. Male Female Unadjusted HPV status No HPV Low risk HPV Probable risk HPV High risk HPV Adjusted HPV status No HPV Low risk HPV Probable risk HPV High risk HPV HR 1.00 1.62 1.78 2.59 1.00 1.25 1.55 2.03 95% CI ref 0.73–3.60 0.64–4.92 1.22–5.49 ref 0.52–3.00 0.54–4.43 0.87–4.71 HR 1.00 1.10 1.20 1.12 1.00 0.89 1.05 1.08 95% CI ref 0.60–2.01 0.60–2.36 0.64–1.98 ref 0.48–1.65 0.54–2.06 0.61–1.91 This table provides results of cox regressions; assessing the risk of mortality across cancer risk groups in unadjusted and fully adjusted models, stratified by sex. HR: hazard ratio. The adjusted model accounted for age, smoking status, health insurance, physical activity, and education. *Indicates significance, p<0.05 https://doi.org/10.1371/journal.pone.0299479.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 9 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. Fig 4. Effects of HPV and MetS on risk of mortality, stratified by sex. These figures provide results of cox regressions to assess risk of mortality across cross-classified cancer risk groups and MetS in unadjusted and fully adjusted models, across males and females. HR = hazard ratio. https://doi.org/10.1371/journal.pone.0299479.g004 low-risk genotypes [35] may have contributed to our under-counting of low-risk HPV sub- types in our sample. Indeed, enhanced screening of low-risk cases, that would not otherwise be picked up in the general population due to no routine use of HPV testing in the U.S. for low- risk strains, could improve accuracy of predictions. At the time of the NHANES data collection primary screening was limited to those with a cervix and would not include screening for low- PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 10 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. risk HPV. Screening for low-risk HPV status lacks clinical utility; as such, knowing low-risk HPV status, or knowing that a low-risk HPV strain is present may not necessarily have an impact on the clinical management of patient with non-malignant conditions such as mucosal warts. In contrast, current screening tailors to the clinical utility of knowing high risk HPV sta- tus as it has an impact on the treatment of precancerous lesions and the prevention of cancer. Previous literature has examined the co-occurrence of MetS and HPV and risk of HPV per- sistence [15, 16], as well as HPV-related cancers and conditions [17–25]. Findings from these studies indicate that MetS is associated with a greater risk of HPV persistence [15, 16], and that MetS or its individual components tend to increase the risk of cancers related to HPV [17–25]. Adding to the literature, we examined MetS risk across HPV cancer-risk groups, and mortality risk across cross-classified groups of MetS and HPV. In these analyses, females with high-risk HPV had lower odds of MetS relative to the group with no HPV, and there were no associations between HPV and MetS observed in males. After adjusting for age, smoking sta- tus, health insurance, physical activity, and education, having MetS and high-risk HPV increased the risk of mortality in females. While there are several possible explanations, these findings may be due in part to sex dif- ferences in vaccination and screening. Indeed, the Centers for Disease Control and Prevention (CDC) have noted sex differences in HPV vaccination from 2013–2018 wherein females were more likely to have ever received one or more dose of HPV vaccine compared to males [36]. In 2014, 60% of females and only 42% of males aged 13 to 17 years old received at least one dose of the HPV vaccine [37]. There are also disparities in HPV-related cancer screening. Beginning at the age of 21, females are advised to undergo cytology (pap) test [38] to detect precancerous cell changes that could lead to cervical cancer. To date, there is no routine HPV-related cancer screening guidelines in place for males. Variations in NHANES HPV testing methodology might account for some observed disparities in mortality across sex. Females were assessed by oral swabs for four cycles (18-69y) and vaginal swabs for seven cycles (18-59y), whereas males were only assessed by penile swabs for two cycles (18-59y) and oral swabs for four cycles (18- 69y). Consequently, men aged 60–64 were only evaluated for HPV in two cycles, potentially leading to an underestimation of mortality. The exact mechanism of association between HPV and MetS remains unclear, but may be related to a persistent inflammatory response and increased oxidative stress [39]. Thus, MetS could put an individual at a higher risk of HPV-related cancers, which pose a higher risk of mortality when co-occurring with an HPV infection. Previous literature has also found ele- vated mortality risk in HPV-related cancers [26, 27], but mortality risk in cancer risk groups remains unclear. Mortality findings in this study indicate greatest risk of mortality in the high- risk HPV groups relative to the group with no HPV, in females. A significant proportion (70%) of cervical cancers are associated with high-risk HPV, specifically type 16 and 18 HPV [9], therefore our findings of elevated mortality risk in high-risk HPV groups was expected. Strengths and limitations Among several strengths of the current analysis is the use of NHANES data which allows for nationally representative estimates using comprehensive health behavior and laboratory infor- mation. This dataset is unique in that it captures HPV and MetS variables from objective labo- ratory data and allows for the mutual adjustment of these factors. The main limitation of this study is a lack of information on HPV persistence (a pre-cursor for cancer), as HPV testing was conducted in NHANES laboratories only once per participant. Furthermore, the preva- lence of HPV in males may be underestimated, as females were assessed by oral swabs for four cycles (2009–2016) and vaginal swabs for seven cycles (2003–2016), whereas males were only PLOS ONE \| https://doi.org/10.1371/journal.pone.0299479 March 7, 2024 11 / 14 PLOS ONEHPV, MetS, and all-cause death; Analysis of the U.S. NHANES 2003-2004 to 2015-2016. assessed by penile swabs for two cycles (2013–2016) oral swabs for four cycles (2009–2016). The prevalence of detectable HPV has shown to be higher through penile swabs (45%) than oral swabs (11%) in US male adults [40]. Because the no HPV group had a relatively shorter average follow-up than the low, probable, and high-risk groups, we can not exclude the possi- bility that further screening would have resulted in a classification as HPV positive. This, how- ever, would have resulted in a bias to the null in the current analysis. Finally, the analytical sample excluded individuals with missing data on HPV, MetS, mortality, or covariates, which may be reflective of treatment seeking behaviors seen in other studies. Conclusion Taken together, results from this study demonstrate the importance of a common cluster of cardiovascular risk factors on mortality risk across HPV subgroups. Future efforts focused on the harmonization of HPV-specific datasets or pooling of subsequent NHANES cycles may allow for broader insight into this question, by examining specific HPV subtypes, highly preva- lent high-risk HPV subtypes, and HPV-related cancers. In the intermediate term, further pro- spective analysis is needed to understand the temporal, age, vaccination, and sex effects of HPV diagnosis on these relationships in studies with more detailed histories of HPV infection and persistence. Supporting information S1 Checklist. (DOCX) Author Contributions Conceptualization: Chris I. Ardern, Catriona J. Buick. Data curation: Akinkunle Oye-Somefun. Formal analysis: Parmis Mirzadeh, Chris I. Ardern. Methodology: Parmis Mirzadeh, Akinkunle Oye-Somefun, Chris I. Ardern, Catriona J. Buick. Project administration: Catriona J. Buick. Supervision: Chris I. Ardern, Catriona J. Buick. Writing – original draft: Parmis Mirzadeh. Writing – review & editing: Parmis Mirzadeh, Akinkunle Oye-Somefun, Chris I. Ardern, Catriona J. Buick. References 1. Human Papillomavirus: A hidden epidemic in the United States [Internet]. PRB. Available from: https:// www.prb.org/resources/human-papillomavirus-a-hidden-epidemic-in-the-united-states/. 2. 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10.1371_journal.pstr.0000101	RESEARCH ARTICLE Strategic styles of hardware product development could accelerate commercialization in cleantech startups Erin LooneyID Tonio Buonassisi1, Ian Marius Peters1,3 1, Andre´ BuscariolliID 2, Maria C. Yang1, Geoffrey RaymondID 2, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Looney E, Buscariolli A, Yang MC, Raymond G, Buonassisi T, Peters IM (2024) Strategic styles of hardware product development could accelerate commercialization in cleantech startups. PLOS Sustain Transform 3(3): e0000101. https://doi.org/10.1371/journal.pstr.0000101 Editor: Ana Delicado, Universidade de Lisboa Instituto de Ciencias Sociais, PORTUGAL Received: March 7, 2022 Accepted: January 31, 2024 Published: March 20, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pstr.0000101 Copyright: © 2024 Looney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All interview questions and those answers that are anonymized are available in the manuscript and Supporting 1 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 2 Department of Sociology, University of California, Santa Barbara, California, United States of America, 3 Forschungszentrum Ju¨lich, Helmoltz-Institut Erlangen- Nu¨rnberg fu¨r Erneuerbare Energien, Erlangen, Germany erin.elizabeth.looney@gmail.com (EL); buonassisi@mit.edu (TB); ian.marius.peters@gmail.com (IMP) Abstract Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step (median of 19 weeks per prototype) regardless of prototype complexity or iteration. Qualitative interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and structured, typified by free-form exploration and rule-based execution, respectively. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and expectations. When pivoting to a new product or market, returning to a natural style is beneficial. Author summary Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step regardless of prototype complexity or iteration. Interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and struc- tured. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 1 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONInformation files. Some data cannot be shared publicly due to privacy of the interview participants. Funding: TB, EEL acknowledge support from U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007535. TB EEL acknowledges support from Singapore’s National Research Foundation through the Singapore-MIT Alliance for Research and Technology’s Low-Energy Electronic Systems (LEES) IRG. All authors acknowledge support from the U.S. Department of Energy and National Science Foundation joint Quantum Energy and Sustainable Solar Technologies (QESST) grant. IMP acknowledges support Bavarian State Government (project "PV-Tera – Reliable and cost- efficient photovoltaic power generation on the Terawatt scale", No. 44- 6521a/20/5). EEL acknowledges support by the National Science Foundation Graduate Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Prototyping styles could accelerate cleantech hardware development expectations. When pivoting to a new product or market, returning to a natural style is beneficial. Introduction Successful commercialization of hardware products is essential to a sustainable future by com- bating climate change with renewable energy technologies, screening for disease with medical diagnostics and devices, and increasing production efficiency with robotics, among many other critical applications. Unfortunately, hardware product development (PD) is often less attractive to investors due to the barriers of long commercialization timelines and large upfront investment [1,2]. Commercialization timelines are shown in Fig 1, with the years to exit (merger, acquisition, or initial public offering) for cleantech, medical, and software sectors are compared for the years 2006 to 2011 [1]. Software companies with three-year modal average exit times are the most attractive to investors who start receiving a return on investment (ROI) at that time. Medical device startups, often slowed by hardware timelines and medical regulations, have longer exit times than soft- ware. Sustainable hardware technologies, called cleantech in this paper, are the slowest to exit and have the fewest total companies overall with a bi-modal distribution and peaks at four and eight years. The slower time-to-market for cleantech is especially problematic due to the urgent need for sustainable solutions today. Furthermore, long timelines lead to less investment overall and therefore less technology development to help with the goal of sustainability long term. The overarching goal of this work was to find the PD steps that most need acceleration and determine levers that practitioners can use to accelerate PD timelines, hastening time-to-mar- ket and attracting more investment for technologies critical to a sustainable future. We focus on hardware product development with an emphasis on sustainable, or clean technologies, which is shortened throughout the work to ‘PD’ for simplicity. Questions we intended to answer include: How long do PD steps take for early-stage hardware startups? What are the parameters determining the lengths of the longest PD steps? What levers are available to accel- erate PD? What are some best practices or strategies to overcome limiting parameters in PD? This work sits in the intersection of engineering and social science, as we aim to understand how to accelerate PD by gathering actionable data from startups using social science methods. Within engineering, there is ample research on innovation and new product development methods and strategies (i.e., the application of the agile product development movement to hardware) [3,4,5,6,7,8]. Within policy, business management, and finance, there is also a large body of social science and technology research that focuses on improving innovation and Fig 1. Years to exit for startups in cleantech, medical, and software sectors. Number of startups that successfully exit in a merger and acquisition or initial public offering plotted against how many years from incorporation it took to exit. The dotted line indicates the envelope around software with which hardware startups compete. Raw data provided by Benjamin Gaddy from [1]. https://doi.org/10.1371/journal.pstr.0000101.g001 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 2 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development development outcomes with solutions and frameworks for either policymakers or institutional investors [9,10,11,12,13,14,15,16]. There are few papers in the literature that address early- stage hardware startup product development from the viewpoint of the practitioners them- selves [17]. This paper focuses on individual companies and their PD timelines to address practitioners within early-stage hardware startups directly. Through a series of semi-structured interviews with practitioners, company workflows are mapped, bottlenecks identified, and a strategic framework for acceleration in hardware startups developed. Analyzing 55 interviews with hardware startups, 20 of whom were cleantech, lasting on average over one hour, this work aims for a unique combination of breadth and depth. Identifying the root-causes for long development timelines in hardware startups requires a broader database than typical case studies can offer. Developing a framework for accelerated development demands a deeper analysis than encompassed in the scope of typical short surveys. With the findings of this work, we hope to inform early-stage cleantech hardware startups and facilitate accelerated hard- ware product development for technologies that can enable a sustainable and prosperous future. Results Interview data collection for qualitative and quantitative analysis We developed and implemented a facilitated survey (interview) combining engineering domain expertise and sociological research methods, both quantitative and qualitative. The interview included sections encompassing product planning, requirements, modeling, concept generation and selection, complexity, prototyping, manufacturing, and more. The final inter- view contained approximately 170 questions, lasted between 45 minutes and two hours, and the majority were conducted in person. Over 80% of the interviews (45) were recorded for transcription and an online survey form [18] was filled out by the interviewer in real time. A geographically diverse set of startup companies participated with 35 (63%) from North America, 7 (13%) from Europe, and 13 (24%) from Asia. All of the companies interviewed cre- ate hardware products, and the focus of the interview was on the development timeline leading up to the first production quality product. Cleantech hardware companies were originally the sole target for this study including start- ups working on battery, wind, and solar technologies as well as energy efficiency, greenhouse gas sensing, EV charging, and more. However, the set of available companies matching this description that had completed at least one prototype was a prohibitively small sample size. Therefore, the facilitated interview was opened to any early-stage hardware startup creating physical products including scientific equipment, medical devices, quantum computing, con- sumer electronics, food technologies, microfluidics, and more. The inherent similarities in early-stage hardware product development independent of sector meant that the other hard- ware startups brought valuable data to the study. They also provide a point of reference to see what differences cleantech startups may have. In the end, almost half of the companies inter- viewed identified as cleantech with the remaining half including companies from medical, robotic, aerospace, and other sectors. In this sample, the majority of companies had core intellectual property (IP) in a novel application or service provided. The remaining companies in the sample had IP in novel mate- rials, processes, assemblies, or business models. Furthermore, the competitive strategy of most companies was in the technology itself rather than customer focus or cost leadership. Over 93% of the interviewees (51) were technically trained as engineers or scientists and have an education level beyond a bachelor’s degree. The position of over 90% of the interviewees (50) at the time of the interview was as an executive and/or founder of their company. The remain- der of the interviewees were lead engineers or scientists. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 3 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Fig 2. Product development workflow timeline. An example of a workflow timeline developed through interviews. Incorporation (orange), prototyping (blue), modeling (red), and operational milestones (green) are key components. https://doi.org/10.1371/journal.pstr.0000101.g002 In this work, a hybrid of quantitative and qualitative analysis often referred to as mixed methods was used to explore what the bottlenecks in the PD process are and how they can be overcome. The quantitative parts of the survey included workflow mapping and product com- plexity metrics. An example of a company workflow mapping and its components are shown in Fig 2. We developed a workflow for each startup interviewed and used the timing needed for each prototype as well as development ‘milestones’ such as incorporation, modeling start date, etc. to calculate times for PD tasks seen below in Fig 3. To our knowledge, the mapping of PD timelines and calculation of task timing for over 50 hardware startups has never been done before. To complement the quantitative data gathered, open-ended questions about each PD step were asked. To analyze these results and find conclusive insights, qualitative research methods from the field of sociology were employed. The main method used in this work is qualitative coding on all transcribed interviews, which is "a way of indexing or categorizing the text in order to establish a framework of thematic ideas about it" [19]. More information about this methodology is provided in the methods section. Using this mixed methods approach, we first endeavor to answer our initial research ques- tion of how long PD steps take for early-stage hardware startups. Prototyping is the longest PD activity with an average time per prototype of 19 weeks By aggregating timelines from the interviews in Fig 3, we find the amount of time in years each PD step takes to complete. Total prototyping time is the longest PD step at around 2.5 years PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 4 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Fig 3. Elapsed time during product development steps, n = 55. Left: product development process times in years, Right: Number of weeks per prototype for the first five prototypes. https://doi.org/10.1371/journal.pstr.0000101.g003 for 5 prototypes while the remaining PD activities have a median of less than one year. Here prototypes are defined as preliminary examples of a product used to evaluate design and per- formance [20]. Gathering user preferences and targeting a market are next in length with medians of over half a year. In the early-stage PD studied herein companies typically create multiple, iterative prototypes before arriving at a final design ready for mass production. There were no significant differences found in the timelines of cleantech vs other hardware startups. To understand prototyping time from a different angle, we plot the amount of time taken per prototype and find a median time between 17.5 and 20.5 weeks. For the first three proto- types, the time to completion grows slightly with each iteration. However, overall, the amount of time it takes to prototype stays close to consistent for each iteration, suggesting a "character- istic prototyping time" of about 19 weeks per prototype. Taking this characteristic prototyping time and multiplying it by 5 prototypes total results in 1.8 years. This number is below the 2.5 years median obtained from the workflow analysis for the overall prototyping period. The dis- crepancy is due to downtime between iterations, which was not included in the time for single prototypes. This downtime accounts for an additional six weeks on average per prototype. This time is often spent using that prototype for demonstrations and marketing as well as determining what to do for the next iteration. To understand how the interviewees perceived the length of each PD step, we asked what PD step slowed them down the most and what they thought could realistically be accelerated. Most of the interviewees believe that prototyping is the slowest PD step, matching reality. Also, they believe uniformly that prototyping could realistically be made faster. There is less confi- dence by the interviewees in speeding up any PD activities other than prototyping. Given this outcome, we now focus our next research question on prototyping as the longest PD step: What are the parameters determining the lengths of the longest PD steps? Product complexity is not correlated with time to prototype One hypothesis we pursued while designing the interview was that complexity is positively correlated with development timeline, meaning the more complex the product, the longer it takes to create. In literature, product or engineering complexity is usually defined by breaking PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 5 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Fig 4. Complexity metrics vs. average time to prototype. Plotted with ordinary least squares regression trend lines. Green is for cleantech and blue is for other sectors. In the top left is the complexity metric that takes into account several complexity parameters: part count, number of custom parts, percentage outsourced of build and design, and design for outdoors. https://doi.org/10.1371/journal.pstr.0000101.g004 the concept into key dimensions or metrics, for example, complexity based on size, component count, interfaces, control, assembly, etc. [21]. In this work, we designed a set of questions for interviewees to gather key complexity parameters adopted from literature and supplemented to target our specific hypothesis. The complexity parameters and literature used are described in detail within S1 Text and S1 Fig. In Fig 4, we plot complexity parameters against prototyp- ing times with ordinary least squares (OLS) regression trend lines. The major finding from Fig 4 is that we found no significant correlation between how com- plex a product is and the time it takes to prototype was found. This is a surprising result as intuition would suggest that a more complex product would take longer to prototype. In Fig 4, the cleantech companies are in green and the other companies are in blue. There are no signifi- cant differences in the complexity parameters between these two categories. The only visible trend is possibly even more counterintuitive: prototyping time decreases with the number of functions the product performs. The cause of this trend is unclear. We speculate that it could be due to several things; one is that for products the interviewee conceived of as simpler, there is a tendency to enumerate more non-essential functions, whereas for complex products, the interviewee focused on the one or two main functions. It could be that of feature creep or PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 6 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development overengineering occur more frequently in simpler products during development. Overengi- neering describes the process of designing a product to be overly complex, having more fea- tures or greater robustness than is necessary for its functions, which leads to inefficiencies. Feature creep is the expansion of product requirements during prototyping by adding features beyond the original scope of the product [20]. Another reason for this could be that product complexity is managed by modularity, and that the more modular the product has, the less time it takes to prototype [21]. To validate this trend a more rigorous definition of essential functions vs. other functions would be needed. This is an area for future research. Due to the lack of correlation between complexity and time to prototype within these com- panies, we continue to search for drivers of prototyping time. To do this, we use qualitative codes to investigate the impact of PD processes on prototyping. Development styles correlate with time to prototype One qualitative grounded code that emerged from the analysis deals with the interviewees placing their development process on a spectrum from organic and natural PD to highly struc- tured PD. The section of the interview on defining requirements prompted many responses pertinent to this spectrum. While responses varied between companies, a division between a more natural approach and a more structured approach was apparent. For example, some companies describe their design decisions mentioning the lack of structure: Interviewer: “So, did you use traditional engineering requirements?” C.6 (consumer electronics company): “No. We didn’t do anything traditional. It was pretty organic.” Interviewer: “Okay. So, would you say official, loosely worded, or no requirements were discussed?” C.6: “I would say the middle one. Less official, loosely worded. Just like, ‘We kind of need this. Let’s try it.’ We built this hacky solution and fixed it.” This exchange demonstrates one approach to PD that we propose to define as natural and organic. No formal requirements were used and there was a description of things “just happen- ing” or evolving naturally as the team worked toward designing and building prototypes. These descriptions were common among the startups interviewed, with descriptions such as “going with the flow,” letting the design “evolve,” and undertaking “organic” or “natural” processes. On the other end of the spectrum were companies that approached PD in a rigorous, struc- tured manner. There were a few categories of these companies: those with an investor or grant that imposed structure, those with experience in industry using structured PD, and those that had recently practiced structured PD in a university setting. For example, one company work- ing in medical device design explained: Interviewer: “Were traditional engineering requirements used?” C.5 (consumer electronics company): “It was imposed, right? It’s required because it’s an FDA regulated product. So, there’s no optionality there. If you want to bring something to the market, you have to because the FDA requires it.” This enforced structure can be beneficial to some companies. For example, another com- pany expressed that the structure mandated by their government grant in the form of mile- stones was a useful strategy: Interviewer: “Looking back, would you spend more or less time on requirements creation?” PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 7 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development C.8 (cleantech company): “On the requirements creation. No, I don’t think so. I mean it definitely seems like a necessary part of the process and it’s good to have those [grant] mile- stones to try and hit. So, I mean, I think it’s a pretty good strategy.” In the above interview excerpts, the division between natural and structured PD models is evident. Before detailing more analysis on how prototyping times are affected by these two development styles, both natural and structured, we define the two sides of the spectrum. We define natural innovation as a flexible process in which prototyping iterations evolve following the intuition of participating engineers generally without rigid planning or require- ments. Natural innovation allows for free-form ideation, rapid switching between designs, and exploration of a comprehensive design parameter space. We define structured innovation as an ordered process in which prototyping iterations fol- low prescribed and codified plans including defined engineering requirements with proce- dures in place for altering plans. Structured innovation creates order that ensures the design meets functional and cost targets, ensures the product can be manufactured, and facilitates communication about the design and the fabrication process. Each interviewed company falls somewhere on the spectrum from natural approaches to more structured approaches. Using analysis from the interviews, we developed a method for placing com- panies on this spectrum. Each time an interviewee mentions in the interview one of the steps on the spectrum in Fig 5, they are assigned one point at that position on the spectrum. We outlined eight steps ranging from “directly mentioning a lack of structure, organic, or natural style” on the natural side, to “following traditional engineering requirements” on the structured side. Once all the tran- scripts were processed, the overall totals were summed, normalized, and a number between 0 and 1 was calculated placing the company in one of the three categories: natural, hybrid, and structured. We find that companies using highly structured processes for prototyping had an average of 31 weeks per prototype including the downtime before the next prototype. Using the average of six weeks downtime, this was 25 weeks per prototype. Companies using natural processes aver- aged 19 weeks for prototyping time plus downtime, or 13 weeks prototyping time, about half the time of structured processes. Hybrid companies were at 28 weeks prototyping time plus downtime. This result points to natural processes being the better choice if prototyping quickly is the only goal. Several excerpts from the interviews back up this finding, including: Interviewer: “If you could go back in time and do things differently, would you spend more or less time on requirements? Could you speed up the process?” C.1 (cleantech company): “I probably would spend a little bit less time arguing about whether it should be this value or that value cause really at this stage, it doesn’t matter. You just have to pick something and go with it. You’re going to learn how to adjust it. . . One of our early angel investors gave me a piece of advice that is a soundbite motto, but I found it very useful. He basically said, ‘There’s no good decisions and bad decisions. There’s just decisions and then you work to make them good.’ So, it’s basically saying don’t waste time analyzing it to all hell.” In this quote, the interviewee is pointing out that early in the PD cycle, flexibility and speed are key to iterating quickly. However, we learn from the interviews that structured processes also have a role in efficient PD. EL: “If you could go back in time and do things differently, would you spend more or less time on requirements? Could you speed up the process?” PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 8 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Fig 5. Natural to structured innovation spectrum for prototype development. Top: Spectrum from natural to structured process with criteria for categorization of companies along that spectrum. Bottom: Startup companies on the spectrum vs. how many weeks it takes to complete one prototype on average. The orange error bars are 25% of median to 75% of median values of the data. https://doi.org/10.1371/journal.pstr.0000101.g005 C.9 (cleantech company): “Actually, I would spend more time on requirements. In the pro- cess, we made a number of different prototypes, which I think looking back is not neces- sary, if we had proper detailed requirements already done. That could also speed up the process.” In this excerpt, we see that the company may have lost time by not having requirements defined and understood early enough leading them down some wrong paths. At some point, once the technology, implementation, and market have aligned, structure in the form of rigor- ous requirements can enhance PD efficiency rather than slow it down. The crux in implementing a development style for startups is the question of when to use a natural approach versus when to deploy a structured approach. From Fig 5 in conjunction with overall analysis of the interviews, it seems that for the first few prototypes natural PD pro- cesses is likely the best approach, as structure can bring paperwork, extra tasks, and careful evaluation which explains the much longer prototyping time. However, at a certain point in the life cycle of a company, structure can actually accelerate PD by avoiding timely and expen- sive distractions. Therefore, there is a balancing act that startups must perform to get through prototyping efficiently, having a natural process long enough to move quickly and creatively at PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 9 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development first, but bringing in structure soon enough to not waste time in the long run. One CEO explained this tension and his compromise between these two extremes in a hybrid approach: Interviewee: “How important was defining requirements for you?” C.26 (electronic equipment company): “It didn’t happen like I sat down and I came up with the requirement. While making the thing it evolved. . . It’s a compromise because you can perfect one design and make sure that it’s going to work in [the] first shot. But the other approach is just to do it fast. Okay, let’s get it, try it, fix it, fix it.” From this qualitative analysis, we have found that a balance between natural and structured PD is one way to potentially accelerate PD for many hardware companies. A case study about the pitfalls of deploying the wrong PD style In this section, we highlight a specific case study of a cleantech company that was efficiently prototyping in a natural regime, but experienced large slowdowns in product development later in the process. For this company, the first five prototypes had a median prototyping time similar to that found for all companies in the study. After the fifth prototype however, there was a total redesign for prototype 6 that fell victim to both overengineering and feature creep, taking a total of 1.5 years to finish. As the company was running out of money and was close to collapse, a new CEO, former investor stepped in to overcome these issues. He explained: C.44 (cleantech company): “Unbeknownst to the salespeople that were trying to sell [the product] and to the board of directors, [the company] undertook a top to bottom redesign of the product. It took over a year before another unit was in the field.” Interviewee: “Did it fix the problem?” C.44: “No. Actually, nothing was better. Everything was worse. It was more complicated, more expensive, more difficult to make, more sources of failure.” After the new CEO was brought on, there was a year-long reversing of the over-complicated prototype, resulting in an extra 2.5 years of development time that could have been shortened or completely avoided (Fig 6): In this example, the first five prototypes were accomplished relatively quickly beginning with a natural development regime and gaining more structure over time. Then for the sixth prototype the company pivoted to a substantially new, untested design that did not emphasize design simplicity, maintainability, or other key requirements. The team was also by this point using a more structured development style, and the product became overly complicated lack- ing rapid testing and user feedback. This demonstrates the need for reverting to a natural development style for each major design pivot as the new concept must be rapidly built, tested, and verified in the hands of the customer. If the company needed to pivot, then using a natural development style may have prevented such a lengthy slowdown. And, if the company had not pivoted and was using a structured style for the long-tested design, then the slowdown may also have been avoided. This example shows the perils of using structure on brand new designs before the technology, implementation of that technology, and market type have coalesced into one product goal [22]. This example could also shed light on why product complexity is not correlated with proto- typing time. One hypothesis is that there is a tendency toward perfectionism and overengi- neering with simpler products as compared to more complex products. In the example above, PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 10 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Fig 6. Example of company delaying time to market by being stuck in the natural style of prototype development. Timeline for company that struggled with overengineering and feature creep which slowed the PD down by over 2.5 years. https://doi.org/10.1371/journal.pstr.0000101.g006 the design pivot made the product overly complicated potentially due to the designers’ percep- tion of the product’s straightforwardness. If a product is known by the team to be complex, there may be a greater prerogative to do “quick and dirty” natural prototyping and decision making due to the constant pressure to get something working. However, for products seen as less complex or “simpler” by the team, there may be a tendency to try to make a prototype per- fect or add unnecessary features rather than just getting something working. These competing effects might cause an averaging out of how long it takes to prototype leading to no trends with complexity. Some of the interviewees working on less complex products mentioned with- out prompting that there was a risk of “feature creep” for their products due to simplicity of core functions. The example above with Company 44 demonstrates the harms of complicating a design with new features especially when working in a structured development regime. This hypothesis and its potential toward revealing why less complex products take the same time to prototype as more complex products is a promising future research direction. Strategically deploying natural and structured styles to accelerate PD We see from the interviews that bringing structure in at the right time could potentially accel- erate overall PD but could also slow down PD if brought in at the wrong time. Therefore, an important question becomes: how do startups know when to migrate from natural to struc- tured PD and back again? Several best practices were distilled from the interviews for when to deploy different PD styles. One point in PD at which bringing in structure is advisable is when the team expands beyond the first founding members. For example, one company CEO, who first approached design naturally said: PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 11 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Interviewer: “Do you use traditional design requirements?” C.15 (cleantech company): "So we kind of go through the prototypes quickly instead of having a really rigorous computation of all the requirements required. . . [We] made some decent progress but then when I brought in other people. . . I noticed that that process just doesn’t work for certain people. . . And then I realize that I need to bring in the more for- malized project management structure. It’s through that that we will write down the differ- ent design requirements." The need for structure in this case involves the need for clear communication and expecta- tions. With new team members who must be brought up to speed and understand company expectations, structure is crucial. Another point at which structure should be brought in is when communication with part- ners becomes necessary. Several companies expressed that a lack of documentation and struc- ture made initial interactions with manufacturers or other outsourcing firms inefficient and a time sink. For example, one company explained that an entire prototype was wasted, as their outsourcing partner did not understand what they verbally described and produced something unworkable. After structure was introduced with formal documentation and review, this prob- lem was overcome. Another example of the need for structure when dealing with outside partners comes from Company 28 in this exchange: Interviewer: "If you could design or build anything differently, or outsource more or less would you have done something differently?" C.28 (consumer electronics company): "We would have worked with our contract manu- facturer and brought him into the design sooner. . . Just documentation. It’s an investment that pays off. The more you do the better. And it’s never too early to give the design to your contract manufacturer once the industrial design is done." Without structure, communication was ineffective between the stakeholders in these exam- ples. Structure is shown to be incredibly important when bringing in outside partners who must understand the intricacies of the product and agree to the conditions of PD. Discussion Innovation is complex. Hardware innovation is even more so, because the long timelines between “investment” and “return” mean that more factors can influence outcomes. Various parameters in the innovation process change with time (e.g., customer needs & willingness to pay, competing tech, and more), thus long innovation cycles face difficulties converging to a marketable product. We see a clear need to accelerate hardware-development timelines. We see evidence in our data, that hardware-development timelines can be compressed by adopting natural and structured development styles strategically throughout PD. What levers are available to accelerate PD? What are some best practices or strategies to overcome limiting parameters in PD? In this work we have focused on prototyping as the main lever to shortening PD timelines, as it is the longest PD activity and the one most practitioners believe can be sped up. We find that technical complexity of the product does not necessary correlate with prototyping times. Changes in complexity cannot then easily be used as a lever to accelerate development. There- fore, we focus instead on the environment external to the product itself such as processes, funding, team makeup, etc. for levers with which to accelerate prototyping. We find several PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 12 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development potential levers through qualitative coding including a division between natural and structured development processes. We find that a natural style of PD is about twice as fast as structured when working with early prototypes, but that a structured style must be adopted at some point for optimal PD. The natural and structured modes of development can help lend depth to the strategies and theories of innovation and product development found in literature. In The Lean Startup, Eric Reis defines a process by which a startup can find success through iterative build-measure- learn cycles with continual customer feedback [23]. At each iteration, during the learn part of the cycle, the team decides whether to pivot to a new design/idea/prototype or to preserve and move forward with the current. Reis suggests that a successful startup would work to accelerate this feedback loop. What the framework of natural and structured development styles brings to this picture is that a prototype’s development cycle needs to be accompanied by the appro- priate development style. When working on a prototype’s development cycle at the beginning of creating a product, a natural process of rapid prototyping with little to no documentation and an emphasis on speed is preferred. However, as the design matures during later develop- ment cycles, if this natural style persists, the prototype will become stuck in a premature stage unable to graduate to a commercializeable product. However, if structure in the form of docu- mentation, requirements, and clear deliverables, is brought in after 2–3 prototypes the product can evolve toward market readiness. A key point that is revealed by this framework is that if the team determines that a pivot is necessary, a reversion to a natural style of prototyping is needed for rapid development of the new idea. The transition from natural to structured development in some ways is organic as more team members are brought on, outsourcing begins, and the design of the product grows more precise. However, a reversion to a natural style of working from a structured one is not as easy. In the Innovator’s Dilemma, Christensen describes some of this difficulty within the context of large incumbent companies being poor innovators or new product developers [24]. Within the natural and structured framework of development, this can be explained by the fact that large companies have entrenched structure and processes for development, making it difficult to work with a natural style within these organizations. Some large companies recognized this and work to create an insulated team or internal division that is not beholden to the structure imposed on the larger organization to foster innovation and new PD. A prime example of the success of such an effort is Lockheed Martin’s Advanced Development Programs, also known as Skunk Works [25]. This internal organiza- tion is given autonomy and freedom to perform research and development activities outside the usual bureaucratic regime of the larger organization. This program was widely successful and the term “skunk works” has been used by many other corporations or institutions trying to create such an agile internal team. These issues are often not thought of as problems that startups face as they are by definition early-stage and should have more flexibility to be in the natural PD regime. However, what we find in this study is that startups must walk a fine line between natural and structured PD to get a product to market with the limited resources available to them. The natural to structured development spectrum outlined herein is a framework that we hope startups can use to inform strategy as they move through prototyping. Within this study we highlight some key milestones that indicate readiness to move from natural to structured development and back depending on the stage of prototype readiness and team makeup. For example, we find that a company is poised to move toward a structured process if the company has completed a design, verified market fit, and started to hire new employees or begun to work with multiple outside partners or vendors. On the other hand, as demonstrated with the case study of C.44 (Fig 6), if a company is pivoting toward a new PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 13 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development market or completely new design, a reversion to natural development is recommended to avoid costly slowdowns. We find in the interviews, that many times transitions between natural and structured development are executed by startups without clear strategic intent as the transition itself hap- pens organically. Conversely, pivots back to a natural process once structure has been intro- duced can be harder to accomplish. We encourage startups to use the natural and structured PD framework to change development styles with strategic intent. This means, during times of pivoting toward new markets or designs, startups would intentionally move back into a natural development style. Furthermore, this means startups would bring structure in strategically when pushing a fully designed and verified product to market, bringing on more team mem- bers, and engaging with manufacturers. With these strategies intentionally deployed, we expect this framework can help accelerate PD for hardware startups. Furthermore, there are emergent productivity tools that can aid these transitions including sentiment analysis for understanding user preferences, generative design tools to explore the parameter space of design while enforcing design for manufacturing principles, and machine learning based tools to accelerate hardware development. We envision these emerging tools making transitions between natural and structured styles of product development easier for startups in the future. Materials and methods Sample selection and data collection Our goal in choosing the subjects was to find a sample that best represents the larger popula- tion of companies generally. For this work, a purposeful sampling technique was used meaning that the sample was selected based on characteristics of the population and our goal of answer- ing the research questions outlined in the introduction [26]. To answer these questions, we needed to sample from hardware startups in general with a focus on the subset of participating cleantech companies. These hardware startups needed to have at least two prototyping itera- tions complete and preferably more to provide the data we needed. With several market seg- ments involved, the data can be compared between sectors including cleantech, medical, consumer electronics, IoT, and others. We were most focused on sustainable, or cleantech companies, and 20 of the final 55 companies identified as cleantech. The companies that agreed to participate came from around the world with 63% from North America, 13% from Europe, and 24% from Asia. Over 70% of the interviewees were engineers or scientists with qualifications beyond a bachelor’s degree, either a Master’s of Science, Doctorate, or Masters of Business Administration. Over 90% of the interviewees were executives (CEO, CTO, etc.) and/or founders of their company. The remainder of the interviewees were lead engineers or scientists. Participants were contacted through online forms, LinkedIn connections, and publicly available email addresses. Of the over 500 cold contacts made, around 40 agreed to the inter- view, an acceptance rate of less than 10%. The companies chosen for an interview were there- fore based on willingness to participate, making it a non-probability sampling technique. This brings some amount of bias to the sample companies as they self-selected to be interviewed. However, there were some factors combating this including a countervailing snowball sam- pling effect. For example, one company CEO would introduce us in person or by email to other company leaders for the study. The likelihood of these subjects participating was much higher than if contacted cold. In the end, 55 companies from around the world agreed to par- ticipate. Out of the 55 interviews, 50 agreed to be recorded, and were subsequently analyzed via audio file and through transcription. This resulted in 1175 pages of transcription to PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 14 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development process. In addition, each answer was documented by the interviewer in Qualtrics survey soft- ware which was then exported into spreadsheets for analysis. The interview questionnaire can be found in S1 Questionnaire. This work was submitted to the Committee on the Use of Humans as Experimental Subjects (COUHES) at MIT for approval as it includes human participants. The study, protocol #1812621468, was determined to be exempt after review by the COUHES pursuant to Federal regulations, 45 CFR Part 46.101(b)(2). Informed consent was received verbally by all partici- pants in the study. Interview methods We chose a facilitated survey format for this interview combining engineering domain exper- tise with sociology research methods both quantitative and qualitative. The sample size, n, was between 30 and 55 for individual questions depending on the company. The sample size and interview length were targeted for a mix of breadth and depth allowing for a broader under- standing than what typical case studies allow as well as a deeper analysis than the scope of typi- cal surveys. This unique study provides a new "bottom-up", interdisciplinary, and mixed- methods perspective on the hardware PD process. The content of the interview is outlined by the Product Design and Development textbook by Ulrich, Eppinger, and Yang [27], used in the MIT product engineering class to guide stu- dent through the PD process as it might happen within a company setting. The generic PD process was adapted from this text for the interview as can be seen below in Table 1. Quantitative and qualitative analysis methods The quantitative parts of the survey include the workflow mapping, complexity metrics, and jobs to be done opportunity landscape [28]. These quantitative metrics are gathered to gener- ate numerical findings and work toward generalizing any conclusions from the timelines. An ordinary least squares (OLS) regression analysis was done on the complexity metrics enumer- ated in Fig 4 to understand if complexity is a driver of prototyping times. We wanted to under- stand, for example, if the total number of parts in the prototype had any correlation with time to prototype. Standard continuous error bars were plotted for the standard deviation of the trendlines. To complement the quantitative data gathered, open ended questions about each PD step are asked. To analyze the results and find conclusive insights, qualitative research methods from the field of sociology are employed. The main method used in this work is qualitative Table 1. Interview Contents from Product Development Process. Section 1 2 3 4 5 6 7 8 9 10 Interview Section Introduction Personal and Company Info Product Planning Requirements Modeling Concept Generation and Selection Complexity Prototyping Manufacturing Overview # of Questions 6 26 16 15 25 26 24 22+ 12 5 https://doi.org/10.1371/journal.pstr.0000101.t001 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 15 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development coding which is "a way of indexing or categorizing the text in order to establish a framework of thematic ideas about it."[19] There are two main types of coding. One is data-driven (grounded) which is when the researcher allows themes to emerge from the documents with no pre-existing framework. The other is concept-driven a priori which is when the researcher applies a pre-existing framework when analyzing the documents. With either of these approaches, coding is an iterative process of code refinement and comparison to the source text [29]. In this work, we used a mixed approach using two kinds of codes, concept-driven and data- driven. Data-driven or grounded coding allows themes to emerge from the documents, while concept-driven a priori coding applies pre-existing theoretical frameworks to analyze the doc- uments [19]. Trained engineer and the interviewer for the study, Erin Looney employed con- cept-driven coding with pre-existing hypotheses and framework. Andre Buscariolli, a trained sociologist, employed data-driven coding with no pre-existing framework. Through a series of meetings, the codes found in common or mutually agreed upon through compelling evidence by both coders are pursued. Through this work, over a dozen codes were found for the 55 interviews and were iterated upon until several specific coded themes developed, and the team focused on the theme that emerged around natural vs. structured development styles. Conclusions Hardware startups generally, and cleantech startups specifically risk having longer times-to- market, deterring investment. Prototyping is the longest development step regardless of proto- type complexity or iteration. To be competitive and therefore promote a sustainable future, the cleantech startup community must find and adopt levers to shorten prototyping times. We find that the startup’s choice of development style is one lever that can affect timeline. On aver- age, the natural PD style defined in this paper takes 35% less time than the structured style, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. We find that startups should transition to a structured style when adding new team members or engaging external partners, which demand clear commu- nication and expectations. We further find that when a startup pivots to a new product or mar- ket, returning to a natural style is beneficial. Using these levers and more, we hope cleantech startups can get to market faster, creating the changes we need to realize a sustainable future. Supporting information S1 Text. Complexity Metrics and Literature: Supporting information on the how the com- plexity metrics for this study were determined and informed by literature. (DOCX) S1 Fig. Quantifiable parameters on product complexity. (TIF) S1 Questionnaire. Interview Questionnaire: Document detailing the facilitated interview questions used in this study. (PDF) Acknowledgments General: The authors deeply thank all the participants in the interviews for engaging in this research. We thank Benjamin Gaddy for providing the raw data used to create Fig 1, and Eugene Fitzgerald for helpful discussions. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000101 March 20, 2024 16 / 18 PLOS SUSTAINABILITY AND TRANSFORMATIONPrototyping styles could accelerate cleantech hardware development Author Contributions Conceptualization: Erin Looney, Geoffrey Raymond, Tonio Buonassisi, Ian Marius Peters. Data curation: Erin Looney. Funding acquisition: Tonio Buonassisi, Ian Marius Peters. Investigation: Erin Looney, Andre´ Buscariolli. Methodology: Erin Looney, Andre´ Buscariolli, Maria C. Yang, Geoffrey Raymond, Tonio Buonassisi, Ian Marius Peters. Project administration: Erin Looney, Tonio Buonassisi, Ian Marius Peters. Resources: Erin Looney. Software: Erin Looney. Supervision: Maria C. Yang, Tonio Buonassisi, Ian Marius Peters. Validation: Ian Marius Peters. Visualization: Erin Looney, Tonio Buonassisi, Ian Marius Peters. 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10.1371_journal.pstr.0000102	RESEARCH ARTICLE To trust or not to trust? Trust landscape of organic animal husbandry: Mapping consumer attitudes and information demands in Germany Elisa BayerID, Sarah Ku¨ hl Marketing for Food and Agricultural Products, Department of Agricultural Economics and Rural Development, University of Go¨ttingen, Go¨ ttingen, Germany elisa.bayer@uni-goettingen.de Abstract A mainly positive attitude of consumers towards organic animal husbandry with its higher keeping standards compared to the legal regulations is evident. However, less is known about consumers’ detailed expectations of organic husbandry and in particular their attitude and trust along the value chain of organic animal products. Which consumers trust the most, and how do they want to be informed about organic animal husbandry? Where along the chain are trust deficits that should be addressed in the future to support sustainable food consumption with high animal welfare standards? To answer these important questions a survey was conducted among 729 German meat consumers. Using a cluster analysis, vari- ous consumer groups were identified based on their trust levels. Further, these groups were characterized regarding their general attitude, their information behavior, their evaluation of current media reporting, and their preferred way to be informed (emotional/rational) about organic animal products. The results revealed three clusters that clearly differ in their trust level of organic husbandry. Respondents assigned to the first cluster are committed organic consumers with high trust and the most positive attitude. The second cluster, combines respondents who are generally open to organic meat consumption and showing the second highest trust level and positive attitude towards organic. They show a slight favor for a more rational presentation of information. The third cluster is the smallest and can be described as the skeptics with a low interest in organic. The results indicate a general moderate to high trust level along the value chain of organic animal products, with the highest trust in organic retailers and farmers and the least trust in processing and conventional retailers. These are important insights for the organic sector in order to improve consumer trust and therewith increase the market share of organic meat products. Author summary In the discussion surrounding sustainable agriculture and consumption, organic produc- tion plays a key role. Especially in terms of high animal welfare standards, organic animal a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Bayer E, Ku¨hl S (2024) To trust or not to trust? Trust landscape of organic animal husbandry: Mapping consumer attitudes and information demands in Germany. PLOS Sustain Transform 3(2): e0000102. https://doi.org/ 10.1371/journal.pstr.0000102 Editor: He´lder Spı´nola, University of Madeira, PORTUGAL Received: June 12, 2023 Accepted: January 31, 2024 Published: February 29, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pstr.0000102 Copyright: © 2024 Bayer, Ku¨hl. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data in this manuscript are available on the Figshare data repository. https://doi.org/10.6084/m9.figshare. 24808338.v1. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 1 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Funding: We are grateful to the Federal Office for Agriculture and Food (BLE) and Federal Programme for Organic Farming and Other Forms of Sustainable Agriculture (BO¨ LN) for financing this study in the project: “Improving social acceptance of organic livestock systems – Analysis of public expectations and development of trust marketing concepts” (grant number 2818OE097 to University of Go¨ttingen (Achim Spiller)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. farming takes a pioneering role. To support market-driven transformation to a more sus- tainable consumption, an understanding of consumers’ attitudes, perspectives, and trust regarding these production systems is essential. Thus, this study identifies trust levels along the production chain of organic animal products and gives insights into the charac- teristics of different consumer groups regarding their trust in organic husbandry. Three clusters could be identified, with the first two clusters showing moderate to high trust lev- els along the production chain and a positive attitude towards organic husbandry. We identified scandals, poor product quality, a high expectation–reality gap and, especially for the second and third clusters, too emotional and uncritical reporting about organic husbandry as possible trust barriers. We provide recommendations for increasing trust in a particular animal welfare-friendly husbandry system based on this study. Introduction For consumers of organic animal products, animal welfare is one of the main reasons to buy organic [1,2], and a generally positive attitude towards organic animal husbandry is evident [3,4,5]. However, less is known about consumers’ detailed expectations of organic animal farming and in particular their attitude and trust along the value chain of organic animal prod- ucts. Organic farming is based on EU standards for organic production. For livestock, this means 100% more space and access to free range for the animals, and that the number of ani- mals on the farm is linked to the area of land (170 kg N/ha). As most of the standards and regu- lations of organic animal farming, such as higher animal husbandry standards or regulations concerning feeding or medicine application, cannot be examined by the consumers them- selves, trust is inherently important when purchasing organic products [6,7,8,9]. Along the production chain, there are many steps where consumers have to trust the value chain and its actors. Here it can be distinguished between systemic and personal trust. Systemic trust is an impersonal form of trust related to the function of a system, such as organic food production and certification. In contrast, personal trust is related to an individual. As today many actors are involved in food chains and food production, systemic trust is the predominant form of trust in food production [10]. Nevertheless, a study showed that consumers have the significantly highest trust in individ- uals in the supply chain for food products, organic and conventional, namely farmers and inspectors. Less trust in food retailers and manufacturers has been found. Overall, consumers place significantly higher trust in the organic food chain than in the conventional sector, and agencies are trusted less than farmers (both conventional and organic) [11]. This is supported by another study which state that systemic trust can be trumped by personal trust. However, the authors also detected high systemic trust in the organic label among Danish consumers [12]. Furthermore, it was found that organic labels can be seen as a major source of trust, pro- vided that they are well known and perceived as trustworthy [9]. The labeling system can serve as a source of trust, as it reduces complexity in the food system [13]. Studies show that trust in organic food is generally quite high [12,14,15] and also robust in the face of disappointment [16]. But e.g. fraud in the organic sector can cause serious trust problems, as consumer trust is abused for profit [17]. Trust can be seen as a multidimensional construct. In literature trustworthiness is defined by the three dimensions of competence, care, and openness/transparency [14,18,19]. In partic- ular, the belief in openness of an actor is related to consumer trust [14]. It was found that open- ness is more important than knowledge exchange with respect to trust in organizations [10]. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 2 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry In contrast, another study found that perceived care for, e.g., public wellbeing of an actor, is the most important trust dimension [19]. However, the impact of different trust dimensions on building general trust varies between actors. For manufacturers, especially the dimension of perceived competence seems to be important [19]. In addition to these dimensions of trust, the dimensions of reliability and skepticism can be added to the construct of trustworthiness. A study showed that a loss of reliability affects trust in a negative way [20]. Further the component of skepticism can be added, referring to a skep- tical view of an actor’s trustworthiness [18]. Moreover, trust is important due to the high information asymmetry in the relationship of the food system and consumers. A high level of trust reduces the need to acquire knowledge about food production [5]. Studies also acknowledge that more knowledge exchange does not generate more trust [21,22]. However, information can be helpful to increase buying intention of high-priced animal welfare products [23]. Furthermore, a positive correlation between trust, knowledge, and a positive attitude towards organics can be found [12,24]. Thus, suitable information is able to bring consumers’ expectations closer to reality. However, most consum- ers have little motivation to inform themselves about organic farming, despite easy access to detailed information about organic husbandry these days. Reasons for this may be a lack of interest, information overload such as too many labels, or a lack of a convenient way of provid- ing information [25]. This results in a more fragile state of “blind trust”, which is not based on information evaluation [5]. Thus “blind trust” can be defined as the opposite of reflexive trust [26]. Besides the dimensions of personal and institutional trust, literature shows different ways of how consumers build their trust. The more rational or reflexive trust type relies on fact- based evaluation of knowledge. The emotional trust type is based on personal trust relations, feelings, and the sharing of similar values [27], whereas habitual trust is built on routinized behavior [28]. To promote sustainable consumption, e.g. of organically produced food, trust in the pro- duction process of these foods is essential. To date, there is a lack of understanding of trust along the value chain of the organic livestock production chain (standards, farmers, control system, processing, sale). It is important for the organic sector to know where trust is most lacking in order to increase it in the future. In addition, this study aims to provide insights into how consumers with different levels of trust in organic farming build their trust, how they per- ceive current media reporting and how they prefer to be approached with information. It is known that animal welfare is important to many consumers [1,2] and may therefore be a par- ticularly emotive issue to communicate so it seems important to gain a deeper understanding of how such information should be provided to specific consumer segments in order to increase trust. Consumer trust is crucial to expand the market share of organic animal prod- ucts [6] and is particularly important for organic animal products, such as meat, where con- sumers have to pay significantly higher prices. A lack of trust in production and/or certification processes can be a reason for the low market share [29] of higher-priced organic meat products, in addition to high prices [7,8,9]. Against the background that European and German policies set the aim to expand the total organic production by 25% (EU) and 30% (Germany) by 2030, it seems necessary to understand how trust in organic animal products is generated and maintained to increase the sales of organic products [30,31]. Summing up, due to the low market shares for organic meat products and the fact that trust in organic animal products is comparatively low in Germany [15], the German organic market was chosen to conduct this study on trust in organic animal farming. In this context, it is known that consumer groups have different trust levels along the value chain for organic animal products [4,24,32]. However, so far no relation has yet been estab- lished with knowledge, information behaviour, perception of media coverage and types of PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 3 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry trust in order to understand these groups more precisely. Thus, we calculated a trust index along the chain of organic meat products, based on the above described five dimensions of trustworthiness (competence, care, openness, reliability and skepticism). These trust indices were used as cluster building variables for a subsequent cluster analysis to identify different consumer groups in terms of their trust in organic livestock farming. Further, we assess how respondents evaluate the current media reporting on organic farming, which provides an ini- tial understanding of how information provision and communication is perceived. In addi- tion, we identify trust deficits along the value chain of organic meat products and investigate whether emotional or rational information is preferred by different consumer groups when promoting organic meat products. The results are important to improve trust along the value chain and to market organic ani- mal products successfully and therewith support the political aim to expand the organic market. Data and methods Research context In terms of transferring food production in the EU to more substantiality, the European Com- mission initiated the “Action plan for organic production in the EU” under the umbrella of the Green Deal. The aim is to increase organic production in the EU to 25% by 2030 [30]. The pro- portion of organic farmland varies between European countries. Austria has the highest share with 26% of organic farm land, which is significantly higher compared to Spain (10%), Ger- many and France (9%) [33]. Germany aims to increase it to 30% by 2023 [31]. This might be challenging as Germany has a comparatively low share of organic farmland so far and trust in organic farming, especially in organic animal farming is comparatively low [15]. Therefore, Germany was chosen as the research setting for this study. In the last years there was a constant rise of organic animal products on the market, resulting in an overall market share of 7% for organic products of [34]. Thereby organic eggs are one of the most bought organic products, whereas the market share for organic meat ranges constantly at 1–2% [29]. In order to achieve the goal of a significant increase in the share of organic food production, consumer demand has to be increase significantly in the future. An understanding of expectations and trust- building behavior is crucial. Research framework The aim of this study was to examine consumer trust along the entire value chain of organic livestock products, to identify which actors in the chain are perceived as trustworthy and where trust is lacking. On the basis of the perceived level of trust along the value chain, con- sumer groups with different trust levels are identified. Furthermore, it has been analysed how these consumer segments build up their trust, how they evaluate current media reporting and how they want to be approached with information about organic animal husbandry (emo- tional/rational). The considered value chain in this study included the following steps and actors: standards, farmers, control system, processing, retailer (organic shop/organic supermarket vs. supermar- ket/discounter). In this study, trustworthiness is defined by five dimensions based on the literature, namely: Competence (skilled people, competent, doing a good job), Care (listening to concerns, acting in public interest) Openness/Transparency (being honest, being sufficiently open and provid- ing relevant information), Reliability (being reliable, to be sure of someone) and skeptical atti- tude (being skeptic about something/someone) [14,18,19]. Table 1 displays the dimensions PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 4 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Table 1. Statements to assess trustworthiness using the example of standards in organic animal husbandry. Trust dimension Statement Competence Care Openness/ Transparency Reliability Skeptical attitude I trust that the organic animal husbandry standards have been developed with the best knowledge for animal welfare. In the development of the husbandry standards for organic animals, animal welfare is the top priority. Potential problems with organic animal husbandry specifications are honestly presented. I believe that the standards of organic animal husbandry can reliably ensure animal welfare. In order for me to feel good about eating products from organic animal husbandry, the regulations must be improved significantly. Adapted from: [14,18,19]; Scale: 1 = Does not apply at all; 5 = Fully applies https://doi.org/10.1371/journal.pstr.0000102.t001 used to assess the trustworthiness of the food chain for organic animal products on the exam- ple of “standards”. These dimensions were also queried regarding the actors in the value chain. A full table with all statements used for the analysis can be found in the appendix (S1 Table). To get an idea of the perceived trustworthiness of each actor along the chain, a trust index was calculated for each actor in the chain, consisting of the mean values of the five statements about trustworthiness. Subsequently, the indexes of the statements on the perceived trustwor- thiness of actors along the chain were used as cluster-building variables. The variables of the cluster analysis are shown in Table 2. Two factor analyses were carried out to consolidate information about different types of trust (habitual, rational, emotional) and the preferred way of information approach (rational/ emotional) (Table 3). The factor analysis on the preferred way how to be addressed with information about organic animal husbandry, identified two factors, namely “preference for more emotional infor- mation” and “refusal of a too idyllic communication”, with the latter factor also indicating a wish for more fact-based communication (see section “clusters and information”). Differences regarding the types of trust (habitual, rational, emotional) were analyzed based on the respective factors: The first factor was named “trust through habit and information” and was based on participants’ statements saying that organic consumption was normal for Table 2. Variables used in the cluster analysis. Cluster-building variables Trustindex: standards Trustindex: farmers Trustindex: control system Trustindex: processing Trustindex: organic retailer (organic shop/organic supermarket) Trustindex: conventional retailer (supermarket/discounter). Descriptive variables Sociodemographic (gender, age, education, income) Consumption of organic animal products General trust towards others Self-assessed knowledge of organic farming Expectations towards organic husbandry Information behavior and evaluation Factor analysis: Preferred way to be addressed with information (emotional/rational) Factor analysis: Trust building behaviour: trust types (habitual, rational, emotional) https://doi.org/10.1371/journal.pstr.0000102.t002 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 5 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Table 3. Statements of the two Factor analysis‘. Statements Factor Analysis on different trust types (rational, emotional, habitual) I’m used to buying organic meat and don’t think too much about it anymore For me there is no alternative to food from organic animal husbandry For me it is normal to trust organic animal husbandry My family has always bought mostly organic meat Buying organic meat makes me feel good I have informed myself in detail about organic animal husbandry to be able to decide whether I can trust it To trust organic animal husbandry, I need to know all the facts To trust organic animal husbandry, I need to look closely at the pros and cons It is important for me to know a farmer so that I can trust him/ her I make my purchase decision mainly based on instinct It is possible to form an opinion about organic animal husbandry without informing oneself in detail I’m happy to read personal stories about organic farmers It is important to present the advantages of organic animal husbandry, e.g., through pleasant reports from farms I prefer photos of organic animal husbandry more than written information Picture book portrayals of organic animal husbandry in advertising raise false expectations Organic animal husbandry is portrayed as a too ideal world Too few facts are communicated about organic animal husbandry Statements Factor Analysis on preferred way of information (emotional/rational) Sources: [27,28] https://doi.org/10.1371/journal.pstr.0000102.t003 them, that there was no alternative to food from organic husbandry, and that buying organic meat made them feel good. Further, a good information base concerning organic animal farm- ing added to this factor. The second factor summarized statements concerning a more rational approach to trusting organic husbandry. Interestingly, the importance to know a farmer to be able to trust organic animal farming was also ascribed to this factor. This second factor can be named “trust through rational analysis and personal connections”. Statements concerning a more emotional way to trust without much information did not form a reliable factor, and no significant differences between the clusters could be detected here (Table 6). The factors resulting from the two factor analysis‘were used to describe the clusters. Survey design A quantitative online survey with 729 German meat consumers was conducted to answer the research questions in this study. The questionnaire began with data protection consent, which was followed by sociodemographic questions. Further, participants were asked about their consumption of organic animal products and how they evaluated their own knowledge in dif- ferent areas of organic farming, such as breeding, husbandry, and slaughter. Afterwards, their expectations towards organic compared to conventional animal farming were assessed. This was followed by a question about their general trust level towards others and by questions about their attitude and trust towards organic animal husbandry in general. Thereafter, PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 6 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry trustworthiness of standards and the control system as well as involved actors in the produc- tion process of organic animal products (farmers, processing, and organic/non-organic retail- ers) were assessed in more detail. At the end of the questionnaire, participants were asked to provide details about their infor- mation behavior concerning organic husbandry, how they evaluate any given information, and what kind of information (emotional/rational) about organic animal farming they prefer, using 5-point Likert scales (1 = Do not agree at all, 5 = Fully agree). Data collection and cleaning The data was collected in February and March 2022. The participants were recruited by an online panel provider. In order to obtain an approximately representative sample of the Ger- man population, quotas were set for gender, age, education, and place of residence according to the population in Germany aged 18 and older (Table 4). Vegetarians and vegans were excluded from the survey, as many questions related to the attitudes towards and buying behavior of (organic) meat, and the focus of the study was on meat eaters’ trust in organic live- stock farming. Due to the length of the questionnaire, the survey was split in two parts (part 1 mean time: 14.36 min; part 2 mean time: 15.14 min). The final sample of the first part after data cleaning consisted of 1,199 participants. Two weeks later, the same participants were invited to answer the second part of the questionnaire. In total, 779 people completed the sec- ond part. In order to identify the participants, the panel provider assigned each participant an individual identification number, which was used to merge the two survey results at the end. Further, we have checked the matched sample for consistency in sociodemographic data, which were determined in both splits, and answering behaviour. During data cleansing, 44 participants were excluded due to speeding or straight-lining behavior (speeder: participants with a shorter response time than half of the median; straight-lining behavior: giving the same answer to all items in statement batteries more than once). Both surveys contained two quality check questions to ensure that participants read all questions thoroughly. Participants who failed these questions were excluded from further participation during the survey. Outliers in the cluster analysis (n = 6) were also excluded from the sample, resulting in a final sample of 729 participants. The sample contained slightly fewer old people (65+ years, 3.4%) and 9.3% less people with a low education compared to the German population. Table 4. Description of the sample; n = 729. Age (Ø; [min, max]) 18–34 years 35–49 years 50–64 years 65+ years Male Female Low education Medium education High education Sources: [35,36,37] https://doi.org/10.1371/journal.pstr.0000102.t004 Sample n = 729 50.7 years [19, 96] 23.5% 24.6% 29.1% 22.9% 49.0% 50.8% 25.2% 33.2% 41.6% German population 51.0 23.9% 22.1% 27.5% 26.3% 49.40% 50.72% 34.5% 31.9% 33.6% PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 7 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Data analyses Data were analyzed using IBM SPSS Statistics 28. The cluster analysis comprised several steps. First, outliers were identified using the single- linkage method. In total, n = 6 outliers were removed. Afterward, a hierarchical cluster analysis with Ward’s algorithm and squared Euclidean distance was used to determine the optimal number of clusters. The dendrogram and agglomeration table (elbow criterion) were exam- ined and suggested a three-cluster solution. Further, the identified number of clusters was checked for its interpretability in terms of content. Afterwards, K-means clustering was used to receive homogeneous clusters [38]. To describe the characteristics of the clusters, an ANOVA and post hoc tests were conducted. Finally, a discriminant analysis was carried out to assess the classification accuracy. The discriminant analysis confirmed a classification accuracy of 99.9%. Further, we conducted two factor analyses concerning the preferred form of information (emotional/rational) and types of trust (habitual, rational, and emotional). A principal compo- nent analysis (PCA) with orthogonal varimax rotation was used. In advance, the data were tested for their suitability to perform a PCA by using the Kaiser-Meyer-Olkin (KMO) criterion and Bartlett’s test [38]. The KMO values were 0.607 and 8.20 and therewith above the recom- mended level of 0.50 [39]. Bartlett’s test was significant for both factor analyses. Further, fac- tors with an eigenvalue greater than 1 were extracted as well as items with loadings below 0.50 and significant double loadings were removed from the analysis [40,41]. After the factors were determined, Cronbach’s alpha was calculated to check the reliability of the factors. It ranged from 0.52 to 0.85 (shown in Tables 6 and 8 below). The threshold for good reliability, Cron- bach’s alpha, should reach a value of 0.7 [42]. However, literature shows that factors with a lower Cronbach’s alpha can also be considered reliable [42,43]. This is in line with a study which stated that low levels of Cronbach’s alpha must not necessarily lead to the exclusion of the respective factor [43]. The factors with lower Cronbach’s alpha levels in this study were considered plausible in terms of contention and meaning. Further, they were only used to describe clusters, which is why we decided to keep these factors despite their lower reliability. Ethics approval The study was approved by the ethics committee at the university before data collection. Par- ticipants were informed about the use of data, and they provided written informed consent online. Results Trust along the value chain of organic animal products Fig 1 presents the trust indexes along the chain of organic animal products and shows that par- ticipants had the highest trust in farmers and organic retailers, whereas processing and con- ventional retailers were trusted less. However, even the lowest trust level (conventional retailer mean: 2.93) was on a moderate level. Cluster analysis according to trust along the value chain Three clusters could be identified with significantly different trust levels, namely high, moder- ate, and low trust in all levels of the value chain (Fig 2). This shows that trust in the different actors was highly interrelated, as none of the clusters showed especially high or low trust in a specific actor. The clusters are described below. Their sociodemographic characteristics, atti- tudes, and general trust towards organic animal husbandry are displayed in Table 5. PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 8 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Fig 1. Trust indexes along the value chain of organic animal products. https://doi.org/10.1371/journal.pstr.0000102.g001 The first cluster (n = 252 ~ 34.6%) can be called “strongly trusting”. Respondents in this clus- ter had the overall highest trust in organic animal husbandry along the value chain. They put especially high trust in organic retailers, inspections, and farmers. Participants in cluster 1 were slightly older than the other clusters (52.5 years) and more often had a high education (44.2%) and a high income than the other clusters. People in this cluster stated to buy organic meat most frequently. Further, they had the most positive attitude towards organic husbandry. These Fig 2. Trust index of actors along the chain of organic animal products, according to the three clusters (n = 729). https://doi.org/10.1371/journal.pstr.0000102.g002 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 9 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONTable 5. Socio-demographics, attitude, and trust in organic animal husbandry. Socio-demographics Cluster distribution Age [years] Female [%] High education [%] <€1,300 [%] €4,500 and more Attitude towards and trust in organic animal husbandry Always or often organic meat buyers [%] Positive attitude: Index of attitude towards organic animal husbandry (Appendix: S2 Table shows the statements; Cronbach’s alpha: 0.826) General trust level: In general, you would say that most people can be trusted or that you can never be too careful when dealing with people. [1 = low trust; 7 = high trust] Own experience Scandals have already affected my trust in organic animal husbandry before. When buying organic animal products, I have often been disappointed by the quality (e.g., spoiled quickly, tough meat, etc.). Knowledge of organic husbandry conditions Knowledge about organic husbandry conditions (self-evaluation) [1 = no knowledge; 5 = high knowledge] Exploring trust in organic husbandry Cluster 1 Cluster 2 Cluster 3 Total 52.5 50.2 44.2 15.5 11.9 35.7 49.7 52.4 40.3 20.2 10.7 26.1 49.3 45.3 38.9 16.8 7.4 7.4 4.29 (0.67)a 3.76 (0.69)b 2.85 (0.99)c 4.13 (1.49)a 3.69 (1.41)b 2.85 (1.41)c 2.96 (1.14)a 2.15 (1.01)a 2.92a 3.41 (1.02)b 2.69 (1.06)b 2.61b 3.97 (1.12)c 3.29 (1.25)c 2.48b 50.6 50.7 41.5 18.1 10.7 26.9 3.83 (0.86) 3.73 (1.49) 3.33 (1.12) 2.56 (1.12) 2.70 Sources: [17,44,45,46,47]; Scales: 1 = I totally disagree; 5 = I totally agree; a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05) https://doi.org/10.1371/journal.pstr.0000102.t005 respondents rated their knowledge about organic husbandry conditions significantly higher than those in the other two clusters. Further, individuals in cluster 1 had the most positive expectations towards organic animal farming (good pasture access, good treatment of the ani- mals, good animal health) and ascribed negative aspects such as “profit orientation” and “expen- sive products” significantly less to organic farming. “Animal welfare problems” were slightly more attributed to conventional husbandry, but there were no significant differences between the three clusters concerning this aspect (see Fig 3). Overall, individuals in this cluster had the highest trust in other humans generally. Moreover, they stated that their trust was least often affected by scandals or that they were disappointed by the quality of organic animal products. The second Cluster (n = 382 ~ 52.4%) is the largest cluster and can be called “moderately trusting”. People in cluster 2 had the second highest trust in organic husbandry. Their average age was 49.7 years and there were slightly more women in this cluster than in the other two clusters (52.4% of females). Participants in cluster 2 had the second highest education and the highest share of people with a low income, but the second highest share of people with a high income. In terms of their attitude towards and trust in organic animal farming, they had in all parts the second highest agreement, which mostly ranged near the average. Regarding self- evaluated knowledge about organic husbandry conditions, people in cluster 2 and 3 rated their knowledge significantly lower than people in cluster 1. The assessment of organic husbandry of cluster 2 members was between that of cluster 1 and 3 members. The third Cluster (n = 95 ~ 13.0%) is the smallest cluster and can be described as “weakly trusting”. Participants in cluster 3 had the lowest trust in organic farming along the value chain, with the lowest trust in processing and conventional retailers. People in this cluster were slightly younger (49.3 years) than people in the others clusters. Members of cluster 3 had the overall lowest share of women (45.3%), and had the lowest share of people with a high education. Fur- ther, they had the smallest share of individuals with a high income. Only very few (7.4%) partic- ipants in this cluster stated to buy organic meat very often or often. The respondents in this cluster had the least positive general attitude towards organic animal farming. Respondents in PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 10 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Fig 3. Assessment of organic animal husbandry compared to conventional animal husbandry (means). https://doi.org/10.1371/journal.pstr.0000102.g003 cluster 3 had a significantly more negative view and more negative expectations of organic hus- bandry than the other two clusters. They ascribed positive aspects, such as “good treatment of the animals”, significantly less to organic husbandry and negative aspects, such as “profit orien- tation”, significantly more to organic farming than the other clusters. Further, individuals in cluster 3 claimed to have been disappointed in the quality of organic animal products most frequently, and their trust was most often affected by scandals compared to the other two clusters. Moreover, respondents in this cluster were characterized by the least trust in other humans. Fig 3 shows consumers’ evaluations of organic animal husbandry compared to conven- tional husbandry. Respondents had to rate different aspects such as “good animal health” or “good pasture access” on a 7-point scale, ranging from 1 = “only in organic husbandry” to 4 = “no differences” to 7 = “only in conventional husbandry”. The results show that organic animal husbandry was associated with “expensive products”, “pasture access”, “good animal treat- ment”, and “good animal health”. “Animal welfare problems” and “profit orientation” were slightly more attributed to conventional husbandry. Clusters and types of trust Differences regarding the types of trust (habitual, rational, emotional) were analyzed based on the respective factors: The first factor was named “trust through habit and information” and was based on participants’ statements saying that organic consumption was normal for them, PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 11 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Table 6. Factor analysis regarding different types of trust (rational, habitual, emotional). Factors “Trust through habit and information” (Cronbach’s alpha: 0.851) “Trust through rational analysis and personal connections” (Cronbach’s alpha: 0.599) Statements to the emotional type of trust (Cronbach’s alpha: 0.215) Cluster 1 0.41 (0.89)a Cluster 2 −0.03 (0.91)b Cluster 3 −0.92 (0.92)c 0.00 (1.01)a −0.02 (0.93)a 0.11 (1.18)a Total 0.00 (1.00) 0.00 (1.00) Displayed are means and standard deviations in parentheses. a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05); explained variance: 58.95%, KMO: 0.820, Bartlett’s test: p < 0.001 https://doi.org/10.1371/journal.pstr.0000102.t006 that there was no alternative to food from organic husbandry, and that buying organic meat made them feel good. Further, a good information base concerning organic animal farming added to this factor. The second factor summarized statements concerning a more rational approach to trusting organic husbandry. Interestingly, the importance to know a farmer to be able to trust organic animal farming was also ascribed to this factor. This second factor can be named “trust through rational analysis and personal connections”. Statements concerning a more emotional way to trust without much information did not form a reliable factor, and no significant differences between the clusters could be detected here (Table 6). Overall, the highest agreement was found for statements referring to rational trust and therewith the need for information (means: 3.95 and 4.05). Respondents agreed less that they were used to buying organic (mean: 2.48) and that it was possible to have an opinion without detailed information (mean: 2.53). With regard to the three clusters, they differ only in terms of the first factor “trust through habit and information”. Participants in cluster 1 agreed signifi- cantly more with this factor than cluster 2, and those in cluster 2 agreed more than those in cluster 3. S3 Table in the Appendix shows all statements of the factor analysis. Clusters and information Table 7 displays consumers’ information behavior and their evaluation of given information concerning organic husbandry. The mean for information frequency was rather low (mean: Table 7. Information behavior and evaluation of current media reporting according to the three clusters. Information behavior Cluster distribution Information frequency [1 = never; 5 = very often] I find it easy to find good, understandable information about organic animal husbandry. I am satisfied with the current information about animal organic foods. Evaluation of current media reporting Trust in provided information [1 = low trust; 5 = high trust] Presentation of organic husbandry [1 = too positive; 5 = too negative] Presentation of organic husbandry [1 = credible; 5 = not credible] Presentation of organic husbandry [1 = too uncritical; 5 = too critical] Cluster 1 Cluster 2 Cluster 3 2.82 (0.75)a 3.47 (0.90)a 3.14 (0.95)a 2.85 (0.95)a 2.75a (0.65) 2.65a (0.84) 2.77a (0.71) 2.69 (0.76)a 3.01 (0.94)b 2.89 (0.88)b 2.40 (0.88)b 2.69a (0.75) 3.05b (0.71) 2.62a (0.80) 2.31 (0.91)b 2.88 (1.02)b 2.82 (1.26)b 1.62 (0.77)c 2.18b (1.02) 3.77c (0.90) 2.35b (1.14) Total 2.69 (0.79) 3.15 (0.96) 2.97 (0.96) 2.45 (0.97) 2.64 (0.78) 3.00 (0.86) 2.63 (0.83) Scales: 1 = I totally disagree; 5 = I totally agree; a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05) https://doi.org/10.1371/journal.pstr.0000102.t007 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 12 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry 2.69; scale from 1 = never to 5 = very often), meaning that most respondents seldomly informed themselves about organic husbandry. Participants in clusters 1 and 2 informed themselves significantly more often than those in cluster 3. In general, trust in information about organic husbandry was rather low (mean: 2.45; scale from 1 = low trust to 5 = high trust). Participants in cluster 1 found it easier to find good infor- mation and were more satisfied with the given information than participants in the other two clusters. The results further show that, on average, the current media reporting on animal husbandry was evaluated as rather balanced (not too positive, not too negative and not too uncritical, not too critical), with a slight tendency that it was perceived as too positive and too uncritical. Especially participants in cluster 3 (significant different to cluster 1 and 2) evaluate current media reporting on organic husbandry as too positive and too uncritical. With regard to credi- bility, participants in cluster 1 rated the media reporting on organic husbandry as significantly more credible than participants in clusters 2 and 3. Regarding information about organic husbandry, consumers had highest trust in organic farmers and in their own friends and family (Fig 4). Also, certification bodies and organic asso- ciations were seen as credible sources of information about organic animal farming. Journal- ists, politics, and retailers were trusted less. Overall, participants in cluster 1 had the highest trust in all actors, followed by participants in cluster 2. The respondents were further asked whom they see as responsible to provide consumers with adequate education about organic husbandry (Fig 5). The results show that consumers considered especially farmers, organic associations, and retailers as responsible for this. Jour- nalists and the consumers themselves were seen as less responsible to ensure proper education about organic farming. There were no major differences between the three clusters. However, it is interesting to note that individuals in the second cluster placed a higher level of responsi- bility on the state and politics to provide proper education. Finally, the factor analysis identified two factors, namely “preference for more emotional information” and “refusal of a too idyllic communication, whish for more facts”, with the latter Fig 4. Trust in actors concerning information about organic animal husbandry, n = 729. https://doi.org/10.1371/journal.pstr.0000102.g004 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 13 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry Fig 5. Responsibility to provide consumers with proper education about organic animal husbandry, n = 729. https://doi.org/10.1371/journal.pstr.0000102.g005 factor also indicating a wish for more fact-based communication. The first cluster preferred a more emotional way of information with pictures and personal stories about farmers, whereas respondents in clusters 2 and 3 desired more fact-based communication about organic animal farming (Table 8). S4 Table in the Appendix shows all statements of the factor analysis. Discussion Trust along the value chain and perception of organic animal production The cluster analysis showed three clearly separated consumer groups according to their trust level along the value chain of organic animal products: cluster 1 “strongly trusting” (34.6%), Table 8. Factor analysis: preference for emotional vs. rational information about organic husbandry. Factors Factor 1: “Preference for more emotional information" (Cronbach’s alpha: 0.60) Factor 2: “Refusal of a too idyllic communication, whish for more facts” (Cronbach’s alpha: 0.52) Cluster 1 0.34 (0.89)a −0.26 (1.07)a Cluster 2 −0.08 (0.93)b 0.05 (0.91)b Cluster 3 −0.70 (1.14)c 0.58 (0.86)c Total 0.00 (1.00) 0.00 (1.00) Displayed are means and standard deviations in parentheses. a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05); explained variance: 55.11%, KMO: 0.607, Bartlett’s test: p < 0.001 https://doi.org/10.1371/journal.pstr.0000102.t008 PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 14 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry cluster 2 “moderately trusting” (52.4%), and cluster 3 “weakly trusting” (13.0%). The results indicate a strong interrelation of trust in the various actors; it was not possible to identify whether respondents had a high level of trust in certain actors and a low level of trust in others. Further, it is evident for all groups that the level of trust is rather high and mostly placed in organic retailers and farmers. The least trust is placed in the processing sector and conven- tional retailers. These findings are in line with other studies, which found an overall moderate to high trust level along the chain of organic food production, with especially high trust in farmers and less trust in food manufacturers [11,14]. The skeptical view of the processing sec- tor could be explained by scandals in this area, where fraud for profit is detected periodically [48,49]. Due to organic price premiums, it is lucrative to label non-organic products as organic to maximize profits. This is a serious problem for the organic sector, as consumers and their trust are abused [17]. The low trust in conventional retailers (mean: 2.93) seems surprising, as most consumers buy their organic products in these locations [50]. Thus, the lower level of trust compared to farmers and organic retailers does not act as a hindrance to buying organic products in these places, which is certainly also due to the practicability in everyday life [51]. Trust is strongly linked to the buying behavior of organic meat products: Participants in cluster 1 stated to buy organic meat most often and had a high level of education and income. These characteristics are common for intensive organic consumers [50,51,52]. Cluster 2 shows the second highest consumption rate of organic meat products, and participants in cluster 3 consume organic meat very rarely. The correlation between trust and buying intention is in line with existing findings. A study found that trust in organic foods significantly influences buying behavior [32]. It is further known that knowledge about and perception of organic food positively influ- ence buying intention [4,24,53,54]: Both are highest in cluster 1 and lowest in cluster 3. The overall very positive attitude of cluster 1 and the rather skeptical attitude of cluster 3 could be explained by the choice-supportive bias, where people tend to attribute more posi- tive/negative features to options they choose/do not choose [55]. Thus, it seems questionable whether respondents in cluster 3 are a potential target group for organic animal products, as their knowledge, interest, and perception are low or negative. In this context it must be noted that the income in cluster 3 is the lowest, and a low income is known to be the main obstacle to buying premium items such as organic products [51,56,57,58,59]. Thus, people in cluster 3 might rate organic animal husbandry more negatively to justify why they do not buy these products [55]. Political actions to make these products also affordable for people with a low income could be real cost accounting for animal products (reducing price differences between organic and conventional products, as organic production has lower environmental costs [60]), subsidization of products with high animal welfare standards, or cutting taxes for low- income households. Therewith, a possible choice-supportive bias could be reduced. Trust types Respondents in cluster 1 show a higher general trust level compared to the other clusters, which supports the findings that people with higher “social trust” also place more trust in actors of the food chain [14]. The general trust level might be a deciding factor in how strongly pronounced the trust in specific actors is, which is consistent with the fact that the respondents trust all actors either to a greater or to a lesser extent. Thus, significantly lower general trust levels in clusters 2 and 3 could make it more difficult to gain and build trust in these two clus- ters, as the lower trust levels might be at least partly related to a personal trait. Furthermore, the trust of respondents in cluster 1 is more based on habit. They also agree more that buying organic makes them feel good. Other studies have found that organic PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 15 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry consumption is often habitual and evokes positive feelings [52,61]. Also, a study concludes that trust in organic products is highly routinized, which would explain why there is a higher agreement to those statements in the cluster with the highest buying frequency of organic products [4]. However, another study states that habit might be the strongest source of trust, as it has a strong influence on people’s everyday behavior [62]. It is further less susceptible to loss of trust [63]. Our results also show that, at least in subjective memory, consumers in this group had fewer bad experiences. It may therefore be possible that no or few negative experi- ences have actually been made and that confidence is therefore high. However, it is also known that people tend to focus on the positive aspects of (purchase) decisions and disregard information that is inconsistent with their decision (choice-supportive bias; [55]). This behav- ior was also detected in organic customers [51]. Additionally, it was found that trust in and buying intention of organic animal products are very consistent among organic consumers, even when they are disappointed by some (non-) existing regulations [16]. Especially in view of possible scandals or in view of unmet expectations, it seems valuable to build up such an impregnable trust among organic customers. The habitual trust is significantly lower in cluster 2 and especially in cluster 3. This seems plausible since consumers in these groups also buy fewer organic products. In addition, these consumers are more likely to agree that they have already had a bad experience with organic products. Here too, the question arises as to whether this is an objective or rather distorted per- ception to justify the purchasing decisions. In general, however, it is known that product qual- ity is an important criterion for safeguarding consumer trust in a producer or a retailer [46,47]. Thus, in general, it is of importance to avoid disappointment as this might lead to mis- trust [64]. Furthermore, the combination of high expectations and low knowledge poses a risk of disappointment in these groups (“halo effect”: [1,65]), which can in turn negatively affect trust, as it is not as solid as in cluster 1 that showed a high level of habitual trust. However, as respondents in all clusters agreed the most with statements of rational trust, and therewith the needed information, a closer view at how this information should be pre- sented and made available seems important. Information behavior and evaluation Respondents in clusters 1 and 2 inform themselves significantly more frequently than partici- pants in cluster 3 but still irregularly. Moreover, consumers in cluster 1 agree more that it is easy to find understandable information, which supports the findings of a study which found that those who buy organic products more often are also more satisfied with the given infor- mation [66]. Further, people in cluster 1 evaluate the provided information and reporting on organic husbandry as significantly more credible than the other clusters. This is again in line with find- ings on the selective exposure theory, which states that people who have a positive attitude towards a subject tend to search predominately for information that is less critical and sup- ports their attitude [67,68]. Nevertheless, in our study, levels of trust in media reporting are moderate, whereas trust in information made available by farmers, certification bodies, and organic associations is clearly higher. This is true for all clusters, but the trust level is again higher in cluster 1. This is supported by another study which found that farmers and third- party certifiers are perceived as an especially trustworthy source of information regarding organic food [54]. When it comes to the question of who has the responsibility to inform con- sumers, there is again a clear preference for farmers and organic associations, but also retailers as the place of purchase. Even though the top three responsibilities are similar in all three groups, there are differences, especially in the attribution of responsibility to consumers and PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 16 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry policymakers. Compared to the other clusters, individuals in cluster 2 most strongly agree that policymakers should be made accountable and that consumers have the least responsibility to educate themselves about organic husbandry. This can be due to two reasons: Either these con- sumers are too uncertain to find helpful information, even though they would like to have some, or they shift the responsibility away to justify their uninformedness. In any case, the rel- evance of a political information campaign becomes evident. The preference for the presenta- tion of information on organic animal husbandry is also different between the groups: Consumers with a high level of trust (cluster 1) have a slight preference for personal stories and photos, whereas this is slightly rejected by respondents in cluster 3 (cluster 2 is in the mid- dle). However, although individuals in cluster 1 like emotional information, they also slightly agree that organic animal husbandry is portrayed too much as an “ideal world”. This statement is even more supported by people in cluster 2 and especially by those in cluster 3. The less criti- cal attitude of participants in cluster 1 regarding actual media reporting and their slight favor of an “emotional way” of the communication might be reasoned by the fact that they prefer appealing photos, but this might also be a result of the high level of trust in and their positive attitude towards organic farming, which they want to maintain. Summing up, the results show that consumer groups with the highest trust in organic ani- mal husbandry seem to be less critical regarding given information and show a slight prefer- ence for emotional information, such as personal stories of farmers and pleasant pictures, whereas less convinced consumer groups reject a too idyllic way of information more and desire more facts. However, all respondents have a high preference for rational information. Especially individuals in cluster 2 seem to need more information/facts or at least do not know where to find them. Thus, information seems to be of importance and should be provided by farmers and other actors of the organic sector (organic associations, certifiers, retailers). Policy and practice recommendations The three identified clusters are significantly different regarding their trust along the value chain. To consolidate trust for these consumer groups, we suggest the following: Cluster 1: Consumer in this cluster have the highest trust level and are already in favor of organic husbandry, with quite unwavering trust and the highest consumption rate of organic meat. They are the most interested in information about organic husbandry. When communi- cating organic husbandry, these consumers can be reached through emotional information (photos, personal stories), but also facts are of importance to this group, while a “too ideal” presentation should be avoided. Cluster 2: The second cluster is of interest, as here people have a positive attitude towards organic husbandry and a moderate trust level. Thus, to strengthen trust in this potential con- sumer group, good product quality along the chain should be ensured, as this group was signif- icantly more often disappointed by the product quality of organic products than the highly trusting people in cluster 1. Further, especially in this group, the expectation–reality gap should be reduced by using a more realistic representation of organic animal farming. Con- sumers in this group should be addressed with a more fact-based communication. A “too idyl- lic” presentation of organic husbandry is seen critically. Scandals, for example fraud in processing and any other areas, should obviously be prevented in any case to gain and main- tain consumer trust. This applies to all organic consumers, but as trust is less stable in clusters 2 and 3, people’s trust in these two clusters could be more affected by scandals. Cluster 3: The third cluster has the lowest trust and interest in organic husbandry and is pri- marily not considered to be a target group for products from organic animal husbandry. Nev- ertheless, as people in this cluster also have the lowest income of all clusters, which might act PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 17 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry as a hindrance to buying organic animal products [58,59], the choice-supportive bias could play a role. Thus, political actions should be implemented to minimize price differences of organic/animal welfare products compared to conventional products and therewith enable people with a low income to choose products with higher animal welfare standards. This most skeptical group should be addressed with facts, as they reject a too idyllic presentation of the sector the most. In general, farmers seem to play a special role in the perception of consumers regarding information about organic husbandry, as they are seen as the most trustworthy in terms of provided information. At the same time, consumer see farmers and other actors in the organic sector as responsible to ensure an adequate information level about organic animal farming. Thus, using farmers to transfer information about organic livestock farming, for example in an information campaign, could be a promising way to convey information in a credible manner. A broad information campaign should be considered to support the aim of extending the mar- ket for organic farming in the near future, as consumers mostly do not see themselves as responsible for educating themselves on organic husbandry. This information campaign should be seen as complementary to the more specific way in which the clusters identified in this study prefer to receive information about organic farming. By considering these points when addressing the target groups purposefully, trust along the chain and the overall positive perception of organic livestock production could be strength- ened and improved. Thus, the basis to expand sales from particularly animal welfare-friendly husbandry systems and sustainable food production can be established. The results of this study may contribute to reach the political aim of expanding organic production in the near future. Limitations and future research In some areas, this study cannot provide a deeper analysis, for example on the reasons why trust in provided information about organic husbandry is low. This should be investigated in further research to be able to improve trust in supplied information. The study results are lim- ited to German consumers, but the results on trust in organic animal husbandry as well as on trust building and information behavior is relevant to a broader public and it can be assumed that the underlying structures for building trust can be transferred to consumers in other countries where there are also known trust issues [15]. The cited literature on trust in organics and about organic consumer groups is partly quite old [18,51,19], but the old findings are sup- ported by more recent data [50] and by newer studies referring to the older references [11], as no current studies are available on this topic. Further, the cluster analysis shows three clusters with high correlations and tendencies regarding trust levels. Thus, also other methods, such as a classification with fixed criteria, could have been considered as a suitable method. However, as we wanted to cluster the groups according to their trust levels and did not want to define fixed category thresholds, we considered the cluster analysis as a suitable method for the aims of this study. Conclusion In summary, trust along the value chain of organic animal products is moderate to high. The processing sector and conventional retailers were identified as actors in which consumers place the least trust. The highest level of trust is placed in organic retailers and farmers. This study identified three distinct clusters with significantly different levels of trust, the distribu- tion and characteristics of which are quite consistent with existing literature. Consumer with high trust show relatively stable habitual trust in organic farming whereas consumer with PLOS Sustainability and Transformation \| https://doi.org/10.1371/journal.pstr.0000102 February 29, 2024 18 / 23 PLOS SUSTAINABILITY AND TRANSFORMATIONExploring trust in organic husbandry moderate trust, and especially with low trust, see organic husbandry less positive and prefer are a more rational/fact-based form of information. In addition to target-group specific com- munication to build trust, a broader information campaign is recommended to improve the knowledge of the general public about organic farming. This is important to support the goal of increasing demand for sustainably produced animal products and to achieve the policy goal of transforming agricultural and food systems towards greater sustainability. Supporting information S1 Table. Statements used for the trust index (cluster-building variables). Scale: 1 = Does not apply at all; 5 = Fully applies. (DOCX) S2 Table. Statements building the index of attitude towards organic animal husbandry. Scale: 1 = Does not apply at all; 5 = Fully applies. (DOCX) S3 Table. Factor analysis regarding different types of trust (rational, habitual, emotional). Explained variance: 58.95%, KMO: 0.820, Bartlett’s test: p < 0.001. Displayed are means and standard deviations in parentheses. Scale: 1 = Totally disagree; 5 = Totally agree. a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05). (DOCX) S4 Table. Factor analysis: preference for emotional vs. rational information about organic husbandry. Explained variance: 55.11%, KMO: 0.607, Bartlett’s test: p < 0.001. Displayed are means and standard deviations in parentheses. Scale: 1 = Totally disagree; 5 = Totally agree). a–c according to post hoc tests, clusters with different letters differed significantly (p � 0.05). (DOCX) Author Contributions Conceptualization: Elisa Bayer, Sarah Ku¨hl. Data curation: Elisa Bayer, Sarah Ku¨hl. Formal analysis: Elisa Bayer. Methodology: Elisa Bayer, Sarah Ku¨hl. Project administration: Elisa Bayer, Sarah Ku¨hl. Supervision: Elisa Bayer, Sarah Ku¨hl. Visualization: Elisa Bayer. Writing – original draft: Elisa Bayer. Writing – review & editing: Elisa Bayer, Sarah Ku¨hl. References 1. Von Meyer-Ho¨ fer M, Nitzko S, Spiller A. Is there an expectation gap? Consumers’ expectations towards organic. An exploratory survey in mature and emerging European organic food markets. British Food Journal 2015. 117(5): 1527–1546. https://doi.org/10.1108/BFJ-07-2014-0252 2. BMEL. O¨ ko-Barometer 2022. Umfrage zum Konsum von Bio-Lebensmitteln 2022. https://www.bmel. de/SharedDocs/Downloads/DE/Broschueren/oeko-barometer-2022.pdf?__blob=publicationFile&v=8. 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10.1371_journal.pone.0264329	RESEARCH ARTICLE Clinically adjudicated deceased donor acute kidney injury and graft outcomes Sherry G. Mansour1,2, Nadeen Khoury3, Ravi Kodali2, Sarthak Virmani2, Peter P. Reese4,5,6, Isaac E. Hall7, Yaqi Jia8, Yu Yamamoto1, Heather R. Thiessen-PhilbrookID Wassim Obeid8, Mona D. Doshi9, Enver Akalin10, Jonathan S. Bromberg11,12, Meera N. Harhay13,14,15, Sumit Mohan16,17, Thangamani Muthukumar18,19, Pooja Singh20, Francis L. Weng21, Dennis G. Moledina1,2, Jason H. Greenberg1,2, Francis P. WilsonID R. ParikhID 1,2, Chirag 8* 8, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mansour SG, Khoury N, Kodali R, Virmani S, Reese PP, Hall IE, et al. (2022) Clinically adjudicated deceased donor acute kidney injury and graft outcomes. PLoS ONE 17(3): e0264329. https://doi.org/10.1371/journal.pone.0264329 Editor: Stanislaw Stepkowski, University of Toledo, UNITED STATES Received: June 11, 2021 Accepted: February 8, 2022 Published: March 3, 2022 Copyright: © 2022 Mansour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: This statement reflects the correct funding associated with this work: This work was supported by the American Heart Association grant 18CDA34110151 and the Patterson Trust Fund to Dr. Mansour; National Institutes of Health (NIH)/ National Institutes of Diabetes and Digestive and Kidney Diseases (NIDDK) grant R01DK-93770, grant K24DK090203 to Dr. Parikh. The funders had no role in study design, data collection and 1 Clinical and Translational Research Accelerator, Yale University School of Medicine, New Haven, CT, United States of America, 2 Department of Internal Medicine, Section of Nephrology, Yale University School of Medicine, New Haven, CT, United States of America, 3 Division of Nephrology, Henry Ford Health System, Detroit, MI, United States of America, 4 Department of Medicine, Renal-Electrolyte and Hypertension Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States of America, 5 Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States of America, 6 Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States of America, 7 Division of Nephrology & Hypertension, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America, 8 Division of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States of America, 9 Division of Nephrology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America, 10 Montefiore-Einstein Kidney Transplant program, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States of America, 11 Division of Transplantation, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, United States of America, 12 Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, United States of America, 13 Department of Internal Medicine, Drexel University College of Medicine, Philadelphia, PA, United States of America, 14 Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, United States of America, 15 Tower Health Transplant Institute, Tower Health System, West Reading, PA, United States of America, 16 Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States of America, 17 Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians & Surgeons, New York, NY, United States of America, 18 Division of Nephrology and Hypertension, Department of Medicine, New York Presbyterian Hospital-Weill Cornell Medical Center, New York, NY, United States of America, 19 Department of Transplantation Medicine, New York Presbyterian Hospital-Weill Cornell Medical Center, New York, NY, United States of America, 20 Division of Nephrology, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University Hospital, Philadelphia, PA, United States of America, 21 Saint Barnabas Medical Center, RWJBarnabas Health, Livingston, NJ, United States of America * chirag.parikh@jhmi.edu Abstract Background Acute kidney injury (AKI) in deceased donors is not associated with graft failure (GF). We hypothesize that hemodynamic AKI (hAKI) comprises the majority of donor AKI and may explain this lack of association. Methods In this ancillary analysis of the Deceased Donor Study, 428 donors with available charts were selected to identify those with and without AKI. AKI cases were classified as hAKI, PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 1 / 15 PLOS ONEanalysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. intrinsic (iAKI), or mixed (mAKI) based on majority adjudication by three nephrologists. We evaluated the associations between AKI phenotypes and delayed graft function (DGF), 1- year eGFR and GF. We also evaluated differences in urine biomarkers among AKI phenotypes. Adjudicated kidney injury and graft outcomes Results Of the 291 (68%) donors with AKI, 106 (36%) were adjudicated as hAKI, 84 (29%) as iAKI and 101 (35%) as mAKI. Of the 856 potential kidneys, 669 were transplanted with 32% developing DGF and 5% experiencing GF. Median 1-year eGFR was 53 (IQR: 41–70) ml/ min/1.73m2. Compared to non-AKI, donors with iAKI had higher odds DGF [aOR (95%CI); 4.83 (2.29, 10.22)] and had lower 1-year eGFR [adjusted B coefficient (95% CI): -11 (-19, -3) mL/min/1.73 m2]. hAKI and mAKI were not associated with DGF or 1-year eGFR. Rates of GF were not different among AKI phenotypes and non-AKI. Urine biomarkers such as NGAL, LFABP, MCP-1, YKL-40, cystatin-C and albumin were higher in iAKI. Conclusion iAKI was associated with higher DGF and lower 1-year eGFR but not with GF. Clinically phe- notyped donor AKI is biologically different based on biomarkers and may help inform deci- sions regarding organ utilization. Introduction Less than 20% of patients on the waiting list receive kidney transplants each year, and approxi- mately thirteen patients die every day awaiting a kidney transplant [1]. Despite this unmet demand, 20% of deceased-donor kidneys are discarded, with kidneys from donors with acute kidney injury (AKI) being procured at lower rates and discarded at higher rates [2–4]. Donor AKI usually occurs in the setting of brain-death and significant hemodynamic changes [5]. Brain-death causes loss of spinal cord sympathetic activity leading to vasodilation, impaired cardiac output and hemodynamic instability with reduction in renal perfusion [6,7]. There- fore, increases in serum creatinine concentration in these settings may be due to hemody- namic changes (pre-renal azotemia), rather than intrinsic damage to the kidneys (acute tubular injury). Despite the inability to distinguish between hemodynamic (hAKI) and intrin- sic AKI (iAKI) by using serum creatinine alone [8], clinical decisions such as whether to pro- cure or accept a deceased donor kidney are partially determined based on serum creatinine- defined AKI. However, deceased-donor hAKI may be a manifestation of appropriate neuro- vascular responses to maintain hemodynamic stability [2,9]. Elucidating relationships between types of donor AKI and graft outcomes may help influence allocation decisions. We hypothe- size that distinguishing between AKI phenotypes by clinical adjudication will assist in under- standing short and long-term graft outcomes. Multiple studies have shown that deceased donor AKI is not associated with adverse recipi- ent outcomes [10–12]. Lack of these associations may be due to the majority of deceased donors having significant hemodynamic changes leading to functional changes (hAKI) rather than structural injury (iAKI). The importance of phenotyping AKI as hAKI or iAKI is highlighted by literature demonstrating that the two processes are transcriptionally different in the kidney tissue of mouse models, with different tubular injury biomarker concentrations in PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 2 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes human urine [13]. In the current study, we determined whether clinical adjudication of deceased donor AKI was associated with recipient outcomes, and whether urine biomarkers distinguish between different phenotypes of AKI. Methods Study design This was an ancillary study from the Deceased Donor Study (DDS) and included 428 deceased donors with available charts from two organ procurement organizations (OPOs); Gift of Life Michigan and New York Organ Donor Network. Overall DDS methods have been described in detail elsewhere [14,15]. For the current study, a trained research coordinator manually abstracted seven demographic variables and 50 longitudinal variables from charts of donor hospitalizations from April 2010 to November 2013. Data were managed using a RedCap elec- tronic database. AKI was defined as �0.3 mg/dL or �50% increase in serum creatinine at any time point during the hospitalization prior to death from the lowest recorded value, irrespec- tive of urine output or duration of time between the two measurements. This corresponded to at least stage 1 AKI by the Acute Kidney Injury Network criteria [16]. We created de-identified donor profiles (S1A and S1B Fig) with the following abstracted donor variables: demographics (age, gender, race), daily trends of hemodynamic status (lowest systolic and diastolic pressures, ejection fraction, central venous pressure, PaO2/FiO2 ratio, hemoglobin, vasopressor use) renal function measures (serum creatinine, maximum delta creatinine during hospitalization, blood urea nitrogen-to-creatinine ratio, net fluid balance, urine output, urine casts, urine pro- tein), medications (angiotensin-converting enzyme inhibitors, angiotensin II receptor block- ers, vancomycin, diuretics), and microbiology (sputum culture, blood culture, urine culture, bronchial culture). We securely distributed these profiles to three board-certified nephrolo- gists, who independently reviewed AKI cases to adjudicate either as hAKI or iAKI. They were asked to use their clinical judgment to assess the phenotype of AKI based on the donor profiles as they would have done in routine clinical practice. All three nephrologists used common clinical markers such as serum creatinine trends, vital signs, volume status, vasopressor use, and presence of infection to accurately adjudicate the cases. They were blinded to the others’ adjudications, recipient outcomes and study urinary biomarker data. If a nephrologist could not confidently adjudicate hAKI or iAKI, they were asked to label the AKI as mixed subtype (mAKI). Final diagnosis was determined by majority adjudication. If all three nephrologists disagreed, the phenotype was designated as mAKI. Biomarker measurement After collection at time of organ procurement, urine samples were centrifuged at 1000×g for 10 minutes at 4˚C, separated into 1 ml aliquots, and immediately stored at -80˚C until bio- marker measurement. The following urine biomarkers were measured: cystatin-C, albumin- to-creatinine ratio (UACR), interferon alpha (IFN), interleukin (IL-) 4, 6, 8, 10,18, kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (LFABP), neutrophil gelati- nase associated lipocalin (NGAL), tumor necrosis factor alpha (TNF-α), chitinase-3-like 1 (YKL-40), epidermal growth factor (EGF), monocyte chemoattractant protein-1 (MCP-1), osteopontin (OPN) and uromodulin (UMOD). NGAL measurement was performed using the Architect platform (Abbott Diagnostics). LFABP was measured using latex-enhanced immu- noturbidimetry with anti-human LFABP mouse monoclonal antibodies (Sekisui Medical). All other urine biomarkers were measured using the Meso Scale Discovery platform (MSD, Gai- thersburg, MD), which uses electrochemiluminescence detection combined with patterned arrays. PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 3 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes Operational definitions Delayed graft function (DGF) in the recipient was defined as the need for any dialysis in the first week post-transplantation. One-year eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration equation using the serum creatinine values reported via chart review from the DDS cohort [17]. If the recipient died prior to 1 year after transplant, we car- ried forward their last reported serum creatinine to calculate 1-year eGFR (this occurred in 21 (2%) of recipients who died within the first year of follow up). If the recipient experienced graft failure (GF) prior to 1 year after transplant, 1-year eGFR was imputed as 10 ml/min/ 1.73m2. Finally 1-year GF was defined as return to dialysis or re-transplantation. Statistical analysis All analyses were two-tailed and p-values less than 0.05 were considered significant. Descrip- tive statistics for continuous variables were reported as median (interquartile range) and for categorical variables as frequencies (%) for the total cohort and stratified by AKI phenotypes. Differences in urine biomarker concentrations and other continuous variables between the three AKI phenotypes were assessed using the Kruskal-Wallis test. Differences in categorical variables including the outcome of GF were assessed using chi-squared test. When evaluating the association between donor AKI phenotypes and outcomes of DGF and 1-year eGFR, we used non-AKI as the reference group. The associations between AKI phe- notypes and the categorical outcome of DGF were analyzed using univariable and multivari- able logistic regression clustered at the donor level. The associations between AKI phenotypes and the continuous outcome of 1-year eGFR were analyzed using univariable and multivari- able linear regression also clustered at the donor level. Beta (β) coefficients were estimated using the linear regression model, where beta was defined as the change in 1-year eGFR associ- ated with AKI phenotype, when all other variables were held fixed. Multivariable models were adjusted for the following donor variables that make up the Kid- ney Donor Profile Index (KDPI): age (years), sex, race, body mass index (BMI), hepatitis C virus (HCV) status, hypertension (HTN), diabetes mellitus (DM), stroke as cause of death, donor donation after cardiovascular determination of death and terminal serum creatinine. In addition to KDPI variables, we adjusted for expanded criteria donor status; transport variables: hypothermic machine perfusion, and cold ischemia time; and recipient variables: age (years), sex, race, DM as the cause of end-stage kidney disease, number of human leukocyte antigen mismatches, panel reactive antibody (%), BMI, pre-emptive transplant status, history of prior kidney transplants and duration of dialysis prior to transplant (months). In secondary analysis, we evaluated deceased donors having persistent AKI at time of organ procurement defined by an increase in serum creatinine of at least 0.3 mg/dL or 50% increase from the lowest to terminal value. In this subset, we evaluated whether biomarkers measured from urine samples collected at organ procurement differ between AKI phenotypes. Lastly, we also evaluated the associations for AKI phenotypes at time of organ procurement with recipi- ent DGF and 1-year eGFR. This study used data from the organ procurement and transplantation network (OPTN). The OPTN data system includes data on all donor, wait-listed candidates, and transplant recip- ients in the US, submitted by the members of OPTN, and has been described elsewhere. The Health Resources and Services Administration, U.S. Department of Health and Human Ser- vices provides oversight to the activities of the OPTN contractor. The analyses are based on OPTN data as of January 2017 and may be subject to change due to future data submission or correction by transplant centers. The OPO scientific review committees and the institutional PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 4 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes review boards for the participating investigators approved this study under a waiver of consent because deidentified data were used. Results Out of 428 donors, 291 met the clinical AKI definition (Fig 1). Among the 291 AKI cases adju- dicated, 106 (36%) had hAKI, 101 (35%) had the mAKI, and 84 (29%) had iAKI. Among the adjudicated cases of hAKI, 54 (51%) had perfect agreement (all three nephrologists agreed). Seventeen (17%) cases had perfect agreement in the mAKI subtype 27 (32%) cases had perfect agreement in the iAKI subtype (S1 Table). Median donor age was 47 years old (IQR: 31, 57) and 40% were female as shown in Table 1. Donor cause of death, KDPI, and admission and terminal serum creatinine significantly differed by AKI phenotype. From the 428 donors eval- uated, there were a total of 856 candidate kidneys for donation, with 669 kidney transplanted; 182 kidneys were discarded and 5 kidneys were excluded from the analysis as they were trans- planted to pediatric recipients. Rates of discard were,60 (22%), 28 (13%), 43 (21%), and 51 (30%) for no-AKI, hAKI, mAKI and iAKI, respectively (p = 0.004). Recipient characteristics stratified by AKI phenotype are shown in Table 2. Recipient age, rate of graft biopsy and hypo- thermic machine perfusion were significantly higher in the iAKI group, whereas recipient panel reactive antibody was less in iAKI as compared to other groups. Among the 669 transplanted kidneys, 487 (73%) had a procurement biopsy. The rates of biopsies were highest in the iAKI at 98 (84%) vs. 147 (70%), 129 (70%) and 113 (71%) for no- AKI, hAKI and mAKI, respectively (p = 0.03). Among kidneys with biopsies, only 65 (13%) had any acute tubular injury (ATI) reported on biopsy. The presence of any ATI as reported on biopsy (mild or moderate to severe) was not significantly different among the AKI Fig 1. Study flow diagram. Shows the breakdown of our study. A total of 428 donors with available charts were included in our study. Among the 428 donors, 291 had AKI at anytime point during the hospitalization. Only donors with AKI were adjudicated, and 106 were found to have hemodynamic AKI, 101 mixed AKI and 84 intrinsic AKI. https://doi.org/10.1371/journal.pone.0264329.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 5 / 15 PLOS ONETable 1. Donor characteristics by AKI phenotype. Adjudicated kidney injury and graft outcomes All (n = 428) No-AKI (n = 137) Hemodynamic (n = 106) Mixed (n = 101) Intrinsic (n = 84) P-value Variables Age (years) Female Black Race BMI (kg/m2) Hypertension Diabetes Cause of Death 47 (31, 57) 171 (40%) 80 (19%) 28 (24, 32) 156 (36%) 38 (9%) Head Trauma 106 (25%) Anoxia 128 (30%) Stroke 177 (41%) Other 11 (3%) Hepatitis C DCD KDPI (%) 6 (1%) 60 (14%) 57 (31, 81) 49 (34, 60) 59 (43%) 19 (14%) 27 (24, 32) 48 (35%) 12 (9%) 37 (27%) 25 (18%) 69 (50%) 2 (1%) 0 (0%) 17 (12%) 57 (31, 83) Admission sCr (mg/dL) 1.0 (0.8, 1.3) 0.9 (0.7, 1.1) Terminal sCr (mg/dL) 1.0 (0.7, 1.4) 0.87 (0.7, 1.2) 47 (31, 54) 46 (43%) 15(14%) 28 (24, 32) 37 (35%) 14 (15%)) 28 (27%) 36 (34%) 36 (34%) 5 (5%) 2 (2%) 17 (16%) 47 (27, 74) 1.1 (0.8, 1.31) 0.80 (0.6, 1.0) 44.5 (28, 54) 39 (39%) 26 (27%) 27 (23, 33) 39 (39%) 7 (8%) 21 (21%) 40 (40%) 38 (38%) 2 (2%) 3 (3%) 11 (11%) 51 (31, 81) 1.1 (0.9, 1.4) 1.1 (0.7, 1.4) 45 (33, 56) 27 (32%) 20 (24%) 29 (24, 32) 32 (39%) 5 (8%) 20 (24%) 27 (33%) 34 (41%) 2 (2%) 1 (1%) 15 (18%) 62 (45, 85) 1.0 (0.8, 1.4) 1.7 (1.2, 3.1) 0.23 0.32 0.05 0.82 0.94 0.28 0.03 0.28 0.47 0.05 <0.0001 <0.0001 Values are represented as medians (interquartile ranges) or n(%). Inference testing was done using Kruskal Wallis test for continuous values, and chi-squared test for categorical values. DCD: Donation after cardiovascular determination of death; KDPI: Kidney donor profile index; sCr: Serum creatinine. https://doi.org/10.1371/journal.pone.0264329.t001 Table 2. Recipient and transport characteristics by AKI phenotype. All (n = 669) No-AKI (n = 209) Hemodynamic (n = 184) Mixed (n = 159) Intrinsic (n = 117) P-value Variables Age (years) Female Black Race BMI (kg/m2) Cause of ESKD 55 (44, 65) 252 (38%) 288 (43%) 28 (24, 32) Unknown/other 124 (19%) Diabetes 216 (32%) Hypertension 158 (24%) Glomerulonephritis 111 (17%) Graft Failure 60 (9%) 56 (46, 65) 84 (40%) 98 (47%) 27 (24, 32) 32 (15%) 76 (36%) 50 (24%) 37 (18%) 14 (7%) ESKD duration (months) 47 (20,74) 48 (23, 73) Pre-emptive transplant Previous kidney transplant Recipient PRA 71 (11%) 93 (14%) 28 (13%) 23 (11%) 0% 467 (70%) 151 (72%) 1–20% 35 (5%) 21–80% 84 (13%) >80% 83 (12%) 5 (4, 5) 486 (73%) HLA mismatch level Kidney biopsied Hypothermic machine perfusion 503 (75%) Cold Ischemia time (hours) 16 (12, 21) 10 (5%) 23 (11%) 25 (12%) 5 (4, 5) 168 (72%) 152 (73%) 16 (12, 21) 53 (44, 63) 63 (34%) 71 (39%) 28 (24, 33) 40 (22%) 54 (29%) 45 (24%) 26 (14%) 19 (10%) 51 (22, 75) 21 (11%) 26 (14%) 121 (66%) 12 (7%) 29 (16%) 22 (12%) 5 (4, 5) 128 (70%) 130 (71%) 16 (12, 22) 52 (40, 65) 65 (41%) 67 (42%) 27 (22, 31) 31 (20%) 51 (32%) 34 (21%) 29 (18%) 14 (9%) 44 (19, 76) 16 (10%) 28 (18%) 106 (67%) 8 (5%) 18 (11%) 27 (17%) 5 (4, 5) 113 (71%) 122 (77%) 16 (11, 20) 59 (48, 66) 40 (34%) 52 (44%) 28 (24, 31) 21 (18%) 35 (30%) 29 (25%) 19 (16%) 13 (11%) 44 (21, 74) 6 (5%) 16 (14%) 89 (76%) 5 (4%) 14 (12%) 9 (8%) 5 (4, 5) 98 (84%) 99 (85%) 15 (12, 20) 0.05 0.43 0.41 0.54 0.80 0.73 0.13 0.35 0.01 0.31 0.03 0.04 0.31 Values are represented as medians (interquartile ranges) or n(%). Inference testing was done using Kruskal Wallis test for continuous values, and chi-squared test for categorical values. BMI: Body mass index; ESKD: End stage kidney disease; HLA: Human leukocyte antigen; PRA: Panel reactive antibody. https://doi.org/10.1371/journal.pone.0264329.t002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 6 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes phenotypes [18 (9%) in no AKI, 19 (10%) in hAKI, 14 (9%) in mAKI and 14 (12%) in iAKI, p = 0.91]. The presence of moderate to severe ATI was also not different among the AKI phe- notypes [13 (6%) in no AKI, 16 (9%) in hAKI, 14 (9%) in mAKI, and 13 (11%) in iAKI, p = 0.28). Distribution of urine biomarkers among AKI phenotypes A total of 17 biomarkers measured from urine collected at organ procurement were evaluated among non-AKI donors and the three AKI phenotypes happening at anytime during hospitali- zation. Eight biomarkers were significantly different after indexing to urine creatinine, with NGAL, EGF, cystatin-C, and UMOD having the highest statistically significant differences (Table 3). For clinical AKI still ongoing at time of organ procurement, however, ten biomark- ers were significantly different- EGF, NGAL, cystatin-C, MCP-1, LFABP, UMOD, UACR, IL- 8, YKL-40, and IL-6 (Table 4). Associations of AKI phenotypes at anytime during hospitalization with DGF Out of 669 kidneys transplanted, 209 were from non-AKI donors, 184 were from donors with hAKI, 159 from donors with the mAKI and 117 from donors with iAKI. DGF occurred in 216 (32%) kidneys, with the highest rate of DGF in kidneys from donors with iAKI, 60 (51%), fol- lowed by hAKI, 59 (32%), mAKI, 44 (28%), and non-AKI, 53 (25%), p<0.0001. In univariable analyses, iAKI had significantly increased odds of DGF compared to non-AKI, but neither hAKI nor mAKI were significantly associated with DGF as shown in Table 5 and Fig 2. Table 3. Distribution of biomarkers among AKI phenotypes at anytime during hospitalization. Urine biomarkers Total (n = 428) No AKI (n = 137) Hemodynamic (n = 106) Mixed (n = 101) Intrinsic (n = 84) P-value EGF (pg/mg) 5601 (3032, 9541) 5676 (3377, 9385) 8038 (4452, 13132) 6004 (3960, 10218) 2963 (1557, 4710) NGAL (ng/mg) 141 (44.63, 832) 75.34 (26.65, 303) 103 (34.29, 362) 232 (63.97, 1011) 817 (106, 2625) Cystatin C (pg/mg) 2.03 (0.72, 6.93) 1.08 (0.62, 4.56) 1.79 (0.70, 5.24) 2.43 (0.81, 6.52) 4.87(1.11, 13.43) UMOD (ng/mg) 4973 (2426, 13184) 5335 (2505, 10766) 4505 (2326,15008) 7479 (3025,19335) 3703 (1685, 6673) LFABP (ng/mg) 54.83 (13.39, 172.85) 33.80 (8.40, 132.48) 37.83 (15.69, 131.69) 67.80 (10.23, 154.63) 110 (28.57, 296) YKL-40 (pg/mg) 2941 (651, 21452) 3167 (871, 18168) 2467 (627, 15367) 1437 (223, 13404) 6335 (986, 128087) MCP-1 (pg/mg) 871 (397, 2133 860 (371, 1988) 701 (309, 1925) 795 (394, 1963) 1197 (647, 3742) UACR (mg/g) 57.59 (25.85, 139.41) 49.52 (24.21, 126.21) 57.87 (24.77, 132.41) 53.70 (27.95, 105.93) 81.54 (35.42, 252.37) IL-8 (pg/mg) IL-6 (pg/mg) OPN (ng/mg) IL-18 (pg/mg) 29.81 (9.39, 100.29) 23.04 (7.44, 94.84) 28.63 (6.13, 69.86) 29.06 (7.50, 133.19) 44.09 (15.98, 148.43) 3.29 (1.15, 14.25) 3.60 (1.19, 13.02) 2.64 (0.88, 13.63) 2.94 (1.21, 12.25) 7.49 (1.53, 30.36) 2888 (1472, 6719) 3208 (1712, 7215) 2539 (1284, 6480) 2077 (1099, 5729) 3440 (1551, 8405) 109.38 (49.97, 304.82) 105.84 (55, 330) 125.78 (56.94, 282.64) 86.14 (45.06, 219) 183 (41.18, 684) KIM-1 (pg/mg) 3476 (1804, 6448) 3813 (1974, 7894) 3639 (1972, 6331) 3178 (1409, 6051) 3330 (1599, 5848) Creatinine (mg/dL) 43.78 (18.86, 83.41) 45.86 (23.62, 89.89) 41.39 (16.43, 88.56) 37.66 (13.52, 82.22) 44.73 (23.68, 79.80) IL-4 (pg/mg) 0.07 (0.04, 0.20) TNF-a (pg/mg) 0.56 (0.24, 2.19) IL-10 (pg/mg) IFN (pg/mg) 0.14 (0.07, 0.34) 2.17 (1.20, 5.86) 0.07 (0.04, 0.15) 0.44 (0.24, 1.99) 0.13 (0.06, 0.29) 2.11 (1.24, 4.29) 0.08 (0.03, 0.21) 0.66 (0.22, 2.38) 0.14 (0.07, 0.27) 2.25 (1.11,5.93) 0.09 (0.04, 0.25) 0.63 (0.23, 1.99) 0.17 (0.07, 0.48) 2.46 (1.18, 7.26) 0.08 (0.04, 0.17) 0.60 (0.29, 3.70) 0.13 (0.07, 0.27) 2.07 (1.23, 4.48) <0.001 <0.001 <0.001 <0.001 0.003 0.008 0.03 0.03 0.06 0.08 0.10 0.16 0.32 0.42 0.44 0.56 0.60 0.71 Values are represented as medians (interquartile ranges). Inference testing was done using Kruskal Wallis test. Abbreviations: IFN, interferon alpha; IL, interleukin; KIM-1, kidney injury molecule-1; LFABP, liver fatty acid binding protein; NGAL, neutrophil gelatinase associated lipocalin; TNF, tumor necrosis factor; YKL-40, chitinase 3-like 1; EGF, epidermal growth factor; MCP-1, monocyte chemoattractant protein-1; OPN, osteopontin; UACR: Urine albumin creatinine ratio; UMOD, uromodulin. https://doi.org/10.1371/journal.pone.0264329.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 7 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes Table 4. Distribution of biomarkers among AKI phenotypes at time of organ procurement. Urine biomarkers Total (n = 428) No AKI (n = 304) Hemodynamic (n = 16) Mixed (n = 45) Intrinsic (n = 63) EGF (pg/mg) 5601 (3032, 9541) 6633 (4334, 11249) 2300 (1342, 8167) 4804 (3433, 6815) 2477 (1308, 3974) NGAL (ng/mg) 141 (44.63, 832) 90.07 (35.9, 381) 188 (64, 772) Cystatin C (pg/mg) 2.03 (0.72, 6.93) 1.57 (0.65, 5.31) 1.74 (0.71, 4.31) MCP-1 (pg/mg) 871 (397, 2133) 708 (349, 1759) 1748 (928, 2647) 369 (146, 1543) 3.02 (1.09, 7.79) 962 (443, 2287) 1103 (239, 2721) 7.34 (1.81, 15.61) 1761 (770, 5246) LFABP (ng/mg) 54.83 (13.39, 173) 38.46 (9.92, 136.23) 39.46 (22.34, 165.51) 77.13 (28.13, 169.20) 128 (41.78, 356.52) UMOD (ng/mg) 4973 (2426, 13184) 5586 (2782, 13692) 2971 (1709, 7099) 6035 (2719, 18258) 3683 (1427, 6673) UACR (mg/g) IL-8 (pg/mg) 57.59 (25.85, 139) 54.60 (22.44, 132) 66.94 (30.98, 108) 51.98 (35.79, 93.60) 95.23 (38.85, 267) 29.81 (9.39, 100) 24.51 (7.46, 74.69) 39.59 (21.59, 93.49) 23.33 (6.50, 184) 70.39 (25.30, 189) YKL-40 (pg/mg) 2941 (651, 21452) 2433 (682, 14555) 3249 (370, 69635) 2188 (156, 17820) 16037 (1650, 204203) IL-6 (pg/mg) IL-18 (pg/mg) KIM-1 (pg/mg) TNF-a (pg/mg) IL-4 (pg/mg) 3.29 (1.15, 14.25) 2.80 (1.07, 11.96) 4.49 (2.05, 10.38) 3.73 (1.05, 12.71) 11.13 (1.79, 39.16) 109 (49.97, 305) 106 (48.96, 231) 245 (61.19, 594) 89.54 (52.56, 220) 219 (47.04, 771) 3476 (1804, 6448) 3466 (1826, 6489) 5327 (3479, 8527) 2795 (1093, 6366) 3528 (1599, 5848) 0.56 (0.24, 2.19) 0.52 (0.23, 2.12) 0.45 (0.21, 0.78) 0.07 (0.04, 0.20) 0.04 (0.04, 0.20) 0.07 (0.03, 0.10) 0.72 (0.23, 2.71) 0.09 (0.03, 0.25) 0.63 (0.33, 3.89) 0.09 (0.05, 0.24) Creatinine (mg/dL) 43.78 (18.86, 83.41) 44.40 (18.51, 86.22) 53.17 (40.88, 112) 42.59 (13.20, 89.38) 39.02 (20.06, 71.36) IFN (pg/mg) OPN (ng/mg) IL-10 (pg/mg) 2.17 (1.20, 5.86) 2.11 (1.18, 5.65) 2.10 (0.77, 3.29) 2.37 (1.05, 7.64) 2.47 (1.50, 6.09) 2888 (1472, 6719) 2828 (1476, 6530) 2886 (1620, 6032) 2062 (1099, 8986) 3436 (1526, 8405) 0.14 (0.07, 0.34) 0.14 (0.07, 0.35) 0.13 (0.06, 0.20) 0.15 (0.06, 0.60) 0.17 (0.08, 0.28) P-value <0.0001 <0.0001 <0.0001 <0.0001 0.001 0.002 0.003 0.005 0.006 0.006 0.12 0.18 0.19 0.34 0.51 0.56 0.73 0.78 Values are represented as medians (interquartile ranges). Inference testing was done using Kruskal Wallis test. Abbreviations: ACR: Albumin creatinine ratio; IFN, interferon alpha; IL, interleukin; KIM-1, kidney injury molecule-1; LFABP, liver fatty acid binding protein; NGAL, neutrophil gelatinase associated lipocalin; TNF, tumor necrosis factor; YKL-40, chitinase 3-like 1; EGF, epidermal growth factor; MCP-1, monocyte chemoattractant protein-1; OPN, osteopontin; UACR: Urine albumin creatinine ratio; UMOD, Uromodulin. https://doi.org/10.1371/journal.pone.0264329.t004 Table 5. Associations between AKI phenotypes and DGF and 1-year eGFR. Variables Event Rate in Recipients (n/total) Univariable [OR (95% CI)] Multivariable a [OR (95% CI)] Association between AKI Phenotypes at anytime during hospitalization and DGF No AKI hAKI mAKI iAKI No AKI hAKI mAKI iAKI 53/209 (25%) 59/184 (32%) 44/159 (28%) 60/117 (51%) (ref) 1.39 (0.87, 2.23) 1.13 (0.67, 1.88) 3.10 (1.87, 5.13) Median (IQR) of 1-year eGFR Univariable [B coefficient (95% CI)] (ref) 1.65 (0.83, 3.27) 1.71 (0.84, 3.50) 4.83 (2.29, 10.22) Multivariable b [B coefficient (95% CI)] Association between AKI Phenotypes at anytime during hospitalization and 1-year eGFR (ref) 0.93 (-4.60, 6.47) 1.01 (-5.04, 7.06) -4.82 (-10.62, 0.97) (ref) 0.88 (-5.56, 7.33) -1.95 (-8.65, 4.74) -11.22 (-19.14, -3.30) AKI: Acute kidney injury; DGF: Delayed graft function; eGFR: Estimated glomerular filtration rate. a Multivariable model was adjusted for donor characteristics: KDPI, expanded criteria donor; transport characteristics: Hypothermic machine perfusion and cold ischemia time; and recipient characteristics: Age, black race, male gender, BMI, diabetes as the cause of ESKD, HLA mismatches, PRA, pre-emptive transplant, prior kidney transplant, and duration on dialysis. b Multivariable model was adjusted for donor characteristics: Age, sex, black race, BMI, HCV status, hypertension, diabetes, stroke as cause of death, donor donation after cardiac death, terminal serum creatinine, expanded criteria donor; transport characteristics: Hypothermic machine perfusion and cold ischemia time; and recipient characteristics: Age, black race, male gender, BMI, diabetes as the cause of ESKD, HLA mismatches, PRA, pre-emptive transplant, prior kidney transplant, and duration on dialysis. https://doi.org/10.1371/journal.pone.0264329.t005 PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 8 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes Fig 2. Associations of AKI phenotypes with DGF and 1-year eGFR. Shows the independent associations between AKI phenotypes as compared to hemodynamic AKI and the outcomes of delayed graft function and 1-year eGFR. The exposure of AKI phenotypes is shown both as defined by AKI happening anytime during donor hospitalization as well as AKI at time of organ procurement. https://doi.org/10.1371/journal.pone.0264329.g002 Adjusting for donor and recipient characteristics, the associations remained significant with iAKI having 5 times the odds of DGF compared to non-AKI [aOR (95% CI): 4.83 (2.29, 10.22)]. There were no significant associations with DGF when comparing the hAKI and mAKI with non-AKI. Full multivariable model is shown in S2 Table. Associations of AKI phenotypes at anytime during hospitalization with 1-year eGFR One-year eGFR was numerically lower in iAKI {iAKI [median (IQR) of 49 (35, 67) mL/min/ 1.73m2], compared to non-AKI [52 (43, 69) mL/min/1.73m2], mAKI [55 (43, 74) mL/min/ 1.73m2] and hAKI [57 (39, 72) mL/min/m2], p = 0.22} but did not reach statistical significance. On multivariable analysis, iAKI was independently associated with an 11 ml/min/1.73m2 decrease in eGFR compared to non-AKI [adjusted B coefficient (95% CI): -11.22 (-19.14, -3.30)] as shown in Table 5 and Fig 2. The full multivariable model is shown in S3 Table. DGF was not a significant effect modifier for the association between AKI phenotypes and 1-year eGFR. Rates of 1-year GF among AKI phenotypes A total of 34 recipients experienced GF by 1-year. The distribution of GF among AKI pheno- types was not significantly different. Among recipients of kidneys from non-AKI donors, 3.8% (8/209) developed GF. Among recipients of kidneys from donors with AKI, 4.3% (8/184) PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 9 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes developed GF in the hAKI group, 6.3% (10/159) in the mAKI group, and 6.8% (8/117) in the iAKI group. These rates were not significantly different, p = 0.32. Associations of AKI phenotypes at time of procurement with DGF and 1-year eGFR We evaluated a subset of donors with persistent AKI at time of organ procurement, and out of 291 donors with AKI during hospitalization, 124 (43%) had persistent AKI at organ procure- ment. Of these, 16 (13%) had hAKI, 45 (36%) had mAKI and 63 (51%) had iAKI. Compared to non-AKI, iAKI was associated with nearly 3-fold odds of having DGF [2.92 (1.47, 5.8)] and lower 1-year eGFR [-8.71 (-0.25, -17.18) ml/min/1.73m2 (Fig 2). In contrast hAKI was associ- ated with higher 1-year eGFR [13.27 (3.16, 23.38) ml/min/1.73m2] compared to non-AKI, but was not associated with DGF. mAKI was not associated with DGF or 1-year eGFR. Discussion We evaluated associations between clinically adjudicated deceased-donor AKI and recipient outcomes in this multicenter study. We found that clinically phenotyped deceased-donor AKI had biological differences as evidenced by urine injury and repair biomarkers. We also found that donor iAKI happening earlier during donor hospitalization or ongoing at organ procure- ment was significantly associated with increased risk of DGF and lower 1-year eGFR but was not associated with early GF. Our study further explores the biological differences between hAKI and iAKI as identified by Barasch et al [13], and contributes to the argument that the sole reliance on serum creati- nine, without phenotyping AKI, neglects relevant prognostic data that associate with graft out- comes [18,19]. To our knowledge, this study is first to demonstrate that this biological difference, as measured by urine biomarkers, exists within clinically adjudicated deceased- donor AKI phenotypes. Current diagnostic strategies such as fractional excretion of sodium (FeNa) and urea are often unable to make the distinction between structural (iAKI) and func- tional (hAKI) disease. Many studies have reported FeNa <1% in iAKI, and although it has moderate discrimination for iAKI, its sensitivity and specificity decrease in patients using diuretics [20–22]. Consequently clinicians are left to rely on retrospective data such as response to fluids to differentiate between hemodynamic and intrinsic etiologies of AKI [23,24]. Our findings validate physicians’ clinical acumen and highlight certain urine biomark- ers as targets for future research to distinguish between AKI phenotypes and to limit subjectiv- ity from this clinically challenging setting. Furthermore, our findings that iAKI is more highly associated with DGF and lower eGFR suggest that phenotyping AKI is important for predict- ing recipient outcomes. More so, these findings may offer an opportunity for treating clini- cians to modify certain risk factors leading to iAKI in donors prior to organ procurement such as avoidance of hypotension, and treatment of any infections. This also highlights the impor- tance of assessing the etiology and phenotype of AKI prior to organ acceptance or rejection. Additionally, clinically adjudicated iAKI was associated with increased risk for DGF and lower 1-year eGFR, further highlighting the potential benefit of accurately phenotyping AKI. hAKI is a functional change in the kidneys with reduction in filtration, but iAKI involves tubu- lar cell injury and structural damage to the kidneys [25–27]. Given the pathophysiologic, and known transcriptional differences in kidney tissue between intrinsic and hemodynamic AKI [13], it is biologically plausible that iAKI in deceased donors is associated with an increase in DGF and lower 1-year eGFR. Furthermore, our findings are in agreement with prior literature, which shows that tubular injury on histology is associated with DGF [28,29]. Our results also highlight that phenotyping AKI both clinically and by biomarkers is important in terms of PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 10 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes recipient outcomes. Pre-procurement identification of donors with iAKI using biomarkers such as NGAL may offer a window for clinicians to intervene to improve future recipient out- comes. We previously identified that urine NGAL among other biomarkers was not associated with recipient outcomes [15]. However, urine NGAL has been shown to be associated with ATI severity in deceased donors [30]. Our current findings suggest that NGAL may potentially have a different association with recipient outcomes in the setting of iAKI. Future studies with larger sample size will need to investigate this further. When evaluating GF, our study was limited by sample size but did not identify differing rates of GF among clinically adjudicated AKI phenotypes. Although future studies with larger sample size are needed to properly investigate the potential association between iAKI and GF, the findings of our study suggest that donor iAKI may lead to significantly lower 1-year eGFR but this decline in graft function may not be clinically meaningful to manifest as graft failure. These findings are consistent with our previously published data, which have shown that deceased-donor AKI, defined by terminal serum creatinine, is not associated with GF [9]. This lack of association with GF is likely due to the unique events surrounding deceased-donor AKI and could be partially explained by the predominance of hAKI among deceased donors as we have shown in this study. Labeling deceased-donor AKI as one disorder by a rise in serum cre- atinine rather than a heterogeneous condition and manifestation of multiple disorders, risks the potential discard of kidneys with good transplant prognosis. In fact, our study identified a subset of donors with ongoing hAKI at time of organ procurement with better 1-year graft function as compared to non-AKI. Our findings need to be interpreted in the context of our study’s limitations. The three adjudicators may not be an accurate representation of the general physician population as all trained at the same institution. The phenotyping of AKI was mainly as nephrologists were encouraged to use their clinical judgment. However, this more accurately reflected real life clinical settings, as physicians rely on their clinical acumen to classify and phenotype AKI. Another limitation in our study involved our definition of AKI, which was based on a rise in serum creatinine, and did not account for potential creatinine level fluctuations in undiag- nosed chronic kidney disease in the donors. In addition, the biomarker differences among phenotypes could have captured the clinical severity rather than the actual etiology of AKI as adjudicators assessed a wide variety of variables including laboratory, medication, as well as demographic data to adjudicate donor AKI cases. Histological confirmation of our clinically adjudicated AKI phenotypes was limited as ATI on biopsy was only found in <15% of kidneys in our study. This is limited by some practical concerns as procurement wedge biopsies are usually interpreted in a rush by non-renal pathologists, and hence tubular injury may not be accurately reported [31]. However, the absence of a relationship between the evidence of ATI on biopsy and clinical AKI phenotypes further calls into question the utility of procurement biopsies [32–34]. Alternatively, biomarkers such as NGAL have been shown to be specific to tubular injury in the kidneys, which we have shown to be significantly higher in the iAKI group [35]. Furthermore, our AKI definition utilized lowest serum creatinine as the baseline and a change of 0.3 mg/dL could have preceded the lowest creatinine measurement. This approach presumes that some donors could have incurred AKI prior to admission and that admission creatinine is not representative of their baseline value. Given the inclusivity of this definition, less severe AKI could have been included in our cohort. Another limitation is the lack of adjustment for multiple comparisons for the number of biomarkers and clinical vari- ables tested. Lastly, our results need to be validated in a larger sample size. Future studies may take an alternative approach with a focus on machine learning techniques and data-driven approaches to identify variables predictive of clinically adjudicated AKI in a smaller subset, which can then be applied to larger subsets to assess the validity of our findings [36]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 11 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes In conclusion, we have shown that clinically adjudicated deceased-donor hAKI and iAKI were biologically different by injury and repair urine biomarkers. iAKI was associated with higher rates of DGF and lower 1-year eGFR but was not associated with GF, whereas higher 1-year eGFR was noted for kidneys with hAKI at time of organ procurement. Clinically pheno- typed deceased donor AKI may help inform decisions regarding organ allocation and utilization. Supporting information S1 Fig. a: Example of a Deceased-Donor Profile (adjudicated as hemodynamic AKI by all three adjudicators). We created de-identified donor profiles abstracted donor clinical variables and distributed these profiles to nephrologists for adjudication. b: Example of a Deceased- Donor Profile (adjudicated as intrinsic AKI by all three adjudicators). We created de-identified donor profiles abstracted donor clinical variables and distributed these profiles to nephrolo- gists for adjudication. (TIF) S1 Table. Breakdown of agreement among nephrologists. Among the adjudicated cases of hAKI, 51% had perfect agreement in the hAKI subtype, 17% had perfect agreement in the mAKI subtype and 32% had perfect agreement in the iAKI subtype. (TIF) S2 Table. Full multivariable model for the outcome of DGF. There were no significant asso- ciations with DGF when comparing the hAKI and mAKI with non-AKI. (TIF) S3 Table. Full multivariable model for the outcome of 1-year eGFR. iAKI was indepen- dently associated with an 11 ml/min/1.73m2 decrease in eGFR compared to non-AKI. (TIF) Acknowledgments We would like to thank all the families of the deceased donors and all the recipients who allowed for this science to progress. We are eternally indebted to your contributions to the field of kidney transplant. The data reported here have been supplied by the United Network for Organ Sharing (UNOS) as the contractor for the OPTN. The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or inter- pretation by the OPTN or the U.S. Government. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. These organizations were not involved in study design, analysis, interpretation, or manuscript creation. Author Contributions Conceptualization: Sherry G. Mansour, Heather R. Thiessen-Philbrook. Data curation: Nadeen Khoury, Ravi Kodali, Sarthak Virmani, Wassim Obeid. Formal analysis: Sherry G. Mansour, Yaqi Jia, Yu Yamamoto, Heather R. Thiessen-Philbrook. Funding acquisition: Sherry G. Mansour, Chirag R. Parikh. PLOS ONE \| https://doi.org/10.1371/journal.pone.0264329 March 3, 2022 12 / 15 PLOS ONEAdjudicated kidney injury and graft outcomes Investigation: Sherry G. Mansour. Methodology: Sherry G. Mansour. Software: Sherry G. Mansour. Supervision: Chirag R. Parikh. Writing – original draft: Sherry G. Mansour. 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10.1371_journal.pmed.1004339	RESEARCH ARTICLE Use of isotretinoin among girls and women of childbearing age and occurrence of isotretinoin-exposed pregnancies in Germany: A population-based study Jonas ReinoldID Ulrike HaugID 1,3¤* 1, Bianca KollhorstID 2, Nadine WentzellID 1, Katharina PlatzbeckerID 1, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany, 2 Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany, 3 Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany ¤ Current address: Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany * haug@leibniz-bips.de OPEN ACCESS Citation: Reinold J, Kollhorst B, Wentzell N, Platzbecker K, Haug U (2024) Use of isotretinoin among girls and women of childbearing age and occurrence of isotretinoin-exposed pregnancies in Germany: A population-based study. PLoS Med 21(1): e1004339. https://doi.org/10.1371/journal. pmed.1004339 Received: September 30, 2022 Accepted: December 21, 2023 Published: January 25, 2024 Abstract Background AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: Exposure to isotretinoin during pregnancy must be avoided due to its teratogenicity, but real-world data on its use are scarce. We aimed to describe (i) isotretinoin use in women of childbearing age in Germany; (ii) the occurrence of isotretinoin-exposed pregnancies; and (iii) malformations among children exposed in utero. Methods and findings Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pmed.1004339 Copyright: © 2024 Reinold et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: As we are not the owners of the data we are not legally entitled to grant access to the data of the German Pharmacoepidemiological Research Database. In accordance with German data protection regulations, access to the data is granted only to Using observational data from the German Pharmacoepidemiological Research Database (GePaRD, claims data from approximately 20% of the German population), we conducted annual cross-sectional analyses to determine age-standardized prevalence of isotretinoin use between 2004 and 2019 among girls and women aged 13 to 49 years. In cohort analy- ses, we estimated the number of exposed pregnancies by assessing whether there was pre- scription supply overlapping the beginning of pregnancy (estimated supply was varied in sensitivity analyses) or a dispensation within the first 8 weeks of pregnancy. Data of live- born children classified as exposed in a critical period according to these criteria were reviewed to assess the presence of congenital malformations. The age-standardized prevalence of isotretinoin use per 1,000 girls and women increased from 1.20 (95% confidence interval [CI]: 1.16, 1.24) in 2004 to 1.96 (95% CI: 1.92, 2.01) in 2019. In the base case analysis, we identified 178 pregnancies exposed to isotreti- noin, with the number per year doubling during the study period, and at least 45% of exposed pregnancies ended in an induced abortion. In sensitivity analyses, the number of exposed pregnancies ranged between 172 and 375. Among live-born children, 6 had major congenital malformations. The main limitation of this study was the lack of information on PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 1 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany the prescribed dose, i.e., the supply had to be estimated based on the dispensed amount of isotretinoin. Conclusions Isotretinoin use among girls and women of childbearing age increased in Germany between 2004 and 2019, and there was a considerable number of pregnancies likely exposed to iso- tretinoin in a critical period. This highlights the importance of monitoring compliance with the existing risk minimization measures for isotretinoin in Germany. Author summary Why was this study done? • Systemic (oral) isotretinoin is used in the treatment of moderate to severe acne. • Given that isotretinoin is one of the strongest human teratogens known today, it is important to monitor the use of isotretinoin in girls and women of childbearing age as well as the occurrence of pregnancies exposed to this drug. • There is a lack of population-based studies addressing these research questions. What did the researchers do and find? employees of the Leibniz Institute for Prevention Research and Epidemiology – BIPS on the premises of the institute and in the context of approved research projects. Third parties may only access the data in cooperation with the Leibniz Institute for Prevention Research and Epidemiology – BIPS and after signing an agreement for guest researchers. Please contact gepard@leibniz-bips. de for help with this process. Funding: The study was partly funded by the German Federal Institute for Drugs and Medical Devices, Bonn (BfArM, V-18281/ 68605 / 2019- 2020) (https://www.bfarm.de). The study proposal was submitted by UH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. JR, NW, KP, BK, and UH are working at an independent, non-profit research institute, the Leibniz Institute for Prevention Research and Epidemiology – BIPS. Unrelated to this study, BIPS occasionally conducts studies financed by the pharmaceutical industry. These are post-authorization safety studies (PASS) requested by health authorities. The design and conduct of these studies as well as the interpretation and publication are not influenced by the pharmaceutical industry. The study presented was not funded by the pharmaceutical industry. • Using a database covering 20% of the German population, we conducted cross-sectional analyses to assess the prevalence of isotretinoin use between 2004 and 2019 in girls and women of childbearing age. • We found that the age-standardized prevalence of isotretinoin use increased from 1.20 Anatomical Therapeutic Abbreviations: ATC, AU : Anabbreviationlisthasbeencompiledforthoseusedinthetext:Pleaseverifythatallentriesarecorrect: Chemical; CI, confidence interval; DDD, defined daily dose; FAERS, FDA Adverse Event Reporting System; GePaRD, German Pharmacoepidemiological Research Database; IQR, interquartile range. to 1.96 per 1,000 girls/women during this period. • In cohort analyses, we estimated the number of pregnancies likely exposed to isotreti- noin in a critical period. In the base case analysis, we identified 178 of such pregnancies. • Sensitivity analyses considering the recommended one-month washout period sug- gested that there could have been additional pregnancies exposed to isotretinoin because they started before the end of the washout period. What do these findings mean? • Isotretinoin use among girls and women of childbearing age increased in Germany between 2004 and 2019, and there were a considerable number of pregnancies likely exposed to isotretinoin in a critical period. • This highlights the importance of monitoring compliance with the existing risk minimi- zation measures for isotretinoin in Germany. • It also seems important to increase awareness regarding the component of the preg- nancy prevention program that recommends contraception also in the month after treatment cessation. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 2 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany • The main limitation of this study was the lack of information on the prescribed dose of isotretinoin. We therefore estimated the dose based on the dispensed amount of isotreti- noin and varied the underlying assumptions. Introduction Systemic (oral) treatment with the vitamin A derivative isotretinoin (13-cis-retinoic acid) is indicated in moderate to severe acne (e.g., nodular or conglobate acne or acne at risk of perma- nent scarring) resistant to therapy with systemic antibiotics and topical anti-acne treatment [1]. Systemic isotretinoin is considered to be the clinically most effective anti-acne therapy, achieving long-term remission or significant improvement in many patients [2,3]. At the same time, isotretinoin is one of the strongest human teratogens known today [4]. Given the age dis- tribution of patients with acne, girls and women of childbearing age are among the patients to whom isotretinoin is prescribed. In 2003, i.e., 20 years after the EU-market authorization for isotretinoin, uniform preg- nancy prevention programs were established in order to avoid isotretinoin exposure during pregnancy. In 2018, these measures were evaluated and updated [5]. Girls and women initiat- ing systemic isotretinoin treatment are required to have monthly pregnancy tests and to use 2 complementary contraceptive methods from 1 month before treatment initiation to 1 month after treatment cessation [5]. The continuation of contraception for 1 month after treatment cessation is recommended due to delayed plasma elimination [3]. Because of this delayed elim- ination, pregnancies beginning shortly after treatment with isotretinoin may also be exposed to potentially harmful plasma concentrations. Given the potential harm to the unborn child, it is important to monitor the use of isotreti- noin in girls and women of childbearing age as well as the frequency of pregnancies exposed to this drug. However, no such data are available from Germany. Also internationally, popula- tion-based studies quantifying the frequency of isotretinoin-exposed pregnancies are only available for few countries, namely the United States, Canada, France, and the Netherlands [6–11], and there are no studies on time trends of isotretinoin use among young women. Fur- thermore, there is a lack of studies systematically exploring the extent to which conceptions occurring in the month after discontinuation of treatment may contribute to the number of exposed pregnancies. Therefore, the aims of this study were (i) to describe the utilization of isotretinoin in girls and women of childbearing age in Germany including time trends; (ii) to describe the fre- quency of pregnancies exposed to isotretinoin, considering also potential exposure due to treatment cessation in the month before pregnancy; and (iii) to explore potential malforma- tions among children exposed to isotretinoin in early pregnancy (not to be mistaken with esti- mating causal effects). Methods Data source We used the German Pharmacoepidemiological Research Database (GePaRD) which is based on claims data from 4 statutory health insurance providers in Germany and currently includes information on approximately 25 million persons who have been insured with one of the par- ticipating providers since 2004 or later. In addition to demographic data, GePaRD contains PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 3 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany information on drug dispensations as well as outpatient (i.e., from general practitioners and specialists) and inpatient services and diagnoses. The data is available on an individual level. Per data year, there is information on approximately 20% of the general population and all geographical regions of Germany are represented [12,13]. The German health care system is based on mandatory private or statutory health insurances [14]. About 90% of the general pop- ulation are covered by statutory health insurances [15]. Core characteristics of the German health insurance system are uniform access to all levels of care and a free choice of providers. In GePaRD, the Anatomical Therapeutic Chemical (ATC) code is used to identify drugs dispensed in the outpatient setting. Systemic isotretinoin treatment was identified based on the ATC code D10BA01. Diagnoses in GePaRD are coded according to the International Clas- sification of Diseases 10th revision, German modification (ICD-10-GM). For research on drug utilization and safety during pregnancy, algorithms to identify and classify pregnancy out- comes [16,17], to estimate the beginning of pregnancy [18], and to link mothers with their children [19] have been developed for GePaRD. This study follows the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines (S1 Checklist). Study design and study population Prevalent use of isotretinoin among girls and women of childbearing age. To determine prevalent use of isotretinoin over time, we conducted year-wise cross-sectional analyses from 2004 to 2019. For each calendar year, we included all girls and women in the numerator who had at least 1 dispensation of isotretinoin, were aged between 13 and 49 years in the respective year, and were insured on June 30 of that year. In the denominator, we included all girls and women aged between 13 and 49 years in the respective year and insured on June 30 of that year. Identification of exposed pregnancies. Using the algorithm for pregnancy outcomes, we identified pregnancies ending between 2004 and 2019 and occurring among girls and women aged 13 to 49 years at beginning of pregnancy. Exposure to isotretinoin during early pregnancy was assumed if the exposure window assigned to the last dispensation before pregnancy overlapped the first day of pregnancy or if there was a dispensation in the first 8 weeks of pregnancy. The latter time period was restricted to 8 weeks rather than 12 weeks because it is then more likely that the dispensed drug was actu- ally used during the first trimester, so this was a conservative approach to avoid overestimating the number of pregnancies exposed during the first trimester. The exposure window assigned to the last dispensation before pregnancy was defined as the dispensation date plus the total number of defined daily doses (DDDs) in the package (1 DDD of isotretinoin is 30 mg). In various sensitivity analyses, we changed the exposure window to take into account (i) delayed elimination (30 days were added to the exposure assigned to the last dispensation); (ii) poten- tial treatment at higher or lower doses (the DDD was multiplied by the factor 0.75, 1.5, and 3.0, respectively, which corresponds to a daily dose of 40 mg, 20 mg, and 10 mg, respectively); and (iii) delayed elimination in combination with treatment at higher or lower doses (the DDD was multiplied by the factor 0.75, 1.5, and 3.0, respectively, and in addition, a 30-day period was added). In addition (iv), we conducted a sensitivity analysis assuming a fixed sup- ply of 30 days after the last dispensation before pregnancy as this is the maximum supply that should be dispensed to girls and women of childbearing age according to the German preg- nancy prevention program for isotretinoin [20]. In each of the analyses, we made sure that the pre-observation period of the mothers included in the respective analysis before beginning of pregnancy was sufficiently long to assess the exposure status. Given that certain incomplete pregnancies in claims data may have no outcome recorded (e.g., miscarriages not requiring medical treatment, induced abortions without medical PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 4 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany indication) and would therefore remain undetected when only applying the outcome algo- rithm, we also searched for this type of incomplete pregnancies. To qualify for this category, there had to be at least a code indicating the expected delivery date and another indicator of a pregnancy (e.g., a pregnancy-related examination) within a plausible time interval after the estimated beginning of pregnancy. The date of the last pregnancy-related examination recorded in the data was assigned as the end of these pregnancies. We determined the expo- sure status of these pregnancies as described above. Pregnancies were followed up from the beginning to the end of pregnancy. Pregnancies that could not be followed up until the end due to an interruption in the mother’s health insur- ance period, the end of the study period or the mother’s death, the pregnancies were not excluded but we classified them as a separate category (“ongoing pregnancies”) given that their outcome could not be determined within the available follow-up. When assessing the distribu- tion of pregnancy outcomes, we only considered non-ongoing pregnancies. Exploration of potential malformations among exposed children. For exposed preg- nancies ending in a live birth, we applied an algorithm linking mothers with their children [19] to explore potential congenital malformations in the children. Among linked children, we identified those with any malformation code (ICD-10-GM: Q00–Q99) occurring up to 1 year after birth. We reviewed all information available on them in GePaRD in order to ver- ify the presence of malformations. The profile review was conducted independently by 2 reviewers (1 physician and 1 epidemiologist, both with expertise in the interpretation of codes in German claims data). Consensus was reached in a subsequent case conference. While reviewers were instructed to consider certain objective criteria confirming the occur- rence of malformations, such as the presence of inpatient codes, repeated coding, and treat- ment or monitoring of the malformations, they were specifically asked to apply their clinical judgment in light of the overall patient history including, e.g., gestational age at birth or chromosomal abnormalities as potential alternative explanations for malforma- tions. In doing so, the primary focus was on malformations categorized as “major” as sug- gested by EUROCAT, but so-called “minor” malformations were also considered if treatment (e.g., surgical) or other information (e.g., physical impairment, malformation- related complications) indicated a higher level of severity [21]. Data analysis In the cross-sectional analyses, we determined—for each year—age-specific and age-standard- ized prevalence of isotretinoin use. For age standardization, we used the age distribution of the German female population on 31 December 2019 as reference. Furthermore, we described the medical specialty of the prescribing physicians. For that purpose, we considered all isotretinoin dispensations in the respective year among included girls and women and assigned the spe- cialty of the prescribing physician based on the information contained in the individual physi- cian number [22]. As for pregnancies, we determined the number of those classified as exposed overall and by calendar year. We described the mothers’ age at the beginning of preg- nancy and the pregnancy outcomes. The distribution of continuous variables was summarized as median (interquartile range [IQR]), while categorical variables were expressed as frequency counts (percentages). We con- ducted all statistical analyses using the software SAS version 9.4. The analyses were planned beforehand and conducted in an analogous way for other drugs with teratogenic potential [23]. There were no data-driven changes to the analysis plan. Two of the sensitivity analyses were conducted in response to reviewers (assuming a daily dose of 40 mg and a fixed supply of 30 days after the last dispensation before pregnancy, respectively). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 5 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany Ethics and approvals In Germany, the utilization of health insurance data for scientific research is regulated by the Code of Social Law. All involved health insurance providers as well as the German Federal Office for Social Security and the Senator for Health, Women and Consumer Protection in Bremen as their responsible authorities approved the use of GePaRD data for this study. Informed consent for studies based on claims data is required by law unless obtaining consent appears unacceptable and would bias results, which was the case in this study. According to the Ethics Committee of the University of Bremen, studies based on GePaRD are exempt from institutional review board review. Specifically, for this study, the Ethics Committee of the Uni- versity of Bremen was again asked whether a retrospective review might be needed. They judged that the information deducible from the publication is not suited—even in combina- tion—to result in an identification of affected persons. They do thus not believe that there is sufficient risk to the individuals to warrant a retrospective review by an ethics committee. From a public health perspective, the information provided in this paper is very important to increase knowledge on the teratogenic effects of isotretinoin including the number and type of affected organs, all the more so as the use of isotretinoin is further increasing in women of childbearing age. Results Prevalence of isotretinoin use among girls and women of childbearing age Overall, there were 50,936 girls and women with at least 1 prescription of isotretinoin in GePaRD between 2004 and 2019. Across all years, 52% to 67% of users were 16 to 30 years old, and 81% to 90% were �40 years old (S1 Table). The overall age-standardized prevalence of iso- tretinoin use per 1,000 girls and women increased from 1.20 (95% confidence interval [CI]: 1.16, 1.24) in 2004 to 1.96 (95% CI: 1.92, 2.01) in 2019, i.e., by 63% (Fig 1). The prevalences increased particularly among girls and women aged 13 to 30 years. For example, in age group 16 to 20 years they increased by 103% (from 2.07 [95% CI: 1.92, 2.23] to 4.20 [95% CI: 4.00, 4.41] per 1,000) and in age group 21 to 25 years they increased by 96% (from 1.96 [95% CI: 1.82, 2.11] to 3.84 [95% CI: 3.67, 4.02] per 1,000). In total, 339,408 prescriptions of isotretinoin were dispensed to girls and women aged 13 to 49 years during the study period (S2 Table). The vast majority of those prescriptions (89%) was issued by dermatologists while general practitioners had a share of 6%. Exposed pregnancies In the base case analysis, there were 178 pregnancies classified as exposed to isotretinoin dur- ing early pregnancy (Table 1). The number of exposed pregnancies per year increased over the course of the study period. On average, there were 7 pregnancies per year from 2004 to 2011 and 15 pregnancies per year from 2012 to 2019. The majority of the 178 pregnancies (62.9%) occurred in the age group 16 to 30 years (S3 Table). The mothers’ median age at the beginning of pregnancy was 28 years (IQR 24 to 33 years). About half of these pregnancies were classified as exposed due to a dispensation of isotretinoin within the first 8 weeks of pregnancy, about one quarter because the exposure window assigned to the last dispensation before pregnancy overlapped the beginning of pregnancy and in the remaining, both criteria were fulfilled. For exposed pregnancies ending during the observation period (n = 164), the distribution of pregnancy outcomes is summarized in Table 1. Overall, 29.3% (n = 48) of exposed pregnan- cies ended in live birth, thereof 6.3% (n = 3) were preterm births, 45.1% (n = 74) ended in an PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 6 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany -specific and age-standardized prevalence with 95% CIs (shaded area) of isotretinoin use per 1,000 girls and women Fig 1. AgeAU : AbbreviationlistshavebeencompiledforthoseusedinFig1andTables1and2:Pleaseverifythatallentriesarecorrect: aged 13–49 years between 2004 and 2019 in the GePaRD. CI, confidence interval; GePaRD, German Pharmacoepidemiological Research Database. https://doi.org/10.1371/journal.pmed.1004339.g001 induced abortion, and 1.8% (n = 3) in a miscarriage. For 20.7% (n = 34) of pregnancies, no outcome was recorded (i.e., they are assumed to also be abortions). Table 1 also shows the number of pregnancies and the distribution of pregnancy outcomes observed in the sensitivity analyses. When delayed elimination (washout period of 1 month) was considered, the number of exposed pregnancies increased by 31% (from 178 to 233), while the proportion of live births and induced abortions remained at a level similar to the base case analysis. If a fixed supply of 30 days was assumed, there was an increase of exposed pregnancies by 7% (from 178 to 190). If a daily dose of 40 mg was assumed, the number of exposed preg- nancies decreased by 3% (from 178 to 172), if a daily dose of 20 mg was assumed, it increased by 6% (from 178 to 188) and if a daily dose of 10 mg was assumed, it increased by 58% (from 178 to 283) compared to the base case analysis. When the one-month washout period was con- sidered for assumed doses of 10 mg, 20 mg, and 40 mg, the increase in the number of exposed pregnancies was 33% (from 283 to 375), 39% (from 188 to 261), and 30% (from 172 to 224), respectively. In all sensitivity analyses, the proportion of live births was below 40% except for those with an assumed daily dose of 10 mg when delayed elimination was considered (46%). Characterization of exposed children In 138 out of 157 (87.9%) pregnancies ending in a live birth and classified as exposed in the base case or the sensitivity analyses, the mother’s and child’s data could be linked. Of these, 6 children had at least 1 major congenital malformation according to EUROCAT definitions (Table 2). Five children had malformations classified as minor according to EUROCAT but are reported here as they required surgical or other intense treatment. Two other children had an atrial septal defect that could not be classified into major or minor according to EUROCAT given that the distinction requires information that is not available in German claims data. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 7 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany Table 1. Number of pregnancies exposed to isotretinoin between 2004 and 2019 in GePaRD, mother’s age at pregnancy beginning and type of pregnancy outcome: base case and sensitivity analyses considering delayed elimination (exposure window assigned to the last dispensation before pregnancy was extended by 1 month), a fixed supply of 30 days, and the possibility that lower or higher doses than the DDD for isotretinoin (30 mg) were used. Base case analysis Daily dose 30 mg (DDD) Sensitivity analyses (A) One- month extension (B) Fixed supply of 30 days (C) Daily dose 40 mg (D) Daily dose 40 mg and one- month extension (E) Daily dose 20 mg (F) Daily dose 20 mg and one- month extension (G) Daily dose 10 mg (H) Daily dose 10 mg and one-month extension 178 233 190 172 224 188 261 283 375 46 (25.8%) 103 (44.2%) 58 (30.5%) 40 (23.3%) 93 (41.5%) 58 (30.9%) 133 (50.9%) 161 (56.9%) 254 (67.7%) 88 (49.4%) 62 (26.6%) 77 (40.5%) 96 (55.8%) 66 (29.5%) 77 (40.9%) 60 (23.0%) 56 (19.8%) 53 (14.1%) 44 (24.7%) 68 (29.2%) 55 (28.9%) 36 (20.9%) 65 (29.0%) 53 (28.2%) 68 (26.1%) 66 (23.3%) 68 (18.1%) 28 (24; 33) 1641 28 (24; 33) 2131 28 (23; 32) 1751 28 (24; 32) 1581 28 (24; 33) 2051 28 (24; 33) 1721 28 (24; 33) 2401 28 (24; 33) 2591 28 (25; 33) 3401 48 (29.3%) 63 (29.6%) 52 (29.7%) 44 (27.8%) 61 (29.8%) 49 (28.5%) 81 (33.8%) 103 (39.8%) 157 (46.2%) 3 (6.3%) 4 (6.3%) 3 (5.8%) 3 (6.8%) 3 (4.9%) 3 (6.1%) 5 (6.2%) 4 (3.9%) 8 (5.1%) 0 (0%) 1 (0.5%) 0 (0%) 0 (0%) 1 (0.5%) 0 (0%) 1 (0.4%) 2 (0.8%) 2 (0.6%) 74 (45.1%) 90 (42.3%) 79 (45.1%) 74 (46.8%) 86 (42.0%) 79 (45.9%) 94 (39.2%) 89 (34.4%) 96 (28.2%) 5 (3%) 7 (3.3%) 6 (3.4%) 5 (3.2%) 6 (2.9%) 5 (2.9%) 6 (2.5%) 6 (2.3%) 7 (2.1%) 3 (1.8%) 3 (1.4%) 4 (2.3%) 4 (2.5%) 3 (1.5%) 3 (1.7%) 3 (1.3%) 3 (1.2%) 3 (0.9%) 34 (20.7%) 49 (23%) 34 (19.4%) 31 (19.6%) 48 (23.4%) 36 (20.9%) 55 (22.9%) 56 (21.6%) 75 (22.1%) Number of exposed pregnancies Exposure overlapping pregnancy beginning, n (%) Dispensation in the first 8 weeks of pregnancy, n (%) Both, n (%) Mother’s age Median (Q1; Q3) Number of exposed and non-ongoing pregnancies1 Pregnancy outcomes Live birth, n (%) thereof preterm birth, n (% of live births) Still birth, n (%) Induced abortion, n (%) Ectopic pregnancy or molar pregnancy, n (%) Miscarriage, n (%) No pregnancy outcome recorded2, n (%) 1This number is lower than the number of all exposed pregnancies in the respective analysis given that some pregnancies were still ongoing at the end of the observation period, so the outcome could not be determined yet. The percentages regarding the different types of pregnancy outcome refer to the number of non-ongoing pregnancies. 2There were clear indicators of a pregnancy but no outcome was recorded. It can be assumed that these pregnancies ended in a miscarriage not requiring medical care or an induced abortion not reimbursed by the health insurance. DDD, defined daily dose; GePaRD, German Pharmacoepidemiological Research Database. https://doi.org/10.1371/journal.pmed.1004339.t001 Of the 6 children with major malformations, 3 were classified as exposed in the base case analyses and 3 were classified as exposed in the sensitivity analyses. The major malformations affected the heart, the eye, the skull, and the nose. Furthermore, 1 child had sacral spina bifida with hydrocephalus and 1 child had multiple major malformations including microcephaly, malformation of the ear and heart. Among children with so-called minor malformations, 2 had congenital hypertrophic pyloric stenosis requiring surgical treatment, 2 had undescended testicles requiring surgical treatment and 1 had metatarsus varus with abnormalities of gait and mobility requiring extensive orthopedic treatment. Four of these 5 children were classified as exposed in the sensitivity analyses only. Table 2 shows—for each of the 13 children with congenital malformations—in which of the analyses they were classified as exposed. In all of them, the exposure window assigned to the last dispensation before pregnancy overlapped the beginning of pregnancy. In the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 8 / 15 PLOS MEDICINETable 2. Malformations observed in live-born children exposed to isotretinoin during pregnancy in GePaRD between 2004 and 2019. Isotretinoin use in young women and during pregnancy in Germany Child Q-Code Description 1 2 3 4 5 6 7 8 9 10 11 12 13 Q24.9 Congenital malformation of heart, unspecified Q40.0 Congenital hypertrophic pyloric stenosis3 Q15.9 Congenital malformation of eye, unspecified Q10.3 Other congenital malformations of eyelid Q02 Microcephaly Q16.5 Congenital malformation of inner ear Q21.0 Ventricular septal defect Stenosis of pulmonary artery4 Q25.6 Q40.0 Congenital hypertrophic pyloric stenosis3 Q53.1 Undescended testicle, unilateral5 Q21.1 Atrial septal defect6 Q75.0 Craniosynostosis Q01.0 Frontal encephalocele Q53.1 Undescended testicle, unilateral7 Q75.8 Other specified congenital malformations of skull and face bones Q30.1 Agenesis and underdevelopment of nose Q66.2 Metatarsus varus8 Q21.1 Atrial septal defect6 Q05.3 Sacral spina bifida with hydrocephalus Classified as “major” according to EUROCAT2 Yes No Yes No Yes Yes Yes Yes No No Unclear Yes Yes No Yes Yes No Unclear Yes Exposure classification Base case Sensitivity analyses1 A B C D E F G H X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X 1In the base case analysis, a daily dose of 30 mg was assumed and the exposure window was not extended beyond the supply provided by the last dispensation before pregnancy. In the sensitivity analyses, the estimated daily dose was varied and/or the exposure window was extended to consider delayed plasma elimination. Specifically, the variations were as follows. A: one-month extension of the exposure window, B: a fixed exposure window of 30 days after the last dispensation was assumed, irrespective of the dispensed amount of isotretinoin, C: daily dose 40 mg, D: daily dose 40 mg and one-month extension of the exposure window, E: daily dose 20 mg, F: daily dose 20 mg and one-month extension of the exposure window, G: daily dose 10 mg, H: daily dose 10 mg and one-month extension of the exposure window. 2For those with minor malformation, information on treatment is provided to explain why they are reported here (see also Methods section). 3Hypertrophic pyloric stenosis was surgically treated. 4Classified as “major” as child was born on term (EUROCAT classification “minor” if gestational age at birth <37 week). 5Undescended testicle was surgically treated. 6The subtype of atrial septal defect is not reported in German claims data. “Unclear” in the column regarding EUROCAT classification means that although additional information coded for the child was considered, it was not clear whether the child had the subtype classified as minor according to EUROCAT (ICD-10 Q21.11) or one of the other subtypes classified as major. For child 7, ICD-10-GM Q21.1 was coded repeatedly (also in the inpatient setting) together with codes for cardiology visits and further diagnostics indicating monitoring beyond the first year of life. For child 12, ICD-10-GM Q21.1 was also coded repeatedly together with codes for cardiac murmur (ICD-10-GM R01), cardiology visits and further diagnostics indicating monitoring beyond the first year of life. 7Undescended testicle was surgically treated; in addition, there were codes for cardiac malformations (ICD-10-GM Q21.1, Q25.0, Q21.8) that are not listed in the table as the child was born prematurely. 8Child received extensive orthopedic treatment and had continuous records of ICD-10-GM R26.8: Other and unspecified abnormalities of gait and mobility. GePaRD, German Pharmacoepidemiological Research Database. https://doi.org/10.1371/journal.pmed.1004339.t002 children classified as exposed in the base case analysis, the mother additionally had a dis- pensation of isotretinoin within the first 8 weeks of pregnancy, while no such dispensations were observed in the mothers of children classified as exposed in the sensitivity analyses only. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 9 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany Discussion In this population-based study covering approximately 20% of the German population, we found that the use of isotretinoin among girls and women of childbearing age increased by 63% between 2004 and 2019, from 1.20 per 1,000 in 2004 to 1.96 per 1,000 in 2019. This increase was particularly pronounced in girls and women up to the age of 30 years. Across the whole study period, more than 80% of users were �40 years (between 3,629 of 4,456 and 6,723 of 7,504), i.e., in age groups in which pregnancies typically occur. Even though risk minimiza- tion measures are in place, we observed—in the base case analysis—178 pregnancies likely exposed to isotretinoin during a time window most critical for fetal development. In sensitivity analyses varying the assumptions on the dose and considering the recommended one-month washout period, this number ranged between 172 and 375 pregnancies. In the base case analy- sis, at least 74 exposed pregnancies ended in an induced abortion (sensitivity analyses: 74 to 96). Among live births classified as exposed in the base case or the sensitivity analyses, there were 6 children with major malformations. Regarding the increase in the prevalence of isotretinoin use among girls and women of childbearing age, there is hardly any data to which we could compare our findings. There is only a single data source reporting—for each year—the total number of DDDs dispensed to all persons with statutory health insurance in Germany (i.e., no denominator/prevalence esti- mate, and no age or sex-specific findings). Even though comparability is limited, that report supports our finding regarding an increase of oral use of isotretinoin (ATC code D10BA01) between 2004 and 2019 (2004: 5.2 million dispensed DDDs; 2019: 6.0 million dispensed DDDs) [24,25]. The mainstays of acne treatment have remained largely unchanged over recent years [26], so the increase in the prevalence of isotretinoin use cannot be explained by changes in treatment guidelines. Also, there have been no reports on major changes in the disease prev- alence during the past 15 years. We can thus only speculate about reasons for this increase. German guidelines, which expired in 2014 without renewal, recommended systemic isotreti- noin as second-line therapy after systemic antibiotics and topical anti-acne therapy, while the European guidelines recommend systemic isotretinoin as first-line therapy in moderate as well as severe acne [1]. In view of the effectiveness of systemic isotretinoin in the treatment of acne and the increasing awareness of the mental health burden associated with acne, German der- matologists might have been inclined to follow the European rather than the German guide- lines. Another potential reason could be an increased awareness of antibiotic resistance related to the antibiotic stewardship programs launched during the past decade [27]. Considering rec- ommendations from experts, regulatory authorities, and the WHO to shorten and reduce the number of treatment episodes with antibiotics, the threshold to proceed to second-line treat- ment with systemic isotretinoin might have become lower when treating acne patients [3,28]. In Germany, there is a long-established pregnancy prevention program for isotretinoin in line with EMA recommendations. In addition, prescriptions of isotretinoin must be filled within 7 days of being issued and the supply per prescription is limited to 30 days of treatment [20]. Despite these measures, we found—in the base case analysis—178 pregnancies likely exposed to isotretinoin in the critical time window. As our database covers approximately one fifth of the German population, it can roughly be estimated that at least 980 such pregnancies occurred across Germany between 2004 and 2019. As a matter of concern, the number of exposed pregnancies per year doubled during the study period, i.e., this problem is continu- ously gaining relevance. Our sensitivity analyses considering delayed plasma elimination and lower-dose treatment showed that the number of exposed pregnancies may even be consider- ably higher than estimated in the base case analysis. Of note, irrespective of the daily dose we assumed, the number of pregnancies classified as exposed increased by 30% to 39% if the one- PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 10 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany month washout period was considered. Since this suggests that pregnancies often occur too soon after the end of isotretinoin treatment, increasing awareness regarding the component of the pregnancy prevention program that recommends contraception also in the month after treatment cessation seems important. Our study confirms reports from other countries about isotretinoin-exposed pregnancies occurring despite long-standing pregnancy prevention programs [29]. However, there is a lack of studies quantifying the frequency of these pregnancies. Dividing the number of exposed preg- nancies by the total number of users in our study yields (roughly) a ratio between 3.5 (base case analysis) and 6.7 (sensitivity analyses) exposed pregnancies per 1,000 isotretinoin users. This is similar to the results of a study from the US based on pregnancy reports from the FDA Adverse Event Reporting System (FAERS) showing between 3.3 and 6.5 exposed pregnancies per 1,000 users between 1997 and 2017 [10]. It is also similar to a study based on Canadian administrative data (data from the provinces British Columbia, Saskatchewan, Manitoba, and Ontario) from 1996 to 2011, in which this ratio varied between 3.1 and 6.2 pregnancies per 1,000 isotretinoin users depending on the exposure definition (high-specificity versus high-sensitivity) [7]. Regarding Europe, population-based studies estimating the frequency of pregnancies per 1,000 users are only available for France where this ratio ranged between 0.32 and 0.95 when combin- ing data from studies conducted between 1987 and 2011 [9]. A population-based study from the Netherlands determined the proportion of isotretinoin-exposed pregnancies among all pregnancies. It reported that between 1999 and 2007 about 2.5 per 10,000 pregnancies were exposed to isotretinoin in the 30 days before or during pregnancy [11]. The number of exposed pregnancies ending in a live birth in our study was 48 of 164 (29%) in the base case analysis and increased to 157 of 340 (46%) in a sensitivity analysis where a dose of only 10 mg and delayed elimination were considered. This illustrates that the propor- tion of live births is sensitive to the exposure definition, which may hamper comparability between existing studies. Furthermore, it is influenced by the extent to which incomplete preg- nancies are captured in the respective database, which may also vary between studies. This may explain variation in the proportion of live births reported in different studies. For exam- ple, a German study using data collected in the context of counseling pregnant women and their health care providers reported a proportion of 18 live births of 91 pregnancies (20%) [30], a study from the United States reported proportions of 68 of 138 (49%) [8], while much lower proportions of 118 of 1,473 (8%) were reported by a Canadian study [7] and 85 of 553 (15%) in French studies [9]. While our study was not designed to quantify risks, it was still striking that 13 live-born children classified as exposed to isotretinoin in early pregnancy in the base case or the sensitiv- ity analyses had congenital malformations, 6 of them with major malformations. Many of these malformations involved organ systems known to be affected by the so-called retinoic acid embryopathy. This finding and the fact that at least 45% of exposed pregnancies (74 of 164) in our study ended in an induced abortion—as compared to 4% when all pregnancies in GePaRD are considered [17]—underline the importance of improving adherence to preg- nancy prevention programs. Some limitations should be considered in the interpretation of our results. First, German claims data do not include the dose prescribed by the physician. Consequently, treatment duration has to be estimated based on the DDD. The DDD represents the dose for adults, but lower doses may be used, particularly in girls and adolescents, or sometimes a higher dose might be used. To address this issue, we performed comprehensive sensitivity analyses varying the exposure windows assigned to each dispensation. Second, as in all pharmacoepidemiologi- cal studies, there is uncertainty whether patients filling a prescription are actually taking the drug. It is also uncertain whether they always start taking the drug after filling the prescription PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 11 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany or whether they may partly start later. Third, while our study was designed to describe preva- lence of isotretinoin use and pregnancies occurring under treatment with isotretinoin, our database would not have been suited to assess whether risk minimization measures were fol- lowed on an individual level. This would have required comprehensive information on con- traceptive measures, which is limited in GePaRD as in most other claims databases [31]. Fourth, with regard to pregnancy outcomes and malformations, our study was merely descrip- tive, so causal conclusions cannot be drawn. Estimating causal effects would have required a different design including the consideration of relevant confounders, as well as a larger sample of exposed children. The latter might be achieved by a consortium of large databases. We strongly advise against simplified calculations in which the proportion of live births with mal- formations in our study is compared to the corresponding proportion reported for all live births in Germany in order to estimate risks. Such a comparison could be very misleading for various reasons (e.g., due to differences in age and thus presumably also in the prevalence of comorbidities, different proportions with induced abortions and thus missing malformation status). Fifth, as in most databases used to investigate drug utilization and safety during preg- nancy, pregnancy outcomes clearly classified as “miscarriages” are underrepresented in our database, i.e., the frequency of this outcome was underestimated in our study. To address this limitation, we also searched for incomplete pregnancies with no outcome recorded. It seems plausible that these were miscarriages not requiring medical treatment or induced abortions without medical indication, i.e., not reimbursed by health insurances. Although not perfect as it still remained unclear whether it was a miscarriage or induced abortion, we think this approach was valuable to capture the number of exposed pregnancies more completely. Sixth, to assess the presence of malformations in children exposed during pregnancy, we conducted an in-depth patient profile review based on all diagnoses and procedure codes available in GePaRD but did not have additional clinical data. A main strength of our study is the large claims database that has been shown to be repre- sentative of persons with statutory health insurance in Germany in terms of drug dispensations [32]. The available data allowed us to assess trends in isotretinoin dispensations over a 15-year period. Due to the use of claims data, our analyses were not affected by recall or non-responder bias. Furthermore, the sophisticated methods developed for GePaRD (i) to identify pregnancy outcomes [17], which were further optimized to capture incomplete pregnancies; (ii) to link mothers’ and babies’ data [19]; and (iii) to estimate the beginning of pregnancy—predomi- nantly based on the estimated date of delivery—which is expected to minimize misclassifica- tion of gestational age [18], are strengths of our study. We also consider it a strength of our study that we conducted sensitivity analyses to systematically assess whether pregnancies may have started during the recommended one-month washout period after treatment cessation. The prevalence of isotretinoin use among girls and women of childbearing age increased in Germany between 2004 and 2019, and there were a considerable number of pregnancies likely exposed to isotretinoin in a critical period. This highlights the importance of monitoring com- pliance with the existing risk minimization measures for isotretinoin in Germany. Supporting information S1 Checklist. STROBE Statement—checklist of items that should be included in reports of observational studies. (DOCX) S1 Table. Number of girls and women aged 13–49 years with at least 1 dispensation of iso- tretinoin between 2004 and 2019 in GePaRD by age group and year of prescription. (DOCX) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004339 January 25, 2024 12 / 15 PLOS MEDICINEIsotretinoin use in young women and during pregnancy in Germany S2 Table. Prescriptions of Isotretinoin dispensed to girls and women aged 13–49 years between 2004 and 2019 in GePaRD: Distribution of the specialty of the prescribing physi- cian. (DOCX) S3 Table. Number of pregnancies exposed to isotretinoin between 2004 and 2019 in GePaRD by age group and year of beginning of pregnancy. (DOCX) Acknowledgments The authors would like to thank Marieke Niemeyer and Philipp Alexander Volkmar for statis- tical programming of analysis datasets and double-independent programming of results as well as all statutory health insurance providers which provided data for this study, namely AOK Bremen/Bremerhaven, DAK-Gesundheit, Die Techniker Krankenkasse (TK), and hkk Krankenkasse. Author Contributions Conceptualization: Jonas Reinold, Nadine Wentzell, Ulrike Haug. Data curation: Bianca Kollhorst. Formal analysis: Bianca Kollhorst. Funding acquisition: Ulrike Haug. Methodology: Bianca Kollhorst, Ulrike Haug. Project administration: Jonas Reinold. Supervision: Ulrike Haug. Visualization: Jonas Reinold. Writing – original draft: Jonas Reinold. Writing – review & editing: Jonas Reinold, Bianca Kollhorst, Nadine Wentzell, Katharina Platzbecker, Ulrike Haug. References 1. Nast A, Dre´no B, Bettoli V, Bukvic Mokos Z, Degitz K, Dressler C, et al. 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10.1371_journal.pone.0300509	RESEARCH ARTICLE Uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the COM-B model Charuai SuwanbamrungID Warissara Suwannakarn2, Sangchom Siripanich1,2, Md. Siddikur RahmanID Haroon StanikzaiID 1,2, Benchawan Srinam2, Pakawan Promkool2, 4* 3, Muhammad a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Suwanbamrung C, Srinam B, Promkool P, Suwannakarn W, Siripanich S, Rahman M.S, et al. (2024) Uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the COM-B model. PLoS ONE 19(3): e0300509. https://doi.org/10.1371/journal. pone.0300509 Editor: Pei Boon Ooi, Sunway University, MALAYSIA Received: August 12, 2023 Accepted: February 27, 2024 Published: March 14, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0300509 Copyright: © 2024 Suwanbamrung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are 1 Excellent Center for Dengue and Community Public Health (EC for DACH), Walailak University, Nakhon Si Thammarat, Thailand, 2 Public Health Research Program, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand, 3 Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh, 4 Department of Public Health, Faculty of Medicine, Kandahar University, Kandahar, Afghanistan * haroonstanikzai1@gmail.com Abstract Background The COVID-19 pandemic has imposed unprecedented suffering on social and individual lev- els worldwide. Vaccines against COVID-19 have been prioritized as a crucial strategy for ending the pandemic as well as minimizing its consequences. Objectives This study aimed to determine the uptake of COVID-19 vaccine among high-risk urban pop- ulations in Southern Thailand using the Capability, Opportunity, Motivation, and Behavior (COM-B) model. Methods We conducted a web-based cross-sectional study in the Hat Yai district, Songkhla province in Southern Thailand, in September and October 2021. The questionnaire was composed of sections on sociodemographic characteristics, COVID-19 vaccination status, and COM-B constructs. We employed a multivariable logistic regression analysis to determine factors associated with the uptake of the COVID-19 vaccine. We set statistical significance at p < 0.05. Results In this study, females constituted 54.7% of the total participants (n = 358), and nearly half of the participants (45.8%) were in the younger age group (18–29). Of all the participants, 59.5% (95%CI: 54.2%-64.6%) received at least one dose of the COVID-19 vaccine. Factors associated with the uptake of COVID-19 vaccine and their adjusted OR (95% CI) were being married: 3.59 (2.06–6.24), having a graduate degree: 2.34 (1.38–3.96), gainfully PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 1 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand within the paper and its Supporting Information Files. employed: 3.30 (1.91–5.67), having a high level of opportunity: 2.90 (1.48–5.66), and having a high level of motivation: 2.87 (1.17–17.08). Funding: This study was financially supported by the Excellent Center for Dengue and Community Public Health [WU-COE-66-16], School of Public Health, Walailak University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Conclusion The uptake of COVID-19 vaccines was moderate in this population. Moreover, the results showed that the COM-B model is useful in predicting COVID-19 vaccine uptake. The find- ings of this study could be used to aid future public health interventions in any event of out- breaks similar to COVID-19 disease in Thailand and beyond. Introduction Over the past three years, the COVID-19 pandemic has resulted in a growing burden of biop- sychosocial problems in developing and developed countries [1, 2]. On an individual level, the disaster has resulted in an overwhelming number of bio-psycho-social deficits in the general population that impacted the physical, psychological, and social aspects of their health and well-being [3, 4]. In response to this largest health crisis of our time, the development and deployment of effective vaccines has been prioritized as a crucial strategy to contain the spread of the virus and reduce its impact on public health [5, 6]. Following the catastrophic first wave of the COVID-19 pandemic, multi-national pharma- ceutical industries made vaccines available sporadically during the second wave and in large amounts after the second wave [6]. The administration of COVID-19 vaccines has dramati- cally shifted the nature of the disease, leading to a marked reduction in the number of cases and deaths [7, 8]. Immediately following the successful development of multiple effective and safe COVID-19 vaccines, the World Health Organization (WHO) urged all countries to vacci- nate at least 70% of their populations by mid-2022, with priority given to vaccinating health workers and the most vulnerable groups (e.g., individuals over 60 years of age, those with com- promised immune system, and those with pre-existing medical conditions) [9]. However, the proportion of people vaccinated against COVID-19 reflects significant global disparities. Moreover, vaccination rates are suboptimal, particularly in the developing coun- tries. Data assert that approximately 70.3% of the world’s population has received at least one dose of the COVID-19 vaccine [10]. In developing countries, however, this proportion is only 32.3% [11]. In Thailand, vaccination escalated rapidly, where about 82.5% of the population has received at least their first dose of the COVID-19 vaccine as of June 2023 [11], with notably lower coverage in remote areas [12]. Although vaccination remains a cornerstone of the COVID-19 pandemic response, broad public support remains elusive. For instance, a recent global systematic review found that the global pooled acceptance rate of the COVID-19 vaccine was 64.9%, with significant variations across WHO regions (range; 60.8%-81.6%) [13]. Other relevant studies have also found that the acceptance rate of the COVID-19 vaccine varies substantially from country to country and even from region to region within a country [14, 15]. In Thailand, the acceptance rates of the COVID-19 vaccine reported ranged from 58% to 95.6% [12, 13, 16]. However, the above stud- ies were mainly among vulnerable populations and emanated from regions other than the South. Consistent findings of extensive research indicate that the decision to vaccinate against COVID-19 is influenced by diverse factors, including sociodemographic characteristics, socio- cultural and religious considerations, political perspectives, trust in healthcare professionals PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 2 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand and current vaccines, the availability and accessibility of vaccination services, and fear of COVID-19 [12, 14, 17–19]. Studies from Thailand reported a strong association of sociodemo- graphics and health system influencers with the COVID-19 vaccination decision [12, 15]. There is a growing recognition that theory-based behavioral models can effectively predict COVID-19 preventive behaviors [20, 21]. The capability, opportunity, motivation, and behav- ior (COM-B) model is recognized as an efficacious framework for preventing COVID-19 dis- ease and acts as a practical framework for designing and promoting preventive behaviors [21, 22]. The results of several studies indicate that the application of this model is successful in COVID-19 prevention [23, 24]. There is little knowledge in the study area of the COM-B model, predicting the uptake of COVID-19 vaccines. This study conducted in 2021 captured a period when the uptake of COVID-19 vaccines was suboptimal worldwide. However, at the time of writing this paper, the situation for COVID-19 vaccine acceptance has improved significantly. Recognizing the challenges encountered at the initial stages of COVID-19 vaccine acceptance, it becomes even more criti- cal to identify gaps in the adoption of healthy behavior to devise effective interventions and implementation approaches. As an example, the COM-B model in COVID-19 vaccine uptake can provide implicit insights from behavior change models as a potential solution to any event of outbreaks similar to COVID-19 disease in Thailand and beyond. Materials and methods Study settings and design A web-based cross-sectional study was conducted in the Hat Yai district, an urban area of Songkhla Province in Southern Thailand, in September and October 2021. The district is divided into sixteen administrative units or sub-districts and is lodging approximately 286,274 people [25]. We selected the Hat Yai district for the present study due to its designation as one of the areas significantly affected by the COVID-19 pandemic. Moreover, this district, charac- terized by certain demographics, cultural factors, or socioeconomic conditions could provide broader implications for public health policies and interventions in other developing regions of the world. Study population We recruited a total of 358 participants who were currently active members of the Facebook group HATY AIZ (a social media platform dedicated to community engagement and connec- tion) to participate in an anonymous survey using the online Google survey forms via Face- book Messenger. Our participants mainly lived in cities, were young, spoke Thai, and were willing to participate in this study. Sample size and sampling procedures The estimated sample size was 325, obtained by a sample size calculation from the Facebook group HATY AIZ records, which documented the number of active adult members (800 mem- bers; July–August 2021) with a 95% confidence level, a 5% margin of error, a design effect of 1.5, and a 10% nonresponse rate. We increased the calculated sample size from a minimum of 325 participants to 379 participants in the prevision of missing data. The final analyses consist of 358 participants with their complete data sets. We used convenience sampling (voluntary participation) to recruit our participants in this study. PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 3 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand Study measures Based on relevant literature, we developed a structured questionnaire with sections on sociode- mographic information, COM-B constructs, and participants’ intentions to get vaccinated against COVID-19. In this study, we used six sociodemographic characteristics as independent variables: sex (male/female); age (18–29, 30–44, 45–59, >60); marital status (currently married, currently unmarried); education (undergraduate, graduate or higher); occupation (occupation with uncertain income, occupation with regular income); and income in Baht (� 26000, > 26000). We employed the comprehensive theoretical model grounded in the COM-B model to understand the factors influencing the individuals’ behavior toward receiving the COVID-19 vaccination [20, 21]. The adapted COM-B model has three constructs: (1) capability, (2) opportunity, and (3) motivation with good psychometric properties for facilitating behavioral change. The capability and opportunity constructs were measured using 12-item scales: capability (two negatives and ten positives) and opportunity (three negatives and nine positives). Each item was scored from 0 (unfavorable response) to 1 (favorable response), which yields a total score from 0 to 12. In each scale, a score of � 10 was used to signify a high level of capability and opportunity for receiving the COVID-19 vaccination, as defined in prior work [20, 26]. We employed a 5-point Likert scale to measure the motivation construct. The motivation construct consists of 12 items, including 6-item positive and 6-item negative subscales. Each item in the positive constructs was scored from 5 (strongly agree) to 0 (strongly disagree) and vice versa in the negative constructs, yielding a total score from 12 to 60. A score of � 48 was used to signify a high level of motivation for receiving the COVID-19 vaccination [26, 27]. The outcome variable of this study was the respondents’ behavior regarding the uptake of the COVID-19 vaccine. The participants in this survey were asked about their COVID-19 vac- cination status and the responses were in a dichotomous ‘yes’ or ‘no’ format. Data collection The questionnaire, which consisted of sections on socio-demographic information, COVID- 19 vaccine-related information, and the COM-B model, was initially drafted in English and later translated into Thai (local language) for the ease of administration. Before the commence- ment of the study, we pretested the questionnaire in another Facebook group with 65 partici- pants to check and revise (if required) its verbal consistency. Additionally, we checked the structure reliability of the questionnaire in the pretested sample, and the Cronbach’s Alpha value (Thai version) for capability, opportunity, motivation, and total was 0.86, 0.77, 0.82, and 0.83, respectively. We distributed the Thai version of the questionnaire through the HATY AIZ Facebook group and were available for responses from September 10, 2021, to October 31, 2021. Once a potential client accessed the online survey form, a consent form appeared on the first page indicating the study description, objectives, and participants’ right to withdraw at any time. If the client would like to participate, they could willfully provide their consent and choose their desired language of the form. Only then did the clients complete and submit the anonymous survey questionnaire. Statistical analysis We employed descriptive statistics to understand participants’ capability, opportunity, and motivation to adopt the new behavior, i.e., acceptance of the COVID-19 vaccine. We employed multivariable logistic regression analysis to determine factors associated with the uptake of the PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 4 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand COVID-19 vaccine. In all analyses, the assumptions for the multivariable logistic regression model were met. We set statistical significance at p < 0.05. Ethical consideration The Ethics Committee for Human Research, Walailak University approved this study (ref. no. WUEC-21-214-01; Dated: August 16, 2021). All participants agreed to the terms of an elec- tronic consent form before they could participate in the study. The electronic consent form included information on study description, objectives, and participants’ right to withdraw at any time. Moreover, we followed the ethical principles outlined in the Declaration of Helsinki. Results Table 1 indicates the sociodemographic characteristics of our study participants. A total of 358 Facebook users from the HATY AIZ group aged 18–60 years were included in this study. In our sample, 54.7% (196) were female, and nearly half of the participants (45.8%, 164) were young (18–29). Approximately one-third (42.7%, 153) of our participants were married, and Table 1. Sociodemographic characteristics of the study participants (n = 358). Variables Frequency (%) Age (In completed years) 18–29 30–44 45–59 � 60 Sex Male Female Marital status Single Married Separated/Divorced Educational status Secondary school (level 1–3) Secondary school (level 4–6) Vocational Certificate/Diploma Bachelor degree Higher education Employment status Employed with regular income Employed with uncertain income Monthly household income (in Baht) � 26000 > 26000 History of travel to/or from another province in the last 14 days Yes No History of exposure to COVID-19 patients in the last 14 days Yes No https://doi.org/10.1371/journal.pone.0300509.t001 164 (45.8) 104 (29.1) 80 (22.3) 10 (2.8) 162 (45.3) 196 (54.7) 192 (53.6) 153 (42.7) 13 (3.6) 9 (2.5) 41 (11.4) 72 (20.1) 220 (61.5) 16 (4.5) 231 (64.5) 127 (35.5) 197 (55.0) 161 (45.0) 35 (9.8) 323 (90.2) 29 (8.1) 329 (91.9) PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 5 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand more than two-thirds (64.5%, 231) were gainfully employed. The monthly household income was � 26000 Baht (750 USD, July 2023) in more than half of the participants (55%, 197). Data on participants’ educational attainment and travel history are summarized in Table 1. Capability to receive COVID-19 vaccine The mean score of the capability construct was 10.80 (± 1.35 SD) with a range of 3–12 points, and nearly two-thirds (249; 69.6%) of participants showed a high level of capability. Table 2 illustrates the detailed reflections of our participants on the capability construct within the COM-B model. Opportunity to receive COVID-19 vaccine The mean score of the opportunity construct was 10.04 (± 1.72 SD) with a range of 2–12 points, and about 41.9% (150) of participants had a high level of opportunity to receive the COVID-19 vaccine. The detailed opportunity construct and participants’ reflections are illus- trated in Table 3. Motivation to receive COVID-19 vaccine The mean score of the motivation construct was 41.18 (± 8.81 SD) with a range of 24–60 points, and a small number of the participants (12%, 43) had a high level of motivation to receive the COVID-19 vaccine. The detailed motivation construct and participants’ reflections are illustrated in Table 4. At the time of this study, around two-thirds (59.5%, 95%CI: 54.2%-64.6%) of study partici- pants had received at least one dose of the COVID-19 vaccine. Table 2. Capability to receive COVID-19 vaccine (n = 358). Items Knowledge of the spread of COVID-19 disease Knowledge of the signs and symptoms associated with COVID-19 disease COVID-19 vaccine can boost immunity Knowledge of the vaccine doses and schedule It is necessary to get enough sleep and refrain from consuming alcohol, tea, and coffee before vaccination Individuals can go home without any surveillance and are not required to wait for 30 minutes to observe any symptoms post vaccination* Knowledge of factors that increase the risk for severe COVID-19 COVID-19 can be transmitted easily within families when a family member has signs and symptoms of COVID-19 disease COVID-19 doesn’t affect individuals’ daily activities* COVID-19 results in unemployment and income loss Vaccination can be reserved online or through public channels Reponses, Frequency (%) Yes 351 (98) 347 (96.9) 329 (91.9) No 7 (2) 11 (3.1) 29 (8.1) 264 (73.7) 94 (26.3) 344 (96.1) 14 (3.9) 75 (20.9) 344 (96.1) 346 (96.6) 82 (22.9) 346 (96.6) 335 (93.6) 283 (79.1) 14 (3.9) 12 (3.4) 276 (77.1) 12 (3.4) 23 (6.4) Decisions to receive the vaccine can be affected by its adverse effects 302 (84.4) 56 (15.6) Level of capability (cut-off point 90%) * Negative capability https://doi.org/10.1371/journal.pone.0300509.t002 High 249 (69.6) Low 109 (30.4) PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 6 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand Table 3. Opportunity to receive COVID-19 vaccine (n = 358). Items You are supported by family and friends to receive the vaccine You are informed to register for the vaccine You live in a high-risk area for the COVID pandemic You think that shopping malls and companies are sources of COVID-19 infection You need a vaccine due to the disease‘s impact on your employment* You need the government to provide vaccines for all citizens You ask for a vaccine that fulfills all your needs* Vaccine is available near your residence The risk of infection is not increased if individuals are not vaccinated* Reponses, Frequency (%) Yes No 324 (90.4) 303 (84.6) 34 (9.5) 55 (15.4) 313 (87.4) 45 (12.6) 326 (91.1) 320 (89.4) 333 (93.0) 32 (8.9) 38 (10.6) 25 (7.0) 228 (63.7) 130 (36.3) 293 (81.8) 65 (18.2) 155 (43.7) 203 (56.7) Adherence to social distancing measures lowers your risk of the COVID-19 disease 316 (88.3) 42 (11.7) You can select the type of vaccine You can travel to receive vaccine Level of opportunity (cut-off point 90%) * Negative opportunity https://doi.org/10.1371/journal.pone.0300509.t003 238 (66.5) 120 (33.5) 342 (95.5) 16 (4.5) High Low 150 (41.9) 208 (58.1) Factors associated with the uptake of COVID-19 vaccine in our sample A multivariable logistic regression analysis indicated that being married (AOR = 3.59, 95%CI: 2.06–6.24), having a graduate degree (AOR = 2.34, 95%CI: 1.38–3.96), employed with a regular income (AOR = 3.30, 95%CI: 1.91–5.67), having a high level of opportunity (AOR = 2.90, 95% CI: 1.48–5.66), and having a high level of motivation (AOR = 2.87, 95%CI: 1.17–17.08) were associated with uptake of the COVID-19 vaccine (Table 5). Table 4. Motivation to receive COVID-19 vaccine (n = 358). Items Responses, Frequency (%) Strongly agree Agree Neutral Disagree Strongly disagree You have fears about the spread of COVID-19 infection* You are worried that you might be infected with COVID-19 in the future* COVID-19 vaccines have side effects* You are afraid of the COVID-19 vaccine side effects* COVID-19 vaccines are necessary for the control of the pandemic COVID-19 vaccines prevent infection rather than severity* Individuals receive vaccines to avoid the COVID-19 infection Most individuals receive vaccines only to maintain their rights* Have trust in COVID-19 vaccines provided by the government Have trust in COVID-19 vaccines as they are registered by FDA Information about COVID-19 vaccines affects the decision to receive the vaccine You trust healthcare professionals‘ advice to receive the COVID-19 vaccine 102 (28.5) 133 (37.2) 1 (0.3) 118 (33.0) 281 (78.5) 78 (21.8) 163 (45.5) 106 (29.6) 66 (18.4) 100 (27.9) 243 (67.9) 236 (65.9) Level of motivation (cut-off point 90%) * Negative motivation https://doi.org/10.1371/journal.pone.0300509.t004 45 (12.6) 58 (16.2) 44 (12.3) 75 (20.9) 6 (1.7) 35 (9.8) 48 (13.4) 77 (21.5) 57 (17.9) 51 (14.2) 79 (22.1) 55 (15.4) 51 (14.2) 24 (6.7) 0 (0.0) 38 (10.6) 67 (18.7) 52 (14.5) 44 (12.3) 47 (13.1) 40 (11.2) 71 (19.8) 42 (11.7) 48 (13.4) 62 (17.3) 74 (20.7) 36 (10.1) 74 (20.7) 78 (21.8) 30 (8.4) 66 (18.4) 44 (12.3) 68 (19.0) 36 (10.1) 2 (0.6) 4 (1.1) High 96 (26.8) 55 (15.4) 224 (62.6) 73 (20.4) 2 (0.6) 123 (34.4) 62 (17.3) 93 (26.0) 120 (33.5) 76 (21.2) 3 (0.8) 14 (3.9) Low 43 (12.0) 315 (88.0) PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 7 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand Table 5. Factors associated with the uptake of COVID-19 vaccine; crude and adjusted odds ratio with 95% CI. Independent Variables Categories Vaccination status Crude Odds Ratio (95% CI) p-value Adjusted Odds Ratio (95% CI) p-value Age Sex Marital status Education status Employment status Level of capability Level of opportunity Level of motivation 18–44 �45 Male Female Currently married Currently unmarried Undergraduate Graduate Uncertain income Regular income Low High Low High Low High Yes 147 66 99 114 115 98 61 152 114 99 190 23 167 46 177 36 No 121 24 63 82 38 107 61 84 117 28 125 20 125 20 138 7 https://doi.org/10.1371/journal.pone.0300509.t005 1 2.26 (1.33–3.82) 1.05 (0.87–1.27) 1 3.30 (2.09–5.22) 1 1 1.81 (1.16–2.82) 1 3.62 (2.21–5.93) 1 0.75 (0.39–1.43) 1 1.72 (0.97–3.05) 1 4.01 (1.73–9.28) 0.01 0.20 0.03 0.01 <0.001 0.21 <0.001 <0.001 - - 3.59 (2.06–6.24) 1 1 2.34 (1.38–3.96) - - 0.02 0.03 1 <0.001 3.30 (1.91–5.67) - 1 2.90 (1.48–5.66) 1 2.87 (1.17–7.08) - <0.001 <0.001 Discussion This study discovered the association between the COM-B constructs and COVID-19 vaccina- tion behaviors. To the best of our knowledge, few documents have been published about the COVID-19 vaccine uptake via the COM-B model constructs. The study is important, as often time is not taken to understand how theory-based behavioral change models influence people’s behavior toward COVID-19 vaccinations and what are the needs for effective immunization programs from a population perspective. This is informative as the population’s behaviors toward an effective immunization program may differ from region to region, with significant implications for policymaking and restructuring immunization services. In this study, we found that 59.5% of our study participants had received at least one dose of the COVID-19 vaccine. The results of previous studies demonstrated that the uptake of the COVID-19 vaccine in the Thai population ranged from 58% to 95.6% [12, 13, 16]. Also, national statistics reported that the uptake of the COVID-19 vaccine in the general population of Thailand was 82.5% [11]. Therefore, the COVID-19 vaccination rate in this study is lower than that recorded in most previous studies and national statistics. This difference may be attributed to a small sample size for the population sourced in this study. The possibility of dif- ferences in sociodemographics, study design, and geographic locations cannot be excluded. The acceptance and uptake of vaccines are affected by multiple factors including geographic location, time, sociodemographics, politics, culture, and vaccine type [13, 28]. Hence, we rec- ommend further studies involving larger and geographically diverse populations to verify these findings. Our study showed that the education level of the participants was significantly associated with the uptake of the COVID-19 vaccine. This finding is similar to earlier pertinent literature that pointed out that higher educational attainment is strongly associated with the acceptance and uptake of the COVID-19 vaccine [13, 28, 29]. Surprisingly, a similar study in Iran found an inverse relationship, which is contrary to our findings [30]. The decrease in acceptance of the COVID-19 vaccine among educated Iranians may coincide with the initiation of PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 8 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand vaccination in Iran before the findings of large clinical trials were announced. However, stud- ies have shown that higher education levels may have a significant effect on immunization cov- erage [13, 28]. Given the findings of this study, providing credible information through various mediums coupled with the enhancement of health literacy through educational inter- ventions and community messages could be useful in adopting a new health behavior, particu- larly for those with lower educational attainment. Moreover, these interventions could also be effective in addressing any remaining COVID-19 vaccine hesitancy in the community. Consistent with relevant literature, our findings indicate that COVID-19 vaccine uptake was lower in currently unmarried participants than those currently married. Studies have found that vaccine uptake was higher in participants who were married [28, 31]. Given these findings, vaccination policies and strategies specific to populations with single/separated mari- tal status for future pandemics are crucial, particularly considering specific concerns or prefer- ences in such cases. We noted that employed participants with a regular income had 3.3 times higher odds of getting vaccinated than those with unreliable income. Similar studies have shown an associa- tion between employment status and COVID-19 vaccination status [28, 30]. Employment vac- cination requirements may have played some role as the data were collected after vaccination mandates by the Thai government. In fact, stable income is a crucial socioeconomic factor influencing people’s health attitudes and behaviors. Therefore, considering socioeconomic fac- tors such as income and employment is crucial when addressing the health needs of the population. It is well known that theory-based behavioral models are crucial in adopting a new behavior [32–34]. We observed that 69.6%, 41.9%, and 12% of our study participants, respectively, had a high level of capability, opportunity, and motivation to receive the COVID-19 vaccine accord- ing to the COM-B model. In addition, we found that participants with high levels of opportu- nity and motivation were more likely to receive their first dose of the COVID-19 vaccine than participants with low levels of opportunity and motivation. Several studies reported that factors that influence behavioral intentions toward the COVID-19 vaccine are multi-dimensional [28, 29]. A study in Thailand found that the constructs in the COM-B model could successfully gen- erate themes of behaviors that affect COVID-19 prevention behaviors [23]. Other studies have revealed that the adapted COM-B model provides refined details for each construct in the vac- cine context [22, 24]. Based on the COM-B constructs, effective interventions could be designed based on the predictors of capability, opportunity, and motivation which can be beneficial in a variety of disaster, geographic and socioeconomic contexts to improve healthy behaviors. Limitations Our findings have several limitations. The smaller sample size allows less precision in the find- ings. Considering the use of an online survey approach, elder participants and participants with low education may be under-represented in our sample. We employed the Thai version of the COM-B model in the context of the COVID-19 vaccine for the first time, while their psycho- metric properties require assertion. We have not assessed the bio-psycho-social health of our subjects, and their socio-cultural affiliations that may have confounded their uttered responses. Finally, the findings of this study are limited to a particular period of the pandemic, and since then there have been substantial improvements in the acceptance of the COVID-19 vaccine. Conclusion We found that 59.5% of our study participants had received at least one dose of a COVID-19 vaccine. The uptake of the COVID-19 vaccine was influenced by the sociodemographics of PLOS ONE \| https://doi.org/10.1371/journal.pone.0300509 March 14, 2024 9 / 12 PLOS ONEUptake of COVID-19 vaccine in Southern Thailand our participants (i.e., educational attainment, marital status, and employment status). We also found that opportunity and motivation constructs of the COM-B model effectively predicted the uptake of the COVID-19 vaccines. The findings of this study could be used to inform evi- dence-based interventions in a variety of disaster, geographic and socioeconomic contexts to improve healthy behaviors. Supporting information S1 Dataset. Microsoft excel file with minimal dataset. (XLSX) Acknowledgments The authors sincerely thank all the people in the high-risk district who were involved in the study for their assistance and support. Author Contributions Conceptualization: Charuai Suwanbamrung, Benchawan Srinam, Warissara Suwannakarn, Sangchom Siripanich, Md. Siddikur Rahman, Muhammad Haroon Stanikzai. Data curation: Muhammad Haroon Stanikzai. Formal analysis: Pakawan Promkool, Warissara Suwannakarn, Muhammad Haroon Stanikzai. Funding acquisition: Charuai Suwanbamrung, Pakawan Promkool. Investigation: Warissara Suwannakarn, Muhammad Haroon Stanikzai. Methodology: Benchawan Srinam, Pakawan Promkool, Warissara Suwannakarn, Md. Siddi- kur Rahman, Muhammad Haroon Stanikzai. Project administration: Pakawan Promkool, Muhammad Haroon Stanikzai. Resources: Pakawan Promkool. Software: Muhammad Haroon Stanikzai. Supervision: Charuai Suwanbamrung, Benchawan Srinam, Sangchom Siripanich, Md. Siddi- kur Rahman, Muhammad Haroon Stanikzai. 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10.1371_journal.pgph.0002529	RESEARCH ARTICLE Dietary intake and associated risk factors among pregnant women in Mbeya, Tanzania 1, Geofrey Mchau1, Hamida Mbilikila1, Kaunara AziziID Erick KillelID Adam Hancy1, Tedson Lukindo1, Ramadhan Mwiru2, Ramadhan Noor2, Abraham Sanga2, Patrick Codjia2, Germana H. LeynaID 1,3, Ray M. MasumoID 1, Nyamizi Ngasa1, 1,4* 1 Department of Community Health and Nutrition, Tanzania Food and Nutrition Centre, Dar es Salaam, Tanzania, 2 The United Nations Children’s Fund (UNICEF), Dar es Salaam, Tanzania, 3 Department of Epidemiology and Biostatistics, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania, 4 Department of Statistics, University of Dar es Salaam (UDSM), Dar es Salaam, Tanzania a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 * rmasumo@yahoo.com Abstract OPEN ACCESS Citation: Killel E, Mchau G, Mbilikila H, Azizi K, Ngasa N, Hancy A, et al. (2024) Dietary intake and associated risk factors among pregnant women in Mbeya, Tanzania. PLOS Glob Public Health 4(1): e0002529. https://doi.org/10.1371/journal. pgph.0002529 Editor: Dickson Abanimi Amugsi, African Population and Health Research Center, KENYA Received: July 10, 2023 Accepted: November 17, 2023 Published: January 5, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgph.0002529 Copyright: © 2024 Killel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All datasets underlying this study are freely available at the public repository https://osf.io/7ysb9/. Poor dietary intake among pregnant women has serious detrimental consequences for pregnancy and offspring both in developed and developing countries. This study aimed to assess dietary intake and associated risk factors among pregnant women. A cross-sectional study was conducted in Mbeya, Tanzania with a sample size of 420 pregnant women attending antenatal clinics to assess the factors associated with dietary intake. Dietary intake was assessed using a piloted questionnaire of the Prime Diet Quality Score. A tested standard questionnaire was also used to collect factors that are associated with dietary intake among pregnant women. The strengths of the associations between the dependent and independent variables were tested using the Pearson chi-square tests and the multivari- ate log-binomial regression method was performed to calculate the adjusted risk ratios (ARR) and 95% confidence interval (CI). The study revealed that out of 420 pregnant women who participated in this study only 12.6% and 29.3% consumed at least four serv- ings of fruits and vegetables per week respectively. Poor dietary intakes were less likely among cohabiting pregnant women [Adjusted RR 0.22 (95% CI 0.09–0.50)] and; those who reported taking Fansidar tablets during the pregnancy [Adjusted RR 0.55 (95% CI 0.31– 0.96)]. Further, we found that poor dietary intakes were more likely among pregnant women who were classified as overweight and obesity by the MUAC above 33cm [Adjusted RR 3.49 (95% CI 1.10–11.06)]. The study results affirm that cohabitation and obesity affect die- tary intakes among pregnant women differently compared to married women in rural set- tings of Tanzania. Further research is needed to investigate the social aspects that link dietary intake outcomes for developing a tailored gestational intervention to improve mater- nal and birth outcomes in sub-Saharan African countries. Introduction Poor dietary intake among pregnant women has serious detrimental consequences for preg- nancy and offspring both in developed and developing countries [1]. In 2017, Pelletier and PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 1 / 14 PLOS GLOBAL PUBLIC HEALTHFunding: The authors have declared that no competing interests exist. Competing interests: The authors received no specific funding for this work. Dietary intake among pregnant women colleagues defined dietary quality as one that is hygienically safe, nutritious, balanced, and well adapted to the needs of individuals in order to prevent disease, ensure a good state of health, as well as proper development [2]. Previous studies have consistently reported that the Mediter- ranean diet is one of the most effective diets in reducing the risk of cardiovascular diseases and overall mortality due to non-communicable diseases [3, 4]. The Mediterranean diet is charac- terized by a high intake of fish, olive oil, non-starchy vegetables, legumes, whole grains (cere- als), fruits, and nuts, as well as a lower intake of dairy products, red and processed meat and a moderate intake of wine [3, 4]. Worldwide dietary guidelines vary and each country adapts to suit its specific needs [5, 6]. The dietary guidelines of the United Kingdom and the American 2020–2025 recommended the consumption of at least two portions of fruit and 3 portions of vegetables a day [7, 8]. A Lancet study conducted in 195 countries on the health effects of dietary risks published that in most of the countries, the intake of healthy food such as whole grains, vegetables and fruits were much less compared to unhealthy foods such as processed foods and soft drinks [5]. An epidemiological study from South Africa by Venter and Winterbach revealed a higher dietary intake of fats than the recommended among mid-adolescents [9]. Evidence emanated from Bahrain consistently reported poor consumption of healthy food items compared to unhealthy food items [10]. There are a growing number of studies on dietary intake among pregnant women in many countries, especially industrialised countries [5, 6], however, pub- lished research regarding dietary intake among pregnant women in Tanzania is minimal. While the results from studies based in other countries provide relevant information related to this subject [9], these results cannot be entirely relatable to pregnant women in Tanzania. A recently published longitudinal study in Dar es Salaam, Tanzania among pregnant women reported high consumption of green leafy vegetables and refined grains [11]. Inconsistent find- ings were reported in another prospective cohort study among 432 pregnant women in the rural settings of Ethiopia where the consumption of vegetables and fruits was poor and associ- ated with a higher risk of adverse pregnancy outcomes [12]. The research work in Tanzania [11] was performed in urban settings and may not represent the dietary intake in rural settings. The two previous National Nutrition Surveys in Tanzania, TNNS of 2014 and 2018 lack detailed information on dietary intake and diet quality [13]. Therefore, further high-quality prospective cohort studies are required in Tanzania to enhance the generalisability of the results and help inform policies and programmes. Dietary intake among pregnant women is affected by various factors, such as socio-demo- graphic and economic status, nutrition status, environmental, cultural, and political [14–18]. Adequate dietary intake is nothing new to sub-Saharan African countries but what is of great concern is the fact that it is one of the issues on which a lot of resources have been spent over a period of time with very limited results. The current pieces of literature provide limited infor- mation on dietary intake among pregnant women in sub-Saharan African countries because the requirements that would enhance the collection and use of those data, including the use of new technology, in these countries rarely exist [19]. Hence, the present study aimed at examin- ing dietary intake and associated risk factors among pregnant women in the Mbeya region, Tanzania. The findings of this study would be a valuable step in developing a tailored gesta- tional intervention to improve maternal and birth outcomes in sub-Saharan African countries. Methods Ethics statement The survey was approved by the Tanzania Ethics Committees i.e. the National Institute for Medical Research with the reference number SZEC-24239/R.A/V.1/151. Date of issue 12th PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 2 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women August 2022. All eligible subjects were informed of the purpose and nature of the survey and those who agreed to participate were asked to sign a written informed consent form. More- over, a written informed consent was obtained from the parent/guardian of each participant under 18 years of age. All procedures followed were per the ethical standards of the Helsinki Declaration of 1975 including the confidentiality and, authors had no access to information that could identify individual participants during or after data collection. Study design A cross-sectional study was conducted among pregnant women in seven districts of Mbeya region in Tanzania. The study was carried out from 15th September to 10th December 2022. Study area Mbeya region has a population of 2,204,543 (1,068,615 male and 1,135,928 female) and 557,574 women of reproductive age [20]. The total deliveries in the Mbeya region in 2020 were 72,076. There are 17 hospitals, 23 health centres and 278 dispensaries, where 251 health facilities provide reproductive and child health services. This study was conducted at 42 Reproductive and Child Health (RCH) Clinics in seven districts of the Mbeya region. The selected RCH clinics in this study are estimated to provide services to approximately 1036 pregnant women [20]. Study population All pregnant women aged between 15 to 49 years, less than 28 weeks of gestation, and who attended antenatal visits in Mbeya were invited to participate in the study. This is according to the minimal risk in research involving pregnant women and offspring [21]. A total of 574 preg- nant women were invited and 420 (response rate of 73.0%) agreed to participate. The study excluded pregnant women taking medication for other reasons except malaria chemoprophy- laxis plus iron and folate supplements. Sample size and sampling procedure A sample size (n = 420) was considered sufficient based on the Lwanga and Lemeshow formula [22]. Prior to carrying out the study, the proportion of women of reproductive age with poor dietary intake was estimated to be 45%, with a margin error of 5%, a confidence level of 95%, and a design effect of 1.5. Another 10% was added to the sample size to account for non- responses. The sampling procedure involved two steps [22], a list of 251 governmental and faith-based health facilities providing antenatal services in the Mbeya region was obtained and used in a random selection of the health facilities from each district based on probability pro- portional to size sampling. A total of 42 facilities from a pool of 251 were randomly selected for the survey. An additional two reserved clusters were included in the survey. Given the sam- pling frame of public health facilities in Mbeya, the probability proportional to size was per- formed to allocate the number of facilities per district for inclusion in the survey. Therefore, a total of 44 health facilities offering antenatal services located in the Mbeya region were visited and surveyed [22]. Data collection Dietary intake assessment. Dietary intake was assessed by the Prime Diet Quality Score (PDQS) developed in the USA using a modified Prime Screen questionnaire as a means to characterize diet quality [23]. The questionnaire was first found to predict factors associated PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 3 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women with the lower risk of coronary heart disease (CHD) in a large population in the USA [23], and diet quality among adults in Bosnia and Herzegovina [18]. In Tanzania, Yang and colleagues employed the questionnaire in a prospective pregnancy cohort study [11]. The PDQS contains 21 food groups; 13 are healthy food and, seven are unhealthy food. The PDQS was assessed using 24-hour recalls, which reflected the feeding practice from the previous morning to the morning of the interview [11, 23, 24]. For this study, the questionnaire was translated into Kis- wahili the main language in Tanzania, spoken proficiently by almost 95% of the population. In the translation process, two translators with different backgrounds independently translated the original questionnaire into Kiswahili. The IMAN project staff in the field reviewed for semantic, experiential, and conceptual equivalence to the original version. Sensitivity to cul- ture and selection of appropriate words were considered. The Kiswahili version of the ques- tionnaire was then given to a translator fluent in both English and Kiswahili to translate back into the original language. This translator was not shown the original English version. Lastly, all translations and the original questionnaire were given to IMAN project staff in the field in order to consolidate all the versions of the questionnaire and achieve equivalence between the original and target versions. Both the Kiswahili version and the original English version of the PDQS were administered to 20 female secondary school teachers in Dar es Salaam in two ses- sions separated by an interval of two weeks to evaluate the quality of the translations in terms of comprehensibility, readability and relevance on face validity and, correlations between the two administrations were calculated. However, a 30-day recall was not a part of this study which might have different comparable outcomes. Participants were asked ‘From when you woke up yesterday till you woke up this morning did you consume the following food items: dark green leafy vegetables, cruciferous vegetables, dark orange vegetables and fruits, other vegetables, citrus fruits, other fruits, legumes, nuts and seeds, poultry, fish, whole grains, vegetable liquid oils, white roots and tubers, red meat as a main dish, processed meats, refined grains and baked products, sugar-sweetened beverages, fried foods away from home, sweets, ice cream and low-fat dairy?’ Responses were given on a 5-point likert scale; 0 = not at all, 1 = once, 2 = twice, 3 = thrice, and 4 = fourth or more. Each occasion of food group consumption was considered as a serving. The mean number of serv- ings was computed over the available recall days for each participant. The mean number of servings for each food group was multiplied by 7 to standardise the number of servings per week, from which points for each food group could be assigned based on whether the food was categorised as healthy or unhealthy [11, 23, 24]. Points were assigned for consumption of healthy food groups as follows: 0–1 serving/week, 0 points; 2–3 servings/week, 1 point; and �4 servings/week, 2 points. Scoring for unhealthy food groups was assigned as follows: 0–1 serv- ing/week, 2 points; 2–3 servings/week, 1 point; and �4 servings/week, 0 points [11, 23, 24]. Demographic and socio-economic factors. The demographic factors were assessed by asking pregnant women attending antenatal services to provide the following information: Age, marital status, education level, and occupation status. Socio-economic status was assessed by household ownership of durable assets (such as ownership of a car, motorcycle, bicycle, cart, refrigerator, television, radio, etc.), housing characteristics (such as the material of dwell- ing floor and roof, toilet facilities, etc.), and access to basic services (such as electricity supply, source of drinking water). Household asset data uses simple questions and therefore suffers from less recall or social desirability bias. Anthropometric measurements. Maternal nutrition status was assessed by measuring weight and height. Weight was measured by the nearest 0.1 kg with a battery-powered elec- tronic scale (Seca, Hamburg, Germany), and height was measured to the nearest 0.1 cm with a height model recommended by UNICEF. Height was measured when pregnant women were not wearing shoes or a head covering. Further, Mid Upper Arm Circumference PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 4 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women (MUAC) assessed by MUAC tapes was used to assess the nutrition status of pregnant women [25]. Laboratory assessment. A trained nurse collected blood samples through vein puncture from consented participants. Blood samples were taken into ethylenediaminetetraacetic acid (EDTA) and non-anticoagulated whole blood vacutainers (Becton Dickenson, NJ, USA). Approximately 6mL of venous blood sample was collected on each vacutainer and protected from light. Whole-blood vacutainers were maintained at 4–8˚C for less than 2 hours before being transported to the temporary laboratories. Malaria was tested by rapid diagnostic test (SD Bioline, Rep. of Korea), and hemoglobin level was measured by HemoCue HB 201+ analy- ser (Hemo Cue, Angelholm, Sweden). Assessment of C-reactive protein (CRP), and alpha-1 acid glycoprotein (AGP) was performed with Roche Cobas Integra 400 Plus analyser (Roche Diagnostics GmbH, German). Hemoglobin levels <12.0 and <8.0 g/dL were used to charac- terise anaemia and severe anaemia, respectively. Serum C- reactive protein (CRP) and Alpha- 1-acid glycoprotein (AGP) values of CRP > 5.0 mg/L and AGP > 1.0 g/L respectively were characterized as high inflammatory marks [26, 27]. Data analysis The data were analysed by using SPSS version 25. The dietary intake as dependent variable was assessed as a categorical variable splitting at the median i.e. 0 = good dietary intake and, 1 = poor dietary intake. An asset-based approach to measuring household socio-economic status is considered an alternative to income and consumption expenditure in low-income countries. Principal Components Analysis (PCA) is a method for determining wealth indices [28]. In this study, household wealth index was assessed as (1) “available and in working condition” or (0) “not available and/or not in working condition” of durable assets, housing characteristics and access to basic services. For constructing a wealth index among pregnant women in Mbeya, the first principal component was used to categorise households into two approximate group’s i.e. lowest and highest group. The strengths of the associations between the depen- dent and independent variables in bivariate analysis were tested using the Pearson chi- square tests because all variables were categorical. Independent variables that were signifi- cant at arbitrary levels in the bivariate analysis were selected for multivariate analysis. We based this on the Wald test with a P-value cut-off of 0.7. In multivariate analysis, Log bino- mial regression method were used first for adjusting confounders and second to identify independent predictors of dietary intake among the study population, and the significance level was set at 5%. Results Reliability The internal consistency reliability scales were examined using Cronbach’s alpha. Test-retest reliability analysis was performed using kappa statistics and Intra class correlation coefficients (ICC). The agreement between the interviewers and the gold standard on the dietary intake assessments on the English and Kiswahili versions were Cohen’s kappa of 0.62 and 0.67 respec- tively. During the field, duplicate interviews were performed randomly with 20 pregnant women. Test-retest reliability of reports on the dietary intake assessment using a Kiswahili ver- sion in terms of ICC was 0.72 (95% CI 0.64–0.78). Thus, acceptable levels of intra-interviews agreement (kappa >0.60) were obtained [29]. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 5 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women The characteristics of the study population Study participants had a mean age of 25.49 ± 6.37 years. Table 1 depicts the characteristics of the study population. More than half of the participants were 15–24 years old, about seventy- two percent had completed at least primary education and, 84.3% were self-employed. Five percent (n = 21) of pregnant women had Mid Upper Arm Circumference (MUAC) of above 33cm and falls in the category of overweight and obesity. Nine percent (n = 38) of pregnant women had serum C- reactive protein (CRP) above 5mg/L and, 19% (n = 80) had Alpha- 1-acid glycoprotein (AGP) above 1 g/L. For the construction of wealth index: One-third of the participants lived in houses with electricity; 72.0% had access to improved sources of drinking water and 65.0% were not sharing toilet facilities. The pit latrine without washable was the most common type of toilet 31.6% (n = 133). About half (51.5%) used cement as the material of the dwelling floor and 7.8% used thatch/palm leaf as material for the roof. Furthermore, less Table 1. Frequency distribution of the socio-demographic and economic characteristics of pregnant women in Mbeya (n = 420). Categories Frequency (n) Percentage (%) Variable Age group (Years) Education Marital status Number of pregnancies Trimester of pregnancy Received of iron and folic acid supplements Received Fansidar (SP) during pregnancy Occupation status Household wealth Index 15–19 20–24 25–29 35+ No education Primary Secondary and above Married Cohabit Single/ Divorced Primigravida Multigravida First trimester (<12 weeks) Second trimester (12–26 weeks) No Yes No Yes Formal employment Self employed Not employed Higher socio-economic Middle socio-economic lower socio-economic Mid Upper Arm Circumference (MUAC) Thin (<23cm) Normal (between 23 and 33cm) Overweight or obesity (above 33 cm) Malaria infection Serum C- reactive protein (CRP) Alpha-1-acid glycoprotein (AGP) https://doi.org/10.1371/journal.pgph.0002529.t001 No Yes CRP�5mg/L CRP>5mg/L AGP< = 1 g/L AGP>1 g/L 82 133 99 106 34 301 85 238 133 49 104 316 109 311 155 265 204 216 15 355 51 140 139 140 16 383 21 402 18 383 38 341 80 19.5 31.6 23.6 25.2 8.1 71.7 20.2 56.5 31.6 11.6 24.7 75.1 26.0 74.0 36.8 62.9 48.5 51.3 3.6 84.3 12.1 33.4 33.2 33.4 3.8 91.2 5.0 95.7 4.3 91.0 9.0 81.0 19.0 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 6 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women Table 2. The patterns and distribution of food groups consumption among pregnant women according to PDQS score (n = 420). Healthy foods Serving per week Dark leafy green vegetables Cruciferous vegetables Dark orange vegetables and fruits Other vegetables Whole citrus fruits Other whole fruits Legumes Nuts and seeds Poultry Fish Whole grains Vegetable liquid oils White roots and tubers Low fat diary Unhealthy foods Serving per week Red meats Sweets and ice cream Fried foods obtained away from Home Processed meat Refined grains and baked goods Sugar sweetened beverages 0–1 serving/week n (%) 142(33.7) 2–3 servings/week n (%) 156 (37.1) �4 servings/week n (%) 123 (29.2) 393(93.4) 263(62.5) 266(63.2) 391 (92.9) 308(73.2) 259 (61.5) 275(65.3) 389(92.4) 277(65.8) 324(77.0) 46(10.9) 189(44.9) 349(82.9) 20(4.8) 105(24.9) 93 (22.1) 25 (5.9) 78(18.5) 104 (24.7) 107(25.4) 21(5.0) 98(23.3) 68(16.1) 134(31.8) 161(38.2) 56(13.3) 8(1.9) 53(12.6) 62(14.7) 5 (1.2) 35(8.3) 58(13.9) 39(9.3) 11(2.6) 46(10.9) 29(6.9) 241(57.2) 71(16.9) 16(3.8) 0–1 serving/week n (%) 311(73.9) 2–3 servings/week n (%) 85(20.2) �4 servings/week n (%) 25(5.9) 349(82.9) 348(82.7) 412(97.9) 74(17.6) 249(59.1) 58 (13.8) 65(15.4) 8(1.9) 145(34.4) 146(34.7) 14 (3.3) 8(1.9) 1(0.2) 202 (48.0) 26(6.2) https://doi.org/10.1371/journal.pgph.0002529.t002 than two percent (n = 6) and 2.4% (n = 10) of the participants owned a motor vehicle and a set of television respectively (not in Table 1). Following the PCA analysis, 140 pregnant women (33.4%) fell under the category of lower socio-economic status as shown in Table 1. Dietary intake and diet quality Among 420 pregnant women who participated in the study, two hundred forty (57.2%) fell into the group of poor dietary intake. The median PDQS was 16 (the 25th and 75th percentiles were 14.0 and 18.0, respectively). Table 2 shows the patterns and distribution of dietary intake among pregnant women according to PDQS on healthy and unhealthy food shows that 57.2 of the study participants consumed more than four servings of edible vegetable liquid oil per week out of the 14 healthy foods assessed. Furthermore, the healthy foods that were less con- sumed per week were cruciferous vegetables (93.4%), whole citrus fruits (92.9%), and poultry (92.4%). However, refined grains and baked goods represented the highest percentage of serv- ings consumed per week out of the six unhealthy foods assessed. Bivariate analysis Table 3 depicts the bivariate analysis; poor dietary intake were significantly associated with the marital status of pregnant women, and those who received Fansidar tablets during pregnancy (p>0.05). However, the age group of pregnant women, their educational level, occupation PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 7 / 14 PLOS GLOBAL PUBLIC HEALTHTable 3. Bivariate analysis on the factors associated with poor dietary quality among pregnant women in Mbeya (n = 420): Chi square test. Variable Categories Good dietary quality Poor dietary quality P-value Dietary intake among pregnant women Age group (Years) Education Marital status 15–19 20–24 25–29 35+ No education Primary Secondary and above Married Cohabit Single/ Divorced Trimester of pregnancy First trimester (<12 weeks) Second trimester (12–26 weeks) Received of iron and folic acid supplements Received Fansidar during pregnancy Occupation status Household wealth Index No Yes No Yes Formal employment Self employed Not employed Higher socio-economic Middle socio-economic lower socio-economic Mid Upper Arm Circumference (MUAC) Thin (<23cm) % (n) 16.8 (30) 30.7 (55) 29.1 (52) 23.5 (42) 8.3 (15) 70.6 (127) 21.1 (38) 70.0 (126) 17.8 (32) 12.2 (22) 27.8 (50) 72,2 (130) 38.5 (69) 61.5 (110) 43.6 (78) 56.4 (101) 5.6 (10) 83.3 (150) 11.1 (20) 33.0 (59) 30.2 (54) 36.9 (66) 3.9 (7) Normal (between 23 and 33cm) 91.6 (164) Overweight or obesity (above 33 cm) 4.5 (8) Malaria infection Serum C- reactive protein (CRP) Alpha-1-acid glycoprotein (AGP) No Yes CRP�5mg/L CRP >5mg/L AGP< = 1 g/L AGP>1 g/L https://doi.org/10.1371/journal.pgph.0002529.t003 96.7 (174) 3.3 (6) 89.4 (161) 50.0 (19) 80.6 (145) 19.4 (35) % (n) 21.6 (52) 32.4 (78) 19.5 (47) 26.6 (64) 7.9 (19) 72.6 (175) 19.5 (47) 46.5 (112) 42.3 (102) 11.2 (27) 24.9 (60) 75.1 (181) 35.7 (86) 64.3(155) 52.3 (126) 47.7 (115) 2.1 (5) 85.5 (206) 12.4 (30) 34.0 (82) 35.3 (85) 30.7 (74) 3.7 (9) 90.9 (219) 5.4 (13) 95.0 (229) 5.0 (12) 92.1 (222) 7.9 (19) 81.3 (196) 18.7 (45) 0.129 0.696 0.000 0.506 0.548 0.077 0.156 0.365 0.079 0.409 0.344 0.542 status, household wealth index, received iron and folic acid supplements, Serum C- reactive protein (CRP) and Alpha-1-acid glycoprotein (AGP) were not significantly associated with poor dietary intake and diet quality among pregnant women (p>0.05). Multivariate analysis Table 4 presented all variables which were significant at arbitrary levels in the bivariate analysis and hence qualify to be included in the multivariate analysis. Using a Log binomial regression method, the study found out that poor dietary intake were less likely among cohabiting preg- nant women [Adjusted RR 0.22 (95% CI 0.09–0.50)] and; those who reported taking Fansidar (Sulfadoxine and Pyrimethamine, SP) tablets during pregnancy in Mbeya region [Adjusted RR 0.55 (95% CI 0.31–0.96)]. Further, the study found that poor dietary intake were more likely among pregnant women who were classified as overweight and obesity by the MUAC [Adjusted RR 3.49 (95% CI 1.10–11.06)] and; slightly significant among pregnant women of PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 8 / 14 PLOS GLOBAL PUBLIC HEALTHTable 4. Multivariate log binomial regression methods were used to assess factors associated with poor dietary intake among pregnant women in Mbeya (n = 420). Dietary intake among pregnant women Variable Age group Marital status Occupational status Education level Household wealth index Category 15–19 20–24 25–29 35+ Married Cohabit Single/divorced Formal employment Self employed Not employed No formal Primary Secondary and above Higher socio-economic Middle socio-economic lower socio-economic Mid Upper Arm Circumference (MUAC) Thin (<23cm) Overweight or obesity (above 33 cm) Normal (between 23 and 33cm) Taken Fansidar during this pregnancy Malaria status Received of iron and folic acid supplements Alpha-1-acid glycoprotein (AGP) Serum C- reactive protein (CRP) Number of pregnancy Trimester of pregnancy The reference group is last category https://doi.org/10.1371/journal.pgph.0002529.t004 Yes No Yes No Yes No AGP< = 1 g/L AGP>1 g/L CRP�5mg/L CRP>5mg/L Primigravida Multigravida First trimester (<12 weeks) Second trimester (12–26 weeks) Adjusted RR 95% confidence interval for Adj. RR Lower bound Upper bound 1.22 1.31 2.05 1 1.35 0.22 1 4.29 1.02 1 0.73 0.95 1 1.40 0.70 1 3.02 3.49 1 0.55 1 1.89 1 1.09 1 0.94 1 0.60 1 1.09 1 1.14 1 0.51 0.66 0.98 1 0.60 0.09 1 0.83 0.45 1 0.21 0.47 1 0.77 0.36 1 0.52 1.10 1 0.31 1 0.54 1 0.62 1 0.48 1 0.24 1 0.54 1 0.64 1 2.89 2.59 4.29 1 3.04 0.50 1 22.17 2.29 1 2.56 1.91 1 2.56 1.35 1 17.50 11.06 1 0.96 1 6.62 1 1.90 1 1.81 1 1.50 1 2.22 1 2.03 1 age group 25–29 years old [Adjusted RR 2.05 (95% CI 0.98–4.29)]. Poor dietary intake was not associated with higher concentrations of inflammatory factors i.e. CRP and AGP. Discussion This study contributes to our understanding on socio-demographic drivers for poor dietary intake among pregnant women legally married or cohabiting in Tanzania. Worldwide, poor dietary intake has negative consequences for pregnancy and born children [1, 12]. This study found that dietary intake among pregnant women in the rural settings of Tanzania was largely characterised by low intakes of fruits and vegetables. The findings are very similar to those PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 9 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women found in Ethiopia [12], Iran [30], and India [31]. However, a study from the urban settings of Tanzania reported a high consumption of vegetables among pregnant women [11]. This could be explained by the difference in research methodology especially the inclusion and exclusion criteria of the study participants, and also the difference in dietary assessment tools used between the studies. Evidence shows that, the nutrients in fruits and vegetables such as fibers, vitamins, minerals, and phytochemicals play a key role in human health and well-being [32– 35]. Tanzania has recently developed its national food based dietary guidelines and, it has not yet been fully operationalized. Guidelines of other countries like UK and USA recommend a plant-based diet, rich in fruit, vegetables, whole grains, and legumes to lower the risk of heart disease, type 2 diabetes, obesity, and other health conditions [8, 27]. In this study, we highlight the factors that affect the dietary intake of pregnant women in the rural settings of Tanzania. Our findings affirmed previous researches on the relationship between cohabitation and dietary intake among pregnant women [36]. Pregnant women who engaged in cohabiting relationships seemed to have better education and financial position and, thus reflected on their decision power over their wealth [36]. From the anecdotal evi- dence, it is known that cohabiting relationships are a common practice in the study area. According to the study of Dinour and colleagues in 2012, they found that marital status is one of strong socio-demographic factors that greatly influence health-related behaviours and out- comes [37]. However, studies emanating from sub-Saharan African countries merge cohabita- tion and marriage into one category of marital status and, this is because pregnant women are reluctant to report the status of cohabitation because of stigma [38]. This study managed to assess separate the marriage and cohabitation statuses because there are often different out- comes for the health and well-being of pregnant women and their children in different set- tings. Further prospective cohort research is needed to investigate the social aspects that link marital transition and dietary intake outcomes among pregnant women in sub-Saharan Afri- can countries. Parallel with globalisation, pronounced changes in the human behaviour and lifestyle such as decreased consumption of fruits and vegetables and increased consumption of unhealthy foods [38], have resulted in escalating rates of overweight and obesity among pregnant women. The trends of overweight and obesity among pregnant women in Tanzania has changed from being considered as a mild disorder to the major causes of morbidity and mor- tality associated with non-communicable diseases [13]. According to MUAC measures, this study found that only twenty one pregnant women had MUAC measures above 33cm [25]. Further, our study revealed that out of 420 pregnant women, only 12.6% and 29.3% consumed at least four servings of fruits and vegetables per week respectively. Similar findings were docu- mented in the previous studies from low-income countries, where overweight and obesity pregnant women consumed less vegetable and fruits [12, 39]. In multivariate analysis, our data revealed that pregnant women who were overweight or obesity were significantly associated with poor dietary intake. Pregnant women with overweight and obesity need nutrition counseling that are supported by scientific evidence and that can be easily understood and translated into everyday life to improve maternal and birth outcomes. Findings from large, long-term randomised controlled trials provide convincing evidence that changes made in physical activity levels and dietary habits are effective in delaying, and possibly preventing, progression from overweight and obesity to non-communicable diseases [40]. Future prospec- tive cohort research is needed to investigate the link between obesity and dietary patterns among pregnant women. This study also highlighted an important finding, the protective effect to poor dietary intake among pregnant women who reported taking SP tablets. In Tanzania, SP tablets are offered to all pregnant women attending antenatal clinics between 16 and 24 weeks and, between 28 and PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 10 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women 32 weeks [41]. The linkage between taking SP tablets and poor dietary intake protection could not established because malaria and nutrition interventions are well integrated into antenatal care in Tanzania, and both impart pregnant women with essential knowledge [42]. Further studies are needed to broaden the understanding on this relationship and, help researchers in sub-Saharan African countries to develop tailored interventions to improve maternal and birth outcomes. Strengths and limitations There are several strengths of this study. First, the study was able to use reliable survey data and blood samples from a large population sample and measured diet during the pregnancy using both the Prime Diet Quality Score tool and 24-hour dietary recall. Second, this study provides important information on factors associated with dietary intake among pregnant women of gestation period less than 28 weeks, which is important for fetal development, given the rapid cell growth, and development of immune cells and organs in the first trimester [43, 44]. Third, the present study is that it links social and biological data with dietary data and allows analysis of dietary intake. However, there were several limitations of the study. First, we inevitably have some level of measurement error in both dietary and social and biological data, as both were based on self-report. This source of error is, however, expected to be largely ran- dom, producing valid estimates for the study population. Second, we derived PDQS scores from 24-hour recalls, and there were limited precedents in published literature for converting these scores to equivalent scores for the food frequency questionnaire (FFQ). The validity of using the PDQS score assessed using 24-hour recall is an area of active research. Notably, the 24-hour recall method is used widely in developing countries and our findings provide support for the use of this metric for deriving PDQS in these settings. Our findings may not be gener- alisable to populations where dietary patterns and determinants outcomes differ from rural Tanzania. Associations may be stronger in populations with more prevalent micronutrients and other deficiencies in pregnant women. Conclusions The results of this study affirm that cohabitation and obesity affect dietary intake among preg- nant women differently compared to marriage in rural settings of Tanzania. Further, the find- ings suggest that public health action is needed to promote the consumption of fruits and vegetables among pregnant women in Mbeya. We recommend prospective cohort research to investigate the social aspects that link poor dietary intake outcomes for developing a tailored gestational intervention to improve maternal and birth outcomes in sub-Saharan African countries. Supporting information S1 Checklist. STROBE statement—Checklist of items that should be included in reports of observational studies. (DOCX) Author Contributions Conceptualization: Erick Killel, Nyamizi Ngasa, Adam Hancy, Tedson Lukindo, Ramadhan Noor, Abraham Sanga, Germana H. Leyna, Ray M. Masumo. Data curation: Adam Hancy, Tedson Lukindo. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002529 January 5, 2024 11 / 14 PLOS GLOBAL PUBLIC HEALTHDietary intake among pregnant women Formal analysis: Erick Killel, Geofrey Mchau, Hamida Mbilikila, Kaunara Azizi, Nyamizi Ngasa, Adam Hancy, Tedson Lukindo, Ramadhan Mwiru, Ray M. Masumo. Investigation: Erick Killel, Kaunara Azizi, Adam Hancy, Tedson Lukindo, Ramadhan Noor, Abraham Sanga. Methodology: Erick Killel, Hamida Mbilikila, Kaunara Azizi, Nyamizi Ngasa, Adam Hancy, Tedson Lukindo, Ramadhan Noor, Ray M. Masumo. Project administration: Erick Killel, Geofrey Mchau, Tedson Lukindo, Ramadhan Noor, Abraham Sanga, Patrick Codjia. Supervision: Ramadhan Mwiru, Ramadhan Noor, Patrick Codjia, Germana H. Leyna, Ray M. Masumo. Validation: Adam Hancy. Writing – original draft: Erick Killel, Geofrey Mchau, Hamida Mbilikila, Kaunara Azizi, Nya- mizi Ngasa, Tedson Lukindo, Ramadhan Mwiru, Abraham Sanga, Patrick Codjia, Germana H. Leyna, Ray M. Masumo. 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10.1371_journal.pone.0296688	RESEARCH ARTICLE Flanged males have higher reproductive success in a completely wild orangutan population Amy M. ScottID Wahyu Susanto6, Tatang Mitra Setia6, Cheryl D. Knott1,7 1,2, Graham L. Banes3,4, Wuryantari Setiadi5, Jessica R. Saragih5, Tri 1 Department of Anthropology, Boston University, Boston, Massachusetts, United States of America, 2 Department of Natural Resources and the Environment, University of New Hampshire, Durham, New Hampshire, United States of America, 3 Wisconsin National Primate Research Center, University of Wisconsin–Madison, Madison, Wisconsin, United States of America, 4 The Orang-Utan Conservation Genetics Project, Madison, Wisconsin, United States of America, 5 Eijkman Research Center for Molecular Biology, National Agency for Research and Innovation (BRIN), The Science and Technology Center of Soekarno, Cibinong, West Java, Indonesia, 6 Departemen of Biology, Faculty of Biology and Agricultural, Universitas Nasional, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia, 7 Department of Biology, Boston University, Boston, Massachusetts, United States of America amscott@bu.edu Abstract Male orangutans (Pongo spp.) exhibit bimaturism, an alternative reproductive tactic, with flanged and unflanged males displaying two distinct morphological and behavioral pheno- types. Flanged males are larger than unflanged males and display secondary sexual char- acteristics which unflanged males lack. The evolutionary explanation for alternative reproductive tactics in orangutans remains unclear because orangutan paternity studies to date have been from sites with ex-captive orangutans, provisioning via feeding stations and veterinary care, or that lack data on the identity of mothers. Here we demonstrate, using the first long-term paternity data from a site free of these limitations, that alternative reproductive tactics in orangutans are condition-dependent, not frequency-dependent. We found higher reproductive success by flanged males than by unflanged males, a pattern consistent with other Bornean orangutan (Pongo pygmaeus) paternity studies. Previous paternity studies disagree on the degree of male reproductive skew, but we found low reproductive skew among flanged males. We compare our findings and previous paternity studies from both Bornean and Sumatran orangutans (Pongo abelii) to understand why these differences exist, examining the possible roles of species differences, ecology, and human intervention. Additionally, we use long-term behavioral data to demonstrate that while flanged males can displace unflanged males in association with females, flanged males are unable to keep other males from associating with a female, and thus they are unable to completely mate guard females. Our results demonstrate that alternative reproductive tactics in Bornean orangutans are condition-dependent, supporting the understanding that the flanged male morph is indicative of good condition. Despite intense male-male competition and direct sex- ual coercion by males, female mate choice is effective in determining reproductive out- comes in this population of wild orangutans. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Scott AM, Banes GL, Setiadi W, Saragih JR, Susanto TW, Mitra Setia T, et al. (2024) Flanged males have higher reproductive success in a completely wild orangutan population. PLoS ONE 19(2): e0296688. https://doi.org/10.1371/journal. pone.0296688 Editor: Honnavalli Nagaraj Kumara, Salim Ali Centre for Ornithology and Natural History, INDIA Received: August 21, 2023 Accepted: December 17, 2023 Published: February 9, 2024 Copyright: © 2024 Scott et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data files are available from the OpenBU database (https://hdl. handle.net/2144/45321). Funding: This research was supported by Adventure Travel Conservation Fund: adventuretravelconservationfund.org; Arcus Foundation (G-PGM-1708- 2235, G-PGM-1506- 1327, G-PGM- 1104-36, 1104-36/PID-01853, 0902-30): www.arcusfoundation.org; Association of Zoos and Aquariums Conservation Endowment Fund (13-1159, 11-1063) and Conservation Grants PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 1 / 20 PLOS ONEFund (15-1296): www.aza.org/conservation- funding; Balikpapan Orangutan Society-Canada: orangutan.ca; Conservation, Food and Health Foundation: cfhfoundation.grantsmanagement08. com; Disney Conservation Fund: impact.disney. com/environment/conservation; Focused on Nature: focusedonnature.org; Hollomon Price Foundation: hollomonpricefoundation.org; Houston Zoo: www.houstonzoo.org; Indonesia Climate Change Trust Fund: www.icctf.or.id; Keidanren Nature Conservation Fund: www.keidanren.net/ kncf/en; The Leakey Foundation: leakeyfoundation. org; Nacey Maggioncalda Foundation: formerly: www.naceymaggioncalda.org, currently: leakeyfoundation.org; National Geographic Society (ECO690-14, GEFNE68-13, 8564-08, C113-07): www.nationalgeographic.org/society; National Science Foundation (BCS-1638823, BCS- 0936199): nsf.gov; Ocean Park Conservation Fund: www.opcf.org.hk/en; Orangutan Conservancy: orangutan.com; Phoenix Zoo: www.phoenixzoo. org; Primate Conservation International; Sea World Busch Gardens Conservation Fund; swbg- conservationfund.org; Tides Foundation: tides.org; US Fish and Wildlife Service (F19AP00798, F18AP00898, F15AP00812, F13AP00920, F12AP00369, 96200-0-G249, 96200-9-G110, 98210-8-G661, 98210-7-G185): www.fws.gov; Wenner-Gren Foundation: wennergren.org; Whitley Fund for Nature: whitleyaward.org; Wildlife Conservation Network: wildnet.org; Woodland Park Zoo Partners for Wildlife: zoo.org; Zoo New England: www.zoonewengland.org; and Zoo Atlanta: zooatlanta.org grants to CDK. This research was supported by Boston University Graduate Research Abroad Fellowship: www.bu. edu/cas/admissions/phd-mfa/fellowship-aid/aid- for-phd-students; Boston University Graduate Student Organization Research Grant: www.bu. edu/gso/travelgrants; Boston University Women’s Guild: www.bu.edu/womensguild; Cora Du Bois Charitable Trust: library.harvard.edu/cora-du-bois- fellowship; The Leakey Foundation: leakeyfoundation.org/; and National Science Foundation Graduate Research Fellowship (Grant DGE-1247312): nsf.gov grants to AMS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Flanged male orangutans have higher reproductive success Introduction Alternative reproductive tactics (ARTs) are the existence of two distinct phenotypes within one sex in the context of reproduction [1]. ARTs occur throughout the animal kingdom and are expected to evolve when there is strong sexual selection [1,2], specifically intra-sexual com- petition [2]. There are two primary explanations for the existence of ARTs. The two pheno- types may be either frequency-dependent evolutionary stable strategies, where the relative fitness of each morph depends on its frequency in the population [3] or condition-dependent, due to difference in the quality (i.e. age, body condition, experience, nutritional state and/or genes) of individuals where one morph is ‘making the best of a bad lot’ [4]. Male orangutans (Pongo spp.) display two ARTs with males exhibiting distinct morphologi- cal (Fig 1) (expressed as bimaturism) and behavioral phenotypes [5–8]. Flanged males (50–90 kg) are up to twice the size of unflanged males, but some unflanged males reach flanged male body size (30–59 kg) [9,10]. Flanged males possess secondary sexual characteristics, including an enlarged throat sac and cheek flanges [6,7,11], and are the only morph capable of producing long calls [5,6]. Flanged males are intolerant of each other, either avoiding or fighting and wounding each other [5,12], but they are typically more tolerant of unflanged males [5]. Con- versely, unflanged males are generally tolerant of each other and tend to avoid flanged males [5,6]. Flanged males are dominant to unflanged males and displace unflanged males in con- sortships with females [5,6,12–14]. It has been suggested that flanged males use consortships to mate guard females, as a means to keep other males from mating with a female [12,15,16]. The male morphs also differ in activity patterns, with unflanged males traveling further per day than flanged males [14,17,18]. Due to these differences, the flanged male mating strategy has been described as “sit, call, and wait” and the unflanged male strategy described as “go, search, and find” [7,14]. For male orangutans, ARTs are plastic and sequential—an immature male first develops the unflanged male phenotype and may develop the flanged male phenotype later, but this transi- tion is irreversible [7,19]. There is tremendous variation in the age of flange development, with wild males reportedly developing flanges from ages 14 to 30, and some males never developing flanges [11,16,20]. Flanged males in poor condition exhibit shriveled flanges and are referred to as past-prime males [21]. Past-prime males are not regularly seen, suggesting that this phase is not reached by all males, and is likely short for the males who do become past-prime. Addi- tionally, the presence of past-prime males indicates that the flanged morph is so costly to maintain that some flanged males that cannot continue to maintain it enter the past-prime state [11]. Understanding how sexual selection acts on traits, such as ARTs, requires considering mul- tiple mechanisms of sexual selection simultaneously [22]. Both male and female reproductive strategies are expected to impact the relative reproductive success of each morph [22], and this is especially true for primates, where male and female strategies are closely tied [23]. Orangu- tans are semi-solitary with large home ranges and adults primarily range alone or adult females range with dependent offspring [11,16,24,25], so reproduction first requires finding a mate. It has been suggested that one function of flanged male long calls is to attract females [26,27], and it may also play a role in male-male competition [26,28,29]. Across study sites, female orangutans prefer flanged males [5,6,21,30,31]. Orangutans also have slow life histories, including the longest interbirth interval of any mammal (7.6 years) [32,33]. Slow life histories push the potential for sexual conflict to an extreme [34]. Both male morphs employ sexual coercion in the form of forced copulations [30,35]. Sexual coercion can override female mate choice, but it is unknown if it increases male reproductive success. Female orangutans do not display overt signals of ovulation, such as the sexual swellings typical of many cercopithecoids PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 2 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Fig 1. Example of male orangutans displaying the two alternative reproductive tactics. An unflanged male (left) lacks cheek pads and a throat sac and has a smaller body size. A flanged male (right) has secondary sexual characteristics including large cheek pads (flanges), a large throat sac, and larger body size. Photos by Tim Laman. https://doi.org/10.1371/journal.pone.0296688.g001 [21,36] and ovulatory status appears to be effectively hidden from males [36,37]. Females pref- erentially mate with prime flanged males when they are ovulating and show increased willing- ness to mate with unflanged and non-prime males when the risk of conception is low [21]. Across primates this mating pattern—mating preferentially with preferred males when the likelihood of conception is highest and mating with non-preferred males when the likelihood of conception is lowest—is argued to be a paternity confusion strategy that reduces the likeli- hood of infanticide [21,38,39]. Quantifying the reproductive success of each morph is essential for testing hypotheses about the evolutionary pressures that resulted in orangutan ARTs. Previous studies of pater- nity in both Bornean (Pongo pygmaeus) and Sumatran (Pongo abelii) orangutans are limited by incomplete maternity data [40–43], the inclusion of ex-captive orangutans who may not display natural mating behaviors, or by provisioning from feeding stations and from veterinary care [13,20,44,45] (Table 1). The first orangutan (P. abelli) paternity study found that the two morphs had similar reproductive success and therefore concluded that the two morphs repre- sent alternative mating strategies that coexist as evolutionary stable strategies [20]. The subse- quent three orangutan (P. pygmaeus) paternity studies all concurred that flanged males had much higher reproductive success than unflanged males [13,44,45]. Each of these studies has unique limitations (Table 1). There are also important island or species differences to consider. P. abelii live in habitats with higher food availability, exist at higher densities, and are more social compared to P. pygmaeus [24,46]. We present paternity data from Cabang Panti Research Station in Gunung Palung National Park, Borneo, Indonesia (GPNP), the first from completely wild orangutans with known mothers. We compare our results against others to discern how study limitations and habitat differences explain contrasting results across sites. Orangutan paternity studies also differ in the degree of male reproductive skew—the degree to which reproduction is monopolized versus shared (Table 1). Characterizing male PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 3 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Table 1. Paternity determination and paternity skew across study sites. Study Site No. offspring sired by flanged males No. offspring sired by unflanged males Ketambe Research Station, Gunung Leuser National Park [18] Kinabatangan Orang- utan Conservation Project, Lower Kinabatangan Wildlife Sanctuary [37] Camp Leakey, Tanjung Puting National Park [36] Sepilok Orangutan Rehabilitation Center [12] Cabang Panti Research Station, Gunung Palung National Park 4 9 10 4 5 6 1 3c 1 0 Total 32 11 Total no. offspring with father assigneda 10 10 14 6c 6c 46 Total no. offspring tested Total no. candidate sires testeda Study Periodb 11 16 25 8 11 16 17 4 1983– 1997 (11) 1985– 2000 (8) 1993– 2009 (13) 2010– 2014 (1) Most successful male’s share (mean) 48.18 Most successful male’s share (range) 33.33–100 33.32 18.18–50 56.57 14.29–100 57.14 NA 13 20 2008– 2014 (3) 33.33 20–40 Limitation Ex-captives in study population Limited population knowledge; Mothers genetically assigned Feeding station; Ex- captives in study population; veterinary care Feeding station; Ex- captives in study population; Only one flanged male sampled Gray background = P. abelii. White background = P. pygmaeus. a = Number of offspring and candidate sires tested are likely an underestimate of the total number of offspring born or candidate sires in the study site due to sampling difficulties. b = Number in parentheses is the number of 5-year periods during the study period. c = Number of offspring sired by flanged males and unflanged males do not add up to the total because there was a male of unknown morph who sired an offspring. https://doi.org/10.1371/journal.pone.0296688.t001 reproductive skew is important for understanding the evolution of ARTs in orangutans. Across primates, the degree of male reproductive skew in multi-male groups is best explained by the degree of female reproductive synchrony and the number of males in the group [47,48]. The orangutan social system, with a high fission-fusion dynamic (social associations vary in size, composition, and cohesion) [11,24,49], and a lack of group formation, makes defining the number of males in a "group" difficult. However, there is clearly a male biased operational sex ratio, with many males competing for a few conception opportunities, due in part to the long interbirth interval [16]. In terms of female reproductive synchrony, reproduction is asynchro- nous, although some sites do see increases in births following periods of high fruit availability [50]. Even without female reproductive synchrony, in a dispersed social system, low male reproductive skew is expected [47]. Additionally, male dominance can lead to higher repro- ductive success through priority-of-access [51], but this is not the case for all species [52]. Here we compare male reproductive skew across sites and use long-term behavioral data to test the ability of flanged males or a single dominant flanged male to mate guard females. We combine long-term behavioral observations and genetic paternity determination from a completely wild orangutan population at Cabang Panti Research Station in Gunung Palung National Park, Borneo, Indonesia, to investigate the evolution of male ARTs in Bornean orangutans. If male ARTs are frequency-dependent evolutionary stable strategies, we would expect the frequency of each morph to be stable and relative fitness of each morph to depend PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 4 / 20 PLOS ONEFlanged male orangutans have higher reproductive success on its frequency in the population [1,3], i.e. if 20% of males are flanged then 20% of offspring will be sired by flanged males. Conversely if the male morphs are condition-dependent strate- gies, then we would expect unequal fitness benefits for each morph, where the morph in ‘poor condition’ has lower reproductive success and takes advantage of alternative tactics [1,4]. First, we determine the relative reproductive success of the two morphs and measure male reproduc- tive skew. Second, we test the ability of flanged males to mate guard females. We then compare our results to those from prior studies in other populations to discern how study limitations and habitat differences might explain contrasting results across studies. Finally, we discuss the implications of these results for our understanding of the evolution of ARTs in orangutans and the interaction between male and female reproductive strategies. Materials and methods Study site and population Orangutans (Pongo pygmaeus wurmbii) were studied in Gunung Palung National Park (GPNP), West Kalimantan, Indonesia, based out of the Cabang Panti Research Station (CPRS) (1˚13´S, 1107´E) (3400 ha), as part of a study that began in 1994 [53]. Most orangutans encountered and followed were habituated and individually identifiable, but unknown and unhabituated individuals were also encountered, due to male dispersal and large home ranges [11,24,54,55]. Each month, phenology data were collected to characterize food availability of orangutan foods from 60 plots (totaling 9 ha) spread across 6 habitat types in the study site [56,57]. Fruit availability was calculated from the top 25 genera of plants that orangutans are known to consume most often at GPNP which represented 80% of fruit in their diet [57,58]. We then normalized that data by calculating modified Z scores from the percentage of stems that had mature or ripe fruits. Food availability was used as a control variable in our statistical models. Behavioral data collection We used long-term data (2008–2019) from orangutans in CPRS collected during focal follows [59] to assess the ability of the two male morphs to effectively mate guard females and to create a male dominance hierarchy. During orangutan follows, an association was recorded when- ever another orangutan came within 50 meters of the focal [60,61]. The identity and age-sex class of all orangutans was recorded. Males were classified by morph—flanged or unflanged. For this analysis, males who had small, developing flanges were classified as unflanged males. We used long-term follow data to tally the number of flanged and unflanged males that were seen in the study site one year prior to and following conception for each offspring, where we were able to identify a father and determine his morph. Females were classified as ‘sexually active’ or ‘non-sexually active’ based on the likelihood that they were fecund and actively mat- ing. The ‘sexually active’ category included nulliparous females, parous females without depen- dent offspring, mothers with offspring over age six, and pregnant females in the first trimester. Females in this population are most proceptive to mating during the first trimester of preg- nancy [21]. The non-sexually active category included parous females with dependent off- spring under age six and pregnant females in the second and third trimester. Since orangutans have a gestation period of approximately eight months [62] and an average interbirth interval of 7.6 years [32], females will on average conceive when a dependent offspring is 6.8 years old and will begin mating 6–12 months before she conceives. Therefore, we used six years as a cut- off because we expected females to begin mating again at approximately that time. Addition- ally, we have previously shown that male-female interactions change when the dependent off- spring reaches age six [63]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 5 / 20 PLOS ONEFlanged male orangutans have higher reproductive success We analyzed all adult male-female associations from 2008–2019 (N = 759), noting the occurrence and outcome of an encounter with a second or ‘extra-pair male’ (EPM). If the asso- ciation between the first male and female was terminated after the second male arrived, and the second male stayed with the female, we defined this as male displacement. Displacement did not necessarily involve agonism or aggression between the males, nor was it necessarily immediate. For each male-female association, the length of the association (in minutes) and all mating events were also recorded. We analyzed all adult male-male interactions from 2008–2014, the period with both behav- ioral data and with paternity determination data, to evaluate male dominance rank. Offspring with known paternities were conceived from January 2010 to August 2014. During this period, nine of the sampled flanged males (Bilbo was still unflanged), an additional three individually recognizable flanged males, and up to seven unknown flanged males were observed in the study site. We examined the outcome of all dyadic interactions between flanged males to eval- uate dominance rank. Dominance was defined by the outcomes of dyadic agonistic interac- tions [64]. We included avoidance, displacement, and chase interactions as dominance interactions with a clear dominant and subordinate individual. Sample collection Fecal samples were collected after observed defecation from known and unknown orangutans from 2008 through 2019. When possible, two samples were collected from one individual on separate occasions. Samples from mother and dependent offspring were collected in the same encounter. Samples were stored in either RNAlater, 70% ethanol, or dried using the two-step ethanol alcohol-silica desiccation method [65,66]. Dried samples were stored at ambient tem- perature (up to 40˚ C) until analysis. Samples stored in RNAlater or 70% ethanol were stored at -20˚ C or -80˚ C. Genotyping and paternity analysis We collected fecal samples from 42 orangutans for genotyping: 13 offspring, their 10 mothers, and 19 candidate fathers (8 unflanged, 10 flanged, and 1 observed as both unflanged and flanged males) in GPNP. Genomic DNA was extracted 2–3 times from each fecal sample using ChimerX stool DNA purification kits. Following Morin et al. [67], we quantified DNA content through qPCR12. We amplified a panel of 12 autosomal tetranucleotide microsatellites [20,44,68–71] (S1 Table). These were first co-amplified in an initial PCR reaction, with suffi- cient replicates to maintain error rates of less than 1% when scoring homozygotes, per Ara- ndjelovic et al. [72], before the products were re-amplified with labelled primers in panels of 3–5 loci. Fragment analysis was performed by the DNA sequencing unit at Eijkman Institute for Molecular Biology, using an Applied Biosystems 3130 Genetic Analyzer to size alleles against a GeneScan™ 500 LIZ™ internal size standard. Peaks were manually scored by two different peo- ple using GeneMapper (v3.7 and v4.0). Scores were concordant irrespective of software ver- sion. Heterozygotes were called when the same two alleles were observed in at least two independent amplifications, and homozygotes were called when only one allele was observed in up to five independent amplifications, per Arandjelovic et al. [72]. Prior to downstream analysis, CERVUS 3.0 [42] and MICRO-CHECKER 2.2.3 [73] were used to assess genotypes for null alleles, allelic dropout, and scoring errors due to stuttering, and to confirm that all 12 microsatellites were in Hardy-Weinberg equilibrium (S2 Table). Individual identity analysis was performed in CERVUS 3.0 to ensure that purported replicates derived from the same individual. Individuals genotyped at a minimum of nine loci were PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 6 / 20 PLOS ONEFlanged male orangutans have higher reproductive success subsequently used in parentage analysis, having met the minimum number of loci needed to tell full siblings apart (PID-sibs <0.001) for the mean observed heterozygosity in our panel of microsatellites (sensu Waits et al. [74]). Paternity analyses were performed in CERVUS 3.0 [42] and in COLONY 2.0.6.7 [75], using both an exclusionary approach and a likelihood approach. In the exclusionary approach, off- spring are required to share one allele at each locus with the known mother and the other allele must be shared with the father. On the other hand, the likelihood approach in CERVUS 3.0 allows for genotyping errors, null alleles, and potential mutations. The advantage of COLONY 2.0.6.7 is that it uses a full-pedigree likelihood approach, rather than dyadic relationships, when inferring both parentage and sibship. Field observation of mother-offspring pairs was confirmed using exclusionary maternity analysis CERVUS 3.0. Mothers were then used as known parents in CERVUS 3.0, increasing the statistical power of paternity assignment. All sampled males were considered candidate fathers for each offspring. Paternity was simulated using 100,000 offspring to obtain critical values of Delta at confidence levels of 80% (relaxed) and 95% (strict), sensu Marshall et al. [43]. For simulation in CERVUS 3.0, the proportion of candidate fathers sampled was inferred at three different values: 0.2, 0.5, 0.65 to simulate the possibility that an unsampled sire fathered offspring. The values 0.65 and 0.2 represent the upper and lower limits of ‘unknown’ males being entirely ‘known’ males or entirely ‘unknown, unique’ males, respec- tively. Each value produced the same results, so we report values using 0.5 as the proportion of candidate fathers. In COLONY 2.0.6.7, analysis was run with the following parameters: female polygamy and male polygamy without inbreeding or clones, ‘long’ length of run, ‘high’ likelihood precision, no updating of allele frequency, and no sibship prior. Reported paternity results take known maternal genotype into account. Again, all sampled males were considered candidate fathers for each offspring. Cross-site comparisons We compared our paternity data from CBRS in GPNP to published paternity results from four other orangutan study sites: Kinabatangan Orangutan Conservation Project, Lower Kinaba- tangan Wildlife Sanctuary [45]; Ketambe Research Station, Gunung Leuser National Park [20]; Camp Leakey, Tanjung Puting National Park [44]; and Sepilok Orangutan Rehabilitation Cen- ter [13]. Reproductive skew We calculated male reproductive skew (2008–2014) using two measures: Nonacs B index [76,77] and the most successful sire’s share as a percentage. We calculated both measures for our study population and the most successful sire’s share for all published orangutan paternity data. Due to the male dispersal and long lives, we were unable to accurately estimate adult male ages required for the multinomial skew index [78], and Nonacs B index was calculated with the Skew calculator 2013 (https://www.dropbox.com/home/2013%20Version, accessed December 2021) [79]. The B index takes residency and number of offspring into account (see S1 File for details of interpretation). We included only sampled males and the two unsampled fathers (of the two offspring for whom we could not identify a father) in our calculation. For the unsampled fathers, we used the average male residency time across this study period, 3 years. PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 7 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Statistical analysis To test the ability of flanged and unflanged males to mate guard females, we used two-sided Fisher’s exact tests to compare the rate at which the two male morphs are displaced by an EPM in association with a female (N = 63). We additionally tested this hypothesis using two-sided Fisher’s exact tests to compare the rate at which the two male morphs are displaced by an EPM in association with only sexually active females (N = 36). Fisher’s exact tests are appropriate for comparisons when values in some categories are less than five [80]. Further, we tested the possibility that flanged male presence alone acts as a deterrent pre- venting EPM from encountering the flanged male-female pair using Chi-square tests of equal proportions and a binomial generalized linear mixed model (GLMM). We used Chi-square tests of equal proportions to compare the rate at which sexually active female (N = 486 associa- tions), non-sexually active female (N = 201 associations), and total female associations (N = 706 associations) with flanged versus unflanged males encounter an additional or EPM. We also tested whether male-female association grouping (sexually active female-flanged male, sexually active female-unflanged male, non-sexually active female-flanged male, and non-sexu- ally active female-unflanged male) impacted the chance of encountering an EPM using a bino- mial GLMM. Data exploration and model residuals revealed no violations of the assumptions of the binomial GLMM [81]. The response variable was the occurrence of an encounter with an EPM (yes/no). We used the length of a male-female association as an offset variable and included the identity of the male and female as random effects. Fruit availability (see Study Site and Population) was included as a fixed effect (control variable) because some study sites show that orangutans are more social during periods of high fruit [82–84] (but see [85,86]). We compared AIC values between models that excluded fixed effects to determine the best model and how to code male morph and female reproductive class (S3 Table). We performed all statistical procedures in R [87]. For the nonparametric post-hoc tests, we used the package PMCMR [88]. For the binomial GLMMs, we used the packages lme4 [89] and arm [90] to calculate confidence intervals. Graphs were made in the packages ggplot2 [91] and cowplot [92]. This study followed the American Society of Primatologists’ ‘Ethical Treatment of Non- Human Primates’ principles. It was non-invasive and observational. All protocols were approved by The Eijkman Institute Research Ethics Commission, Boston University IACUC (protocol no. 11–045 and 14–043) or deemed exempt by Boston University IACUC. All proto- cols were approved by the Indonesian State Ministry for Research and Technology (RISTEK), the Ministry of Home Affairs and the Indonesian Institute of Sciences (LIPI), the Center for Research and Development in Biology (PPPB), and Balai Taman Nasional Gunung Palung (BTNGP). Sample collection was approved by Balai Taman Nasional Gunung Palung (BTNGP), permit numbers: 86/YPPN/SK/XII/2009-2019. Results Male reproductive success Each of the three methods of paternity determination (exclusionary and likelihood approaches in CERVUS and full-pedigree likelihood approach in COLONY) were concordant in paternity assignment (Table 2). Paternity could be assigned for five out of seven offspring conceived during the sampling period (2008–2019) and one individual conceived prior to the sampling period. The flange-status of this sire at the time of conception (ca. 2005) is unknown, but he was flanged at first observation in 2009. Over a six-year period (2009–2014), four flanged males sired five offspring, indicating that male morph plays an important role in male PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 8 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Table 2. Paternity assignment at cabang panti research station in GPNP. Offspring Est. Birth Year Mother Trio mis-match Pe Next best mis-match Delta Assigned Father CERVUS Exclusion Likelihood COLONY Prob. Dagul Rossa Berani Ijal Telur Uok Januari Benny Dolia Hannah Vanna Tawni Bayasa 2002 Delly 2004 Veli 2005 Bibi 2005 Irmaa 2007 Tari 2007 Umi 2009 JT 2010 Beth 2011 Dewi 2012 Heraa 2012 Veli 2014 Tari 2015 Bibi — — 0 — — — — 0 0 — 0 1 0 — — — — — — — 0.999 0.999 0.999 0.999 0.999 0.998 — — 4 — — — — 3 2 — 1 4 2 — — — — — — — 13.7 ‡ 7.76 4.30 6.50 4.08 5.29 ‡ ‡ ‡ ‡ ‡ — — 0.999 — — — — 0.961 0.023 — 0.880 0.023 0.072 — — Codet — — — — Prabu Senjaa — Prabu Mandab Moris Gray = conceptions within the sampling (fecal and behavioral data collected) period. a = genotyped at 11 loci, not all 12 loci. b = genotyped at 10 loci, not all 12 loci. Bold = male is known to have been flanged at the time of conception. Non-bolded father means that his phenotype was unknown at the time of conception. Trio mismatch = the number of loci that are a mismatch in the trio of offspring, mother and assigned father. Pe = exclusion probability, calculated in CERVUS 3.0 using allele frequencies from all 48 individuals genotyped. Next best mismatch = refers to the trio of offspring, mother, and the male with the closest match after the assigned father. ‡ = the trio delta value meets the strict (95%) confidence level. https://doi.org/10.1371/journal.pone.0296688.t002 reproductive success (Table 2). During each of these conception periods, there were never more flanged males than unflanged males observed in the study site (S4 Table). The mothers of these five offspring were parous at the time of conception. For the two offspring for whom fathers could not be determined, the COLONY pedigree results inferred different fathers. Male reproductive skew We found low reproductive skew in our study population. From 2008 to 2014, the period with both behavioral data and offspring genetic sampling, the most successful sire’s share was 28.57% and Nonacs B index was 0.0004 (Npotential sires = 17, Noffspring = 6, P = 0.502, 95% CI = -0.121–0.188) (Table 1). Our Nonacs B values indicated either a random or equal distribution of male reproductive skew (equalB = 0.121, monopolyB = 0.922, see S1 File for details of inter- pretation). Unfortunately, we were not able to construct a male dominance hierarchy because only six interactions between flanged males were observed during this same period, with 3434 flanged male observation hours (S5 Table). Even with few observations, we did not find a strict relationship between male dominance and reproductive success. For example, Senja, who sired one known offspring during this period, was subordinate to Codet, who did not sire any known offspring during this period (Tables 1 and S5). Cross-site comparisons Combining paternity assignment data across the five study sites showed that, overall, flanged males sired a greater proportion of offspring (69.57%) than did unflanged males (23.91%) PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 9 / 20 PLOS ONEFlanged male orangutans have higher reproductive success (Table 1). This was especially true for Bornean sites, where flanged males sired 77.78% of off- spring (Table 1). There was also variation in the degree of male reproductive skew across study sites (Table 1). Across study sites, the mean most successful sire’s share (for 5-year periods) ranged from 33.33%-57.57% (Table 1). Mate guarding Our paternity results demonstrated that a single male was unable to monopolize paternity within our study site during any time period. To examine this from a behavioral perspective, we examined the ability of males to mate-guard females. We tested whether the presence of a male in association with a female served to deter a second or ‘extra-pair male’ (EPM) from interacting with that female. Further, we examined the outcome of those interactions to deter- mine if flanged males were able to displace unflanged males. We observed no significant difference in the rate at which female associations with flanged and unflanged males encountered an EPM (χ2 = 0.120, df = 1, P = 0.730, N = 706). On average, an EPM was encountered every 56.65 hours of unflanged male-female associations, and every 54.08 hours of flanged male-female associations (S6 Table). Likewise, there was not a signifi- cant difference in the rate at which sexually active female associations (χ2 = 2.412, df = 1, P = 0.120, N = 486) or non-sexually active female associations (χ2 = 0.746, df = 1, P = 0.388, N = 201) with flanged and unflanged males encountered an EPM. On average, sexually active females in association with flanged males encountered an EPM every 95.85 hours and in asso- ciation with unflanged males encountered an EPM every 70.78 hours (S6 Table). In contrast, non-sexually active females in association with flanged males encountered an EPM every 15.28 hours and in association with unflanged males encountered an EPM every 43.08 hours, on average (S6 Table). Our best binomial Generalized Linear Mixed Model (GLMM) found that food availability and both male and female age-sex classes significantly impacted the likelihood that a male-female association would encounter an EPM (S7 Table). Flanged males with non- sexually active females were significantly more likely to encounter an EPM than were either flanged or unflanged males with sexually active females (S7 Table and Fig 2). However, after an EPM was encountered, there was a statistically significant difference between male morphs in the proportion of encounters in which the first male associating with a female was displaced (Fisher’s Exact Test, N = 50, P = 0.0004) (Fig 3A). Unflanged males were displaced in 60% of encounters with an EPM. Of the 18 times that unflanged males were displaced, 61% of the EPM were flanged. Flanged males were only displaced in 10% of encoun- ters with an EPM and they were never displaced by unflanged males. This 10% represents one instance in which one flanged male chased off another flanged male in the presence of two non-sexually active females. When considering only sexually active females, flanged males were statistically significantly less likely to be displaced than unflanged males (Fisher’s Exact Test, N = 36, P = 0.003) (Fig 3B). Conversely, when considering only non-sexually active females, there is no difference in the rate of displacement between male morphs (Fisher’s Exact Test, N = 20, P = 0.379) (Fig 3C). Thus, both female reproductive state and male morph are important determinants of orangutan mating behavior [21]. Discussion Male reproductive success and skew Our paternity results (6 assigned paternities over 10 years) most closely align with those of Kinabatangan [45], finding low reproductive skew among flanged males. While Kinabatangan inferred maternity from genetic data, our results confirm the same overall pattern. Only these two studies are from wholly wild and unprovisioned orangutans in primary rainforest habitat PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 10 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Fig 2. The proportion of male-female associations in which the dyad encounters an ‘Extra-Pair Male’ (EPM) by male-female association group type. FL-SA = flanged male/sexually active female association. FL-NSA = flanged male/non-sexually active female association. UF-SA = unflanged male/sexually active female association. UF-NSA = unflanged male/non-sexually active female association. N values at the top of each column show the number of male-female associations in each group type. Dark gray represents encounters with an EPM and light gray represents no encounter with an EPM. Significance values from the binomial GLMM (* = P < 0.05). https://doi.org/10.1371/journal.pone.0296688.g002 without feeding stations, ex-captive orangutans, or veterinary care. This suggests that flanged males have higher reproductive success than unflanged males in completely wild Bornean pop- ulations, and that a single flanged male cannot monopolize paternity. In contrast, provisioning from feeding stations at Tanjung Putting [44] and Sepilok [13], in conjunction with veterinary interventions, may explain why a single flanged male was able to monopolize paternity at these two sites. Feeding stations may create an unnaturally high concentration of female orangutans in one area, increasing the ability of a single male to monopolize females. One unexpected out- come of feeding stations may be a reduction in genetic diversity in subsequent generations due to high male reproductive skew. It is likely that without feeding stations, either (1) dominant males are unable to monopolize females across large areas or (2) male dominance hierarchies are less strict when males are not competing over access to a feeding station. Due to the rarity of interactions between flanged males (6 interactions in 7 years), we could not construct a dominance hierarchy, but all observed interactions suggest a linear hierarchy with no PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 11 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Fig 3. Male displacement in male-female associations. The proportion of (a) all male-female associations, (b) a subset of male-female associations where the female is a sexually active, and (c) a subset of male-female associations where the female is a non-sexually active in which the first male is displaced by an ‘extra-pair male’ (EPM). Male displacement is represented by darker shading. N values at the top of each column show the number of male-female associations by male morph. Dark gray represents encounters with displacement and light gray represents encounter with no displacement. Significance values from Fisher’s exact tests (* = P < 0.05, ns = P > 0.05). https://doi.org/10.1371/journal.pone.0296688.g003 observations of rank reversals or rank instability. Continued study of paternity and male inter- actions at GPNP and more orangutan study sites could differentiate between these two possibilities. Paternity results from Ketambe, Sumatra [20], starkly contrast with those from Borneo— unflanged males at Ketambe had higher reproductive success than flanged males [20]. It is unclear if this represents a species difference or unusual population parameters. The combina- tion of male rank instability, first-time mothers, and ex-captive females in that study [20,93] may have resulted in an inflated reproductive advantage for unflanged males. For instance, Sepilok and Tanjung Puting also found that the offspring of first-time mothers were sired by unflanged males [13,44], although in GPNP nulliparous females formed preferential mating relationships with flanged males [94]. If there truly is a species difference between the relative reproductive success of flanged and unflanged males, it is likely due to differences in the dura- tion of the unflanged stage and variation in the relative proportions of each morph between the islands [19,86]. However, it is important to note that orangutan paternity studies are limited by small sam- ple sizes (Table 1) due to their long interbirth intervals and semi-solitary social structure. Smaller samples are more subject to random stochasticity, which may also play a factor in explaining the differences between sites, but comparison of data across five different sites adds robustness to these comparisons. Small sample sizes may contribute to the finding of lower reproductive skew. In this comparative perspective, the two Bornean sites with completely wild orangutans (GPNP and Kinabatangan) agree that flanged males have higher reproductive success than unflanged males and reproductive success is spread broadly across many flanged males. But with only one Sumatran site in the sample [20], where there are also ex-captives, it is unclear if that pattern holds for Sumatran orangutans. Half of the offspring (5 out of 10) in Ketambe were born to matrilines with ex-captive mothers, and 4 of these 5 offspring were PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 12 / 20 PLOS ONEFlanged male orangutans have higher reproductive success sired by unflanged males [93]. If mating strategies are learned through the observation of the mother, then ex-captive matrilines might not display the same mate choice preferences as wild orangutans. Orangutan reproductive strategies Orangutans exhibit male-male competition, sexual coercion by males, and female mate choice. Reproductive success is impacted by the interaction between each of these male and female reproductive strategies. Due to the highly dispersed spatial distribution of female orangutans [11,24,25], it is expected that a single male cannot monopolize females or conceptions, result- ing in low male reproductive skew [47]. Our paternity data concluded that a single male can- not monopolize paternity, and the behavioral data on male-female association encounter rates with EPMs further addresses the inability of a single flanged male to monopolize females. Con- sistent with other studies [6,16,30], we found that flanged males at GPNP were able to displace unflanged males associating with females, specifically with sexually active females. However, the mere presence of a flanged male with a female does not keep other males away. But once a sexually active female is an association with a male, regardless of male morph, the pair is less likely to encounter an EPM. Female orangutans at GPNP display a mixed mating strategy pref- erentially associating with prime, flanged males when they are most likely to conceive and with non-prime, unflanged males when they are less likely to conceive [21,38]. Thus, females may be choosing who to associate with based on their probability of conception [21,38]. The limited ability of flanged males to mate guard further highlights the importance of female choice in facultative associations and mating. Therefore, female preference for flanged males, coupled with the flanged male ability to displace unflanged males, operate in parallel leading to higher reproductive success for flanged males, and flanged male inability to completely mate guard females, leads to low reproductive skew among these flanged males. Because both flanged and unflanged males perform forced copulations [11,35], our results cannot speak to the efficacy of that form of sexual coercion in leading to reproductive success. Since unflanged males have lower reproductive success, it appears that harassment by unflanged males is not a successful reproductive strategy. Instead, female preference for flanged males and flanged male competi- tive ability are operating in the same direction, leading to higher reproductive success for flanged males. Alternative reproductive strategies Our results also have important implications for understanding ARTs in male orangutans. ARTs have been hypothesized to be either frequency-dependent evolutionary stable strategies [3] or due to difference in the quality of individuals [1,4]. The first published study of orangu- tan paternity, and still the only study in Sumatran orangutans, found that the two morphs had comparable reproductive success at Ketambe, and thus argued that the two morphs were evo- lutionary stable strategies [7,20]. Now, 20 years later with data from an additional four Bor- nean sites, it is clear, that at least in Borneo, flanged males have higher reproductive success than unflanged males. The relative numbers of flanged and unflanged males are pivotal to our interpretation of paternity data, but accurate counts of the numbers of males in a study site are difficult to obtain due to large home ranges and the difficulty of visually identifying orangutans who tran- sition from unflanged to flanged males. Cross-site comparisons agree that in Sumatra there are approximately twice as many unflanged males as flanged males, whereas in Borneo there is more inter-site variation, but the morphs exist in approximately equal proportions [12,19]. Long-term demographic data at GPNP agrees with these approximations [35]. These PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 13 / 20 PLOS ONEFlanged male orangutans have higher reproductive success proportions indicate that males remain in the unflanged morph longer in Sumatra than in Bor- neo [19]. Because the first paternity study found that unflanged males sired 60% of offspring in a Sumatran population, it was argued that flanged and unflanged male morphs represent alter- native mating strategies that coexist, representing evolutionary stable strategies [7,20]. If this result is representative of all of Sumatra, it could explain why Sumatran males remain unflanged for longer than Bornean males. In this case, unflanged males avoid the energetic and competitive costs of becoming a flanged male [11], while achieving reproductive success. However, the paternity data from Borneo does not support the understanding that male alter- native reproductive strategies are frequency-dependent evolutionary stable strategies. In Bor- neo, 77% of offspring are sired by flanged males while flanged males only represent approximately 50% of all males, demonstrating that the flanged morph is absolutely and rela- tively more successful. Because the reproductive success of the morphs is not related to their proportion in the population, Bornean orangutan ARTs are not frequency-dependent. For Bornean orangutans, ARTs are due to individual differences in quality, with the flanged morph indicating higher quality and unflanged morph indicating lower quality. Our results support the view that the unflanged morph is a transitional stage, where unflanged males are in a ‘waiting room’, avoiding the costs associated with the flanged morph, and ‘making the best of a bad situation’ until they are able to flange [7,93]. The variation in results at different study sites highlights the dynamic nature of ARTs; the ability of each morph to attain repro- ductive success is likely highly dynamic, depending on the relative proportions of each male morph and density of orangutans, which is influenced by food availability [24,95]. More data on the relative proportion of each male morph, male dominance hierarchies, and paternity data from additional study sites of P. abelii in Sumatra will clarify if island differences are due to ecological factors or if there are true species differences. Additionally, studies of the recently described Tapanuli orangutan (Pongo tapanuliensis)—who live on Sumatra, south of Lake Toba, but inhabit less productive forests, live at lower densities, and are less social and thus more similar to Bornean orangutans than Sumatran orangutans [84]—will further help to clar- ify the relative roles of ecology and species differences. These combined paternity results across sites align with the model of developmental arrest in male orangutans developed by Pradhan et al. [95] which explains differences in the ratio of flanged to unflanged males across sites through ecology. According to this model, longer delays in the development of flanges are expected when females are monopolizable by the dominant male because, in this situation, non-dominant flanged males will have lower repro- ductive success; thus, males should remain unflanged to avoid the costs of the flanged male morph [24,46,95]. Dominant male monopolization of females is expected when orangutans live at higher densities and are more gregarious, which is related to increased food availability [24,46]. Thus, where orangutans live at higher densities (i.e., Ketambe, Sumatra), a dominant flanged male is expected to be able to monopolize females, and a smaller proportion of flanged males are expected. It is worth noting that at Ketambe, there are periods of both high repro- ductive skew, where a single flanged male sires many offspring, and periods of low reproduc- tive skew which correspond to times of rank instability. Conversely, where orangutan habitat is less productive and orangutans live at lower densities (i.e., Borneo), a dominant flanged male is not expected to be able to monopolize females, and a greater proportion of flanged males are expected. Paternity results from the two Bornean sites with completely wild orangu- tans agree with this model, showing that in the absence of feeding stations, a single male is not able to monopolize females. And in the Bornean case, we also see short developmental arrest, resulting in relatively more flanged males. In Borneo, with low reproductive skew among flanged males, there is a reproductive benefit to flanging. PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 14 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Despite intense male-male competition [5,12,96] and sexual coercion [11,35], female choice remains an important factor in determining orangutan reproductive outcomes. The impor- tance of female choice may explain why it is that in all primate species with male ARTs, the morphs are attributed to individual differences in quality [23]. In taxa where the costs of repro- duction are disproportionately borne by one sex, we expect strong sexual selection, including mate choice to evolve [97]. In the case of mammals where obligate female gestation and lacta- tion mean that females must invest heavily in reproduction and parental care, we expect female choice to evolve [98], and thus is not surprising that male ARTs in orangutans are signals of male quality that are subject to female choice. We predict that in species with both strong mate choice, driven by differential costs of reproduction, and ARTs, the ARTs will be condition- dependent, rather than frequency-dependent. This study of orangutan paternity determination in GPNP is the first study of orangutan paternity from a completely wild population in a primary rainforest site, without feeding sta- tions, rehabilitant orangutans, or veterinary care, and with known maternal-offspring relation- ships. This enables us to better understand why previous orangutan paternity studies disagree on which morph has higher reproductive success and the degree of male reproductive skew. We show that the ARTs are condition-dependent. Flanged males have higher reproductive success, and unflanged males are ‘making the best of a bad situation’. Supporting information S1 Table. Panel of microsatellite primers used for genotyping. (DOCX) S2 Table. Summary statistics for the 12 microsatellite loci used. (DOCX) S3 Table. GLMM comparisons. (DOCX) S4 Table. Ratio of flanged to unflanged males during each of the five conception periods where paternity and male morph were determined. (DOCX) S5 Table. Flanged male-flanged male dyadic dominance interactions from 2008 to 2014. (DOCX) S6 Table. Rates at which male-female associations encounter an additional or Extra-Pair Male (EPM), expressed as the average number of hours of association per encounter with an EPM. (DOCX) S7 Table. GLMM testing the probability that a male-female pair encountered an EPM. (DOCX) S1 File. Reproductive skew calculation and nonac’s b interpretation. (DOCX) Acknowledgments We thank the Universitas Nasional (UNAS), the Eijkman Research Center for Molecular Biol- ogy, National Research and Innovation Agency–BRIN (Eijkman Institute for Molecular Biol- ogy–EIMB), the Universitas Tanjungpura (UNTAN), the Directorate of Natural Resource PLOS ONE \| https://doi.org/10.1371/journal.pone.0296688 February 9, 2024 15 / 20 PLOS ONEFlanged male orangutans have higher reproductive success Conservation and Ecosystems (KSDAE)–Ministry of Environment and Forestry, the Gunung Palung National Park office (BTNGP), National Research and Innovation Agency (BRIN, for- merly LIPI), the Center for Research and Development in Biology (PPPB), and the Ministry of Research and Technology/National Research and Innovation Agency (RISTEK/BRIN) for their sponsorship, collaboration, and permissions to conduct research in Gunung Palung National Park. We are grateful to all the dedicated field assistants, research assistants, field managers, and students who assisted with project maintenance and data collection. Thank you to the Genome Diversity and Disease Laboratory and DNA Sequencing Unit at EIMB and Hannah Gorman, Morgana Haub, and Jay Coogan for help with genotyping. We would like to thank Carolyn Hodges-Simeon, Christopher Schmitt, and Larissa Swedell for constructive sug- gestions on the manuscript. Author Contributions Conceptualization: Amy M. Scott, Cheryl D. Knott. Data curation: Amy M. Scott, Cheryl D. Knott. Formal analysis: Amy M. Scott, Cheryl D. Knott. Funding acquisition: Amy M. Scott, Cheryl D. Knott. Investigation: Amy M. Scott, Jessica R. Saragih. Methodology: Amy M. Scott, Graham L. Banes, Cheryl D. Knott. Project administration: Amy M. Scott, Tri Wahyu Susanto, Cheryl D. Knott. Resources: Wuryantari Setiadi, Tri Wahyu Susanto, Tatang Mitra Setia, Cheryl D. Knott. 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10.1371_journal.ppat.1012064	RESEARCH ARTICLE Rubisco small subunit (RbCS) is co-opted by potyvirids as the scaffold protein in assembling a complex for viral intercellular movement Li Qin1, Hongjun Liu1, Peilan Liu1, Lu Jiang1,2, Xiaofei Cheng3, Fangfang Li2, Wentao Shen4, Wenping Qiu5, Zhaoji Dai1, Hongguang CuiID 1 1 Key Laboratory of Green Prevention and Control of Tropical Plant Diseases and Pests (Ministry of Education) and School of Tropical Agriculture and Forestry, Hainan University, Haikou, China, 2 State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China, 3 College of Plant Protection/Key Laboratory of Germplasm Enhancement, Physiology and Ecology of Food Crops in Cold Region of Chinese Education Ministry, Northeast Agricultural University, Harbin, China, 4 Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Haikou, China, 5 Center for Grapevine Biotechnology, William H. Darr College of Agriculture, Missouri State University, Mountain Grove, United States of America * zhaoji.dai@hainanu.edu.cn (ZD); hongguang.cui@hainanu.edu.cn (HC) Abstract Plant viruses must move through plasmodesmata (PD) to complete their life cycles. For viruses in the Potyviridae family (potyvirids), three viral factors (P3N-PIPO, CI, and CP) and few host proteins are known to participate in this event. Nevertheless, not all the proteins engaging in the cell-to-cell movement of potyvirids have been discovered. Here, we found that HCPro2 encoded by areca palm necrotic ring spot virus (ANRSV) assists viral intercel- lular movement, which could be functionally complemented by its counterpart HCPro from a potyvirus. Affinity purification and mass spectrometry identified several viral factors (includ- ing CI and CP) and host proteins that are physically associated with HCPro2. We demon- strated that HCPro2 interacts with both CI and CP in planta in forming PD-localized complexes during viral infection. Further, we screened HCPro2-associating host proteins, and identified a common host protein in Nicotiana benthamiana–Rubisco small subunit (NbRbCS) that mediates the interactions of HCPro2 with CI or CP, and CI with CP. Knock- down of NbRbCS impairs these interactions, and significantly attenuates the intercellular and systemic movement of ANRSV and three other potyvirids (turnip mosaic virus, pepper veinal mottle virus, and telosma mosaic virus). This study indicates that a nucleus-encoded chloroplast-targeted protein is hijacked by potyvirids as the scaffold protein to assemble a complex to facilitate viral movement across cells. Author summary Potyviridae is the largest family of RNA viruses in the plant kingdom, consisting of geneti- cally diverse members that adversely affect agriculturally and economically important a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Qin L, Liu H, Liu P, Jiang L, Cheng X, Li F, et al. (2024) Rubisco small subunit (RbCS) is co- opted by potyvirids as the scaffold protein in assembling a complex for viral intercellular movement. PLoS Pathog 20(3): e1012064. https:// doi.org/10.1371/journal.ppat.1012064 Editor: Ying Wang, University of Florida, UNITED STATES Received: October 17, 2023 Accepted: February 21, 2024 Published: March 4, 2024 Copyright: © 2024 Qin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting information files. Funding: This work was supported by grants from the National Natural Science Foundation of China (32060603, 32372484 to HC and 32360651 to ZD), the 111 project (D20024 to HC), and Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture, Hainan University PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 1 / 33 PLOS PATHOGENS(XTCX2022NYB11 to HC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Rubisco small subunit and viral intercellular movement crops. However, viral and host components in the local movement of potyvirids, an essen- tial step for viruses to spread through the whole plant, are still not fully understood. Thus far, three viral factors (P3N-PIPO, CI and CP) and several host proteins are known to be engaged coordinately in this event. Here, we found that another viral protein, HCPro2, also aids a potyvirid in the intercellular movement. HCPro2 facilitates the virus to move through plasmodesmata (PD), the gate between two plant cells, by forming a complex with CI and CP. More intriguingly, we found that NbRbCS, one of the most abundant proteins in a plant cell, interacts with all the above viral factors in mediating viral move- ment across plant cells. Reduction of NbRbCS levels greatly impairs the intercellular movement of four tested potyvirids. These data suggest that a common chloroplast pro- tein is co-opted as a pro-viral factor in assembling a complex for viral movement. This finding provides a new insight in our understanding of potyvirids’ movement in plants. Introduction Plasmodesmata (PD) are plasma-membrane-lined nanochannels that cross rigid cell wall between adjacent cells, allowing the exchange of signals and resources among cells for develop- mental regulation and stress responses in higher plants [1–3]. Functional plasmodesmata are also found in bryophytes [4,5]. Plant viruses, as the obligate intracellular parasites, take full advantage of PD to spread intercellularly to establish systemic infection. However, the small aperture of PD allows small molecules to diffuse, but physically restricts the passage of macro- molecules or macromolecular complexes such as viral ribonucleoprotein complexes (vRNPs) and virions [1,6,7]. To overcome the barrier, plant viruses encode diverse types of movement proteins (MPs) that interact with host proteins to modify PD to translocate vRNPs or virions [7–11]. Based on the characteristics of MPs and their interactions with PD, three modes of cell-to-cell movement are assigned to different plant viruses, and herein readers are directed to several excellent reviews [7,9,11–13]. However, the cell-to-cell movement for viruses in the Potyviridae family (potyvirids), representing the largest group of plant-infecting RNA viruses, has not been definitively categorized [7]. All potyvirids excluding bymoviruses possess one positive-sense, single-stranded RNA genome (~ 9.7 kb), which contains a long, full-genome open reading frame (ORF) and another relatively short ORF (PIPO) embedded in P3-coding region [14,15]. PIPO becomes translat- able in frame with the coding region of P1 through the N-terminus of P3 (P3N) from viral genomic subpopulation, which is produced by viral RNA polymerase (NIb) slippage during viral replication [16,17]. Upon translation, two different polyproteins are proteolytically pro- cessed by virus-encoded proteases into 10 to 12 mature units [18,19]. None of them is anno- tated as MP, whereas three factors, P3N-PIPO (a translational fusion of P3N with PIPO), CI (cylindrical inclusion protein), and CP (coat protein), are known to regulate viral intercellular trafficking in a coordinated manner. P3N-PIPO is a PD-localized viral factor, facilitating its own cell-to-cell movement [10,20,21]. Disrupting the generation of P3N-PIPO in different potyvirids restricts viral cell- to-cell movement but does not affect viral replication [22–25]. CI is a multifunctional viral protein [26]. Accumulating genetic evidence assigns an independent role for CI in viral inter- cellular movement [26–28]. CI is recruited to PD via an interaction with P3N-PIPO, and forms conical structures that anchor to and extend through PD [7,20,25]. The CI conical struc- tures bind CPs or virions to aid viral intercellular passage [29–31]. Artificial mutation in CP that disrupts viral particle assembly compromises intercellular spread as well [32–35], PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 2 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement suggesting that viral cell-to-cell movement occurs in the form of virion [7]. Helper compo- nent-protease (HCPro) is another multifunctional protein, and its function in RNA silencing suppression (RSS) was well-studied [36]. HCPro likely participates in cell-to-cell movement: i) HCPro of a virus in Potyvirus genus (potyvirus) has the capacity of trafficking between cells and increasing the size exclusion limit (SEL) of PD [37]; ii) HCPro stabilizes CP and enhances the yield of virions [38,39], suggesting its indirect role in viral intercellular movement [7]; iii) HCPro or CI of potato virus A (PVA) forms a protrusion at one end of virion [31,40]. Never- theless, the connections between HCPro and viral intercellular movement have been not dem- onstrated thus far. Cell-to-cell movement of plant viruses usually depends on the coordinated action of viral MPs and host proteins [41,42]. Several host proteins have been identified to interact with poty- virid movement-related proteins. A hydrophilic plasma membrane-associated cation-binding protein (PCaP1) is recruited to PD via interaction with P3N-PIPO to promote viral intercellu- lar movement, in cases of turnip mosaic virus (TuMV) and tobacco vein banding mosaic virus [10,24,43]. PCaP1 might function in anchoring P3N-PIPO to PD, or serving actin filaments inside PD to enlarge their SEL [10,43]. Another plasma membrane protein, synaptotagmin A, facilitates the trafficking of TuMV P3N-PIPO through PD [44]. An α-expansin in N. benthami- ana (NbEXPA1) promotes both replication and cell-to-cell movement of TuMV [45]. The subject of chloroplast-virus interplay has been attracting great interest for a long time [46–48]. An increasing number of chloroplast proteins are co-opted by different viruses for replication, movement or/and counteracting host defense response [49–53]. Ribulose 1, 5-bisphosphate carboxylase/oxygenase (Rubisco) catalyzes the first rate-limiting step in CO2 fixation in photosynthesis. Rubisco is comprised of eight large subunits (RbCL; 50–55 kDa) and eight small subunits (RbCS; 12–18 kDa) which form a hexadecameric L8S8 complex [54– 56]. RbCL is encoded by chloroplast genome, while RbCS is nucleus-encoded [54]. RbCS interacts with tobamoviral MPs at PD for viral intercellular and long-distance movement [57]. RbCL or RbCS interact with both P3 and P3N-PIPO in cases of several potyviruses [58]. RbCL interacts with HCPro of bean common mosaic virus, and CP of potato virus Y [59,60]. How- ever, the biological relevance of these interactions has not clearly defined. Previously, we characterized two novel viruses, areca palm necrotic spindle-spot virus (ANSSV) and areca palm ring spot virus (ANRSV), which are clustered into a new genus in the Potyviridae family [18,61,62]. Both viruses share a distinct pattern of leader proteases—two copies of HCPro (HCPro1-HCPro2) [63], which prompted us to investigate the functions of HCPro1 and HCPro2 during viral infection. In the present study, we found that HCPro1 is dispensable for viral infection, whereas HCPro2 is indispensable. Besides acting as the viral suppressor of RNA silencing (VSR), HCPro2 participates in viral cell-to-cell movement, which could be functionally complemented by its counterpart from a potyvirus. HCPro2 interacts with both CI and CP in planta, and these three viral proteins form the complexes around PD during viral infection. More interestingly, we identified a common host protein, NbRbCS, that likely acts as a scaffold in the formation of a viral complex for viral cell-to-cell movement. Reduced NbRbCS gene expression greatly impairs viral intercellular movement and systemic infection for ANRSV and three potyviruses tested. Results HCPro1 is dispensable for ANRSV infection To examine the function(s) of HCPro1 during ANRSV infection, its coding region was removed from pRS-G to produce pRS-G(ΔHCPro1) (Fig 1A). pRS-G and pRS-G(ΔHCPro1) were each inoculated into ten N. benthamiana seedlings via agroinfiltration (OD600 = 0.5 per PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 3 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 1. HCPro1 is dispensable for ANRSV infection. (A) Schematic diagrams of pRS-G and pRS-G(ΔHCPro1). The P3—NIa represents the coding region for seven viral factors, including P3, P3N-PIPO, 6K1, CI, 6K2, VPg and NIa-Pro. (B) Infectivity test of pRS-G and pRS-G(ΔHCPro1) in N. benthamiana. The representative N. benthamiana plants inoculated with the indicated virus clones were photographed under daylight (upper) and UV light (lower). Mock, empty vector control. Bars, 5 cm. (C) Western blot analysis of GFP accumulation in inoculated N. benthamiana plants. Total proteins were extracted from top non-inoculated leaves at the indicated time points. The hybridization signal intensity was quantitatively analyzed with ImageJ software [106]. Coomassie blue staining of RbCL was used as a loading control. (D) Real-time RT-qPCR analysis of viral RNA accumulation in inoculated plants. Total RNAs were extracted from top non-inoculated leaves, followed by real-time RT-qPCR analysis. The values represent the mean ± standard deviation (SD) from three independent biological replicates. The average values for pRS-G were designated 100 to normalize the data. Statistically significant differences, determined by an unpaired two-tailed Student’s t test, are indicated by asterisks. , 0.001<P<0.01; , P<0.001. https://doi.org/10.1371/journal.ppat.1012064.g001 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 4 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement clone). At different time points, all plants inoculated with either pRS-G or pRS-G(ΔHCPro1) exhibited dwarfism and leaf rugosity symptoms, as well as obvious GFP signals along veins in top non-inoculated leaves (Fig 1B). Interestingly, more severe symptoms along with stronger fluorescence intensity were observed in plants inoculated with pRS-G(ΔHCPro1) (Fig 1B). Consistently, both GFP and viral genomic RNA accumulated to higher levels in these plants (Fig 1C and 1D). The genomic region, corresponding to partial 50 UTR (110 nucleotides [nts]), complete HCPro2, and partial P3 (150 nts) for virus progeny derived from pRS-G (ΔHCPro1), was sequenced, and the spontaneous mutations of nucleotide sequence were not observed. Given that the deletion of HCPro1-coding sequence shortens viral genome size, it is uncertain if the enhancement effect on viral infectivity is caused by an alteration of viral genome or a negatively regulatory role exerted by HCPro1 protein. Nevertheless, our data sup- port that HCPro1 is dispensable for ANRSV infection in N. benthamiana plants. HCPro2 functions in viral cell-to-cell movement, which is functionally complemented by its counterpart—HCPro from a potyvirus To investigate the functions of HCPro2 during viral infection, we deleted HCPro2-coding sequence in pRS-G to generate pRS-G(ΔHCPro2) (Fig 2A). Infectivity test showed that all eight plants inoculated with pRS-G displayed obvious GFP fluorescence in upper non-inocu- lated leaves at 8 dpi and 16 dpi, whereas those inoculated with pRS-G(ΔHCPro2) did not (Fig 2B). RT-PCR confirmed the absence of viral infection in non-inoculated leaves of all plants treated with pRS-G(ΔHCPro2) (S1 Fig). ANSSV HCPro2 (ssHCPro2) expresses the RSS activ- ity [63]. Thus, we tested the RSS activity of ANRSV HCPro2. For this, we constructed three T-DNA vectors for expressing HA-tagged HCPro1 (HCPro1-HA), HCPro2 (HCPro2-HA) and HCPro1-HCPro2 (HCPro1-HCPro2-HA) of ANRSV, respectively. Each of them, together with a plasmid for expressing GFP reporter [64] were co-inoculated into N. benthamiana leaves. Co-expression of GFP along with either empty vector or HA-tagged ssHCPro2 (ssHCPro2-HA) was included as the negative and positive controls, respectively. At 60 hours post-inoculation (hpi), the leaf patches co-expressing HCPro2-HA/GFP, HCPro1-HC- Pro2-HA/GFP or ssHCPro2-HA/GFP displayed strong GFP fluorescence, whereas no obvious fluorescence was observed on the leaf patches co-expressing HCPro1-HA/GFP or negative control (S2A Fig). Consistently, a higher abundance of GFP at both protein and RNA levels was detected in leaf patches co-expressing HCPro2-HA/GFP, HCPro1-HCPro2-HA/GFP or ssHCPro2-HA/GFP (S2B and S2C Fig), indicating that HCPro2 is the VSR of ANRSV. Both potyvirus-encoded HCPro (the counterpart of ANRSV HCPro2) and tombusvirus- encoded P19 are well-known VSRs [65–67]. To explore additional functions of HCPro2 beyond RSS, we substituted HCPro2 in pRS-G with either TuMV HCPro (tuHCPro) or tomato bushy stunt virus P19 (tbP19) to produce two hybrid clones, pRS-G(tuHCPro) and pRS-G(tbP19) (Fig 2A). N. benthamiana seedlings (n = 10 per clone) were inoculated with them, followed by observations under UV light in every one- or two-day interval for one month. At 8 dpi, obvious fluorescence spots were observed in upper non-inoculated leaves of plants inoculated with pRS-G(tuHCPro). All plants inoculated with either pRS-G(tuHCPro) or wild-type pRS-G displayed the comparable distribution pattern and intensity of fluores- cence signals at 13, 16 and 30 dpi (Figs 2B and S3A). In contrast, only three out of 10 plants inoculated with pRS-G(tbP19) showed scattered fluorescence spots in only one non-inoculated leaf at 30 dpi (S3A Fig). For virus progeny derived from three hybrid clones, the genomic sequence, covering partial HCPro1 (200 nts), complete tuHCPro / tbP19, and partial P3 (150 nts) was determined, and the alternations of nt sequences were not identified. Altogether, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 5 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 2. The effects of deletion of HCPro2 or its substitution with different VSRs on viral infectivity. (A) Schematic diagrams of the derivatives of pRS-G. TuMV HCPro and TBSV P19 are represented by tuHCPro and tbP19, respectively. (B) Infectivity test of the derivatives of pRS-G in N. benthamiana. Representative photographs were taken under UV light at the indicated time points. The close view of leaf regions indicated by dashed boxes is shown. White arrows indicate fluorescence spots. Mock, empty vector control. Bars, 5 cm. (C) Time course observation of viral cell-to-cell movement for the indicated virus clones. Viral intercellular movement was monitored at 48 hpi, 72 hpi, and 96 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 6 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement hpi. Bars, 100 μm. (D) Statistical analysis of the size of viral spreading area at 96 hpi. For each clone, at least 25 infection foci from a total of six plants in three independent experiments were analyzed. The size of infection foci was calculated by ImageJ. The data are presented as the mean ± SD (n � 25). The average value for pRS-G was designated 1×105 μm2 to normalize the data. Statistically significant differences, determined by an unpaired two-tailed Student’s t test, are indicated by asterisks. , P<0.001. (E) The effects of hybrid virus clones on viral genomic RNA accumulation. Relative viral genomic RNA accumulation was determined by real-time RT-qPCR with a pair of primers RS9200F/ RS9350R (S2 Table) targeting viral CP region. N. benthamiana leaves inoculated with the indicated clones (OD600 = 0.3 per clone) were sampled at 60 hpi for the assay. Error bars denote the SD from three biological replicates. , 0.001<P<0.01; NS, no significant difference. https://doi.org/10.1371/journal.ppat.1012064.g002 these results suggested that HCPro2 implements additional function(s) beyond RSS, which can be largely complemented by its counterpart in TuMV. Further, we examined the performance of hybrid viruses in intercellular movement. Agro- bacterial cultures harboring pRS-G, pRS-G(tuHCPro), pRS-G(tbP19) or pRS-G(ΔGDD) (a replication- and movement-null mutant that lacks a strictly-conserved GDD motif in viral RNA polymerase) were highly diluted to 0.0001 of OD600, and infiltrated into N. benthamiana leaves. Single cells emitting GFP fluorescence, representing primarily-transfected cells, were observed for all clones at 48 hpi and 60 hpi (Figs 2C and S3B). Clear viral spreading from pri- marily-transfected to peripheral cells started at 72 hpi for pRS-G, and 84 hpi for pRS-G(tuHC- Pro) (Figs 2C and S3B). Thus, replacement of HCPro2 with tuHCPro partially inhibited viral intercellular movement (Figs 2D and S3C). In contrast, pRS-G(tbP19), similar to pRS-G (ΔGDD), was deficient in cell-to-cell movement (Figs 2C and S3B). Moreover, we assessed the performance of hybrid viruses in viral genomic RNA accumulation. N. benthamiana leaves inoculated with these clones were used in real-time RT-qPCR to measure virus accumulation at 60 hpi as viral intercellular movement did not occur at this time point (S3B Fig). As shown in Fig 2E, no significant difference was found between wild-type pRS-G and each of hybrid clones. Conclusively, HCPro2 also functions in viral cell-to-cell movement. HCPro2 forms PD-localized punctate inclusions in virus-infected cells To investigate the cellular compartment distribution of HCPro2 in virus-infected cells, we fused a GFP-coding sequence at the beginning of HCPro2 in pRS to obtain pRS-GFP-HCPro2 (Fig 3A). Infectivity test showed all inoculated plants (n = 10) exhibited chlorosis and obvious fluorescence signals along veins in upper non-inoculated leaves (Fig 3B), indicating that the recombinant clone is viable. The fused GFP-HCPro2 (61.21 kDa) was detected (Fig 3C). Virus-infected leaf tissues were sampled for subcellular fractionation assay. Immunoblot anal- ysis revealed that GFP-HCPro2 was present in different fractions with a varied degree, includ- ing nuclei-chloroplast-cell wall fraction (P3), membranous fraction (P30), and cytoplasmic fraction (S30) (Fig 3D). As a control, free GFP produced in pRS-G sample was mainly present in S30 fraction (Fig 3D). Next, we examined the subcellular localization pattern of HCPro2 in virus-infected cells. N. benthamiana leaves infiltrated with either pRS-GFP-HCPro2 or pRS-G (OD600 = 0.1) at 72 hpi were subjected to confocal microscopy observation. Both free GFP in pRS-G sample and GFP-HCPro2 in pRS-GFP-HCPro2 were observed to be diffused into cytoplasm and nucleus (Fig 3E). Differently, GFP-HCPro2 was also aggregated in punctate structures, and the distri- bution pattern resembles that of PD-localized markers (Fig 3E). To test this idea, leaf samples of pRS-GFP-HCPro2 were stained with aniline blue, which reacts with callose deposited at PD necks. As expected, about 70% of GFP-HCPro2 punctate (82 out of 120 punctate observed) colocalized with aniline blue-stained callose (Fig 3F). CI is a PD-localized viral protein in viral infection [20]. We produced a T-DNA construct for the expression of mCherry-fused CI (CI- mCherry). The construct together with pRS-GFP-HCPro2 were co-inoculated into N. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 7 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 3. Cellular compartment distribution and subcellular localization of HCPro2 in virus-infected cells. (A) Schematic diagram of pRS-GFP-HCPro2. For the clone, the complete GFP-coding sequence was fused at the N-terminus of HCPro2. (B) Infectivity test of pRS-GFP-HCPro2 in N. benthamiana. The upper non-inoculated leaf was photographed under daylight and UV light at 8 dpi. Mock, empty vector control. Bars, 2.5 cm. (C) Immunoblot detection of GFP-HCPro2 accumulation. The upper non-inoculated leaves of N. benthamiana plants infiltrated with pRS-GFP-HCPro2 or pRS-G were assayed by Western blot at 8 dpi. Coomassie blue staining of RbCL was used as a loading control. (D) Subcellular fractionation coupled with immunoblot detection of GFP-HCPro2. The upper non-inoculated leaves of N. benthamiana plants infiltrated with pRS-GFP-HCPro2 or pRS-G were collected at 8 dpi for PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 8 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement subcellular fractionation assay. The resulting fractions were subjected to immunoblot detection by using anti-GFP anti-body. S1, the supernatant following centrifugation of crude homogenate at 1000 g; S3 and P3, the corresponding supernatant and pellet following the centrifugation of S1 at 3700 g; S30 and P30, the corresponding supernatant and pellet following the centrifugation of S3 at 30000 g. (E) Subcellular localization of GFP-HCPro2 in virus-infected cells. N. benthamiana leaves were inoculated with pRS-GFP-HCPro2 or pRS-G, followed by confocal microscopy observation at 72 hpi. The regions indicated by dashed boxes are enlarged. Bars, 50 μm. (F) Subcellular co-localization of GFP-HCPro2 and the callose at PD. At 72 hpi, the inoculated leaves with pRS-GFP-HCPro2 were stained with aniline blue, followed by confocal microscopy observation. Bars, 25 μm. (G) Co-localization of HCPro2 and CI at PD in virus-infected cells. N. benthamiana leaves were co-inoculated with pRS-GFP-HCPro2 together with a construct for expressing CI-mCherry (final OD600 = 0.2 per clone), followed by staining with aniline blue at 72 hpi and confocal microscopy observation. Bars, 25 μm. https://doi.org/10.1371/journal.ppat.1012064.g003 benthamiana leaves. The majority of GFP-HCPro2 punctate structures (112 out of 150 inclu- sions observed) were overlapped with both CI-mCherry inclusions and aniline blue-stained callose structures at 72 hpi (Fig 3G). Taken together, HCPro2 is distributed into different cellu- lar compartments, and in particular forms PD-localized inclusions in virus-infected cells, pro- viding an important clue on the involvement of HCPro2 in viral cell-to-cell movement. Purification and identification of viral and host proteins that physically associate with HCPro2 in the context of viral infection To get insight into the role of HCPro2 in viral intercellular movement, a twin-Strep sequence (2×Strep) was fused with the first nucleotide of HCPro2 in pRS-G (Fig 4A) to purify viral and host proteins that physically associate with HCPro2 in the context of viral infection. Infectivity test showed that pRS-G-2×Strep-HCPro2 is viable, but much weaker than wild-type pRS-G (Figs 4B and S4). The fused 2×Strep-HCPro2 (37.57 kDa) was detected from upper non-inocu- lated leaves (Fig 4C). Unfortunately, streptavidin purification failed to enrich 2×Strep-HCPro2 along with its associating proteins via SDS-PAGE analysis and immunoblot detection. Consid- ered that HCPro1 deletion significantly increases both viral RNA load and protein expression (Fig 1C and 1D), we used pRS-G(ΔHCPro1) instead to fuse the 2×Strep with HCPro2, and obtained pRS-G(ΔHCPro1)-2×Strep-HCPro2 (Fig 4A). Infectivity test showed that the clone was more aggressive in both virus-triggered symptoms and systemic spreading (Fig 4B). A sig- nificantly higher abundance of 2×Strep-HCPro2 and viral RNA accumulation was detected (Figs 4C and S4). The upper non-inoculated leaves of plants were subjected to affinity purifica- tion. SDS-PAGE analysis revealed the presence of a putative band corresponding to the expected size of 2×Strep-HCPro2 and several other bands in co-purified products, whereas these bands were absent in the parallel control pRS-G (Fig 4D, upper panel). The presence of 2×Strep-HCPro2 was verified by immunoblotting (Fig 4D, lower panel). The affinity-purified products from both samples were analyzed by liquid chromatography tandem mass spectrom- etry (LC-MS/MS). A total of 58 protein species, including six viral proteins (HCPro2, P3, 6K1, CI, NIb, and CP) and 52 host proteins, were uniquely identified in co-purified products with 2×Strep-HCPro2 (Fig 4E and 4F, and S1 Table). HCPro2 interacts with CI and CP in planta Both CI and CP (potyvirid movement-related proteins) are co-purified with HCPro2, prompt- ing us to envisage that HCPro2 might regulate viral intercellular movement via interactions with CI and CP. Thus, we examined the interactions of HCPro2 with three viral movement- related factors (CI, CP, and P3N-PIPO) by using yeast two-hybrid (Y2H). Their coding sequences were cloned into pGBKT7-DEST or pGADT7-DEST. Co-transformation of yeast cells did not detect the interaction between BD-HCPro2 and AD-CI, AD-CP or AD-P3N-PIPO (Fig 5A). A consistent result was obtained when co-expressing AD-HCPro2 and BD-CI, BD-CP or BD-P3N-PIPO (Figs 5A and S5). HCPro2 did not interact with the PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 9 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 4. Purification and identification of viral and host proteins that associate with HCPro2 during ANRSV infection. (A) Schematic diagrams of pRS-G- 2×Strep-HCPro2 and pRS-G(ΔHCPro1)-2×Strep-HCPro2. (B) Infectivity test of the indicated clones in N. benthamiana. The representative plants were photographed at 12 dpi. Bars, 2.5 cm. (C) Immunoblot detection of 2×Strep-HCPro2 in upper non-inoculated leaves at 12 dpi. Coomassie blue staining of RbCL was used as a loading control. The bands corresponding to the expected size of 2×Strep-HCPro2 (37.57 kDa) are indicated by black arrow. The red arrow indicates unspecific bands. (D) SDS-PAGE analysis and immunoblot detection of co-purified proteins with 2×Strep-HCPro2. The upper non-inoculated leaves of plants infiltrated with pRS-G (as the parallel control) or pRS-G(ΔHCPro1)-2×Strep-HCPro2 were collected at 12 dpi for affinity-purification with streptavidin. Elution fractions (E1-E3) were used for SDS-PAGE with silver staining (upper panel) and immunoblot detection (lower panel). The black arrow indicates putative bands corresponding to 2×Strep-HCPro2. The bands (indicated by red arrows) likely represent a HCPro2-containing complex with a high-molecular-mass. (E) LC-MS/MS identification of co-purified products with 2×Strep-HCPro2. The co-purified products from both pRS-G and pRS-G(ΔHCPro1)-2×Strep-HCPro2 samples were analysed by LC-MS/MS. The protein species, uniquely identified from co-purified products with 2×Strep-HCPro2, together their corresponding peptides were summarized. (F) Summary of viral proteins co-purified with 2×Strep-HCPro2. https://doi.org/10.1371/journal.ppat.1012064.g004 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 10 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 5. HCPro2 interacts with CI and CP in planta. (A) Y2H tests the interactions of HCPro2 with P3N-PIPO, CI and CP. The coding sequences of HCPro2, P3N-PIPO, CI and CP were cloned into pGBKT7-DEST or pGADT7-DEST for the expression of these proteins fused with GAL4 BD or AD domain. Yeast competent cells (Y2H Gold) were co-transformed to express the indicated pairs of proteins. The transformed cells were subjected to 10-fold serial dilutions and plated on the SD/- Trp/-Leu and SD/-Trp/-Leu/-His/-Ade mediums. The plates were cultured at 28˚C for four to six days before photographing. Co-transformation of a pair of plasmids for simultaneous expression of AD-T7-T and BD-T7-53 was included as the positive control. (B) BiFC tests the interactions of HCPro2 with CI, CP and P3N-PIPO. The coding sequences of HCPro2, CI, CP and P3N-PIPO were individually engineered into pEarleyGate201-YN and pEarleyGate202-YC for the expression of these PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 11 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement proteins fused with the YN or YC part of YFP. N. benthamiana leaves were co-inoculated for the expression of the indicated pairs of proteins. YFP signals (shown in green) were observed by fluorescence microscope at 72 hpi. Bars, 50 μm. (C, D) Co-IP tests the interactions of HCPro2 with CI and CP. The inoculated leaves of N. benthamiana plants for co-expression of GFP-HCPro2 / Myc-CI (C) or GFP-HCPro2 / Myc-CP (D) were sampled at 72 hpi for Co-IP assays using GFP-Trap Agarose. Total protein extracts prior to (Input) and after immunoprecipitation (IP) were analyzed by immunoblotting using anti-Myc and anti-GFP antibodies. https://doi.org/10.1371/journal.ppat.1012064.g005 remaining viral factors in Y2H either (S6 Fig). As well, the interactions of HCPro2 with CI, CP, and P3N-PIPO were not identified when tested by membrane yeast two hybrid (MYTH) (S7 Fig). Next, we examined whether HCPro2 interacts with CI, CP and P3N-PIPO in planta using bimolecular fluorescence complementation (BiFC). Their coding sequences were individually engineered into both pEarleyGate201-YN and pEarleyGate202-YC. HCPro2-YC along with P3N-PIPO-YN, CI-YN or CP-YN were co-expressed in N. benthamiana leaves. Obvious fluo- rescence signals with punctate distribution were observed for the co-expression of HCPro2-YC and CI-YN (Fig 5B, left panel), indicating that HCPro2 interacts with CI in planta. A consis- tent result was obtained when using a combination of HCPro2-YN and CI-YC for the test (Fig 5B, right panel). In addition, we observed strong fluorescence signals in leaf samples co- expressing HCPro2-YN and CP-YC (Fig 5B). In contrast, no interaction was detected between HCPro2 and P3N-PIPO (Fig 5B). Further, the interactions of HCPro2 with CI and CP were tested by co-immunoprecipitation (Co-IP). For this, we developed a series of T-DNA con- structs for transient expression of free GFP, GFP-tagged HCPro2 (GFP-HCPro2), and 4×Myc- tagged CI (Myc-CI) and CP (Myc-CP). GFP-HCPro2 was co-expressed with Myc-CI or Myc- CP in N. benthamiana leaves. Co-expression of GFP and Myc-CI or Myc-CP was included as the parallel controls. Total proteins were subjected to co-immunoprecipitation with GFP-Trap Agarose. Immunoblot analysis showed that both CI and CP were co-immunoprecipitated with GFP-HCPro2, but not with free GFP in control groups (Fig 5C and 5D). HCPro2, CI and CP form the complexes at PD in viral infection We further investigated whether the interactions of HCPro2 with CI and CP occur at PD in viral infection. For this, two constructs for co-expression of HCPro2-YN and CI-YC, along with pRS, were co-inoculated into N. benthamiana leaves. At 72 hpi, obvious fluorescence sig- nals with punctate structures, an indication of the interaction between HCPro2 and CI, were observed. Statistically, 130 out of 150 inclusion observed (approximately 87%) were overlapped with aniline blue-strained callose structures at PD (Fig 6A). Similarly, HCPro2-YN and CP-YC interact to form punctate structures, and a large number of them (126 out of 150 inclu- sions observed) localized at PD either (Fig 6B). Next, we investigated whether the three viral factors form the complexes at PD in viral infection. Three constructs for simultaneous expres- sion of HCPro2-YN, CI-YC, and CP-mCherry, together with pRS, were inoculated into N. benthamiana leaves. At 72 hpi, approximately 65% of the punctate structures (85 out of 130 inclusions observed), resulting from the interaction between HCPro2 and CI, overlapped with the structures formed by CP-mCherry at PD (Fig 6C). When HCPro2-YN, CP-YC and CI- mCherry were co-expressed, 95 out of 120 punctate inclusions observed (an indication of the interaction between HCPro2 and CP) colocalized with CI-mCherry structures at PD (Fig 6D). The above results indicate that HCPro2, CI, and CP likely form the complexes at PD. To fur- ther prove the existence of HCPro2-CI-CP complex, a Co-IP assay was performed. Two con- structs for expressing Myc-CI and Myc-CP, together with pRS-GFP-HCPro2 or pRS-G (as the parallel control), were co-inoculated into N. benthamiana leaves. Total proteins were subjected to co-immunoprecipitation with GFP-Trap Agarose. Immunoblot analysis showed that both PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 12 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 6. HCPro2, CI and CP form the complexes at PD in viral infection. (A) HCPro2 interacts with CI at PD. N. benthamiana leaves were co-inoculated with two constructs corresponding to HCPro2-YN and CI-YC together with viral clone–pRS (final OD600 = 0.2 per clone), followed by staining with aniline blue at 72 hpi and observation by confocal microscopy. Bars, 25 μm. (B) HCPro2 interacts with CP at PD. N. benthamiana leaves were co-inoculated with two constructs corresponding to HCPro2-YN and CP-YC together with pRS (final OD600 = 0.2 per clone), followed by staining with aniline blue at 72 hpi and observation by confocal microscopy. Bars, 25 μm. (C) Confocal microscopy observation of N. benthamiana leaves co-expressing HCPro2-YN, CI-YC and CP-mCherry in viral infection. N. benthamiana leaves were co-inoculated with three constructs for simultaneous expression of HCPro2-YN, CI-YC, and CP-mCherry together with pRS (final OD600 = 0.2 per clone), followed by staining with aniline blue at 72 hpi and observation by confocal microscopy. Bars, 25 μm. (D) Confocal microscopy observation of N. benthamiana leaves co-expressing HCPro2-YN, CP-YC and CI-mCherry in viral infection. N. benthamiana leaves were co-inoculated with three constructs for simultaneous expression of HCPro2-YN, CP-YC and CI-mCherry along with pRS (final OD600 = 0.2 per clone), followed by staining with aniline blue at 72 hpi and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 13 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement observation by confocal microscopy. Bars, 25 μm. (E) Both CI and CP were coimmunoprecipitated with GFP-HCPro2 in viral infection. N. benthamiana leaves are co-inoculated with two constructs for simultaneous expression of Myc-CI and Myc-CP along with viral clone pRS-GFP-HCPro2. At 72 hpi, total proteins were extracted for Co-IP assay using GFP-Trap Agarose. Total protein extracts prior to (Input) and after immunoprecipitation (IP) were immuno-detected using anti-Myc and anti-GFP polyclonal antibodies. https://doi.org/10.1371/journal.ppat.1012064.g006 CI and CP were coimmunoprecipitated with GFP-HCPro2 (Fig 6E). Conclusively, the three viral proteins (HCPro2, CI and CP) form an interactive complex at PD in viral infection. A common host protein (NbRbCS) facilitates the interactions of HCPro2 with CI or CP, and CI with CP Given that HCPro2 interacts with CI and CP by BiFC and Co-IP but not by Y2H and MYTH (Figs 5 and S7), we proposed that one or more host proteins mediate these interactions. To test this hypothesis, Y2H was employed to screen the interactions between HCPro2 and its associ- ating host proteins. The candidate proteins identified by LC-MS/MS with the score above 25 (S1 Table) were selected. The results revealed a strong interaction between HCPro2 and NbRbCS (Fig 7A). This interaction was verified by BiFC and Co-IP (Fig 7B and 7C). Both N- terminal region (N2) and C-terminal cysteine protease region (D2) of HCPro2 interacts with NbRbCS (S8 Fig). Subsequently, we tested whether NbRbCS interacts with CI and CP. Y2H assays showed that AD-NbRbCS interacts with both BD-CI and BD-CP (Fig 7A). The interac- tions were verified by BiFC (Fig 7B). NbRbCS does not interact with P3N-PIPO, assayed by either Y2H or BiFC (Fig 7A and 7B). In addition, we performed Y2H assays to examine the interactions of NbRbCS with the remaining viral factors (HCPro1, P3, 6K1, 6K2, VPg, NIa- Pro and NIb). Remarkably, the strong interactions of NbRbCS with HCPro1, P3, VPg, and NIb were detected (S9 Fig). To further illustrate the role of NbRbCS in mediating these interactions, we employed tobacco rattle virus (TRV)-based virus-induced gene silencing (VIGS) to knockdown NbRbCS in N. benthamiana. At 12 dpi, the plants inoculated with TRV-NbRbCS exhibited abnormal development phenotype such as dwarfism in size and foliar yellowing, which was absent in control plants (TRV-GUS) (S10A Fig). Real-time RT-qPCR confirmed that NbRbCS mRNA transcripts are significantly reduced in plants inoculated with TRV-NbRbCS (S10B Fig). The upper leaves of NbRbCS-silenced and control plants were subjected to co-expression of HCPro2-YN and CI-YC. At 72 hpi, strong fluorescence signals resulting from the interaction between HCPro2 and CI were monitored in control samples, whereas the signals were nearly undetectable in NbRbCS-silenced plants (Fig 7D and 7E). Both HCPro2-YN and CI-YC in NbRbCS-silenced plants accumulate at a comparable level with those in control plants (S11A Fig). The abundance of RbCL is controlled by its interaction with RbCS to form L8S8 complex [68]. Supporting this notion, we observed that NbRbCL accumulated less in NbRbCS-silenced plants (S11A Fig, lower panel). Co-IP confirmed that the interaction of HCPro2 with CI was greatly weakened in NbRbCS-silenced plants (Fig 7F). Similarly, the interaction between HCPro2 and CP was significantly attenuated in NbRbCS-silenced plants when tested by BiFC and Co-IP assays (Figs 7G–7I and S11B). Silencing of NbRbCS destroys photosynthetic path- way, leading to abnormal physiological phenotype. To discriminate whether the effects of knocking down NbRbCS on the interactions of HCPro2 with CI and CP are caused by the defi- ciency-of-photosynthesis, we silenced another key gene—Ferredoxin-NADP reductase (FNR) in photosynthetic pathway. As expected, silencing of NbFNR leads to the similar abnormalities as observed in NbRbCS-silenced plants (S10A and S10C Fig). Both BiFC and Co-IP revealed that silencing of NbFNR did not affect the interactions of HCPro2 with CI and CP, in contrast to those observed in NbRbCS-silenced plants (Figs 7D–7I and S11A and S11B). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 14 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 7. A common host protein—NbRbCS mediates the interactions of HCPro2-CI, HCPro2-CP and CI-CP. (A) Y2H tests the interactions of NbRbCS with HCPro2, CI, CP and P3N-PIPO. Co-transformation of a pair of plasmids for the expression of AD-T7-T and BD-T7-53 was included as the positive control. (B) BiFC tests the interactions of NbRbCS with HCPro2, CI, CP, and P3N-PIPO. N. benthamiana leaves were co-inoculated for expressing the indicated pair of proteins (final OD600 = 0.2 per plasmid). The fluorescence signals (shown in green) were observed by a fluorescence microscope at 72 hpi. Bars, 50 μm. The co-expression of YC or YN along with an indicated protein was included as the negative control. (C) Co-IP tests the interaction of NbRbCS with HCPro2. N. benthamiana leaves for co-expression of Myc-NbRbCS and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 15 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement GFP-HCPro2 or GFP (final OD600 = 0.3 per plasmid) were sampled at 72 hpi for Co-IP assay using GFP-Trap Agarose. The bands indicated by red arrows likely represent a putative complex that contains GFP-HCPro2 and Myc-NbRbCS. (D, G, J) BiFC assays test the interactions of HCPro2-CI, HCPro2-CP, and CI-CP in NbRbCS- and NbFNR-silenced N. benthamiana plants. At 12 dpi, the upper fully-expanded leaves were co-inoculated for co-expression of HCPro2-YN / CI-YC (D), HCPro2-YN / CP-YC (G), or CI-YN / CP-YC (J). The OD600 value for each plasmid is finally adjusted to 0.2. The samples were observed by a fluorescence microscopy at 60 hpi (G) or 72 hpi (D, J). Bars, 100 μm (D, G) or 50 μm (J). (E, H, K) Statistical analysis of fluorescence signal intensity. The fluorescence signal intensity for HCPro2-YN / CI-YC (E), HCPro2-YN / CP-YC (H), or CI-YN / CP-YC (K), was quantified by ImageJ. At least 20 scans per treatment from three independent experiments were analyzed. Data are presented as the mean ± SD (n � 20). *, P<0.001; ns, no significant difference. (F, I, L) Co-IP assays test the interactions of HCPro2-CI, HCPro2-CP and CI-CP in NbRbCS- and NbFNR-silenced plants. N. benthamiana plants were pre-inoculated with the indicated TRV-based constructs. Twelve days later, the upper fully-expanded leaves were subjected to co-expression of GFP / GFP-HCPro2 and Myc-CI (F), GFP / GFP-HCPro2 and Myc-CP (I), or GFP-CP and Myc-CI (L) (final OD600 = 0.2 per plasmid). The leaf samples were collected at 60 hpi (I) or 72 hpi (F, L) for Co-IP assays using GFP-Trap Agarose. Total protein extracts prior to (Input) and after immunoprecipitation (IP) were analyzed by immunoblotting using anti-Myc and anti-GFP antibodies. The numbers 1, 2 and 3 in circle indicate N. benthamiana plants pre-inoculated with TRV-GUS, TRV-NbRbCS and TRV-NbFNR, respectively. https://doi.org/10.1371/journal.ppat.1012064.g007 Potyvirid CP or virion binds with CI-forming conical structures to aid viral cell-to-cell movement, whereas the interaction of CI and CP was detected in planta in most cases. Since NbRbCS interacts with both CI and CP, we proposed that NbRbCS mediates the interaction between CI and CP either. To test this hypothesis, CI-YN and CP-YC were co-expressed in NbRbCS-silenced leaves. At 72 hpi, strong fluorescence signals resulting from CI-CP interac- tion were observed in control samples, whereas this interaction was significantly compromised in NbRbCS-silenced plants (Figs 7J and 7K and S11C). Co-IP further confirmed the above results (Fig 7L). Notably, the removal of chloroplast transit peptide (CTP) in NbRbCS, pre- venting its entering into chloroplast, does not affect its interactions with CI and CP, indicating that the portion of NbRbCS in cytoplasm is sufficient to mediate these interactions (S12 Fig). Altogether, NbRbCS acts as a common host protein to mediate the interactions of HCPro2 with CI or CP, and CI with CP. Interactions of NbRbCS with HCPro2, CI and CP occur at PD in viral infection As illustrated above, HCPro2, CI, and CP form the complexes at PD during viral infection, and the interactions among them are mediated by a common host protein—NbRbCS. These prompted us to speculate that NbRbCS interacts with the three viral factors at PD during viral infection. Each pair of T-DNA constructs for co-expression of RbCS and HCPro2, CI or CP, together with viral clone pRS, were co-inoculated into N. benthamiana leaves, followed by aniline blue staining at 72 hpi. Confocal microscopy revealed that the interactions of NbRbCS with HCPro2, CI, and CP consistently form punctate inclusions, which are largely overlapped with aniline blue-stained callose structures at PD (Fig 8A– 8C). Statistically, 90 out of 115 inclusions observed for NbRbCS-HCPro2, 95 out of 110 for NbRbCS-CI, and 40 out of 65 for NbRbCS-CP co-localized with callose structures at PD. RbCS is a nucleus-encoded protein and transported into chloroplast via its N-terminal transit peptide. Hence, we examined whether NbRbCS was recruited to PD during viral infection. For this, we generated a construct for expressing a mCherry-tagged NbRbCS (NbRbCS-mCherry). As shown in Fig 8D, NbRbCS-mCherry, when expressed alone, exactly localized at chloroplast, but not at PD at all. Intriguingly, when NbRbCS-mCherry was co-expressed along with viral clone pRS, it was diffused in the cytoplasm or formed punctate inclusions (Fig 8E and 8F). Statistically, approximately 41% of punctate structures (35 out of 110 inclusions observed) were overlapped with alanine blue-stained callose at PD (Fig 8E and 8F). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 16 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 8. NbRbCS interacts with HCPro2, CI and CP at PD during viral infection. (A-C) The interactions of NbRbCS with HCPro2, CI, and CP occur at PD in viral infection. A pair of constructs for co-expression of NbRbCS-YC / HCPro2-YN (A), NbRbCS-YN / CI-YC (B), or NbRbCS-YN / CP-YC (C), together with viral clone—pRS were co-inoculated into N. benthamiana leaves (final OD600 = 0.2 per clone). The inoculated leaves were stained with aniline blue at 72 hpi, followed by confocal microscopy observation. Bars, 25 μm. (D) NbRbCS-mCherry targets chloroplast when expressed alone in planta. N. benthamiana leaves were inoculated with the construct of NbRbCS-mCherry (OD600 = 0.2), followed by aniline blue staining at 72 hpi and confocal microscopy observation. Bars, 25 μm. (E, F) Subcellular localization of NbRbCS-mCherry at PD in viral infection. N. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 17 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement benthamiana leaves were co-expressed with NbRbCS-mCherry and viral clone (pRS) (OD600 = 0.2 per clone), followed by aniline blue staining at 72 hpi and confocal microscopy observation. A close-view of co-localization of NbRbCS-mCherry with aniline blue-stained callose at PD is shown in panel (F). Bars, 25 μm. https://doi.org/10.1371/journal.ppat.1012064.g008 Knockdown of NbRbCS significantly attenuates viral cell-to-cell movement and systemic infection for ANRSV and other three tested viruses in Potyvirus genus The effects of NbRbCS on ANRSV infection were investigated. N. benthamiana seedlings (n = 8 per clone) were pre-inoculated with TRV-NbRbCS, TRV-GUS or TRV-NbFNR (the parallel control). At 12 dpi, NbRbCS or NbFNR mRNA transcripts were significantly reduced (S10 Fig). Immediately, these plants were challenged with ANRSV-GFP via sap rub-inocula- tion. Ten days later, strong fluorescence signals, indicative of ANRSV-GFP infection, were observed in upper leaves of all pre-treated plants with TRV-GUS or TRV-NbFNR, whereas NbRbCS-silenced plants exhibited scattered fluorescence signals along veins (Fig 9A). Real- time RT-qPCR and immunoblotting assays confirmed that ANRSV infection was largely restricted in NbRbCS-silenced plants (Fig 9B and 9C). The effects of NbRbCS on viral intercel- lular movement were also examined. Agrobacterial culture harboring pRS-G (OD600 = 0.001) was inoculated into NbRbCS-silenced and control plants. At 108 hpi, the size of viral spreading from primarily-transfected to peripheral cells was much smaller in NbRbCS-silenced leaves (Fig 9D and 9E). We employed a similar strategy to test the effects of NbRbCS on viral infectiv- ity for other three potyviruses, pepper veinal mottle virus (PVMV), telosma mosaic virus (TelMV), and TuMV. The results showed that NbRbCS-silencing significantly impairs sys- temic infection and intercellular movement for PVMV (Fig 9F–9I), TuMV (Fig 9J–9M) and TelMV (Fig 9N–9Q), indicating that NbRbCS plays a general regulatory role in potyvirid infection. Discussion HCPro assists viral movement, but how does it connect with intercellular movement has been not demonstrated. This study provides genetic and biochemical evidence supporting a role of HCPro2 (a homolog of potyviral HCPro) in viral intercellular movement. HCPro2, together with CI and CP, form the complexes at PD. The interactions among them do not involve cell membranes, but are indeed facilitated by a host protein NbRbCS, abundant in a plant cell. The fraction of RbCS involved in the interactions is distinct from the chloroplast pool. Knockdown of NbRbCS by gene silencing impairs their interactions, and viral intercellular movement and systemic infection. Therefore, we envisage a scenario that the nucleus-encoded RbCS is hijacked as a pro-viral factor to mediate the assembly of intercellular movement complex to promote viral cell-to-cell movement (Fig 10). The model might be generally applied to other potyvirids, based on following considerations: i) The interactions among the three viral factors (HCPro, CI, and CP) have been documented for numerous potyvirids, however, these interac- tions were usually detected in planta, rarely in vitro [28–31,69–77], suggesting a potential role of RbCS in mediating these interactions; ii) Silencing of NbRbCS significantly attenuates the cell-to-cell movement for ANRSV but also other three tested potyviruses. The genomic 50-terminal regions of potyvirids encode two types of leader proteases: serine- protease (P1) and cysteine-protease (HCPro), which differ greatly in the arrangement and sequence composition among inter-genus viruses [18,78]. One of leader proteases expresses RSS activity for each potyvirid. The arepaviruses have two copies of HCPro PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 18 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Fig 9. Knockdown of NbRbCS largely inhibits viral intercellular movement and systemic infection for ANRSV and other three tested potyviruses. (A) Silencing of NbRbCS significantly restricts ANRSV infection. N. benthamiana seedlings at 3- to 5-leaf stage were pre-inoculated with TRV-GUS, TRV-NbRbCS or TRV-NbFNR. At 12 dpi, these plants were challenged with ANRSV-GFP via sap rub-inoculation. The representative plants were photographed under daylight and UV light at ten days post-challenging inoculation (dpci). Bars, 2.5 cm. (B) Real-time RT-qPCR analysis of viral genomic RNA accumulation. Leaf samples were collected at 10 dpci for real-time RT-qPCR assay. Error bars denote the standard errors from three biological replicates. , 0.001<P<0.01; ns, no significant difference. (C) Immunoblot analysis of GFP accumulation at 10 dpci. (D) Viral intercellular movement from single primarily infected cells at 108 hours post-challenging inoculation (hpci). Bars, 100 μm. (E) Statistical analysis of the size of viral infection foci at 108 hpci. For each treatment, a total of 25 infection foci from a total of six plants in three independent experiments were analyzed by ImageJ. The size of infection foci is presented as the mean ± SD (n = 25). The average value for TRV-GUS / ANRSV-GFP was designated 1×104 μm2 to normalize the data. *, P<0.001. (F, J, N) The effects of RbCS-silencing on the infectivity of three potyviruses. The representative plants were photographed under UV light at 6 dpci for PVMV-GFP (F) and TuMV-GFP (J), and at 13 dpci for TelMV-GFP (N). Bars, 2.5 cm. (G, K, O) Real- time RT-qPCR analysis of viral genomic RNA accumulation. Viral genomic RNA accumulation was determined at 6 dpci for PVMV-GFP (G) and TuMV-GFP (K), and at 13 dpci for TelMV-GFP (O). Error bars denote the standard errors from three biological replicates. , 0.001<P<0.01; *, P<0.001. (H, L, P) Viral intercellular movement from single primarily infected cells. Viral intercellular movement was recorded at 108 hpci for PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 19 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement PVMV-GFP (H), and at 84 hpci for TuMV-GFP (L) and TelMV-GFP (P). Bars, 100 μm. (I, M, Q) Statistical analysis of the size of viral infection foci. The infection foci were determined at 108 hpci for PVMV-GFP (I) and at 84 hpci for TuMV-GFP (M) and TelMV-GFP (Q). For each treatment, a total of 20 infection foci from a total of six plants in three independent experiments were analyzed. The size of infection foci is presented as the mean ± SD (n = 20). The average value for control groups was designated 1×105 μm2 to normalize the data. , P<0.001. https://doi.org/10.1371/journal.ppat.1012064.g009 (HCPro1-HCPro2), with HCPro2 as the VSR. HCPro1 is dispensable for ANRSV infection in N. benthamiana. The lethality of HCPro1 deletion in ANSSV [63] might be explained by the fact that N. benthamiana is less susceptible to ANSSV [79]. The phenomenon that the leader protease without RSS activity is dispensable has been reported for several potyvirids [80–82]. Although the HCPro of wheat streak mosaic virus with loss-of-RSS activity is dispensable for viral infection, it is a determinant in eriophyid mite-vectored transmission [83]. In the case of plum pox virus (PPV), P1 protein is not essential during viral infection, but it elaborately mod- ulates viral replication to evade host immune response [81]. Here, we attempt to speculate that HCPro1 might play an accessory role in viral infection or function in insect-vectored transmission. This study performed a comprehensive investigation on ANRSV HCPro2, and provided substantial evidence to support its role in cell-to-cell movement: i) Replacement of HCPro2 with an unrelated VSR (P19) does not affect viral RNA accumulation, but nearly abolishes Fig 10. A working model depicting that NbRbCS is co-opted as the scaffold protein in mediating the assembly of viral intercellular movement complex. NCLS, nucleus; CHL, chloroplast; PM, plasma-membrane; CW, cell wall; PD, plasmodesmata. https://doi.org/10.1371/journal.ppat.1012064.g010 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 20 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement viral cell-to-cell movement. ii) Substitution of HCPro2 with its counterpart from a potyvirus efficiently complements viral intercellular movement, indicating that inter-genus HCPros might be functionally interchangeable in aiding viral intercellular movement. iii) Movement- related proteins CI and CP are co-purified with HCPro2. iv) HCPro2, CI and CP form the PD- targeting complexes, which is pivotal in viral cell-to-cell movement. The above results, together with previous observations [37] and the fact that HCPro interacts with CI or/and CP in planta for numerous potyvirids [26,36,84,85], suggest that different potyvirid HCPros might share a common function in aiding viral intercellular movement. Nevertheless, the underlying molec- ular mechanism is still unknown at this time. Previous studies revealed that both PPV and PVA HCPros have the capacity to stabilize CP and enhance the yield of viral particles [38,39], suggesting that HCPro aids viral intercellular movement in an indirect manner. Intriguingly, the steady-state of CP mediated by HCPro was observed either in a context of viral infection or in the presence of viral proteins P3-to-CP, whereas the co-expression of HCPro and CP does not [38,39]. Consequently, we propose that HCPro facilitates to stabilize CP and enhance viral particle yield likely via the formation of HCPro-RbCS-CP-CI complex (Fig 10). HCPro2 is distributed, with a varied degree, into different cellular compartments in viral infection. The HCPro2-formed inclusions mainly targeted to PD, but a small portion of them are elsewhere. In recent years, a significant progress has been achieved with regard to the aggregates induced by PVA HCPro. The aggregates (called as PVA-induced granules, PGs) are multifunctional during viral infection, including viral genome translation, RSS, encapsidation and systemic spread [86–88]. Whether the HCPro2 inclusions that are not targeted to PD behave similar functions to PGs awaits to be investigated. Among five viral proteins co-puri- fied with HCPro2, four (P3, 6K1, CI, and NIb) are components of 6K2-induced replication complex [19,84]. HCPro was also identified from 6K2-induced replication vesicles for PVA [89]. It is logical to speculate that HCPro2 might also participate in viral replication, which would be a promising research direction. The chloroplast has long been recognized as a common target by many plant viruses. Plant viruses may directly modify chloroplast membranes to assemble viral replication complex, or co-opt chloroplast proteins for viral replication, movement or/and counteracting host defense. The rubisco is highly expressed in plants, and believed to be the most abundant protein on the planet [90]. However, only one document is dedicated to the description of RbCS-virus inter- action and its biological relevance [57]. In this study, we provide multi-disciplinary evidences to support that RbCS is co-opted to mediate multiple interactions among viral movement- related proteins, likely functioning in the assembly of movement complex (Fig 10). Here, we discuss five critical points that need to be clarified in future: i) How is RbCS recruited to PD? Potyvirid CI, when expressed alone in planta, is localized in cytoplasm in the form of irregular aggregates. Once P3N-PIPO is co-expressed, CI is recruited to PD and forms cone-shaped structures [20]. Thus, it is speculated that CI might recruit RbCS to PD via the interaction in viral infection (Fig 10). ii) Whether the multiple interactions among HCPro2, RbCS, CP, and CI (including potential self-interactions) have synergistic enhancement effect awaits to be investigated. iii) It is so fascinating that RbCS, such a small molecule, interacts with three viral movement-related proteins. In the RbCS-mediated complex, it is unclear whether one mole- cule of RbCS simultaneously interacts with HCPro2, CI and CP, or more molecule are needed. To clarify this point, a fine mapping of interaction sites between RbCS and HCPro2, CI or CP should be performed. iv) Why would a variety of viral factors and host proteins be needed for the intercellular movement of potyvirids? As stated in introduction, three viral factors (CI, CP, and P3N-PIPO), together with HCPro2 or HCPro identified in this study, participate in viral intercellular movement, although the actual roles of them have not been clearly defined. Noticeably, these proteins do not contain a typical transmembrane domain. How could they PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 21 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement be translocated to PD to facilitate viral intercellular movement? In line with this point, several plasma membrane- or PD-localized proteins were identified to be potentially involved in this event [10,43–45]. As depicted in the model (Fig 10), it is very possible that P3N-PIPO is anchored to PD by a cellular membranous protein—PCaP1 [10]. The CI is recruited to PD by P3N-PIPO [20]. RbCS is co-opted to act as a mediator to aggregate both HCPro2 and CP/ virion at PD. v) It is worth noting that RbCS interacts with tobamoviral MPs to facilitate viral intercellular and long-distance movement, although the underpinning molecular mechanism was not reported [57]. It seems that plant viruses likely evolved different strategies to utilize such an abundant chloroplast protein in viral infection, which deserves more studies in the future. A previous report showed that RbCS interacts with P3 for several potyviruses [58]. Besides P3, other three protein (HCPro1, VPg and NIb) of ANRSV interact with RbCS (S9 Fig). Coin- cidentally, two of them (P3 and NIb) are co-purified with HCPro2. VPg plays multifunctional roles during viral infection. Among them, VPg is targeted to membranous factories and plays a key role in viral replication [91,92]. Taken together, we envisage that RbCS might also partic- ipate in viral replication via its interactions with replication-related viral proteins. Again, it is amazing that an abundant chloroplast protein has the capacity of interaction with multiple viral proteins. A fine mapping of interaction sites among them might help design resistance strategy of conferring broad resistance to potyvirids. Materials and methods Plant materials and virus resources N. benthamiana plants were maintained in a growth cabinet set under the conditions of 16 h of light at 25˚C and 8 h of darkness at 23˚C, with 70% relative humidity. In sap rub-inoculation assays, homogenates containing GFP-tagged telosma mosaic virus (TelMV-GFP), pepper veinal mottle virus (PVMV-GFP) and TuMV-GFP were prepared from infected leaf tissues of N. benthamiana plants pre-inoculated with pPasFru-G, pHNu-GFP and pCBTuMV-GFP/ mCherry, respectively [35,93,94]. An infectious cDNA clone of ANRSV-ZYZ (pRS), as well as its derivative pRS-G (GFP-tagged ANRSV clone) were previously developed [79]. Development of ANRSV-derived cDNA clones Either pRS-G or pRS-G was used as the backbone to construct a series of ANRSV-derived virus clones, including pRS-G(ΔHCPro1), pRS-G(ΔHCPro2), pRS-G(tuHCPro), pRS-G (tbP19), pRS-GFP-HCPro2, pRS-G-2×Strep-HCPro2 and pRS-G(ΔHCPro1)-2×Strep- HCPro2. These clones were constructed by a similar strategy, mainly based on standard DNA manipulation technologies such as overlapping PCR. Herein, the detailed description for the creation of pRS-G(ΔHCPro2), in which the complete HCPro2-coding sequence in ANRSV was deleted, was stated. Two PCR reactions with pRS-G as the template were performed using corresponding primer sets PCB301-F/SOE-HP2-R and SOE-HP2-F/RSV-3-R (S2 Table). A mixture of resulting PCR products was used as the template for overlapping PCR with primer set PCB301-F/ RSV-3-R (S2 Table). The obtained fragment was inserted back into pRS-G by using Pme I / Mlu I sites to generate pRS-G(ΔHCPro2). The pRS-G(ΔGDD), a replication- defective virus clone, was created via the removal of strictly-conserved GDD motif in viral RNA polymerase (NIb). Two fragments upstream and downstream of GDD motif in pRS-G were amplified with corresponding primer sets RSV-5-F/SOE-GDD-R and SOE-GDD-F/ RSV-5-R (S2 Table), and then mixed as the template for overlapping PCR with primer set RSV-5-F/RSV-5-R (S2 Table). The obtained fragment was inserted back into EcoR I / Sal I- treated pRS-G to generate pRS-G(ΔGDD). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 22 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Plasmids construction For the RSS assay, four plasmids, including pCaM-HCPro1-HCPro2-HA, pCaM-HC- Pro1-HA, pCaM-HCPro2-HA, and pCaM-ssHCPro2-HA, were constructed for respective expression of HCPro1-HCPro2-HA, HCPro1-HA, HCPro2-HA and ssHCPro2-HA. The cod- ing regions of them were amplified from pRS-G or pSS-I-G, and individually integrated into a binary plant expression vector pCaMterX [95] by using Xho I / Kpn I sites. The complete sequences of NbRbCS and NbFNR sequences are deposited in NCBI GenBank database with accession numbers as QCS40508.1 and QAV53876.1. We referred to these sequences to design primers in this study (S2 Table). For TRV-based VIGS analysis, SGN VIGS Tool (https://vigs. solgenomics.net) was employed to design two pairs of primers TRV-NbRbCS-F/ TRV-NbRbCS-R and TRV-NbFNR-F/TRV-NbFNR-R (S2 Table) for amplifying two ~300 bp- fragments corresponding to NbRbCS and NbFNR. The obtained fragments were individually cloned into pTRV2 [96] by utility of BamH I / Xho I sites to obtain pTRV2-NbRbCS and pTRV2-NbFNR (S2 Table). For Y2H, BiFC and Co-IP assays, the corresponding plasmids were generated by using Gateway cloning technology. Briefly, the coding sequences of indi- cated cistrons were engineered into the entry clone—pDONR221, and then transferred into the desired gateway-compatible destination vectors, including pGADT7-DEST, pGBKT7-DEST, pEarleygate201-YN, pEarleygate202-YC, and pBA-FLAG-4myc-DC [97–99]. In addition, we constructed four plasmids (pCaM-GFP-HCPro2, pCaM-GFP-CP, pCaM-CI- mCherry, pCaM-CP-mCherry, and pCaM-NbRbCS-mCherry) for respective expression of GFP-HCPro2, GFP-CP, CI-mCherry, CP-mCherry, and NbRbCS-mCherry. For them, we amplified complete GFP and mCherry sequences from pVPH-GFP//mCherry [100], individu- ally engineered them to pCaMterX, and obtained two intermediate vectors—pCaM-GFP and pCaM-mCherry. Then, the coding sequences of HCPro2, CI, CP, and NbRbCS were individu- ally integrated into pCaM-GFP or pCaM-mCherry to produce the four plasmids via seamless cloning or restriction endonuclease digestion-T4 DNA ligation strategy. For MYTH assay, the HCPro2-coding sequence was integrated into the bait vector—pBT3-STE by using Sfi I site to produce pBT3-STE-HCPro2 for the expression of HCPro2-Cub-LexA. CI, CP and P3N-PIPO were individually cloned into the prey vector—pPR3-N(DEST) [101] via Gateway cloning technology for respective expression of Nub-CI, Nub-CI, and Nub-P3N-PIPO. All plasmids in this study were verified by Sanger DNA sequencing. Agroinfiltration and sap rub-inoculation Agrobacterium (strain GV3101)-mediated transformation was performed following previous description [63,79]. Fully expanded leaves of N. benthamiana seedlings were infiltrated with agrobacterial cultures harboring relevant plasmids. N. benthamiana seedlings at 3- to 5-leaf stage were used for infectivity test of ANRSV-derived cDNA clones. The seedlings at 6- to 8-leaf stage were used for transient expression of genes of interest. For TRV-VIGS assays [96], two agrobacterial cultures harboring pTRV1 along with pTRV2-GUS (TRV-GUS), pTRV2-NbRbCS or pTRV2-NbFNR were mixed (final OD600 = 0.3 per culture), and infiltrated into N. benthamiana seedlings at 3- to 5-leaf stage. Sap rub-inoculation assays were essentially performed according to a previously described protocol [79]. Y2H and MYTH Yeast two-hybrid (Y2H) assays were performed according to the Yeastmaker Yeast Transfor- mation System 2 User Manual (Clontech). Each pair of indicated genes were cloned into pGBKT7-DEST for fusing with GAL4 DNA binding domain (BD) or pGADT7-DEST for fus- ing with GAL4 activation domain (AD). Yeast competent cells (Y2H Gold) was co- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 23 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement transformed with bait and prey constructs, followed by 10-fold serial dilution and plating onto synthetic defined (SD) yeast leucine and tryptophan dropout medium (SD/-Leu/-Trp) or leu- cine, tryptophan, histidine and adenine dropout medium (SD/-Leu/-Trp/-His/-Ade). The transformants were allowed by 4- to 6-day growth on the dropout mediums at 28˚C. For immunoblot detection of protein expression in yeast, the co-transformed yeast cells were prop- agated in liquid SD/-Leu/-Trp medium, and the cultures were harvested at OD600 of 0.5. The total proteins were extracted by using Yeast Protein Extraction Reagent (Takara), followed by immunoblot detection with anti-HA monoclonal or anti-Myc polyclonal antibody (Abcam). Membrane yeast two hybrid (MYTH) assays were exactly performed according to the user manual of DUALmembrane starter kits (Dualsystems Biotech). Yeast competent cells (NMY51) were co-transformed with each pair of the indicated constructs, and plated onto SD/-Leu/-Trp and SD/-Leu/-Trp/-His/-Ade mediums. BiFC Each pair of indicated genes were integrated into pEarleyGate201-YN and pEarleyGate202-YC [98] for the expression of desired proteins fused with N-terminal half (YN) or C-terminal half of YFP (YC). Two agrobacterial cultures harboring YN- or YC-constructs were mixed (final OD600 = 0.3 per culture), and infiltrated into fully developed leaves of N. benthamiana. The inoculated leaves were examined by an inverted fluorescence microscope (BX53, OLYMPUS) at the indicated time points. To relatively quantify the intensity of protein-protein interaction among different treatments by BiFC, the YFP fluorescence signals were captured when all the conditions, i.e., 10×objective, U-FBNA filter (BP470-495; BA510-550), burner status set: 50% or 100% (U-HGLGPS), and the value of exposure time, were kept constant. Co-IP Total proteins were extracted from one gram of co-inoculated leaves of N. benthamiana by using 2 mL of ice-cold immunoprecipitation buffer (10% [v/v] glycerol, 25 mM Tris-HCI, pH 7.5, 150 mM NaCl, 10 mM DTT, 1 mM EDTA, 1 × Protease Inhibitor Cocktail, For Plant Cell (Sangon Biotech), and 0.15% [v/v] Nonidet P-40). Protein extracts were incubated with GFP-Trap beads (ChromoTek) for 1h at 4˚C. The beads were collected and washed with the buffer (10 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.05% Nonidet P40, 0.5 mM EDTA). Total protein extracts prior to (Input) and after immunoprecipitation (IP) were analyzed by immu- noblotting using anti-GFP and anti-Myc polyclonal antibodies (Abcam), essentially as previ- ously described [63]. Streptavidin affinity purification and LC-MS/MS The upper non-inoculated leaves were collected from N. benthamiana plants infiltrated with pRS-G-2×Strep-HCPro2, pRS-G(ΔHCPro1)-2×Strep-HCPro2 or pRS-G at 12 dpi. Streptavi- din affinity purification, SDS-PAGE and immunoblot analysis, and LC-MS/MS identification were conducted essentially as described by Hu and colleagues [102]. Subcellular fractionation assay Several previous documents were referred to perform subcellular fractionation assay [103– 105]. One gram of leaf tissues per treatment were fine homogenized in 4 mL of lysis buffer (50 mM Tris-HCl, pH 7.4, 15 mM MgCl2, 10 mM KCl, 20% glycerol, 1 × Protease Inhibitor Cock- tail). The homogenate was centrifuged at 1000 g for 5 min at 4˚C to remove the debris, and the supernatant (S1) was obtained. S1 was centrifuged at 3700 g for 10 min at 4˚C, resulting in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 24 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement supernatant (S3) and crude pellet (P3). P3 fraction includes nuclei, chloroplasts and cell wall. S3 was centrifuged at 30000 g for 50 min at 4˚C to separate soluble (S30) and crude membrane (P30). Both P3 and P30 pellets were resuspended in the lysis buffer (4 mL per pellet). An ali- quot of 10 μL per sample was used for immunoblot analysis. Northern blot and real-time RT-qPCR Norther blot assays were performed to detect GFP mRNA abundance, essentially as previously described by Qin and colleagues [63]. Real-time RT-qPCR was employed to relatively quantity viral genomic RNAs or endogenous gene transcripts, following a previously described protocol by Hu and colleagues [102]. The primers used in the assays were listed in S2 Table. Aniline blue staining Aniline blue solution is prepared before use via mixing 0.1% aniline blue (Sigma-Aldrich)- water solution with 1 M glycerol solution in a ratio of 2:3. The mixture was infiltrated into N. benthamiana leaves by using a 1 mL needle-free syringe. Thirty minutes later, aniline blue fluorescence was observed under confocal microscope. Confocal microscopy The epidermal cells of inoculated leaves with relevant plasmids were observed under a confocal microscopy (FV1000, OLYMPUS) with a 20× water immersion objective. Excitation wave- lengths and emission filters were 488 nm/bandpass 500–530 nm for GFP or YFP, 543 nm/ bandpass 580–620 nm for mCherry, and 405 nm/band-pass 442–472 nm for aniline blue fluorochrome. Supporting information S1 Data. Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Figure panels 1D, 2D, 2E, 7E, 7H, 7K, 9B, 9E, 9G, 9I, 9K, 9M, 9O, 9Q, S3C, S4, S10B and S10C. (XLSX) S1 Table. List of host proteins that are uniquely identified from co-purified products with 2×Strep-HCPro2 by LC-MS/MS. (PDF) S2 Table. List of primers used in this study. (PDF) S1 Fig. RT-PCR detection of ANRSV and its derivatives. The upper non-inoculated leaves of N. benthamiana plants were assayed at 16 dpi. RT-PCR was conducted with primer set 8900F/ 9300R (S2 Table) that target viral CP region. (TIF) S2 Fig. RNA silencing suppression test of HCPro1, HCPro2, and HCPro1-HCPro2 of ANRSV. (A) Representative photographs of co-infiltrated N. benthamiana leaves were taken under UV light at 72 hpi. Each of three plasmids (for the transient expression of HCPro1-HA, HCPro2-HA, HCPro1-HCPro2-HA, respectively), together with a GFP-expressing plasmid, were co-inoculated into N. benthamiana leaves via agroinfiltration. Co-expression of GFP along with either empty vector—pCaMterX (Vec) or HA-tagged ANSSV-encoded HCPro2 (ssHCPro2-HA) were included as negative and positive controls, respectively. (B) Immunoblot PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 25 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement detection of GFP accumulation in co-inoculated leaf patches at 72 hpi. Coomassie blue stain- ing of RbCL was used as a loading control. (C) Northern blot analysis of GFP transcript accu- mulation in co-inoculated leaf patches at 72 hpi. Ethidium bromide staining of ribosomal RNA (rRNA) was served as a loading control. (TIF) S3 Fig. The effects of deletion of HCPro2 or its substitution with different VSRs on viral infectivity. (A) Infectivity test of pRS-G and its derivatives in N. benthamiana. Representative photographs were taken under UV light at 13 dpi and 30 dpi. The leaf region indicated by dashed box is enlarged. Mock, empty vector control. Bars, 5 cm. (B) The observation of viral cell-to-cell movement for the indicated virus clones at 60 hpi and 84 hpi. Bars, 100 μm. (C) Sta- tistical analysis of the size of viral spreading area at 84 hpi. For each clone, at least 25 infection foci from a total of six plants in three independent experiments was analyzed. The size of infec- tion foci is calculated by ImageJ. The data are presented as the mean ± SD (n � 25). The aver- age value for wild-type pRS-G was designated 1×105 μm to normalize the data. , 0.001<P<0.01. (TIF) S4 Fig. Real-time RT-qPCR analysis of viral genomic RNA accumulation. The upper non- inoculated leaves of N. benthamiana plants were sampled at 12 dpi for the assay. RT-qPCR with primer set RS9200F/RS9350R (S2 Table) targeting viral CP region was performed. Error bars denote the SD from three biological replicates. The average value for pRS-G-2×Strep- HCPro2 was designated 1.0 to normalize the data. , 0.01<P<0.05; , 0.001<P<0.01. (TIF) S5 Fig. Immunoblot analysis of the expression of BD- and AD-fused proteins in yeast. The bands, indicated by red asterisks, correspond to the predicted size of recombinant proteins (~54.5 kDa for AD-HCPro2, 97.87 for BD-CI, 57.46 for BD-P3N-PIPO, and 54.51 for BD-CP). The arrowhead, AD-HCPro2. Coomassie blue staining of the total proteins (CBB) was used as a loading control. (TIF) S6 Fig. Y2H tests the interactions of HCPro2 with six viral proteins. Yeast competent cells (Y2H Gold) were co-transformed to express the indicated pairs of proteins. The transformed cells were subjected to 10-fold serial dilutions and plated on SD/-Trp/-Leu and SD/-Trp/- Leu/-His/-Ade mediums. The plates were cultured at 28˚C for four to six days before photo- graphing. (TIF) S7 Fig. MYTH tests the interactions of HCPro2 with CI, CP and P3N-PIPO. Yeast compe- tent cells (NMY51) were co-transformed to express the indicated pairs of proteins. The trans- formed cells were subjected to 10-fold serial dilutions and plated on SD/-Trp/-Leu and SD/- Trp/-Leu/-His/-Ade mediums. The plates were cultured at 28˚C for four to six days before photographing. Co-transformation of a pair of constructs for simultaneous expression of soy- bean mosaic virus (SMV) P3-Cub-LexA and Nub-EIF4A [101] was included as the positive control. (TIF) S8 Fig. NbRbCS interacts with both N2 and D2 domains of HCPro2. (A) Schematic diagram of HCPro2 showing N2 and D2 domains. The red box represents the cysteine protease domain of HCPro2. (B) The interactions of NbRbCS with N2 and D2 domains were tested by Y2H assays. The coding sequence of NbRbCS was cloned into pGADT7-DEST and pGBKT7-DEST PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 26 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement for respective expression of GAL4 AD-fused (AD-NbRbCS) and BD-fused NbRbCS (BD-NbRbCS). The coding sequences of N2 and D2 domains of HCPro2 were cloned into pGADT7-DEST for respective expression of AD-fused N2 (AD-N2) and D2 (AD-D2), and cloned into pGBKT7-DEST for respective expression of BD-fused N2 (BD-N2) and D2 (BD-D2). The co-transformed yeast cells for co-expressing the indicated pairs of proteins / domains were subjected to 10-fold serial dilutions and plated on SD/-Trp/-Leu/-His/-Ade mediums. (C) The interactions of NbRbCS with N2 and D2 domains were tested by BiFC assays. The coding sequences of N2 and D2 were individually integrated into pEarleyGa- te201-YN for expressing YFP YN-fused N2 (N2-YN) and D2 (D2-YN). N. benthamiana leaves were co-inoculated for the expression of indicated pairs of proteins. YFP signals (shown in green) were observed by fluorescence microscope at 72 hpi. The co-expression of YC and the indicated protein was included as the negative controls. Bars, 100 μm. (TIF) S9 Fig. Y2H tests the interaction of NbRbCS with seven viral factors. The co-transformed yeast cells for co-expressing the indicated pairs of proteins were subjected to 10-fold serial dilutions and plated on SD/-Trp/-Leu and SD/-Trp/-Leu/-His/-Ade mediums. Co-transforma- tion of yeast cells for simultaneous expression of AD-T7-T and BD-T7-53 was included as the positive control. (TIF) S10 Fig. Silencing of NbRbCS or NbFNR in N. benthamiana. (A) Phenotypic observation of NbRbCS- or NbFNR-silenced in N. benthamiana. N. benthamiana seedlings at 3- to 5-leaf stage were inoculated with pTRV1 along with pTRV2-NbRbCS (TRV-NbRbCS) or pTRV2-NbFNR (TRV-NbFNR), and photographed at 12 dpi. Co-inoculation of pTRV1 and pTRV2-GUS was included as the parallel control. Bars, 2.5 cm. (B, C) Real-time RT-qPCR analysis of NbRbCS or NbFNR mRNA transcript accumulation. The samples were collected at 12 dpi for the assay. Error bars denote the standard errors from three biological replicates. The average value for TRV-GUS was designated 1.0 to normalize the data. *, P<0.001. (TIF) S11 Fig. Immunoblot analysis of co-expression of the indicated proteins in NbRbCS- and NbFNR-silenced plants. The co-inoculated leaves for co-expression of HCPro2-YN / CI-YC (A), HCPro2-YN / CP-YC (B) or CI-YN / CP-YC (C) were sampled at 60 hpi (B) or 72 hpi (A, C) for immunoblot analysis using anti-GFP antibody. As the abundance of RbCL was greatly decreased along with RbCS-silencing, Coomassie blue staining of protein bands (indicated by red asterisks) was used as a loading control. (TIF) S12 Fig. NbRbCS(ΔCTP) interacts with HCPro2, CI and CP. (A) Schematic diagram of NbRbCS(ΔCTP). NbRbCS(ΔCTP) is a truncated version of NbRbCS, with a removal of chloro- plast transit peptide (CTP). (B) Y2H tests the interactions of NbRbCS(ΔCTP) with HCPro2, CI and CP. The transformed yeast cells for co-expression of the indicated proteins were sub- jected to 10-fold serial dilutions and plated on SD/-Trp/-Leu/-His/-Ade mediums. Co-trans- formation of a pair of constructs for the expression of AD-T7-T and BD-T7-53 was included as the positive control. (B) BiFC assay tests the interactions of NbRbCS(ΔCTP) with HCPro2, CI and CP. N. benthamiana leaves were co-inoculated for the expression of the indicated com- bination of proteins. YFP signals (shown in green) were observed by fluorescence microscope at 72 hpi. Bars, 50 μm. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012064 March 4, 2024 27 / 33 PLOS PATHOGENSRubisco small subunit and viral intercellular movement Acknowledgments We thank Prof. Jingsheng Xu (Fujian Agriculture and Forestry University) for the assistance in aniline blue staining, and Dr. Aiming Wang (Agriculture and Agri-Food Canada) and Dr. Guanwei Wu (Ningbo University) for critical suggestions. Author Contributions Conceptualization: Li Qin, Zhaoji Dai, Hongguang Cui. Formal analysis: Li Qin, Hongjun Liu, Peilan Liu, Lu Jiang, Xiaofei Cheng, Fangfang Li, Wen- tao Shen, Wenping Qiu, Zhaoji Dai, Hongguang Cui. Funding acquisition: Zhaoji Dai, Hongguang Cui. Investigation: Li Qin, Hongjun Liu, Peilan Liu, Lu Jiang. Methodology: Li Qin, Hongjun Liu, Peilan Liu, Lu Jiang. Project administration: Hongguang Cui. Supervision: Hongguang Cui. Writing – original draft: Li Qin, Hongguang Cui. 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10.1371_journal.pntd.0011798	RESEARCH ARTICLE Predictors for participation in mass-treatment and female genital schistosomiasis re- investigation, and the effect of praziquantel treatment in South African adolescents Takalani Girly NemungadiID Arachchige2, Pavitra Pillay3, Svein Gunnar Gundersen4, Birgitte Jyding Vennervald5, Patricia Doris Ndhlovu6, Myra Taylor1, Saloshni Naidoo1, Eyrun Floerecke Kjetland1,2 1, Elisabeth Kleppa2, Hashini Nilushika Galappaththi- 1 Discipline of Public Health Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa, 2 Norwegian Centre for Imported and Tropical Diseases, Oslo University Hospital, Oslo, Norway, 3 Department of Biomedical and Clinical Technology, Durban University of Technology, KwaZulu- Natal, Durban, South Africa, 4 Institute for Global Development and Planning, University of Agder, Kristiansand, Norway, 5 Section for Parasitology and Aquatic Pathobiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 6 BRIGHT Academy, Ugu District, South Africa takalaninemungadi@gmail.com Abstract Objective Female Genital Schistosomiasis (FGS) causes intravaginal lesions and symptoms that could be mistaken for sexually transmitted diseases or cancer. In adults, FGS lesions [grainy sandy patches (GSP), homogenous yellow patches (HYP), abnormal blood vessels and rubbery papules] are refractory to treatment. The effect of treatment has never been explored in young women; it is unclear if gynaecological investigation will be possible in this young age group (16–23 years). We explored the predictors for accepting anti-schistosomal treatment and/or gynaecological reinvestigation in young women, and the effects of anti- schistosomal mass-treatment (praziquantel) on the clinical manifestations of FGS at an ado- lescent age. Method The study was conducted between 2011 and 2013 in randomly selected, rural, high schools in Ilembe, uThungulu and Ugu Districts, KwaZulu-Natal Province, East Coast of South Africa. At baseline, gynaecological investigations were conducted in female learners in grades 8 to 12, aged 16–23 years (n = 2293). Mass-treatment was offered in the low-trans- mission season between May and August (a few in September, n = 48), in accordance with WHO recommendations. Reinvestigation was offered after a median of 9 months (range 5–14 months). Univariate, multivariable and logistic regression analysis were used to mea- sure the association between variables. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Nemungadi TG, Kleppa E, Galappaththi- Arachchige HN, Pillay P, Gundersen SG, Vennervald BJ, et al. (2024) Predictors for participation in mass-treatment and female genital schistosomiasis re-investigation, and the effect of praziquantel treatment in South African adolescents. PLoS Negl Trop Dis 18(3): e0011798. https://doi.org/10.1371/journal.pntd.0011798 Editor: Eva Clark, Baylor College of Medicine, UNITED STATES Received: November 15, 2023 Accepted: March 12, 2024 Published: March 27, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pntd.0011798 Copyright: © 2024 Nemungadi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data cannot be shared publicly because of confidentiality. Data are PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 1 / 17 PLOS NEGLECTED TROPICAL DISEASESavailable from the Centre for Bilharzia and Tropical Health Research for researchers who meet the criteria for access to confidential data. Email address for request: brightresearch.cbthr@gmail. com. Funding: This work was supported by the University of KwaZulu-Natal College of Health Sciences PhD Scholarship (student number 216073797). The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no. PIRSES-GA-2010-269245, University of Copenhagen with the support from the Bill and Melinda Gates Foundation, Grant # OPPGH5344, and South-Eastern Regional Health Authority, Norway project no. 2016055. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Mass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Results Prevalence: Of the 2293 learners who came for baseline gynaecological investigations, 1045 (46%) had FGS lesions and/or schistosomiasis, 209/1045 (20%) had GSP; 208/1045 (20%) HYP; 772/1045 (74%) had abnormal blood vessels; and 404/1045 (39%) were urine positive. Overall participation rate for mass treatment and gynaecological investigation: Only 26% (587/2293) learners participated in the mass treatment and 17% (401/2293) participated in the follow up gynaecological reinvestigations. Loss to follow-up among those with FGS: More than 70% of learners with FGS lesions at baseline were lost to follow-up for gynaecological investigations: 156/209 (75%) GSP; 154/ 208 (74%) HYP; 539/722 (75%) abnormal blood vessels; 238/404 (59%) urine positive. The grade 12 pupil had left school and did not participate in the reinvestigations (n = 375; 16%). Follow-up findings: Amongst those with lesions who came for both treatment and reinves- tigation, 12/19 still had GSP, 8/28 had HYP, and 54/90 had abnormal blood vessels. Only 3/ 55 remained positive for S. haematobium ova. Factors influencing treatment and follow-up gynaecological investigation: HIV, current water contact, water contact as a toddler and urinary schistosomiasis influenced participa- tion in mass treatment. Grainy sandy patches, abnormal blood vessels, HYP, previous preg- nancy, current water contact, water contact as a toddler and father present in the family were strongly associated with coming back for follow-up gynaecological investigation. Challenges in sample size for follow-up analysis of the effect of treatment: The low mass treatment uptake and loss to follow up among those who had baseline FGS reduced the chances of a larger sample size at follow up investigation. However, multivariable analysis showed that treatment had effect on the abnormal blood vessels (adjusted odds ratio = 2.1, 95% CI 1.1–3.9 and p = 0.018). Conclusion Compliance to treatment and gynaecological reinvestigation was very low. There is need to embark on large scale awareness and advocacy in schools and communities before imple- menting mass-treatment and investigation studies. Despite challenges in sample size and significant loss to follow-up, limiting the ability to fully understand the treatment’s effect, mul- tivariable analysis demonstrated a significant treatment effect on abnormal blood vessels. Author summary Female genital schistosomiasis (FGS) is a neglected tropical disease and it affects many women and young girls in schistosomiasis endemic areas. A lot of research is still needed to understand the characteristics of FGS, its prevention, as well as the timing for treat- ment. As a result of the limited information, some women who suffer from FGS end up being misdiagnosed with diseases such as human papilloma virus or other sexually trans- mitted diseases. The study highlights issues that need to be taken into considerations when providing treatment or conducting mass treatment for schistosomiasis and FGS or planning gynaecological investigations to inform FGS control programmes. In this study of adolescent girls and young women of KwaZulu-Natal Province of South Africa, we PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 2 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS sought to explore the factors that influence participation in mass treatment and gynaeco- logical investigation, and investigating the effect of praziquantel treatment on FGS. Fac- tors that influenced participation in mass treatment included HIV, current water contact, water contact as a toddler and urinary schistosomiasis. Factors that influenced participa- tion in follow up gynaecological investigation included grainy sandy patches, abnormal blood vessels, homogenous yellow patches, previous pregnancy, current water contact, water contact as a toddler and father present in the family. There was low uptake and loss to follow up for mass treatment, and this contributed to small sample size for follow up gynaecological investigations to understand the effect of treatment. However, multivari- able analysis showed that treatment had effect on the abnormal blood vessels and not on grainy sandy patches and homogenous yellow patches. Introduction Female Genital Schistosomiasis (FGS) is a complication of schistosomiasis that results from trapped schistosome eggs, which damage tissues and organs [1,2]. It is known to affect females of all age groups. FGS has a complex disease manifestation spectrum, including lesions of the cervix and vagina (grainy sandy patches and homogenous yellow patches), surface bleeding, abnormal blood vessels and rubbery papules [3,4]. FGS has also been associated with sub-fer- tility or infertility, ectopic pregnancy, spontaneous abortion, premature birth, and increased susceptibility to HIV transmission and possibly progression. Reports suggest that eggs are evenly distributed in the genital organs [2,5–7]. There are many case reports of concurrent FGS with cervical intra-epithelial neoplasia and one paper to date has shown lower Human Papillomavirus (HPV) clearance in FGS positive women or increased susceptibility to HPV [3,8–12]. WHO recommends mass drug administration in schistosomiasis endemic schools for pre- vention of morbidity, and studies indicate that early treatment may be necessary to prevent genital lesions in adulthood [13–15]. In South Africa, there is no mass treatment or mass drug administration programme for schistosomiasis and treatment is case-by-case upon con- sultation by the affected individuals. The mass treatment that was conducted between 1998 and 2001 in KwaZulu-Natal was discontinued due to resource constraints [16,17]. FGS is not known among most health care workers, clinicians and community members and only approximately 160 gynaecologists were previously informed about FGS in South Africa [16– 19]. As a result, FGS is not considered a priority condition. There is therefore limited data and research and this leads to a low index of suspicion by clinicians and FGS lesions often being misclassified as sexually transmitted infections or cervical cancer [2,5,20–27]. There have been efforts by authorities in South Africa to mobilise WHO-donated praziquantel in order to implement the mass treatment as per the recommended WHO strategy over the past years. However, this has been a challenge since, the donated praziquantel is not registered in the country and is not permitted to be used [28]. Subsequently, treatment has been mobilised from within the country and mass treatment is being planned for implementation [29]. In order to prevent low uptake and to properly plan for the roll-out of the mass treatment, it is important to identify factors that will influence participation, and to determine the effect of treatment on FGS symptoms. It is also important to identify factors that will influence par- ticipation in gynaecological investigation in order to improve treatment and other preven- tion and control measures as well as research information that will provide new knowledge. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 3 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Between 2011 and 2013, we conducted a follow-up study and sought to explore the feasibil- ity of the implementation of mass drug administration and gynaecological investigations in school-going young women in rural KwaZulu-Natal, and to explore the effects on lesions and symptoms. This is in line with the WHO recommendations of promoting regular de-worming and expanding the reach of at risk adolescent girls and women of reproductive age [14]. Materials and methods Ethics statement The study was approved by the Biomedical Research Ethics Committee (BREC), University of KwaZulu-Natal (Ref BF029/07), KwaZulu-Natal Department of Health (Reference HRKM010- 08) and the Regional Committee for Medical and Health Research Ethics (REC), South Eastern Norway. The ethical committees were aware that minors were invited into the study and spe- cifically approved independent minor consent without parental consent. All study participants were offered anti-schistosomal treatment and, if applicable, treatment for sexually transmitted diseases, and/or referral to the local health system for treatment of HIV. Participants and area The study was conducted between 2011 and 2013 in the KwaZulu-Natal Province of South Africa. The participants were female learners aged 16–23 years, from randomly selected high schools in Ilembe, uThungulu and Ugu Districts on the East Coast of South Africa that had not undergone anti-schistosomal mass-treatment before the first investigation as shown in Table 1. The participants were recruited from schools that were classified as rural by the South African Department of Basic Education and were below the altitude of 400 meters above sea level, with an estimated prevalence of S. haematobium of 10% or more based on an initial show of hands for red urine in Ugu District and a haematuria dipstick survey in Ilembe and uThun- gulu districts [30]. Gynaecological examinations were performed in two research clinics (North of Durban and South of Durban); virgins, pregnant, and severely ill females were excluded. Schools with prevalence of less than 10% were excluded. Questionnaires and clinical examinations Group information, individual information and the consent form procedure was done at the school over a 2-week-period. Consent forms and parent information were distributed in advance [39–41]. The investigation has been described previously. Briefly, a questionnaire on water contact, reproductive history, genital and abdominal symptoms was administered indi- vidually to participants in isiZulu (the local language) prior to gynaecological examination [30]. Teachers would suggest days when it would be suitable to fetch learners from schools to go to the research clinic. Teenagers had indicated they were embarrassed to give urine in school so urine was collected only while they were at the clinic. On pre-reserved days, a female driver, trained and well-informed about the study, would pick up 4–13 participants (depend- ing on the vehicle size) for the drive to the clinic for gynaecological examinations by trained medical doctors to determine the presence or absence of FGS lesions using a colposcopy. For quality control, the computer images analysis was done afterwards by an FGS specialist as the supervisor who has worked in five countries since 1994. Treatment In accordance with the WHO policy for areas of high schistosomiasis endemicity, mass treat- ment of participants and other learners in enrolled schools was carried out during the winter PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 4 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Table 1. Study design and practicalities, their rationale, and the potential unintended consequences. Inclusion considerations Reason Participants included were 16 years and above, including Grade 12 KwaZulu-Natal was chosen as the study area Although some young women with multiple lesions were treated at the clinic, most were not treated immediately after examination at the clinic. In order to explore reversibility of lesions, we wished to invite young women at an earliest possible time for a gynaecological investigation. However, gynaecological investigations are not practically feasible before the age of 16 for cultural, legal and sometimes anatomical reasons [31]. Average sexual debut age is 18.7 years in South Africa and many hide sexual debut from e.g. their parents and teachers for as long as they can [32,33]. Moreover, some may be worried about the "loss of virginity" during speculum examination [34,35]. Visual inspection of the cervix, fornices and vaginal surfaces before sexual debut may therefore be offensive to their sensibilities. Firstly, at this tender age, chances were high that these young women would be invited for their first gynaecological examination. Some amongst the pupils might be found to have been raped and a gynaecological examination might trigger psychological reactions. Therefore, we looked for a study area where psychologists and support centres would be available for study participants in a schistosomiasis endemic area. Secondly, there are more than 4,000 rural schools in KZN and we estimated that we would be able to reach the necessary sample size indicated below in the sample calculation under material and method [36,37]. Treatment with praziquantel was done at schools, not at the research clinics for the following reasons: 1) Mass Drug Administration is recommended so that siblings and class mates are treated simultaneously [15] 2) Anti-schistosomal treatment with praziquantel should be given in the low-transmission season [28,38] 3) Praziquantel may cause nausea and sometimes vomiting; if treatment had been done at clinic immediately after investigation, some learners might become car sick on the way from the clinic, it could potentially scare others from participating [15]. https://doi.org/10.1371/journal.pntd.0011798.t001 season (low-transmission season) after the baseline gynaecological examination. Treatment began after lunch to ensure that the children had eaten their free school lunches before treat- ment and to save the costs on food for the programme. Depending on the size of school, the treatment team comprised of 2–4 nurses and 2–4 assistants [27]. Bread and bananas were dis- tributed to learners who had not received a school lunch. Learners with signed consent forms were weighed, and the number of praziquantel tablets was calculated for the dose of 40 mg per kg of body weight. As described previously, a designated nurse directly observed all tablet ingestion by counting the number of tablets in each learner’s hand and watching for hand-to- mouth intake [27]. The treatment team stayed in the school for an hour after the last dose to allow learners to report any side effects. All learners, even the untreated, were allowed to come back for follow-up gynaecological examination. Sample size calculation and statistical analyses Demographic and clinical variables were explored as predictors for (1) participation in mass- treatment and (2) returning to a follow-up gynaecological investigation. We planned a study of independent cases and controls with 3 control(s) per case. Downs et al described that only 2 out of 9 women with FGS lesions were healed 6 months months/weeks after treatment with PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 5 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS praziquantel [42]. However, there was no control group. There is only one paper that has explored the effect of treatment (3 and 12 months) on the FGS lesions and compared it with the untreated [43]. We planned to study independent treated and untreated FGS positive females. In this young population, where lesions might possibly be reversible, the authors decided to explore 50% reversibility of the FGS lesions in the treated. This is low, a very conser- vative estimate, due to the lack of prior data. We therefore estimated the probability of healing among the treated FGS positive to be 0.5. Amongst the untreated, we assumed that the proba- bility of healing would be 0.1. We would therefore need to study 19 FGS positive patients and 19 FGS negative patients to be able to reject the null hypothesis that the exposure rates for case and controls are equal with probability (power) 0.8. The Type I error probability associated with this test of this null hypothesis is 0.05. We used an uncorrected chi-squared statistic to evaluate this null hypothesis. In order to study simultaneously the impact of several variables, logistic regression analysis was applied with a 5% significance level; as a general rule variables were included if the p-value from the crude association was less than 0.2 and if the Spearman rank correlation coefficient was below 0.7. Age was forced into the model. To assess the overlap of the FGS lesions among the learners, we created Venn diagrams (presented under results) using Venny version 2.1 (Juan Carlos Oliveros, https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html) Results Description of the study population A total of 2293 adolescent girls and young women from 70 secondary schools were enrolled and examined gynaecologically for FGS lesions and urinary schistosomiasis at baseline. As shown in Fig 1, 1045/2293 (46%) learners had genital lesions and/or urinary schistosomiasis at baseline. None of them had rubbery papules. Abnormal blood vessels were the most common findings. As shown in Fig 2, 271 learners had more than one FGS lesions while 556 had only one FGS lesion (ABV: abnormal blood vessels, GSP: grainy sandy patches, HYP: homogenous yellow patches). Mass treatment participation Treatment with praziquantel (40mg/kg) was given in the low-transmission season [South Afri- can winter between May and August, including a few in September (n = 48)]. Only 26% (587 of the 2293) learners were treated between baseline and follow-up. Multivariable analysis showed that HIV, current water contact, water contact as a toddler and urinary schistosomiasis positively influenced learners’ willingness to participate in the mass treatment (Table 2). A Fig 1. Genital lesions and schistosomiasis amongst adolescent girls and young women in Kwa-Zulu-Natal, 2012–2013. https://doi.org/10.1371/journal.pntd.0011798.g001 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 6 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Fig 2. Venn diagram showing an overlap of baseline FGS lesions among learners. https://doi.org/10.1371/journal.pntd.0011798.g002 greater loss to follow-up for mass treatment was observed in the Southern research clinic as compared to the Northern clinic (odds ratio = 2.1, 95% CI 1.7–2.6 and p <0.001). Gynaecological reinvestigation Table 3 shows that a total of 709 learners (31% of the 2293 learners) were interviewed at follow up. Of the 709, 398 agreed to participate in the follow up gynaecological investigation; the majority (32%) felt that they were not ready. The willing participants were reinvestigated after a median of 9 months (range 5–14 months). The median age of those followed-up was 19 years (range 16–23 years), and those who were lost to follow-up had a median age of 18 years (range 16–23 years). Table 4 show factors that may have contributed to compliance with follow up gynaecologi- cal investigation. The three FGS lesions (grainy sandy patches, abnormal blood vessels and PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 7 / 17 PLOS NEGLECTED TROPICAL DISEASESTable 2. Underlying factors and compliance with mass-treatment. Mass treatment, gyneacological investigation and effect of praziquantel treatment on FGS History Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 Current water contact Urinary schistosomiasis Abnormal blood vessels Water contact as toddler HIV Red urine previously or now Mother present in the family Homogenous yellow patch Has ever been pregnant Heavy bleeding with clots Father present in the family Genital itch Genital burn Genital sore/ulcer Genital lump Urge with leak Urge Dysuria Bloody discharge Watery discharge Abnormal discharge smell Grainy sandy patch Participated in mass treatment (%) Did not participate in mass treatment (%) Univariate analysis Multivariable analysis Odds ratio 95% Confidence Interval p-value Adjusted odds ratio 95% Confidence Interval p- value 1 0.7 0.4 0.5 0.6 1.4 0.7 1.1 1.1 1.4 0.7 0.8 1.3 0.2–2.3 0.2–1.2 0.2–1.5 0.2–0.7 0.1–1.8 0.600 0.113 0.249 0.346 0.003 0.5–0.9 0.037 0.8–1.3 0.9–1.4 1.1–1.8 0.680a 0.245b 0.002 0.5–0.9 0.7–1.1 0.015 0.138 0.9–1.6 0.055 8/585 (1) 34/585 (6) 135/585 (23) 244/585 (42) 164/585 (28) 391/587 (59) 10/1046 (1) 45/1046 (4) 287/1046 (27) 474/1046 (45) 230/1046 (22) 596/1050 (57) 90/517 (17) 193/895 (22) 215/587 (37) 337/1050 (32) 339/587 (68) 611/1050 (58) 86/555 (15) 155/587 (26) 210/999 (21) 320/1045 (31) 375/587 (64) 625/1045 (60) 60/587 (10) 90/1050 (09) 280/587 (48) 504/1050 (48) 176/408 (43) 350/755 (46) 136/587 (23) 251/1045 (24) 361/587 (61) 243/587 (41) 114/585 (19) 93/583 (16) 270/587 (46) 202/585 (35) 297/587 (35) 104/583 (17) 369/583 (63) 260/583 (45) 658/1046 (63) 464/1046 (44) 206/1043 (20) 176/1038 (17) 499/1045 (48) 346/1044 (33) 550/1044 (52) 198/1043 (19) 638/1039 (61) 490/1042 (47) 49/587 (08) 102/1050 (08) 1 0.9 0.6 0.6 0.9 1.5 0.8 1.2 1.5 0.7 0.8 1.2 1.2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.1 0.9 0.9 1.1 0.9 0.8 0.3–2.6 0.2–1.5 0.3–1.7 0.3–2.3 0.913 0.274 0.389 0.813 1.2–1.9 <0.001 0.6–1.0 0.060 0.9–1.5 0.063 1.2–1.9 <0.001 0.5–0.9 0.6–1.0 0.008 0.072 0.9–1.5 0.105 0.9–1.7 0.267 0.8–1.2 0.907 0.7–1.1 0.292 0.8–1.2 0.698 0.8–1.2 0.7–1.1 0.8–1.3 0.7–1.2 0.8–1.1 0.9–1.3 0.8–1.1 0.7–1.2 0.9–1.3 0.7–1.1 0.573 0.246 0.898 0.602 0.496 0.569 0.418 0.569 0.452 0.346 0.6–1.2 0.359 amultivariable with abnormal blood vessels and current water contact bmultivariable with abnormal blood vessels and water contact as a toddler. In order to study simultaneously the impact of several variables, logistic regression analysis was applied with a 5% significance level; as a general rule variables were included in the multivariable analysis if the p-value from the crude association was less than 0.2 and if the Spearman rank correlation coefficient was below 0.7. https://doi.org/10.1371/journal.pntd.0011798.t002 homogenous yellow patches), previous pregnancy, current water contact, water contact as a toddler and father present in the family were strongly associated with coming back for a fol- low-up investigation in both univariate and multivariable analysis. Multivariable analysis of the three FGS lesions individually (correlation) with current water contact, previous PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 8 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Table 3. Reasons for refusing follow up gynaecological examination (n = 709). Reasons for denying or aborting gynaecological examination Came for follow up Total (%) Did not deny or abort Fear Not ready Menstruation Virgin Pregnant Other TOTAL No (%) Yes (%) 16 (3.9) 26 (100) 127 (100) 24 (92) 14 (100) 53 (100) 48 (98) 308 (43) 398 (96) 0 (0) 0 (0) 2 (8) 0 (0) 0 (0) 1 (2) 414 (100) 26 (100) 127 (100) 26 (100) 14 (100) 53 (100) 49 (100) 401 (57) 709 (100) https://doi.org/10.1371/journal.pntd.0011798.t003 pregnancy and water contact as a toddler had no effect on the association. A greater loss to fol- low-up for gynaecological investigation was observed in the Southern research clinic as com- pared to the Northern clinic (odds ratio = 1.9, 95% CI 1.5–2.4 and p <0.001). On the baseline investigation day, participants were informed about genital lesions (grainy sandy patches, homogenous yellow patched and abnormal blood vessels) but urine microscopy results were not readily available as the laboratory investigation were still going on. However, only approximately 25% of the young women with lesions returned for follow-up examina- tions (Fig 3, bold frame). As shown in Fig 3 (hatched frame), more than 70% of learners that had genital lesions at baseline were lost to follow up for gynaecological investigation, and 23% of learners with urinary schistosomiasis were lost to follow-up. The effect of treatment Multivariable analysis of the effect of treatment on baseline FGS showed that treatment only had an effect on the abnormal blood vessels (adjusted odds ratio = 2.1, 95% CI 1.1–3.9 and p = 0.018) (Table 5). Treatment was protective against urinary schistosomiasis; only 3 learners among the 55 that were treated remained positive for urinary schistosomiasis at follow up (adjusted odds ratio = 0.4, 95% CI 0.01–0.1 and p < 0.001). Before accounting for FGS lesions at baseline (including those with and without FGS lesions at baseline), the univariate analysis showed a strong association between treatment and two FGS lesions (homogenous yellow patches and abnormal blood vessels); at multivariable analysis this association remained unchanged for the abnormal blood vessels (adjusted odds ratio = 2.3, 95% CI 1.5–3.6 and p <0.001) (Table 5) whereas it became insignificant for homog- enous yellow patches. Discussion The abnormal blood vessels were refractory to praziquantel treatment. Abnormal blood vessels and grainy sandy patches were previously found to be significantly associated with the pres- ence of live worms as opposed to the homogenous yellow patches [44]. The association between praziquantel treatment and abnormal blood vessels may be an indication that abnor- mal blood vessels are an early stage of the grainy sandy patches and are easy to eliminate as opposed to the grainy sandy patches and homogenous yellow patches. It is important to note that different investigators define the investigation findings differently and that the investiga- tors of gynaecological examinations were different in baseline and follow-up studies. However, the computer images were analysed by the FGS specialists afterwards for quality control. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 9 / 17 PLOS NEGLECTED TROPICAL DISEASESCurrent water contact 257/401 (64) 110/398 (28) 1124/1892 (59) 571/1883 (30) Table 4. Underlying factors associated with coming back for follow-up gynaecological investigations. Mass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Came for follow-up (%) Did not come for gynaecological investigation Univariate analysis Multivariable analysis Odds ratio 95% Confidence Interval p-value Adjusted odds ratio 95% Confidence Interval p-value History Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 Grainy sandy patch* Abnormal blood vessels* Homogenous yellow patch* Has ever been pregnant Water contact as toddler Red urine previously or now Father present in the family Genital lump Condom use Heavy bleeding with clots HIV Mother present in the family Genital itch Genital burn Genital sore/ulcer Urge with leak (urgenic1) Urge but no leak (urge1) Dysuria Bloody discharge Watery discharge Abnormal discharge smell Urinary schistosomiasis 5/400 (1) 24/400 (7) 120/400 (30) 214/400 (54) 37/400 (9) 56/401 (14) 185/401 (46) 18/1863 (1) 92/1863 (5) 488/1863 (26) 742/1863 (40) 523/1863 (28) 153/1892 (8) 537/1892 (28) 55/401 (14) 153/1892 (8) 236/401 (60) 838/1892 (44) 260/401 (65) 1119/1892 (59) 71/398 (19) 454/1881 (24) 65/394 (17) 130/319 (41) 113/259 (44) 83/386 (22) 231/399 (58) 253/399 (63) 179/399 (45) 77/398 (19) 200/398 (50) 311/1872 (17) 649/1604 (41) 628/1403 (45) 356/1794 (20) 1136/1880 (60) 1155/1884 (61) 809/1884 (43) 347/1879 (19) 882/1883 (47) 137/392 (35) 639/1881 (34) 217/398 (55) 70/397 (18) 252/396 (64) 179/399 (45) 982/1882 (52) 356/1875 (19) 1168/1872 (62) 878/1869 (47) 82/367 (22) 322/1579 (20) 1 0.8 0.8 0.9 0.2 1.8 2.2 1.8 1.8 1.4 0.8 0.7 0.3–2.5 0.3–2.2 0.3–2.5 0.1–0.6 0.724 0.635 0.803 0.004 1.3–2.6 <0.001 1.7–2.7 <0.001 1.3–2.5 0.001 1.5–2.3 <0.001 1.1–1.7 0.009 1.1–1.7 0.6–1.1 0.012 0.140 0.5–0.9 0.010 1 0.9 0.9 1.0 0.3 1.8 2.3 1.8 1.8 1.3 1.2 0.9 0.7 0.9 1.0 0.9 1.1 0.9 1.1 1.1 1.1 1.1 1.0 1.1 0.9 1.1 0.9 1.1 0.3–2.8 0.3–2.4 0.4–2.8 0.1–0.7 0.910 0.813 0.941 0.010 1.3–2.6 <0.001 1.7–2.7 <0.001 1.3–2.5 <0.001 1.4–2.2 <0.001 1.0–1.6 <0.034 1.4 0.9–1.5 0.7–1.1 0.082 0.287 0.5–0.9 0.007 0.7–1.3 0.8–1.3 0.7–1.2 0.8–1.4 0.7–1.1 0.9–1.4 0.9–1.3 0.8–1.4 0.9–1.4 0.955 0.923 0.736 0.461 0.349 0.433 0.482 0.682 0.216 0.8–1.3 0.837 0.9–1.4 0.7–1.2 0.8–1.3 0.7–1.1 0.395 0.530 0.642 0.442 0.9–1.5 0.407 *Were analysed separately because some participants were affected by more than one lesion and there may be causal relationship between the three FGS lesions. https://doi.org/10.1371/journal.pntd.0011798.t004 South Africa is not implementing mass drug administration [45]; the remaining gynaecolo- gical symptoms among the treated were high and may be an indication that for effectiveness, several rounds of treatment are required and should be started at an earlier age. A similar study in Zimbabwe, which investigated the effect of praziquantel on FGS, reported a result consistent to ours where a standard single-dose of praziquantel did not have effect on the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 10 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS Fig 3. Follow-up amongst adolescent girls and young women investigated for Female Genital Schistosomiasis in Kwa-Zulu-Natal. Hatched frame: lost to follow-up for gynaecological investigation, Bold frame: Remaining findings at follow-up gynaecological investigation. https://doi.org/10.1371/journal.pntd.0011798.g003 inflammatory lesions of FGS that had formed and developed over a few years [13]. The timing for follow up investigations may have been too soon for some learners to determine the true effect of treatment (the median time period between praziquantel (40mg/kg) treatment and reinvestigation was 5 months (range 5–14 months). Treatment with praziquantel is the only recommended effective way of killing adult hel- minths and mature live eggs, but has no effect on the calcified ova; early treatment is therefore crucial before the ova are deposited in the tissue and cause damage [6,46]. This may possibly explain the remaining gynaecological symptoms among the treated, including those who did not have the lesions at baseline. A retrospective study found that treatment at an early age seems to prevent gynaecologic morbidity [13]. Lack of a regular mass treatment programmes in South Africa, repeated exposure to risky water, and re-infections could have led to continuous FGS lesions even after a single dose of praziquantel. Further investigations and follow up studies are therefore needed in countries that are implementing mass drug administration and areas that are not implementing mass Table 5. Effect of treatment—remaining FGS lesions among the treated and untreated. FGS lesions Treated (%) Untreated (%) Univariate analysis Multivariable analysis Effect of treatment among those with FGS a baseline OR 95% CI p-value AOR 95% CI p-value Grainy sandy patches Homogenous yellow patches Abnormal blood vessels Urinary schistosomiasis 12/19 (63) 08/28 (29) 54/90 (60) 3/55 (5) 23/34 (68) 03/26 (12) 40/93 (43) 0.8 0.3–2.7 3.1 0.7–3.2 1.9 1.1–3.6 0.741 0.120 0.022 0.5 6.7 2.1 1.1–2.1 0.9–48 1.1–3.9 73/183 (53) 0.5 0.02–0.2 <0.001 0.4 0.01–0.1 Overall FGS rate at follow up (after treatment and including FGS among those who did not have FGS at baseline) Grainy sandy patches Homogenous yellow patches Abnormal blood vessels Urinary schistosomiasis 25/167 (15) 24/167 (14) 81/167 (49) 07/351 (02) 35/229 (15) 17/229 (07) 66/229 (29) 91/681 (13) 0.9 0.6–1.7 2.1 1.1–4.0 2.3 1.5–3.5 0.1 0.1–0.3 0.931 0.025 <0.001 <0.001 0.9 1.8 2.3 0.1 0.5–1.7 0.9–3.5 1.5–3.6 0.1–0.3 0.369 0.058 0.018 <0.001 0.903 0.096 <0.001 <0.001 https://doi.org/10.1371/journal.pntd.0011798.t005 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 11 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS drug administration. These studies will help with information to fully understand the differ- ences between the FGS lesions and whether praziquantel treatment can eliminate these lesions, including the right timing to possibly eliminate the lesions. Currently, only one study has investigated the impact of treatment on gynaecological lesions and found no significant change in the adult sandy patches and contact bleeding over a 12-month period, even though urinary egg excretion ceased [43]. Another study found that schistosomiasis PCR remained positive in the genitals after treatment [42]. The lack of information and complexity in conducting research on how long it takes for the FGS lesions to manifest following infection made it diffi- cult to determine whether the FGS lesions diagnosed at follow-up were due to schistosomiasis re-infection or were progressive lesions that had not yet manifested and therefore diagnosed at baseline. Lack of a regular mass treatment programme in South Africa, repeated exposure to risky water, and re-infections could have led to continuous FGS lesions even after a single dose of praziquantel. Identification of HIV, current water contact, water contact as a toddler and urinary schisto- somiasis as factors that influenced learners to participate in mass treatment may be a sign of some awareness of risk factors of schistosomiasis, i.e. schistosomiasis as a risk factor for HIV, and water contact as a risk for schistosomiasis. The pre-treatment health education may have played a role in influencing these participants to accept the treatment. Learners who felt they do not have a problem with these identified factors may have perceived it unnecessary for them to get treated. Furthermore, the three FGS lesions (grainy sandy patches, abnormal blood vessels and homogenous yellow patches), previous pregnancy, current water contact, water contact as a toddler and father present in the family were strongly associated with returning for follow-up investigation. It is anticipated that the investigators at the Northern clinic had gained more experience in both health education and community engagement as opposed to when they conducted investigations at the Southern clinic (the 1st investigation site) where there was a greater loss to follow up. Therefore, active ongoing community engage- ment targeting both learners, parents and the entire community at risk is crucial in order to empower the community with the full knowledge on prevention and control that will enable them to make informed decisions. South Africa is endemic for schistosomiasis with some focal areas having high and moderate infections [44], and FGS studies among the young population may help identify targeted inter- ventions. Study findings revealed that more than 20% of the adolescent women in KwaZulu- Natal province had three well-known genital mucosal manifestations of FGS [30]; in Limpopo Province, FGS accounted for 87.6% of the female cases in a study that described the pathology of biopsy diagnosed schistosomiasis [47]. However, treatment in South Africa is still case-based and, as in many other countries, most community members do not seek early treatment or do not seek treatment at all [17,27,48]. Therefore, those infected and those that complicate to FGS remain infected or with FGS complication; some individuals are misdiagnosed as sexually trans- mitted infections (STIs) or cervical cancer [2,5,20–27]. In this study, loss to follow up was a major challenge. Involving community health workers in FGS studies may assist with managing those who are lost to follow up because it would be easier for them to maintain contact and fol- low up with the learners in their catchment area, especially those found to have FGS symptoms. It is important to note that gynaecological examination was not possible among the early and middle adolescent group due to virginity, fear, pregnancy, and privacy and other related issues. Early adolescence and some middle adolescence may only be targeted for prevention and only examined at the late adolescence stage to check if they developed any FGS after repeated treatment. Observational studies in young adolescents and adults are critical in deter- mining the effect of early treatment on schistosomiasis and FGS. It is similarly important to determine the burden of infertility among those living in endemic areas and determine the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 12 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS association with FGS. Therefore, it is important for countries such as South Africa to establish mass drug administration programmes (targeting communities at risk, prioritizing enrolled and unenrolled school age children, as well as adolescent girls and women of reproductive age) in order to prevent sustained infections that can lead to FGS. The high loss to follow up is worrying because more knowledge is required through research to understand FGS pathogenesis and improve the knowledge of clinicians. For instance, it is important to note that the high loss to follow-up during mass treatment in this study contributed to small sample size to determine the effect of treatment on FGS. Currently, the lack of knowledge of FGS among most clinicians and affected communities is concerning due to the reported high prevalence of genital lesions [49]. It has been reported that up to 75% of all women and girls infected with urinary S. haematobium have lesions in the uterus, cervix, vagina or vulva [4]. Many women have been reported to have genital schistosomiasis without urinary excretion of S. haematobium eggs [2,4,13,50,51]. In sub-Saharan Africa, it is estimated that 56 million women have FGS [2]. In addition to the identified unintended consequences due to inclusion criteria and research actions reported under results, the high loss to follow-up and low treatment coverage in this study could also be attributed to factors that were reported by another study in South Africa in Ugu Distict of KwaZulu-Natal Province. This Ugu District study was conducted among grade 10–12 learners, teachers, community health workers and traditional healers [16,17]. Factors that contributed to low coverage in this Ugu District study were reported to be: older age group, lack of knowledge, attending a large school, parental control and a closer teacher follow-up in younger children and in small schools, misconceptions that schistosomia- sis is a self-healing disease and symptoms confused with sexually transmitted infections, the chronicity of the disease is not known to the general population, teasing and stigma, schistoso- miasis-related absenteeism that may reach 30% on some days, tablets must be distributed by health professionals in South Africa, and schools reluctant to provide more than one day for treatment in order to minimise disturbance [15,17,27]. The lower loss to follow up at the North of Durban clinic may be attributed to the experi- ence gained while operation at the first clinic (South of Durban). A praziquantel mass treat- ment programme was implemented in South Africa, in Ugu District of KwaZulu-Natal Province between 1998 and 2001 among grade 10–12 learners, teachers, community health workers and traditional healers, and only reached 44% of the learners instead of the WHO and the South African National Department of Health recommended target of 75% coverage [27]. This shows a need to embark on large scale awareness and advocacy campaign in schools and communities, coordinated by the Department of Health and in collaboration with the Depart- ment of Basic Education, non-governmental organisations and community leaders, before implementing FGS studies among young people and mass treatment administration, to improve participation in research that will inform control measures. Study limitations include the small sample size to determine the effect of treatment due to low treatment coverage and loss to follow up. Another limitation was that treatment was not done immediately after baseline, and therefore many learners were not treated between base- line and follow up either due to factors described above by Lothe et al in the qualitative studies in Ugu District, KwaZulu-Natal Province [16,17]. As a result, some learners that were not treated between baseline and follow up were only treated during follow-up. Some of the learn- ers under 16 years might already have had chronic lesions, and inclusion of the Grade 12 learn- ers could have contributed to loss to follow up because of those who passed matric and moved to tertiary institutions the following year. In summary, it is critical to conduct vigorous ongoing community engagements for aware- ness and buy-in prior to mass treatment and gynaecological investigations in order to improve PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011798 March 27, 2024 13 / 17 PLOS NEGLECTED TROPICAL DISEASESMass treatment, gyneacological investigation and effect of praziquantel treatment on FGS community participation and increase sample size. The effect of treatment was investigated among a small sample size and there was loss to follow-up which could have been prevented by treatment immediately after gynaecological investigation at the clinic at baseline. It is there- fore difficult to draw firm conclusions about the effect of treatment on FGS lesions. In addition to the vigorous community engagement, future studies should prioritize treatment immedi- ately after baseline investigation as another strategy for improving sample size. Acknowledgments We are appreciative of the support from Roy Manyaira, Silindile Gagai and other staff at BRIGHT Research in KwaZulu-Natal, South Africa. We thank all the girls and young women who participated in the study. Author Contributions Conceptualization: Takalani Girly Nemungadi, Elisabeth Kleppa, Hashini Nilushika Galap- paththi-Arachchige, Pavitra Pillay, Svein Gunnar Gundersen, Birgitte Jyding Vennervald, Patricia Doris Ndhlovu, Myra Taylor, Eyrun Floerecke Kjetland. Data curation: Takalani Girly Nemungadi. Formal analysis: Takalani Girly Nemungadi. Funding acquisition: Eyrun Floerecke Kjetland. Investigation: Takalani Girly Nemungadi, Elisabeth Kleppa, Hashini Nilushika Galappaththi- Arachchige, Patricia Doris Ndhlovu, Eyrun Floerecke Kjetland. Methodology: Takalani Girly Nemungadi, Eyrun Floerecke Kjetland. Project administration: Eyrun Floerecke Kjetland. Supervision: Saloshni Naidoo, Eyrun Floerecke Kjetland. Validation: Takalani Girly Nemungadi, Eyrun Floerecke Kjetland. Writing – original draft: Takalani Girly Nemungadi. Writing – review & editing: Takalani Girly Nemungadi, Elisabeth Kleppa, Hashini Nilushika Galappaththi-Arachchige, Pavitra Pillay, Svein Gunnar Gundersen, Birgitte Jyding Venner- vald, Patricia Doris Ndhlovu, Myra Taylor, Saloshni Naidoo, Eyrun Floerecke Kjetland. References 1. Hegertun IEA, Sulheim Gundersen KM, Kleppa E, Zulu SG, Gundersen SG, Taylor M, et al. S. haema- tobium as a common cause of genital morbidity in girls: A cross-sectional study of children in South Africa. PLoS Negl Trop Dis. 2013; 7(3):e2104 (1–8). 2. UNAIDS, World Health Organization. No more neglect. Female genital schistosomiasis and HIV. Inte- grating reproductive health interventions to improve women’s lives [Internet]. Geneva, Switzerland; 2019 [cited 2020 Mar 12]. Available from: https://www.unaids.org/sites/default/files/media_asset/ female_genital_schistosomiasis_and_hiv_en.pdf 3. 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10.1371_journal.pone.0298960	RESEARCH ARTICLE Prevalence and associated factors of refractive error among adults in South Ethiopia, a community-based cross-sectional study Marshet Gete Abebe1☯, Abiy Maru Alemayehu2☯, Minychil Bantihun Munaw2☯, Mikias Mered Tilahun2☯, Henok Biruk AlemayehuID 1☯* 1 Department of Ophthalmology and Optometry, Hawassa University, Comprehensive Specialized Hospital, Hawassa, Ethiopia, 2 Department of Optometry, School of Medicine, University of Gondar, Comprehensive Specialized Hospital, Gondar, Ethiopia ☯ These authors contributed equally to this work. * Henokbiruk37@gmail.com Abstract Introduction The increasing prevalence of refractive error has become a serious health issue that needs serious attention. However, there are few studies regarding the prevalence and associated factors of refractive error at the community level in Ethiopia as well as in the study area. Therefore, providing updated data is crucial to reduce the burdens of refractive error in the community. Objective To assess the prevalence and associated factors of refractive error among adults in Hawassa City, South Ethiopia, 2023. Method A community-based cross-sectional study was conducted on 951 adults using a multistage sampling technique from May 8 to June 8, 2023, in Hawassa City, South Ethiopia. A pre- tested, structured questionnaire combined with an ocular examination and a refraction pro- cedure was used to collect data. The collected data from the Kobo Toolbox was exported to a statistical package for social sciences for analysis. Binary and multivariable logistic regres- sion analyses were performed. A P-value of less than 0.05 was considered statistically sig- nificant in the multivariable analysis. Result A total of 894 study participants were involved in this study with a 94.1% response rate. The prevalence of refractive error was 12.3% (95% CI: 10.2, 14.5%). Regular use of electronic devices (adjusted odds ratio = 3.64, 95% CI: 2.25, 5.91), being diabetic (adjusted odds ratio a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Abebe MG, Alemayehu AM, Munaw MB, Tilahun MM, Alemayehu HB (2024) Prevalence and associated factors of refractive error among adults in South Ethiopia, a community-based cross- sectional study. PLoS ONE 19(3): e0298960. https://doi.org/10.1371/journal.pone.0298960 Editor: Fidan Aghayeva, Chiemsee Augen Tagesklinik, Technical University of Munich, GERMANY Received: August 31, 2023 Accepted: February 1, 2024 Published: March 25, 2024 Copyright: © 2024 Abebe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: he author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 1 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia = 4.02, 95% CI: 2.16, 7.48), positive family history of refractive error (adjusted odds ratio = 2.71, 95% CI 1.59, 4.61) and positive history of cataract surgery (adjusted odds ratio = 5.17, 95% CI 2.19, 12.4) were significantly associated with refractive error. Conclusion and recommendation The overall magnitude of refractive error in our study area was high. Regular use of elec- tronic devices, being diabetic, positive family history of refractive error, and a positive history of cataract surgery were associated with refractive error. Introduction Refractive error (RE) is a condition where the optical system of the eye fails to focus parallel rays of light on the retina. The RE occurs when there is an imbalance between the axial length and the refractive power of the eye [1]. Symptoms of RE include blurring of vision, headaches, eyestrain, and problems with focusing and seeing details at any distance. Globally, the preva- lence of RE was 12% [2]. The prevalence of RE ranges from 6% to 72% in developed countries [3, 4]. In Sub-Saharan Africa, the prevalence of RE was approximately 46% [5, 6]. Hospital- based studies conducted in Gondar, Borumeda, and Arba Minch, Ethiopia showed that the prevalence of RE was 76.3%, 18.3%, and 27.5% respectively [7–9]. Globally, 2.2 billion people suffer from visual impairment (VI), and RE accounts for 88.4 million cases [10]. RE is the most common cause of visual impairment worldwide. Around 50% of the world’s vision impairment and blindness caused by RE are found in Asia [11]. According to Ethiopian national surveys, RE accounts for 33.4% of low vision and is the sec- ond leading cause of VI after cataracts [12]. RE can undermine individual performance, reduce social participation, and reduce employability. RE can also increase the economic burden on the country. Approximately US$202 billion is attributed to VI due to uncor- rected RE [13]. Those above conditions result in a reduced quality of life for individuals with RE [11]. Among the top 20 causes of disability-adjusted life years, RE is one of the four non-fatal disorders [14]. Some of the factors, such as age, educational level, history of cataract surgery, family history of RE, and history of diabetes mellitus were associated with the development of RE, as reported by studies [15, 16]. Although RE cannot be completely prevented, it can be treated easily. RE can be treated with spectacle, contact lens, or refractive surgery [17]. To address the issue, multi-tiered points of delivery for refractive care services and optical dispensing units were established, together with highly qualified optometry personnel [18]. Ethiopia launched the Vision 2020 global initiative to develop a comprehensive and sustain- able eye care system that will eliminate the major causes of avoidable blindness [19]. The increasing prevalence of RE in both developed and developing nations remains an urgent public health problem that needs serious attention [10, 11]. Although RE is prevalent across the world, there is limited evidence on the burden and predictors of RE among adults at the community level in Ethiopia. Hence, conducting the prevalence and associated factors gives updated information that contributes to reducing the burden of RE. In addition, this study can be used as baseline information for policymakers, the Ministry of Health, and other researchers to allocate resources for eye care service delivery. PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 2 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia Method and materials Study design A community-based cross-sectional study was conducted. Study area and period The study was conducted in Hawassa City, South Ethiopia from May 8, 2023, to June 8, 2023. Hawassa is the capital city of the Southern Nations, Nationalities, and Peoples Region as well as the Sidama Regional State. It is located 273 kilometers (170 miles) south of Addis Ababa. According to the Ethiopian National Housing and Census Statistical Agency, the population of Hawassa city administration is expected to be 403,025 people, and out of this, 266,331 peo- ple live in the urban with an estimated household of 63,412 [20]. There are 20 kebeles (The smallest administrative unit of Ethiopia, contained within a woreda) in the city. Five govern- ment health centers and four hospitals are found in Hawassa City. In general, there are four private eye clinics and one comprehensive, specialized hospital that provides a comprehensive eye care service that serves more than 16 million people in the catchment area. In addition, there is one general hospital that provides eye care services. Source and study population All adults who lived in Hawassa City were the source population and all adults aged �18 years who lived for at least 6 months in households of selected kebeles in Hawassa City were the study population. Inclusion and exclusion criteria All adults aged �18 years who lived for at least 6 months in households of selected kebeles in Hawassa city were included in the study and adults aged �18 years with ocular comorbidities (like corneal opacity, and active eye infection) that obscure retinoscopy reflex during the refraction, adults aged �18 years with an absolute blind eye, adults aged �18 years who were unable to respond due to serious illness, and mental illness were excluded from the study. Sample size and sampling procedure Sample size determination. A single population proportion formula was used by consid- ering the following assumptions: n ¼ ðZa=2Þ2Pð1 (cid:0) PÞ d2 Where; n = sample size Z = Value of z statistic at 95% confidence interval = 1.96 α (level of significance) = 5% P = proportion of RE from a study in Eritrea 6.4% [21] d = allowable maximum margin of error 2% Sample size ¼ 3:84 � 0:064 � 0:936 0:022 ¼ 576 Design effect = 1.5 and Non response rate = 10% The final sample size was 951 PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 3 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia Fig 1. Schematic presentation of sampling technique and procedures for prevalence and associated factors of refractive error among adults in Hawassa City, South Ethiopia, 2023. https://doi.org/10.1371/journal.pone.0298960.g001 Sampling technique and procedure. In Hawassa city, there are 20 kebeles. A multistage sampling technique was employed to select a representative sample from the city. The list of the total of kebeles was obtained from the Hawassa city administration. The four kebeles were chosen by lottery using simple random sampling. The selected four kebeles contained 12,363 of the city’s total households (63,412). The appropriate household was then picked by system- atic random sampling with a K interval after the sample size was proportionally assigned based on the household size of each selected kebele Fig 1. The K interval was calculated by dividing the number of total households in the selected kebele by the total sample size (i.e., 12,363 / 951; K = 13). Then, at random, we chose a number between 1 and 13 to choose the first family to be included in the sample, and every 13th household was included after that. For families with more than one person eligible for the study, a lottery approach was used to choose study par- ticipants. When the eligible individual was not present at the time of data collection, the resi- dence was revisited twice. When there were no eligible persons who met the inclusion criteria in the selected household, a household listed immediately was evaluated. Operational definitions RE was defined as a spherical equivalent of > +0.50 or < -0.50 diopter in either eye on subjec- tive refraction. Myopia was defined as a spherical equivalent of < -0.50 D. High myopia was defined as a spherical equivalent of > -6.00 D [22]. Hyperopia was defined as a spherical equiv- alent of > +0.50 D. Astigmatism was defined as cylinder power > 0.50 D, without taking the PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 4 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia direction of the axis into account [23]. Smoking was defined as those who smoked one stick of cigarette within the last month [24]. Sleeping Duration was defined as a longer duration when an individual sleeps for 6 hours or more and a short duration when an individual sleeps for less than 6 hours [25]. History of cataract surgery was defined as the examiner, facing the patient, shining the light source on the patient’s eye to see Purkinje’s reflexes like small shining bubbles. Regular use of electronic devices was defined as using mobile phones or computers, and other electronic devices at least once a day for at least two hours [26]. Family history of RE was defined as a family member (mother, father, brother, and sister) of RE diagnosed by pro- fessionals or any spectacle use [27]. History of diabetes mellitus and hypertension was defined if the individual has/had a diagnosed diabetic mellitus/ hypertension or undergoing anti-dia- betes mellitus/antihypertensive treatment [28]. Data collection tools, procedures, and quality control Data collection tools, procedures. In this study, data were collected in three sections which were face-to-face interviews, ocular examinations, and refraction procedures. The data were collected by five qualified and well-trained Optometrists. A brief explanation of the pur- pose of the study was provided then verbal informed consent was obtained before collecting the information. An electronic data collection tool called Kobo Toolbox version 2022.4.4 was used to collect the data. A pre-tested and semi-structured interviewer-administered question- naire adapted from previous studies [9, 29, 30] was used to conduct the data collection. The questionnaires consist of several questions to assess socio-demographic characteristics, behav- ioral factors, systemic co-morbidity, and clinical factors (S1 File). One supervisor (MSc in Clinical Optometry) from Hawassa University supervises the data collector every day during the data collection time. Ophthalmic examination. Following the interview, all study participants received an ophthalmic examination and refraction. Optometrists performed ophthalmic examinations, which began with a VA test. Monocular and binocular unaided VA, and VA after refractive correction were measured using reduced Snellen acuity charts measured at 3 meters under normal illumination. When participants could not see a letter at 3 meters their VA was tested by reducing the testing distance and when the participant could not see letters at 1 meter, VA was determined by counting fingers, hand motion, light perception, and no light perception. Following the recording of the VA, a torch was used to inspect for the presence of any corneal opacity, cataracts, or pseudophakia/aphakia. Finally, the optometrist set up a semi-dark room within the participant’s home for the static retinoscopy technique and retinoscopy was performed for each study participant. Objective refraction was performed using streak retinoscopy. The objective retinoscopy result was then refined using monocular subjective refraction. Subjective refraction was then recorded for each eye. Finally, the spherical equivalent was calculated for the result of subjective refraction. Study participants with a spherical equivalent of > +0.50 or < -0.50 diopter in either eye were categorized as having RE. Finally, for individuals with refractive problems, a spectacle pre- scription was supplied to the participant. Data quality control To ensure the consistency of the data, the questionnaire was translated from English to Amharic and back again. A pre-tested Amharic version of semi-structured questions was used to ensure the reliability of the questionnaires. Before collecting data, a pretest of 48 participants (5% of the sample size) was conducted in Yirgalem, Sidama, to ensure that the questionnaire was clear, acceptable, and understandable. PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 5 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia To increase the quality of the data, the data collectors and one supervisor received one day of training before the actual data collection day. Training on how to utilize the Kobo Toolbox, examination procedures, and interviewing techniques was given. The supervisor closely moni- tored the data collection activities in the field and ensured that the collected data was complete and consistent. Data processing and analysis The data collected in the Kobo Toolbox was checked for completeness and consistency. The data were exported to Microsoft Excel, cleaned, and coded with SPSS 26, and then further anal- ysis was conducted by using SPSS. Descriptive statistics like percentage and frequency were used to summarize demographic data and categorical variables. A binary logistic regression was used to identify factors related to RE. In the bivariable analysis, variables having a P-value of less than 0.2 were entered on the multivariable logistic regression (S2 File). The variance inflation factor (VIF) and tolerance test have been used to determine whether the independent variables were multi-collinear, and a value less than 1.05 with a tolerance less than 0.955 was found. The model’s fitness was evaluated using the Hosmer and Lemeshow goodness of fits, and the P-value was 0.76. To demonstrate the relationship between the inde- pendent and dependent variables, an adjusted odds ratio with a 95% confidence interval was computed. A P-value of less than 0.05 was considered statistically significant. Result Socio-demographic characteristics of study participants A total of 894 participants were involved in the study, the remaining 57 individuals were non- respondents making a response rate 94.1%. 3 cases with corneal opacity and 2 cases with infec- tion were excluded during the study. The median age of the participant was 37 years, with an interquartile range (IQR) (28–50). Out of 894 study participants, 466 (52.1%) were male, (23.0%) were private employees and 478(53.5%) had college/university educational status (Table 1). Table 1. Socio-demographic characteristics of study participants among adults in Hawassa City, South Ethiopia, 2023 (n = 894). Variables Age (year) Sex Educational status Occupational status Categories 18–28 29–37 38–50 51–80 Male Female Unable to read and write Read and write Primary school Secondary school College/ University Unemployed Farmer Housewife Student Merchant Government employee Private employee n = sample size https://doi.org/10.1371/journal.pone.0298960.t001 Frequency (N) Percent (%) 238 197 242 217 466 428 15 63 71 267 478 106 22 128 93 140 199 206 26.6 22.0 27.1 24.3 52.1 47.9 1.7 7.0 7.9 29.9 53.5 11.9 2.4 14.3 10.4 15.7 22.3 23.0 PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 6 / 14 PLOS ONETable 2. Systemic comorbidities, clinical and behavioral characteristics of study participants among adults in Hawassa City, South Ethiopia, 2023 (n = 894). Prevalence and associated factors of refractive error among adults in South Ethiopia Categories Frequency(N) Percent (%) Variables Diabetes mellitus Hypertension Eye examination Duration of eye examination(year) (n = 380) Mode of an eye examination (n = 380) Family history of RE History of wearing spectacle Having cataract History of cataract surgery Smoking Yes No Yes No Yes No >3 � 3 Home Traditional medicine Hospital/clinic Yes No Yes No Yes No Yes No Smoker Non-Smoker Sleeping duration (hour) Longer duration shorter duration Regular use of electronic devices Yes No https://doi.org/10.1371/journal.pone.0298960.t002 69 825 58 836 380 514 27 353 2 3 375 124 770 24 870 85 809 30 864 32 862 608 286 201 693 7.7 92.3 6.5 93.5 42.5 57.5 7.1 92.9 0.5 0.8 98.7 13.9 86.1 2.7 97.3 9.5 90.5 3.4 96.6 3.6 96.4 68.0 32.0 22.5 77.5 Systemic comorbidities, clinical and behavioral characteristics of study participants This study reported that 69(7.7%), 58(6.5%), and 124(13.9%) of the study participants had a history of diabetic mellitus, hypertension, and a family history of RE respectively. Besides, reg- ular use of electronic devices was found among 201(22.5%) of the study participants (Table 2). Prevalence of RE Among the total of 894 participants, 110 (12.3%) [95% CI: 10.2, 14.5%] had a RE. The preva- lence of uncorrected RE was 11.1%. This study revealed that from the total RE 43.8% of them had myopia and 2.7% had high myopia (Fig 2). Factors associated with RE Bivariable and multivariable binary logistic regression was performed to identify the associated factors with RE. In bivariable binary logistic regression analysis, older age, being male, regular use of electronic devices, longer sleeping duration, positive history of diabetes mellitus, family history of RE, having cataract, and history of cataract surgery were associated with RE. Those variables in the bivariable analysis that had a P-value less than 0.2 were entered into a multivariable binary logistic regression. A family history of RE, regular use of electronic devices, a positive history of diabetes mellitus, and a history of cataract surgery were associated with RE in multivariable logistic regression with a P-value of less than 0.05. The odds of having RE among participants aged 51–80 years were two times more likely compared with participants aged 18–28 years (AOR = 2.08, 95% CI: 1.01–4.31). PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 7 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia Fig 2. Types of refractive error among adults in Hawassa City, South Ethiopia, 2023 (n = 110). https://doi.org/10.1371/journal.pone.0298960.g002 Regular use of electronic devices was also significantly associated with RE. The odds of having RE among participants with regular use of electronic devices were 3.64 times higher compared to participants who had no regular use of electronic devices (AOR = 3.64, 95% CI: 2.25–5.91). The odds of having RE among participants who had a positive history of diabetes mellitus were 4.02 times higher than those who had no history of diabetes mellitus (AOR = 4.02, 95% CI: 2.16–7.48). The odds of having RE among Participants who had a family history of RE were 2.71 times more likely than participants who had no family history of RE (AOR = 2.71, 95% CI: 1.59– 4.61). The odds of having RE among participants who had a history of cataract surgery were 5.17 times higher compared to participants who had no history of cataract surgery (AOR = 5.17, 95% CI: 2.19–12.4) (Table 3). Discussion The prevalence and associated factors of RE were assessed in this community-based cross-sec- tional study among adults in Hawssa City, South Ethiopia. The finding of this study revealed that the prevalence of RE was 12.3% (95% CI: 10.2– 14.5%). This result was in line with the study conducted in Bogota, Colombia 12.5% [29]. Both studies used similar study designs, which may account for this similarity. On the other hand, the finding of this study was lower than studies conducted in Gondar Northwest Ethiopia 35.6% [31], Borumed Ethiopia 18.3% [7], and London United Kingdom 54% [32]. In this case, the discrepancy may be due to the socio-demographic characteristics of the study population and the study setting. As an example, the study done in Gondar was con- ducted among pregnant women. During pregnancy, corneal curvature and central corneal thickness increase substantially, while intraocular pressure decreases. Those physiological changes contribute to RE, which may lead to an increase in the prevalence of RE [33]. Further- more, the study in Borumed, Ethiopia, was hospital-based. Given that most patients go to the hospital for vision difficulties, this could overestimate the magnitude of RE. Furthermore, a study in London, United Kingdom, was conducted among older persons, as age causes struc- tural changes in the ocular system, which increase the magnitude of RE [34]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 8 / 14 PLOS ONETable 3. Bivariable and multivariable binary logistic regression analysis for factors associated with RE among adults in Hawassa City, South Ethiopia, 2023 (n = 894). Prevalence and associated factors of refractive error among adults in South Ethiopia Variable Age (year) 50–80 38–50 29–37 18–28 Sex Male Female Regular use of electronic devices (hours) Yes No Sleeping duration (hour) Longer Shorter Diabetes mellitus Yes No Family history RE Yes No Having cataract Yes No History of cataract surgery Yes No RE Yes 41 26 23 20 64 46 51 59 85 25 30 80 31 79 17 93 16 94 NO 176 216 174 218 402 382 150 634 523 261 39 745 93 691 68 716 14 770 COR: crude odds ratio AOR: adjusted odds ratio https://doi.org/10.1371/journal.pone.0298960.t003 COR (95%CI) AOR (95%CI) 2.53(1.43–4.49) 1.31(0.71–2.42) 1.44(0.76–2.70) 1.00 2.08(1.01–4.31) 1.51(0.76–2.99) 1.78(0.89–3.57) 1.00 1.32(0.88–1.98) 1.00 1.18(0.75–1.85) 1.00 P-value 0.047 0.237 0.100 0.457 3.65(2.41–5.53) 1.00 3.64(2.25–5.91) 1.00 < 0.001 1.69 (1.06–2.71) 1.00 1.38(0.83–2.30) 1.00 0.208 7.16 (4.2–12.1) 1.00 4.02(2.16–7.48) 1.00 < 0.001 2.91 (1.82–4.65) 1.00 2.71(1.59–4.61) 1.00 < 0.001 1.92 (1.08–3.41) 1.00 1.60(0.79–3.25) 1.00 0.187 9.36 (4.42–19.79) 1.00 5.17(2.19–12.4) 1.00 < 0.001 The current study’s results were greater than those obtained in Eritrea 6.4% [35], Kenya 7.4% [36], and Durban South Africa [37]. This difference may be due to variations in the method they employed and cut-off points for RE. The study in Eritrea employed a definition of RE with a VA of 6/12 or worse, which excluded participants who had RE with a VA better than 6/12, which may reduce the prevalence of RE. A study done in Durban, South Africa only included 15- to 24-year-olds, but this study included all persons 18 years and above. Several ocular diseases (diabetic retinopathy, glaucoma, and cataracts) and structural changes (retinal degeneration) in the ocular system are common among older adults and thus lead to RE. Since ocular growth stabilizes at older ages, RE risk factors will likely differ from those of younger ages due to ocular growth stability and slight changes in biometrics [34]. Because of age-related ocular disorders that increase the prevalence of RE, the above condition causes an increase in RE. Furthermore, the result of a study conducted in Bangladesh 4.7% [38] was lower than in this study; this discrepancy might be caused by the difference in the study population. The odds of having RE among participants who had a history of diabetes mellitus were 4.02 times higher compared to participants who had no diabetes mellitus. This result is comparable with the studies conducted in Borumed, Ethiopia, and Yunnan, China [7, 39]. Clinical research has demonstrated that transient RE shifts are related to blood glucose levels. Increasing glucose may decrease the osmotic pressure of aqueous humor, leading to a flow of water from the aqueous humor into the lens, resulting in functional and morphologic changes in the lens. As a result of changes in lens refractive index, diabetics are more likely to develop RE [40, 41]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 9 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia The odds of having RE among participants who underwent cataract surgery were 5.17 times higher than those participants who had no history of cataract surgery. This result was supported by the study conducted in South India [42]. Cataract surgery induces RE in different ways, which can be in preoperative (errors in biometry parameters, Pre-existing systemic & ocular comorbidities, Pre-existing uncorrected corneal astigmatism >1.00 DC), intraoperative (surgical variations of incision size, incision location, Use of sutures), or postoperative (shift in IOL position) conditions [43–45]. The odds of having RE in participants who had a family history of RE was 2.71 times higher than in participants who had no family history of RE. This result is comparable with the studies conducted in Arba Minch, Ethiopia, and East China [9, 30]. Studies have found considerable relationships between first-degree relatives’ RE. Research has shown that RE aggregates signifi- cantly within families. It has been reported that the heritability of RE ranges from 50% to 90% within various populations [46, 47]. The odds of having RE among participants who have regular use of electronic devices were 3.64 times higher than participants who have no regular use of electronic devices. This result was consistent with a study conducted in Gondar, Northwest Ethiopia, and Rohtak India [48, 49]. Staring at the computer for an extended period causes prolonged accommodation and muscle fatigue, which might result in a transient shift in the refractive status of the eye [50]. In addition, staring at the computer for an extended time will cause dry eye, which will affect the refractive power of the cornea [51]. Strengths and limitations of the study Both objective and subjective full refraction procedure was performed to determine the refrac- tive status of the eye. As the study is community-based it is more representative than institu- tion-based studies. A cross-sectional study design does not reveal a cause-and-effect relationship between dependent and independent variables. Recall bias was another issue due to the nature of the questionnaire to assess family history of RE and smoking. Conclusion As a conclusion, the prevalence of RE in this study area was 12.3%. A family history of RE, reg- ular use of electronic devices, a positive history of diabetes mellitus, and a history of cataract surgery were significantly associated with RE. Since most of these associated factors are modi- fiable (regular use of electronic devices, a positive history of diabetes mellitus, and a history of cataract surgery), eye care professionals should primarily focus on the prevention of these modifiable causes. To mitigate the burden of RE, it is recommended that eye care professionals prioritize early screening of individuals with diabetes. From a perspective of minimizing post- operative RE following cataract surgery, there is a need to enhance preoperative evaluation and intraoperative care. Supporting information S1 File. English version of questionnaire. (DOCX) S2 File. Data used for analysis including data on refractive error and associated factors. (SAV) PLOS ONE \| https://doi.org/10.1371/journal.pone.0298960 March 25, 2024 10 / 14 PLOS ONEPrevalence and associated factors of refractive error among adults in South Ethiopia Acknowledgments The authors would like to acknowledge study participants for their participation in the study and also, we would like to acknowledge data collectors (optometrists). Ethical consideration The University of Gondar, College of Medicine and Health Sciences, School of Medicine, and the Ethical Review Committee provided us with ethical approval, approval ID 06/01/622/ 2015EC, and the regional administrative office gave us a letter of support. All study partici- pants provided verbal informed consent after they were provided with an information sheet receiving a full explanation of the study’s objective and being informed that they have the right to question and withdraw from the study at any moment during data collection. this was approved by the IRB. There was no reward or risk for the study participants who were chosen. By avoiding any personal identifiers in the data-gathering tools and using password-protected data on a com- puter, confidentiality was maintained. In addition, the collected data on the data collector’s phone was discarded after sending the daily collected information to the principal investigator to maintain confidentiality. Author Contributions Conceptualization: Marshet Gete Abebe, Mikias Mered Tilahun. Data curation: Marshet Gete Abebe, Abiy Maru Alemayehu, Minychil Bantihun Munaw, Henok Biruk Alemayehu. Formal analysis: Marshet Gete Abebe, Henok Biruk Alemayehu. Methodology: Marshet Gete Abebe, Henok Biruk Alemayehu. Software: Marshet Gete Abebe, Abiy Maru Alemayehu, Minychil Bantihun Munaw, Henok Biruk Alemayehu. Supervision: Abiy Maru Alemayehu. Validation: Minychil Bantihun Munaw, Mikias Mered Tilahun. Visualization: Abiy Maru Alemayehu, Mikias Mered Tilahun, Henok Biruk Alemayehu. 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10.1371_journal.pone.0290569	RESEARCH ARTICLE Measuring facial mimicry: Affdex vs. EMG Jan-Frederik WestermannID, Ralf Scha¨ fer, Marc Nordmann, Peter Richter, Tobias Mu¨ ller, Matthias Franz Medical Faculty, Clinical Institute for Psychosomatic Medicine and Psychotherapy, University Hospital of the Heinrich-Heine-University, Du¨sseldorf, Germany westermannjan@yahoo.de Abstract Facial mimicry is the automatic imitation of the facial affect expressions of others. It serves as an important component of interpersonal communication and affective co-experience. Facial mimicry has so far been measured by Electromyography (EMG), which requires a complex measuring apparatus. Recently, software for measuring facial expressions have become available, but it is still unclear how well it is suited for measuring facial mimicry. This study investigates the comparability of the automated facial coding software Affdex with EMG for measuring facial mimicry. For this purpose, facial mimicry was induced in 33 sub- jects by presenting naturalistic affect-expressive video sequences (anger, joy). The response of the subjects is measured simultaneously by facial EMG (corrugator supercilii muscle, zygomaticus major muscle) and by Affdex (action units lip corner puller and brow lowerer and affects joy and anger). Subsequently, the correlations between the measure- ment results of EMG and Affdex were calculated. After the presentation of the joy stimulus, there was an increase in zygomaticus muscle activity (EMG) about 400 ms after stimulus onset and an increase in joy and lip corner puller activity (Affdex) about 1200 ms after stimu- lus onset. The joy and the lip corner puller activity detected by Affdex correlate significantly with the EMG activity. After presentation of the anger stimulus, corrugator muscle activity (EMG) also increased approximately 400 ms after stimulus onset, whereas anger and brow lowerer activity (Affdex) showed no response. During the entire measurement interval, anger activity and brow lowerer activity (Affdex) did not correlate with corrugator muscle activity (EMG). Using Affdex, the facial mimicry response to a joy stimulus can be measured, but it is detected approximately 800 ms later compared to the EMG. Thus, electromyogra- phy remains the tool of choice for studying subtle mimic processes like facial mimicry. 1 Introduction Mimicry describes the imitation of facial expressions, intonation, and body posture between two interaction partners [1]. Facial mimicry is of particular importance because it has a specific emotional meaning as it represents a congruent mimic response to an emotional facial expres- sion [2]. It is detectable in the electromyogram (EMG) after only 200–400 ms [3, 4] and occurs unconsciously and automatically [5]. Facial mimicry has been shown to be triggered even when affective cues are perceived only unconsciously [4, 6]. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Westermann J-F, Scha¨fer R, Nordmann M, Richter P, Mu¨ller T, Franz M (2024) Measuring facial mimicry: Affdex vs. EMG. PLoS ONE 19(1): e0290569. https://doi.org/10.1371/journal. pone.0290569 Editor: Peter A. Bos, Leiden University, NETHERLANDS Received: December 23, 2022 Accepted: August 9, 2023 Published: January 2, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0290569 Copyright: © 2024 Westermann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All Data files are available from the Figshare database: Links: S1_Measuring Facial Mimicry Affdex vs. EMG_Psychometric Data https://figshare.com/ articles/dataset/Untitled_ItemMeasuring_Facial_ PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 1 / 22 PLOS ONEMimicry_Automated_Facial_Coding_vs_EMG_ Psychometric_Data/21777281 S2_Measuring Facial Mimicry Affdex vs. EMG_EMG Data https:// figshare.com/articles/dataset/Measuring_Facial_ Mimicry_Automated_Facial_Coding_vs_EMG_ EMG_Data/21777275 S3_Measuring Facial Mimicry Affdex vs. EMG_Affdex Data https:// figshare.com/articles/dataset/Measuring_Facial_ Mimicry_Automated_Facial_Coding_vs_EMG_ Affdex_Data/21777200 S4_Measuring Facial Mimicry Affdex vs. EMG_Code for Statistics in R https://figshare.com/articles/software/S4_ Measuring_Facial_Mimicry_Affdex_vs_EMG_ Code_for_Statistics_in_R/22639150. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Measuring facial mimicry: Affdex vs. EMG The role of facial mimicry in the recognition of others’ emotions is controversial. In a widely accepted concept, facial mimicry leads to emotional contagion through a feedback mechanism [7]. This is thought to improve affect perception and thus the ability to empathize. This concept has been further developed as part of an embodiment approach to emotion rec- ognition [8]. According to this, facial mimicry facilitates the decoding of observed emotions [9]. This hypothesis is supported by the fact that it has been shown that emotion recognition can be impaired when the subject’s facial mimicry is impaired. [10, 11]. Similarly, an increase in mimic muscle activity (e.g., due to a task such as biting on a pen or holding a pen with the lips), may result in lower accuracy in facial expression recognition. In contrast, there is also evidence that facial mimicry does not improve emotion recognition [12, 13] and no consistent evidence for the feedback hypothesis could be found in reviews [14]. A broader consensus exists for the assumption that mimicry has a positive influence on social relationships [15]. It has been demonstrated that facial mimicry occurs to varying degrees depending on the situa- tion in which the subject finds himself. Thus, the probability for facial mimicry to occur is higher when there is a desire to cooperate and lower in a competitive situation [16, 17]. According to this assumption, facial mimicry can be understood as an affiliative behavior and supports the establishment and maintenance of interpersonal relationships.[18–20]. Interac- tion partners are perceived as more likable when they subtly imitate the others behavior [21]. This makes it easier for an individual to receive acceptance in a group. The individual thus sat- isfies his or her need to belong to the group and, at the same time, the collective achievement of relevant goals is facilitated [22]. Facial mimicry occurs with varying likelihood depending on the valence of the affect. Consistent with an attachment-reinforcing function, smiling is more frequently imitated as an expression of a happy facial expression [20]. In encounters between strangers, a frown (7%) is imitated significantly less often than a smile (53%) [23]. The gold standard for measuring facial mimicry is EMG measurement of affect-relevant mimic target muscles. Measurement of EMG activities of the zygomaticus and corrugator muscles is commonly used to distinguish between hedonic and anhedonic affects [24–26]. Activation of the corrugator muscle results in a frown and the contraction of the zygomaticus muscle results in a smile. Dimberg [24] showed that the presentation of happy faces led to an increase in EMG activity in the zygomaticus muscle and the presentation of angry faces led to an increase in EMG activity in the corrugator muscle. While zygomaticus and corrugator mus- cles are good indicators of the valence of mimicry, it has been shown that many emotions are subject to a specific pattern of muscle activity and that this pattern is also reflected in the mim- icry response [27]. It has also been shown that the mimic response to angry and happy faces results in visible congruent changes in facial expression [28]. This allows to measure facial mimicry using the Facial Action Coding System (FACS) [29]. The FACS is currently the most comprehensive method for coding facial expressions. Using videotaped faces, specially trained human coders can encode so-called action units. According to FACS, there are a total of 44 action units, with each action unit describing a specific facial mimic activity. Ekman assumes that certain combi- nations of action units can be used to infer the basic affects of fear, disgust, joy, sadness, sur- prise, and anger. From this, Ekman derived his own coding system, the Emotional Facial Action Coding System (EMFACS) [30]. Although EMFACS has never been published in a peer review process, it is widely used. Besides the aspects mentioned above, the measurement of facial mimicry is also relevant from a clinical perspective. Some mental disorders that lead to impaired interpersonal com- munication and thus to distress and further mental comorbidities are associated with altered facial mimicry. For example, slowed [31] or decreased [32] facial mimicry has been demon- strated in patients with autism on EMG. Individuals with alexithymia [33] and Parkinson´s PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 2 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG disease [34] also exhibit a reduced facial mimicry response. Other mental disorders with impaired facial mimicry include schizophrenia [35, 36], borderline personality disorder [37], and depression [38]. The extent to which impaired facial mimicry moderates the severity and distress of these disorders is debated. As already described, the measurement of facial mimicry is technically demanding. Mea- surement by EMG requires a complex measuring apparatus and experience in the application and interpretation of EMG signals. The analysis of video footage by FACS requires specially trained FACS raters and is very time-consuming. Recent methods for machine-learning assis- ted videographic measurement of mimic activity promise time-efficient and easy-to-interpret analysis. This could open up a large area of application in affect research. The software Affdex (developed by Affectiva) investigated in this study is used on the iMo- tions platform. Affdex is one of the most widely used automated facial coding software. It promises ease of use and synchronization with other psychophysiological measures. It can be used to synchronously measure and evaluate various psychophysiological signals. Should a val- idation for the measurement of facial mimicry be successful, complex experimental paradigms could thus be performed in a relatively user-friendly manner. Affdex is based on a machine learning principle. A database of approximately 27,000 human FACS-encoded videos of affect- expressive faces is available. To evaluate the likelihood of activity of action units of new videos, Affdex compares them with the database [39]. In a further step, the combined activity of spe- cific action units is used to derive the probability of the presence of a basic affect based on EMFACS [39]. However, the exact operation of the underlying algorithm is not disclosed, making it difficult for researchers to examine the software in detail. There are already some studies available that have investigated different Automated Facial Coding (AFC) software concerning to certain features. Sto¨ckli et al. [40] compare Affdex with the AFC software Facet and conclude that AFC has difficulties in detecting subtle affects. In another study, subjects were asked to mime happy and angry faces while both Affdex and EMG activity of zygomaticus and corrugator muscle were measured. A high positive correla- tion was found between the probability of joy and zygomaticus muscle activity and between anger and corrugator muscle activity [41]. While strong prototypical affect expressions were measured here, Ho¨fling et al. [42] compare the ability of the AFC software Facereader (Nol- dus) with EMG to measure subtle affect expressions. Here, subjects were not asked to imitate the affect stimuli, but to behave passively. There was a congruent EMG activity for anger and joy, indicating a facial mimicry response, whereas the Facereader software had difficulty mea- suring the negative valence for the anger stimuli. The ability of Affdex to measure facial mimicry has not yet been investigated. Furthermore, there have been no studies to date on the extent to which the AFC measurement for the lip cor- ner puller and brow lowerer action units correlate with the EMG activity of the zygomaticus and corrugator muscles. This question is of interest because these muscles represent the under- lying anatomical structures for the action units. The present study attempts to answer these open questions. The aim of this study is to compare electromyography with the Affdex AFC software for measuring facial mimicry response to angry and happy faces. For this purpose, a healthy cohort was shown videos of faces dynamically accumulating affect over time. The stimulus material consisted of video sequences of adult faces showing the basic affects of anger and joy. EMG measurements of the zygomaticus and the corrugator mus- cles and Affdex measurements were performed simultaneously. Subsequently, EMG activity of the zygomaticus and corrugator muscles was directly compared to the FACS-oriented Affdex action units lip corner puller and brow lowerer [43]. This allows for a direct comparison of measurement sensitivity, as these action units represent the visible correlates to the underlying PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 3 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG muscles [29]. Affdex uses additional information from the face for the measurements besides the lip corner puller and brow lowerer action units. Therefore, EMG activity was additionally compared with affect probabilities for joy and anger measured by Affdex. We expected a positive correlation between the EMG-activity of zygomaticus muscle and action unit lip corner puller for affect joy. We also expected a positive correlation between the EMG-activity of corrugator muscle and action unit brow lowerer for affect anger [41]. How- ever, there is also evidence that measuring subtle affect expressions may be more difficult for the Affdex software [40, 42]. 2 Materials and methods This study is part of a study project on differences in facial responsiveness of alexithymic and non-alexithymic subjects using EMG and Affdex [33]. In the study project, the mimic responses to video sequences with dynamically animated affective facial expressions of adults and children (anger, joy, disgust, surprise, sadness) were investigated. In the present study, only the response of the healthy control group to the adult stimuli of joy and anger was exam- ined. The extent to which the EMG activity of the zygomaticus and corrugator muscles corre- lated with the Affdex measurements of lip corner puller, joy, brow lowerer, and anger was investigated. 2.1 Psychometric instruments To ensure a psychologically healthy subject sample, exclusion criteria were screened by two structured interviews (Structured Clinical Interview for DSM-IV (SCID), Toronto Structured Interview for Alexithymia (TSIA)), questionnaires (Short version of the Autism Spectrum Quotient (AQ-short), Beck Depression Inventory II (BDI-II), Patient Health Questionnaire (PHQ-9), 20-item Prosopagnosia Index (PI-20), Toronto Alexithymia Scale (TAS-20)), and functional tests in the laboratory immediately before the start of the experimental part of the study. The Structured Clinical Interview for DSM-IV (SCID) [44] used to identify psychiatric diagnoses according to the Diagnostic and Statistical Manual Fourth Edition (DSM-IV). The SCID is divided into two parts. The first part captures DSM-IV Axis I disorders (SCID-I) and the second part captures DSM-IV Axis II personality disorders (SCID II). In this study, only schizoid personality traits were recorded for the SCID-II, thus excluding schizoid traits in the subjects. The Toronto Structured Interview for Alexithymia (TSIA) [45] is an instrument used for clinical and scientific purposes to identify alexithymic disorders, the regulation and processing of affects. In each case, the respondent is asked to name a corresponding situation from his or her life for different cases. A detailed coding catalog allows a three-level assessment of alexithy- mia development per item. Only non-representative norm data are available to serve as a guide. Reliability estimates of intraclass correlation correspond to 0.90 (p < 0.01) and reliabil- ity estimates to 0.88 (p < 0.01). A combined use of TAS and TSIA is suggested for effective assessment of alexithymia [46]. The short version of the Autism Spectrum Quotient (AQ-short) [47] consists of the three factors: interaction and spontaneity, imagination and creativity, and communication and reci- procity. The internal consistency of the factors ranged from 0.65 to 0.87, and the sensitivity analysis resulted in a cut-off value of 18. The Beck Depression Inventory II (BDI-II) [48] is a self-report questionnaire with 21 multi- ple-choice questions. Cut-offs for BDI-II are as follows: 0–13 points no or minimal depressive symptoms, 14–19 points mild, 20–28 points moderate, 29–63 points severe depressive PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 4 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG symptoms. Retest reliability during one week is r = 0.93 with internal consistency in clinical and non-clinical samples of 0.84 � α � 0.94. The Patient Health Questionnaire (PHQ-9) [49] is a nine-item component of the PHQ. Each item can be scored as 0 (not at all), 1 (on a single day), 2 (more than half of the days), or 3 (almost every day). Overall, the PHQ-9 score ranges from 0 to 27. Major depression can be diagnosed if any of the items indicate depressed mood and 5 or more items have a score of 2 or higher. Internal reliability was Cronbach’s α = 0.89 in a representative primary care study. The 20-item Prosopagnosia Index (PI-20) [50] is used to identify prosopagnosia traits. The index is a self-report instrument used to assess experience with face recognition. It is scored using a five-point scale (strongly agree to strongly disagree). The Cronbach’s α of 0.96 shows a high internal consistency of the 20 items. Cut-off scores are 65–74 for mild, 75–84 for moder- ate and 85–100 for severe developmental prosopagnosia. The Toronto Alexithymia Scale (TAS-20) [51] is a questionnaire that refers to people who tend to minimize emotional experience and focus attention externally and who have trouble describing and identifying emotions. The TAS-20 uses cut-off scoring � 51 = nonalexithymia, � 61 = alexithymia. Scores of 52 to 60 = possible alexithymia [52]. It is recommended to use the 33rd percentile corresponding to � 45 (threshold for being surely nonalexithymic) and the 66th percentile value corresponding to � 52 (threshold for being alexithymic) for experimental studies. To ensure correct group classification [53]. We were able to determine reliability coef- ficients Cronbach’s α = .86 for the TAS-20 from the screening sample (N = 2924). 2.2 Participants Subjects were recruited via posters and advertisements on social networks. The study proce- dure and data protection regulations were described in detail to the cted parties. Each subject received financial compensation of 25 Euro for expenses and signed an informed consent form. Subsequently, subjects accessed an online questionnaire [54] in which sociodemo- graphic variables (age, gender, siblings, education), the PHQ-9, and the TAS-20 were collected and severe neurological or psychiatric disorders were queried. Exclusion criteria were insuffi- cient knowledge of the German language, left-handedness, age under 18 or over 50 years, seri- ous medical conditions such as endocrine disorders or coronary heart disease, use of psychotropic drugs, vigilance disorders, substance abuse, visual disorders, neurological disor- ders (including neuropathy and botulinum toxin use), or psychiatric disorders. The non-alex- ithymic control group studied here was characterized by a TAS-20 sum score <45 and originally included 38 participants. For technical reasons, 5 subjects were excluded from this study. Reasons for this were misplaced Affdex measurement points due to unfavorable lighting conditions or glasses although not every subject wearing glasses had to be excluded. Thus, 33 participants between the ages of 20 and 42 years (mean age = 25.24, SD = 5.73, SE = 0.99, 22 females, 11 males) were included. The clinically defined thresholds of AQ-short (cut-off value = 18), BDI-II (cut-off value = 13), PI20 (cut-off value = 65), PHQ-9 (cut-off value = 9) and TAS-20 (cut-off value = 51) were not exceeded by any of the subjects. The results of the subjects’ psychometric tests are shown in Table 1. 2.3 Stimulus material The stimulus material consisted of video sequences of adult faces showing the five basic affects (fear, joy, sadness, surprise, anger). Each video began with a neutral face that continuously built up a maximum affect expression (apex) over 2 seconds, which was presented for one sec- ond afterwards. Original portraits of adult individuals were taken from the Karolinska Directed Emotional Faces image set [55]. Deindividualized affect-expressive portraits for each PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 5 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Table 1. Descriptive statistics, n = 33. Instrument AQ-short BDI-II PI20 PHQ-9 TAS-20 mean 4.64 2.00 34.39 2.73 31.94 sd 2.29 2.21 7.60 2.00 5.34 median 4.00 2.00 33.00 3.00 31.00 se 0.40 0.38 1.32 0.35 0.93 AQ-short, Autism-Quotient short version sum score, cut-off value = 18; BDI-II, Beck Depression Inventory II sum score, cut-off value = 13; PHQ-9, 9-item depression module of the Patient Health Questionnaire sum score, cut-off value = 9; PI20, 20 item prosopagnosia index sum score, cut-off value = 65; sd, standard deviation; se, standard error; TAS-20, 20-item Toronto Alexithymia Scale sum score, cut-off value = 51. https://doi.org/10.1371/journal.pone.0290569.t001 gender and affect (five basic affects and neutral) were developed from the most valid portraits for each affect category [56]. This was realized by a digital overlay of the individual faces and resulted in affect prototypical facial patterns of basic affects in a purified way. These averaged affect prototypical portraits served as visual source material for the creation of video sequences of each basic affect and gender. For this reason, a software based morphing algorithm was used, which generated a naturalistic affect enrichment by interpolating video frames from neu- tral to maximal affect expression within 2000 ms. The final videos show dynamic sequences of naturalistic sliding facial affect amplification (2000 ms), followed by a static presentation of the apex of each basic affect (1000 ms). Both the averaged portraits and the dynamic video sequences were created and edited by using the software package Abrasoft Fantamorph Deluxe 5. The whole process of stimulus development and the proof of validity of the dynamic stimu- lus material was demonstrated by Mu¨ller et al. [57]. Here, specific mimic responses could be detected for each basic affect. 2.4 Procedure The study was approved by the Ethics Committee of the Medical Faculty of Heinrich Heine University under the registration number 2016116024. Subjects were recruited via posters and advertisements on social networks. The study procedure and data protection regulations were described in detail to the interested parties. Each subject received financial compensation of 25 Euro for expenses and signed an informed consent form. Before starting the experiment, all participants had to pass simple functional tests for checking the reactivity and function of the facial nerve and visual perceptual ability. Subsequently, subjects completed the various psycho- metric instruments and clinical interviews (TAS-20, BDI-II, SCID, TSIA, PI20, and AQ- short). Only participants whose test scores were below the defined clinical threshold were admitted to the study. At the beginning of the experiment, subjects were shown the investiga- tion cabin and it was explained that affect-expressive faces would be presented as videos and "bodily signals" would be measured simultaneously. To this end, participants were told to watch the videos attentively and empathize with the affects shown without imitating them. The texts and images were presented on a 24-inch TFT screen (AMW) with a resolution of 1920 x 1080 (60 Hz), at a distance of 1 m. Coordination of the experiment and presentation were controlled using PsychoPy v1.82.01 software [58]. The EMG activity was measured bipo- lar with Ag/AgCl miniature electrodes (Easy Cap E220N-CS-120) according to the guidelines of Fridlund and Cacioppo [59]. The electrodes were filled with electrolyte paste and attached to the left and right zygomatic and corrugator muscle regions. In addition, two reference elec- trodes were attached to the mastoids, and two additional electrodes were attached to the PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 6 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG temporal bone region for measurement of the electrooculogram (for later correction of arti- facts). To ensure impedances below 10 k [59], the skin of the subjects was cleaned with alcohol and rubbed with an abrasive electrode paste before attaching the electrodes. After these proce- dures, the experiment was started. The stimulus material was presented for 3 seconds as described above (videos of affect-expressive faces, 2 seconds of affect enrichment, 1 second of apex). A black fixation cross on a white background was presented for a mean inter-stimulus– interval time of 5 seconds before each video presentation. The videos were presented in ran- domized order. Each subject watched 40 videos (five affects, two age groups, two genders, two runs). Subjects were filmed throughout the procedure to enable offline Affdex measurement and to monitor their cooperation in following the instructions and their compliance (vigilance, attention, involvement). The filming was performed with a digital camera, which took frontal video recordings of the subjects at a distance of 1 m. The resulting videos were first stored locally and later imported into the iMotions software and analyzed in iMotions using Affdex software. 2.5 Measurement of facial EMG EMG Data were acquired from both sides of the face (left and right) from each muscle (zygo- maticus and corrugator muscle). EMG activity during stimulus presentation was measured digitally with a sampling rate of 2000 Hz (digital polygraph EEG 1100 G; Nihon Kohden). The EMG signal was further processed offline using the Brain Vision Analyzer. A high-pass filter at 10 Hz and a low-pass filter at 1000 Hz were used. A notch filter (50 Hz) was also used to reduce electromagnetic interference. Before the start of the measurements, the subjects were asked to grimace in order to check the function of the measuring chain based on the initial EMG sig- nals. The recorded signals were stored on a hard disk for further offline analyses and parame- trization. Two independent reviewers checked the EMG measurements for artifacts (e.g. subject movement, electrode movements, current voltage drifts). Subsequently, the EMG sig- nal was rectified and integrated stepwise for each 200 ms interval over 5000 ms. For better comparability with the interstimulus interval, 1 s before stimulus presentation was included in the analysis. Due to the dynamic affect buildup during the first 2 s of the stimulus presentation and the expected delayed facial response, an additional 1 s after stimulus presentation was eval- uated. For subsequent analysis, a total of 25 200 ms intervals were used, i.e., 1 s before stimulus presentation, 3 s during stimulus presentation, and 1 s after stimulus presentation were each included in the measurement. EMG activity was determined baseline-corrected. The baseline was defined as the mean of the last 1000 ms before stimulus presentation. The preprocessed EMG data were imported into the statistical software package R for further analysis. 2.6 Measurement with Affdex Affdex is a software program for automatic recognition of facial expressions based on the Facial Action Coding System [29]. First, the Viola-Jones algorithm [60] is used to recognize faces and mark the area relevant to the facial expression with a rectangular frame. Within this frame, 34 relevant measurement points on the face are identified and marked, and histogram- of-oriented-gradient features are obtained from the relevant measurement area. Using sup- port-vector-machine classifiers trained with 10000 manually coded facial expressions, percen- tile ranks are obtained for each facial expression-relevant motion. Subsequently, affect- expressive facial expressions are inferred from the combination of different facial movements using the Emotional Facial Action Coding System and percentile ranks are also determined for the occurrence of one of the basic affects (anger, disgust, fear, joy, sadness, surprise, contempt) [61]. The FACS action units lip corner puller (action unit 12 according to FACS) and brow PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 7 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG lowerer (action unit 4 according to FACS) examined in this study were renamed Smile and brow furrow by Affectiva. However, we continue to use the official FACS nomenclature in this paper. During the EMG measurement, subjects’ faces were filmed with a video camera (C920 HD Pro Webcam), in a resolution of 1920x1080 (30 frames per second), which was located above the presentation screen, providing frontal footage of the subjects throughout the experiment. Because the camera was turned on a few minutes before the experiment began, the videos were initially trimmed to the actual length of the experiment. During initial trial measurements with Affdex, it was noticed that measurement points in the eyebrow area partially jumped into the area of the EMG electrodes that were responsible for the measurements of the corrugator muscle. To avoid erroneous measurements, the electrodes were retouched using video editing software (DaVinci Resolve, Blackmagic design), in consultation with iMotions technical sup- port. For this purpose, the electrodes were covered with skin-colored areas and tracked over the entire course of the video in every single frame (framerate: 30 FPS). As a result the covers reliably covered only the area of the electrodes during facial movements of the subjects, but did not affect any areas relevant for measurement. The resulting videos were then imported into the iMotions software (iMotions version 7.2). Within iMotions, markers were now added to the time segments in which the stimulus presen- tations took place, which enabled an assignment to the affect-expressive stimuli. The automatic marker import of iMotions often led to an inaccurate placement of the markers. Therefore, the markers were placed manually. To ensure that the markers were placed at the correct times, there was a small red light behind the subjects that was controlled by PsychoPy and turned off each time a stimulus presentation began. The correct order of the stimulus presentation could be viewed in PsychoPy. "Postprocessing" by the Affdex algorithm and subsequent data export now took place. The resulting data sets were imported into R (Version 4.1.0) for further parameterization and analysis. 2.7 Data reduction and analysis Rectified individual EMG data were integrated for each 200 ms interval over 5000 ms (1000 ms before stimulus onset and 1000 ms after stimulus termination). Integrals were then aver- aged for both sides of the face left and right, for female and male stimuli and for first and sec- ond measurement, resulting in 25 x 200 ms averaged EMG integrals for each affect and subject. The output of the Affdex data was in 40 ms intervals. These were first averaged over 200 ms. Subsequently, the data were averaged according to the EMG data for female and male stimulus material and for the first and the second measurement. For the correlation calcula- tions, first and second measurement were not averaged. No distinction was made between the left and right half of the face by Affdex. Measurements of response to children stimuli were excluded. Spearman correlations were then calculated between EMG activity and Affdex measure- ments at each measurement time point. For the presentation of the joy stimulus, the correla- tions between the zygomatic muscle and the lip corner puller action unit and the joy probability were calculated. For the presentation of the anger stimulus, the correlations between the corrugator muscle and the brow lowerer action unit and the anger probability were calculated. The correlation probabilities were tested for significance, and the significance level was set at α = 0.05. Because of repeated measures, Hochberg-Benjamini corrections were applied for p values � 0.05. The Affdex data, the EMG data, and the respective correlations were plotted graphically together in Figs 1–4. Measurement time points at which Affdex and PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 8 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG EMG were significantly correlated (α�0.05) were marked with an . In addition, cross correla- tions were performed between the following time series: Zygomaticus muscle (EMG)- lip cor- ner puller (Affdex); Zygomaticus muscle (EMG)- joy (Affdex); Corrugator muscle (EMG)- brow lowerer (Affdex); Corrugator muscle (EMG)- anger (Affdex). 3 Results Fig 1 shows the facial mimicry response of the observer represented by the course of the EMG activity of the zygomaticus muscle and the probability of the lip corner puller action unit calcu- lated by Affdex during the measurement interval when the video was displaying joy. In addi- tion, the course of the Spearman correlation between EMG and Affdex at each measurement time point is shown. Fig 2 shows the course of the EMG activity of the corrugator muscle and the probability of the brow lowerer action unit calculated by Affdex. In addition, the course of the Spearman correlation between the two values is shown. Starting with the neutral face and extending to the apex, EMG activity for both affects is congruent with the affect enhancement of the stimuli. Approximately 400 ms after stimulus onset, EMG activity increases in parallel with the increasing affect expression of the stimulus. It increases to its maximum after 2000 ms, which corresponds to the arising apex of affect expression in the stimulus videos. In addi- tion to the increase in mean values, an increase in variance is also evident. As expected, presen- tation of the joy stimulus led to an increase in zygomaticus muscle activity and presentation of the anger stimulus led to an increase in corrugator activity. The curve of the zygomaticus mus- cle reaches its maximum at a value of approx. 1800 μVx200 ms and drops to approx. Fig 1. Stimulus joy, measurement of zygomaticus muscle (EMG) and lip corner puller (Affdex). Electromyographical activity [μV integrated over 25 x 200 ms interval (μV x 200 ms) +/- standard error] of zygomaticus muscle (blue line) and Affdex measurement (% +/- standard error) for the activity of the lip corner puller action unit (red line) in response to video clips of affect expressing faces of adults for the affect joy, whiskers represent the standard error, black line represents the Spearman correlation between electromyographical activity and Affdex at each measurement point, the symbol indicates p � 0.05. https://doi.org/10.1371/journal.pone.0290569.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 9 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Fig 2. Stimulus anger, measurement of corrugator muscle (EMG) and brow lowerer action unit (Affdex). Electromyographical activity [μV integrated over 25 x 200 ms interval (μV x 200 ms) +/- standard error] of corrugator muscle (blue line) and Affdex measurement (% +/- standard error) for the brow lowerer actionunit (red line) in response to video clips of affect expressing faces of adults for the affect anger, whiskers represent the standard error, black line represents the Spearman correlation between electromyographical activity and Affdex at each measurement point, the symbol * indicates p � 0.05. https://doi.org/10.1371/journal.pone.0290569.g002 1200 μVx200 ms at the end of the measurement interval. The curve of corrugator muscle rises to a maximum value of 1300 μVx200 ms and drops to a value of approx. 500 μVx200 ms by the end of the measurement interval. Table 2 contains the results of Spearman correlation calculations between zygomaticus muscle and lip corner puller including alpha error corrected p-values. Table 3 contains the results of Spearman correlation calculations between corrugator muscle and brow lowerer including alpha error corrected p-values. The activities determined by Affdex for lip corner puller and brow lowerer, respectively, run differently. The activity of the action unit lip corner puller starts to increase 1200 ms after stimulus onset. The activity increases over a period of 2000 ms to its maximum of 6% (3000 ms after stimulus onset and 1000 ms after stimulus apex). As with the EMG measurement, the variance also increases in addition to the mean values. Thus, both the EMG of the zygomaticus muscle and the Affdex measurement of the lip corner puller action unit show an increase in activity upon presentation of the joy stimulus. However, lip corner puller activity proceeds with a latency of 800 ms compared with the course of the stimulus material and EMG activity. Brow lowerer activity calculated by Affdex shows an increase from 6% during stimulus onset to its maximum of 8% approximately 2500 ms after stimulus onset. Unlike EMG activity, there is no change in variance, suggesting that this is not a stimulus-associated increase in brow lowerer activity calculated by Affdex but a random fluctuation. The calculation of the Spearman correlation between the EMG activity for the zygomaticus muscle and the activity of the lip corner puller action unit calculated by Affdex for the affect PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 10 / 22 PLOS ONETable 2. Stimulus joy, Spearman correlation between zygomaticus muscle (EMG) and lip corner puller (Affdex). Time [ms] n-value r-value -1000 -800 -600 -400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 0.1389497 0.03903398 -0.1423648 -0.0714157 -0.1102954 -0.1694939 -0.1941923 -0.0209880 0.1098222 0.1252305 0.1943012 0.2110547 0.1922236 0.2111252 0.2271216 0.3006293 0.4328895 0.4593286 0.542301 0.5338247 0.5420904 0.467337 0.460306 0.4806718 0.4313402 https://doi.org/10.1371/journal.pone.0290569.t002 Effect size 0.01930702 0.001523652 0.02026774 0.005100206 0.01216508 0.02872818 0.03771065 0.0004404974 0.01206092 0.01568268 0.03775296 0.04454409 0.03694991 0.04457385 0.05158422 0.09037798 0.1873933 0.2109828 0.2940904 0.2849688 0.293862 0.2184039 0.2118816 0.2310454 0.1860544 df lower ci 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 -0.1066704 -0.2049349 -0.3715936 -0.3081226 -0.3431673 -0.3953098 -0.4166425 -0.2616915 -0.1358208 -0.1204532 -0.0500870 -0.0326456 -0.0522396 -0.0325719 -0.0157785 0.06319469 0.2131938 0.2444747 0.3456343 0.3350861 0.3453717 0.2540387 0.2456397 0.270057 0.2113746 upper ci 0.3685868 0.2784362 0.1032248 0.173618 0.1353505 0.07564198 0.0501999 0.2221733 0.3427446 0.356459 0.416736 0.4310676 0.414951 0.4311277 0.4447084 0.505855 0.6109147 0.6311903 0.6933309 0.6870841 0.693176 0.6372858 0.6319354 0.6473885 0.6097192 Measuring facial mimicry: Affdex vs. EMG p-value Corrected p-value 0.2658492 0.7556689 0.254162 0.568795 0.3779834 0.1736616 0.1181946 0.8671591 0.3800456 0.316397 0.1179845 0.0889287 0.1220435 0.0888196 0.0666633 0.01417941 0.00028311 0.00010461 2.56027e-06 3.91689e-06 2.58782e-06 7.61925e-06 0.00010075 4.40978e-05 0.00029936 - - - - - - - - - - - - - - - 0.3544852 0.00707775 0.00261525 6.400675e-05 9.792225e-05 6.46955e-05 0.0001904812 0.00251875 0.001102445 0.007484 joy shows an increase in Spearman correlation shortly after the onset of stimulus presentation, but without becoming significant. The Spearman correlation coefficients become significant (p � 0.05) from 2200 ms after the onset of stimulus presentation and increase to a maximum of approximately 0.5 3000 ms after stimulus presentation. Zygomaticus muscle activity and lip corner puller action unit activity calculated by Affdex correlate significantly with each other until the end of the measurement interval 4000 ms after stimulus presentation. Calculation of Spearman correlation between EMG activities for corrugator muscle and brow lowerer action unit calculated by Affdex for affect anger shows no relevant increase in Spearman correlation. Corrugator muscle activity does not significantly correlate with brow lowerer action unit activity calculated by Affdex at any time point Fig 3 shows again the course of the EMG activity of the zygomaticus muscle and the proba- bility of joy calculated by Affdex. In addition, the course of the Spearman correlation between the two values is shown. Fig 4 shows the course of the EMG activity of the corrugator muscle and the probability of anger calculated by Affdex. The probability for the affect joy calculated by Affdex shows an increase 1400 ms after stimulus onset from 0% to 2.5%. The curve reaches its maximum 500 ms after the apex of the affect expression of the stimulus (2500 ms after stim- ulus onset). As with the EMG measurement and the action unit measurement, the variance increases here in addition to the mean values. Thus, both the EMG of the zygomaticus muscle and the Affdex measure of joy probability show an increase upon presentation of the joy stim- ulus. However, the joy probability calculated by Affdex progresses with a latency of 1000 ms compared with the progress of the stimulus material and EMG activity. PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 11 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Table 3. Stimulus anger, Spearman correlation between corrugator muscle (EMG) and brow lowerer action unit (Affdex). Time [ms] n-value r-value -1000 -800 -600 -400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 -0.1437431 0.001796796 0.01142858 -0.06701946 0.07088798 -0.00206789 -0.04331385 0.01619883 -0.06906 -0.09947335 -0.1558416 -0.1362269 -0.05674876 0.06390056 0.08782626 -0.03159638 -0.04826307 0.08563286 0.03358398 0.05541565 0.07864943 0.04100801 0.07333763 0.1148427 0.0182875 Effect size 0.02066208 3.228476e-06 0.0001306124 0.004491608 0.005025106 4.276153e-06 0.00187609 0.0002624021 0.004769284 0.009894947 0.0242866 0.01855777 0.003220422 0.004083282 0.007713452 0.0009983312 0.002329324 0.007332987 0.001127884 0.003070894 0.006185733 0.001681657 0.005378408 0.01318885 0.1352313 df lower ci 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 -0.3728058 -0.2403405 -0.2312438 -0.3041192 -0.1741324 -0.2439785 -0.2823863 -0.2267228 -0.3059784 -0.3334774 -0.3834125 -0.366186 -0.2947333 -0.1809305 -0.1575557 -0.2715525 -0.286944 -0.1597101 -0.210157 -0.1891541 -0.1665538 -0.20304 -0.1717436 -0.1308265 -0.1104154 upper ci 0.1018325 0.2437236 0.2527622 0.177899 0.3076425 0.2400851 0.2008242 0.2572231 0.1759131 0.1460763 0.08956966 0.1094133 0.1878643 0.3012738 0.3229932 0.2120581 0.1960599 0.3210123 0.2733945 0.2935117 0.3146918 0.2802591 0.3098702 0.3472241 0.3653074 p-value 0.2495456 0.988576 0.9274329 0.5928798 0.5716619 0.9868526 0.7298522 0.8972833 0.5816441 0.4268122 0.2114737 0.275422 0.6508473 0.6102384 0.4831607 0.8011581 0.7003679 0.4941963 0.7889302 0.6585348 0.5301898 0.7437263 0.5584113 0.3585125 0.2789792 n-value, Number of cases per correlation, r-value, correlation coefficient, df, degrees of freedom, lower ci, lower confidence interval, upper ci, upper confidence interval https://doi.org/10.1371/journal.pone.0290569.t003 Table 4 contains the results of Spearman correlation calculations between zygomaticus muscle and Joy including alpha error corrected p-values. Table 5 contains the results of Spear- man correlation calculations between corrugator muscle and anger including alpha error cor- rected p-values. The probability for affect anger calculated by Affdex shows no increase and remains at 0% throughout the stimulus presentation. The calculation of Spearman correlation coefficients between EMG activity of the zygoma- ticus muscle and the probability of detecting joy calculated by Affdex during the presentation of the joy video shows a value of -0.2 until it increases to 0 1000 ms after stimulus onset and increases from 1600 ms to a maximum value of 0.4, 2600 ms after stimulus onset. The Spear- man correlation coefficients become significant (p � 0.05) at 3400 ms, 3800 ms and 4000 ms. Calculation of Spearman correlation between EMG activities for corrugator muscle and anger probability for the anger affect calculated by Affdex shows an increase of Spearman cor- relation to nearly 0.2 from 3800 ms after stimulus presentation. Fig 5 shows the results of the cross-correlation calculation between the EMG activity of the zygomaticus muscle and the lip corner puller activity determined by Affdex during the presen- tation of the joy stimulus. the largest cross-correlation coefficient was found at a lag of 5 and was 0.256, indicating lagged matching in temporal patterns between EMG and Affdex. PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 12 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Fig 4. Stimulus anger, measurement of corrugator muscle (EMG) and affect anger (Affdex). Electromyographical activity [μV integrated over 25 x 200 ms interval (μV x 200 ms) +/- standard error] of corrugator muscle (blue line) and Affdex measurement (% +/- standard error) for the affect anger (orange line) in response to video clips of affect expressing faces of adults for the affect anger, whiskers represent the standard error, black line represents the Spearman correlation between electromyographical activity and Affdex at each measurement point, the symbol * indicates p � 0.05. https://doi.org/10.1371/journal.pone.0290569.g004 Table 6 contains the cross-correlation coefficients of all measurements shown so far. For the calculation of the cross-correlation coefficient between zygomaticus muscle and the affect joy during the presentation of the joy stimulus, the largest value was also shown at a lag of 5. The cross-correlation coefficient here was 0.174. During the presentation of the anger stimu- lus, the cross-correlation coefficients show no relevant increase. 4 Discussion This study is the first in which the facial mimicry response of healthy subjects to dynamic affect-enhancing videos (joy and anger) was measured simultaneously using EMG and Affdex to compare the suitability of these two measurement methods. EMG measurement has been the gold standard for measuring facial mimicry. However, it requires a complex measuring apparatus and experience in the application and interpretation of EMG signals. Affdex promises a time-saving and easy-to-interpret analysis of facial expres- sions. In addition, measurement electrodes in the face would not be necessary. This could open up a wide range of applications in affect research. First, the EMG activity of zygomaticus muscle and corrugator muscle was directly com- pared to each other using the Affdex action units lip corner puller and brow lowerer, which are based on FACS [43]. This allows for a direct comparison of measurement sensitivity, as these action units represent the visible correlates to the underlying target muscles [29]. Second, EMG activity was compared to affect probabilities measured by Affdex, as Affdex uses other PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 13 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Fig 3. Stimulus joy, measurement of zygomaticus muscle (EMG) and affect joy (Affdex). Electromyographical activity [μV integrated over 25 x 200 ms interval (μV x 200 ms) +/- standard error] of zygomaticus muscle (blue line) and Affdex measurement (% +/- standard error) for the affect joy (orange line) in response to video clips of affect expressing faces of adults for the affect joy, whiskers represent the standard error, black line represents the Spearman correlation between electromyographical activity and Affdex at each measurement point, the symbol * indicates p � 0.05. https://doi.org/10.1371/journal.pone.0290569.g003 data for measurement in addition to the lip corner puller and brow lowerer action units according to EMFACS [39]. We expected comparable results for Affdex and EMG measurements [41, 42]. However, there was also evidence for reduced measurement performance of Affdex for subtle affect expressions, as expected for facial mimicry [40]. In healthy subjects, it has been shown that facial mimicry could be induced by affective stimulus material. The muscle activity of the zygomaticus and corrugator muscles measured by EMG reflected the valence of the presented affects joy and anger [33, 62]. The Affdex measurement for the lip corner puller action unit and the affect joy also showed an increase and a significant correlation with the EMG measurement of the zygomaticus mus- cle during the presentation of the joy stimulus. However, the rise of the Affdex trace for lip cor- ner puller did not begin until 1200 ms after stimulus onset and approximately 800 ms after the rise of the EMG trace. Sato et al. [63] demonstrated that human FACS coders detect the facial mimicry response to a dynamic happy stimulus after 817 (±200) ms after stimulus onset. The Affdex trace for joy rose 200 ms later than the trace for lip corner puller. The Affdex trace for lip corner puller reached its maximum at an average of 6.02% and the Affdex trace for joy at an average of 2.51%. These values correspond to relatively low expressions. Kulke et al. [41] stud- ied a healthy cohort who imitated faces with maximum affect expression. Here, Affdex mea- sured a maximum mean of 69.56% for lip corner puller and a maximum mean of 67.53% for joy when imitating joy. Thus, it was shown that Affdex is generally capable of measuring the facial mimicry response to the joy stimulus, however it´s reactivity starts much later. The PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 14 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Table 4. Stimulus joy, Spearman correlation between zygomaticus muscle (EMG) and affect joy (Affdex). Time [ms] n-value r-value Effect size df lower ci -1000 -800 -600 -400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 -0.0854732 0.1178771 0.06178529 -0.0896203 -0.1555527 -0.2120662 -0.1696811 -0.1932284 -0.2055995 -0.0854732 -0.2147373 0.01678515 0.03147738 0.02088638 0.1979175 0.2154148 0.3833971 0.3502786 0.3916563 0.3758274 0.3703136 0.3192721 0.3043612 0.3106878 0.007305663 0.01389501 0.003817422 0.008031798 0.02419664 0.04497207 0.02879168 0.03733721 0.04227115 0.007305663 0.04611211 0.0002817413 0.0009908255 0.0004362409 0.03917134 0.04640354 0.1469933 0.1226951 0.1533947 0.1412462 0.1371322 0.1019347 0.09263574 0.09652691 0.03315688 -0.062706 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 -0.320868 -0.1278019 -0.1829838 -0.3246119 -0.38316 -0.4319294 -0.3954725 -0.4158145 -0.4264132 -0.320868 -0.434203 -0.2261664 -0.2121718 -0.22227 -0.0463349 -0.0280821 0.155824 0.1182729 0.1652921 0.1471827 0.1409103 0.08370778 0.06728495 0.07423745 0.1820903 upper ci 0.1598668 0.3499264 0.2993417 0.1557918 0.0898633 0.03158785 0.07545029 0.05119882 0.03834118 0.1598668 0.02879192 0.2577707 0.2714422 0.2615967 0.4198389 0.4347793 0.5723215 0.5460204 0.5788205 0.566344 0.5619774 0.5210417 0.5089052 0.5140647 0.4062202 p-value 0.4950049 0.345869 0.6221349 0.4742325 0.212332 0.08737338 0.1731798 0.1200672 0.09769558 0.4950049 0.08336919 0.8935867 0.8018917 0.8677967 0.111167 0.08237689 0.00148503 0.00393588 0.00114594 0.00187239 0.00220921 0.00897530 0.01296778 0.01111744 0.1433829 Corrected p-value - - - - - - - - - - - - - - - - 0.03712575 0.098397 0.0286485 0.04680975 0.05523025 0.2243825 0.3241945 0.277936 - n-value, Number of cases per correlation, r-value, correlation coefficient, df, degrees of freedom, lower ci, lower confidence interval, upper ci, upper confidence interval, corrected p-value (Benjamini Hochberg correction for multiple testing) https://doi.org/10.1371/journal.pone.0290569.t004 Affdex measurement for the action unit brow lowerer showed no stimulus-associated change and no significant correlation with the EMG measurement for the corrugator muscle at any time during the measurement. The Affdex trace for affect anger showed no deflection during stimulus presentation. Higher levels are found in a healthy cohort that imitated anger stimuli [41]. Here, Affdex measured a maximum mean of 36.72% for brow lowerer and a maximum mean of 8.88% for anger when imitating anger. Affdex thus performs relatively poor in our tri- als measuring the facial mimicry response to the anger stimulus. Since the measurements are time series, we also calculated cross-correlation correlations. These additional calculations provided statistical evidence about a lagged matching in tempo- ral patterns between EMG and Affdex. For the measurements of corrugator muscle, brow lowerer and anger, the cross-correlation coefficients show no relevant increase. Similar studies already indicated low sensitivity of automated affect detection for subtle affect expressions [40, 42, 64]. However, hedonic affect could be measured better than anhe- donic affect [65], which is consistent with the results of present study. It remains unclear why Affdex detects the mimicry response for joy but not for anger, although the EMG measures muscle activity in both cases. Other studies also showed weaker recognition performance of Automated Facial Coding for anger compared to joy [40, 66]. One PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 15 / 22 PLOS ONETable 5. Stimulus anger, Spearman correlation between corrugator muscle (EMG) and affect anger (Affdex). Measuring facial mimicry: Affdex vs. EMG Time [ms] n-value r-value -1000 -800 -600 -400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 -0.0045819 0.04370906 -0.0091734 0.004527421 0.0121565 0.1085402 0.05565026 0.0129309 0.006851618 -0.1680629 -0.1378292 -0.1923704 -0.04926441 0.0970532 0.07548504 0.0339959 0.07830402 0.1555797 0.1603847 0.1347437 Effect size 2.099417e-05 0.001910482 8.415112e-05 2.049754e05 0.0001477805 0.01178098 0.003096951 0.0001672082 4.694467e-05 0.02824514 0.01899689 0.03700637 0.002426982 0.009419324 0.005697991 0.001155721 0.00613152 0.02420504 0.02572325 0.01815586 0.08484137 0.007198058 0.166375 0.1750089 0.2580593 0.2467782 0.02768064 0.03062812 0.0665946 0.06089948 df lower ci upper ci p-value Corrected p-value 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 -0.2463415 -0.2004442 -0.2506497 -0.2377659 -0.2305546 -0.1370941 -0.1889272 -0.2298211 -0.2355718 -0.3940664 -0.3675991 -0.4150772 -0.2878648 -0.1484671 -0.1696472 -0.2097628 -0.1668917 -0.0898359 -0.0849456 -0.110906 -0.1604869 -0.0788322 -0.0699885 0.01709419 0.00504685 0.2377145 0.2827506 0.2333775 0.2462903 0.2534436 0.341599 0.2937268 0.2541682 0.2484723 0.07710626 0.1077997 0.05208755 0.1950946 0.3313036 0.3118209 0.273776 0.3143786 0.3831835 0.38738 0.364877 0.320297 0.3925986 0.4000945 0.4706931 0.4612606 0.9708735 0.7274827 0.9417245 0.9712199 0.9228245 0.3856662 0.6571793 0.9179247 0.9564572 0.1773758 0.2697614 0.1217531 0.6944542 0.4382027 0.5469167 0.7864023 0.5320032 0.2122518 0.1982959 0.2807322 0.4982106 0.1818309 0.1598766 0.0364382 0.0457665 - - - - - - - - - - - - - - - - - - - - - - - 0.910955 1 n-value, Number of cases per correlation, r-value, correlation coefficient, df, degrees of freedom, lower ci, lower confidence interval, upper ci, upper confidence interval https://doi.org/10.1371/journal.pone.0290569.t005 reason for this could be that the joy stimulus leads to higher and longer-lasting muscle activity than the anger stimulus. While the zygomaticus muscle activity increases to about 1800 μV x 200 ms and remains at over 1200 μV x 200 ms until the end of the measurement interval, the corrugator muscle activity only increases to a maximum of about 1300 μV x 200 ms and decreases to about 500 μV x 200 ms until the end of the measurement interval. The course of EMG activity could provide clues to the activity of the action units measured by Affdex. It is possible that the muscle activity of the corrugator muscle is not high enough to activate the brow lowerer action unit to a sufficient extent to detect it measurable by Affdex. Other studies also showed a stronger EMG response to hedonic stimuli than to anhedonic stimuli [42]. Even if EMG activity can be reliably measured, quantitatively these are extremely small increases in activation in the EMG. It is conceivable that EMG can measure muscle activity that does not result in any visible change in the face. Another explanation could be the simultaneous application of EMG by skin electrodes and Affdex measurement. Kulke et al. [41] found that when measuring imitated affect with Affdex and simultaneous EMG measurement, the measurement result was only slightly worsened by the EMG electrodes used on the face. We observed that the Affdex measurement points, which are regularly located at the eyebrows, jumped over longer time intervals to the EMG electrodes located at the forehead above the eyebrows. This occurred even though the electrodes did not cover the areas relevant to Affdex. Therefore, in this study, to achieve consistently functioning PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 16 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Fig 5. Stimulus joy, cross-correlation coefficients between zygomaticus muscle (EMG) and lip corner puller (Affdex). Cross- correlation coefficients between the EMG activity of the zygomaticus muscle and the lip corner puller activity determined by Affdex during the presentation of the joy stimulus (black vertical lines). The blue dashed line represents the 95% confidence interval. https://doi.org/10.1371/journal.pone.0290569.g005 Affdex measurements, the EMG electrodes in the video footage of the test subjects observing the video sequences were retouched after consultation with the iMotions support team. After retouching, the measurement points were consistently located on the eyebrows, so that the Aff- dex measurement may no longer have been affected. Of course, it is conceivable that the retouching obscured needed cues, and thus impaired Affdex’ anger and brow lowerer detec- tion in particular. In this case, this study is not suitable to assess Affdex’s performance for mea- suring brow lowerer activity. The measurement points responsible for measuring Affdex’ lip corner puller action unit and joy were in the correct positions throughout the measurement interval. In conducting this study, in addition to the above mentioned advantages of Affdex com- pared to EMG disadvantages of Affdex were also noticed. A total of 5 subjects were excluded due to incorrect measurements by Affdex caused by unfavorable lighting conditions or glasses. To prevent further measurement errors, EMG electrodes had to be retouched as described above, which was technically challenging and very time-consuming. We performed the Affdex measurements on post-processed videos. The import of stimulus markers provided by iMo- tions for this procedure sometimes resulted in temporally offset markers. As a result, the mark- ers had to be inserted manually to accurately mark the times at which stimuli were presented. This procedure was also very time-consuming. PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 17 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Table 6. Cross-correlation coefficients by lag. Stimulus joy, cross-correlation coefficients between zygomaticus muscle (EMG) and lip corner puller (Affdex) by lag 0 0.178 13 -0.033 1 0.201 14 -0.053 2 0.220 15 -0.064 3 0.240 16 -0.062 4 0.255 17 -0.049 5 0.256 18 -0.022 6 0.240 19 0.008 7 0.211 20 0.041 8 0.173 21 0.074 Stimulus anger, cross-correlation coefficients between corrugator muscle (EMG) and brow lowerer (Affdex) by lag 0 0.038 13 0.007 1 0.045 14 0.013 2 0.051 15 0.019 3 0.055 16 0.025 4 0.058 17 0.031 5 0.055 18 0.039 6 0.049 19 0.045 7 0.039 20 0.050 Stimulus joy, cross-correlation coefficients between zygomaticus muscle (EMG) and joy (Affdex) by lag 0 0.114 13 -0.024 1 0.128 14 -0.038 2 0.143 15 -0.047 3 0.157 16 -0.044 4 0.170 17 -0.029 5 0.174 18 -0.010 6 0.161 19 0.008 7 0.135 20 0.029 Stimulus anger, cross-correlation coefficients between corrugator muscle (EMG) and anger (Affdex) by lag 0 -0.004 13 -0.007 1 -0.001 14 -0.007 2 0.003 15 -0.005 3 0.007 16 -0.004 4 0.011 17 0.000 5 0.014 18 0.007 6 0.014 19 0.013 7 0.009 20 0.026 8 0.033 21 0.058 8 0.103 21 0.051 8 0.005 21 0.038 9 0.129 22 0.107 9 0.023 22 0.067 9 0.073 22 0.074 9 0.001 22 0.037 10 0.081 23 0.137 10 0.013 23 0.072 10 0.045 23 0.096 10 -0.001 23 0.038 11 0.038 24 0.162 11 0.006 24 0.079 11 0.019 24 0.116 11 -0.004 24 0.042 12 -0.001 25 0.187 12 0.003 25 0.085 12 -0.004 25 0.132 12 -0.006 25 0.047 Numbers 0–25, lags, EMG, Electromyography https://doi.org/10.1371/journal.pone.0290569.t006 Technical improvements could resolve these problems and significantly improve the application. As mentioned earlier, retouching the electrodes was time consuming. Future studies should either not use electrodes when measuring simultaneously with Affdex or place them on the face and cover them up so that they do not interfere with Affdex measurement. Future studies could additionally check the subject videos with human FACS raters. This type of validity check would be very time-consuming but would clarify whether Affdex does not detect changes in mimic musculature that are visible to humans. The present study focused on measuring the facial mimicry of the most commonly studied affects, joy and anger. Future studies could investigate other affects such as fear, disgust, sad- ness, and surprise. 4.1 Conclusion The present study demonstrates that Affdex can measure a facial mimicry response for the affect joy. Despite the delayed measurement compared to the established EMG measurement, Affdex shows a valid performance. Nevertheless, it still does not match the highly sensitive EMG and therefore needs further improvement for measuring subtle affect expressions. It remains unclear how well Affdex detects the facial mimicry response to an angry stimulus, because in this study the electrodes measuring the corrugator muscle probably confounded Affdex. Should the measurement performance of Affdex improve significantly in the future and enable the measurement of subtle affect expressions, it could develop into a promising measurement instrument with a broad range of applications. Especially naturalistic experi- mental settings that require non-contact measurement of affective responses could benefit from Affdex. However, EMG has been superior in capturing the temporal and dynamic course characteristics of affect-expressive mimicry, at least for the basic affects studied here. EMG thus remains the gold standard for measuring facial mimicry. PLOS ONE \| https://doi.org/10.1371/journal.pone.0290569 January 2, 2024 18 / 22 PLOS ONEMeasuring facial mimicry: Affdex vs. EMG Acknowledgments We would like to thank Lotte Wagner-Douglas, Claudius Rehagel and Alexandra Schwatlo for help with data collection and data analysis. Author Contributions Conceptualization: Jan-Frederik Westermann, Marc Nordmann, Matthias Franz. Data curation: Jan-Frederik Westermann, Ralf Scha¨fer, Marc Nordmann, Peter Richter. Formal analysis: Jan-Frederik Westermann, Peter Richter. Investigation: Jan-Frederik Westermann. Methodology: Ralf Scha¨fer, Tobias Mu¨ller, Matthias Franz. Project administration: Matthias Franz. Resources: Matthias Franz. Software: Jan-Frederik Westermann. Supervision: Ralf Scha¨fer, Matthias Franz. Validation: Ralf Scha¨fer, Matthias Franz. Visualization: Jan-Frederik Westermann. Writing – original draft: Jan-Frederik Westermann. Writing – review & editing: Tobias Mu¨ller, Matthias Franz. References 1. 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10.1371_journal.pgph.0002821	RESEARCH ARTICLE Preventing iatrogenic HCV infection: A quantitative risk assessment based on observational data in an Egyptian hospital Paul HenriotID Wafaa M. Hussein3, Dalia Sos3, Isis Magdy3, Ke´ vin JeanID 1,2, Wagida A. AnwarID 3, Maha El Gaafary3, Samia Abdo4, Mona Rafik4†, 1,2, Laura TemimeID 1,2 1 MESuRS Laboratory, Conservatoire national des arts et me´tiers, Paris, France, 2 PACRI Unit, Conservatoire national des arts et me´ tiers, Institut Pasteur, Paris, France, 3 Department of Community, Environmental and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt, 4 Department of Clinical Pathology, Faculty of Medicine, Ain Shams University, Cairo, Egypt † Deceased. paul.henriot@protonmail.com Abstract When compliance with infection control recommendations is non-optimal, hospitals may play an important role in hepatitis C (HCV) transmission. However, few studies have ana- lyzed the nosocomial HCV acquisition risk based on detailed empirical data. Here, we used data from a prospective cohort study conducted on 500 patients in the Ain Shams hospital (Cairo, Egypt) in 2017 with the objective of identifying (i) high-risk patient profiles and (ii) transmission hotspots within the hospital. Data included information on patient HCV status upon admission, their trajectories between wards and the invasive procedures they under- went. We first performed a sequence analysis to identify different hospitalization profiles. Second, we estimated each patient’s individual risk of HCV acquisition based on ward-spe- cific prevalence and procedures undergone, and risk hotspots by computing ward-level risks. Then, using a beta regression model, we evaluated upon-admission factors linked to HCV acquisition risk and built a score estimating the risk of HCV infection during hospitaliza- tion based on these factors. Finally, we assessed and compared ward-focused and patient- focused HCV control strategies. The sequence analysis based on patient trajectories allowed us to identify four distinct patient trajectory profiles. The risk of HCV infection was greater in the internal medicine department, compared to the surgery department (0�188% [0�142%-0�235%] vs. 0�043%, CI 95%: [0�036%-0�050%]), with risk hotspots in the geriatric, tropical medicine and intensive-care wards. Upon-admission risk predictors included source of admission, age, reason for hospitalization, and medical history. Interventions focused on the most at-risk patients were most effective to reduce HCV infection risk. Our results might help reduce the risk of HCV acquisition during hospitalization in Egypt by targeting enhanced control measures to ward-level transmission hotspots and to at-risk patients iden- tified upon admission. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Henriot P, Anwar WA, El Gaafary M, Abdo S, Rafik M, Hussein WM, et al. (2024) Preventing iatrogenic HCV infection: A quantitative risk assessment based on observational data in an Egyptian hospital. PLOS Glob Public Health 4(2): e0002821. https://doi.org/10.1371/journal. pgph.0002821 Editor: Reuben Kiggundu, Management Sciences for Health, UGANDA Received: October 30, 2023 Accepted: January 25, 2024 Published: February 15, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgph.0002821 Copyright: © 2024 Henriot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data analysed in this study is available upon request only. Indeed, de-identified data cannot be publicly shared, as our PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 1 / 15 PLOS GLOBAL PUBLIC HEALTHstudy involves sensitive data on human participants, and could be indirectly identifying based on multiple patient characteristics. Individual data requests may be sent to the CorC (secr- CORC@pasteur.fr). Funding: PH was funded by Agence Nationale de Recherches sur le Sida et les He´patites Virales (ANRS), Grant Number 12320 B115. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Preventing iatrogenic HCV infection in Hospitals Introduction Hepatitis C virus (HCV) is a bloodborne pathogen usually transmitted during iatrogenic pro- cedures or unsafe injections like drug use. Even though a direct-acting antiviral (DAA) treat- ment is available, an estimated 58 million people were still living with hepatitis C worldwide in 2019, with significant morbidity and mortality consequences, mostly due to liver cirrhosis and hepatocellular carcinoma [1]. While HCV prevalence varies widely between countries, Egypt has historically been the most affected country worldwide, with an HCV prevalence still over 10% in the adult population in the mid 2010’s [2]. The scaling-up of DAA treatments has tem- porarily yielded the hope that a large test-and-treat strategy could be sufficient to eliminate the epidemics [2]. In this context, Egypt launched an ambitious national treatment programme in 2014, followed by an intensive screening and treatment programme in 2018, with the objective of eliminating HCV within the country by 2021. In 2018 and 2019, almost 50 million Egyptian residents were screened (80% of the adult population), resulting in an HCV seroprevalence reduced to 4�6% in the adult population [3, 4]. It is estimated that more than 90% of the HCV infected population were treated during this campaign, and the 2021 general prevalence was estimated at around 0.5%, thus achieving WHO elimination target. Despite these huge achievements, 20% of the adult population were not screened and treatment does not prevent for reinfections, which is especially problematic in specific population such as injectable drug users, whom could introduce HCV in hospitals. Hence, articulating primary HCV prevention, especially in healthcare settings [5], together with treatment and cure remains key to accelerate HCV elimination in the country. While the implementation of infection control measures has substantially reduced the risk of nosocomial HCV transmission, hospitals may still play an important role in the epidemic dynamics of HCV, due to potential exposure to infected patients and contaminated equipment [6, 7]. This is particularly true in low-to-middle-income countries, such as Egypt [8, 9], How- ever, HCV outbreaks in healthcare settings are seldom detected and investigated, so that the transmission routes often remain unknown. A few studies quantified the HCV acquisition risk in hospitals [10–16]. However, these studies were mostly focused on the occupational risk to healthcare workers, most used data from the literature rather than actual observations, and none accounted for procedure-specific risk levels. In this context, the first objective of this work is to assess the risk of nosocomial HCV infection for the patients hospitalized in an Egyptian hospital. To that aim, we propose a probabilistic risk assessment framework informed by detailed empirical data recently collected in this hospital. Based on this assessment, the second objective is then to identify transmission hotspots as well as at-risk patient profiles to better manage the HCV risk within the hospital. Material and methods Ethical considerations Ethical approval was obtained from the Institutional Review Board of the Faculty of Medicine of Ain Shams University and from the Sheffield University, School of Health and Related Research. All methods were carried out in accordance with relevant guidelines and regula- tions and informed consent was obtained from all subjects and/or their legal guardian(s). Data and setting Data was collected as part of a prospective cohort study (ANRS 12320 IMMHoTHep project, “Investigative Mathematical Modeling of Hospital Transmission of Hepatitis C’’) conducted over a 6-month period in 2017 [17]. This study focused on patients hospitalized in the internal PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 2 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals Table 1. Odds-ratios (OR) of HCV infection associated with exposure to iatrogenic procedures, based on a previ- ously published meta-analysis [18]. The 15 procedure types in the IMMHoTHep data (second column) are aggre- gated into 8 of the 10 procedure groups defined in the meta-analysis and sorted from higher to lower risk. No procedures from the remaining 2 groups defined in the meta-analysis (dental care and transplantation) were observed in the IMMHoTHep data. Procedure groups in meta-analysis Wound care Blood transfusion IV—Catheter Surgery Other procedures Haemodialysis Injection Endoscopy Procedures in data Stitches, Wound dressing Blood transfusion Intravenous, Cardiac catheter Surgery Other invasive procedures, drainage catheter Dialysis Injection, Blood glucose, Blood sample Endoscopy, Endotracheal intubation, gastric lavage OR [CI 95%] 2.83 [1.85–4.32] 2.60 [2.09–3.22] 2.42 [1.68–3.51] 2.30 [1.77–3.00] 2.28 [1.43–3.64] 2.02 [0.98–4.17] 1.67 [1.17–2.38] 1.48 [0.95–2.3] https://doi.org/10.1371/journal.pgph.0002821.t001 medicine (organized in 15 wards) and surgery (organized in 10 wards) departments of the Ain Shams University Hospital in Cairo, Egypt. Five hundred hospitalized patients (aged more than 21) were included upon their admis- sion to the hospital, either through the outpatient clinics or the emergency departments. Their demographic characteristics and medical history were collected through a structured question- naire upon admission. Their HCV status upon admission was retrieved, and infections were confirmed by HCV-RNA detection. Patients’ individual trajectories were then followed up over the course of their entire hospitalizations: this included information on their geographical movements between departments and wards within the hospital and the invasive procedures they underwent within these locations. Procedures performed were aggregated into 15 groups following expert opinion (Table 1). Further information on the study is available in Anwar et al., (2021) [17]. Trajectory analysis To identify typical hospitalization profiles, we performed a sequence analysis based on patient trajectories between seven locations (Surgery department, Internal medicine department, Emergency room, ICU, Endoscopy building, MRI building, Outpatients clinic) within the hos- pital. Sequence analysis is a non-parametric approach to investigate and cluster longitudinal life course data between individuals [18, 19]. Here, sequences were composed of five-minute- long events over the course of hospitalization, completed by the post-hospitalization status (i.e., deceased or discharged), so that all trajectories had the same length as the longest one. Therefore, each sequence was composed of at least two out of nine states, describing location within the hospital (seven states) and post-hospitalization status (two states). To compute differences between sequences, substitution and indel costs were calculated based on the observed transition rates between the states previously defined. We used the opti- mal matching (OM) method to compute the distance matrix between individual sequences [20]. Then, we compared partitions built with the Ward’s minimum variance method [21] using the Point Biserial Correlation (PBC) [22] to find the optimal number of clusters. All sequence analyses were performed using the R package TraMineR [23]. Per-procedure risk estimation We firstly estimated the risk of iatrogenic HCV infection following a procedure performed with contaminated equipment, for each of the 15 procedure types identified in the data. This PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 3 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals was based on a previous meta-analysis studying the association between HCV infection and ten groups of iatrogenic procedures [24]. The 15 procedure types in the data were aggregated to match the groups considered in this meta-analysis (Table 1). Odds-ratio (OR) distributions were considered log-normal with mean equal to the average ORs and standard deviation derived from the associated confidence intervals. The risk of getting HCV-infected through injection by contaminated equipment was then used as a reference to determine the other procedure-specific risks. Ross et al. [11] estimated this risk at 2�20% (plausible interval, 1%-9�2%). Here, we translated this as a PERT-distributed [25] risk distribution, with a median of 2�20% and an analytically calculated mode of 1�23% (S1 Text): Riskinjection � PERTð1; 1 � 23; 9 � 2Þ ð1Þ The procedure-specific risk of HCV infection due to contaminated equipment was calcu- lated for each procedure p as the ratio between the OR of this procedure (denoted ORp) and the injection OR, multiplied by the risk due to injection with contaminated equipment, as fol- lows: Riskp ¼ ORp ORinjection �Riskinjection ð2Þ Individual risk assessment For each patient, the cumulative risk of HCV acquisition over the entire hospitalisation was computed from the within-hospital individual trajectory, the ward-specific HCV prevalence and procedure-associated risks, as follows: R ¼ 1 (cid:0) Yn Ymi i¼1 j¼1 ð1 (cid:0) rj;p � Pi � ð1 (cid:0) AÞÞ ð3Þ where n is the total number of wards visited by the patient, mi the total number of procedures undergone by the patient in ward i, rj,p the risk of HCV acquisition while undergoing the jth procedure if the equipment is contaminated, A the probability of proper equipment handling (i.e., equipment decontamination or use of disposable equipment) and Pi the HCV prevalence in ward i. The risk rj,p was computed as described in the previous section, based on the procedure type p. The ward-specific prevalence Pi, was used as a proxy of the ward-specific probability of medical equipment being contaminated by HCV prior to infection control procedures. It was considered to be constant over time and equal to the proportion of HCV-positive patients among all patients that passed through ward i in our database. For simplicity, the probability of correct infection control in equipment handling was assumed independent of the procedure type. Syringe reuse was taken as a proxy to estimate this probability at 97%, based on a study by Anwar et al. [26] which found that 3% of nurses from two hospital departments in Egypt and Saudi Arabia reused syringes between patients. Finally, to maximize statistical power in the identification of hotspots and at-risk profiles, we performed this individual risk assessment using the data from all 500 patients included in the IMMHoTHep study, irrespective of their HCV status upon admission, even though in real- ity the initially HCV-positive patients were not at-risk. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 4 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals Ward-level risk assessment for hotspot identification To determine the risk of HCV infection associated with each ward in the internal medicine and surgery departments, we calculated for each patient the risk of getting infected through invasive procedures undergone within each unique ward (based on subsets of their trajecto- ries), as in the previous subsection. The distribution of a ward-specific risk was composed of the average risks of all patients visiting it. To shed light on the components of this risk, the ward-level HCV prevalence and average number of procedures per patient and procedure group were also calculated. Statistical analyses of patient-level determinants of the HCV infection risk We investigated differences between the clusters identified through the patient trajectory anal- yses across: (i) age, HCV infection risk, duration of hospitalization and average number of pro- cedures per patient as quantitative variables; and (ii) gender, education level, marital status, source of admission, source of admission, patient localization, history of hospitalization, hospi- talization reason and status at the end of follow-up as categorical variables. Differences were computed using the χ2 test for qualitative variables and the Kruskal-Wallis test for quantitative variables. We performed a beta regression to identify upon-admission factors associated with nosoco- mial HCV risk [27]. As some patients had an infection risk equal to 0, data was transformed following this formula: y0 ¼ ðy � ðn (cid:0) 1Þ þ 0 � 5Þ=n, where y is the risk data and n is the sam- ple size [28]. Explanatory variables included: age, gender, source of admission, patient localiza- tion, history of hospitalization, and hospitalization reason, previous anti-schistosomiasis treatment and history of multiple invasive procedures. A backward selection was performed to discriminate the best model based on the Akaike information criterion (AIC). Finally, using logistic regression, we assessed the capacity of a score based on the variables appearing in the best beta regression model to identify at-high-risk patients. We defined high- risk patients as those belonging to the upper 25% of the risk distribution (over the 75th percen- tile). The training data was composed of 70% of the entire dataset whereas the other 30% was used for the testing dataset. If data unbalance was detected, up-sampling was used to equalise sample sizes for both groups. Cross-validation was performed over 50 folds. Area under the ROC curve (AUC), specificity and sensitivity were computed using the R packages caret [29] and Mleval [30]. A sensitivity analysis was performed on the cut-off for dichotomization; for each case, the Informedness [31] metric was calculated and considered as a proxy of the quality of the model. Assessment of patient and ward-focused strategies We assessed the potential effectiveness of two strategies on the reduction of the HCV infection risk: i. A patient-focused strategy, assuming the probability A of proper equipment handling to be 1 for the most at-risk patients, selected following two sub-strategies: a) randomly (Random- selection) and b) using upon admission the calculated score based on our beta regression model. Here all potential HCV-positive patients within the most-at risk group were consid- ered HCV-negative upon admission, so that they did not impact the risk of other patients visiting the same wards. Strategies targeted at 200 (40%), 150 (30%), 100 (20%), and 50 (10%) at-risk patients among 500 were explored. ii. A ward-focused intervention, assuming the probability A of proper equipment handling to be 1 within the most at-risk wards. Wards were ranked from higher to lower risk and the PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 5 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals number of targeted wards was chosen based on the cumulative number of patients visiting at least once these particular wards, so that the total number of patients was the closest pos- sible to the number of patients targeted in the corresponding patient-focused scenario. Results Patient trajectory description The sequence analysis identified four groups of patients (Fig 1, S2 Fig). Their sizes were hetero- geneous (356, 54, 14 and 76 patients, respectively). Group one (the largest one) included patients with a short hospital stay in internal medicine or surgery, Groups two and four repre- sented patients with intermediate lengths of stay in surgery and internal medicine, respectively, and Group three was composed of patients with long stays in internal medicine. In the latter, 36% of patients were deceased at the end of follow-up. Patients in Group three were older than in the other groups (median: 64 years old, IQR [46–67]), had longer hospital stays (20�4 days [17�5–23�2]) and underwent more invasive procedures (median: 43 [15 – 77]) (S1 Table). HCV infection risk assessment The estimated per-procedure median risk of HCV infection due to contaminated equipment ranged from 1�961%% [IQR 1�339% - 2�923%] for endoscopy up to 3�750% [IQR 2�566% - 5�584%] for wound care (S1 Fig). The median patient HCV infection risk over the entire database was 0�043% [0�026%- 0�093%] (Mean: 0�114%, IC95% [0�091%-0�137%]). This risk differed significantly between Fig 1. Chronograms for each of the four clusters of patients identified after sequence analysis. Dotted lines represent the average length of stay for each group of patients. https://doi.org/10.1371/journal.pgph.0002821.g001 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 6 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals patient groups (P<0�001), with a greater risk of getting HCV infected in group 3 (median: 0�470%, IQR [0�081% - 0�823%]) (S1 Table). Ward-level risk assessment and hotspot identification Overall, the risk of HCV infection was higher in the internal medicine hospital compared to the surgery hospital (0�043%, CI 95%: [0�036%-0�050%] vs. 0�188% [0�142%-0�235%], t.test P<3�6210−9). Within internal medicine, HCV prevalence was found highest in the tropical medicine ward (50% CI 95% [32�100% - 67�900%]), followed by the GIT (31% [15�620%-45�980%]), and geriatric wards (20% [0%-55�060%]) (Fig 2A). Conversely, the average number of procedures within the ER ICU ward was found to be the highest with 33�1 acts, followed by the geriatric ward with 21�6 acts, while the tropical medicine and GIT wards only held the 9th and 10th places among the 15 wards, with 10�45 and 8�24 procedures per patient on average (Fig 2B). The median estimated risk of HCV infection was highest in the geriatric ward (0�621% IQR [0�114%-0�649%], mean: 0�431%) represented by 5 patients, followed by the ward of tropical medicine (0�271% [0�146%- 0�599%], mean: 2�850%), represented by 30 patients and ER ICU ward (0�242% IQR [0�180%-0�811%], mean: 0�474%), represented by 11 patients (Fig 2C). Within surgery, HCV prevalence was high within the urosurgery, orthopaedics, and neuro- surgery wards, at 20% [0%-55�060%], 57�140% [20�480% -93�800%] and 5�330% [0% - Fig 2. Panel of ward characteristics for each ward in the internal medicine hospital. (A) HCV prevalence in each ward with their associated 95% confidence intervals. (B) Average number of procedures per patient. Procedure types are represented from the high-risk ones to the low-risk ones (from left to right). (C) Boxplots of average ward-specific risk of HCV infection, coloured according to the number of patients visiting these wards. Mean values are represented by purple diamond dots. https://doi.org/10.1371/journal.pgph.0002821.g002 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 7 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals Fig 3. Panel of ward characteristics for each ward in the surgery hospital. (A) HCV prevalence in each ward with their associated 95% confidence intervals. (B) Average number of procedures per patient. Procedure types are represented from the high-risk ones to the low-risk ones (from left to right). (C) Boxplots of average ward-specific risk of HCV infection, coloured according to the number of patients visiting these wards. Mean values are represented by purple diamond dots. Three wards are not represented because no patients underwent invasive procedures within them. https://doi.org/10.1371/journal.pgph.0002821.g003 15�790%] (Fig 3A). The ICU ward was associated with the highest number of invasive acts, with an average of 12�75 procedures per patient. Within the orthopaedics and neurosurgery wards, patients underwent 8�5 and 7�4 invasive procedures on average, respectively, and only 1�4 in the urosurgery ward (Fig 3B). The highest risk was found in the urosurgery ward (0�045% IQR [0�044%- 0�046%], mean: 0�101%), but only 7 patients visited it. This was fol- lowed by the orthopaedics (0�037% IQR [0�024%- 0�053%], mean: 0�046%) and the neurosur- gery (0�021% IQR [0�021%- 0�089%], mean: 0�165%) wards, represented by 75 and 6 patients respectively (Fig 3C). Identification of at-risk patient profiles upon admission The best beta regression model explaining the patient HCV infection risk from upon-admission variables is described in Table 2. The hospitalisation cause came out as a key driver of HCV risk, reflecting the higher risk in internal medicine patients, as well as a particularly elevated risk in patients with liver or gastro-intestinal (GIT) complaints. In addition, patients with a history of anti-schistosomiasis treatment were found at higher risk of HCV infection. Other variables selected in the best model were the source of admission and age, with a higher risk in patients admitted via the emergency room or older; and a history of injection or endoscopy. A score based on these explanatory variables allowed to discriminate high-risk patients upon admission. The calculated AUC was 0�79 (95% CI: [0�71–0�87]) with a sensitivity of 0�73 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 8 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals Std. Error p-value - 0.051 0.002 - 0.075 0.089 0.068 0.092 - 0.084 - 0.052 - 0.072 0.114 0.065 0.395 <0.01* <0.01 <0.001* - 0.043* - 0.062 - 0.102 Table 2. Result of the multivariate beta-regression analysis. Characteristic Source of admission Outpatient clinic Emergency room Age Reason for hospitalisation General surgery Special surgery General IM Special IM Liver/ GIT complaint Previous anti-schistosomiasis treatment No / Doesn’t remember Yes Previous injection No / Doesn’t remember Yes Previous endoscopy No / Doesn’t remember Yes IM: internal medicine. GIT: gastro-intestinal https://doi.org/10.1371/journal.pgph.0002821.t002 β - 0.081 0.003 - 0.064 0.276 0.195 0.443 - 0.169 - 0.097 - 0.118 [0�65–0�79], and a specificity of 0�68 [0�54–0�79] (S3 Table). Based on a sensitivity analysis, we found that using a cut-off at the 90th percentile of the overall distribution led to the best logistic regression based on the Informedness criteria (S2 Table). Assessment of patient and ward-focused strategies All simulated interventions focusing on at least 20% of patients led to at least a two-fold reduc- tion of the overall risk, except those based on randomly selected patients (Fig 4, S3 Table) In addition, patient-focused interventions were generally found to be more effective than ward- focused (Fig 4, S3 Table). Nevertheless, for interventions targeting 20% (100) of patients and less, focusing on the most at-risk wards was more efficient at reducing the risk than score- based patient targeting. Discussion This study aimed at better understanding patient trajectories within an Egyptian hospital to help manage the HCV infection risk. Our work was based on data collected on 500 patients within Ain Shams hospital, Egypt, and on a meta-analysis investigating the risk of HCV infec- tion for multiple hospital-based procedures [24], from which we computed HCV infection risks for all patients over the course of their hospitalisation. While we estimated a low overall HCV infection risk, we found that some upon-admission patient characteristics were related to a higher risk: age, reason of hospitalization, and history of previous invasive procedures. We proposed a score to detect high risk patients upon their admission and assessed the effect of simulated interventions on the overall risk of HCV infec- tion during hospitalization. Selecting patients using our score was always more effective than randomly selecting patients upon-admission. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 9 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals Fig 4. Average effect of simulated intervention on the overall risk of HCV infection during hospitalization. Labels under bars correspond to the proportion of concerned patients for a given intervention for the four sub-scenarios considered in the analysis (Comparison groups A, B, C and D). As proportions of patients for the ward-focused scenario were chosen based on the number of cumulative patients in these wards, they were not exactly equal to the proportions given for patient-based scenarios. https://doi.org/10.1371/journal.pgph.0002821.g004 Due to high uncertainty, notably on per-procedure infection control practices, our risk esti- mates should be considered relatively, rather than focusing on their absolute values. For instance, we propose a prioritisation of wards in terms of HCV infection risk in the surgery and internal medicine departments. In particular, we found that the internal medicine depart- ment is the most at-risk of HCV infection, with the geriatric, tropical and endoscopy wards within this department identified as potential “hotspots”. This is consistent with a previous risk assessment study conducted in a German hospital in 2008 in which the highest risk of bloodborne pathogen infection for HCW was within the internal medicine departments [32]. Our results might not be generalizable outside Egypt, as the epidemiology of HCV in this country is unique. In particular, in our database, most of the infected patients were older than non-infected ones and most of them had a history of anti-schistosomiasis treatment performed during the mass treatment campaign initiated by the government between 1950 and 1980, which led to a massive diffusion of HCV within the country [33]. While this may impact both the hotspots we identified within the hospital and the at-risk patient profiles we determined, the approach we propose to assess the HCV infection risk may still be extended to other set- tings and contexts beyond Egypt, provided the necessary data is available. To the best of our knowledge, only a few earlier studies have attempted to assess the indi- vidual risk of HCV infection for patients and healthcare workers within hospitals. Among these studies, only two described models investigating the patient-to-patient transmission risk PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 10 / 15 40%40%40.6%30%30%31.8%20%20%16.4%10%10%10.6%0%20%40%60%80%DCBAComparison groupAverage risk reduction (%)InterventionPatient−focused (Model−based selection)Patient−focused (Random selection)Ward focusedPLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals using a probabilistic approach [14, 15]. The first one [14] described the individual risk for patients hospitalized in a haemodialysis unit and the other one quantified the risk of blood- borne pathogen infection during an invasive procedure [15]. Although these studies used the HCV prevalence level within wards to estimate the HCV infection risk for hospitalized patients, none used detailed longitudinal data to investigate the individual infection risk dur- ing hospitalization. In addition, a few studies directly reported HCV incidence in hospitalized patients. However, all were focused on haemodialysis units [34–36]. Finally, as far as we know, no earlier study has investigated the impact on HCV infection risk of patient-based and ward- based interventions similar to those proposed in our work. Some studies investigated the effect of increased prevention and control in healthcare workers on the incidence of occupational exposure to bloodborne pathogens [37, 38] but none seemed to focus on the effect of these pre- vention measures on the HCV incidence in patients. Several data-related and methodological limitations could be highlighted in our study. First, we were limited by our data and had few observations of patients for multiple wards. In particular, the highest estimated risks were observed in wards that received a low number of patients (Neurosurgery and geriatric). In future studies, investigating these specific risks based on more patient visits might give more accurate estimates of the corresponding ward-level HCV infection risks. Second, we did not have access to HCV status upon patient discharge. A study assessing the HCV status after hospitalisation in addition to the HCV status upon admission would be needed to really quantify the risk of getting HCV infected during a hospital stay from data. However, we believe that the analyses we performed, based on detailed data on patient trajec- tories and per-procedure risk ranking, do provide valid conclusions in terms of the identifica- tion of possible hotspots and at-risk patient profiles within the hospital. Third, we only accounted for transmission between patients within the same ward and did not investigate potential transmission from healthcare workers to patients, which can be another HCV gateway [39]. This may have led us to under-estimate all the HCV acquisition risks, but should not have affected the prioritisation we propose in terms of geographical hot- spots and patient profiles upon admission. Indeed, an earlier study performed in the same hos- pital on a larger staff population confirmed that HCV RNA positive healthcare workers were very rare, and that highest proportions of HCV-infected healthcare workers were found in the internal medicine department [40]. Fourth, our beta-regression model identified several upon-admission variables as associated with the risk of HCV infection during hospitalization. These associations should not be inter- preted as causal. For example, patients with a history of anti-schistosomiasis treatment were found to be more at risk of HCV infection, possibly reflecting the high prevalence among other patients they are exposed to. In a context of limited budget and human resources, this work may help better manage the HCV risk within Egyptian hospitals in two ways. First, infection control could be reinforced locally in the hotspots we identified. This could for instance imply systematic HCV screening for patients newly admitted to these specific wards, hiring of dedicated hygiene personnel within these wards, or allocation of the available disposable equipment to these wards. Second, the score we proposed could be systematically computed upon admission for all newly admit- ted patients. Those identified as at high risk could then be “flagged” for reinforced precautions over the course of their hospitalisation. When comparing such ward-focused and patient- focused strategies, we found that interventions targeted at identified at-risk patients upon admission were most effective. However, interventions focused on ward hotspots also allowed to reduce the risk more than two-fold and may in practice prove both easier to implement logistically and more acceptable from an ethical point of view. In addition, when hospital PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002821 February 15, 2024 11 / 15 PLOS GLOBAL PUBLIC HEALTHPreventing iatrogenic HCV infection in Hospitals resources only allowed to target less than 20% of patients for reinforced infection control, ward-focused interventions were actually most effective. Even if the Egyptian government implemented a large HCV test and treat campaign in 2018 that led to a significant prevalence reduction, these results may still help to reach more easily WHO HCV elimination objectives by pointing out the most at risk patients and wards. In addition, the framework we developed could be extended to assess and manage iatrogenic HCV risks in other hospitals or risks associated with other blood-borne pathogens such as HIV or HBV. This would require, in the first case, the collection of data on patient trajectories similar to the IMMHoTHep data in other hospitals; and in the second case, estimates of the per-procedure infection risks associated with these other pathogens. Finally, in future work, the detailed data we collected on patient trajectories and invasive procedures could be used to inform mechanistic models simulating dynamically the transmis- sion of HCV or other blood-borne pathogens within the hospital. Such models would allow to assess the impact of potential control measures in a more accurate way than the very simplified assessment proposed here. Supporting information S1 Checklist. Inclusivity in global research. (DOCX) S1 Table. Characteristics of each group found after sequence analysis. (DOCX) S2 Table. Summary of the impact of patient and ward-focused strategies on the risk of HCV infection during hospitalization. (DOCX) S3 Table. Sensitivity analysis for the cut-off value of the risk considered in the logistic regression. (DOCX) S1 Fig. Distributions of the procedure–specific risks of HCV infection in case of contami- nated equipment. (JPG) S2 Fig. Point Biserial Correlation (PBC) for 1 to 20 clusters. PBC was very similar for 3, 4 and 5 partitions. Therefore, we chose to build 4 clusters of patients (vertical dashed line). (PNG) S1 Text. Mode calculation. (DOCX) Author Contributions Conceptualization: Paul Henriot, Ke´vin Jean, Laura Temime. Data curation: Wagida A. Anwar, Maha El Gaafary, Samia Abdo, Mona Rafik, Wafaa M. Hus- sein, Dalia Sos, Isis Magdy. Formal analysis: Paul Henriot. Funding acquisition: Paul Henriot. Methodology: Paul Henriot, Ke´vin Jean, Laura Temime. 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10.1371_journal.pone.0297957	RESEARCH ARTICLE Low profile high gain RHCP antenna for L- Band and S-Band using rectangular ring metasurface with backlobe suppression Sundas Farooq Khan, Bilal Muhammad KhanID, Tariq Mairaj Rasool Khan National University of Sciences and Technology, Karachi, Pakistan bmkhan27@gmail.com Abstract In this reported work a single feed, miniaturized, dual layer, and low profile antenna is pre- sented for 1.575GHz frequency band. The proposed antenna offers high gain, lower noise bandwidth, with better sensitivity and range. The ground choke technique is used for back lobe suppression. The prototype is fabricated on FR 4 substrate using manual fabrication technique. This offers an inexpensive and readily available fabrication. Therefore, fabricated antenna is small size, low cost, easily fabricated and tested for satellite communication. The antenna comprises of two layers, containing a patch radiator and a Metasurface layer with 3x3 rectangular ring resonators. The layers are separated using foam with a 12mm width. The proposed prototype is radiating at 1.575GHz and 2.33GHz with an overall dimension of 85.6 x 68.4 x 15.204 mm. The developed antenna provides a gain of 5.9 dBi. The simulated results are verified using VNA and anechoic chamber testing. Moreover, the developed antenna has been successfully tested for L-Band Satellite communication in real time sce- nario without any LNA. Higher Gain due to Metasurface increase the efficiency of the sys- tem. The promising results indicate the aptness of the developed antenna for real-world applications of L-Band and S-Band. 1. Intorduction METASURFACES are two-dimensional metamaterials i.e. a geometry spatially arranged in such a way that it exhibits unusual but homogenous properties so it can be used as a single building block and facilitates general application. According to Snells’s law, altering surface impedance create phase shift so by altering impedance of the surface we can control the reflection parame- ters. Metasurface consist of arrays of subwavelength scatterers that can be designed to manipu- late the phase, amplitude, and polarization of electromagnetic waves in a highly controlled manner. By introducing variable phase discontinuities across the surface, desired reflection and refraction properties can be achieved. Thus, making it suitable for variety of applications. They have been widely studied in the field of optics and have been used to create a wide range of functional devices such as waveplates [1–3], polarizers [4–8], and beam shapers [9–13]. One of the key advantages of metasurfaces is that they can be used to create highly compact and a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Khan SF, Khan BM, Mairaj Rasool Khan T (2024) Low profile high gain RHCP antenna for L- Band and S-Band using rectangular ring metasurface with backlobe suppression. PLoS ONE 19(2): e0297957. https://doi.org/10.1371/journal. pone.0297957 Editor: Yuan-Fong Chou Chau, Universiti Brunei Darussalam, BRUNEI DARUSSALAM Received: October 16, 2023 Accepted: January 14, 2024 Published: February 8, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0297957 Copyright: © 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript. Funding: The author(s) received no specific funding for this work. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 1 / 14 PLOS ONECompeting interests: The authors have declared that no competing interests exist Low profile RHCP antenna lightweight devices that can be integrated into a wide range of systems. Extensive research has been made in the field of metasurfaces with expensive and customized substrates with multi- layer design and complex printing procedure. There is a dire need to explore the field with commonly available materials and simple configuration. The growing use of satellite communication has made it a crucial tool for connecting the disconnected [14]. The goal of satellite communication is reliability. Although higher fre- quency band give higher bandwidth and smaller antenna size however they are more suscepti- ble to signal degradation also known as rain fading. Therefore, lower frequency bands are preferred when reliability is required. Using metasurface over conventional antenna not only reduce size but also increase gain and bandwidth without compromising on reliability of the system. Moreover, circular polarized antenna is preferred as they are insensitive to the physical alignment of receiver. High gain at narrow bandwidth reduces the noise bandwidth at receiver end hence provide better sensitivity and higher read range. A polarization converter is pre- sented in [15] for frequency 4.386.32GHz with 6.05 dBic gain. A reconfigurable antenna with 5.6dBi gain for 5-6GHz frequency range is presented in [16] for multidirectional beam forma- tion. In [17] S-shaped Metasurface antenna is designed for 5.3–6.6 GHz. An annular ring slot design report results for 4.8–7.5GHz range in [18]. In [19, 20] low profile antennas are pre- sented for higher frequency spectrum. However, [21–23] are presented for higher frequency ranges than 1.575GHz of GPS band. Survey on Metasurface literature suggests very few papers have been published for L-Band. Furthermore, no low profile (FR 4 based) Metasurface antenna is reported for L band to the best of author’s knowledge. In summary, this work introduces a novel low-profile metasurface antenna designed for the L-band in satellite communication. Utilizing commonly available materials and a simplified configuration, the proposed antenna aims to reduce size, enhance gain and bandwidth, while maintaining system reliability. Addressing a notable gap in existing literature, this research contributes to the exploration of metasurface applications in satellite communication, particularly in the less-explored L-band frequency range using commonly available FR 4 substrate 2. Design and fabrication The proposed design consists of two layers. First layer consists of a patch antenna with coaxial feed. This layer has ground plan with etched slots in it for back lobe suppression. Second layer consist of Metasurface unit cells. This layer does not have direct feeding or ground plan. Patch Antenna is designed using governing design equations of microstrip patch antenna [24]. Length of the patch was calculated with (1). Where f is resonating frequency and εr per- mittivity of substrate is 4.3. From (1) calculated length of patch is 45mm. However, best results were observed with rectangular patch with aspect ratio of 1.15 therefore, length and width is 48.5mm and 42.175mm respectively. L ¼ Vo p 2f ffiffiffiffi εr whereas feed point was calculated by locating point where input impedance matches 50O xo ¼ xf � cosðyÞ yo ¼ xf � sinðyÞ ð1Þ ð2Þ ð3Þ Where xf = 17.0 and θ = 35 degree while feed is in the 2nd quadrant. Backward radiation is generated at patch antenna on the finite ground plane due to the ground plane edge diffraction PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 2 / 14 PLOS ONELow profile RHCP antenna Table 1. Dimensions of parameter. Parameter Value (mm) Parameter Value (mm) W 85.6 L2 17.4 L 68.4 W3 4 https://doi.org/10.1371/journal.pone.0297957.t001 W1 48.5 L3 4 L1 42.17 W4 5.8 W2 20 L4 30 [25]. The slotted ground choke is created by etching four slots at the corner of the ground plane of the FR4 substrate as dimensions tabulated in Table 1. Rectangular rings are subwavelength scatterer which are � λ/10 with aspect ratio of 1.14. It is studied in [26] that rectangular scatterers give wider bandwidth. Aspect ratio is a characteris- tic of substrate. A complete model of antenna is presented in Fig 1. Parameters are tabulated in Table 1. Functionality of Metasurface layer can be understood through superstrate or resonant cav- ity Antenna, Metasurface layer can be applied as superstrate or around patch to suppress sur- face wave or reshape [27–29]. By appropriately designing the scaterrers and controlling the phase and amplitude of wave it’s possible to enhance the amplitude of the waves in the desired direction, effectively increasing the gain of the antenna in that direction. Left- handed Fig 1. Antenna Configuration: (a)Perspective view, (b) Coaxial fed rectangular patch antenna (c) Ground plan (d) Metasurface Layer. https://doi.org/10.1371/journal.pone.0297957.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 3 / 14 PLOS ONELow profile RHCP antenna Fig 2. (a)Unit Cell (b)Floquet Port View in CST (c) Working Principle Design & Methadology. https://doi.org/10.1371/journal.pone.0297957.g002 Metametrial (LH-MTMs) or Double negative (DNG) materials as well as Electronic bandgap structure (EBG) also enhance gain and directivity when primary source antenna is paired with them [30–32]. In [27] double closed ring resonator (DCR) as reflective metasurface unit cell was investigated. It was found that at 2.2 GHz and 2.9GHz DCR shows unusual properties such as permeability close to zero. Retrieved effective parameters shows DCR lies in Mu Near Zero (MNZ) & Epsilon Negative medium (ENG) region at 2.2 and 2.9GHz frequency thus enhancing gain. For our desired frequency i.e. 1.575GHz we designed the single closed ring resonator with bigger dimensions as shown in Fig 2(A). Only unit cell was also designed separately through floquet port of CST MW. It was concluded that metasurface in subject case is collimating the wave nature thus increasing directivity and gain. Further study on retrieved effective parame- ters is in progress. Manual fabrication technique is used for prototype development. This includes etching of substrate through Ferric Chloride solution. A fabricated prototype is presented in Fig 3. 3. Meaurement & testing Antenna was tested on vector network analyzer for reflection parameter. It was then tested in an anechoic chamber for gain and radiation patterns. Fig 4. Shows the VNA testing and anechoic chamber testing. Developed Antenna is tested for satellite communication using test bench. Open ground testing was conducted to receive real GPS signals. Prototype was connected with USRP X310 Fig 3. Fabricated antenna. https://doi.org/10.1371/journal.pone.0297957.g003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 4 / 14 PLOS ONELow profile RHCP antenna Fig 4. Testing of Prototype (a) VNA testing (b) Anechoic Chamber testing. https://doi.org/10.1371/journal.pone.0297957.g004 SDR without any external LNA. Test setup is shown in Fig 5(A). Whereas Fig 5(B). explain the block diagram of test setup. Device under test (DUT) is connected with Software Defined radio (SDR) An SDR is a transceiver system consisting of a radio front end (RFE) and a digital back end with a variety of on-board DSP capabilities. This test involved analyzing the received signal for visualizing how many satellites were captured also utilizing a GNU Radio flowgraph to measure the power of the signals received by DUT. 4. Result and discussion The developed Antenna is designed for satellite communication and is functional for 1.575GHz (L-band) as well as for 2.33GHz (S-Band) with reflection coefficient less than -10dB. Fig 6. Shows measured and simulated results. In order to qualify antenna for satellite commu- nication reception its reflection coefficients, polarization and gain was checked. Simulated results show less prominent reflection parameter at 2.33 GHz. However, measured results show that antenna is capable of transmitting 90% of the power at 2.33GHz as well as, S11 is below -10dB at said frequency. Fig 7. Shows axial ratio of antenna which is below for entire PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 5 / 14 PLOS ONELow profile RHCP antenna Fig 5. Open ground Testing of Prototype (a) Test Setup (b) Block Diagram of Test Set Up. https://doi.org/10.1371/journal.pone.0297957.g005 Fig 6. Measured and simulated S11 of antenna. https://doi.org/10.1371/journal.pone.0297957.g006 Fig 7. Axial ratio of antenna. https://doi.org/10.1371/journal.pone.0297957.g007 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 6 / 14 PLOS ONELow profile RHCP antenna Fig 8. Radiation pattern after Backlobe Suppression (a)Absolute Gain 5.91 dBi (b) LHCP Gain -1.22dBi (c) RHCP Gain 4.976dBi. https://doi.org/10.1371/journal.pone.0297957.g008 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 7 / 14 PLOS ONELow profile RHCP antenna Fig 9. Radiation pattern Before & after Backlobe Suppression (a) Without Choke (b) after Choke. https://doi.org/10.1371/journal.pone.0297957.g009 spectrum of 1.575GHz thus circularly polarized. From Fig 6 it is evident that bandwidth is quite narrow at both frequencies thus providing low noise figure. It was learned when a COTS (commercial of the shelf) LNA with 5dB noise figure was used as external amplifier. This spe- cific COTS LNA was chosen due its low cost. In this experiment no satellite communication was established due high noise figure on channel. Each component on channel adds noise on received signal. It is concluded through experiments that noise is an important factor to take into account while reception. Therefore, having narrow bandwidth filters out the unwanted signals at desired frequency thus better. Antenna is circularly polarized at 1.575GHz however it is linearly polarized at 2.33GHz. But it can be further optimized to make it circularly polarized at 2.33GHz as well. Radiation pattern of antenna is presented in Fig 8. Antenna offers only -1.22dBi Gain in LHCP and 4.98dBi Gain in RHCP regions making it a right hand circularly polarized antenna. Overall Gain of the antenna is 5.91dBi. A 104 degree beam width ensures excellent hemispherical coverage. Back lobe suppression technique reduced the back lobe. Before fabricating choke on ground plane antenna had considerable back lobe. A side-by-side comparison of impact of choke is presented in Fig 9. It is evident from graph, that without Choke more power was transmitted backward than in beamforming region. This not only reduce the efficiency of antenna but also could cause a potential hazard of unwanted RF exposure. Measured results of antenna in anechoic Chamber is presented in Fig 10.Testing was done using a linearly polarized horn antenna at transmitting end and rotating it. Fig 10A) shows the radiation pattern in polar plot while Fig 10(B) shows normalized values in Cartesian plot. It can be seen in normalized values that difference of power between co polarized and cross polarized at main lobe is very less prove it to be circularly polarized. The working of the antenna can be explained with the behavior of wave. A guided wave is a plane wave while the free-space wave is a spherically expanding wave. An antenna is region of transition between guided wave to free space. The presented technique breaks the region of transition into two layers, first layer i.e. patch antenna, radiate. Whereas the second layer colli- mates the spherically expanding wave with sub wavelength scatterer. This second layer is placed at an optimized distance to have constructive interference. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 8 / 14 PLOS ONELow profile RHCP antenna Fig 10. Measured Radiation pattern of antenna (a) Polar Plots (b) Normalized Plots. https://doi.org/10.1371/journal.pone.0297957.g010 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 9 / 14 PLOS ONELow profile RHCP antenna Fig 11. (a) Electric Field Scattering at 1.575GHz (b) Surface Current Distribution. https://doi.org/10.1371/journal.pone.0297957.g011 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 10 / 14 PLOS ONELow profile RHCP antenna Fig 12. (a) 06 Satellites Received (b) Power Received at Flowgraph. https://doi.org/10.1371/journal.pone.0297957.g012 The Metasurface layer or the 2nd layer not only improves gain but also reshapes the reflec- tion coefficient parameter by shifting it to its left. With Metasurface layer, S11 of antenna improves impedance matching as low as below -20dB. Fig 11(A) presents the Electric field scattering at 1.575GHz and different time variants. It can be seen from the pattern that electric field in patch as well as in each Metacell is rotating in circular motion. Surface current distribution presented in Fig 11(B) shows majority of current is in center of patch and Metasurface. While choke at ground is bringing edge current back to center thus suppressing backlobe. Test setup successfully track and locked the position of 06 satellites present in the region as shown in Fig 12(A). Once channel is established position velocity and time of the Satellite was calculated. Once satellite started sending subframes to the receiver channel is successfully established. Antenna successfully established links with six satellites. This data acquisition and tracking validated the hypothesis that it can be used for satellite communication. Received Gain at Flowgraph was 52.80dB as presented in Fig 12(B). The presented prototype has an edge over its predecessor in size reduction, gain enhance- ment and simplicity. As previously reported, work for same frequency i.e., 1.575GHz was fab- ricated using customized Rogers substrate. In [33] 2-layer design was presented with 9dBi gain PLOS ONE \| https://doi.org/10.1371/journal.pone.0297957 February 8, 2024 11 / 14 PLOS ONELow profile RHCP antenna Table 2. Comparison with other paper. Frequency Size (mm) Gain Substrate Layers Proposed Design 1.575GHz 85.6x68.4 x15.204 5.9dBi FR 4 2 Paper [33] 1.575GHz 175x175x21 9dBi Paper [34] 1.575GHz 75x75x13 5dBi Rogers Customized Rogers Customized 2 3 https://doi.org/10.1371/journal.pone.0297957.t002 but with large dimensions. In [33] 3-layer design was presented adding complexity in manufacturing process and 5dBi gain. This paper presents a design which gives more than 50% size reduction over [33] and better gain than [34]. Moreover, it has added advantage of easy fabrication on readily available substrate. A comparison is delineated in Table 2. 5. Conclusion A novel rectangular ring shaped Metasurface based patch antenna with slotted ground choke is presented for L-band and S-band. Antenna is circularly polarized due to its diagonal feed. Stacked approach of antenna enhanced the overall Gain of antenna. It is fabricated on FR 4 substrate and can be used for satellite communication. The developed antenna, with an overall dimension of 85.6 x 68.4 x 15.204 mm provides a gain of 5.9 dBi. The simulated results are ver- ified using VNA and anechoic chamber testing. Moreover, the developed antenna has been successfully tested for L-Band Satellite communication for data acquisition of commercial sat- ellites. The promising results indicate the efficacy of the developed antenna for real-world applications of L-Band. Thus fabricated prototype antenna is small size, low cost, easily fabri- cated and readily available for satellite communication. Study of retrieved effective parameters, further optimization of antenna for S-Band to increase multi-functionality is the proposed future work. Further size reduction and exploring the design on different substrate are also termed as future activities. Author Contributions Conceptualization: Sundas Farooq Khan, Bilal Muhammad Khan. Data curation: Sundas Farooq Khan, Tariq Mairaj Rasool Khan. Formal analysis: Sundas Farooq Khan, Bilal Muhammad Khan. Methodology: Sundas Farooq Khan, Bilal Muhammad Khan, Tariq Mairaj Rasool Khan. Project administration: Bilal Muhammad Khan. Software: Tariq Mairaj Rasool Khan. Supervision: Bilal Muhammad Khan. Writing – original draft: Sundas Farooq Khan. Writing – review & editing: Sundas Farooq Khan, Bilal Muhammad Khan. References 1. Deng Y., Cuo Wu, Chao Meng, Sesrgey I. 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10.1371_journal.pone.0295362	RESEARCH ARTICLE Effects of high-intensity interval training on strength, speed, and endurance performance among racket sports players: A systematic review Yixuan LiuID, Borhannudin Bin Abdullah, Hazizi Bin Abu Saad Faculty of Educational Studies, Department of Sports Studies, Universiti Putra Malaysia, Serdang, Malaysia a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 borhannudin@upm.edu.my Abstract OPEN ACCESS Citation: Liu Y, Abdullah BB, Abu Saad HB (2024) Effects of high-intensity interval training on strength, speed, and endurance performance among racket sports players: A systematic review. PLoS ONE 19(1): e0295362. https://doi.org/ 10.1371/journal.pone.0295362 Editor: Leonardo Vidal Andreato, Amazonas State University, BRAZIL Received: September 19, 2023 Accepted: November 17, 2023 Published: January 5, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0295362 Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting information files. This study aims to present a critical review of the existing literature on the effects of High- Intensity Interval Training (HIIT) on strength, speed, and endurance performance among racket sports athletes. This study conducted a systematic literature review by PRISMA guidelines. Various well-known academic and scientific databases were used for research collection, including PubMed, EBSCOhost, Scopus, Web of Science, and Google Scholar. Out of 27 relevant studies, 10 were selected for inclusion in this systematic review, all meet- ing the required inclusion criteria. The quality of each study was assessed using the PEDro scale, with scores ranging from 3 to 5 for the selected studies. HIIT was found to improve racket players’ VO2 max (maximum oxygen uptake), running and repetitive sprint perfor- mance, jumping performance, and hitting speed during play. Current findings indicate that HIIT can significantly benefit athletic performance. Long-term HIIT allows athletes to enhance their power while improving crucial variables related to both aerobic and anaerobic endurance. This anaerobic endurance and explosive power type is particularly vital for racket sports players. For example, athletes in table tennis and badminton must exert maxi- mum effort during high-intensity middle and back-court play. Racket athletes also need to maintain a stable state while preserving ball speed and positioning, and must quickly recover to prepare for the next rally. This training mechanism can assist athletes in honing their skills and achieving more efficient hitting quality. Therefore, this paper recommends that racket sports athletes incorporate HIIT into their regular training routines. The sug- gested frequency is three times per week, with each training session lasting 30–40 minutes, and a total duration of six to eight weeks. Trial registration. Systematic Review Registra- tion: [https://inplasy.com/], identififier[INPLASY20230080]. 1. Introduction Racket sports are a subset of ball games and may also be classified as those games where tools, such as rackets, are used to propel or strike a ball [1,2]. These matches typically involve two to four players and are centered around the objective of striking the ball so that the opponent PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 1 / 19 PLOS ONEFunding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. HIIT on strength, speed, and endurance performance among racket sports players cannot return it [3,4]. Racket sports fall into two main categories: net sports, played on desig- nated courts like tennis, badminton, and table tennis, and non-net sports, played on shared courts, such as squash [2,5]. Racket sports necessitate a combination of high-intensity interval exercise and low-inten- sity exercise. To succeed, athletes in this field must possess a blend of speed, strength, and exceptional aerobic endurance [6,7]. During competitions, the body alternates between peri- ods of high-intensity work, using intramuscular phosphate and glycolysis to replenish energy stores and restore homeostasis [8,9]. The ATP-CP energy system is vital for brief bursts of energy and shifts as energy demands change during the match. Initially, during vigorous exer- cise, the body relies on the ATP-CP system, emphasizing the importance of explosive power [3,10]. However, as the activity continues, the rapid depletion of ATP and CP stores due to their limited capacity prompts a transition to the lactate system when these reserves prove inadequate for sustained physical exertion [4,11]. In racquet sports, the body perpetually shifts between energy systems as the intensity and duration of play evolve. Short, explosive move- ments over limited distances are reliant on the ATP-CP system, while protracted rallies and enduring competitions engage the lactate system [12–14]. Comprehensive fitness programs for racket players typically encompass training for both anaerobic and aerobic energy systems to enhance performance [15,16]. Some studies have analyzed athletes’ technical movements during competitions, showing that the thighs push against the ground to convert gravitational, elastic, and chemical energy into kinetic energy [17–19]. The energy transfer from the lower to the upper limbs is facilitated by the core area, involving a continuous muscle contraction, thereby making strength vital for performance [8,12,16]. Racket players require speed and explosiveness to generate striking power [20]. A more powerful drive enables faster, more accurate, and more challenging ball hits for the opponent [21]. Strength also positively affects movement balance and injury prevention, helping athletes maintain proper body positioning and meet the demands of prolonged competition [22]. Fur- thermore, quick movements and reflexes are crucial for racket sports. Excellent speed allows for more effective court coverage and better-timed strikes [8,23]. Thus, speed is important for both defending against and launching attacks [24,25]. Moreover, the accumulation of lactic acid during high-intensity exercise can affect internal stability and metabolic processes, leading to fatigue [5,26]. Therefore, endurance is another key aspect; athletes’ muscles must be condi- tioned to withstand prolonged, repetitive, high-intensity activity. Training programs that take endurance into account are essential for maintaining high-performance levels during matches [4,27]. The scientific method of strength training serves as the optimal guideline for enhancing athletes’ sports performance [28,29]. Racket sports are categorized as middle-level in terms of exercise intensity, necessitating attention to both the anaerobic and aerobic capacities of ath- letes [8,12,30]. One study demonstrated that racket-playing athletes experienced an increase in average blood lactate levels as match intensity escalated [25]. This suggests that players tend to rely on a relatively high proportion of aerobic energy during matches, due to the extended intervals between games in racket sports [22,31]. Enhancing endurance can mitigate the risk of fatigue, thereby preserving accuracy and decision-making abilities. It also facilitates quicker recovery between points and helps players rebound from challenging situations [32]. HIIT reigns as a widely embraced and potent exercise methodology, celebrated for its capacity to significantly enhance cardiovascular endurance and foster overall strength [33–35]. At its essence, HIIT thrives on the rhythmic alternation of brief, vigorous exercise intervals with equally succinct interludes of rest or low-intensity activity [36]. This harmonious inter- play of intense training and strategic recovery cycles bestows upon HIIT the mantle of a stellar choice for those who seek a profoundly efficient and impactful workout [37–39]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 2 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Simultaneously, the fitness industry boasts a plethora of terminology to define training meth- ods featuring the cyclic integration of high-intensity workouts and periods of rest or low- intensity intervals [40,41]. Terms such as ’high-intensity interval exercise’ and ’high-intensity intermittent training’ align with the core principles of HIIT [40,42,43]. Nevertheless, HIIT has emerged as the prevailing and universally embraced terminology in the fitness community [43,44]. This consensus streamlines communication and fosters better understanding among coaches, athletes, and fitness enthusiasts, rendering it the primary choice for describing this training method in most contexts [18,35]. Current research on HIIT confirms its efficacy in improving individual strength and promoting muscle hypertrophy (muscle growth) in athletes [4,45]. HIIT activates fast-twitch muscle fibers, which are responsible for generating high levels of strength and power. This activation promotes muscle adaptation and an increase in strength [13]. Additionally, HIIT stimulates the production of anabolic hormones like testosterone and growth hormone, which are instrumental in muscle protein synthesis and, consequently, in muscle growth and hypertrophy [19]. HIIT also induces metabolic stress and causes micro- tears in muscle fibers, triggering physiological responses that promote anabolic signaling and growth factors, leading to increased muscle strength and hypertrophy [8,12,46]. HIIT engages both aerobic and anaerobic energy systems, offering athletes the opportunity to enhance their speed and power in racket sports by targeting high-strength muscle fibers [8,47]. HIIT not only improves anaerobic capacity but also emulates the speed and duration of activities specific to various sports, thereby aiding athletes in developing energy systems and muscle adaptations tailored to their needs [21,44]. HIIT can also significantly improve an athlete’s VO2 max, rep- resenting maximal oxygen utilization during exercise. Elevated VO2 max levels indicate better aerobic capacity and endurance, enabling sustained submaximal-intensity training over extended periods [28,48]. Compared to traditional steady-state cardio or prolonged strength training sessions, HIIT workouts are notably shorter. These high-intensity, short-duration exercises lead to metabolic adaptations like increased mitochondrial density and enhanced glucose utilization, both crucial for strength development [29]. While existing literature confirms the impact of HIIT on sprinting and cardiorespiratory performance in adolescents [49], studies across various sports have shown associations with HIIT in metrics like VO2 max, shuttle performance, strength, repetitive sprinting, and jump- ing [37,40], however, there remains a gap in the literature concerning systematic reviews spe- cifically addressing the physiological impacts of HIIT on athletes engaged in racquet sports. Therefore, the primary objective of this study is to explore the influence of HIIT on the strength, speed, and endurance performance of racket sports athletes, employing a systematic literature review approach. 2. Materials and methods 2.1 Protocol and registration The Preferred Reporting Items for Meta-Analysis (PRISMA) guidelines were followed in this literature review to systematically gather, select, and analyze data. The review was registered on the INPLASY website at [https://inplasy.com/], under the identifier INPLASY202320080 [50]. 2.2 Search strategy This study adhered to the PRISMA statement in both its design and execution. A comprehen- sive literature search was conducted across four reputable academic databases: PubMed, EBS- COhost, Scopus, and Web of Science. Google Scholar and Reference were also used as search engines. For each independent database, a carefully crafted search query was implemented, PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 3 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players focusing on the title and abstract. The search employed predefined keywords, utilizing the fol- lowing query: ("High-Intensity Interval Training" OR "HIIT" OR "High-Intensity Intermittent Training") AND ("Strength" OR "Power" OR "Speed" OR "Endurance") AND ("Table Tennis Players" OR "Tennis Players" OR "Badminton Players" OR "Racket Players"). 2.3 Eligibility criteria The systematic searches followed the PICOS framework, which encompasses the following key concepts: 1. Population, 2. Intervention, 3. Comparison, 4. Outcome, and 5. Study design. As outlined in Table 1, the PICOS framework served as the inclusion criteria for publications. The following specific requirements were considered to determine a study’s eligibility for inclusion: 1. The research population must include racket players, such as those in table tennis, tennis, and badminton, irrespective of gender or age. 2. The intervention in the research should specifically focus on HIIT. Distinct and explicit comparisons with alternative training methods must be discussed separately. 3. HIIT should be compared with other training programs. 4. The study must examine at least one aspect of the effect of HIIT on strength, endurance, or speed in racket athletes. 5. The study should include experimental articles, which may consist of two-group controlled trials (randomized or non-randomized) or single-group trials. 2.4 Study selection After two independent authors selected articles that met the inclusion criteria, this review employed an EndNote citation management system to identify and remove duplicates. The titles and abstracts of the papers were assessed by Liu and Borhannudin to determine their suitability for inclusion in this study. In cases where the two authors disagreed on the selection of an article, a third author conducted a comprehensive analysis of the full article to make the final decision. 2.5 Data extraction and quality assessment Upon completing the data retrieval phase, the study extracted key insights from eligible research articles. These insights included crucial details such as author names, publication years, and extensive population characteristics like participant numbers, types, age groups, and gender distribution. Furthermore, meticulous documentation of intervention characteris- tics, including the type, specific measures employed, and frequency, was conducted [51]. To rigorously assess trial quality, the study used the well-established PEDro scale, originally Table 1. PICOS Eligibility criteria. PICOS Population Intervention Comparison Outcome Study design Detailed Information Racket players (table tennis, badminton, tennis) High-intensity interval training (HIIT) HIIT vs. Other training programs The effect of HIIT on strength, endurance, and speed among racket players Two-group controlled trials (randomized/non-randomized)/single-group trial https://doi.org/10.1371/journal.pone.0295362.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 4 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players proposed by Herbert and Elkins [52]. This scale evaluates four essential methodological aspects: randomization, blinding procedures, comparisons between study groups, and the robustness of data analysis. The PEDro scale, built on the foundation of the Delphi list initially developed by Verhagen et al. [51], consists of 11 items, each contributing to a comprehensive assessment of methodo- logical integrity. Two independent raters, specially trained for this task, systematically assessed the quality of trials within the PEDro database. Any discrepancies or conflicts that arose during this evaluation process were carefully considered and resolved through the input of a third, impartial rater [52]. In summary, the PEDro scale, with scores ranging from 1 to 10, serves as a valuable tool for gauging the methodological quality of the studies included. Higher scores indicate superior methodological rigor. Articles that received scores between 8 and 10 were classified as exhibiting excellent methodological quality. Those with scores ranging from 5 to 7 were considered to dem- onstrate a high level of research quality. Articles that scored between 3 and 4 were characterized as having moderate research quality, while those with scores below 3 were deemed to lack suffi- cient methodological rigor and were consequently excluded from this comprehensive study [51]. 3. Results 3.1 Study selection A total of 22 articles were identified during the initial database search: seven from Web of Sci- ence, nine from PubMed, four from EBSCOhost, and two from Google Scholar. Duplicate arti- cles were meticulously removed using EndNote software. Subsequently, a second round of screening eliminated two non-full-text articles and three non-journal articles. In the third screening phase, 17 full-text articles were assessed for eligibility. Seven articles were excluded because they did not align with the subject area of interest. Ten relevant publications satisfied the inclusion criteria and were selected for qualitative synthesis. The detailed process under- taken in this study is visually depicted in Fig 1. 3.2 Study quality assessment As presented in Table 2, the assessment conducted via the PEDro scale revealed that the aver- age score for the data included in this study falls within the range of 3–5. While the quality of the incorporated studies is generally commendable, it is noteworthy that all these studies encountered challenges related to hidden allocation, blinding of participants, and criteria about evaluators, therapists, and intent-to-treat analysis. From among the included studies, five were able to specify eligibility criteria, ensure comparability of baseline groups, conduct comprehensive between-group comparisons, provide precise point estimates, and account for variability. The intervention under scrutiny involves HIIT, which inherently carries associated risks of professional and sports-related injuries, thus achieving complete blinding of partici- pants, evaluators, and therapists becomes a formidable challenge. This highlights the need for future research endeavors to prioritize higher-quality study designs and elevate the level of evi- dence in this domain. 3.3 Participant characteristics Table 3 outlines the key characteristics of the ten studies that met the inclusion criteria. 1. Athlete Categorization: One study focused on table tennis players [53]. Four studies cen- tered around badminton players [54–56,58]. Five studies examined tennis players [24,36,57,59,60]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 5 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Fig 1. The PRISMA flow chart for the search, screening, and selection strategy for the eligible studies. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://wvw.prisma-statement.org/. https://doi.org/10.1371/journal.pone.0295362.g001 2. Sample Size: Across these ten studies, a total of 227 participants were involved, with sample sizes ranging from 13 [59] to 32 subjects [55]. The median sample size was 20.5, and the mean was 22.3. 3. Gender: All ten studies focused on racket athletes [24,36,53–60]. Two studies specifically involved female athletes [53,57]. The remaining eight studies included males at three stud- ies did not include data on subjects’ weight, height, or BMI [36,57]. 4. Age: All ten studies provided information on the age of their subjects, along with the age range [24,36,53–60]. The analysis revealed that participants’ ages ranged from 12 years [57] to 22 years [54]. 5. Body Mass Index (BMI): Five studies reported both height and weight [54–56,58,60]. 3.4 Intervention characteristics Key characteristics of the intervention, including its type, duration, and frequency, were exam- ined across the ten included studies. All studies employed HIIT as the primary intervention, with some studies referring to it simply as interval training. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 6 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Table 2. Summary of methodological quality assessment scores. Study Eligibility Criteria Random Allocation Allocation Concealment Baseline Comparability Blind Therapist Blind Assessor Follow up Intention to Treat Analysis Between Group Comparisons Point Measure and Variability Total PEDro Score 1 0 0 1 0 0 0 0 1 1 TH,P (2017) [53] Liu et al. (2021) [54] Ko et al. (2021) [55] Fuentes et al. (2021) [36] Suppiah, 2019 [56] Fernandez et al. (2012) [57] Wee et al. (2017) [58] Kilit and Arslan, (2019) [24] Rodrı´guez et al. (2017) [59] Fernande et al. (2017) [60] 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 https://doi.org/10.1371/journal.pone.0295362.t002 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 5 5 3 5 4 4 4 4 5 5 The intervention characteristics reported in the ten studies were based on the following: 1. Training Duration: The shortest intervention period was four weeks [36,55,57]. Minimum intervention expected 2 times [59]. Other studies had intervention durations of six weeks [24,57] and eight weeks [54,57], two study had an intervention period of 10 weeks [53,56]. 2. Training Frequency: An analysis of the ten research reports revealed that training frequency ranged from two to three times a week. Specifically, one studie reported a training fre- quency of twice a week [58]. The remaining eight studies conducted training sessions three times per week. 3. Heart Rate: In 5 out of the 10 studies, heart rate was not explicitly documented, while two studies measured heart rate using the Hrmax method [54,55]. Another study utilized Team2 Pro as the criterion for heart rate measurement [53]. 4. Interval Time: Nine of the ten studies employed a 30-second interval, whereas one study utilized a 15-second interval [57]. 3.5 Outcome In this study, the results were organized by categorizing racket athletes based on their perfor- mance in strength, speed, and endurance, all within the context of HIIT. Each author indepen- dently classified the papers according to the specific topics explored within the components of PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 7 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Table 3. Characteristics of the studies examined in the present review. Study Type of athletes Population characteristics Interventions TH, P. (2017) [53]. adolescent table tennis players EG1 = 10, CG = 10 Age:12±1.6yr., WT: 19.68± 2, HT; NR, CG = 12, Liu et al. (2021) [54] Elite Badminton Player Sex: F and M, EG1 = F/8M/8, CG = F/8M/8 Age: M/ 20.0 ± 1.3, F/20.5 ± 1.4, WT: M/73.8±6.9, F/62.6 ± 4.2, HT: M/179.6 ± 3.6cm, F/ 168.5 ± 4.2cm Ko et al. (2021) [55] Adolescent Badminton Players Sex:M, EG1 = 16,CG = 16Age: M/20.0 ± 1.3, F/20.5 ± 1.4, WT: M/73.8±6.9, F/62.6 ± 4.2,HT: M/179.6 ± 3.6cm, F/ 168.5 ± 4.2cm Fuentes et al. (2021) [36] Recreational Tennis Players EG1 = 16, CG = 16 Age: 21.40 ±1.52,AEP = 0.8year WT:NR HT:NR Suppiah et al., (2019) [56] Collegiate athletes (badminton players) Sex:M,Age:20±1,WT = 65.3 ±11, HT = 173.0 ± 5.3, EG = 9, CG = 9 Freq.: 3 times/week, time: 2h-3h, length: 10 weeks Heart rate; Record the whole process Interval Time: 30s Heart Rate: NR Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: NR, length: 8 Weeks Heart rate;50% to 90%HRmax Interval Time:30s Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: 30min, length: 4 weeks Heart rate; Maintain 50% HRmax to Beyond 90% HRmax Interval Time:30s Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: 0.78hours, length: 4weeks Interval Time:30s Heart rate: NR Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: 30mins length: 10weeks Interval Time:30s Heart rate: NR Whether it meets the HIIT training intensity and interval time: Yes Type of exercise training High-intensity interval training group (EG), control group (CG) Measures index Outcomes Endurance; Heart rate (Team2 Pro), Filmate Pro (Vo2 max), Accoutered Plus meter(Blood lactate) Vo2 max" Blood lactate" High-intensity interval training (EG), traditional training (CG) YO-YO IR2 intermittent recovery test, increasing load gas metabolism analysis, and lactate clearance rate test. ventilatory anaerobic threshold", the ventilatory anaerobic threshold in the percentage of VO2max ", lactate clearance", High-intensity interval training (EG), Moderate continuous training (CG)) Wingate test, isokinetic muscle function test peak power" fatigue index" Heart Rate$ Isokinetic Muscle strength" High-intensity interval training (EG), Stroop training (CG)) Serve ball speed, Lower body muscular power, Spirometry, and Isometric hand strength, 、 hitting speed" accuracy score, spirometry$ 20 m Multistage Fitness, Four Corner Agility, 20 m Sprint. VO2max", sprinting " High-intensity interval training (EG), traditional training (CG) (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 8 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Table 3. (Continued) Study Type of athletes Population characteristics Tennis players Fernandez et al. (2012) [57] Sex: M, EG1 = 11, EG2 = 12, CG = 9, Age = 12.0 ± 3.6 years, WT = NR, HT = NR Wee et al. (2017) [58] Collegiate athletes (badminton players) Sex: M, EG = 9, CG = 9 Age: age = 20±1; WT = 65.3±11kg; HT = 173.0±5.3cm Kilit and Arslan, (2019) [24] Young Tennis Players. Sex: M, EG = 14, CG = 15, Age: 13.8 ± 0.4 years, WT = NR, HT = NR Fuentes- Garcı´a (2021) [36] Young tennis player Sex: M, EG = 6, CG = 7, Age:17 ±2 HT: 176.5±4.4, WT: 69.5 ±3.4kg, Fernande et al. (2017) [60] Recreational Tennis Players Sex: M, EG = 10, CG = 10, Age: 14±0.1y, WT = 63.8kg, HT: 174.7±4.8cm Interventions Freq.: 3 times/week, time: 30min, length: 6weeks Interval Time:30s Heart rate: NR Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: NR length: 4weeks Interval Time:15s Heart rate: 165- 185bpm Whether it meets the HIIT training intensity and interval time: Yes Freq.: 3 times/week, time: 20-30min length: 6weeks Interval Time:30s Heart rate: NR Whether it meets the HIIT training intensity and interval time: Yes Freq.: 2 times/week, time: NR length: 3 mouths Heart rate:80% to 140% Wmax Interval Time:30s Whether it meets the HIIT training intensity and interval time: Yes Freq.: 2 times/week, time: 15-30min length: 8 weeks Heart rate: NR Interval Time:30s Whether it meets the HIIT training intensity and interval time: Yes Measures index Outcomes incremental treadmill test, Hit and Turn Tennis Test, Vertical Jumping, Twenty-meter Sprint Run, Repeated-Sprint Ability Shuttle Test VO2max", Hitting speed", 20-m sprint$, 400m" Type of exercise training High-intensity interval training (EG1), repeated- sprint training (EG2), traditional training (CG) High-intensity interval training (EG), traditional training (CG) VO2 max Test, Wingate Ergometer Test, Countermovement Vertical Jump, Drop Jump, and Illinois Agility Test. VO2max", mean power", reactive strength " High-intensity interval training (EG), court tennis training (CG) Maximum oxygen consumption, sprinting, jumping, 400-m running time 10m, 20m sprinting ", VO2max", 400-m running time", sprinting", Standing long jump", High-intensity interval training (EG), intermittent interval training (CG) High-intensity interval training (EG), sport- specific drill training (CG) Average HR, Lactate, Borg scale, Number of shots, Number of errors HR$, Lactate$, Number of shots", Number of errors" Laboratory Test, 30–15 Intermittent Fitness Test, Speed Test, Vertical Jumping, VO2max", 5m$, 10m$, 20m$, jumping strength" 400m" EG, experimental group; CG, control group; WT, weight; NR, notreported; HR: Heart rate; HT, height; Freq.,frequency; M, Male;F, Female; YR, year; ", signifificant within-group improvement from pretest to post-test; $, non-signifificant within-group change from pretest to post-test. https://doi.org/10.1371/journal.pone.0295362.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 9 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players the papers. Any disagreements that arose during this classification process were thoroughly discussed among all the authors until a unanimous consensus was reached. Utilizing this clas- sification framework, the experimental findings from the ten included studies were systemati- cally compiled and subjected to comprehensive summarization and analysis. 3.5.1 Effect of HIIT training on speed in racket sports players. Among the ten studies included in this systematic review, six focused on assessing the impact of HIIT on speed per- formance. These six studies collectively involved 56 male athletes aged 14 to 18 years [24,36,51,56,59,60]. The speed assessments conducted covered a range of distances, including 5 meters, 10 meters, 20 meters [57], 10 and 400 meters [24,57], and a repeated sprint ability test [57,60] The studies examined a diverse group of young athletes, including table tennis players [53], badminton players [55,56,58], and tennis players [24,36,54,57,59,60]. Five of these studies reported notable enhancements in straight-line sprint tests. Further improvements were observed in direction change sprint tests [34,60] and sprint ability tests [24,60]. Additionally, significant improvements were noted in the 400-meter run [24]. One study even reported a significant enhancement in batting speed [36]. The results demonstrated statistically significant differences in most performance tests between the pre-intervention and post-intervention periods within the experimental groups. Specifically, in the straight-line sprint tests, effect sizes (d-values) ranged from 0.40 to 1.10 (p < 0.05). In the direction change sprint tests, d-values ranged from 0.77 to 0.88 (p < 0.05). The repeated sprint ability tests showed a noteworthy reduction in mean sprint time by 3.8% (p < 0.05) [57]. In the 400-meter run, the HIIT group’s performance significantly increased by 5.2% (p < 0.05) [24]. 3.5.2 Effect of HIIT training on strength in racket sports players. Strength is a pivotal factor in racket sports, as it directly influences skill and tactical performance, subsequently impacting the overall quality of sports performance [24,36,56,59,60]. Athletes in racket sports recognize the significance of enhancing their strength to elevate their performance levels, maintain competitiveness, and refine the precision and quality of their play [24,51,56]. In total, nine studies explored the realm of strength training, involving a combined total of 90 male athletes aged 14 to 18 years, with a mean age of 15.5 ± 2.2 years. Among these studies, some reports indicated a significant enhancement in the ATP-CP system among licensed ath- letes following HIIT [24]. Additionally, two studies highlighted the substantial impact of HIIT on lower body strength, as demonstrated in jump strength tests and sprint tests [24,60]. Another study reported an overall improvement in athletes’ explosive power [54]. However, it’s worth noting that one study did not find a significant increase in sprint performance [57]. While numerous studies have evaluated both lower and upper extremity strength, varia- tions often arise due to differences in loading strategies [41,44]. Further research is warranted to delve deeper into the area of strength enhancement in racket sports. In the context of blood lactate concentrations, significant differences were observed between the two time periods (pre-intervention and post-intervention) within the experimental groups during most of the strength tests. These differences were reflected in effect sizes (d-values) ranging from 4.09 ± 0.19 to 3.52 ± 0.76 (p < 0.05) for various strength tests, including lower body jumping and sprint tests. However, it’s essential to acknowledge that one study did not identify a signifi- cant improvement in sprint performance [57]. Given the substantial body of literature examining both lower and upper extremity strength, variations in findings often stem from differences in loading strategies [41,49]. Therefore, additional research efforts are essential for a comprehensive understanding of strength enhancement in this context. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 10 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players 3.5.3 Effect of HIIT training on endurance in racket sports players. Endurance require- ments in sports vary widely, with physical activities categorized based on their static and dynamic components as well as the involvement of various energy systems. Racket sports, notably, fall into the category of moderate-intensity activities. From this perspective, it’s evi- dent that both anaerobic and aerobic energy systems play crucial roles in meeting the energy demands of these sports [10,11,14,61,62]. A total of nine studies in our analysis focused on endurance training, collectively involving 110 male athletes aged 14 to 18 years, with a mean age of 15.5 ± 2.2 years. Notably, Three of the studies showed significant improvements in tennis-specific endurance, with significant reductions in mean HIIT sprint time and improvements in anaerobic capacity [24,36,59]. For badminton players, a similar peak improvement was observed in the 400-meter run- ning time test, accompanied by a substantial 5.2% average increase in vanadium dioxide and a significant 2.4% improvement in performance response. Additionally, there was a notable 6.0% increase in VO2 max levels [55–58]. 4. Discussion This systematic review offers an in-depth analysis of the impact of HIIT on key physical attri- butes—namely strength, speed, and endurance—in racket athletes. Our principal findings underscore significant improvements in athletes’ physical performance attributable to HIIT interventions. Of particular note is HIIT’s pronounced efficacy in enhancing submaximal endurance performance, as demonstrated by increased running speed and oxygen uptake at various thresholds. Additionally, we observed improvements in repetitive sprint performance and linear sprint running when compared to multiple control regimens. Strength, speed, and endurance are fundamental components underpinning racket sports success. Given the uniformly positive outcomes seen in these studies, it becomes evident that HIIT serves as an effective intervention for improving the performance of racket athletes. Building on the analytical framework outlined in the Results section, we have carefully scruti- nized the variables examined in these studies, thereby elucidating the multifaceted effects of HIIT on the physical capabilities of racket athletes. 4.1 Effect of HIIT training on speed in racket sports players Speed is universally acknowledged as a crucial component of motor skills across various sports disciplines [63,64]. In racket sports, speed’s significance is even more pronounced, as it directly affects a player’s hitting velocity and, consequently, the match’s outcome [36,65]. It is impor- tant to note that empirical evidence supporting the efficacy of HIIT in enhancing a tennis play- er’s stroke speed remains relatively limited, with one study standing as an exception [36]. Several studies do suggest, however, that batting speed may be influenced by factors such as motor skill proficiency and muscle contraction velocity [9,36,65]. Additionally, existing research emphasizes the positive impact of HIIT on repetitive sprint performance and linear sprint running [1,24,55,56]. One plausible explanation for these out- comes relates to the nature of HIIT, which places the body in a state conducive to fat loss, mus- cle retention, and even muscle growth. This, in turn, enlarges the muscle cross-sectional area, improving force transmission and, subsequently, speed [66–68]. Another possible reason cen- ters on HIIT’s role in strengthening core muscles [69]. The strategic manipulation of load and intensity enhances the synergy of multiple muscle groups, aiding athletes in maintaining their required center of gravity and coordinating movements, whether for short-term sprints or lat- eral speed bursts [1,24,55,56]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 11 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players HIIT consists of high-intensity exercise regimens executed according to a specific plan. Typically, the training pushes the heart rate to exceed 80% of its maximum, with strict interval timing [39]. Generally, two types of HIIT exist: one features high-intensity, low-interval exer- cises, requiring the heart rate to reach between 100% and 120% of the maximum and lasting for 10–30 seconds, with equivalent rest periods. The other is high-intensity, long-interval training, where the heart rate must reach between 80% and 95% of the maximum and last for 1–3 minutes, with corresponding rest periods [34,44]. A recent study systematically summarized HIIT as characterized by high intensity, heavy load, and interval-limited exercise [18,33]. This form of strength training not only enhances an athlete’s speed but also improves the efficiency of complex movements across different sports. 4.2 Effect of HIIT training on strength in racket sports players Research findings on the beneficial effects of HIIT have contributed substantially to our under- standing of strength development in racket athletes. Two studies offer compelling evidence that HIIT enhances performance in jump tests among these athletes [24,60]. This assertion is corroborated by literature that includes badminton and tennis players [54,60]. Additionally, several metrics within the jump tests—such as jump height, ground contact time, and peak power—showed statistically significant improvements. An intriguing parallel can be drawn from a study involving male soccer players, in which the athletes not only improved their strength levels but also achieved greater technical preci- sion through specific HIIT exercises [70]. Collectively, these findings emphasize the positive influence of HIIT interventions on athletes’ strength. Furthermore, HIIT’s capacity to stimu- late athletes’ sensory nerves and unlock their latent potential and athleticism—enabling them to excel in competitive environments—is noteworthy. HIIT primarily functions to augment the body’s energy supply capacity within the glycoly- sis air supply system and the mixed metabolic system of phosphate and glycolysis air supply [13,71,72]. These results contrast with those reported by Wee et al. (2017), which covered bad- minton players, table tennis players, and tennis players, and suggested that the strength train- ing program under investigation may not have adequately activated the neuromuscular system associated with strength [60,73]. In swimmers, HIIT has been found to positively impact explosive muscle strength [74]. For racket players, the strength of their hitting ability depends not just on the upper arm, shoulder, elbow, and wrist strength during the hitting motion, but also on power transmission from the lower body to the core, as well as the coordinated engagement of various muscle groups in the upper body [8,29,62]. Within this kinetic chain, explosive force plays a vital role [25]. The accelerated running speeds required by HIIT training lead to increased neural drive, resulting in enhanced anaerobic glycolytic activation and the recruitment of additional fast-twitch motor units for brief durations [45]. Consequently, HIIT training programs prove effective in bolstering the muscular power of racket athletes. 4.3 Effect of HIIT training on endurance in racket sports players The influence of HIIT on the endurance of racket athletes can be divided into two compo- nents: aerobic and anaerobic endurance [16]. Racket sports are inherently moderate-intensity activities that balance both static and dynamic elements, utilizing both anaerobic and aerobic energy systems [36]. As a result, endurance is a vital attribute for racket athletes, with elite competitors generally displaying superior levels of it [21]. Recent research indicates that even brief bouts of HIIT can significantly improve an indi- vidual’s maximal oxygen uptake and muscle oxidase activity, thereby enhancing performance PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 12 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players in long-term endurance tasks. From an energy metabolism perspective, anaerobic endurance primarily relies on the phosphagen and glycolysis systems. Notably, HIIT primarily employs the glycolytic energy supply system and a blended metabolic system incorporating both phos- phate and glycolysis [35,71,72,75]. One study demonstrated that HIIT significantly improves table tennis players’ VO2max and lactic acid uptake in blood [53]. Although HIIT is classified as anaerobic exercise, the after-burn effect induced by intervals allows athletes to engage in continuous, moderate-inten- sity exercise [18,24]. Moreover, research has shown that HIIT positively impacts the aerobic capacity of racket athletes, particularly by increasing their maximum oxygen uptake and meta- bolic capacity [36,54,60]. Maximum oxygen uptake, commonly referred to as VO2 max, serves as a key objective measure for assessing both anaerobic and aerobic endurance [70,75,76,77]. Higher oxygen consumption during intense physical activities signifies increased energy production through aerobic metabolism [30,45,68,70]. Numerous studies indicate that HIIT can elevate peak VO2 max among seasoned racket athletes [55–58]. The efficacy of HIIT in boosting maximal oxy- gen uptake stems from the high-intensity intervals that enable athletes to reach or briefly exceed their anaerobic thresholds, followed by periods of lower aerobic intensity. This pattern overloads the cardiorespiratory system, inducing continuous adaptations in heart and lung functions and ultimately increasing VO2 max. Moreover, HIIT improves not only aerobic but also anaerobic endurance in racket athletes [22,24,36]. "A study by Ko et al. (2021) found that HIIT elevated the lactate and anaerobic thresholds as percentages of VO2max by four percent compared to athletes who underwent moderate continuous training (MCT) with rackets. This finding aligns with the hypothesis that HIIT can generate higher levels of intensity in the heart, lungs, and muscles, thereby enhancing anaerobic metabolism. The lactate threshold is a crucial indicator of endurance, marking the point where energy supply shifts from aerobic to anaerobic metabolism. During HIIT sessions, the intensity is so high that oxygen uptake falls short of fulfilling aerobic metabolism demands, causing a surge in anaerobic energy production. This leads to an accumulation of lactic acid, contributing to muscle fatigue [10,11,14,22]. Additional research confirms HIIT’s positive effects on anaerobic capacity; it not only increases the body’s resilience to lactic acid but also its ability to metabo- lize and effectively clear or recycle it. This leads to an elevated lactate threshold, meaning that during more intense exercises, there is no significant accumulation of lactic acid, signaling an improvement in anaerobic capacity [24,36,51,55,56,59]. 5. Limitations While this review offers valuable insights into the effects of HIIT on speed, strength, and endurance in racket athletes, it is important to acknowledge certain limitations and areas for improvement: Limited Scope: The existing literature predominantly focuses on HIIT in team-based racket sports like tennis and badminton, with less emphasis on individual sports such as squash and table tennis. Further research addressing the unique needs and benefits of HIIT in these indi- vidual sports is warranted. Comparative Analysis: Although the effectiveness of HIIT has been explored in comparison to traditional training methods, the literature currently lacks a detailed comparison with emerging training techniques. Investigating how HIIT fares against these newer methods could provide a more comprehensive understanding of its impact. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 13 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players 6. Conclusion This systematic review illuminates the growing body of evidence supporting the effectiveness of HIIT in enhancing speed, strength, and endurance in athletes participating in racket sports like tennis, table tennis, and badminton. These findings emphasize HIIT’s potential to improve overall athletic performance compared to other training methods. However, it is worth noting that limited research exists on how HIIT specifically influences skill components in racket sports, such as stroke techniques and match strategies. Future research should focus on these areas to offer comprehensive insights that could help athletes improve their skill performance in competitive settings. 7. Practical application In the world of racket sports—be it tennis, badminton, or table tennis—athletes require a diverse skill set that includes speed, strength, endurance, and rapid recovery. These sports demand quick movements, fast reflexes, and sustained performance, making physical condi- tioning a cornerstone for success. Enter HIIT, a training methodology that has garnered signif- icant attention for its ability to boost athletic performance across various domains. When applied to racket sports, HIIT serves as a potent tool for athletes aiming to elevate their game. Known for its efficiency in expending a substantial amount of energy within a short time frame, HIIT has particular relevance for racket sports, where it can lead to improved physiol- ogy, particularly in terms of speed and power. The brief, intense bursts of activity in HIIT align well with the explosive movements required in these sports. By incorporating HIIT into their training regimens, athletes can markedly improve their ability to execute quick, impactful plays on the court or table. Additionally, HIIT has the unique ability to stimulate sensory nerves, contributing to improved proprioception and agility. This enhanced sensory awareness allows athletes to make split-second decisions with greater precision, a vital skill in any racket sport. Metabolically, HIIT aligns well with the energy demands of racket sports, primarily engaging the glycolytic energy system vital for short bursts of high-intensity effort. This form of training mimics the stop-and-start nature of these sports, allowing athletes to optimize their speed, strength, and endurance while managing energy resources efficiently. In summary, HIIT acts as a performance enhancer for racket athletes, fine-tuning their physiological and sensory attributes, and leading to notable improvements in speed, strength, and endurance. Therefore, it is strongly recommended that racket athletes incorporate HIIT into their regular training routines, ideally at a minimum frequency of two sessions per week over four weeks. This structured approach could help athletes unlock their full potential and excel in competi- tive environments. Supporting information S1 Checklist. PRISMA 2020 checklist. (DOCX) S1 Data. (DOCX) Author Contributions Conceptualization: Yixuan Liu. Data curation: Yixuan Liu. Formal analysis: Borhannudin Bin Abdullah. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295362 January 5, 2024 14 / 19 PLOS ONEHIIT on strength, speed, and endurance performance among racket sports players Investigation: Yixuan Liu. Methodology: Yixuan Liu. Project administration: Borhannudin Bin Abdullah. Resources: Borhannudin Bin Abdullah. Supervision: Hazizi Bin Abu Saad. Validation: Hazizi Bin Abu Saad. Visualization: Hazizi Bin Abu Saad. Writing – original draft: Yixuan Liu. Writing – review & editing: Yixuan Liu. 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10.1371_journal.pgph.0002801	RESEARCH ARTICLE Improving maternal and neonatal outcomes among pregnant women who are HIV-positive or HIV-negative through the Saving Mothers Giving Life initiative in Uganda: An analysis of population-based mortality surveillance data Maureen NabatanziID Sandra Nabatanzi2, Phoebe Nabunya1, Benon KwesigaID Patrick Komakech5 1¤, Julie R. Harris2, Phoebe NamukanjaID 1, Alex R. Ario4, 2, Steven N. Kabwama3, 1 Uganda Public Health Fellowship Program, Ministry of Health, Kampala, Uganda, 2 Division of Global Health Protection, US Centers for Disease Control and Prevention, Kampala, Uganda, 3 Department of Community Health and Behavioral Sciences, Makerere University School of Public Health, Kampala, Uganda, 4 Uganda National Institute of Public Health, Ministry of Health, Kampala, Uganda, 5 Office of Health and HIV, US Agency for International Development, Kampala, Uganda ¤ Current address: Center for Development Research, University of Bonn, Bonn, Germany maureen.nabatanzi@uni-bonn.de, mnabatanzi@musph.ac.ug Abstract HIV infection is associated with poor maternal health outcomes. In 2016, the maternal mor- tality ratio (MMR) in Uganda was 336/100,000, and the neonatal mortality rate (NMR) was 19/1,000. Saving Mothers, Giving Life (SMGL) was a five-year maternal and neonatal health strengthening initiative launched in 2012 in Uganda. We extracted maternal and neonatal data for 2015–2016 from the initiative’s population-based mortality surveillance system in 123 health facilities in Western Uganda. We collected data on the facilities, HIV status, anti- retroviral drug (ARV) use, death, birth weight, delivery type, parity, Apgar scores, and com- plications. We compared mother and baby outcomes between HIV-positive or HIV- negative, computed risk ratios (RR) for adverse outcomes, and used the chi-square to test for significance in differences observed. Among 116,066 pregnant women who attended and gave birth at SMGL-implementing facilities during 2015–2016, 8,307 (7.7%) were HIV- positive, of whom 7,809 (94%) used antiretroviral drugs (ARVs) at the time of delivery. Dur- ing birth, 23,993 (21%) women experienced �1 complications. Neonate Apgar scores <7 (8.8%) and maternal haemorrhage during birth (1.6%) were the most common outcomes. Overall facility MMR was 258/100,000 and NMR was 7.6/1,000. HIV infection increased risk of maternal death (RR = 3.6, 95% Confidence Interval (CI) = 2.4–5.5), maternal sepsis (RR = 2.1, 95% CI = 1.3–3.3), and infant birth weight <2,500g (RR = 1.2, 95% CI = 1.1–1.3), but was protective against maternal complications (RR = 0.92, 95% CI = 0.87–0.97) and perina- tal death (RR = 0.78, 95% CI = 0.68–0.89). Among the HIV-positive, ARV non-use increased risk of maternal death (RR = 15, 95% CI = 7.1–31) and perinatal death (RR = 2.3, 95% CI = 1.6–3.4). SMGL reduced facility MMR and NMR below national rates. HIV-infection was associated with maternal sepsis and death. Failure to use ARVs among women living with a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Nabatanzi M, Harris JR, Namukanja P, Kabwama SN, Nabatanzi S, Nabunya P, et al. (2024) Improving maternal and neonatal outcomes among pregnant women who are HIV-positive or HIV-negative through the Saving Mothers Giving Life initiative in Uganda: An analysis of population- based mortality surveillance data. PLOS Glob Public Health 4(2): e0002801. https://doi.org/ 10.1371/journal.pgph.0002801 Editor: Nazmul Alam, Asian University for Women, BANGLADESH Received: August 21, 2023 Accepted: December 12, 2023 Published: February 1, 2024 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: The datasets upon which our findings are based belong to the US Centers for Disease Control and Prevention. For confidentiality reasons, the datasets are not publicly available. However, the data sets can be availed upon reasonable request from the secretariat of the Uganda Public Health Fellowship program at jnamagulu@musph.ac.ug, and with PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 1 / 12 PLOS GLOBAL PUBLIC HEALTHpermission from the US Centers for Disease Control and Prevention. Funding: The United States President’s Emergency Plan for AIDS Relief (PEPFAR) funded the Saving Mothers Giving Live initiative which generated data analyzed in this project. The PEPFAR through the US Centers for Disease Control and Prevention Cooperative Agreement number GH001353–01 and through Makerere University School of Public Health supports the Uganda Public Health Fellowship Program, MoH. The first author undertook this project as part of the Public Health Fellowship Program training but did not receive specific funds for the project. The staff of the funding body provided technical guidance in the design of the study, ethical clearance and collection, analysis, and interpretation of data and in writing the manuscript. Competing interests: The authors have declared that no competing interests exist. Maternal and neonatal outcomes among women who are HIV-positive or HIV-negative HIV increased the risk of maternal and perinatal death. Use of the SMGL approach and complementary interventions that further strengthen HIV care, may continue to reduce MMR and NMR. Introduction In the last 30 years, both maternal and neonatal mortality have been declining globally due to concerted strategies to improve equitable access to quality maternal and newborn health care [1, 2]. In 2015, the maternal mortality rate (MMR) in low-income settings was 482/100,000 (compared with 11/100,000 in high-income settings), while the neonatal mortality rate (NMR) was 30/1,000 live births (compared with 3/1,000 in high-income settings) [3, 4]. There are mul- tiple contributors to these high rates in low-income settings, including limited access to health services around the time of delivery to treat hemorrhage, infections (including HIV), high blood pressure, unsafe abortions, and obstructed labor [5, 6]. Similarly, maternal infections and complications around the time of birth, such as asphyxia and congenital birth defects increase the risk of neonatal death [2]. The link between HIV infection in pregnancy and adverse maternal and neonatal outcomes including maternal anaemia, tuberculosis, miscar- riages, still births, preterm births and low birth weight babies is well established [7, 8]. In low- income settings, risk of adverse outcomes among HIV infected pregnant women is further complicated by co-infections such as malaria and micronutrient deficiencies like anaemia [9, 10]. Even in the presence of other effective interventions, untreated HIV infection, and/or low CD4 counts has an independent impact on both maternal and neonatal mortality [7, 11]. Most maternal and newborn deaths can be prevented by improving women’s access to facility-based skilled health services. For pregnant women, these include treatment for hypertension and malaria, drugs and transfusions to manage severe bleeding during childbirth, and the use of antiretroviral drugs (ARVs) for women living with HIV [6]. Essential care for newborns includes thermal protection, umbilical cord care, breastfeeding, preventive ARVs if exposed to HIV, immunization, nutrient supplements, and responding to danger signs like feeding diffi- culties and breathing problems, reduced activity, fever or convulsions [2, 6]. Despite the avail- ability of many of these interventions in Sub-Saharan Africa, as of 2015, the regional neonatal mortality rate was the highest in the world [4, 5]. The 2016 Uganda demographic health survey reported maternal mortality and neonatal mortality rates of 336 per 100,000 and 19 per 1,000 live births, respectively [12]. To accelerate progress towards reducing MMR and NMR, in 2015, the United Nations set global Sustainable Development Goal targets of MMR at <70/100,000 and NMR at <12/1,000 by 2030 [13]. Countries implemented interventions to achieve these through health financing reforms, antenatal, obstetric, and postnatal care, ARVs for HIV-infected pregnant and post- partum women, and essential newborn care [14–16]. One such intervention was the Saving Mothers, Giving Life (SMGL) initiative, funded by the United States President’s Emergency Plan for AIDS Relief (PEPFAR) and launched in 2012 in Uganda and Zambia to reduce mater- nal and neonatal mortality [17]. In targeted districts, public and private health networks received support to reduce women’s time to seeking, reaching, and receiving quality maternal, newborn, and HIV health services including HIV testing for pregnant women and provision of ARVs [17]. Within these districts, health centres implementing SMGL ensured that every pregnant woman had access to safe delivery and testing of newborns exposed to HIV. In the event of complications, life-saving emergency obstetric and newborn care was provided within PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 2 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative two hours [18]. Provision of HIV services for pregnant women is especially important in coun- tries with high HIV prevalence, such as Uganda, in which the prevalence of HIV among adult women was 7.6% in 2016 and 7.1% in 2020 [19, 20]. In Uganda, SMGL was piloted in 2012 in Kibaale, Kabarole, Kyenjonjo, and Kamwenge Districts in Western Uganda [18, 21]. In addition to obstetric and newborn care during the first year of implementation in Uganda, 82% of SMGL facilities provided HIV testing and anti- retroviral drugs (ARVs) for expectant women who were HIV-positive [22]. All SMGL facilities together achieved a 35% reduction in MMR in this first year [21]. In 2014, SMGL was expanded to six districts in Northern Uganda [18]. By the end of the five-year initiative, SMGL had successfully reduced maternal mortality by 44% in target facilities in Uganda [17]. How- ever, it was unclear if both HIV-positive and HIV-negative populations attending SMGL facili- ties had achieved the same benefits in maternal and perinatal outcomes. We compared maternal and perinatal outcomes and mortality rates among women who were HIV-positive or HIV-negativein SMGL facilities in the four pilot districts of western Uganda during 2015 and 2016 to inform future interventions modelled on the SMGL initiative. Methods Study site In Uganda, maternal and perinatal healthcare services are provided based on the level of health facility a woman visits. The lowest level facilities, known as health centre (HC) IIs, provide essential obstetric care, HIV services, and intermittent preventive treatment for malaria, while HC IIIs provide normal and assisted deliveries, basic emergency obstetric care, and first aid for obstetric complications. In addition to the services provided by the lower levels, HC IVs and hospitals provide comprehensive emergency obstetric care such as Cesarean sections and blood transfusions, and regional and national referral hospitals receive referrals for women with high-risk pregnancies [23]. This study utilized secondary data from health facilities that implemented the SMGL initia- tive in the four pilot districts: Kibaale, Kabarole, Kyenjonjo, and Kamwenge in Western Uganda during 2015–2016. In the four districts, SMGL initiative data used were from 123 health facili- ties for 2015 and 2016. Pregnant women who attended the SMGL implementing health facilities accessed the maternal and perinatal services and thus participated in the initiative. Data source and inclusion criteria We used routinely-collected data for pregnant women collected between 2015 and 2016 and kept in the SMGL database from the four pilot facilities. All pregnant women who were resi- dents of the districts of the facilities were included in the SMGL initiative. In total, 116,066 pregnant women participated in the initiative. These secondary data were collected using Preg- nancy Outcomes Monitoring System, a population-based mortality surveillance system used for the SMGL initiative. The Pregnancy Outcome Monitoring System captured pregnant women and their babies’ data on district, death, birth weight, delivery type, Apgar scores, com- plications, and ARV use (for pregnant women who are HIV-positive). Apgar scores are used to assess newborns’ physiology immediately after birth and to monitor response to resuscita- tion [24]. During Apgar scoring, the newborn’s color, heart rate, reflexes, muscle tone, and res- piration are assessed for signs of insufficient blood flow and scored. The scores are recorded at 1 minute after birth and afterwards at intervals of 5 minutes; scores between 7 to 10 are consid- ered positive, while scores below 7 are considered low [24]. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 3 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Data analysis The MMR was computed as a ratio of maternal (pregnancy or childbirth-related) deaths occurring within 42 days of delivery to total deliveries per 100,000 [4]. NMR was computed as a proportion of neonatal deaths (live births during the first 28 completed days of life) to total births per 1,000 [3]. Maternal outcomes were monitored up to 42 days after delivery. We ana- lyzed using Stata [25] and described maternal district, age, and parity, HIV prevalence, and ARV use among women and babies as means, medians, and proportions. We computed the proportion of pregnant women who participated in the SMGL initiative at each health facility level, delivery types, and outcomes using Stata and plotted graphs of delivery types using Microsoft Excel. Maternal outcomes (death, hemorrhage, sepsis, malaria, and anemia) and infant outcomes (neonatal death, stillbirth, Apgar scores, and birth weight) were analyzed as proportions. We compared the risk of adverse maternal and perinatal outcomes among women were HIV-positive and women who were HIV-negative and used the chi-square to test for associa- tion. Statistical significance was set at p<0.05. Ethics statement This study used secondary data collected by the SMGL initiative (2014–2016) during routine standard of care activities embedded in the district health care system. These data were accessed on 18 July, 2019 for this study. The data were aggregated with no individual patient identifiers and the authors did not have access to information that could identify individual participants. The study protocol was reviewed and approved locally by the Makerere Univer- sity Higher Degrees, Research and Ethics committee, as well as the US Centers for Disease Control and Prevention (CDC) through the Science Integrity Branch of the Division of Global HIV and TB in accordance with CDC human research protection procedures and was deter- mined to be non-research. Data were only accessed by the study team. This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. §See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq. Results Sites of the SMGL initiative Our study focused on 123 health facilities that implemented the SMGL initiative in Kibaale, Kabarole, Kyenjonjo, and Kamwenge Districts in Western Uganda (Fig 1). Among the four districts, 116,066 pregnant women participated in the SMGL initiative at their facility of attendance (Fig 1). Of the health facilities, 68 (55%) were HC III (Fig 2). Of the 116,066 pregnant women who participated in the SMGL initiative, 48,580 (42%) accessed the services at HC IIIs (Fig 2). Characteristics of women and newborns in the SMGL initiative We abstracted maternal and perinatal data for 116,066 women; the median age was 23 years, mean parity was 2.6 children, and 8,307 (7.2%) were HIV-positive. Among the 8,307 women who were HIV-positive, 7,809 (94%) used ARVs, and 7,173 (86%) of their babies were enrolled on ARVs immediately after birth (Table 1). PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 4 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Fig 1. Number of health facilities and pregnant women participating in the SMGL initiative in four districts in Western Uganda, 2015–2016. https://doi.org/10.1371/journal.pgph.0002801.g001 Maternal and perinatal outcomes at SMGL-implementing facilities Among the 116,066 pregnant women at SMGL-implementing facilities, 288 died (MMR = 258/ 100,000). Haemorrhage (n = 1,891, 1.6%) and sepsis (n = 666, 0.57%) were the most common adverse maternal outcomes. Five hundred and ninety-five (<1%) women had malaria at the time of delivery, and 237 (0.2%) were anaemic. There were 830 neonatal deaths (NMR = 7.6 per 1,000). Low Apgar score (<7) at 1 min (n = 9,494, 8.8%) and low birth weight (<2,500g) (8,218, 7.9%) were the most common neonatal and perinatal adverse outcomes (Table 2). For the 114,899 (99%) women for whom delivery type was recorded, 94,807 (83%) had a spontaneous vaginal delivery (SVD), 20,092 (17%) required an assisted delivery, 14,212 (12%) had a Cesarean section, and 108,887 (95%) received services for active management of the third stage of labor (Fig 3). The study data included 97 types of maternal complications. In total, 23,993 (21%) experienced �1 complication, including obstructed labor (3,621, 15%) and abortion (3,868, 3.2%). PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 5 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Fig 2. Levels of health facilities and proportion of pregnant women that attended the facilities during the SMGL initiative in Western Uganda, 2015–2016. https://doi.org/10.1371/journal.pgph.0002801.g002 Associations between HIV status, ARV use and outcomes of women and newborns in the SMGL initiative The risk of maternal death at SMGL sites was 3.6 times higher among pregnant women living with HIV than those who were HIV-negative (RR = 3.6, 95% CI = 2.4–5.5) (Table 3). Pregnant Table 1. Demographic characteristics of women and babies in the SMGL initiative in four pilot districts in West- ern Uganda, 2015–2016. Characteristic Women in SMGL Median maternal age HIV-positive ARV use among HIV-positive ARV use among babies who were HIV-exposed Mean parity SD = Standard Deviation https://doi.org/10.1371/journal.pgph.0002801.t001 Statistic 116,066 23 (Range = 11–57) 8,307 (7.2%) 7,809 (94%) 7,173 (86%) 2.6 (SD±2.1) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 6 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Table 2. Maternal and perinatal or neonatal outcomes among pregnant women in the SMGL initiative in Western Uganda, 2015–2016. Outcomes (n = 116,066) Maternal outcome Hemorrhage Sepsis Malaria Maternal death Anaemia Perinatal or Neonatal outcome Low Apgar score at 1min Low birth weight Low Apgar score at 5min Fresh still birth Macerated still birth Neonatal death n (%) 1,891 (1.6) 666 (0.57) 595 (0.51) 288 (0.26) 237 (0.20) 9,494 (8.8) 8218 (7.9) 4,911 (4.9) 1,740 (1.6) 1,339 (1.2) 830 (0.76) https://doi.org/10.1371/journal.pgph.0002801.t002 women living with HIV also had an increased risk of maternal sepsis (RR = 2.1, 95% CI = 1.3– 3.3) and low birthweight for their infants (RR = 1.2, 95% CI = 1.1–1.3). However, being HIV- positive was mildly protective against having a maternal complication (RR = 0.95, 95% CI = 0.87–0.97) and perinatal death (RR = 0.78, 95% CI = 0.68–0.89) (Table 3). Fig 3. Types of delivery among pregnant women in the SMGL initiative in Western Uganda, 2015–2016. https://doi.org/10.1371/journal.pgph.0002801.g003 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 7 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Table 3. Associations between HIV status and maternal and perinatal or neonatal outcomes in the SMGL initiative in Western Uganda, 2015–2016. Outcomes Maternal outcome Death Sepsis Anemia Assisted delivery Hemorrhage Any complication Perinatal or neonatal outcome Low birth weight Neonatal death Low Apgar score at 1min Fresh still birth Macerated still birth Low Apgar score at 5min statistically significant association https://doi.org/10.1371/journal.pgph.0002801.t003 HIV-positive n = 8,307 (%) HIV-negative n = 99,339 (%) RR (95% CI) 28 (0.34) 19 (0.23) 10 (0.12) 1,071 (13) 122 (1.5) 1,208 (15) 697 (8.4) 55 (0.66) 555 (6.7) 94 (1.1) 76 (0.91) 275 (3.3) 93 (0.094) 111 (0.11) 98 (0.99) 12,616 (13) 1,458 (1.5) 15,698 (16) 7,212 (7.3) 723 (0.73) 8,381 (8.4) 1,489 (1.5) 1,199 (1.2) 4,340 (4.4) 3.6 (2.4–5.5) 2 (1.3–3.3)* 1.2 (0.64–2.3) 1 (0.96–1.1) 1 (0.83–1.2) 0.92 (0.87–0.97)* 1.18 (1.09–1.3)* 0.91 (0.69–1.19) 0.81 (0.75–0.88)* 0.76 (0.61–0.93)* 0.76 (0.60–0.96)* 0.76 (0.6–0.87)* Pregnant women living with HIV who did not use ARVs had an increased risk of having any complication (RR = 1.9, 95% CI = 1.6–2.2). This group also had a higher risk of maternal sepsis (RR = 19, 95% CI = 7.7–47), maternal death (RR = 15, 95% CI = 7.1–31), and their babies were twice as likely to die (RR = 2.3, 95% CI = 1.6–3.4) compared to pregnant women living with HIV who used ARVs (Table 4). Discussion At health centres implementing the SMGL initiative that were evaluated in Uganda, common adverse outcomes among pregnant women included hemorrhage and sepsis; among new- borns, common adverse outcomes included low Apgar scores and low birth weight. Being HIV positive and failing to use ARVs among the women who were HIV-positive increased the risk of adverse maternal and perinatal outcomes. In our study, the MMR was 258 per 100,000, lower than the pre-SMGL (baseline) district- wide MMR of 452 per 100,000 in 2012 (P < 0.0001) and the national rate of 336 per 100,000 Table 4. Associations between ARV use and maternal and perinatal or neonatal outcomes among women living with HIV enrolled in SMGL initiative in Western Uganda, 2015–2016. Outcomes among HIV positive Didn’t use ARVs n = 561 (%) Used ARVs n = 7,746 (%) Maternal sepsis Maternal death Maternal malaria Maternal anaemia Perinatal death Neonatal death Any maternal complication Low birth weight Maternal hemorrhage 11 (1.9) 14 (2.5) 7 (1.2) 2 (0.36) 30 (5.3) 7 (1.2) 146 (26) 55 (9.8) 10 (1.8) statistically significant association https://doi.org/10.1371/journal.pgph.0002801.t004 8 (0.10) 14 (0.18) 13 (0.17) 8 (0.10) 194 (2.5) 48 (0.62) 1,062 (14) 642 (8.3) 112 (1.4) RR (95% CI) 19 (7.7–47) 15 (7.1–31)* 7.4 (2.9–19)* 3.5 (0.73–16) 2.3 (1.6–3.4)* 2.1 (0.99–4.6) 1.9 (1.6–2.2)* 1.3 (0.9–1.6) 1.2 (0.65–2.3) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 8 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative live births in 2016 (P = 0.0008) [12, 18, 26]. Similarly, the SMGL facility NMR of 7.6 per 1,000 was lower than the baseline pre-discharge NMR of 8.4 per 1,000 in 2012 (p = 0.04) and the national rate of 19 per 1,000 in 2016 (p<0.0001) [12, 18, 26]. The improvements in maternal and neonatal mortality rates reported by SMGL facilities indicate the potential of this intensive approach for lowering deaths among pregnant women and newborns in Uganda. When appropriately implemented, the SMGL approach can limit the risk of the leading causes of maternal deaths in Uganda, which are delays in seeking and accessing quality care to manage complications [17, 27]. The prevalence of anaemia at the time of delivery in this group was less than one percent. In contrast, a 2021 Ugandan metanalysis reported a prevalence of anaemia in pregnancy of 30% [28]. Although the Ministry of Health recommends the provision of iron and folic acid supplementation to pregnant women to prevent anemia, low adherence and drug shortages interfere with program effectiveness [29]. At SMGL facilities, pregnant women had access to supplements and close monitoring of anaemia, which could have contributed to the low prevalence. Through SMGL implementation, accessible lifesaving emergency care for pregnant women and optimal newborn care for newborns can further reduce complications arising during labor, delivery, and immediately after birth [17, 27]. The HIV prevalence in this group (7.2%) was comparable to the 2017 national prevalence of 7.6% among women [19]. Pregnant women living with HIV in our study had a higher risk of maternal sepsis, death, and of having babies with low birth weight. HIV infection in preg- nancy is associated with altered immune responses in both mother and child and an increased risk of adverse outcomes [8, 30]. At health centres implementing SMGL, HIV testing and treat- ment were integrated into routine labour and delivery services [22], providing mothers who tested positive with opportunities for maternal care. This could potentially explain the reduced risk of maternal complication and of perinatal death among babies of women who were women observed in our study. However, further investigation would be needed to confirm this hypothesis. ARV use in this group (94%) was comparable to the national ARV use rates among women who are HIV-positive (95%) [19]. In 2012, Uganda launched the Option B+ prevention of mother to child transmission (PMTCT), in which all HIV-positive pregnant women and their exposed babies received ARVs, regardless of their CD4 levels [19]. Furthermore, HIV testing and provision of ARVs was an integral component of the SMGL approach [22]. Although ARV use among women who were HIV-positive in our analysis was high, women who did not use ARVs had a significantly increased risk of specific complications, malaria, sepsis, and of having babies with low birthweight. HIV -induced immune suppression is linked to increased susceptibility to infections like tuberculosis, pneumonia, and sepsis during pregnancy and the postpartum period [8]. ARVs suppress viral replication, and can reduce susceptibility to infec- tions that may affect maternal and perinatal outcomes [31]. Our findings are consistent with previous reports that preterm births, low birthweight, and still births are higher among HIV- infected non-ARV users than those using ARVs [7, 11]. Despite Uganda’s success in the implementation of PMTCT, challenges in wider access to and utilization of services remain. Delays to initiate and sustain antenatal care (ANC), long patient waiting times, HIV-related stigma, and transportation to the health facilities are some of the barriers that affect adherence [32, 33]. Even in health centres where quality obstetric and neonatal care and ARVs are accessible, such as in SMGL-implementing facilities, strengthen- ing measures to promote adherence, such as the provision of spaces that are conducive for communication between health care providers and women who are HIV-positive, can improve attendance at antenatal care and potentially early enrolment on ARVs [32]. Research on innovative complementary services to encourage women who are HIV-positive to adhere PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 9 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative to using ARVs could be useful. Our findings complement evidence that the SMGL approach provides life-saving health services for mothers and newborns [17]. Limitations The analysis was carried out using previously collected data, and therefore it was not possible to assess other factors that could affect maternal and perinatal outcomes like ANC attendance, partner support, transport or access to the health centre, and HIV viral load. The SMGL sites were purposively selected for the program implantation and this could limit the generalizabil- ity of the findings. The conclusions of the study are, however, based on a significant sample size of 116,066 women collected over a two-year period. Conclusion Health facilities that implemented the SMGL initiative had lower MMR and NMR than national rates. There was an increased risk of maternal death, sepsis, and low birth weight among women who were HIV-positive. Not using ARVs increased the risk of all adverse maternal and perinatal outcomes. To some extent, lessons learnt from SMGL are being sus- tained in Uganda’s maternal health care system package [17]. Commitment to improving health care continues through improved policies and capacity building by the Ministry of Health; empowered community health workers continue to advocate for improved services at rural facilities [18]. In addition to sustaining the SMGL approach to improving maternal and newborn health services, strengthening care for pregnant women who are HIV-positive and their newborns in health centres in Uganda and developing additional approaches that can sustainably improve antiretroviral therapy enrolment and adherence among this group, even at facilities already equipped with maternal and newborn health services, may continue to reduce MMR and NMR. Acknowledgments We would like to acknowledge the Uganda MoH, the US Centers for Disease Control and Pre- vention and the Saving Mothers Giving Live initiative implementors for allowing us to use these data. We appreciate Dr Alice Assimwe of Baylor College of Medicine Children’s Founda- tion, Kampala, Uganda for her contribution to the initiative’s implementation in Uganda. Author Contributions Conceptualization: Maureen Nabatanzi, Patrick Komakech. Data curation: Maureen Nabatanzi, Phoebe Nabunya. Formal analysis: Maureen Nabatanzi, Sandra Nabatanzi, Phoebe Nabunya. Methodology: Maureen Nabatanzi. Project administration: Steven N. Kabwama, Alex R. Ario. Software: Maureen Nabatanzi. Supervision: Julie R. Harris, Phoebe Namukanja, Steven N. Kabwama, Benon Kwesiga, Alex R. Ario, Patrick Komakech. Validation: Patrick Komakech. Visualization: Maureen Nabatanzi. Writing – original draft: Maureen Nabatanzi. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002801 February 1, 2024 10 / 12 PLOS GLOBAL PUBLIC HEALTHMaternal and neonatal outcomes among women who are HIV-positive or HIV-negative Writing – review & editing: Maureen Nabatanzi, Julie R. Harris. References 1. World Health Organization. Strategies toward ending preventable maternal mortality (EPMM) Geneva, Switzerland; 2015. 52. Available from: https://www.who.int/publications/i/item/9789241508483 2. World Health Organization. Newborn Mortality 2022 [updated 28 January 202230 March 2023]. Avail- able from: https://www.who.int/news-room/fact-sheets/detail/levels-and-trends-in-child-mortality- report-2021#:*:text=Preterm%20birth%2C%20intrapartum%2Drelated%20complications,causes% 20of%20most%20neonatal%20deaths 3. World Bank. Mortality rate, neonatal (per 1,000 live births) - High income, Low income 2023 [6 April 2023]. Available from: https://data.worldbank.org/indicator/SH.DYN.NMRT?locations=XD-XM&view=chart 4. WHO U, UNFPA, World Bank Group, and the United Nations Population Division. Maternal mortality ratio (modeled estimate, per 100,000 live births) - Low income, High income Geneva: World Health Organization; 2019 [6 April 2023]. Trends in Maternal Mortality: 2000 to 17. 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Kiwanuka TS, Ononge S, Kiondo P, Namusoke F. Adherence to iron supplements among women receiving antenatal care at Mulago National Referral Hospital, Uganda-cross-sectional study. BMC Res Notes. 2017; 10(1):510. Epub 2017/10/27. https://doi.org/10.1186/s13104-017-2834-z PMID: 29070052; PubMed Central PMCID: PMC5657073. 30. Pfeifer C, Bunders MJ. Maternal HIV infection alters the immune balance in the mother and fetus; impli- cations for pregnancy outcome and infant health. Curr Opin HIV AIDS. 2016; 11(2):138–45. https://doi. org/10.1097/COH.0000000000000239 PMID: 26679415. 31. Garvy BA, Feola DJ, Thornton AC. Effects of Antiretroviral Therapy on Immunity in Patients Infected with HIV. Current pharmaceutical design. 2006; 12(9):1015–22. https://doi.org/10.2174/ 138161206776055886 PMID: 16515483 32. Wanyenze RK, Goggin K, Finocchario-Kessler S, Beyeza-Kashesya J, Mindry D, Birungi J, et al. 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10.1371_journal.pmed.1004313	RESEARCH ARTICLE Impact on wine sales of removing the largest serving size by the glass: An A-B-A reversal trial in 21 pubs, bars, and restaurants in England Eleni MantzariID J. HollandsID 1, Minna VentselID 1,2, Theresa M. MarteauID 1* 1, Emily Pechey1, Ilse LeeID 1, Mark A. PillingID 1, Gareth a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Behaviour and Health Research Unit, University of Cambridge, Cambridge, United Kingdom, 2 EPPI Centre, UCL Social Research Institute, University College London, London, United Kingdom * tm388@cam.ac.uk Abstract OPEN ACCESS Background Citation: Mantzari E, Ventsel M, Pechey E, Lee I, Pilling MA, Hollands GJ, et al. (2024) Impact on wine sales of removing the largest serving size by the glass: An A-B-A reversal trial in 21 pubs, bars, and restaurants in England. PLoS Med 21(1): e1004313. https://doi.org/10.1371/journal. pmed.1004313 Academic Editor: Jurgen Rehm, University of Toronto, CANADA Received: February 20, 2023 Accepted: October 25, 2023 Published: January 18, 2024 Copyright: © 2024 Mantzari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the results presented in the study are available from the Open Science Framework (https://osf.io/ 5ne6g). Funding: The work of this report was funded in whole by Wellcome [PI: TMM: ref 206853/Z/17/Z (Collaborative Award in Science: Behaviour Change by Design: Generating and Implementing Evidence to Improve Health for All)] The funders had no role in study design, data collection and analysis, Interventions that alter aspects of the physical environments in which unhealthy behaviours occur have the potential to change behaviour at scale, i.e., across populations, and thereby decrease the risk of several diseases. One set of such interventions involves reducing serv- ing sizes, which could reduce alcohol consumption. The effect of modifying the available range of serving sizes of wine in a real-world setting is unknown. We aimed to assess the impact on the volume of wine sold of removing the largest serving size by the glass from the options available in licensed premises. Methods and findings The study was conducted between September 2021 and May 2022 in 21 licensed premises in England that sold wine by the glass in serving sizes greater than 125 ml (i.e., 175 ml or 250 ml) and used an electronic point of sale till system. It used an A-B-A reversal design, set over 3 four-weekly periods. “A” represented the nonintervention periods during which stan- dard serving sizes were served and “B” the intervention period when the largest serving size for a glass of wine was removed from the existing range in each establishment: 250 ml (18 premises) or 175 ml (3 premises). The primary outcome was the daily volume of wine sold, extracted from sales data. Twenty-one premises completed the study, 20 of which did so per protocol and were included in the primary analysis. After adjusting for prespecified covariates, the intervention resulted in −420�8 millilitres (ml) (95% confidence intervals (CIs) −681�4 to −160�2 p = 0�002) or −7�6% (95% CI −12�3%, −2�9%) less wine being sold per day. There was no evidence that sales of beer and cider or total daily revenues changed but the study was not powered to detect differences in these outcomes. The main study limita- tion is that we were unable to assess the sales of other alcoholic drinks apart from wine, beer, and cider, estimated to comprise approximately 30% of alcoholic drinks sold in partici- pating premises. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 1 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: ABV, alcohol by volume; CI, confidence interval; CL, centilitre; EPOS, electronic point of sale; IMD, index of multiple deprivation; IRR, incident rate ratio; ML, millilitre; VAT, value added tax; WHO, World Health Organization. Conclusions Removing the largest serving size of wine by the glass from those available reduced the vol- ume of wine sold. This promising intervention for decreasing alcohol consumption across populations merits consideration as part of alcohol licensing regulations. Trial registration ISRCTN https://doi.org/10.1186/ISRCTN33169631; OSF https://osf.io/xkgdb. Author summary Why was this study done? • Reducing the portion size of food reduces the amount of food people eat. • Reducing the size of servings of alcoholic drinks sold by the glass in licensed premises could reduce alcohol consumption but there is no real-world evidence for this. What did the researchers do and find? • We asked 21 licensed premises in England to remove the offer of their larger serving size of wine by the glass (usually 250 ml) from available options for 4 weeks. We com- pared the total daily volume of wine sold during the intervention period to that sold during nonintervention periods. • Removing the largest serving size of wine by the glass (usually 250 ml) in licensed prem- ises reduced the volume of wine sold by 7.6%. There was no evidence that it impacted sales of beer or cider, or total daily revenues. What do these findings mean? • This intervention merits consideration for inclusion in alcohol licensing regulations. • These findings are limited by our inability to assess the sales of cocktails and spirits and the potential for people to have compensated for reducing wine consumption by drink- ing more cocktails and spirits. Introduction Alcohol consumption is the fifth largest contributor to premature death and disease globally [1]. In 2016, it was estimated to have caused approximately 3 million deaths worldwide and was responsible for 5.1% of the global burden of disease [2]. Reducing alcohol consumption across populations is a global public health priority [3]. This is reflected in the World Health Organization’s (WHO) SAFER initiative (https://www.who.int/initiatives/SAFER), launched in 2018 with the aim of helping governments reduce harmful alcohol consumption and its PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 2 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass related consequences [4]. This global priority is also reflected in WHO Europe’s recent deci- sion to commit all member states to a comprehensive plan for accelerating action on reducing alcohol consumption across the continent [5]. Many cues in physical and economic environments influence alcohol consumption across populations. These include advertising, marketing [6–9], product labelling [10–12], the avail- ability of alcohol [13–17], and price [18,19]. Interventions, therefore, that target cues in physi- cal and economic environments have significant potential, based on indirect evidence, to exert effects scalable to populations [20,21]. Most of the focus to date has been on interventions that increase the price of alcoholic drinks, and control their marketing and licensing [22,23]. Although these interventions are effective at reducing consumption across populations, more interventions are needed to reduce consumption further. One set of promising interventions involves reducing the portion or serving sizes of products that harm health. When presented with smaller portions, packages, or related tableware, such as plates or glasses, people consume less [24]. While a small minority of people use smaller serving sizes to regulate their alcohol consumption [25], this well-documented “portion size effect” for food has—until recently— been neglected as a focus of study in relation to alcoholic drinks and its potential as an alcohol control policy. Reducing the size of containers, including the glasses and bottles in which alcohol is pack- aged and served has the potential to reduce alcohol consumption. Larger 370 ml wine glasses increased the volume of wine sold, and therefore consumed, compared to 300 ml glasses in res- taurants by approximately 7% [26], while in homes, smaller 290 ml wine glasses reduced the amount of wine drunk by around 6�5% compared with 350 ml glasses [27]. Bottle size may also influence alcohol consumption. Drinking wine at home from 50 cl bottles, compared with 75 cl bottles, reduced the amount consumed by 4�5% [28]. In a subsequent study assessing drink- ing at home from 37�5 cl compared with 75 cl bottles, a smaller and less certain reduction of 3�6% was observed [27]. Reducing the serving sizes of alcoholic drinks available in licensed premises could also reduce consumption. Two studies, 1 conducted in a laboratory and 1 in a semi-naturalistic context of a pub in which drinks were served in sizes predetermined by the researchers, found a reduction in alcohol consumed on a single occasion when serving sizes were smaller [29]. In the first of these, participants were randomised to 1 of 2 groups and served either larger serving sizes of cider (460 ml), lager (460 ml) or wine (165 ml), or smaller serving sizes (cider/lager: 345 ml; wine: 125 ml). Alcohol consumption was 20% lower in the group served small sizes. In the second study, participants were recruited to the study to attend 1 of 4 quiz nights during which they were randomised either to the offer of pints (568 ml) of beer/cider and 175 ml glasses of wine, or to the offer of serving sizes reduced by 33%, i.e., 2/3 pints (379 ml) for beer/ cider and 125 ml for wine. Sales were reduced by 28% when serving sizes were smaller. How- ever, the impact of offering reduced serving sizes has yet to be evaluated in real-world settings. The current study is the first to our knowledge to be conducted in a real-world field setting of licensed premises operating commercially. The study targeted wine consumption, given wine is the most commonly drunk alcoholic beverage in Europe, including the United King- dom, where over a third of the population prefers wine over other alcoholic beverages, such as beer [30]. It was designed to estimate the impact on the volume of wine sold of removing the largest serving size by the glass from the options available. We hypothesised that removing the largest available serving of wine by the glass would reduce the volume of wine sold. The secondary aims of the study were to assess the impact of the intervention on: (i) the vol- ume of wine sold in different serving sizes, in order to explore the serving sizes of choice in the absence of the largest serving of wine by the glass; (ii) the volume of beer and cider sold, in order to assess whether the absence of the largest serving of wine led to a shift in beer and PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 3 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass cider consumption; and (iii) total revenue, to explore whether removal of the largest serving of wine had an impact on earnings. Methods Ethics statement The study was approved by the University of Cambridge Psychology Research Ethics Commit- tee (reference no: PRE.2019.035). The study protocol (S1 Study protocol) was preregistered (ISRCTN: ISRCTN33169631 https://doi.org/10.1186/ISRCTN33169631; Open Science Frame- work: registration https://osf.io/xkgdb/; protocol: https://osf.io/sxe9t; statistical analysis plan (S1 Statistical Analysis Plan): https://osf.io/6n9xh). Study design The study used an A-B-A treatment reversal design comprising 3 consecutive four-week peri- ods in which “A” represented the nonintervention periods during which standard serving sizes were available, and “B” represented the intervention period during which the largest serving size of wine by the glass available in that establishment (either 250 ml or 175 ml) was removed from sale. Setting The study was conducted in pubs, bars, and restaurants. Participants Participants were 21 licensed premises in England. Their location and other characteristics are shown in Table 1. The majority of these (86%) were pubs, located in London (62%), in more deprived areas, as determined by their IMD (index of multiple deprivation) scores, with 76% of premises located in the first and second most deprived quintiles of the city. To be eligible to take part in the study, licensed premises had to meet the following criteria: i. sell wine by the glass in serving sizes greater than 125 ml (i.e., 175 ml or 250 ml) ii. sell a minimum of 100 glasses of wine on average per week iii. be willing to remove the largest serving size for a glass of wine iv. have an electronic point of sale (EPOS) till system to record daily sales of all drinks and their served sizes v. be primarily indoor, permanent establishments in a fixed location; i.e., not purposefully tem- porary or time-limited (e.g., pop-up), or mobile venues (e.g., vans). The flow of premises through the study is shown in Fig 1. Twenty-one licensed premises were recruited from 1,778 that were contacted in targeted geographical areas, a recruitment rate of just over 1%. Sample size calculation. The current study was initially planned with a minimum of 5 licensed premises. Based on available resources, recruitment was increased to 21 licensed premises. There was prior data from a pilot study in 2 licensed premises—both student bars— using an A-B-A design with each period lasting 4 weeks. Based on a linear mixed effects model of the daily data for the 2 premises, power simulations [31] suggested that 85 premises were need to be recruited to provide at least 80% power to detect a predicted effect of −226�8 ml PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 4 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Table 1. Characteristics of recruited licensed premises. Premises number Location Index of multiple deprivation quintile* Premises type Baseline daily revenue (£) (mean (sd)) Largest serving of wine by glass* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Hackney, London South Cambridgeshire Stroud, Gloucester Stroud, Gloucester Lewisham, London Islington, London Lewisham, London Hackney, London Islington, London Eastleigh, Southampton Hackney, London Brighton and Hove Lambeth, London Southwark, London Southwark, London Southwark, London Brighton and Hove Brighton and Hove Brighton and Hove Greenwich, London Hammersmith and Fulham, London 2 5 5 3 2 4 2 2 2 5 2 3 2 3 2 2 1 1 2 2 4 Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Pub Bar Pub Restaurant and Bar Pub Pub Champagne and Cocktail Bar 1,327�8 (1,245�9) 898�6 (372�4) 1,471�9 (555�1) 846�0 (550�8) 2,599�6 (1,288�6) 2,795�0 (1,690�6) 2,724�1 (1,231�2) 2,672�1 (2,064�6) 1,776�6 (1,152�6) 1,422�6 (1,318�6) 941�1 (765�9) 1,181�0 (642�4) 4,504�9 (1,693�2) 4,840�2 (2,795�0) 3,841�1 (1,278�8) 3,553�0 (2,993�6) 1,789�5 (1,106�4) 981�9 (10,061�0) 520�9 (484�1) 1,015�0 (526�6) 1,671�1 (2,014�9) 250 ml 250 ml 250 ml 250 ml 250 ml 175 ml 250 ml 250 ml 250 ml 250 ml 250 ml 250 ml 250 ml 250 ml 250 ml 250 ml 175 ml 250 ml 250 ml 250 ml 175 ml 1 = most deprived; 5 = least deprived. * Description of premises type taken from each premises’ website. * A 250 ml glass of wine contains on average 2.5 standard drinks or 3 units and a 175 ml glass of wine contains on average 1.7 standard drinks or 2.3 units. https://doi.org/10.1371/journal.pmed.1004313.t001 wine sold during the intervention. It was not feasible to recruit this number of premises. Given these simulations, power to detect possible effects with 21 establishments was expected to be low. The study was therefore considered opportunistic, providing preliminary evidence to inform future research. Fig 1. CONSORT diagram of flow of premises through study. https://doi.org/10.1371/journal.pmed.1004313.g001 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 5 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Intervention Licensed premises reduced their range of serving sizes for glasses of wine by removing the largest serving from their available options. This was either 250 ml or 175 ml, with 125 ml sizes always available in keeping with current regulations for selling alcohol in licensed premises. Eighteen premises offered 250 ml, 175 ml, and 125 ml servings during their nonintervention periods. During the intervention, these premises offered only 175 ml and 125 ml servings. Three premises offered 175 ml and 125 ml servings during their nonintervention periods and only 125 ml servings during the intervention. Menus and signage were updated to reflect changes. Sizes of bottles and carafes remained unchanged. Within the TIPPME intervention typology for changing environments to change behav- iour [21], the type of intervention used in the current study was “Size,” and focused on the “Product” itself, i.e., the alcoholic drink (as opposed, for example, to aspects of the wider environment). Measures Primary outcome. Daily volume (in ml) of all wine sold (by the glass, as well as sales by the bottle and carafe, if offered), extracted from electronic records of sales. Secondary outcomes. The following outcomes were extracted from electronic records of sales: i. Daily volume (in ml) of wine sold by each serving size: - 125 ml - 175 ml - 250 ml - 500 ml carafe - 750 ml bottle - 1,000 ml carafe ii. Daily volume (in ml) of beer and cider sold iii. Daily revenue (in £) from food, alcoholic, and nonalcoholic drinks. Note that information on spirits and other alcoholic drinks could not be extracted from sales reports. Cocktails and spirits are estimated to have comprised approximately 30% of alco- holic drinks sold in participating premises [32]. Covariates. Given that daily temperature, day of the week, season, and holidays can influ- ence alcohol sales [33,34], the following covariates were considered: i. Maximum daily local temperature ii. Special events (e.g., Bank Holidays, other holidays, major sporting events) iii. Total revenue iv. Day of the week v. Study day from start of a period vi. Season at start of study: autumn or winter. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 6 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Procedure Potentially eligible pubs, bars, and restaurants were identified through a publicly available database (www.whatpub.com). Those based in 1 of 8 geographical areas were invited via email to participate in the study. These areas were selected so that either the research team or collab- orators could readily travel there to conducted fidelity checks. Those interested in taking part were sent more information about the study and were assessed for eligibility over the tele- phone. Eligible premises wishing to participate provided written informed consent for taking part. Consent from individual consumers was not deemed necessary as individual-level data were not collected. Recruited premises changed their available serving sizes for wine on 2 occasions over a period of 12 weeks, first to remove their largest serving size by the glass during the intervention period (B), and second to return it during the second nonintervention period (A). Till systems, menus, and signs were updated as appropriate to reflect the available serving sizes. Premises leads were contacted 1 day before each change to remind them of the required change. Premises leads and staff were asked not to mention the study to any patrons who asked about the serving size changes, being given a simple explanation in the event of such an inquiry: “We have been receiv- ing requests for differently sized drinks, so we are trying out some changes for a few weeks.” Fidelity to the protocol was checked twice to establish whether the correct serving sizes were on offer during the intervention period (B) and subsequent return to nonintervention period (A). Checks were conducted during the first week of each study period. No premises failed any fidelity checks. Data were collected between September 2021 and May 2022. Premises were paid £1,000 (plus 20% value added tax (VAT)), later increased to £1,500 (plus 20% VAT) to help increase the recruitment rate, for taking part in the study and providing all requested data. Premises were also reimbursed for the costs of any necessary changes to menus and signs. Data analysis Premises provided daily reports that listed sales of each individual product. The format of the reports differed between premises. The data cleaning process involved aggregating sales according to product type (i.e., beer and wine) and serving size. Analyses were performed on R (4.0.3). Unadjusted summaries of the volume of wine sold during the nonintervention and intervention periods were calculated both overall and for each specific serving size. Outliers in the daily data were identified using range checks, scatter plots, median absolute deviation val- ues, and histograms on daily data, for further checking that these were true values. The poten- tial outliers identified were all deemed genuine values and it was assumed that the model covariates (total revenue—proxy for site busyness—and specials events) could handle these to ensure no outliers in the model residual diagnostics. Primary analysis. A generalised linear mixed model (generalised additive models that can accommodate heterogeneity) was used to estimate daily volume of wine sales, the primary out- come, according to study period (A versus B). Premises were treated as a random factor and heterogeneity (in terms of size and other characteristics which result in different amounts of variability) between premises was modelled. The analysis included prespecified covariates for day of the week, study day (number ranging from 1 to 84, to allow for potential linear trends over time), and total revenue from all food and drink (as a proxy for premises busyness). An overall effect was estimated from this model. The mean difference and associated 95% confi- dence intervals (CIs) and p-value, as well as a Cohen’s d effect size and its 95% CI were calcu- lated. All regression model diagnostics (residual plots, worm plots) were checked and were satisfactory. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 7 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Only premises that met the following 3 conditions were included in the primary analysis: i) completed the study in full, i.e., all 12 weeks ii) provided primary outcome data for the 12 weeks of the study iii) adhered to the protocol for intervention implementation, i.e., they passed the fidelity checks and their data did not suggest that the largest serving size of wine by the glass was sold during Period B. Sensitivity analyses. Four sets of sensitivity analyses were conducted to check the robust- ness of the primary analysis. For each, an overall effect was estimated, as well as mean differ- ences and associated 95% CIs and p-values: 1. Regression analysis, repeating the primary analysis but taking into account 3 additional covariates: (i) the total number of special events in each period; (ii) season at the start of the study (autumn or winter); (iii) maximum daily local temperature. 2. Regression analysis, repeating the primary analysis but adding daily-level data from all premises, including those that violated the protocol for intervention implementation. 3. Regression analysis, repeating the primary analysis but including the 2 nonintervention periods as separate factor levels (i.e., using A1, B, and A2 levels for the periods). 4. As data might be less variable when aggregated at the period level, a regression analysis was conducted using period-level data to compare mean daily sales during period A (aggregate value for 2 four-week A period) and mean daily sales during period B (aggregate value for 1 four-week B period). Mean daily sales for each period were calculated by adding the total volume of wine sold and dividing by the number of days the premises were open during each “A” and “B” period. No covariates were included in this analysis due to the inclusions of only 2 data points per site (i.e., aggregate of A periods and aggregate of B). Secondary analyses. For the secondary outcomes generalised linear mixed models were used, with the distribution of the data assessed by model diagnostics dictating which model was most appropriate (e.g., Poisson regression). For each, an overall effect was estimated, as well as mean differences and associated 95% CIs and p-values: The following secondary analyses were conducted: 1. Regression analyses to estimate the number of wine drinks sold in each serving size (125 ml, 175 ml, 250 ml, 500 ml carafes, 750 ml bottle, 1000 ml carafes) according to the study Period (A versus B). 2. A regression analysis to estimate the daily volume of beer and cider sold according to the study Period (A versus B). The analysis included covariates for day of the week, study day (number ranging from 1 to 84), and total revenue from all food and drink. 3. A regression analysis to estimate total revenue from all food and drink according to the study Period (A versus B). The analysis included covariates for day of the week and study day (number ranging from 1 to 84). Results One premises was excluded from the primary analysis for violating the protocol and selling the largest serving of wine by the glass during the intervention period, as identified by inspection of the data. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 8 / 17 PLOS MEDICINETable 2. Mixed effects generalised additive linear mixed model (GAM) main results (95% CI) estimating the volume (ml) of wine sold per day (n = 20). Impact on wine sales of removing the largest serving size by the glass Estimate (SE) 856�06 (208�12) −420 79 (132 96) 540�92 (238�34) 833.23 (235�23) 928�55 (238�16) 1,160�84 (249�29) 364�94 (243�92) 900�88 (239�29) −1�70 (2�62) 1�84 (0�047) t-value P-value Lower Upper 95% CI for estimate 4�11 −3�16 2�27 3�54 3�89 4�66 1�49 3�76 −0�65 38�79 <0�001 0�002 0�024* <0�001 <0�001 <0�001 0�135 <0�001 0�517 <0�001 448�1 −681�4 73�4 372�2 461�8 672�2 −113�1 431�9 −0�68 1�75 1,263�9 −160.2 1,008�1 1,294�3 1,395�3 1,649�5 843�0 1,369�9 3�44 1�94 Intercept Study period (ref: nonintervention) Day of the week_Tuesday (ref: Monday) Day of the week_Wednesday (ref: Monday) Day of the week_Thursday (ref: Monday) Day of the week_Friday (ref: Monday) Day of the week_Saturday (ref: Monday) Day of the week_Sunday (ref: Monday) Study day Total revenue Significant at the p < 0�05 level. Significant at the p < 0�01 level. CI, confidence interval; SE, standard error. https://doi.org/10.1371/journal.pmed.1004313.t002 Primary outcome: Volume of wine sales The unadjusted mean daily volume of wine sold per premises during the nonintervention peri- ods (A) was 5,198�7 ml (sd = 5,021�9) and 4,814�1 ml (sd = 5,188�4) during the intervention period (B). After accounting for prespecified covariates (day of the week; study day; total reve- nue), there was a significant effect of study period, with −420�8 ml (95% CI −681�4 to −160�2) or −7�6% (95% CI −12�3%, −2�9%) less wine sold per day during the intervention period (B) compared to the 2 nonintervention periods (A) (Table 2). There was heterogeneity between premises (sigma coefficients were statistically significant at p < 0�001 (Table 3)). Fig 2 shows the effect of the intervention on wine sales overall and for each individual premises. Sensitivity analyses Results and conclusions were unchanged when performing an intention-to-treat analysis (n = 21) that included the one premises that had violated the protocol, with −424�8 ml (95% CI −679�9 to −169�8, p = 0�001) or −7�6% (95% CI −12�2% to −3�1%) less wine being sold per day during the intervention period (B) compared to the nonintervention periods (A) (S1 Table). Including 3 additional covariates in the model (total number of special events such as Bank Holidays in each period; season at the start of the study; maximum daily local temperature) also had no effect on the main result. The model showed that −418�6 ml (95% CI −675.7 to −161�6, p = 0�001) or −7�5% (95% CI −12�2% to −2�9%) less wine was sold per day during the intervention period (B) compared to the nonintervention periods (A) (S2 Table). Using period-level data (i.e., an aggregate value for sales during the nonintervention periods and an aggregate value for the intervention period) rather than daily-level data, generated results that were consistent with the conclusion that wine sales were lower during the interven- tion period (B) compared to the nonintervention period (A) (−306�3 ml; 95% CI −804�9 to 192�3) but the difference was not statistically significant (p = 0�233). In order to assess whether the 2 nonintervention periods were comparable, an additional analysis was conducted in which the 2 nonintervention periods were added to the model sepa- rately. The results showed that sales of wine did not significantly differ during the 2 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 9 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Table 3. Mixed effects generalised additive linear mixed model (GAM) variance estimates from estimating the volume (ml) of wine sold per day for each individual licensed premises (reference: Premises 1). Modelling of the variance (log link): Estimate (SE) t-value P-value Intercept Premises 2 Premises 3 Premises 4 Premises 5 Premises 6 Premises 7 Premises 8 Premises 9 Premises 10 Premises 11 Premises 12 Premises 13 Premises 14 Premises 15 Premises 16 Premises 17 Premises 18 Premises 19 Premises 20 0.474 (0.079) 0.059 (0.113) 0.384 (0.115) −0.006 (0.117) 1.014 (0.111) 0.457 (0.110) 0.372 (0.113) 0.262 (0.113) 0.404 (0.110) 0.081 (0.113) 0.542 (0.112) 0.938 (0.111) 1.05 (0.115) 0.562 (0.110) 0.893 (0.121) 0.508 (0.112) 0.702 (0.131) 0.404 (0.118) 0.939 (0.110) 0.953 (0.118) 5.96 0.52 3.32 −0.05 9.12 4.14 3.29 2.31 3.67 0.71 4.83 8.41 9.15 5.10 7.34 4.53 5.37 3.41 8.50 8.06 <0.001 0.600 0.001* 0.953 <0.001 <0.001 0.001 0.020 <0.001 0.474 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Significant at the p < 0.05 level. *Significant at the p < 0.01 level. CI, confidence interval; SE, standard error. https://doi.org/10.1371/journal.pmed.1004313.t003 nonintervention periods (A) (−85 ml less wine per day was sold during the second noninter- vention period compared to the first; 95% CI −99�6 to 82�5; p = 0�854) (S3 Table), justifying the modelling choice of combining data from both nonintervention periods for the primary analysis. Fig 2. Predicted change in wine sold (% (95% CI)) after removing the largest serving sizes by the glass, derived from the generalised additive linear mixed model. https://doi.org/10.1371/journal.pmed.1004313.g002 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 10 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Secondary outcomes Wine sales by serving size. The unadjusted mean daily volume of wine sold in 125 ml serving sizes during the nonintervention periods (A) was 351�1 ml (sd = 450�7) and 583�5 ml (sd = 926�7) during the intervention period (B). A Poisson regression showed that the number of glasses of wine sold in serving sizes of 125 ml increased during the intervention compared to the nonintervention periods (incident rate ratio (IRR) = 1�64; 0�49 95% CI 0�44 to 0�55; p < 0.001). The unadjusted mean daily volume of wine sold in 175 ml serving sizes during the nonin- tervention periods (A) was 1,487�9 ml (sd = 1,418�2) and 2,372�6 ml (sd = 2,455�5) during the intervention period (B). A Poisson regression showed that the number of glasses of wine sold in serving sizes of 175 ml increased during the intervention compared to the nonintervention periods (IRR = 1�54; 0�43 95% CI 0�40 to 0�46; p < 0�001). The unadjusted mean daily volume of wine sold in 500 ml carafes during the noninterven- tion periods (A) was 116�9 ml (sd = 523�6) and 160�7 ml (sd = 1,128�9) during the intervention period (B). There was no evidence of a difference in the number of 500 ml carafes of wine sold during the intervention compared to the nonintervention periods (0�07 95% CI −0�13 to 0�27; p = 0�49). The unadjusted mean daily volume of wine sold in 750 ml bottles during the noninterven- tion periods (A) was 1,623�7 ml (sd = 2,829�8) and 1,687�5 ml (sd = 2,930�9) during the inter- vention period (B). There was no evidence of a difference in the number of 750 ml bottles of wine sold during the intervention compared to the nonintervention periods (−0�01 95% CI −0�08 to 0�07; p = 0�73). The unadjusted mean daily volume of wine sold in 1,000 ml carafes during the noninter- vention periods (A) was 9�35 ml (sd = 133�3) and 6�80 ml (sd = 100�9) during the intervention period (B). There was no evidence of a difference in the number 1,000 ml carafes of wine sold during the intervention compared to the nonintervention periods (0�17 95% CI −1�08 to 1�43; p = 0�79). Volume of beer and cider sales. The unadjusted mean daily volume of beer and cider sold during the nonintervention periods (A) was 1,002,500 ml (sd = 1,008,900) and 1,005,900 ml (sd = 1,219,700) during the intervention period (B). There was no evidence of a difference in the volume (ml) of beer and cider sold per day between the intervention and noninterven- tion periods (306�74 ml 95% CI −1,355�2 to 1,968�7; p = 0�72). Revenue. The unadjusted mean daily revenue during the nonintervention periods (A) was £2,051�6 (sd = 1,824�1) and £2,089�1 (sd = 2,058�9) during the intervention period (B). There was no evidence of a difference in total daily revenue (£) between the intervention and nonintervention periods (−1�90, 95% CI −63�37 to 59�56; p = 0�95). Discussion Removing the largest serving size of wine by the glass from the range of options available in licensed premises, after controlling for other factors, reduced the volume of wine sold by 7�6%. Sales of the smaller serving sizes of wine by the glass—125 ml and 175 ml—were increased. There was no evidence of a change in sales of wine by the bottle and sales of beer and cider or a change in daily revenues but the study was not powered to detect differences in these outcomes. Findings in context Removing the largest serving size of wine by the glass in licensed premises had the hypothe- sised effect of reducing the volume of wine sold. This is in keeping with the wealth of PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 11 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass literature assessing the impact of smaller serving sizes on food consumption [24] and the very limited literature on alcohol consumption, comprising 2 studies conducted in semi-nat- uralistic contexts [29]. The results of the current study suggest that when the largest serving size of wine by the glass (typically 250 ml) was not available, people shifted towards the smaller options (125 ml and 175 ml) and neither drank the equivalent amount of wine nor more, for example, by opt- ing to buy wine by the carafe or bottle. The increase in sales was slightly larger for 125 ml serv- ings compared to 175 ml serving. Given that in most premises the largest serving was 250 ml, this implies that people did not automatically choose the next available size (175 ml). It is not clear why this was the case. One possibility is that when customers who had planned to drink a large glass of wine (250 ml) were told it was not available, they planned to drink two 125 ml services but stopped after one. People have the tendency to consume a specific number of “units” (e.g., number of glasses or bottles, number of cookies or slices of cake), regardless of portion or package size [35]. This helps explain why smaller serving sizes reduce alcohol con- sumption: people tend to order a pre-set number of glasses, and with less alcohol in each glass they drink less overall. The current study found no evidence that the intervention affected beer and cider sales, suggesting people did not compensate for their reduced wine consumption by drinking more of these alcoholic drinks. Importantly, there was also no evidence that the intervention affected total daily revenues, implying that participating licensed premises did not lose money as a result of removing the largest serving size for glasses of wine. This might reflect the pricing of glasses of wine, with 125 ml servings usually having a higher profit margin than 250 ml glasses [36]. Important to note is that the study was not powered to provide statistically meaningful data on secondary outcomes. Strengths and limitations To our knowledge, this is the first study to estimate the impact on sales—a proxy for consump- tion—of removing the largest serving of sizes of alcoholic drinks in pubs, bars, and restaurants. Further strengths include features of the design that reduce the risk of bias, including the use of objective measures to assess the primary and secondary outcomes, i.e., electronic records of sales, as well as the large number of participating premises and the high retention rate. The study has several limitations. First, due to the complexity of the sales reports provided by participating premises, it was not possible to assess the sales of all alcoholic drinks. While we could reliably assess sales of wines, beers, and ciders—estimated to make up more than 70% of alcoholic drinks in licensed premises in the UK [32]—we were unable to assess sales of spirits or cocktails. It is not known, therefore, whether people compensated for reduced wine consumption by drinking more of these other alcoholic drinks. It is also not known whether customers compensated for the reduced serving sizes by drinking wines higher in % alcohol by volume (ABV). This possibility, however, is minimal given that there were very small varia- tions in the % ABV of wines sold by premises, ranging from 12% to 14%, information very rarely mentioned in wine lists anyway. Also, wine lists did not change during the study and none of the premise managers interviewed at the end of the study mentioned any changes in the drinking patterns of their customers during the intervention period relating to wine strength. Second, we were also unable to control for the number of patrons visiting the prem- ises during each study period, to assess whether differences in sales could be attributed to dif- ferences in how busy premises were. We attempted to take this into consideration by using total revenue as a proxy measure of busyness. Third, the majority of premises were in London and constituted a small proportion of those approached, which potentially restricts the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 12 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass generalisability of the findings. Fourth, the study used an A-B-A reversal design, which has a higher risk of bias than an experimental design, although the analyses accounted for notable events including public holidays and temperature fluctuations, which may have confounded the effects. Fifth, although outcomes were assessed using objective measures, they concerned sales rather than actual consumption. Measuring consumption directly at scale in these kinds of real-world settings is not feasible. Sales are, however, a valid—as well as practicable—proxy for consumption [37] and are commonly used in behavioural research [38–40]. Finally, the impact of removing the largest serving size of wine was assessed only during a four-week period. Whether observed effects are sustained over time remains to be assessed in future research. Implications for research and policy Although the intervention resulted in a relatively small reduction in the volume of wine sold by each premises (7.6% or 421 ml per day, equivalent to approximately 4 standard drinks or 5 units per premises), given that no level of alcohol consumption is currently considered safe for health with even light and moderate consumption contributing to the development of many cancers [41], such a reduction could meaningfully contribute to population health. In England, legal serving sizes of wine by the glass are 125 ml, 175 ml, and multiple of these sizes [42]. It is a requirement that the smallest size—125 ml—is offered to customers but there is no restric- tion on the largest serving size that can be offered. Restricting the sale of the largest serving of wine by the glass (250 ml) in licensed premises—similarly to the ban proposed by the mayor of New York City in 2012 on serving sizes of sugary drinks larger than 16 ounces [43]—could contribute to policies for reducing alcohol consumption at the population level and merits consideration as part of alcohol licensing regulations. This is the first study to assess the impact of this intervention. The findings, therefore, require replication in future studies. If effects are replicated, it would strengthen the case for a such consideration. It is unknown whether similar effects would result from removing the largest serving size for other types of alcoholic drinks including beer, as no real-world studies assessing this exist. We attempted to conduct such a study to estimate the impact of removing the largest serving size for beer—usually a pint (568 ml)—and replacing it with a two-thirds measure, but were unable to find any pubs, bars, or restaurants willing to do this from almost 2,000 contacted. This likely reflects that the pint has been the customary serving size for beer in the UK for cen- turies [44]. In contrast, it was not until relatively recently that licensed premises started serving 250 ml glasses of wine. Smaller glasses containing 125 ml wine were once considered the stan- dard size for serving wine by the glass [45]. In the UK, this default has now been replaced with the 175 ml measure. The size of wine glasses has also increased in recent years, almost doubling in the last thirty years [46], and likely contributing to an increase in wine consumption [26]. Regulating serving sizes in licensed premises could help shift social norms for what consti- tutes an appropriate serving size [47], both for consumption out of the home, such as in pubs and bars, and for consumption at homes where most drinking occurs [48]. This possible indi- rect effect of the intervention awaits study. Whether regulating serving sizes interacts with existing alcohol controls concerning the pricing, marketing, and licensing regulations [22,23] —also awaits study. Interventions that reduce serving or package sizes are generally less supported by the public than information-based interventions, such as health warning labels [49–51]. In the current study, managers reported receiving few complaints from customers when the largest serving size was removed (4/21 premises reported receiving some complaints). Although the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 13 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass intervention would potentially be acceptable to premises’ managers given there is no evidence that it can result in a loss in sales, were it to be implemented as part of alcohol control policies it would likely lead to opposition from the alcohol industry, given its potential to reduce sales of targeted drinks [52]. The impact of such opposition on policy-makers will in part be modi- fied by the level of public support for the intervention [53,54]. Public support for this and indeed a range of policies in health and other domains is sensitive to evidence of the policy’s effectiveness [55,56]. Communicating the effectiveness of a policy to achieve a valued outcome increases its public support. While we are unaware of any studies examining this in the context of alcohol serving sizes, a policy that increased the price of alcohol by introducing a minimum unit price of £1 was supported by 63% of a representative sample of the English population when informed of its effectiveness at reducing crime and hospital admissions, compared with 43% when not given this information [57]. Research is needed to explore the acceptability of sizing interventions for reducing alcohol consumption, as well as methods for increasing low levels of public support. Conclusion Removing the largest serving of wine by the glass from the range of options available in licensed premises reduced the volume of wine sold. This suggests this is a promising interven- tion for decreasing alcohol consumption across populations, which merits consideration as part of alcohol licensing regulations. Supporting information S1 CONSORT Checklist. CONSORT checklist of information relating to study. (DOC) S1 Study protocol. The impact of altering serving sizes of beer and wine on alcohol con- sumption: A field study. (DOCX) S1 Statistical analysis plan. The impact of altering serving sizes of wine on alcohol con- sumption: ANALYSIS PLAN for Intervention 2 (wine study). (DOCX) S1 Table. Mixed effects GAM regression estimates (95% CI) for volume (ml) of wine sold per day (n = 21)—intention to treat analysis. (DOCX) S2 Table. Mixed effects GAM regression estimates (95% CI) for volume (ml) of wine sold per day (n = 20)—additional covariates. (DOCX) S3 Table. Mixed effects GAM regression estimates (95% CI) for volume (ml) of wine sold per day (n = 20)—separating nonintervention periods. (DOCX) Acknowledgments The views expressed in this publication are those of the authors and not necessarily those of Wellcome. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004313 January 18, 2024 14 / 17 PLOS MEDICINEImpact on wine sales of removing the largest serving size by the glass Author Contributions Conceptualization: Eleni Mantzari, Ilse Lee, Gareth J. Hollands, Theresa M. Marteau. Data curation: Eleni Mantzari, Minna Ventsel, Emily Pechey. Formal analysis: Mark A. Pilling. Funding acquisition: Theresa M. Marteau. Methodology: Eleni Mantzari. Project administration: Eleni Mantzari, Minna Ventsel. Supervision: Eleni Mantzari, Theresa M. Marteau. Writing – original draft: Eleni Mantzari. Writing – review & editing: Eleni Mantzari, Emily Pechey, Ilse Lee, Mark A. Pilling, Gareth J. Hollands, Theresa M. Marteau. References 1. Gakidou E AA, Abajobir AA, Abate KH, Abbafati C, Abbas KM. 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10.1371_journal.pgph.0002772	RESEARCH ARTICLE Factors associated with COVID-19 vaccine uptake and hesitancy among healthcare workers in the Democratic Republic of the Congo 1, Jean de Dieu Kamenga1, Christophe Luhata Lungayo2, Aime Cikomola Michel K. NzajiID Mwana Bene2, Shanice Fezeu Meyou3, Anselme Manyong Kapit1, Alanna S. FogartyID 4,6, Kristen B. Stolka3 Dana SessomsID 3, Pia D. M. MacDonald3,5, Claire J. StandleyID 4, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Social, Statistical and Environmental Sciences, RTI International, Kinshasa, Democratic Republic of Congo, 2 Expanded Programme on Immunization, Ministry of Public Health, Kinshasa, Democratic Republic of Congo, 3 Social, Statistical and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, United States of America, 4 Center for Global Health Science and Security, Georgetown University, Washington, District of Columbia, United States of America, 5 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America, 6 Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany Claire.standley@georgetown.edu OPEN ACCESS Abstract Citation: Nzaji MK, Kamenga JdD, Lungayo CL, Bene ACM, Meyou SF, Kapit AM, et al. (2024) Factors associated with COVID-19 vaccine uptake and hesitancy among healthcare workers in the Democratic Republic of the Congo. PLOS Glob Public Health 4(2): e0002772. https://doi.org/ 10.1371/journal.pgph.0002772 Editor: Sarah E. Brewer, University of Colorado Denver - Anschutz Medical Campus: University of Colorado - Anschutz Medical Campus, UNITED STATES Received: October 4, 2023 Accepted: November 27, 2023 Published: February 1, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgph.0002772 Copyright: © 2024 Nzaji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Vaccination is a critical intervention to reduce morbidity and mortality and limit strain on health systems caused by COVID-19. The slow pace of COVID-19 vaccination uptake observed in some settings raises concerns about COVID-19 vaccine hesitancy. The Demo- cratic Republic of the Congo experienced logistical challenges and low uptake at the start of vaccine distribution, leading to one of the lowest overall COVID-19 vaccine coverage rates in the world in 2021. This study assessed the magnitude and associated factors of COVID- 19 vaccine uptake among healthcare workers (HCWs) in seven provinces in DRC. We implemented a cross-sectional Knowledge, Attitudes, and Practices (KAP) questionnaire targeting HCWs, administered by trained data collectors in Haut-Katanga, Kasaï Orientale, Kinshasa, Kongo Centrale, Lualaba, North Kivu, and South Kivu provinces. Data were sum- marized and statistical tests were performed to assess factors associated with vaccine uptake. HCWs across the seven provinces completed the questionnaire (N = 5,102), of whom 46.3% had received at least one dose of COVID-19 vaccine. Older age, being mar- ried, being a medical doctor, being a rural resident, and having access to or having previ- ously worked in a COVID-19 vaccination site were all strongly associated with vaccination uptake. Vaccinated individuals most frequently cited protection of themselves, their families, and their communities as motivations for being vaccinated, whereas unvaccinated individu- als were most concerned about safety, effectiveness, and risk of severe side effects. The findings suggest an opinion divide between vaccine-willing and vaccine-hesitant HCWs. A multidimensional approach may be needed to increase the acceptability of the COVID-19 vaccine for HCWs. Future vaccine campaign messaging could center around the positive impact of vaccination on protecting friends, family, and the community, and also emphasize PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 1 / 15 PLOS GLOBAL PUBLIC HEALTHData Availability Statement: All underlying data are available in the manuscript or supporting materials. Funding: This publication was supported by funding from Cooperative Agreement NU2HGH000047 funded by the US Centers for Disease Control and Prevention. Individuals from the US CDC provided technical input into the design of the study but were not involved in the analysis or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. COVID-19 vaccine uptake and hesitancy in DRC healthcare workers the safety and very low risk of adverse effects. These types of messages may further be useful when planning future immunization campaigns with new vaccines. Introduction Coronavirus disease 2019 (COVID-19), which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first recognized in late 2019 and declared a global pandemic by the World Health Organization in March 2020 [1]. While SARS-CoV-2 can result in serious complications[2], COVID-19 vaccines have been shown to be effective in pre- venting severe disease [3]. Real-world data have also shown that COVID-19 vaccines reduced the risk of COVID-19–associated deaths, regardless of the emergence of the Delta and the Omicron variants [4]. The speed of development and production of the COVID-19 vaccine is unprecedented; however, some data suggest this could contribute to poorer perceptions of the vaccine’s efficacy and safety [5]. Healthcare workers (HCWs), defined as any individual who directly or indirectly delivers care or services to the sick [6], are at high risk of occupational exposure to and transmission of SARS-CoV-2, which prioritized them for early vaccination against COVID-19 [7]. HCWs also play an important role in immunization programs because they not only administer vaccines but they also educate, influence, and build trust with patients around vaccination [8]. In this way, communities treat HCWs as role models for their attitudes toward vaccination and refer to them for vaccine information [9]. Consequently, vaccine uptake among HCWs may encour- age widespread uptake in vaccination among the general population. Conversely, if HCWs are hesitant to be vaccinated, it can be directly detrimental to the response effort if they suffer higher rates of infection and morbidity and this, in turn, can influence negative vaccine per- ceptions in the public. Thus, assessing the factors and reasons associated with HCW uptake and hesitancy is important to help inform targeted approaches for reducing vaccine hesitancy and increasing confidence in vaccines. As of 23 April 2023, the Democratic Republic of the Congo (DRC) has reported just under 96,000 confirmed COVID-19 cases and 1,465 COVID-19–related deaths since the beginning of the pandemic. These are likely substantial underestimates of the true impact of COVID-19 in DRC, given low testing rates and observed high test positivity rates across successive epide- miological waves [10]; estimates based on excess mortality calculations suggest a much higher fatality rate than reported [11]. DRC has one of the lowest rates of COVID-19 vaccine coverage in the world, with only 15.5% of the population having received at least one dose by April 2023 [12]. The vaccine campaign in DRC was also slow to get underway; by the end of 2021, fewer than 1% of the population had received a single dose of the vaccine. By contrast, almost 80% of people in Vietnam (similar total population size to DRC), 19% of people in Liberia (similar gross domestic product per capita as DRC), and 16% of people in Algeria (similar land area to DRC) had received at least one dose of COVID-19 vaccine by this time [13]. While the vac- cine rollout in DRC was hindered by operational and logistical factors, including availability of doses [14], previous studies, which were mostly conducted in single locations or with rel- atively small samples, have demonstrated that vaccine hesitancy was also a factor [15,16]. Consequently, this study aimed to assess the magnitude and associated factors of COVID- 19 vaccine uptake and hesitancy among a large number of HCWs across seven DRC provinces. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 2 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Methods Study area Between 24 December 2021 and 1 March 2023, a Knowledge, Attitudes, and Practices (KAP) ques- tionnaire was administered to HCWs in seven DRC provinces: Haut-Katanga, Kasaï Orientale, Kinshasa, Kongo Centrale, Lualaba, North Kivu, and South Kivu (Fig 1). Provinces were selected as part of an effort to implement intra-action reviews (IARs) in priority provinces. The question- naires were administered in the 2 weeks prior to the IAR to contribute to learning and sharing of best practices and challenges around COVID-19 vaccination at the provincial level [17,18]. Study population Public health facilities and private hospitals located in and around the capital cities of the seven targeted provinces were selected through convenience sampling and the questionnaires Fig 1. Map of seven provinces and dates when KAP questionnaire was administered in DRC, December 2021-March 2023. Figure prepared using base layer from the CIA World Factbook (https://www.cia.gov/static/b2fcc8d80f910b0c91f4a74d33b5c7e6/DRC_Administrative.pdf). https://doi.org/10.1371/journal.pgph.0002772.g001 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 3 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers were administered widely to all available HCWs working in these workplaces. Participating HCWs included doctors, nurses, midwives, laboratory technicians, administrative personnel, and others, aged 18 or older, who provided informed consent. The Cochrane formula [19], where n equals minimum sample size, Z represents the stan- dard normal deviate corresponding to 5% significant level, and p equals proportion of HCWs who are COVID-19 vaccine hesitant, was used to estimate the target sample size per province. Because we did not find a reference study on vaccine hesitancy in DRC at the time of the study design, we estimated 50% of HCWs to be hesitant to vaccination against COVID-19, d = tolerable error of margin set at 0.05; therefore, Z = 1.96. A confidence level of 95% and a margin of error of 5% were used and resulted in a minimum sample size of 484 participants per province after accounting for a nonresponse rate or 10% incomplete response. Data collection and analysis The questionnaire consisted of questions that assessed demographics, health history, COVID- 19 vaccine uptake (at least one dose), perception of risk and exposure to COVID-19, confi- dence in the COVID-19 response, stated reasons for acceptance or rejection of the COVID-19 vaccine, exposure to information about COVID-19, and intention to vaccinate. The questions —and for some questions, response options—in the questionnaire were derived from the liter- ature on vaccine hesitancy and acceptability (S1 File) [20,21]. Each trained data collector con- ducted a pretest of the questionnaire tool with 10 HCWs and convened after the pretest to provide feedback on their experience. The final questionnaire was administered to HCWs by trained data collectors and the data were entered electronically into the questionnaire pro- grammed in KoboCollect (https://www.kobotoolbox.org/). Completed questionnaires were exported from KoboCollect to Microsoft Excel for cleaning and coding. Responses were analyzed using SPSS Enterprise Guide Version 22, with verifica- tion of results, and calculation of confidence intervals, performed in StatCal (EpiInfo 7). Asso- ciations between independent variables and the primary outcomes (vaccinated or not vaccinated) were tested using Student’s t-tests or chi-square tests, as appropriate. Student’s t- tests and ANOVA were used to test for differences between means of Likert scale variables. Nonbinary variables were dichotomized against the reference variable and a step-by-step, bot- tom-up Wald analysis was performed to define the variables to be included in the final multi- variable logistic regression model. The p-value was set at alpha = 0.05 for significance testing. Ethical statement The study was approved by the ethics committee of the School of Public Health at the Univer- sity of Lubumbashi, DRC (approval letter No UNILU/CEM/104/2022). All study participants provided verbal informed consent prior to completing the questionnaire. Documentation for verbal consent was not required due to the one-off nature of the study (no follow-up with par- ticipants) and as the methods represented minimal risk to the subjects. Results Overall, 5,102 individuals provided responses to the questionnaire, of whom 832 were in Haut Katanga, 550 in Kasai Oriental, 900 in Kinshasa, 896 in Kongo Central, 591 in Lualaba, 422 in North Kivu, and 911 in South Kivu. The full dataset of coded responses is provided in the Sup- plemental Material (S2 File). Sociodemographic characteristics of the respondents, by prov- ince, are provided in Table 1. Overall, 46.3% of respondents reported having received one or more doses of the COVID- 19 vaccine, but with substantial variation between provinces (Table 2). Three-quarters of the PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 4 / 15 PLOS GLOBAL PUBLIC HEALTHTable 1. Sociodemographic characteristics of participants, by DRC province (N = 5,102 except where otherwise indicated). Haut Katanga (%) Kasai Oriental (%) Kinshasa (%) Kongo Central (%) Lualaba (%) North Kivu (%) South Kivu (%) Total (%) COVID-19 vaccine uptake and hesitancy in DRC healthcare workers Age range 18–29 30–39 40–54 >55 Provincial n Sex Female Male Level of education None Elementary school Middle school University or higher Healthcare worker categories Nurse Doctor Pharmacist Midwife Laboratory technician Other Marital status Single Divorced/ Separated Married Cohabitation Widowed Religion Animist 162 (19.5%) 274 (32.9%) 285 (32.3%) 111 (14.3%) 832 421 (50.6%) 411 (48.4%) 16 (1.9%) 30 (3.6%) 171 (20.6%) 615 (73.9%) 364 (43.8%) 195 (23.4%) 24 (2.9%) 47 (5.6%) 67 (8.1%) 135 (16.2%) 126 (15.1%) 15 (1.8%) 649 (78.0%) 13 (1.6%) 29 (3.5%) 4 (0,5%) 103 (18.7%) 162 (29.5%) 205 (37. 3%) 80 (14.5%) 550 272 (49.5%) 278 (50. 5%) 1 (0.2%) 40 (7.3%) 200 (36.4%) 309 (56.2%) 268 (48.7%) 78 (14.2%) 10 (1.8%) 25 (4.5%) 50 (9.1%) 119 (21.6%) 52 (9.5%) 9 (1.6%) 463 (84.2%) 1 (0.2%) 25 (4.5%) 2 (0,4%) 78 (8.7%) 362 (40.2%) 374 (41.6%) 86 (9.6%) 900 453 (50.3%) 447 (49.7%) 4 (0.4%) 6 (0.7%) 108 (12.0%) 782 (86.9%) 398 (44.2%) 256 (28.4%) 30 (3.3%) 71 (7.9%) 70 (7.8%) 75 (8.3%) 129 (14.3%) 13 (1.4%) 539 (59.9%) 198 (22.0%) 21 (2.3%) 2 (0,2%) 61 (6.8%) 258 (28.8%) 474 (52.9%) 103 (11.5%) 896 534 (59.6%) 362 (40.4%) 14 (1.6%) 24 (2.7%) 305 (34.0%) 553 (61.7%) 458 (51.1%) 87 (9.7%) 11 (1.2%) 62 (6.9%) 128 (14.3%) 150 (16.6%) 119 (13.3%) 35 (3.9%) 560 (62.5%) 148 (16.5%) 34 (3.8%) 16 (1,8%) 152 (25.7%) 200 (33.8%) 218 (36.9%) 21 (3.6%) 591 314 (53.1%) 277 (46.9%) 12 (2.0%) 11 (1.9%) 59 (10.0%) 509 (86.1%) 270 (45.7%) 47 (8.2%) 62 (10.5%) 86 (14.6%) 50 (8.5%) 76 (12.9%) 182 (30.8%) 7 (1.2%) 360 (60.9%) 2 (0. 5%) 40 (6.8%) 2 (0,3%) 128 (30.3%) 159 (37.7%) 120 (28.4%) 15 (3.6%) 422 179 (42.4%) 243 (57.6%) 8 (1.9%) 16 (3.8%) 79 (18.7%) 319 (75.6%) 165 (39.1%) 73 (17.3%) 19 (4.5%) 23 (5.5%) 19 (4.5%) 123 (29.1%) 121 (28.7%) 2 (0.5%) 284 (67.3%) 11 (2.6%) 4 (0,9%) 2 (0,5%) 197 (21.6%) 325 (35.7%) 294 (32.3%) 95 (10.4%) 911 392 (43.0%) 519 (57.0%) 5 (0.5%) 33 (3.6%) 191 (21.0%) 682 (74.9%) 433 (47.5%) 78 (8. 6%) 52 (5.7%) 130 (14.3%) 78 (8.6%) 140 (15.4%) 197 (21.6%) 9 (1.0%) 685 (75.2%) 3 (0.3%) 17 (1.9%) 0 (0%) 881 (17.3%) 1,740 (34.1%) 1,970 (38.6%) 511 (10.0%) 5,102 2,565 (50.3%) 2,537 (49.7%) 60 (1.2%) 160 (3.0%) 1,113 (21.8%) 3,769 (73.9%) 2,356 (46.2%) 814 (16.0%) 208 (4.1%) 444 (8.7%) 462 (9.1%) 818 (16.0%) 926 (18.1%) 90 (1. 8%) 3,540 (69.4%) 376 (7.4%) 170 (3.3%) 28 (0,5%) (Continued ) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 5 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Table 1. (Continued) Christian Muslim Without religion Other Place of residence Urban Rural Haut Katanga (%) Kasai Oriental (%) 793 (95,3%) 27 (3,2%) 8 (1,0%) 0 (0%) 773 (92.9%) 59 (7.1%) 522 (94,9%) 9 (1,6%) 12 (2,2%) 5 (0,9%) 470 (85.5%) 80 (14.5%) Other vaccine uptake, excluding routine childhood immunizations Yes No 152 (18.3%) 680 (81.7%) Types of other vaccines taken (N = 1965) Cholera Ebola Yellow fever Meningitis Tetanus Other Existing chronic illness Yes No I don’t know 90 (50.2%) 3 (1.6%) 69 (38.5%) 4 (2.2%) 8 (4.5%) 5 (2.8%) 111 (13.3%) 693 (83.3%) 28 (3.4%) 106 (19.3%) 444 (80.7%) 66 (60.0%) 2 (1.8%) 9 (8.2%) 0 (0.00%) 29 (26.4%) 4 (3.7%) 85 (15.5%) 456 (82.9%) 9 (1.6%) Kinshasa (%) 879 (97,7%) 9 (1,0%) 4 (0,4%) 6 (0,7%) 898 (99.8%) 2 (0.2%) 412 (45. 8%) 488 (54.2%) 13 (2.9%) 30 (6.8%) 377 (85. 1%) 0 (0.00%) 21 (4.7%) 2 (0.5%) 157 (17.4%) 683 (75. 9%) 60 (6.7%) Kongo Central (%) 836 (93,3%) 3 (0,3%) 6 (0,7%) 35 (3,9%) 896 (100%) 0 (0.00%) 476 (53.1%) 420 (46.9%) 1 (0.2%) 1 (0.2%) 463 (84. 7%) 0 (0.00%) 32 (5.8%) 6 (1.1%) 63 (7.0%) 809 (90.3%) 24 (2. 7%) Lualaba (%) 537 (90,9%) 13 (2,2%) 17 (2,9%) 22 (3,7%) 526 (89.0%) 65 (11.0%) 91 (15.4%) 500 (84.6%) 2 (2.2%) 1 (1.1%) 83 (94.6%) 1 (1.3%) 5 (5.4%) 0 (0.00%) 68 (11.5%) 510 (86.3%) 13 (2.2%) North Kivu (%) South Kivu (%) 402 (95,3%) 6 (1,4%) 4 (0,9%) 8 (1,9%) 330 (78.2%) 92 (21.8%) 305 (72.3%) 117 (27.7%) 105 (21.9%) 256 (53.4%) 45 (9.4%) 50 (10.4%) 14 (2.9%) 9 (1.8%) 46 (10.9%) 357 (84.6%) 19 (4.5%) 892 (97,9%) 9 (1,0%) 7 (0,8%) 3 (0,3%) 595 (65.3%) 316 (34.7%) 423 (46.4%) 488 (53. 6%) 156 (28.5%) 197 (35.9%) 76 (13.9%) 22 (4.0%) 70 (12.8%) 27 (3.1%) 32 (3.5%) 856 (94.0%) 23 (2.5%) Total (%) 4861 (95,3%) 76 (1,5%) 58 (1,1%) 79 (1,5%) 4488 (88.0%) 614 (12.0%) 1965 (38.53%) 3137 (61.5%) 433 (18.4%) 490 (20.8%) 1122 (47.6%) 77 (3.3%) 179 (7.6%) 53 (2.3%) 562 (11.0%) 4364 (85.5%) 176 (3.4%) Percentages are calculated across rows. Reference variable noted in the OR column. OR = odds ratio; CI = confidence interval. https://doi.org/10.1371/journal.pgph.0002772.t001 respondents believed they were at either moderate or high risk with respect to contracting COVID-19, although less than 44% had ever been tested for COVID-19, and only about a third reported having been in contact with a COVID-19 patient. The multivariable logistic regression suggested that several factors were significantly associ- ated with receiving at least one dose of COVID-19 vaccine (Table 3). Being in the older age group (55 years or older), being a doctor (compared with all other types of HCWs), being mar- ried, and being a rural resident were all associated with being vaccinated, as was having received other adult non-COVID-19 vaccinations. Other significant factors for respondents related to having access to vaccination through their work or within their health structure or having knowledge about vaccination efforts. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 6 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Table 2. COVID-19 beliefs and practices, by province (N = 5,102). Haut Katanga (%) Kasai Oriental (%) Kinshasa (%) Kongo Central (%) Lualaba (%) North Kivu (%) South Kivu (%) Total (%) Are you vaccinated against COVID-19? Yes–at least one dose 355 (42.7%) 408 (70.8%) 382 (42.4%) 347 (38.7%) 268 (45.3%) 138 (32.7%) 466 (51.2%) No 477 (57.3%) What is your risk of contracting COVID-19? No risk Low Moderate High 17 (2.0%) 125 (15.0%) 421 (50.6%) 269 (32.3%) Have you ever been tested for COVID-19? Yes No 431 (51.8%) 401 (48.2%) 142 (25.8%) 97 (17.6%) 80 (14.5%) 149 (27.1%) 224 (40.7%) 107 (19.5%) 443 (80.5%) 518 (57.6%) 22 (2.4%) 111 (12.3%) 525 (58.3%) 242 (26.9%) 494 (54.5%) 406 (45.1%) Knowledge of availability of different COVID-19 vaccines in province Yes No 775 (93.1%) 156 (6.9%) 480 (87.3%) 70 (12.7%) 839 (93,2%) 61 (6.8%) Awareness of routine vaccination against COVID-19 in province or local area Yes No 768 (92.3%) 64 (7.7%) 348 (63.3%) 202 (36.7%) 791 (87.9%) 109 (12.1%) 549 (61.3%) 119 (13.3%) 144 (16.1) 412 (46.0%) 221 (24.7%) 423 (47.2%) 473 (52.8%) 740 (82.6%) 156 (17.4%) 683 (76.2%) 213 (23.8%) Aware of the planned vaccination campaign against COVID-19 in province or local area Yes No 678 (81.5%) 154 (18.5%) Vaccination within respondent’s facility Yes No 754 (90.6%) 78 (9.4%) Previous work at a COVID-19 vaccination site Yes No 166 (20.0%) 666 (80.0%) 502 (91.3%) 48 (8.7%) 367 (66.7%) 183 (33.3%) 122 (22.2%) 428 (77.8%) 878 (97.6%) 22 (2.4%) 796 (88.4%) 104 (11.6%) 275 (30.6%) 625 (69.4%) Willingness to take a COVID-19 vaccination if available in the province Yes No 477 (57.3%) 355 (42.7%) 473 (86.0%) 77 (14.0%) 563 (62.6%) 337 (37.4%) https://doi.org/10.1371/journal.pgph.0002772.t002 879 (98.1%) 17 (1.9%) 467 (52.1%) 429 (47.9%) 123 (13.7%) 773 (86.3%) 555 (61.9%) 341 (38.1%) 323 (54.7%) 71 (12.0%) 222 (37.6%) 189 (32.0%) 109 (18. 4%) 204 (34.5%) 387 (65.5%) 568 (96.1%) 23 (3.9%) 571 (96.6%) 20 (3.4%) 517 (87.5%) 74 (12.5%) 388 (65.7%) 203 (34.3%) 162 (27.4%) 429 (72.6%) 490 (82.9%) 101 (17.1%) 284 (67.3%) 18 (4.3%) 73 (17. 3%) 111 (26.3%) 220 (52. 1%) 184 (43.6%) 238 (56.4%) 358 (84.8%) 64 (15.2%) 191 (45.3%) 231 (54.7%) 392 (92.9%) 30 (7.1%) 422 (100.0%) 0 (0.0%) 422(100.0%) 0 (0.0%) 246 (58.3%) 176 (41.7%) 445 (48.8%) 24 (2.6%) 107 (11.7%) 342 (37.5%) 438 (48.1%) 373 (40.9%) 538 (59.1%) 817 (89.7%) 94 (10.3%) 811 (89.0%) 100 (11.0%) 899 (98.7%) 12 (1.3%) 633 (69.5%) 278 (30.5%) 270 (29.6%) 641 (70.4%) 626 (68.7%) 285 (31.3%) 2364 (46.3%) 2738 (53.7%) 368 (7.2%) 862 (16.9%) 2149 (42.1%) 1723 (33.8%) 2216 (43.4%) 2886 (56.6%) 4577 (89.7%) 525 (10.3%) 4163 (81.59%) 939 (18.4%) 4745 (93.0%) 357 (7.0%) 3827 (75.0%) 1275 (25.0%) 1540 (30.2%) 3562 (69.8%) 3430 (67.2%) 1672 (32.8%) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 7 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Table 3. Significant factors associated with COVID-19 vaccination. Age 55 years or older (vs. 18–55) Married (vs. not married) Previously tested for COVID-19 (Yes vs. No) Been tested for COVID-19 (Yes vs. No) Rural resident (vs. Urban) Perception of risk (Yes vs. No) Has received other adult non-COVID-19 vaccinations (Yes vs. No) Would take a COVID-19 vaccine if they had one available in their province/commune/neighborhood or village/routine vaccination sites (Yes vs. No) Has a vaccination site within their health structure (Yes vs. No) Has ever worked in a COVID-19 vaccination site (Yes vs. No) Would take a COVID-19 vaccine if they knew that several vaccines against COVID-19 are present in their province/commune/district or village/vaccination sites (Yes vs. No) aOR (CI 95%) 1.74 (1.3–2.34) 1.48 (1.22–1.79 1.23 (1.05–1.43) 1.26 (1.07–1.48) 2.29 (1.77–2.96) 1.84 (1.28–2.64 1.77 (1.50–2.09) 2.22 (1.74–2.83) 3.05 (2.51–3.69) 1.87 (1.56–2.24) 1.55 (1.11–2.17) P-value < .001 < .001 .039 < .001 < .001 < .001 < .001 < .001 < .001 < .001 .011 Outputs are from a multivariable logistic regression model fitted using the step-by-step Wald method. https://doi.org/10.1371/journal.pgph.0002772.t003 One demographic factor that was significant in the univariable analyses (but not in the mul- tivariable logistic regression) was gender, with respondents identifying as male more likely to report being vaccinated (OR = 1.46; 95% CI = 1.30–1.62; p < .001) (S1 Table). Likewise, the univariable regression suggested an association between having a known chronic illness and being vaccinated (OR = 1.51; 95% CI = 1.29–1. 81; p < .001). Respondents were also asked about the factors that influenced them to either accept vacci- nation or not. Individuals who had received at least one vaccine could select one or more moti- vating factors from a list. The most frequently selected motivation, representing almost half of all selected responses, was “to protect myself and protect others” (Table 4). This was also the Table 4. Motivation factors for uptake, among vaccinated respondents, by province. Kinshasa (%) Kongo Central (%) Lualaba (%) North Kivu (%) South Kivua (%) Total times response selected (% of total responses) To protect myself and protect others To help stop transmission of the virus Belief in vaccination and science To facilitate own travel To return to “normal” life without restrictions To not die Other specified reasons Total responses per province (% of total responses) Haut Katanga (%) 345 (41.9%) 156 (19.0%) 79 (9.6%) 103 (12.5%) 96 (11.7%) 43 (5.2%) 1 (0.001%) 823 (17.87%) Kasai Oriental (%) 367 (39.4%) 249 (26.7%) 113 (12.1%) 86 (9.2%) 42 (4.5%) 70 (7.5%) 5 (0.5%) 358 (38.1%) 234 (24.9%) 163 (17.3%) 82 (8.7%) 57 (6.1%) 43 (4.6%) 3 (0.3%) 320 (45.7%) 183 (26.1%) 75 (10.7%) 34 (4.9%) 63 (9.0%) 16 (2.3%) 9 (1.3%) 188 (39.1%) 132 (27.4%) 21 (4.4%) 47 (9.8%) 70 (14.6%) 18 (3.7%) 5 (1.0%) 137 (51.9%) 50 (19.0%) 40 (15.2%) 11 (4.2%) 12 (4.5%) 14 (5.3%) 0 (0.0%) 432 (92.7%) 15 (3.2%) 6 (1.3%) 10 (2.1%) 2 (0.4%) N/A* 1 (0.2%) 466 (10.12%) 2147 (46.61%) 1019 (22.21%) 497 (10.79%) 373 (8.10%) 342 (7.43%) 204 (4.42%) 24 (0.52%) 4,606 (100%) 932 (20.23%) 940 (20.41%) 700 (15.20%) 481 (10.44%) 264 (5.73%) Respondents were able to select more than one response. aSouth Kivu respondents were requested to only select one primary motivation. “To Not Die” was not listed as an option in South Kivu. https://doi.org/10.1371/journal.pgph.0002772.t004 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 8 / 15 PLOS GLOBAL PUBLIC HEALTHTable 5. Primary reason for refusal of the COVID-19 vaccine, among unvaccinated respondents, by province. COVID-19 vaccine uptake and hesitancy in DRC healthcare workers Kasaï Oriental (%) Kinshasa (%) Kongo Central (%) Lualaba (%) Insufficient data on the safety of the new vaccine Concern regarding vaccine ineffectiveness Concern regarding vaccine side effects I am against vaccines in general Lack of trust because of the short time frame to manufacture vaccines God’s protection is enough, there is no need for a vaccine Because of the Westerners or Illuminati’s plan to eliminate the Africans through vaccines Previous adverse reaction to any vaccine Concern about acquiring COVID-19 infection from the vaccine itself I do not perceive myself to be at high risk for COVID-19 infection I perceive myself as not being at considerable risk of developing complications if I am infected with COVID-19 Vaccine administration is painful or inconvenient. I have already had an infection with COVID-19 Total responses (% of total responses) https://doi.org/10.1371/journal.pgph.0002772.t005 Haut Katanga (%) 162 (34.1%) 86 (18.1%) 72 (15.2%) 34 (7.2%) 51 (1.1%) 13 (2.7%) 40 (8.4%) 3 (0.6%) 3 (0.6%) 4 (0.8%) 6 (1.3%) 0 (0%) 1 (0.2%) 475 (17.4%) 49 (34.5%) 24 (16.9%) 25 (17.6%) 2 (1.4%) 5 (3.5%) 18 (12.7%) 3 (2.1%) 9 (6.3%) 2 (1.4%) 4 (2.8%) 1 (0.7%) 0 (0%) 0 (0%) 142 (5.2%) 59 (11.4%) 129 (25.0%) 173 (33.5%) 63 (12.2%) 29 (5.6%) 17 (3.3%) 4 (0.8%) 21 (4.1%) 2 (0.4%) 6 (1.2%) 7 (1.4%) 3 (0.6%) 3 (0.6%) 110 (20.1%) 97 (17.7%) 71 (13.0%) 113 (20.6%) 21 (3.8%) 23 (4.2%) 29 (5.3%) 26 (4.7%) 23 (4.2%) 14 (2.6%) 8 (1.5%) 13 (2.4%) 0 (0%) 12 (3.7%) 55 (17.1%) 39 (12.1%) 31 (9.7%) 95 (29.6%) 48 (15.0%) 26 (8.1%) 8 (2.5%) 1 (0.3%) 1 (0.3%) 1 (0.3%) 2 (0.6%) 2 (0.6%) North Kivu (%) 64 (22.5%) 71 (25.0%) 113 (39.8%) 3 (1.1%) 1 (0.4%) 4 (1.4%) 2 (0.7%) 14 (4.9%) 3 (1.1%) 3 (1.1%) 3 (1.1%) 2 (0.7%) 1 (0.4%) South Kivu (%) 112 (25.3%) 87 (19.7%) 53 (12.0%) 45 (10.2%) 8 (1.8%) 52 (11.8%) 25 (5.7%) 24 (5.4%) 14 (3.2%) 11 (2.5%) 10 (2.3%) 0 (0%) 1 (0.2%) Total times selected as primary motivation (% of total responses) 568 (20.8%) 549 (20.1%) 546 (20.0%) 291 (10.7%) 210 (7.7%) 175 (6.4%) 129 (4.7%) 105 (3.8%) 48 (1.8%) 43 (1.6%) 36 (1.3%) 20 (0.7%) 8 (0.3%) 2,728 (100%) 516 (23.6%) 548 (25.2%) 321 (14.7%) 284 (13.0%) 442 (20.3%) most frequently selected single response, which was chosen as the only motivating factor by 32.1% of respondents. “To stop transmission of the virus” was the second most frequently selected motivating factor overall, although it represented fewer than a quarter of all responses. These also were the top two most frequently selected motivations in each province, although the third most frequently selected motivation varied between “belief in vaccination and sci- ence” (in Kasai Oriental, Kinshasa, Kongo Central and North Kivu), “to facilitate own travel” (Haut Katanga and South Kivu), and “to return to ‘normal’ life without restrictions” (Lualaba). Nonvaccinated respondents were asked to select their primary reason for refusal, out of a pre-prepared list of potential responses. Overall, perceived insufficiency of data over vaccine safety was the most frequently cited reason for refusal and also the most frequently selected in Haut Katanga, Kasaï Oriental, and South Kivu provinces (Table 5). The second most fre- quently cited reason overall was concern regarding vaccine ineffectiveness, although this did not appear as the top reason in any individual province. Instead, other most frequently cited reasons at the provincial level were concerns over vaccine side effects (Kinshasa and North Kivu), lack of trust because of the short time frame for manufacture of the vaccines (Lualaba), and being against vaccines in general (Kongo Central). Reasons relating to perceived lower risk of infection or complications with COVID-19, or existing natural immunity through infection, were among the least frequently selected responses overall and within each province. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 9 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Both vaccinated and unvaccinated respondents were asked to provide their level of agree- ment with a series of statements related to factors that might influence or incentivize them to receive a COVID-19 vaccination, and to other statements related to trust and effectiveness of the national COVID-19 response. Vaccinated respondents had significantly higher agreement levels with every provided influencing factor compared with unvaccinated individuals (S2 Table). However, both groups had the strongest agreement rates with the same two factors: “If I were convinced that getting vaccinated would help protect vulnerable members of my family or community” (vaccinated respondents: mean = 3.21, standard deviation [SD] = 1.36; unvac- cinated respondents: mean = 2.62, SD = 1.47) and “If I were sure that the vaccine is effective and that people who are vaccinated do not get sick with COVID-19” (vaccinated respondents: mean = 3.04, SD = 1.41; unvaccinated respondents: mean = 2.75, SD = 1.50). Both groups also had the same statements with which they agreed the least, related to receiving food or money as incentives for getting vaccinated (vaccinated respondents: mean = 1.66 and 1.64; SD = 1.00 and 1.04, respectively; unvaccinated respondents: mean = 1.46 for both, SD = 0.84 and 0.88, respectively). Vaccinated respondents had higher agreement with all the statements related to trust in the authorities, media, health system, and government actions and measures related to the COVID-19 response (S3 Table). Discussion Vaccination is the most effective method of averting vaccine-preventable diseases. However, vaccine hesitancy can compromise vaccination considerably [22], and lack of uptake in HCWs, who are at elevated risk for occupational exposure to diseases like COVID-19 [23], is particularly important for health systems resilience during epidemics. The overall percentage of individuals who had received at least one dose of COVID-19 vac- cine among the over 5,100 HCWs surveyed was 46.3%, which was similar to the level observed for the continent of Africa as a whole, in a 2022 meta-analysis [24]. However, that study showed an overall rate of acceptance in Central Africa of 28%, which could suggest higher rates of acceptance in DRC, or perhaps a temporal change in acceptability. Overall, vaccine hesitancy among HCWs in DRC, and Africa as a whole, is higher than in other regions of the world; one scoping review found acceptance in HCWs of over 75% globally [25]. Our survey findings suggest that in DRC, despite being higher than expected for the region, the uptake is much less than targets; for example, the WHO suggests that countries should aim for a vacci- nation rate of 100% of HCWs to achieve 70% coverage of the overall population [26]. The main reasons for vaccine hesitancy among HCWs in this study are related to safety, side effects, and effectiveness. This aligns with other findings, including a scoping review of 12 studies, that the reasons for vaccine hesitancy in all studies cited safety, side effects, or adverse events [24]. Additional factors cited in other studies, but which were less predominant motiva- tions for vaccine hesitancy among the DRC HCWs surveyed here, included the short duration of clinical trials, lack of trust in the vaccine sources, the low severity of COVID-19, and the risk of acquiring COVID-19 from the vaccine [24,27–29]. We also observed that younger individu- als were less likely to be vaccinated, and in the univariable analysis, women were also signifi- cantly more hesitant than men, findings also seen in studies among HCWs in Ghana and Ethiopia, for example, as well as globally [28,30–34]. Regarding age, it is possible that older HCWs are more aware of the strong link between age and severe COVID-19 outcomes; how- ever, we did not find a clear association between self-perceived risk of contracting COVID-19 and vaccination status, let alone stratified by age. Taken together, the combination of younger women being more hesitant could suggest that misinformation, specifically around side effects PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 10 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers relating to infertility or other reproductive impacts, is affecting uptake among HCWs in DRC, which would mirror observations from other studies and settings [35–37]. Our study also showed that place of residence was significantly associated with vaccine uptake, with rural populations more likely to be vaccinated. However, our sample was skewed quite heavily toward urban residents, as we specifically targeted urban healthcare facilities for distribution of the survey. Our findings contrast with those from other countries, such as India and the United States, where rural communities are consistently more vaccine-hesitant than urban populations [34,38,39]. However, in DRC and other African settings, rural populations are less likely to use mobile phones and to use them to access the internet compared with urban dwellers [40,41]. Given the highly impactful role of social media in spreading misinfor- mation, it could mean that in countries like DRC, HCWs and the general population in urban settings are more exposed to media that might contribute to hesitancy. Vaccinated individuals in this study described their primary motivations for receiving a vaccine as being predominantly to protect themselves from disease and to protect their friends, family, and loved ones, a finding mirrored in other studies of vaccine uptake in Africa [42]. Vaccinated respondents also noted that they would be willing to take vaccines if they are pro- vided in their health structure or local area; which, together with additional messaging empha- sizing the positive impact of vaccination on family and community, could be a helpful strategy to promote completion of vaccination courses or uptake of boosters. While vaccinated individuals reported significantly higher trust scores in government, the health authorities, and other actions of the COVID-19 response, the absolute values were still quite low. This suggests that the government could increase efforts to build trust among this key population, especially with respect to preparedness for future epidemics, as trust has been shown to be a key factor in promoting compliance to response measures, and lower mortality outcomes, during health emergencies [43,44]. We did not observe any factors that would strongly motivate unvaccinated individuals to receive vaccine doses. As seen in other settings, vaccine-hesitant HCWs may therefore benefit from tailored messaging to assuage concerns related to safety and side effects in particular, while also attempting to build trust [32]. Future research could also aim to investigate trusted sources of information among vaccine-hesitant HCWs and leverage those channels for more targeted communication approaches. This study had several limitations. First, the method of sampling health facilities in prox- imity to the capital cities of the provinces may lead to results that are not generalizable to HCWs throughout the province. Second, administration of the questionnaire by data col- lectors instead of through an anonymous method, may have led respondents to respond less accurately or honestly about their vaccination status and beliefs and practices related to vaccination. Third, the KAP questionnaires were administered in the seven provinces between December 2021 and November 2022, during which time the government’s vacci- nation campaigns continued to roll out. Consequently, surveying HCWs in provinces that were in different phases of vaccine rollout may have contributed to differences in vaccine uptake. Conclusion Hesitancy to vaccinate against COVID-19 among health professionals may have a negative impact on progress to build public confidence in the COVID-19 vaccination program. Our results suggest the need to develop tailored strategies to address the concerns identified in the study to ensure optimal vaccine acceptance among HCWs in DRC. Future research, which should include qualitative data collection, should seek to understand specific concerns with PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 11 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers respect to side effects and safety in unvaccinated individuals to inform the development of more targeted vaccination messaging. Supporting information S1 Table. Univariate regression analyses of factors significantly associated with COVID-19 vaccine status (N = 5,102). Percentages are calculated across rows. Reference variable noted in the OR column. OR = odds ratio; CI = confidence interval. (DOCX) S2 Table. Decisions influencing COVID-19 vaccination, by vaccination status. Responses were recorded on a Likert scale, with 1 the least level of agreement and 5 the strongest level of agreement. A response of 3 was described as “partially agree”. (DOCX) S3 Table. Participant’s level of confidence and social trust in government authorities in the fight against COVID-19, by vaccination status. Responses were recorded on a Likert scale, with 1 the least level of agreement and 5 the strongest level of agreement. A response of 3 was described as “partially agree”. (DOCX) S1 File. Knowledge, Attitudes, Practices Questionnaire (in French and English). (DOCX) S2 File. Dataset of KAP questionnaire responses. (CSV) Acknowledgments We would like to thank all the healthcare workers who contributed their time and perspectives to this study, the data collectors who administered the surveys, as well as the support of the Ministry of Health and other COVID-19 response partners. Additionally, we would like to thank colleagues from the US Centers for Disease Control and Prevention for their input into the design of the study: Brooke Aksnes, Melissa Dahlke, Reena H. Doshi, Norbert Soke Gna- kub, Richard Luce, and Robert Perry. This publication was supported by Cooperative Agree- ment NU2HGH000047 funded by the US Centers for Disease Control and Prevention. Author Contributions Conceptualization: Michel K. Nzaji, Christophe Luhata Lungayo, Aime Cikomola Mwana Bene, Anselme Manyong Kapit, Pia D. M. MacDonald, Kristen B. Stolka. Data curation: Michel K. Nzaji, Shanice Fezeu Meyou, Dana Sessoms, Claire J. Standley, Kris- ten B. Stolka. Formal analysis: Michel K. Nzaji, Jean de Dieu Kamenga, Shanice Fezeu Meyou, Alanna S. Fogarty, Dana Sessoms, Claire J. Standley. Funding acquisition: Anselme Manyong Kapit, Pia D. M. MacDonald, Kristen B. Stolka. Investigation: Michel K. Nzaji, Jean de Dieu Kamenga, Christophe Luhata Lungayo, Aime Cikomola Mwana Bene, Shanice Fezeu Meyou, Anselme Manyong Kapit, Kristen B. Stolka. Methodology: Michel K. Nzaji, Jean de Dieu Kamenga, Anselme Manyong Kapit, Pia D. M. MacDonald, Kristen B. Stolka. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002772 February 1, 2024 12 / 15 PLOS GLOBAL PUBLIC HEALTHCOVID-19 vaccine uptake and hesitancy in DRC healthcare workers Project administration: Shanice Fezeu Meyou, Anselme Manyong Kapit, Pia D. M. MacDon- ald, Kristen B. Stolka. Resources: Anselme Manyong Kapit. Supervision: Christophe Luhata Lungayo, Anselme Manyong Kapit, Pia D. M. MacDonald, Claire J. Standley, Kristen B. Stolka. Validation: Michel K. Nzaji, Jean de Dieu Kamenga, Christophe Luhata Lungayo, Aime Ciko- mola Mwana Bene, Anselme Manyong Kapit, Alanna S. Fogarty, Claire J. Standley, Kristen B. Stolka. Visualization: Michel K. Nzaji, Shanice Fezeu Meyou. Writing – original draft: Michel K. Nzaji, Pia D. M. MacDonald, Claire J. Standley, Kristen B. Stolka. Writing – review & editing: Michel K. Nzaji, Jean de Dieu Kamenga, Christophe Luhata Lun- gayo, Aime Cikomola Mwana Bene, Shanice Fezeu Meyou, Anselme Manyong Kapit, Alanna S. 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10.1371_journal.pone.0294847	RESEARCH ARTICLE A phase 2 open-label study of the safety and efficacy of weekly dosing of ATL1102 in patients with non-ambulatory Duchenne muscular dystrophy and pharmacology in mdx mice 1,2,3, George Tachas4, Nuket Desem4, Peter J. Houweling2,3, Ian R. WoodcockID Michael Kean5, Jaiman Emmanuel5, Rachel KennedyID 1,2,6, Kate Carroll1,2, Katy de Valle1,2,6, Justine Adams2, Shireen R. Lamande´ 2,3, Chantal Coles2, Chrystal Tiong2, Matthew Burton2, Daniella Villano1, Peter Button7, Jean-Yves Hogrel8, Sarah Catling- Seyffer1,2, Monique M. Ryan1,2,3, Martin B. Delatycki9,10, Eppie M. Yiu1,2,3 1 Department of Neurology, The Royal Children’s Hospital, Melbourne, Australia, 2 The Murdoch Children’s Research Institute, Melbourne, Australia, 3 Department of Paediatrics, University of Melbourne, Melbourne, Australia, 4 Antisense Therapeutics Ltd, Melbourne, Australia, 5 Department of Medical Imaging, The Royal Children’s Hospital, Melbourne, Australia, 6 Department of Physiotherapy, University of Melbourne, Melbourne, Australia, 7 McCloud Consulting Group, Sydney, Australia, 8 Institut de Myologie, GH Pitie´ - Salpêtrière, Paris, France, 9 Victorian Clinical Genetics Service, Melbourne, Australia, 10 Murdoch Children’s Research Institute, Bruce Lefroy Centre for Genetic Health Research, Melbourne, Australia a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS ian.woodcock@rch.org.au Citation: Woodcock IR, Tachas G, Desem N, Houweling PJ, Kean M, Emmanuel J, et al. (2024) A phase 2 open-label study of the safety and efficacy of weekly dosing of ATL1102 in patients with non-ambulatory Duchenne muscular dystrophy and pharmacology in mdx mice. PLoS ONE 19(1): e0294847. https://doi.org/10.1371/ journal.pone.0294847 Editor: Julie Dumonceaux, UCL: University College London, UNITED KINGDOM Received: June 29, 2023 Accepted: October 19, 2023 Published: January 25, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0294847 Copyright: © 2024 Woodcock et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Background ATL1102 is a 2’MOE gapmer antisense oligonucleotide to the CD49d alpha subunit of VLA- 4, inhibiting expression of CD49d on lymphocytes, reducing survival, activation and migra- tion to sites of inflammation. Children with DMD have dystrophin deficient muscles suscepti- ble to contraction induced injury, which triggers the immune system, exacerbating muscle damage. CD49d is a biomarker of disease severity in DMD, with increased numbers of high CD49d expressing T cells correlating with more severe and progressive weakess, despite corticosteroid treatment. Methods This Phase 2 open label study assessed the safety, efficacy and pharmacokinetic profile of ATL1102 administered as 25 mg weekly by subcutaneous injection for 24 weeks in 9 non- ambulatory boys with DMD aged 10–18 years. The main objective was to assess safety and tolerability of ATL1102. Secondary objectives included the effect of ATL1102 on lymphocyte numbers in the blood, functional changes in upper limb function as assessed by Perfor- mance of Upper Limb test (PUL 2.0) and upper limb strength using MyoGrip and MyoPinch compared to baseline. PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 1 / 16 PLOS ONEData Availability Statement: All relevant data are within the paper and its Supporting information files. Funding: The ATL1102 in DMD clinical trial was funded in its entirety by the sponsor Antisense Therapeutics Ltd. Authors Ms Desem and Dr Tachas are employees of the sponsor and so received payment for services from the sponsor as employees. Ms. Desem and Dr Tachas hold an equity interest in the sponsor. Dr Tachas and Ms Desem along with other sponsor employees and sub-contracted specialists were involved in the study design and data analysis. Authors Dr Woodcock and Dr Ryan were at the time of the trial employees of the Royal Children’s Hospital and Murdoch Children’s Research Institute and are not affiliated with the sponsor in any way and have not received any direct personal payment or honoraria from the sponsors, nor do they or their family members hold a financial interest or stock in the sponsor company. Dr Woodcock is still an employee of the above institutions, but Dr Ryan has since left the employment to take up public office. Dr Woodcock and Dr Ryan were involved in the trial design as unpaid consultants. As this was a clinical trial, publication was always planned from trial inception. No employees of the sponsor were involved in the data collection, although Ms Desem did liaise closely with the MCRI/RCH site staff and Clinical Trial Organisation throughout the trial. Author Dr Button was paid for services as the study statistician. None of the other authors received any payment from the sponsor to conduct this study. All other authors had input into writing or revising this manuscript. Competing interests: The ATL1102 in DMD clinical trial was funded in its entirety by the commercial sponsor Antisense Therapeutics Ltd. Antisense Therapeutics Ltd is a publicly traded company, listed on the Australian ASX. At the time of the trial, authors Ms Desem and Dr Tachas were employees of the sponsor and so received payment for services from the sponsor as employees. Ms. Desem and Dr Tachas hold an equity interest in the sponsor. Dr Tachas and Ms Desem along with other sponsor employees and sub-contracted specialists were involved in the study design and data analysis. Ms Desem has since left the company and no longer is employed by the sponsor. Authors Dr Woodcock and Dr Ryan were at the time of the trial employees of the Royal Children’s Hospital and Murdoch Children’s Research Institute and are not affiliated with the sponsor in any way and have not received any direct personal payment or honoraria from the sponsors, nor do they or their family members Phase 2 clinical trial of ATL1102 in non-ambulatory DMD Results Eight out of nine participants were on a stable dose of corticosteroids. ATL1102 was gener- ally safe and well tolerated. No serious adverse events were reported. There were no partici- pant withdrawals from the study. The most commonly reported adverse events were injection site erythema and skin discoloration. There was no statistically significant change in lymphocyte count from baseline to week 8, 12 or 24 of dosing however, the CD3+CD49d+ T lymphocytes were statistically significantly higher at week 28 compared to week 24, four weeks past the last dose (mean change 0.40x109/L 95%CI 0.05, 0.74; p = 0.030). Func- tional muscle strength, as measured by the PUL2.0, EK2 and Myoset grip and pinch mea- sures, and MRI fat fraction of the forearm muscles were stable throughout the trial period. Conclusion ATL1102, a novel antisense drug being developed for the treatment of inflammation that exacerbates muscle fibre damage in DMD, appears to be safe and well tolerated in non- ambulant boys with DMD. The apparent stabilisation observed on multiple muscle disease progression parameters assessed over the study duration support the continued develop- ment of ATL1102 for the treatment of DMD. Trial registration Clinical Trial Registration. Australian New Zealand Clinical Trials Registry Number: ACTRN12618000970246. Introduction Duchenne muscular dystrophy (DMD), a severe, progressive, X-linked genetic muscle disease is the most common muscle disorder in boys, affecting 1 in 5000 live male births worldwide [1]. Boys with DMD have onset of progressive muscle weakness in the first decade of life, with death due to cardiorespiratory failure expected in the late third or early fourth decades [2]. Currently the only disease modifying medical treatment is corticosteroid therapy, which delays loss of ambulation by a median 3 years, to 13 years of age [3–5] but carries a significant treat- ment burden of adverse effects [5]. DMD is associated with absence of dystrophin from muscle. This causes increased suscepti- bility to contraction-induced muscle damage, with activation of the innate immune macro- phages in turn activating the adaptive immune system T lymphocytes, leading to upregulation of pro-inflammatory cytokines, including the extracellular structural protein osteopontin, resulting in chronic inflammation, fibrosis and reduced muscle strength [6]. CD49d, the alpha chain subunit of integrin very late antigen 4 (VLA-4) is expressed widely on immune cells in this cascade and can bind osteopontin [7].In patients with DMD, the number of CD49d high expressing T lymphocytes is inversely proportional to ambulation speed, with highest concen- tration seen in non-ambulant patients [8]. Patients with higher concentrations have more severe weakness and are more likely to lose ambulation before 10 yrs of age despite corticoste- roid use, suggesting CD49d may be a biomarker of disease severity or activity [9]. In ex vivo studies a monoclonal antibody to VLA-4 prevented patient T-cell binding to muscle cells and transendothelial migration, highlighting a potential therapeutic avenue [9]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 2 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD hold a financial interest or stock in the sponsor company. Dr Woodcock is still an employee of the above institutions, but Dr Ryan has since left the employment to take up public office as a Member of the Australian Parliament. Dr Woodcock and Dr Ryan were involved in the trial design as unpaid consultants. Dr Woodcock has received honoraria for work performed including educational activities and attendance at advisory board meetings from pharmaceutical companies Biogen, Novartis, Roche and Avidity and an educational travel bursary to attend an international conference in 2016 from Biogen. Dr Woodcock has received grants for research work from FSHD Global Research Foundation, FSHD Society and Fulcrum Therapeutics. Dr Woodcock has been principal investigator on a number of industry-sponsored clinical trials. None of these disclosures affected the work Dr Woodcock performed on this clinical trial. Dr Ryan has received honoraria for work performed including educational activities and attendance at advisory board meetings from pharmaceutical companies Biogen, Novartis, Roche. Dr Ryan has been principal investigator on a number of industry-sponsored clinical trials. None of these disclosures affected the work Dr Ryan performed on this clinical trial. Dr Yiu has received advisory board honoraria from Biogen and Roche, and has received research support from Biogen, Roche, Pfizer and PTC therapeutics unrelated to the content of this manuscript. Dr Yiu has been principal investigator on a number of industry-sponsored clinical trials. None of these disclosures affected the work Dr Yiu performed on this clinical trial. Prof. Delatycki has received grant awards from NHMRC and is principal investigator in industry sponsored clinical trials including trials sponsored by Rearta and PTC. As this was a clinical trial, publication was always planned from trial inception. No employees of the sponsor were involved in the data collection, although Ms Desem did liaise closely with the MCRI/RCH site staff and Clinical Trial Organisation throughout the trial. As the study statistician, author Dr Button was paid a consultancy fee for his services from the trial sponsor commercial company Antisense Therapeutics Ltd. Authors Dr Houweling, Dr Coles and Dr Tiong were recipients of a grant to perform the MDX studies. This grant was paid by the sponsor Antisense Therapeutics Ltd. None of the other authors received any payment from the sponsor to conduct this study. All other authors had input into writing or revising this manuscript. The authors confirm that the involvement of employees of the sponsor Antisense Therapeutics Ltd in the trial design, data analysis and decision to ATL1102 is a second-generation immunomodulatory 2’MOE gapmer antisense oligonucle- otide which specifically targets human CD49d RNA. After binding to the RNA of CD49d, intracellular RNase H attaches resulting in downregulation of CD49d RNA. ATL1102 has pre- viously been trialled to treat Relapsing Remitting Multiple Sclerosis (RRMS), noting that CD49d high expressing T cells are the effector and central memory T cells in RRMS. In this phase 2 RRMS clinical trial, ATL1102 dosed 200mg three times weekly in the first week and twice weekly to 8 weeks substantially reduced inflammatory brain lesions by 88.5% and circu- lating lymphocytes and T lymphocytes by 25% [10]. Reported here, collaborators from the same institution ran two separate but complementary studies. The initial trial, a phase 2 clinical trial examining for the first time the safety and effi- cacy of a low dose ATL1102 treatment for 24 weeks in non-ambulant patients with DMD on concomitant corticosteroid treatment. The second, a pre-clinical study in the mdx mouse model for DMD, conducted using a mouse specific second generation CD49d ASO (ISIS 348574) to show that monotherapy treatment can reduce CD49d mRNA expression in muscle and decrease contraction induced muscle damage. Methods Ethics statement The clinical trial received approval from the Royal Children’s Hospital Human Research Ethics Committee with assigned number HREC/17/RCHM/121. The trial was subsequently regis- tered at the Australian New Zealand Clinical Trials Registry (ACTRN12618000970246). An independent Data Safety Monitoring Board (DSMB) was established to provide safety over- sight for the trial. Participant consent to participate in the trial was sort from parents. Pre-clin- ical mdx mouse analyses were approved by the Murdoch Children’s Research Institute (MCRI) animal care and ethics committee (ACEC; approval number A899). Pre-clinical studies in mice The mdx mouse model is commonly used to study DMD. We tested efficacy of the second gen- eration 2’MOE gapmer mouse specific CD49d ASO ISIS 348574 as ATL1102 is specific to human CD49d RNA and not homologous to mouse. Mdx mice do not have circulating lym- phocytes with high CD49d but have high CD49d expressing lymphocytes in the lymph nodes at 9 weeks [11]. Symptomatic 9 week old mdx mice were treated for 6 weeks to determine the effect of ISIS 348574 on CD49d mRNA expression and muscle function measures. Male mdx and age matched C57Bl10/J wild-type controls were purchased from the Jackson laboratories at 5 weeks of age and acclimatised to the MCRI facility for a total of 4 weeks. Ani- mals were housed in a specific-pathogen-free environment at a constant ambient temperature of 22˚C and 50% humidity on a 12 h light-dark cycle, with ad libitum access to food and water. The mdx mice (n = 12 /group) were randomly assigned to 4 treatment groups (saline, low (5mg/kg) and high (20mg/kg) dose ISIS 348574, and a control gapmer oligonucleotide with the same 20 nucleotides scrambled such that it is not complementary to CD49d or other RNA (20mg/kg)). Mdx Mice received weekly subcutaneous injections of either saline, ISIS348574 and control oligonucleotide for a total of 6 weeks. Wild-type controls (n = 12) received saline only. The high dose (20mg/kg/week) equates to 1.6mg/kg/w dose in human equivalent body surface area (BSA) and the low dose (5mg/kg/week) equates to 0.4mg/kg/week dose on BSA. After treatment mice were anaesthetised using inhaled isoflurane (0.6 ml per min) and muscle function was examined using the Arora Scientific 1300A whole mouse test system and 701C stimulator as previously published [12]. Mice were then euthanised by cervical dislocation and the spleen and skeletal muscle (quadriceps) were collected for further analysis. PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 3 / 16 PLOS ONEpublish this data does not alter our adherence to PLOS ONE policies on sharing data and materials. Phase 2 clinical trial of ATL1102 in non-ambulatory DMD Flow cytometry The mdx spleen was perforated to isolate the splenic sub-cellular content and incubated in red blood cell lysis buffer (Thermofisher) for ten minutes at 4˚C. Splenocytes were centrifuged 1500 g for five minutes at 4˚C. Cell pellets were washed in wash buffer (PBS:1% BSA (bovine albumin serum)). CD4+ and CD8+ T cell populations were identified using anti-CD4-V450 (Biolegend, San Diego, CA, USA) and anti-CD8a-allophycocyanin–cyanine 7 (anti-CD8a- APC-Cy7) (Biolegend, San Diego, CA, USA). Stained cells were analysed using BD LSRFor- tessa™ X-20 Cell Analyzer to identify populations of CD4+ and CD8+ T cells. Clinical trial design This was a phase 2 open-label clinical trial assessing the safety of ATL1102 in non-ambulant boys with DMD concomitantly with their usual corticosteroid therapy, in all but one partici- pant who discontinued corticosteroids years prior to the study. CONSORT flow diagram of study design is shown in Fig 1. Eligibility criteria for the clinical trial are included as S1 Fig. Participants were recruited from a single site in Melbourne, Australia with the study running from August 2018 until January 2020. After providing written informed consent, participants received weekly subcutaneous injec- tions of 25mg of ATL1102 for twenty-four weeks. Injections were administered into subcuta- neous fat of the abdomen by a registered nurse or trained parent. Injection sites were rotated in quadrants around the umbilicus. Parents monitored injection sites for cutaneous reactions and participant discomfort for fourty-eight hours post-administration. Adverse events were recorded in a diary which was returned to the study coordinators at each fortnightly visit. Participants underwent fortnightly venepuncture for exploratory and safety blood tests. This included monitoring of haematology, biochemistry and inflammatory markers and at point of care urinalysis dipstick to monitor kidney function. Participants were seen monthly by the study team for physical examination and respiratory function assessments. Participant safety was monitored routinely and at regularly scheduled meetings by the inde- pendent DSMB. Fig 1. CONSORT flow diagram of study design. https://doi.org/10.1371/journal.pone.0294847.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 4 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Outcome measurements The primary endpoint of the trial was safety of ATL1102 as assessed by the frequency and intensity of adverse events, including injection site reactions and any laboratory value derangement. Secondary outcome measures included both laboratory and functional efficacy endpoints. Laboratory efficacy outcome measures included lymphocyte-modulation activity determined by cell surface flow cytometry measuring variation in the number and percentage of total lym- phocytes as well as those CD4 and CD8 T lymphocytes expressing high levels of CD49d (CD49dhi) to week twenty-four of treatment and to week twenty-eight, four weeks past the last treatment. Changes in upper limb muscle strength and function were measured at baseline and again at weeks five, eight, twelve and twenty-four. Muscle function was assessed by a questionnaire- based outcome measure of disease burden (Egen Klassifikation Scale version 2—EK2) and per- formance-based measures (the Performance of the Upper Limb scale version 2 (PUL 2.0), and the Moviplate 30 second finger tapping score of the MyoSet tool). The Myoset tool also mea- sured distal upper limb strength as determined by the MyoPinch (key pinch strength) and MyoGrip (hand grip strength) scores [13–15]. Other outcome measures assessed were respira- tory function (forced vital capacity (FVC) and forced expiratory volume in one second (FEV1)), and quality of life assessed using the neuromuscular module of the Pediatric Quality of Life Instrument (PedsQL NMD™). Muscle Magnetic Resonance Imaging (MRI) Participants underwent MRI of the dominant forearm at baseline, week twelve and week twenty-four. Unilateral upper-limb MRI was performed at 1.5T (Siemens Aera; Siemens, Erlangen, Germany) using a flexible surface matrix coil (4-Channel Flex Coil) wrapped around the forearm. Participants lay in the scanner in the head-first supine position, with the arm to be imaged lying in a comfortable position on the scanner bed alongside the torso. Two point- Dixon images were acquired (3D gradient-echo TE1/TE2/TR = 2.39/4.44/6.99ms, flip angle 10˚, nine 6mm axial slices, slice gap 0mm, FOV 18x18cm, matrix 320×320, pixel size 0.56×0.56mm, NEX = 4). Fat fraction maps were obtained using on scanner tools. Change over time in muscle composition (atrophy, oedema and fatty infiltration) was mea- sured on the muscles of the central, proximal, and distal forearm using Short Tau Inversion Recovery (STIR) and 3-point Dixon sequences on MRI. Changes in muscle composition were scored using the semi-quantitative visual scoring Mercuri method and by quantitative fat frac- tion analysis [16–18]. Due to fatty infiltration, identification of individual muscles was chal- lenging, such that a compartment composite score of volar, dorsal and ECRLB Br (extensor carpi radialis longus/brevis and brachioradialis) compartments was used as per a previous published study [19]. The lean muscle mass was calculated using previously published meth- ods: Cross-sectional muscle compartment area x ((100 –total muscle compartment fat percent) / 100) [19]. Statistical analysis Based upon data from a previous study of ATL1102 in RRMS patients analyzing blood 3 days after the last dose in week 8, the laboratory efficacy end point of lymphocyte modulation potential was established as a reduction in total lymphocyte count of 0.47x109/L (25% reduc- tion) [10]. For the sample size calculation, the level of significance was set to 0.05 with a 2-sided paired t-test, mean difference of 0.47 (x109/L) from baseline to end of treatment, and standard deviation of 0.428 (x109/L). Using nQuery (Version 8.5.2.0, Table MOT1-1 Paired t- PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 5 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD test for differences in means, Statistical Solutions Ltd.), a sample size of 9 participants was cal- culated as required to achieve a power of 80%. Nine participants were considered sufficient to investigate the safety, tolerability and PK and PD profile of ATL1102 in this rare target patient population. Data were analysed using SAS1 Version 9.4. primary and secondary efficacy mea- sures were analysed using the paired t-test and the non-parametric Wilcoxon sign-rank test. The study was not powered to see a change on the secondary efficacy endpoints from baseline to end of treatment. The study protocol and statistical analysis plan are available as supplemen- tary data in “Protocol” and eSAP1 and eSAP2 respectively. Repeated measures analysis of lym- phocytes and T lymphocyte and NK lymphocyte subsets in a post hoc analysis was conducted comparing baseline to a linear combination of values measured three days post-dose at weeks 8, 12 and 24. Associations between variables was tested using a Pearson correlation test. Preclinical mdx mouse analyses were performed in Graphpad Prism (V9, Graphpad Soft- ware Inc.). For in situ muscle function, one-way ANOVA with Tukey correction for multiple testing was performed (n = 9 animals / treatment). Unpaired T-Tests were used for CD4 and CD8 T-cell analyses (n = 5–6 samples / treatment). All data shown as mean with 95% confi- dence intervals, unless otherwise stated in the figure legends. Results Proof of concept preclinical study using mdx mice Ex vivo analyses of monocytes collected from mdx mice (n = 3), showed that the ASO to mouse CD49d, ISIS 348574 can reduce the expression of CD49d mRNA (Fig 2A). We then examined the in vivo response to ISIS 348574 in mdx mice treated for 6 weeks which showed that CD49d mRNA expression was reduced in skeletal muscle by approximately 40% when treated with either a low (5mg/kg/week) or high (20mg/kg/week) dose of ISIS 348574, com- pared to saline controls (Fig 2B, One-way ANOVA, summary p<0.01, with Tukey correction displayed on the graphs, p = * <0.05, ** <0.01). This study also found that mdx mice treated with the 20mg/kg/week dose of ISIS 348574 showed a reduction in the percentage of splenic CD4+ (30%, p<0.05) and CD8+ (21%, p = 0.058) T lymphocytes, compared to saline treated mice (Fig 2C and 2D). Furthermore in situ muscle funcation analyses show that the high dose (20mg/kg/week) treated mdx mice were protected from the effects of eccentric muscle damage, producing 72% of the original muscle force (P<0.01). This was in contrast to mdx mice treated with either saline, scrambled control or low dose (5mg/kg) ISIS 348574, which generated approximately 50% of the original force following eccentric muscle contractions. (Fig 2E and 2F, One-way ANOVA summary p = <0.001, with Tukey correction displayed on the graphs, p = * <0.05, <0.01, * <0.001). Clinical trial results Eleven adolescent males with DMD were screened for participation. All had been non-ambu- lant for at least six months prior to screening. Two screened participants were excluded (one participant had started cardioprotective medication within three months of the initial visit and the other participant exceeded the pre-determined weight limit for inclusion). Nine participants were enrolled into the open-label study. All had a confirmed pathogenic variant in DMD, with a clinical phenotype consistent with DMD as assessed by their treating/ referring clinician and the study investigator. Participant demographics are summarized in Table 1. Safety. There were no serious adverse events (SAEs) or suspected or unexpected serious adverse reactions (SUSARs). A total of 136 adverse events were recorded (Table 2), with all PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 6 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Fig 2. Pre-clinical data using the mdx mouse model of DMD to test the effects of ISIS 348574 (mouse specific Cd49d oligonucleotide to ATL 1102) in vivo. A) Monocytes were isolated from the spleens of mdx mice (n = 3) and exposed to a single dose of ISIS 348574 for 48hrs in vitro to show that we could achieve a reduction in CD49d mRNA using ISIS 348574 to mouse CD49d RNA. B) Following 6 weeks of treatment CD49d mRNA expression was reduced in mice treated with either the low (5mg/kg/week) or high (20mg/kg/week) dose of ISIS 348574, compared to saline controls (One-way ANOVA with Tukey correction, p = * <0.05, ** <0.01). C and D) Proportion of CD4+ and CD8+ T cells from the spleens of mdx mice with and without ISIS 348574 drug treatment. Cells are expressed as a proportion of total live cells isolated from the spleen. One way ANOVA with Fishers LSD test, * p < 0.05. E and F) In situ muscle physiology analyses shows that mdx mice treated with either saline (red, ~45% force recovery), scrambled (grey, ~45% force recovery) or low dose (orange, ~ 50% force recovery) ISIS 348574 were susceptible to eccentric muscle contraction damage compared to wild-type (black) controls, whereas the mice treated with a high dose of ISIS 348574 were resistant to the effects of eccentric muscle damage and produced 72% of the original force following the eccentric PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 7 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD muscle damaging protocol. This was still significantly less than the 95% force recovery seen in WT mice, however this improvement in force following a muscle damage protocol suggests that the use of a 20mg/kg/week dose of ISIS 348574 was able to protect the muscles of mdx mice. One-way ANOVA with Fishers LSD test, p = * <0.05, <0.01, * <0.001), ***<0.0001). https://doi.org/10.1371/journal.pone.0294847.g002 participants reporting at least one adverse event. Sixty-three percent of the reported adverse events were injection site related, with all but one participant experiencing transient erythema within twenty-four hours of the administration of ATL1102. Six (67%) participants had mild post-inflammatory hyperpigmentation of the skin of their abdomen which was persistent; four had resolved and two were ongoing but improving at the post completion follow-up study visit. The hyperpigmentation was noticed in the first participant after receiving eleven weekly doses of ATL1102. The DSMB was made aware and the participant informed consent form updated. In the five subsequent participants who had a similar reaction it was seen after four to eleven doses. The hyperpigmentation was not regarded as a clinical safety concern by the DSMB. Pain, discomfort or atrophy of the subcutaneous tissues were not reported, and there were no signs of systemic involvement. No participants withdrew from the study. There were no other significant adverse events felt to be related to ATL1102 or its administration. Efficacy. Lymphocyte Count: There was no statistically significant decrease in lymphocyte count from baseline to week eight, week twelve or week twenty-four of dosing (Table 3). There was no statistically significant decrease in CD49d+CD3+CD8+ or CD49d+CD3+CD4+ T lym- phocytes seen between baseline, weeks 8, 12 or 24 (Table 3). This 9 participant trial did not achieve the pre-specified laboratory activity outcome measure of a significant -0.47x109/L (25% reduction) in total lymphocyte count. There was, though, a consistent trend toward declines in the mean number of lymphocytes, and CD49d+ T lymphocytes measured 3 days post-dose at week 8, 12 and 24. The mean num- ber of CD3+CD49d+ T lymphocytes (i.e. CD3+CD4+ and CD3+CD8+ expressing CD49d) measured at week 28 was statistically significantly higher compared to end of dosing at week 24 (mean change 0.40x109/L 95%CI 0.05, 0.74; paired T-Test, p = 0.030) (Table 3). Repeated measures analysis of CD3-CD49d+ NK lymphocytes in a post hoc analysis comparing baseline to a linear combination of values measured three days post-dose at weeks 8, 12 and 24 was sig- nificantly lower compared to baseline (p = 0.018), with comparable NK lymphocyte numbers at week 28 (Fig 3). Functional outcome measures. There were no statistically significant changes in any upper limb functional outcome measures at week 24 compared to baseline (Table 4). The PUL2.0 score remained stable with no significant change between baseline and week twenty- Table 1. Summary of participant demographics. Characteristic Sex Age (years) Weight (kg) Height (cm) BMI Time since non-ambulant (years) Corticosteroid Medication https://doi.org/10.1371/journal.pone.0294847.t001 Category Male Yes Prednisolone Deflazacort Statistic n (%) Mean (SD) Median (range) Mean (SD) Mean (SD) Mean (SD) Median (range) n (%) ATL1102 N = 9 9 (100) 14.9 (2.1) 14.0 (12–18) 52.7 (9.8) 141.1 (10.0) 27.1 (7.4) 2.2 (0.6–9.2) 8 (88.9) 3 (33.3) 5 (55.6) PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 8 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Table 2. Treatment emergent adverse events reported in at least two participants. SYSTEM ORGAN CLASS Preferred Term Participants reporting any AEs All N = 9 Participants (%) [No. of Events] 9 (100.0%) [136] GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS Injection site erythema Injection site pain Injection site swelling Injection site bruising Pyrexia SKIN AND SUBCUTANEOUS TISSUE DISORDERS Skin discolouration GASTROINTESTINAL DISORDERS Vomiting Constipation RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS Cough Nasal congestion Oropharyngeal pain INFECTIONS AND INFESTATIONS Lower respiratory tract infection Nasopharyngitis NERVOUS SYSTEM DISORDERS Migraine https://doi.org/10.1371/journal.pone.0294847.t002 8 (88.9%) [59] 5 (55.6%) [7] 3 (33.3%) [6] 4 (44.4%) [4] 2 (22.2%) [4] 6 (66.7%) [7] 2 (22.2%) [4] 2 (22.2%) [2] 2 (22.2%) [2] 2 (22.2%) [2] 2 (22.2%) [2] 2 (22.2%) [2] 2 (22.2%) [2] 2 (22.2%) [2] four with a mean increase in PUL2.0 score of 0.9 (95%CI -1.33, 3.11, where a higher score indi- cates better function) EK2 scores were stable throughout the trial period. ATL1102 treatment showed no significant effect on lung function throughout the 24 week trial period (Table 4). The components of the Myoset all reflected stable grip and pinch strength over the course of the trial. Table 3. Summary of lymphocytes mean change from baseline to weeks 24 and 28. White blood cell type (X109 cells per litre) Mean Baseline count (x109 cells per litre) Mean Change from baseline Median percentage change from baseline (%) Paired T-Test (p value) of mean change Lymphocytes CD3+ T cells CD3+ CD49d+ T cells CD4+ T cells CD4+ CD49d+ T cells CD8+ T cells CD8+ CD49d+T cells Week 8 Week 12 Week 24 Week 28 Week 8 Week 12 Week 24 Week 28 between week 28 and 24 -0.56 -0.53 -0.50 -0.30 -0.28 -0.20 -0.22 -0.53 -0.33 -0.39 -0.20 -0.22 -0.09 -0.12 -0.28 -0.18 -0.28 -0.15 -0.19 -0.02 -0.05 +0.19 +0.25 +0.11 +0.11 +0.01 +0.14 +0.11 -4.63 -10.9 -12.3 -12.3 -12.5 -9.35 -10.9 -7.14 -5.46 -10.0 -5.23 -7.09 -5.21 -7.32 -4.22 +11.81 0.86 +17.11 -9.78 -1.12 -16.7 -2.62 -5.79 +9.93 +16.50 +1.73 +17.99 +13.37 0.051 0.056 0.03 0.063 0.073 0.068 0.064 3.68 2.93 2.44 1.57 1.20 1.22 1.17 The Lymphocyte mean number of cells at week 24 (at the end of dosing) is trending significantly lower vs week 28 (p = 0.051 paired T test). The mean number of CD3+CD49d+T lymphocytes (CD4+CD49d+ and CD8+CD49d+ T lymphocytes) at week 24 is statistically significantly lower vs week 28 (p = 0.030 paired T test). https://doi.org/10.1371/journal.pone.0294847.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 9 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Fig 3. Showing lymphocyte baseline, week 8, 12 and 24 week data 3 days post each dose and the week 28 data, four week past end of dosing data is shown as a bee swarm plot with mean expression values of lymphocytes. https://doi.org/10.1371/journal.pone.0294847.g003 MRI. In this trial, there was some variation in the quality of proximal and distal slices due to variable positioning of the participant’s forearm in consecutive scans. The quality of the cen- tral slices through the muscle body in the forearms muscles was not compromised and so this measurement was chosen for detailed comparison between baseline and week 24 scans for each participant. No significant change was apparent between baseline and week twenty-four for the mean Mercuri semi-quantitative score of fatty infiltration (0.1 point change), atrophy (0 point change) and muscle oedema (0.3 point change) measuring the central slice around the elbow (Table 5). There was a trend towards minor improvement in the percentage fat fraction in all muscle groups measuring the central slice, although statistical significance was not achieved. There was no significant pattern of change in cross-sectional muscle area of any muscle group (Table 6). Table 4. Change in functional outcome measures from baseline to week 24. Change from Baseline to Week 24 MyoGrip (dom) (% Pred) MyoPinch (dom) (Kg) MyoPinch (dom) (% Pred) MoviPlate Score (dom) % Predicted FVC % Predicted PEF EK2 Patient No. PUL 2.0 01–001 01–002 01–003 01–004 01–006 01–008 01–009 01–010 01–011 +2 +2 0 +2 -3 +7 0 0 -2 MyoGrip (dom) (Kg) -0.63 0.22 0.68 1.09 -0.27 1.00 -0.33 0.05 0.11 -4.49 0.49 1.02 1.01 -0.60 1.11 -3.75 0.11 -1.31 0.03 -0.02 -0.40 0.37 0.07 0.30 -0.22 0.06 -0.18 -0.62 -0.29 -6.59 2.99 0.94 2.77 -4.97 0.72 -3.63 Mean Change (95% CI): +0.9 (-1.33, 3.11) +0.2 (-0.25, 0.67) -0.7 (-2.33, 0.90) 0.0 (-0.18, 0.19) -1.0 (-3.56, 1.63) *Higher score = greater disability. #Reduction in Fat Fraction (%) = improvement. https://doi.org/10.1371/journal.pone.0294847.t004 -14.0 13.0 -3.0 7.0 8.0 3.0 7.0 -15.0 11.0 1.9 (-6.08, 9.85) -3.20 -14.8 -9.10 0.80 -6.50 -7.70 -9.10 -0.40 -1.10 6.30 -17.3 8.70 7.20 6.90 -18.2 -4.30 9.20 2.00 +1 +1 +2 +2 -6 -1 +2 -1 +2 -5.68 (-9.60, -1.76) 0.06 (-8.33, 8.44) 0.2 (-1.80, 2.25) PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 10 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Table 5. Mercuri visual semi-quantative score in whole forearm compartment from baseline to week 24. Fatty Infiltration Atrophy Oedema Whole Forearm Baseline 5.2 1.6 2.6 Wk 24 Change 5.3 1.6 2.9 0.1 0.0 0.3 https://doi.org/10.1371/journal.pone.0294847.t005 Table 6. Change in the MRI Central reading fat fraction, cross sectional area and lean muscle mass from baseline to week 24. Mean Change (95% CI) from Screening/Baseline to Week 24 MRI Parameter Fat Fraction (%) Volar Muscle Dorsal Muscles ECRLB-Br Average Fat Fraction Cross Sectional Muscle Area (mm2) Volar Muscle Dorsal Muscles ECRLB-Br Total Area Lean Muscle Mass (mm2) N MRI Central Reading Mean (95%CI) 9 9 9 9 9 9 9 9 9 -0.57 (-7.81, 6.68) -0.88 (-3.41, 1.65) -0.12 (-6.42, 6.17) -0.52 (-5.62, 4.58) 22.78 (-31.2,76.73) 0.89 (-18.9,20.65) -1.33 (-8.94, 6.28) 22.33 (-36.8,81.42) 13.9 (72.6, 100.4) ECRLB-Br = extensor carpi radialis longus/brevis and brachioradialis. Volar Muscles; flexor digitorum profundus and flexor pollicis longus (FDP), flexor digitorum superficialis and palmaris longus (FDS), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR). Dorsal Muscles: Extensor carpi ulnaris (ECU), extensor digiti minimi (EDM), extensor digitorum (ED), extensor pollicis longus (EPL), abductor pollicis longus (APL), extensor carpi radialis longus/brevis and brachioradialis (ECRLB-Br), but the ECRL-BR are not included in the Dorsal muscle measurement in the Central Reading. Change in the MRI Proximal reading average Fat Fraction (%) from baseline to week 24 was -2.14 [95%CI -7.60; 3.3] for the 9 patients. https://doi.org/10.1371/journal.pone.0294847.t006 Correlation of parameters assessed in the Phase 2 study. Correlation analyses were per- formed across assessment measures PUL2.0, Myoset and MRI. Positive correlations were observed in the Phase 2 study between the different measures of muscle function of Moviplate scores and the PUL 2.0 scores of the distal domain (r = 0.664) which support the consistency of the observed changes across the measures assessed in the study over the 24 week ATL1102 treatment period (S2 Fig). Positive correlations were also observed in the Phase 2 study between the MRI results of the lean muscle area (non-fat) and MyoGrip results (r = 0.604), suggesting a consistency of results across the different parameters of muscle structure and muscle strength (S3 Fig). Discussion Safety This open-label phase 2 clinical trial met its primary safety end point. All but one participant experienced post-injection site erythema, swelling or discomfort suggesting that the investi- gation product is a mild irritant, as has been observed with other MOE antisense drugs, and PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 11 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD is commonly reported with subcutaneiously injected drugs. Future clinical trials of ATL1102 could consider including using ice as a pre-injection site treatment to minimise these reac- tions. Six participants experienced an unexpected post-inflammatory skin hyperpigmenta- tion which resolved or faded at the completion of the study. Interestingly this reaction has not been previously reported in other clinical trials of ATL1102 and was not viewed as a safety concern [10]. The dose chosen for this 24 week trial (25mg/week) was considered as a presumed safe dose in this patient population. In a previous phase 2 trial in individuals with RRMS a loading dose of 200mg every other day for one week was administered then 400mg/week (twice weekly 200mg) for seven weeks [10]. This DMD trial was the first clinical trial to investigate the safety of ATL1102 over a six-month period. Lymphocyte modulation Given the previously observed action of ATL1102 of reducing lymphocytes in the RRMS study, the trial sample size was calculated to see a 25% reduction in total lymphocyte count when the drug is at equilibrium from week 8. This activity endpoint was not met in this trial. There was however a consistent trend toward declines in the mean number of lymphocytes at week 8, 12 and 24 each measured 3 days past dosing, and statistically significant reductions in the mean number of CD49d+ NK lymphocytes at week 8, 12 and 24 weeks of treatment, using repeated measures analysis. The mean number of CD49d+ T lymphocytes (i.e CD3+CD4 + and CD3+CD8+ that are CD49d+) was statistically significantly higher at week 28 compared to week 24, indicating a rebound elevation of CD49d+ T lymphocytes four weeks post the last treatment dose. These results collectively suggest that ATL1102 suppresses CD49d expressing lymphocytes at a dose of 25mg per week. It is anticipated that higher doses will increase the level of lymphocyte reduction whilst maintaining a favourable safety profile in part due to sparing of the majority of T lymphocytes and NK lymphocytes. Future studies will look at dose escalation as supported by this study, and modelling with ATL1102. PUL2.0 and EK2 upper limb function PUL2.0 measures shoulder, elbow, and wrist finger dimensions of disease burden and is a reli- able measure of disease severity and progression in DMD where a lower score indicates loss of function. The mean increase from baseline to week twenty-four in the PUL2.0 was 0.9 (95% CI -1.33 to 3.11). Although the Minimal Clinical Important Difference (MCID) for the PUL2.0 has not been established, an external historical cohort with same inclusion criteria as in the ATL1102 phase 2 trial, showed a decrease in PUL2.0 score of 2.0 (standard deviation 3.02) from baseline over a six month period [20]. In the ATL1102 phase 2 study four of the nine patients achieved an increase in their PUL2.0 score of +2, and another three patients were sta- bilized in the PUL2.0 score (Table 4). This is an encouraging trend that warrants further inves- tigation. A previously published data from a historical cohort also reported that over a twelve month period a mean decrease in PUL2.0 score of 2.17 can occur, albeit in a cohort not directly comparable to the participants in the phase 2 study due to older age and larger propor- tion not on corticosteroids [21]. There was no change in the EK2 over the course of the trial period. This composite outcome measure encompasses multiple aspects of disease burden and as such is a useful clinical moni- toring tool (higher score equals greater disease burden) but is not likely to be as responsive as the PUL2.0 measure to small changes in upper limb function. As such, a stabilisation over the six month trial periods is encouraging and needs to be confirmed in future studies. PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 12 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Myoset tests: MoviPlate, MyoGrip, and MyoPinch The MyoSet functional outcome measures consist of the MoviPlate muscle function assess- ment of repetitive flexion extension of the wrist and fingers, and MyoGrip and MyoPinch assessment of muscle grip and pinch strength [13,14]. These measures have been validated for use in clinical trials of DMD; MyoGrip and MyoPinch in particular have been shown to be sensitive to change in non-ambulant boys and to correlate well with lean muscle mass on MRI [15]. There was no change in any of these measures over the trial period. Previous natural his- tory studies have shown significant deterioration over six months (Grip -0.5kg [95%CI -1.01; 0.002] and Pinch -0.38kg [95%CI -0.53; -0.22]) [15,18]. Matching the MyoGrip and MyoPinch protocol with that of a previously published natural history cohort allowed for comparison of change in grip and pinch strength over a six month period, yielding a statistically significant improvement on grip (p = 0.03) and pinch (p = 0.003) strength [19]. The lack of decline in these measures with ATL1102 during the trial period is once again encouraging and warrants further investigation. MRI of upper limb Magnetic Resonance Imaging of muscle is increasingly used as a biomarker for disease stage and progression. The most widely used scoring method is the Mercuri Score, which requires a skilful investigator to visually score the chosen muscles based upon a standardised set of crite- ria encompassing degree of atrophy, oedematous changes and fatty infiltration of the muscle, to create an aggregate score. The more recent development of automated fat fraction analysis reduces the inter-user variability and provides a more quantitative measure of assessment. Matching the MRI protocol with that of a previously published natural history cohort allowed for direct comparison of change in fat fraction over a six month period [19]. From this pub- lished natural history data, disease is expected to progress with a mean increase of central fore- arm muscle fat fraction percentage of 3.9% (95%CI 1.9,5.7) over six months. The apparent trend towards a decrease in mean forearm muscle fat fraction of 0.52% (95% CI -5.62, 4.58; Median 1.4%) seen after six months of treatment with ATL1102 may suggest that ATL1102 could be modifying the rate of fatty infiltration into these muscles. These changes were repli- cated in the proximal and distal muscle groups. For future MRI studies it would be important to set a clear protocol with imaging tags placed over surface landmarks to ensure uniformity of subsequent scans. Conclusion The proof of concept pre-clinical data supports a potential protective effect of an antisense oli- gonucleotide to CD49d RNA in the mdx mouse model of DMD. This phase 2 open-label clini- cal trial has shown that ATL1102 has a good safety profile and is well tolerated with minor injection site reactions the only treatment-related adverse events reported. The positive obser- vations in functional efficacy outcomes suggesting stabilization, and results compared with historical natural history data, particularly the PUL2.0, MyoGrip, MyoPinch and MRI fat frac- tion analysis, justifies the ongoing drug development program of ATL1102 in non-ambulant boys with DMD and provides a rationale to proceed with larger placebo-controlled studies of this novel therapeutic agent. Supporting information S1 Checklist. TREND statement checklist. (PDF) PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 13 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD S1 Fig. Inclusion and Exclusion criteria for the clinical trial. (DOCX) S2 Fig. Scatter Plot showing the association between the Moviplate scores and the PUL 2.0 distal dimension scores over the 24 Week ATL1102 treatment period with a linear regres- sion line plotted. (TIF) S3 Fig. Scatter plot showing the association of the Grip Strength scores and the MRI data of the forearm lean muscle area (non-fat) over the 24 week ATL1102 treatment period: The MRI is the determined lean muscle mass compartment across the mid (central) domi- nant forearm. The linear regression line is also plotted. (TIF) S4 Fig. Gating strategy for identification of murine CD4+ and CD8+ T cell populations isolated from spleen of mdx mice treated with ISIS 348574. A) Singlets (FSC-H vs FSC-A), B) Live cells (Propidium Iodide vs FSC-A) and C) Lymphocytes (SSC-A vs FSC-A) were gated to remove doublets, dead cells, debris and large/granular cells. D) Anti-CD4-V450 and anti- CD8a APC-Cy7 to were used to gate populations of CD4+ and CD8+ T cells. (TIF) S5 Fig. Correlation of the Moviplate scores and the PUL 2.0 distal dimension scores over the 24 Week ATL1102 treatment period. (TIF) S6 Fig. Correlation of the Grip Strength scores and the MRI data of the lean muscle area (non-fat) across the mid (central) dominant forearm over the 24-week ATL1102 treatment period. (TIF) S7 Fig. Table of Participant specific genetic variant within Dystrophin Gene. (DOCX) S1 File. (PDF) S2 File. (PDF) S3 File. (PDF) S4 File. (PDF) Acknowledgments The authors wish to acknowledge the contribution of Isabelle Ledoux and Simone Birnbaum of the Insitute of Myology, Paris, France who provided support with the quality control and analysis of the MyoSet functional outcome measures; Valeria Ricotti of the Dubowitz Neuro- muscular Centre, London UK, who provided support on the comparative analysis of the MRI observations; and Annabell Leske and Vicky Beal and the team at Avance Clinical, Adelaide, Australia. PLOS ONE \| https://doi.org/10.1371/journal.pone.0294847 January 25, 2024 14 / 16 PLOS ONEPhase 2 clinical trial of ATL1102 in non-ambulatory DMD Author Contributions Conceptualization: George Tachas, Peter J. Houweling, Monique M. Ryan. Data curation: Ian R. Woodcock, Peter J. Houweling, Michael Kean, Jaiman Emmanuel. Formal analysis: Ian R. Woodcock, Nuket Desem, Jaiman Emmanuel, Chantal Coles, Chrystal Tiong, Peter Button, Jean-Yves Hogrel. Funding acquisition: George Tachas. Methodology: Ian R. Woodcock, George Tachas, Peter J. Houweling, Chrystal Tiong, Moni- que M. Ryan. Project administration: Ian R. Woodcock, Nuket Desem, Peter J. Houweling, Rachel Ken- nedy, Kate Carroll, Katy de Valle, Sarah Catling-Seyffer. Resources: George Tachas, Shireen R. Lamande´. Software: Michael Kean, Peter Button. Supervision: Peter J. Houweling, Kate Carroll, Shireen R. Lamande´, Daniella Villano, Moni- que M. Ryan, Martin B. Delatycki, Eppie M. Yiu. Writing – original draft: Ian R. 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10.1371_journal.pntd.0012056	RESEARCH ARTICLE The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling 1, John Chiphwanya2, Square Mkwanda2, Dorothy E. Matipula2, Carrie BarrettID Paul Ndhlovu2, Limbikani Chaponda2, Joseph D. Turner1, Emanuele Giorgi3, Hannah Betts1, Sarah Martindale1, Mark J. Taylor1, Jonathan M. Read3☯, Louise A. Kelly- Hope1,4☯ 1 Centre for Neglected Tropical Disease, Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, United Kingdom, 2 National Lymphatic Filariasis Elimination Programme, Ministry of Health, Lilongwe, Malawi, 3 Lancaster Medical School, South West Drive, Bailrigg, Lancaster, United Kingdom, 4 Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom ☯ These authors contributed equally to this work. Carrie.Barrett@lstmed.ac.uk a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Abstract Citation: Barrett C, Chiphwanya J, Mkwanda S, Matipula DE, Ndhlovu P, Chaponda L, et al. (2024) The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling. PLoS Negl Trop Dis 18(3): e0012056. https://doi.org/10.1371/journal. pntd.0012056 Editor: Alberto Novaes Ramos, Jr, Federal University of Ceara´, Fortaleza, Brazil, BRAZIL Received: September 29, 2023 Accepted: March 10, 2024 Published: March 25, 2024 Copyright: © 2024 Barrett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its supporting information files. Funding: This study was supported by the Centre for Neglected Tropical Diseases, Liverpool School of Tropical Medicine, through funding from the Department for International Development (DFID) and GlaxoSmithKline (GSK) awarded to LK-H & MJT for programmatic support for the elimination of lymphatic filariasis as a public health problem. Background In 2020 the World Health Organization (WHO) declared that Malawi had successfully elimi- nated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distri- butions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national data- base and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information. Methodology LF clinical cases identified through programme house-to-house surveys across 90 sub-dis- trict administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia preva- lence spatial estimates. Principle findings The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 1 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi This work was supported by a Medical Research Council Doctoral Training Partnership for CB [project number 2267306]. The funders of this study had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country. Conclusions The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies. Author summary Lymphatic filariasis (LF) is a disfiguring and painful Neglected Tropical Disease, transmit- ted by mosquitoes, and impairs affected individual’s mental wellbeing, social participa- tion, and ability to work. The two most common clinical manifestations are hydrocoele (scrotal swelling) and lymphoedema (swelling of the limbs). Estimates of LF clinical case numbers are required to provide national and local care needs assessment, and for elimi- nation and surveillance purposes. Clinical case prevalence is currently not readily available or is unknown across many sub-Saharan African countries, however Malawi is unique as the LF Programme has conducted extensive house-to-house patient mapping activities across one third of the country. We used this clinical data in combination with measure- ments of LF infection prevalence and high-resolution climate information, to develop geostatistical models, which estimate the number of clinical cases in unmapped areas. This led to the development of a national database and map of clinical case estimates that will help the Malawi LF Elimination Programme to optimize limited resources, target morbidity management and disability prevention, and improve quality of life of the people affected by this disabling and disfiguring disease. Introduction In sub-Saharan Africa lymphatic filariasis (LF) is a mosquito-borne disease caused by the para- sitic nematode, Wuchereria bancrofti [1,2]. LF is targeted for elimination in 27 African coun- tries (77%) by 2030 described in the World Health Organization (WHO) Neglected Tropical Disease (NTD) road map 2021–2030 [3,4]. To achieve validation of elimination of LF as a pub- lic health problem granted by the WHO, countries are required to submit and meet WHO dos- sier requirements [5]. In addition, countries must be implementing post-validation activities for surveillance and integrating morbidity management into existing health systems [5–7]. In 2018, approximately 51 million individuals were estimated to be infected with LF, which has reduced by 74% since the Global Programme to Eliminate LF (GPELF) began [8]. In 2000, clinical case estimates in sub-Saharan Africa ranged from 46 to 51 million, which are now out- dated and clinical case estimates are unavailable in many countries [9]. The two most common chronic clinical manifestations of LF are hydrocoele (scrotal swelling) and lymphoedema (swelling of the limbs) that cause pain, profound disfigurement and large financial, social and mental health losses [2,10]. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 2 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi The Malawi LF Elimination Programme has achieved certification of LF elimination as a public health problem in 2020 from the WHO [3,11]. Over the past two decades the programme successfully implemented prevalence mapping, effective anti-filarial mass drug administration (MDA), impact assessments, morbidity management and disability prevention (MMDP) and operational research activities as outlined in Chiphwanya et al. [11]. The widespread endemic nature of LF across the country became evident in the early 2000s when LF antigenaemia preva- lence surveys were conducted in villages using antigen-based immunochromatographic rapid tests (ICTs), although this data was geographically sparse in comparison to large district areas [12–14]. More recently, attention has focussed on obtaining better estimates of clinical burden, and the programme conducted a series of large-scale house-to-house mapping activities, across 23 districts in 90 sub-district administrative areas known as Traditional Authorities (TAs) between 2014–2021. The extensive clinical case mapping covered an area of over 33,000km2 populated by 5.6 million people, representing approximately 35% of the geographical area of Malawi, identifying 8,856 clinical cases: male hydrocoele = 6,333 (71.5%; average age 50.5); male lymphoedema = 854 (9.6%; average age 54.4; female lymphoedema = 1,585 (17.9%; aver- age age 50.5); male both = 84 (1.0%; average age 58.8) [11]. In Malawi, estimating the number of hydrocoele and lymphoedema cases was important to allow for the planning and provision of services that are available within the primary care sys- tem in all areas with known affected people, in line with the WHO essential package of care recommendations which include: hydrocoele surgeries; treatment for episodes of adenolym- phangitis (ADL) through antibiotic treatment and symptomatic management; management of lymphoedema (trained health workers able to provide and teach patients self-care measures of hygiene, skin and wound-care, elevation, and exercise) [15]. In addition, short term studies have shown the inclusion of diaphragmatic deep-breathing exercises, lymphatic massage, and dietary changes to improve lymphoedema status and significantly reduce frequency and dura- tion of ADLs [16–18]. The case estimates also helped to direct actions and provide documenta- tion for the WHO dossier on (1) the number of hydrocoele and lymphoedema case estimates in all endemic implementation units (IUs); (2) assess the availability and quality of available funding and resources to (3) provide full geographical coverage of essential package of care within all endemic IUs [5]. In many endemic countries clinical case estimates are lacking, which is likely due to the sig- nificant time, human, and financial resources required [10]. Only one country in Asia has developed a national database and map of LF cases, and also found that disease prevalence was positively correlated with antigenaemia prevalence (prior to MDA) [6], which supports find- ings from historical studies [19,20]. The close relationship between disease and antigenaemia prevalence suggests that these data may be used together to model and predict clinical cases and prevalence rates in unmapped TAs. The use of multiple types of prevalence data has proven useful when data is sparse and resources limited, as shown in other disease mapping activities [21]. In addition, the use of climate covariates such as temperature, rainfall, humidity, may also help model predictions as found in antigenaemia and microfilaria prevalence studies as they impact the abundance of mosquito vectors and LF transmission rates, therefore may be useful in understanding disease distributions and risk [22–25]. To support the Malawi LF Elimination Programme with obtaining LF case estimates across all endemic areas of the country, this study aimed to develop a national map of LF clinical cases, using a geostatistical modelling approach from a combination of clinical case data, anti- genaemia prevalence and climate information. A two-step geostatistical analysis was con- ducted. First, we modelled antigenaemia prevalence (prior to MDA) in relation to climate covariates to predict national antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on these antigenaemia prevalence spatial estimates. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 3 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi Methods Ethics statement Ethical approval was obtained from the Research Ethics Committee at Liverpool School of Tropical Medicine, UK (protocol number 12.22) and the National Health Sciences Research Committee, Ministry of Health, Malawi (protocol number 1260). Data sources LF clinical case data. LF clinical case data was collected across 90 TA areas approximately 35% of Malawi, between 2014 and 2017. National health surveillance assistants, trained to identify and report individual clinical cases (lymphoedema and hydrocoele) using an innova- tive phone reporting tool [11,26,27]. Data were reported by health facility catchments, which were generally aligned with TA administrative boundaries defined by Humanitarian Data Exchange [28]. Two instances where TA and health facility catchment boundaries were not continuous were resolved by merging TA boundaries for analysis purposes. LF antigenaemia data. The LF antigenaemia prevalence data was collected from three surveys conducted in selected 57 villages across all districts in Malawi between 2000 and 2003, prior to initiation of MDA [12,13,29]. The ICT diagnostic tool was used to detect the presence of W. bancrofti-specific circulating filarial antigen in whole-blood samples. Details on the sur- veys’ methodology are available in the original studies [12,13,29]. 21 TAs with available antige- naemia data and LF clinical case data was summarised in S2 Table. Statistical analysis Analytical overview. Exploratory analysis identified antigenaemia as a predictor of clinical case prevalence in TAs where both observations were available, see S1 File. How- ever, antigenaemia data was not available for all areas of Malawi (only 57 villages). Step 1: A geostatistical model was fitted to available antigenaemia prevalence data which incorpo- rated climate covariates to predict and map antigenaemia prevalence in areas with and without available antigenaemia prevalence data. Step 2: A second geostatistical model using the predicted antigenaemia prevalence from step 1 was used to predict LF clinical case estimates. Predicting antigenaemia prevalence–geostatistical model 1. To predict antigenaemia prevalence in areas with and without data available data, we used available antigenaemia data and climate covariates identified from the literature and based on the assumption of their impact on mosquito vector abundance and LF transmission rates [24,25,30–32]. We fitted a geostatistical model of antigenaemia prevalence, P(x), where xi is the geolocation of village i. Available antigenaemia prevalence was determined by the number of positives, Y, divided by the number of individuals tested, N, following a binomial distribution. Climate covariates: average annual temperature (˚C); average annual humidity (kPa); elevation (m); and average annual rainfall (mm) between 1970–2000 were obtained from World Clim, a database for 1km spatial resolution climate surfaces for global land areas. [33]. A Principle Component Analysis (PCA) was performed on the climate covariates to mitigate for collinearity ─ the non-indepen- dence of predictor covariates [34]. The first component of the PCA of the chosen climate covariates for each village location, d(x), was the explanatory variable. To explain the spatial variation in antigenaemia prevalence we include an unobserved stochastic process, S(x), to represent the variation in P(x) that is not explained by d(x). Finally, random variation U assuming a Normal Distribution was included in the model, which gave the following step one PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 4 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi geostatistical equation: � � log PðxiÞ 1 (cid:0) PðxiÞ ¼ d xið Þ þ S xið Þ þ Ui Predicting LF clinical cases–geostatistical model 2. Mðx∗ j Þ ¼ aðx∗ j Þtb þ logðPopðx∗ j ÞÞ þ Sðx∗ j Þ þ Uj ðEq1Þ ðEq2Þ To predict LF clinical cases in the TA with no data (i.e., unmapped), we fitted a second geosta- tistical model of LF clinical cases M(x) using available clinical case data, where x∗ j is the geolo- cation for each TA, j, assuming a Poisson distribution. Note x in geostatistical model 2 has a different spatial scale to x in geostatistical model 1. The explanatory variable, a(x), predicted antigenaemia prevalence, from geostatistical model 1 was fitted to each centroid geolocation of TA, x with available clinical case data, including an offset term of the population size (Pop) for each TA, taken from 2018 census mapping [35]. An unobserved stochastic process, S(x) was included in the model to represent the variation in M(x) that is not explained by a(x). Finally, random variation U assuming a Normal distribution was included in the model to give the step two geostatistical equation. All geostatistical analyses were performed in R programming software using the PrevMap R package [36,37]. To assess the goodness of fit of the geostatistical model 2 predictions, the mean predicted LF clinical cases were compared to programme-collected clinical cases num- bers within TAs observations [11]. Within S2 File, the goodness of fit of the geostatistical model 1 predictions were assessed by comparison against antigenaemia prevalence. Mapping Three maps were produced, the first showing predicted antigenaemia prevalence (%) from the geostatistical model 1 using QGIS software 3.22.5 [38] on 4.5km (longitude) x 4.6km (latitude) scale. The second map showing LF clinical cases and the third showing LF clinical case preva- lence per 100,000 population [35] for TA areas [28] from geostatistical model 2 predictions and available programme-collected data. National estimates National and TA boundary LF clinical case estimates and prevalence (case per 100,000 popula- tion) were summarised from geostatistical model predictions for the 320 unmapped TA areas. S1 Table summarises this for each TA boundary in Malawi. The 95% credible intervals for national LF clinical case estimates were calculated from geostatistical model predictions and summarised for predicted case numbers. National sex-specific hydrocoele and lymphoedema clinical cases were estimated by partitioning the national estimated LF clinical cases by pro- gramme-identified observed proportions. Results Antigenaemia prevalence mapping–geostatistical model 1 We found a positive association between antigenaemia prevalence and clinical case prevalence within TAs, see S1 File. The antigenaemia prevalence predicted from the first geostatistical model using climate covariates was mapped across Malawi and is presented in Fig 1. LF antige- naemia prevalence varied between 0.5% and 69.3% across the country on a spatial scale of 4.5km x 4.6km cells. For the majority of the country the prevalence was low (0.9% ─ 15.7%), PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 5 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi Fig 1. LF prevalence (antigenaemia) map from step one geostatistical analysis. Note: Maps were produced in QGIS mapping software (https://qgis.org) using the base layer from OpenStreetMap (https://www.openstreetmap.org/), and country administrative boundaries available from the Humanitarian Data Exchange [28]. https://doi.org/10.1371/journal.pntd.0012056.g001 however the prevalence was high (1.7% ─ 50.5%) in the northern district Karonga, districts along the southern shore of Lake Malawi (Salima, Dedza, Ntcheu, Mangochi, Balaka and Machinga) (1.4% ─ 33.0%), and highest in the southern districts of Chikwawa and Nsanje (16.4% ─ 69.3%). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 6 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi Clinical case mapping–geostatistical model 2 The number of predicted LF clinical cases for each TA across Malawi from the second geosta- tistical model was presented in Fig 2A. The predicted LF clinical case prevalence per 100,000 population for each TA is presented in Fig 2B. The number of cases within each TA were sum- marised in S1 Table. The number of LF clinical cases varied between TAs, particularly within the middle region of the country, in areas along the southern shore of Lake Malawi and programme-collected TA data, however the prevalence of cases is more consistent amongst predicted TAs in the middle region of the country, suggesting differing population size does impact the number of cases found within each TA. Two TAs with the highest number of LF clinical cases in the middle region of the country were in Lilongwe district, called TA Mazengera and TA Kabudula. Simi- lar to the antigenaemia prevalence, LF clinical cases and case prevalence were high in the northern Karonga district, areas along the southern shore of Lake Malawi, and highest in the southern districts of Chikwawa and Nsanje. Note: Maps were produced in QGIS mapping software (https://qgis.org) using the base layer from OpenStreetMap (https://www.openstreetmap.org/), and country administrative boundaries available from the Humanitarian Data Exchange [28]. Assessing the goodness of fit To assess the goodness of fit of the geostatistical model in step 2, the predicted prevalence LF clinical cases was compared to the number of programme identified clinical case prevalence per 100,000 population shown in Fig 3. Many TA observations that fell on, or very close to, the line, suggesting that the geostatistical model has a good predictive ability. Fig 2. National LF clinical case (A) numbers and (B) prevalence (cases per 100,000 population) and (C) population size taken from Census Report in 2018 [35] summarised at Traditional Authority level. https://doi.org/10.1371/journal.pntd.0012056.g002 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 7 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi Fig 3. Predicted clinical case prevalence from geostatistical analysis compared against programme identified LF clinical case prevalence per 100,000 population. https://doi.org/10.1371/journal.pntd.0012056.g003 National LF clinical cases From geostatistical modelling, the overall estimated number of LF clinical cases was 29,794 cal- culated with 95% credible intervals (CrI) 26,957 to 32,927 across all endemic districts in Malawi. The geostatistical analysis identified a further estimated 20,938 (95% CrI 18,091 to 24,071) cases in the 320 unmapped TA areas (70.3%), in addition to the 8,856 cases identified by the programme house-to-house surveys (29.7%), see Table 1. We estimated that there were Table 1. LF clinical cases across mapped and unmapped Traditional Authority (TA) areas in Malawi. Clinical cases 95% Credible Interval Total Population Clinical Case Prevalence (cases per 100,000 population) Mapped TA Areas Unmapped TA Areas All TA Areas 8,856 20,938 29,794 NA 18,091–24,071 26,957–32,927 5,613,230 11,950,519 17,563,749 158 175 170 * Population size taken from Census Report in 2018 [35]. https://doi.org/10.1371/journal.pntd.0012056.t001 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 8 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi 21,306 (71.5%) male hydrocoele cases, 2,873 (9.6%) male lymphoedema, 5,332 (17.9%) female lymphoedema and 283 (1.0%) male both. Discussion This study makes Malawi the first LF endemic African country to produce a national level database of LF clinical cases, estimating 29,794 (26,957–32,927) from the 8,856 (29.7%) pro- gramme-identified cases and 20,938 (18,091–24,071) cases predicted from geostatistical analy- sis. As well as producing a set of risk maps of LF clinical case and antigenaemia prevalence estimates. The highest numbers of LF clinical cases and antigenaemia prevalence from geosta- tistical model predictions and programme data were observed in Chikwawa and Nsanje dis- tricts in the Southern Region and Karonga district in the Northern Region of the country. The results presented in this study provide the Malawi LF programme and health system with an informative understanding of the clinical case distributions across local regions, allowing them to target resources for MMDP in identified high risk TA areas, where cases were predicted as high as 524 (Thyolo district, TA Nchilamwela). The maps produced in this study demonstrate the widespread distribution of LF clinical cases and prevalence across Malawi. The detailed antigenaemia prevalence map was produced from data collected between 2000–2003, prior to MDA, using climate information from 1970– 2000. Following implementation of MDA initiated in 2008, antigenaemia prevalence has decreased [39]. The map shows the highest LF case prevalence occurred in the northern region in Karonga district and southern region, Nsanje and Chikwawa districts, which may be due to these areas having optimum climate conditions that drive transmission and abundance of mosquito vectors. Within these southern regions of Malawi, Nsanje and Chikwawa districts, predominantely Anopheles funestus, as well as A. arabiensis and A. gambiae have been catego- rised as main LF vectors [11,40]. Along Lake Malawi shore, antigenaemia prevalence was observed to be higher compared to in-land areas, thus likely due to mosquito breeding sites driving W. bancrofti infection as well as human populations inhabiting areas close to water bodies [41]. From LF clinical case predictions and programmatic data presented in this study, the high- est number of cases occurred within districts with highest antigenaemia prevalence, Chik- wawa, Nsanje and Karonga. However, middle regions of Malawi showed a higher prevalence of cases where antigenaemia prevalence was found to be low, suggesting that population may play an important role in defining risk areas for clinical cases. In Bangladesh similar gender ratios and age distributions have been observed [6]. Higher proportions of LF clinical case prevalence been found to be associated with people living in rural areas, poverty, and poor san- itation [42]. Similar comparisons can be drawn to highly endemic districts in Malawi, Chik- wawa and Nsanje, although more research is needed to identify risk factors for LF cases within this context. S1 Table describes the case predictions made from this study analysis predictions and programme-identified cases for each TA boundary in Malawi. Our findings suggest that historic antigenaemia prevalence may be a good predictor for highly endemic regions, but more research is needed from other countries and ecological zones to solidify this relationship [6,19,20]. Our geostatistical approach offers an alternative to national patient searching. This approach may be an improvement over other methods, i.e. community drug distributor esti- mations of cases during MDA programmes, which potentially underestimate clinical cases [43,44]. In the absence of expertise and resources for more complex geostatistical modelling, we advocate that antigenaemia prevalence data could be used to estimate LF clinical case distri- butions. Other country elimination programmes may refer to the S1 File, which features the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 9 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi relationship between antigenemia prevalence and LF clinical cases, and to use this as a guide to estimate clinical cases within their implementation unit boundary if antigenemia prevalence is available. Geostatistical modelling has shown to substantially outperform current WHO guide- lines to collect case estimates, in studies of other NTDs, including soil-transmitted helminth infections in Kenya, Sierra Leone, and Zimbabwe [45]. Geostatistical modelling approaches can offer improved precision of cases estimates for a reduced field-sampling effort, particularly use- ful for NTDs where resources are often limited. More work utilising geostatistical approaches to estimate and map clinical cases in sub-Saharan Africa would be beneficial as current estimates are limited and many countries have antigenaemia data that could be used [4]. For men with hydrocoeles, we estimate thousands of men living with symptoms are likely to require surgery in the next decade. Assessing the capacity of hospitals, the infrastructure, human and financial resources required for these surgeries is important to determine whether they can be provided routinely through the health system. A hospital facility assessment to determine the readiness and quality of hydrocelectomy services has been conduced in Malawi in 2019 [46]. To reduce the backlog of cases, hydrocoele campaigns were conduced periodi- cally from 2008 completing more than 1500 surgeries in high burden districts, Karonga, Chik- wawa and Nsanje [11]. In Malawi, hydrocoele surgeries were found to have significant improvements in men’s quality of life, as well as life-time economic benefits to the individual, his family, and his community which greatly outweigh the low cost of surgery (estimated at US $68 during campaigns) [47,48]. However, since 2015 when the LF programme hydrocoele campaigns have ceased, the capacity of hospital’s to address the backlog of hydrocoeles remains a challenge in TA’s where LF clinical cases are high. For people with lymphoedema, life-long home-based MMDP strategies is required to man- age their and ADLs, to prevent and hinder progression of their symptoms [15]. Studies have shown that lymphoedema predominately affects women [26,49–51], although the reasons for this are not well understood [52]. The home-based care recommended by WHO includes daily hygiene, skin care, limb exercise and elevation [15], with recent research showing the benefits of additional exercises including lymphatic massage, deep diaphragm breathing techniques, skin mobilization, seated and standing exercises [16–18]. Most mild stages of lymphoedema will manage symptoms with home-based care activities, however the more severe stages require more specialist care [50]. Adopting a holistic approach which addresses the physical, psychological and social implications of lymphoedema is required due to the chronic, stigma- tizing nature of this condition [3]. In Malawi, lymphatic management training has been provided to all health surveillance assistants and two staff 259 health centres. More than 4000 community health workers have been trained in lymphoedema management and to provide further training in the lymphoe- dema home-based care to all persons affected identified within their catchment area. All health centres across the country provide free of charge services during ADL episodes, such as antibi- otics or pain relief [11]. The findings from this study show the wide spread distribution LF clinical cases across the country, that will all require lymphoedema management training to hinder the progression of their disease and reduce further disability. The majority of LF clinical cases in Malawi are located within rural communities, where access to healthcare or trained healthcare staff remains a challenge, as well as equitable access to quality healthcare services for women [53]. Additional challenges include addressing the psychosocial consequences of lymphoedema [51]. As many countries move into the LF elimination phase, strategies to focus on alleviating suffering of affected individuals through morbidity management is critical. The Malawi LF programme is in the process of planning and continuing the scale-up of a home-based enhanced self-care in all endemic districts, however more funding is required to continue this PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 10 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi vital work. The informative LF case numbers presented in this study will allow the programme to identity where they need to verify the numbers and plan to allocate resources, including training of health staff and health surveillance assistants. This will help the integration of the essential packages of care into health systems for increased sustainability. In addition, it is a WHO post-elimination requirement to estimate LF case numbers at the implementation unit level, as well as conducting post-elimination surveillance [5]. Limitations and considerations There are several limitations to this study; we will discuss those related to study analysis first. As there was limited data on antigenaemia and LF clinical case mapping, we did not conduct a validation of our geostatistical prediction. This lack of validation means we could not test the reliability of our predictions, therefore more research needs to be done to vali- date some of the predicted areas through ground truthing field surveys. Additionally, esti- mates for sex specific hydrocoele and lymphoedema cases were based on the assumption that there were no differences in gender ratios in different TA areas of Malawi and that the programme collected data distributions of hydrocoele and lymphoedema were the same for our model predictions. Limitations from a public health perspective include that most endemic country pro- grammes have limited access to geostatistical modelers, which restrict the scale up of such work, however a pool of key experts may be convened and work with national programmes to obtain better estimates. Finally, the generalizability in this study is limited to countries with available clinical case data across endemic regions. Conclusion Malawi is the first LF endemic African country to produce a national level database and a set of risk maps of LF clinical case and antigenaemia prevalence estimates. This has been achieved by the extensive detailed patient mapping conducted by the Malawi LF programme, which was used in combination with LF prevalence data, climate information and geostatistical analytical methods. This study highlights the value of combining different data in resource limited set- tings to help to save time, human and financial resources. Additionally, these predictions equip the Malawi national programme and Ministry of Health with information to help assess the readiness and quality of services needed to act on and deliver targeted care to the people who need it most. As well as providing lessons for many of country programmes close to elimi- nating LF as a public health problem. Supporting information S1 STROBE Checklist. Strobe statement. (DOC) S1 Table. Predicted LF clinical cases and prevalence for Traditional Authority Boundary Administrative Units in Malawi from predictions from geostatistical analysis or pro- gramme-collected data. (XLSX) S2 Table. Traditional Authorities with available antigenaemia prevalence data and LF clin- ical cases data in Malawi. (DOCX) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012056 March 25, 2024 11 / 15 PLOS NEGLECTED TROPICAL DISEASESThe national distribution of lymphatic filariasis cases in Malawi S1 File. Additional Methods and Results for Exploratory Analysis: Antigenaemia and Clin- ical Case Prevalence in Malawi. (DOCX) S2 File. Assessing goodness of fit of geostatistical model 1. (DOCX) Acknowledgments The authors wish to thank the health surveillance assistants, programme coordinators and managers, district health workers, district and regional officials, community leaders and par- ticipants who made this study possible. Author Contributions Conceptualization: Carrie Barrett, Louise A. Kelly-Hope. Data curation: John Chiphwanya, Square Mkwanda, Sarah Martindale. Formal analysis: Carrie Barrett, Jonathan M. Read. Funding acquisition: John Chiphwanya, Square Mkwanda, Mark J. Taylor, Louise A. Kelly- Hope. Investigation: Dorothy E. Matipula, Paul Ndhlovu, Limbikani Chaponda, Sarah Martindale. Methodology: Carrie Barrett, Emanuele Giorgi, Hannah Betts, Sarah Martindale, Jonathan M. Read, Louise A. Kelly-Hope. 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10.1371_journal.pmed.1004343	RESEARCH ARTICLE Health outcomes after myocardial infarction: A population study of 56 million people in England Marlous HallID A. BattyID 1,2, Lesley SmithID 3, Jianhua WuID 1,2, Paul C. Lambert5,6, Harry HemingwayID 2,4, Chris Hayward1,2, Jonathan 7,8,9,10, Chris P. Gale1,2,11 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Hall M, Smith L, Wu J, Hayward C, Batty JA, Lambert PC, et al. (2024) Health outcomes after myocardial infarction: A population study of 56 million people in England. PLoS Med 21(2): e1004343. https://doi.org/10.1371/journal. pmed.1004343 Academic Editor: Andre P. Kengne, South African Medical Research Council, SOUTH AFRICA Received: June 26, 2023 Accepted: January 5, 2024 Published: February 15, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pmed.1004343 Copyright: © 2024 Hall et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the results presented in the study are available from NHS England’s Data Access Request Service https://dataaccessrequest.hscic.gov.uk/. HES data 1 Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, 2 Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, 3 Leeds Institute for Health Sciences, University of Leeds, Leeds, United Kingdom, 4 Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom, 5 Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom, 6 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 7 Institute of Health Informatics, University College London, London, United Kingdom, 8 Health Data Research UK, University College London, London, United Kingdom, 9 NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, University College London, London, United Kingdom, 10 Charite´ Universita¨ tsmedizin, Berlin, Germany, 11 Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom m.s.hall@leeds.ac.uk Abstract Background The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health out- comes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. Methods and findings This nationwide cohort study includes all individuals aged �18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subse- quent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative inci- dence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 1 / 26 PLOS MEDICINEare managed and released by NHS Digital. The specific extract provided to the research team can only be used for the stated purpose of the study and for the length of time necessary to conduct the study. The extract cannot be shared outside of the research team or for any other purpose according to the legally binding terms under which they were released. Please see our privacy notice for further information on the purpose and legal basis of our use of these data: https://digital.nhs.uk/data-and- information/data-tools-and-services/data-services/ hospital-episode-statistics. Access to HES data is available by direct application to NHS Digital and is available to anyone who has a legal basis for accessing these data, meets the requirements for safe and secure use of these data and intends to use these data for demonstrable benefit to health and social care in the UK. A full HES data dictionary, information of how to apply and the costs associated with data applications are available publicly via the NHS digital website: https://digital.nhs.uk. All diagnostic and procedure codes used to define specific study outcomes are provided in the supplementary online material released at time of publication. Aggregated data of the age, sex and deprivation specific post MI absolute risk of new onset disease (as presented in heat maps (Fig 5)) are available to explore freely via: https://multimorbidity-research-leeds.github.io/ research-resources Anyone wishing to use these aggregate data to generate their own graphical summaries may do so providing full reference is given to this publication. Funding: MH received funding from the Wellcome Trust https://wellcome.org/ (Sir Henry Wellcome Postdoctoral Fellowship ref: 206470/Z/17/Z), British Heart Foundation https://www.bhf.org.uk/ (ref: PG/19/54/34511) and British Heart Foundation-Alan Turing Cardiovascular Data Science Award https://www.bhf.org.uk/for- professionals/information-for-researchers/what- we-fund/bhf-turing-cardiovascular-data-science- awards (ref: BHF-Turing-19/02/1022). JAB was funded by Wellcome Trust 4ward North Clinical Research Training Fellowship (ref: 227498/Z/23/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The researchers have acted independently from funders and all authors had access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: MH declares research grant income from the Wellcome Trust, Health outcomes after myocardial infarction among the population of England [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. Conclusions In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched indi- viduals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study. Author summary Why was this study done? • Myocardial infarction (MI; heart attack) can have major long-term impact on individu- als and result in a wide range of further health conditions. • Existing studies have focussed on determining the short-term risk of a second heart attack, stroke, or major bleeding, but research describing the long-term risk of major health outcomes for specific age, sex, and deprivation groups was lacking. • Nationally representative and robust information of a wide range of long-term health outcomes following a heart attack is critical for the development of treatment recom- mendations, which take account of an individuals’ specific risk. What did the researchers do and find? • From the population of 56 million adults in England, we analysed hospital records for 34 million adults admitted to hospital (constituting 145 million admission records) to investigate the long-term health outcomes following a heart attack compared with indi- viduals without a heart attack. • Of 433,361 individuals with a heart attack, up to a third developed heart failure or renal failure, 7% had further heart attacks, and 38% died within the 9-year study period. • Heart failure, atrial fibrillation, stroke, peripheral arterial disease, severe bleeding, renal failure, diabetes, and depression occurred more frequently for people who had a heart PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 2 / 26 PLOS MEDICINEBritish Heart Foundation and Alan Turing Institute. JAB declares research grant income from the Wellcome Trust. CPG has received funding, not in relation to this study, from Abbott Diabetes, Bristol Myers Squibb and the European Society of Cardiology, and consulting fees from AI Nexus, AstraZeneca, Amgen, Bayer, Bristol Myers Squibb, Boehrinher-Ingleheim, CardioMatics, Chiesi, Daiichi Sankyo, GPRI Research B.V., Menarini, Novartis, iRhyth, Organon as well as payment for honoraria or lectures from AstraZeneca, Boston Scientific, Menarini, Novartis, Raisio Group, Wondr Medical, Zydus. CPG declares participation on Data Safety Monitoring or Advisory boards for the DANBLCOK and TARGET CTCA trials and editorial and committee membership of the NICE Indicator Advisory Committee, EHJ Quality of Care and Clinical Outcomes and ESC Quality Indicator Committee. CH, LS, JW, HH, and PCL have no competing interests to declare. Abbreviations: aHR, adjusted hazard ratio; CABG, coronary artery bypass graft; CI, confidence interval; CIF, cumulative incidence function; DAPT, dual antiplatelet therapy; GDPR, General Data Protection Regulation; GP, general practitioner; HDR UK, Health Data Research United Kingdom; HES, Hospital Episode Statistics; IMD, Index of Multiple Deprivation; LASER, Leeds Analytic Secure Environment for Research; MI, myocardial infarction; MINAP, Myocardial Ischaemia National Audit Project; NHS, National Health Service; PCI, percutaneous coronary intervention; PH, proportional hazard; PPIE, patient and public involvement and engagement; SD, standard deviation. Health outcomes after myocardial infarction among the population of England attack compared with those who did not, but the risk of cancer was lower overall and the risk of dementia did not differ overall. What do these findings mean? • Efforts should be made to prevent or limit the development of long-term health out- comes that follow a heart attack—the likelihood of which differ depending on the age, sex, and deprivation of an individual. • These findings are based on the full population of adults admitted to hospital in England, address limitations of previous studies, and can be used to inform preventative strategies tailored to specific individuals surviving a heart attack. • The study was limited to hospitalisation data only—therefore, some diagnoses made outside of hospital may have been missed. Introduction Information about the health outcomes of people with myocardial infarction (MI) is required to determine individual health needs, enable earlier detection and treatment of new onset dis- ease, and inform health service planning. MI is a major contributor to further cardiovascular, renometabolic, and neuropsychiatric conditions [1–5]. Although estimating 10-year cardio- vascular disease risk is an established part of primary prevention [6,7], comprehensive evi- dence for the long-term impact of MI on subsequent major health outcomes is lacking. Such information is critical not only for the development of guideline recommendations but also to underpin shared decision-making in the post-MI period [8]. Effective communication of the likely course of disease and risk of adverse long-term outcomes between individuals and healthcare professionals can promote positive lifestyle changes, facilitate treatment compli- ance, and improve patient understanding and quality of life [9,10]. Electronic health records are a powerful resource for understanding a diverse range of health outcomes over many years of follow-up [11]. While the largest study to date of post-MI health outcomes provides temporal trends over 2 decades (4.3 million patients in the United States (US)), outcomes were limited to 1-year mortality, readmission, and recurrent MI [4]. Indeed, the majority of studies of new onset disease following MI focussed only on short-term recurrent MI, bleeding, or stroke [12–26] (literature review S1 Text and S1 Table). Short- and long-term heart failure incidence following MI has been studied extensively [27–32]—but esti- mates vary widely (14% to 36%) [33]. While studies reporting the determinants of heart failure account for confounding and competing risk of death—absolute risk was commonly reported without adjustment, which is prone to bias and lacks generalisability [3,34–36]. Long-term post-MI incidence of atrial fibrillation [37–39] and depression [40–42] has been reported with- out adjustment for sociodemographic factors, pre-existing disease, and differential exposure times, and studies of depression were small (<300 patients). While data on the post-MI inci- dence of cancer (9% within 17 years; n = 2.1 million) [43] and dementia (9% within 35 years, n = 314,911) [2] were more robust—patient demographic-specific absolute risks remain unknown. To our knowledge, there were no contemporary, nationally representative studies of new onset peripheral arterial disease, chronic renal failure, or diabetes for survivors of MI (except one study of newly diagnosed diabetes at time of MI in young adults) [44]. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 3 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England To our knowledge, none of the studies reporting non-fatal health outcomes post-MI to date account for confounding as well as censoring and competing risks of death to quantify the absolute risk of outcomes over continuous follow-up time. Understanding the clinical and public health importance of health outcomes after MI requires consideration of the absolute and relative risks beyond age- and sex-matched general populations. To the best of our knowledge, there are no studies of the long-term relative, abso- lute, and detailed patient demographic-specific risk of major cardiovascular and non-cardio- vascular health outcomes following MI. Therefore, we used hospital admission data in England to determine the risk of all-cause mortality and 11 non-fatal health outcomes following MI, including (1) health outcomes tar- geted through existing secondary prevention guidelines following MI (subsequent MI and heart failure); (2) health outcomes with shared risk factor profiles with MI, but which were not part of secondary prevention (peripheral arterial disease, cerebrovascular disease, and chronic renal failure); (3) health outcomes for which early detection is crucial for improved outcomes (severe bleeding, diabetes, atrial fibrillation, and cancer); and, finally, (4) health outcomes, which are difficult to prevent but have significant impact on individuals quality of life or life expectancy (depression and dementia). We hypothesised that post-MI disease incidence dif- fered to that expected during a life course without MI. Therefore, we determined the excess incidence, adjusted absolute risk, and age, sex, deprivation, and time-specific risk for each of these health outcomes following MI compared with matched controls. Methods We conducted a cohort study of all individuals aged �18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017. We analysed 11 non-fatal health outcomes identified a priori (subsequent MI and first hospitalisa- tion for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all- cause mortality for individuals hospitalised with MI compared with a risk-set matched control cohort. Our study hypothesis was that risk of major health outcomes following MI differed from that expected during a life course without MI. The hypothesis and methodology were defined a priori—exceptions to this, including data-driven decisions and analyses conducted in response to peer review, are labelled as such throughout. Data access Hospital Episode Statistics (HES) data were extracted from the Admitted Patient Care dataset by NHS Digital and linked with all-cause mortality data from the Office for National Statistics (censoring date 27 March 2017). HES data contain prospectively collected clinical, demo- graphic, and organisational data for every hospitalisation to NHS hospitals in England, as pre- viously described [45]. In brief, an individual’s admission to hospital is recorded via a number of single episodes each containing a primary diagnosis, up to 19 secondary diagnoses and 24 operations, procedures, or interventions (coded according to the International Classification of Disease [ICD-10] and Office of Population Censuses and Surveys Classification of Interven- tions and Procedures [OPCS4.5], respectively) [45]. Phenotype definitions Individuals with MI and each of the 11 non-fatal health outcomes were identified using ICD- 10 and OPCS4.5 codes derived from the Health Data Research United Kingdom (HDR UK) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 4 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Phenotype Library (healthdatagateway.org) (S2 Table) [46]. In addition, we identified ICD-10 codes for key subgroups including stroke, aortic disease, gastrointestinal bleeding, vascular dementia, acute and chronic renal failure, and colorectal, lung, breast, and prostate cancer (S2 Table). Index MI, as well as the first occurrence of each outcome, was extracted from all primary and secondary diagnostic codes and all procedure codes across all hospitalisation episodes per individual. To identify index events within our study period, all patients with MI or any of the a priori health outcomes in any HES record prior to the study start date were excluded. We ascertained index MI from all diagnoses fields, as planned a priori, given that the presence of another dominant disease may impact on ascertainment from the first diagnostic position only [47]. Subsequent MI was defined as any MI more than 2 months from initial MI. We con- ducted sensitivity analyses for all outcomes in which follow-up was restricted to 2 or more months following MI to assess the impact of potential bias from the high number of events observed shortly after study entry. This was a data-driven decision (S2 Text). Data cleaning steps are outlined in S3 Text. Analytical cohort and matching process Individuals were categorised into (1) a primary analytical cohort of those with an MI hospitali- sation record and (2) a cohort of all hospitalised individuals who did not have MI (Fig 1). Our a priori analyses plans focussed on the comparison of outcomes between these 2 cohorts. How- ever, in order to minimise bias arising from different demographic profiles between groups as well as to avoid immortal time bias, we instead employed an exact risk-set matching procedure [48]. This was determined prior to peer review, in favour of propensity score matching, to avoid the need for pruning of data resulting in efficiency loss and potential risk of reintroduc- ing bias [49]. Risk-set matching involved matching any new case of MI occurring at time t to any 5 indi- viduals who had not yet developed MI by time t. Matching was based on single year of age, sex, month and year of hospital admission, and NHS Trust. Due to the longitudinal nature of our data, and as per risk-set matching guidance, individuals who later went on to develop MI were censored at the date of MI for the control cohort but contribute to both MI and matched con- trol cohorts in analyses [48]. Study entry was either the date of the first episode with MI or first matched episode. Statistical analyses Patient demographics including age (continuous), sex (male/female), year (single year of study entry), socioeconomic deprivation (Index of Multiple Deprivation [IMD] [50]—the official score of relative deprivation for small areas of England categorised into groups from least deprived [quintile 1] to most deprived [quintile 5]), crude mortality (Kaplan–Meier failure rate at 30 days, 1 year, and 5 years), total diagnoses by ICD-10 chapter heading, total person years of follow-up, and percentage of missing data for each demographic variable were sum- marised for the MI, non-MI, and matched control cohorts, respectively. Following peer review, baseline data relating to cardiovascular risk (including hypertension, dyslipidaemia, obesity, tobacco smoking, and alcohol excess) and use of an invasive coronary strategy for index MI (invasive coronary angiography, percutaneous coronary intervention [PCI], or coronary artery bypass graft [CABG]) (ICD-10 and OPCS4.5 code lists provided; S2 Table) were included. Baseline cardiovascular risk was based on diagnoses observed prior to, or on study entry, within the study period only as historical data were not available in-house due to information governance minimisation requirements (see Data governance section). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 5 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Fig 1. Data extraction and cohort definitions for the population of England, 2008–2017. aPopulation estimates extracted from the Office for National Statistics Population Estimates for England, Wales, Scotland, and Northern Ireland. bHeart failure, atrial fibrillation, cerebrovascular disease, peripheral vascular disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer. cDuplicate HES episodes, which contain the same data across admission start and end dates, episode start and end dates, primary and secondary diagnoses codes, and procedure codes, are administrative duplications where incorrect or new entries have been created and are removed from analyses (S3 Text). dRecords that are missing core and essential information are deemed of too poor a quality to be included in analyses. These include records with missing, conflicting, or out-of-range finished consultant episode start and end dates, unknown spell begin and end indicators, or unknown episode order. eIndividuals were matched according to single year PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 6 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. HES, Hospital Episode Statistics; MI, myocardial infarction; NHS, National Health Service. https://doi.org/10.1371/journal.pmed.1004343.g001 Excess rate of health outcomes and all-cause mortality. Unadjusted rates of disease per 1,000 person-years of follow-up, attained age, and adjusted excess rate of post-MI disease for each outcome compared with matched controls were calculated. Excess rate of post-MI disease was based on adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) from flexible parametric survival models per outcome [51]. Models adjusted for age, sex, calendar year, and deprivation score. Nonlinearity of age was modelled using restricted cubic splines (3 degrees of freedom). To provide an overall estimate of excess risk of post-MI disease, comparable with existing literature, aHRs were modelled as standard proportional hazards [PHs] (i.e., fixed constant over time). Subsequently, the PH assumption was relaxed to provide higher resolu- tion insight into absolute risk of outcomes over continuous follow-up time (described below). Absolute risk of health outcomes and all-cause mortality. The adjusted absolute risk of each outcome was calculated through standardised cumulative incidence functions (CIFs) stratified by a cohort over 9 years of follow-up, based on the same set of adjusted survival mod- els specified above, treating death without outcome as a competing risk and additionally including a time-dependent effect for MI versus matched controls to relax the PH assumption and reflect variation in the difference in cumulative incidence between cohorts over continu- ous follow-up time (using “standsurv” in Stata MP v17) [52]. Age, sex, and deprivation-specific risk charts. Standardised cumulative incidence for each outcome were calculated for all combinations of age group (<40, 40 to <50, 50 to <60, 60 to <70, 70 to <80, 80 to <90, and �90 years of age), sex, and socioeconomic deprivation quintile. Following peer review, age, sex, and deprivation-specific risk for the MI cohort were additionally adjusted for receipt of invasive coronary angiography, PCI, or CABG at time of MI. Risk charts were generated for each cohort at 60 days, 1 year, and 5 years of follow-up and presented in heat maps and an interactive web-based application. Multiple imputation for missing data was not performed owing to (i) the minimal amount of missing data in core data fields and (ii) the significant increase in computational power required versus the minimal gain in analytical accuracy for data of this scale. Data governance, IT infrastructure, ethics, and reporting standards This research adheres to General Data Protection Regulation (GDPR) (privacy notice). Data minimisation standards were met through pseudonymisation, month/year aggregation of date, exclusion of patients aged <18 years old, and individuals with pre-existing conditions from a priori outcomes at source. Data were stored and processed within the Leeds Analytic Secure Environment for Research (LASER), University of Leeds. Ethical approval was not required for this study, which solely relies on the secondary use of routinely collected, non- confidential healthcare data. This study is reported as per the Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) guideline and CODE-EHR mini- mum standards (S1 and S2 Checklists). Patient and public involvement and engagement (PPIE) In the period prior to obtaining research funding, the research team consulted with individuals who have had, or cared for someone who had, an MI. Shared experiences of those individuals shaped early study design and reporting of this research. Individuals raised concerns about the lack of information provided regarding future health risks following their heart attack. They PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 7 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England noted that there was a particular focus on changes in diet and exercise in the immediate period after a heart attack, with little or no information available of “red flags to look out for” in the long term. Individuals identified the need for tools to enable doctors to “risk assess us” and tell us more about future health prospects. These discussions directly informed our research design and ensured we retained a long-term focus, instead of curtailing outcomes at 1-year post-MI to align with existing evidence. Furthermore, we developed an interactive, open-access tool that can be used by healthcare professionals, individual patients, and their carers to better understand and communicate the absolute risks of developing a range of health outcomes motivated by our patient and public involvement and engagement (PPIE) discussions. Individuals felt that this knowledge may provide greater incentivisation for positive lifestyle changes following a heart attack as well as allow individuals and healthcare professionals to “act early rather than react late.” Finally, the research team hosted a workshop attended by a further 8 individuals with car- diovascular and other multiple long-term health conditions providing direct feedback on this study and guiding the dissemination strategy and direction of follow-on studies. The PPIE group identified the need for joined up thinking between different healthcare providers, given the risk of both cardiovascular and non-cardiovascular conditions, which we raise in our man- uscript discussion. They noted the adoption of a long-term perspective is particularly impor- tant given that many expect good life expectancy after MI. The PPIE group advocated for the importance of dissemination to general practitioners (GPs) as well as cardiologists and identi- fied the need for clear lay summaries of the work to ensure it is accessible by all. The research team will coproduce lay summaries with our PPIE members and disseminate findings to rele- vant patient groups, including through the British Heart Foundation’s Heart Voices network. Results Of the 55,619,430 populace of England, 34,116,257 individuals aged 18 years and above were admitted to hospital amounting to 145,912,852 hospital episodes in NHS hospitals in England over the study period. The analytical cohorts comprised 433,361 individuals with MI (2,972,215 episodes), 33,429,669 individuals without MI (129,307,574 episodes), and a subset of 2,001,310 matched controls (17,304,985 episodes) (Fig 1). There were 18,322 matched con- trols who went on to develop MI (0.92%) and therefore contribute data to both cohorts. Indi- viduals with MI were admitted to hospital at a mean age of 67.4 years (standard deviation [SD] 14.4), were predominantly male (65.5% [n = 283,742]), and had a 30-day mortality rate of 9.9% [n = 42,882] (Table 1). For matched controls, the age and sex profile was similar to those with MI by design (mean age 66.8 [SD 14.2] and [65.7% male, n = 1,314,388]) with 3.1% (n = 59,991) 30-day mortality. There were minimal differences in deprivation between cohorts (20.7% [n = 77,008] versus 19.2% [n = 375,734] were in the most deprived category for MI and matched controls, respectively). The proportion of individuals with hypertension, dyslipidae- mia, obesity, and tobacco smoking were higher among those with MI compared with matched controls but lower for alcohol excess (Table 1). A total of 319,439 (73.7%) individuals with MI received an invasive coronary management strategy for index MI. Missing data in core data fields were limited, including 50,438 (0.1%) for age, 33,871 (0.1%) for sex, 0 missing for month and year of admission, but higher for deprivation (n = 4,025,757, 11.8%). There were 18,343,361 diagnoses codes covering all conditions for individuals with MI throughout the study period, of which 24.7% [n = 4,526,829] were unique nonfatal diagnoses codes per individual (Table 1). The majority of diagnosis codes related to the circulatory system (44.3% [n = 2,003,429]), which, along with “endocrine, nutritional, and metabolic diseases” (9.6% [n = 433,545]), appeared more frequently than in matched controls (20.2% [n = 3,631,165] and 9.0% [n = 1,616,515] for circulatory and “endocrine, nutritional, and metabolic diseases,” PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 8 / 26 PLOS MEDICINETable 1. Patient characteristics for people with MI, without MI, and age, sex, and year matched controls hospitalised in England, 2008–2017. Health outcomes after myocardial infarction among the population of England Age at study entry, years (mean, SD)a Male Sex (Numerator/denominator, %) Study entry period (Numerator/denominator, %) 2008–2010 2011–2013 2014–2017 Socioeconomic Status (Numerator/denominator, %)b 1 –Least deprived 2 3 4 5 –Most deprived Cardiovascular risk factors (N, %)c Hypertension Dyslipidaemia Obesity Tobacco smoking Alcohol excess Invasive coronary strategyd Invasive coronary angiography, PCI, or CABG Crude mortality (N, KM failure function) 30 days 1 year 5 years Total follow-up period (9 years) Total primary and secondary diagnoses (ICD-10 Chapters I–XIV)e (Numerator/denominator, %) Certain infectious and parasitic diseases (A00–B99) Neoplasms (C00–D48) Diseases of the blood and blood-forming organs (D50–D89) Endocrine, nutritional, and metabolic diseases (E00-E90) Non-MI N = 33,429,669 41.7 (26.1) 14,747,198/33,395,663 (44.2%) 14,473,148/33,429,669 (43.3%) 10,428,354/33,429,669 (31.2%) 8,528,167/33,429,669 (25.5%) 5,474,307/29,417,676 (18.6%) 5,684,181/29,417,676 (19.3%) 5,883,886/29,417,676 (20.0%) 6,061,653/29,417,676 (20.6%) 6,313,649/29,417,676 (21.5%) 6,839,977/33,429,669 (20.5%) 2,360,150/33,429,669 (7.1%) 1,541,944/33,429,669 (4.6%) 3,974,449/33,429,669 (11.9%) 1,316,047/33,429,669 (3.9%) Analytical Cohort MI N = 433,361 67.4 (14.4) Matched control N = 2,001,310 66.8 (14.2) 283,742/443,298 (65.5%) 1,314,388 (65.7%) 155,563/433,361 (35.7%) 143,841/433,361 (33.3%) 133,957/433,361 (31.1%) 77,008/419,597 (18.4%) 84,328/419,597 (20.1%) 86,689/419,597 (20.7%) 84,751/419,597 (20.2%) 86,821/419,597 (20.7%) 211,386/433,361 (48.8%) 127,921/433,361 (29.5%) 700,484/2,001,310 (35.0%) 704,323/2,001,310 (35.2%) 596,503/2,001,310 (29.8%) 383,572/1,954,523 (19.6%) 410,442/1,954,523 (21.0%) 405,714/1,954,523 (20.8%) 379,061/1,954,523 (19.4%) 375,734/1,954,523 (19.2%) 738,075/2,001,310 (36.9%) 237,305/2,001,310 (11.9%) 25,706/433,361 (5.9%) 74,851/2,001,310 (3.7%) 124,773/433,361 (28.8%) 228,557/2,001,310 (11.4%) 17,031/433,361 (3.9%) 92,905/2,001,310 (4.6%) NA 319,439/433,361 (73.7%) NA 357,416 (1.1%) 1,030,354 (3.1%) 2,416,967 (8.6%) 3,003,970 (13.9%) 42,882 (9.9%) 67,356 (15.7%) 106,958 (28.6%) 119,695 (39.6%) 59,991 (3.1%) 202,713 (10.3%) 435,196 (26.1%) 496,180 (37.3%) 4,207,444/134,983,929 (3.1%) 4,861,221/134,983,929 (3.6%) 2,672,524/134,983,929 (2.0%) 7,472,910/134,983,929 (5.5%) 105,005/4,526,829 (2.3%) 91,264/4,526,829 (2.0%) 91,216/4,526,829 (2.0%) 433,545/4,526,829 (9.6%) 632,462/17,995,217 (3.5%) 1,064,275/17,995,217 (5.9%) 489,230/17,995,217 (2.7%) 1,616,515/17,995,217 (9.0%) (Continued ) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 9 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Table 1. (Continued) Mental and behavioural disorders (F00-F99) Diseases of the nervous system (G00–G99) Diseases of the eye and adnexa (H00–H59) Diseases of the ear and mastoid process (H60–H95) Diseases of the circulatory system (I00–I99) Diseases of the respiratory system (J00–J99) Diseases of the digestive system (K00–K93) Diseases of the skin and subcutaneous tissue (L00–L99) Diseases of the musculoskeletal system and connective tissue (M00–M99) Diseases of the genitourinary system (N00–N99) Total unique 3-digit ICD10 codesf Total non-unique 3-digit ICD10 codesg Total person-years of follow-up Total episodes, N Non-MI N = 33,429,669 6,575,834/134,983,929 (4.9%) 3,400,963/134,983,929 (2.5%) 3,311,967/134,983,929 (2.5%) 971,522/134,983,929 (0.7%) 9,899,036/134,983,929 (7.3%) 7,663,859/134,983,929 (5.7%) 10,866,943/134,983,929 (8.1%) 3,193,502/134,983,929 (2.4%) 7,844,158/134,983,929 (5.8%) 7,159,761/134,983,929 (5.3%) 134,983,929 340,047,260 165,450,465 129,307,574 Analytical Cohort MI N = 433,361 224,846/4,526,829 (5.0%) 77,527/4,526,829 (1.7%) 108,405/4,526,829 (2.4%) 23,627/4,526,829 (0.5%) 2,003,429/4,526,829 (44.3%) 348,782/4,526,829 (7.7%) 396,322/4,526,829 (8.8%) 76,490/4,526,829 (1.7%) 296,028/4,526,829 (6.5%) 250,343/4,526,829 (5.5%) 4,526,829 18,343,361 1,603,181 2,972,215 Matched control N = 2,001,310 1,001,335/17,995,217 (5.6%) 553,259/17,995,217 (3.1%) 755,446/17,995,217 (4.2%) 141,062/17,995,217 (0.8%) 3,631,165/17,995,217 (20.2%) 1,569,536/17,995,217 (8.7%) 2,646,415/17,995,217 (14.7%) 507,402/17,995,217 (2.8%) 1,905,382/17,995,217 (10.6%) 1,481,733/17,995,217 (8.2%) 17,995,217 84,154,930 7,902,483 17,304,985 aAge at study entry reflects age at first MI, age at first hospitalisation for any cause, and age at first matched hospitalisation for any cause for the MI, non-MI, and matched control cohorts, respectively. bMeasured according to quintiles of the IMD. cBaseline cardiovascular risk data relate to diagnoses codes recorded on or prior to individual study entry dates but do not include data prior to 1 January 2008. In addition, tobacco smoking, alcohol excess, and obesity are known to be under recorded in hospitalisation data and likely represent only the extremes of the patient population. dInvasive coronary angiography, PCI, or CABG. eEach unique 3-digit ICD-10 code is counted only once per individual before aggregating to ICD-10 chapter heading level providing a summary of diagnoses codes across the study period. fAny 3-digit ICD10 diagnoses code, counted once per individual, within ICD-10 chapters I to XIV (A00 to N99), and serves as the denominator for chapter heading counts. gAny 3-digit diagnoses code in ICD-10 chapters 1 to 14 (A00-N99), including repeated diagnoses per individual. Missing data were minimal including 50,438 (0.1%) for age, 33,871 (0.1%) for sex, 0 missing for month and year of admission, and 4,025,757 (11.8%) for socioeconomic deprivation across the analytical cohorts. CABG, coronary artery bypass graft; ICD, International Classification of Diseases; KM, Kaplan–Meier; MI, myocardial infarction; NA, not applicable; PCI, percutaneous coronary intervention; SD, standard deviation. https://doi.org/10.1371/journal.pmed.1004343.t001 respectively). For matched controls, diagnoses relating to neoplasms (20.2%, [n = 3,631,165]) and diseases of the digestive system (14.7% [n = 2,646,415]) occurred most frequently (Table 1). Excess rate of health outcomes and all-cause mortality The most frequent health outcomes following MI, prior to standardisation, were heart failure (crude rate 86.4; 95% CI [85.9,86.9] per 1,000 person-years [1,000 pyrs]), atrial fibrillation (64.3; 95% CI [63.9,64.7] per 1,000 pyrs), renal failure (56.5; 95% CI [56.1,56.9] per 1,000 pyrs), PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 10 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Fig 2. Number of individuals, attained age, and excess ratea of 11 nonfatal health outcomes, 9 key subgroups, and all-cause mortality following MI compared with age, sex, and year matched controls in England. aExcess rate of post-MI hospitalisations and all-cause mortality presented as aHRs and 95% CIs based on a series of flexible parametric survival models for each outcome adjusted for age at admission, sex, calendar year of admission, and deprivation score. Age was modelled using restricted cubic spline functions with 3 degrees of freedom to allow for its potential nonlinear association with outcomes, and death without event was treated as a competing risk. Complimentary sensitivity analyses, in which follow up was restricted to begin a minimum of 2 months after study entry, are provided in S8 Table. aHR, adjusted hazard ratio; CI, confidence interval; MI, myocardial infarction; NA, not applicable; SD, standard deviation. https://doi.org/10.1371/journal.pmed.1004343.g002 and diabetes mellitus (53.7; 95% CI [53.3,54.1] per 1,000 pyrs) (S3 and S4 Tables). There was an excess rate of heart failure (aHR 4.93; 95% CI [4.89,7.97]; p < 0.001), atrial fibrillation (aHR 1.98; 95% CI [1.97,2.00]; p < 0.001), cerebrovascular disease (aHR 1.25; 95% CI [1.23,1.26]; p < 0.001), peripheral arterial disease (aHR 1.86; 95% CI [1.83,1.89]; p < 0.001), severe bleed- ing (aHR 1.22; 95% CI [1.20,1.23]; p < 0.001), renal failure (aHR 1.77; 95% CI [1.75,1.78]; p < 0.001), diabetes mellitus (aHR 1.62; 95% CI [1.61,1.64]; p < 0.001), vascular dementia (aHR 1.13; 95% CI [1.10,1.16]; p < 0.001), and depression (aHR 1.06; 95% CI [1.04,1.07]; p < 0.001) following MI compared with matched controls (Fig 2). There was no difference in the rate of dementia overall (aHR 1.01; 95% CI [0.99,1.02]; p = 0.468) and a reduced rate of cancer (aHR 0.56; 95% CI [0.56,0.57]; p < 0.001) (Fig 2). Absolute risk health outcomes and all-cause mortality Overall, the adjusted cumulative incidence at 9 years post-MI was highest for all-cause mortal- ity (37.8%; 95% CI [37.6,37.9]) followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal fail- ure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes mellitus (17.0%; 95% CI [16.9,17.1]), cancer (13.5%, 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0─7.2]), and peripheral arterial disease (6.5%; 95% CI [6.3,6.5]) (Figs 3 and 4 and S5 Table). Cumulative PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 11 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Fig 3. Adjusted absolute riska over continuous time of all-cause mortality, subsequent MI, heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, and severe bleeding following MI compared with matched controlsb in England. aCalculated according to the standardised CIF, treating death without outcome as a competing risk, adjusted for nonlinear age using restricted cubic splines, sex, calendar year and deprivation score and a time-dependent effect for MI versus matched controls. Full CIFs and CIs by time point provided in S5 Table, and sensitivity analyses, in which follow-up was restricted to begin a minimum of 2 months after study entry, presented in S1 Fig and S6 Table. Numbers at risk at 1, 5, and 9 years of follow-up are provided in S7 Table. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cy-Axis range for all-cause mortality differs to plots for nonfatal health outcomes. CI, confidence interval; CIF, cumulative incidence function; ICD, International Classification of Disease; MI, myocardial infarction; NHS, National Health Service. https://doi.org/10.1371/journal.pmed.1004343.g003 incidence was greater among the MI cohort compared with matched controls for all outcomes except gastrointestinal bleeding—where it was higher in the short term (3.8%; 95% CI [3.8,3.9] versus 3.2% 95% CI [3.1,3.2] at 1 year) and similar in the long term (9.0%; 95% CI [8.9,9.1] PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 12 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Fig 4. Adjusted absolute riska over continuous time of renal failure, diabetes mellitus, dementia, depression, and cancer following MI compared with matched controlsb in England. aCalculated according to the standardised CIF, treating death without outcome as a competing risk, adjusted for nonlinear age using restricted cubic splines, sex, calendar year and deprivation score and a time-dependent effect for MI versus matched controls. Full CIFs and CIs by time point provided in S5 Table, and sensitivity analyses, in which follow-up was restricted to begin a minimum of 2 months after study entry, presented in S2 Fig and S6 Table. Numbers at risk at 1, 5, and 9 years of follow-up are provided in S7 Table. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cIncludes all cancer types (ICD10 codes C00–C97), i.e., this category is not restricted to the sum of breast, prostate, lung, and colorectal cancer). CI, confidence interval; CIF, cumulative incidence function; ICD, International Classification of Disease; MI, myocardial infarction; NHS, National Health Service. https://doi.org/10.1371/journal.pmed.1004343.g004 versus 8.8%; 95% CI [8.8,8.9]); dementia—where incidence was higher in the short term (2.1%; 95% CI [2.1,2.2] versus 1.79; 95% CI [1.77,1.81] at 60 days) and lower in the long term (7.8%; 95% CI [7.7,7.9] vesus 8.34%; 95% CI [8.28,8.41] at 9 years); and cancer—where inci- dence was lower throughout the follow-up period (13.5%; 95% CI [13.3,13.6] versus 21.5%; 95% CI [21.4,21.6] at 9 years) (sensitivity analyses S1 and S2 Figs and S6 Table; numbers at risk S7 Table). Age, sex, and deprivation-specific risk charts for 11 nonfatal health outcomes and all-cause mortality following MI There was an increasing risk with age post-MI for men and women across all deprivation quin- tiles for heart failure (cumulative incidence at 5 years ranging from 13.5%; 95% CI [13.0,14.0] to 48.9%; 95% CI [48.0,49.7] for men in deprivation quintile 3 aged <40 years and �90 years, respectively), atrial fibrillation (2.5%; 95% CI [2.3,2.7] to 36.6%; 95% CI [35.8,37.4] for men in deprivation quintile 3 aged <40 years and �90 years, respectively), and renal failure (4.0%; 95% CI [3.7,4.3] to 46.8%; 95% CI [45.9,47.7]) for men in deprivation quintile 3 aged <40 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 13 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England years and �90 years, respectively) (Fig 5). The association of age with subsequent MI, periph- eral arterial disease, cerebrovascular disease, diabetes, and cancer was less pronounced. In con- trast, post-MI depression risk was highest among younger age groups at each time point, particularly for those in the most deprived quintile (5-year CIFs for men in deprivation quin- tile 5 were 11.5%; 95% CI [10.9,12.0]; 10.0%; 95% CI [9.7,10.3]; 8.5%; 95% CI [8.2,8.7]; 6.8%; 95% CI [6.6,6.9]; 5.3%; 95% CI [5.1,5.5]; 4.4%; 95% CI [4.3,3.6]; and 3.8%; 95% CI [3.5,4.0] for those aged <40 years, 40 to <50 years, 50 to <60 years, 60 <70 years, 70 to <80 years, 80 to <90 years, and �90 years respectively). The risk of depression post-MI was also higher among women compared with men (5-year CIFs for women 21.5%; 95% CI [20.5,22.5], 18.9%; 95% CI [18.3,19.5]; 16.1%; 95% CI [15.6,16.6]; 13.0%; 95% CI [12.6,13.4]; 10.3%; 95% CI [10.0,10.6]; 8.7%; 95% CI [8.4,9.0]; 7.5%; 95% CI [7.0,7.9] for those aged <40 years, 40 to <50 years, 50 to <60 years, 60 to <70 years, 70 to <80 years, 80 to<90 years, and �90 years, respectively (Fig 5). Complimentary risk charts for matched controls and sensitivity analyses provided in S3, S4 and S5 Figs and interactive versions accessed via https://multimorbidity- research-leeds.github.io/research-resources. Discussion In this study of over 145 million hospitalisations in England, we provide nationwide evidence from a single health system of the specific burden of a wide range of health outcomes following MI. Up to a third of patients with MI developed heart failure or renal failure, 13% cerebrovas- cular disease, 9% depression, 7% had further MI or peripheral arterial disease, and 38% died within 9 years (compared with 35% deaths for individuals without MI). Rates of all health out- comes, except dementia and cancer, were significantly higher than expected during the normal life course without MI. Increased incidence of heart failure post-MI is well recognised [3], but estimates have been inconsistent, often lack confounder adjustment, and were unavailable by detailed demographic groups. Our study provides adjusted estimates of 21.2% heart failure at 1 year, rising to 29.6% at 9 years following MI compared with 2.9% and 9.8% at 1 and 9 years for matched controls, respectively. Further, our study shows earlier onset of heart failure following MI for the most socioeconomically deprived individuals. While we did not assess secondary preventative medi- cation directly, our findings may reflect previously reported underuse of secondary preventa- tive medication among socioeconomically deprived groups after MI [53]. We found that almost one-fifth of patients were admitted to hospital with severe bleeding following MI. While we were unable to study dual antiplatelet therapy (DAPT), recent data indicated that a reduction of major bleeding complications may be achieved through shortened DAPT regimes [54]. The incidence of post-MI diabetes mellitus, peripheral vascular disease, and renal failure had not been previously reported. Here, we quantify a small excess incidence of diabetes melli- tus (17% versus 14%) and peripheral arterial disease (6% versus 4%) at 9 years, and a marked difference in the incidence of renal failure (27% versus 20%) following MI compared with controls. New hospitalisations for depression occurred in 1 in 11 individuals after MI and was more frequent at younger ages of MI, for those in the most deprived quintile, and among women. Given the increasing trend in cardiovascular risk factors among young adults [55], and the increasing proportion of MI among young adults and women [56], the incidence of depression following MI will likely rise. Our study showed a lower incidence of cancer overall following MI, as well as for breast, colorectal, lung, and prostate cancer compared with controls. Two existing large-scale studies also describe significantly reduced risks of breast and prostate cancer among individuals with PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 14 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Fig 5. Absolute riska of subsequent MI, heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, cancer, and all-cause mortality at 60 days, 1 year, and 5 years following MI by age group, sex, and deprivationb in England (N = 433,361 individuals). aCalculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, deprivation score, and receipt of invasive coronary angiography, percutaneous coronary intervention, or coronary artery bypass graft. bDeprivation is measured using the IMD where 1 indicates those in the least deprived PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 15 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England fifth, and 5 indicates those in the most deprived fifth. S3, S4 and S5 Figs show CIFs for the matched control cohort and the main and matched control sensitivity analyses. Interactive version of these data are provided (https://multimorbidity-research-leeds.github.io/research-resources). CIF, cumulative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction. https://doi.org/10.1371/journal.pmed.1004343.g005 MI [43] and cardiovascular disease more broadly [57]. Our study builds on this, providing demographic-specific absolute risk overall and for key cancer subgroups. In contrast with pre- viously reported data, we additionally show a reduced risk of lung and colorectal cancer and all cancers combined (compared with previously reported increased incidence of lung cancer and nonsignificant difference in colorectal cancer following MI [43] and increased risk of lung, colorectal, and all cancers for individuals with cardiovascular disease [57]). The reason for conflicting evidence may be explained by (1) different population demographics of previ- ously published work (smaller and younger population of MI [43] and broader cardiovascular population [57]) and (2) lack of accounting for competing risk of death [57], which was likely to have a marked impact on findings given the high early mortality rate observed for individu- als with cardiovascular disease. Mechanisms underpinning reduced risk of cancer following MI remain unclear and warrant further investigation. While trial data have assessed the role of aspirin in the onset, progression, and mortality of specific cancer subtypes [58–60], broader evidence for its beneficial effect remains unclear [61]. We are unable to state whether aspirin contributed to the reduced risk of cancer observed, given medication data were unavailable. While some evidence points to reduced screening for cancer among individuals with cardio- vascular disease [62], a surveillance bias may also act in the opposite direction. Finally, given that we focussed specifically on development of new cancer following MI, we included only individuals living long enough cancer-free to develop MI before cancer, and this may further explain our findings. The incidence of dementia overall was 7.8% and 2.3% for vascular dementia within 9 years of MI. There was no difference in the risk of dementia overall, but there was a 13% increased risk of vascular dementia following MI compared with controls—consistent with previous work [2]. We improve on previous data by showing an increased risk of dementia overall in the short term (2.1% versus 1.8% at 60 days) but reduced risk in the long term (7.8% versus 8.3% at 9 years) and a consistently increased risk of vascular dementia at all time points follow- ing MI compared with controls. While we use large-scale nationwide data and robust methodology to produce generalisable results, we do acknowledge the study limitations. Our focus was on hospitalised events and we did not have access to diagnoses made in primary care–with may have (1) underestimated the totality of post-MI disease and (2) led to some individuals with pre-existing comorbidities being missed from our exclusion criteria—which may vary by outcome. While the steep increase in events after study entry could in part reflect underestimation of pre-existing dis- ease, the observed pattern of events was expected given that individuals enter the study at a key clinical event, signifying more severe or complex disease, rather than in a healthy state. To fur- ther mitigate this risk, we present sensitivity analyses delaying follow up to 60 days after study entry, while acknowledging that our primary findings are substantiated by other studies reporting high rates of rehospitalisation within 30 days following MI [63], reflecting conditions likely diagnosed shortly after MI due to screening for risk factors (e.g., diabetes) and sequelae (e.g., heart failure) or due to complications (e.g., bleeding). We further ensured high ascertain- ment of preexisting disease by making use of a look-back period in excess of 10 years and cap- turing conditions via comprehensive code lists. We acknowledge that, for some individuals, the look-back period may have been limited (e.g., due to immigration), which we could not quantify. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 16 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England Case ascertainment of MI within HES is known to be high; indeed, individuals with long- term conditions as well as MI are more likely to be captured by HES than by the UK’s Myocar- dial Ischaemia National Audit Project (MINAP) [47]. We acknowledge that case ascertain- ment and changes in coding practices may vary by health outcome and that reliance on ICD coding alone may have led to under reporting of some conditions such as dementia and depression. While we were unable to account for this directly, confounding of temporal changes in ascertainment and coding practices is partially captured by inclusion of calendar year. Furthermore, high sensitivity and specificity of comorbidity recording have been reported for HES, in particular for diabetes (97.7% sensitivity, 96.1% specificity [64]). While dementia diagnoses are delayed by approximately 1.6 years in HES versus primary care, case ascertainment is high (85%) [65]. We did not account for severity of hospitalisation for indi- viduals without MI and were unable to distinguish between subtypes of MI; however, we include the full range of admissions without restriction to less severe disease, and specific MI subtype coding criteria were implemented towards the end of our study period, making future stratification possible [66]. We acknowledge the likely under reporting of lifestyle-related risk factors within hospitalisation records, and baseline cardiovascular risk data, which were restricted to within the study period only. These data were therefore provided in summary for- mat only and represent only the extremes of the population distribution. HES does not capture information with regard to secondary preventative medication, and there is currently no national individual-level hospital prescribing database for England for linkage without consent for research [45]. While we could not quantify the impact of medication on post-MI incidence of health outcomes, we do adjust for invasive management of MI. Finally, we acknowledge that the largest proportion of data relate to outcomes within 1 year of MI due to a drop-off in numbers at risk over follow up, but note that there were sufficient data to provide statistically robust estimates of long-term outcomes (>110,000 individuals and 2,400 individuals per out- come at 5 and 9 years, respectively). The use of nationally representative health record data provided a depth of analyses allow- ing risk stratification into clinically relevant groups, for many outcomes. While high-quality system wide healthcare databases are increasingly accessible, major barriers remain in (1) timely data access; (2) access to scalable computational facilities to handle size, complexity, and security standards; and (3) stringent data minimisation, which limited the scope to condi- tions identified a priori, rather than the full breadth of possible outcomes. Although clearly a public health focus must be the prevention of MI, evidence from our study has implications for clinical care in Cardiology and beyond. We evidence the excess inci- dence of conditions that are targeted through current secondary preventative guidelines (heart failure), conditions not currently directly included in secondary preventative guidelines (chronic renal failure and cerebrovascular disease), conditions that would benefit from early detection for improved outcomes (severe bleeding and atrial fibrillation), and conditions that have a significant impact on quality of life (depression). While we did not assess impact of sec- ondary preventative medication on outcomes directly, implications of our research in context of previous studies indicate that (1) improved secondary preventative medication for younger individuals in the most socioeconomically deprived group may tackle the high incidence of post-MI heart failure observed among this demographic [53]; and (2) a reduction in the long- term high incidence of major bleeding complications may be achieved through shortened DAPT regimes [54] and longer-term surveillance following MI. High incidences of chronic renal failure, cerebrovascular disease, and peripheral arterial disease following MI suggest opportunity for intensified secondary prevention of shared modifiable risk factors and enhanced post-MI health surveillance to mitigate against increased healthcare usage and pre- mature death. Moreover, screening interventions for the most at risk of post-MI depression PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 17 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England (including individuals who are younger, female, or socioeconomically deprived) should be considered. While the incidence of vascular dementia contributes only a small proportion of the post-MI disease burden, causal links and opportunities for secondary prevention between MI and vascular dementia warrant further study, given the excess incidence observed. When extrapolated to the 1.4 million survivors of MI in the UK in 2022, our study implies an estimated 414,400 new diagnoses of heart failure, 312,200 atrial fibrillation, 175,000 cere- brovascular disease, peripheral vascular disease, 266,000 severe bleeding, 380,800 renal failure, 238,000 diabetes mellitus, 109,200 dementia, 124,600 depression, and 189,000 cancer in the next decade, in addition to 99,400 individuals with subsequent MI and up to 529,200 dying within 9 years of first MI. Our sociodemographic stratified risk charts provide a crucial step in translating future health outcomes to support informed and shared healthcare decision-making. Effective com- munication of the likely course of disease and risk of adverse long-term outcomes between individuals and healthcare professionals promote positive lifestyle changes, facilitate treatment compliance, and improve patient understanding and quality of life [9,10]. Informed by PPIE, our graphics have been designed in an easy-to-use format via a publicly accessible website, pro- viding healthcare professionals and patients with a tool to discuss relevant demographic-spe- cific risk to direct appropriate care. Moreover, these data have the potential to underpin public health policies aimed at reducing the health inequalities observed and reducing the significant ongoing burden of disease for the increasing number of survivors of MI—some of whom have many potential years of life left. Future work should focus on stratifying risk by specific MI phenotype and identifying modifiable risk factors associated with the increased burden of health outcomes evidenced. In conclusion, individuals frequently accrue major comorbidities across a range of body systems in the decade following MI—with 3 in 10 developing heart failure or renal failure and 4 in 10 dying. Health inequalities relating to age, sex, and socioeconomic deprivation are clearly evidenced—socioeconomically deprived individuals are more likely to have MI earlier in their life course and experience an increased burden of post-MI health outcomes at an ear- lier age. Improved post-MI preventative strategies, encompassing enhanced surveillance and detection, are required to tackle the high incidences of heart failure, atrial fibrillation, cerebro- vascular disease, and renal failure observed in this population. Finally, sociodemographic strat- ified risk charts should be used to inform decision-making about health and well-being for specific patient groups in the post-MI period and underpin public health policies aimed at reducing health inequalities. Supporting information S1 Checklist. REporting of studies Conducted using Observational Routinely collected Data (RECORD) Standard. (DOCX) S2 Checklist. CODE-EHR framework: Best practice checklist to report on the use of struc- tured electronic healthcare records in clinical research date of completion: 20 January 2023. Study name: Health outcomes after myocardial infarction: A population study of 56 mil- lion people in England. (DOCX) S1 Text. Review of the prior evidence. (DOCX) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 18 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England S2 Text. Sensitivity analyses methods. (DOCX) S3 Text. Hospital Episode Statistics (HES) data cleaning. (DOCX) S1 Table. Summary of the available evidence of post-MI new onset disease incidence, 1946–October 2023. ACEi/ARB, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers; ACS, acute coronary syndromes; AF, atrial fibrillation; CAD, coronary artery disease; CIF, cumulative incidence function; COPD, coronary obstructive pulmonary disease; CPRD, Clinical Practice Research Database; EHR, electronic healthcare record; HES, Hospital Episode Statistics; HF, heart failure; HFrEF, heart failure with reduced ejection frac- tion; HR, hazard ratio; IRR, incidence rate ratio; IQR, interquartile range; KM, Kaplan–Meier; MI, myocardial infarction; MINAP, Myocardial Ischaemia National Audit Project; NSETMI, non ST-elevation myocardial infarction; PCI, percutaneous coronary intervention; PH, pro- portional hazards; PPCI, primary percutaneous coronary intervention; PTSD, posttraumatic stress disorder; SCAD, spontaneous coronary artery dissection; SD, standard deviation; STEMI, ST-elevation myocardial infarction; USA, United States of America; vs., versus. (DOCX) S2 Table. Code definitions for health outcomes, vascular risk factors, and invasive coro- nary strategy for MI according to the International Classification of Diseases (ICD10) and operating Procedure Code Supplement Classification of Interventions and Procedures (OPCS4.5). ICD10 and OPCS Coding lists adapted from published: https://www. caliberresearch.org/portal. NEC, not elsewhere classifiable; NOC, not otherwise specified. (DOCX) S3 Table. Crude rate of post-MI disease per 1,000 person years (pyrs) for those with MI compared with a matched control groupa in England, 2008–2017. aIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. bCases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service. (DOCX) S4 Table. Crude rate of post-MI disease occurring more than 2 months following study entry (sensitivity analyses) per 1,000 person years (pyrs) for those with MI compared with a matched-control groupa in England, 2008–2017. aIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk- set matching approach. bCases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service; SD, standard deviation. (DOCX) S5 Table. Cumulative incidencea of all-cause mortality and first hospitalisation for all out- comes following MI treating death without event as a competing risk compared with matched controlsb at 60 days, 1 year, 5 years, and 9 years of follow-up in England, 2008– 2017. aCumulative incidences are presented as percentage of cases expected to develop each outcome by each respective time point and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score—treating death without outcome as PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 19 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England a competing risk. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cCases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service; SD, standard deviation. (DOCX) S6 Table. Cumulative incidencea of all-cause mortality and first hospitalisation for all out- comes at least 2 months following MI (sensitivity analyses) treating death without outcome as a competing risk, compared with matched controlsb at 1 year, 5 years, and 9 years of fol- low-up in England, 2008–2017. aCumulative incidences are presented as percentage of cases expected to develop each outcome by each respective time point and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score—treating death without outcome as a competing risk. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cCases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates of subsequent MI for this cohort were not included. CI, confidence interval; MI, myocardial infarction; NA, not applicable; NHS, National Health Service; SD, standard deviation. (DOCX) S7 Table. Numbers at risk for the cumulative incidence analysis over time for post-MI out- comes in England, 2008–2017. aNumbers at risk at 1, 5, and 9 years follow-up are equal for those in the main analyses and the sensitivity analyses by design. MI, myocardial infarction. (DOCX) S8 Table. Excess ratea of all-cause mortality and first hospitalisation for all outcomes at least 2 months following MI (sensitivity analyses) over and above matched controlsb in England, 2008–2017. aExcess rate is presented as the aHR for each outcome comparing matched controls with individuals with MI and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score—treating death without out- come as a competing risk. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cCases within the matched control cohort who went on to develop MI were censored at time of first MI; therefore, estimates comparing the HR of subsequent MI between the MI cohort and matched controls are not applicable. aHR, adjusted hazard ratio; CI, confidence interval; MI, myocardial infarction; NA, not applicable. (DOCX) S1 Fig. Adjusted absolute riska over continuous time of subsequent MI, heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, and severe bleeding occur- ring at least 2 months following index MI (sensitivity analyses) compared with matched controlsb in England. aCalculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline func- tions, sex, calendar year, and deprivation score. bIndividuals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. CIF, cumulative incidence function; MI, myocardial infarction; NHS, National Health Service. (TIF) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 20 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England S2 Fig. Adjusted absolute riska over continuous time of renal failure, diabetes mellitus, dementia, depression, and cancer occurring at least 2 months following index MI (sensitiv- ity analyses) compared with matched controlsb in England. aCalculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlin- ear age using restricted cubic spline functions, sex, calendar year, and deprivation score. bIndi- viduals were matched according to single year of age, sex, month and year of hospital admission, and NHS Trust using a 5:1 risk-set matching approach. cIncludes all cancer types (ICD10 codes C00–C97), i.e., this category is not restricted to the sum of breast, prostate, lung, and colorectal cancer). CIF, cumulative incidence function; MI, myocardial infarction; NHS, National Health Service. (TIF) S3 Fig. Absolute riska of 11 non-fatal health outcomes occurring at least 2 months follow- ing MI at 1 year and 5 years of follow-up (sensitivity analyses) as well as by age group, sex, and deprivationb in England (N = 433,361). aCalculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, deprivation score, and receipt of invasive coronary angiography, percutaneous coronary intervention, or coronary artery bypass graft. bDeprivation is measured using the IMD where 1 indicates those in the least deprived quintile, and 5 indicates those in the most deprived quintile. Interactive version of these data are pro- vided (https://multimorbidity-research-leeds.github.io/research-resources). CIF, cumulative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction. (TIF) S4 Fig. Absolute riska of 11 non-fatal health outcomes and all-cause mortality at 60 days, 1 year, and 5 years of follow-up by age group, sex, and deprivationb among matched individ- uals without MI in England (N = 2,001,310). aCalculated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score. bDeprivation is measured using the IMD where 1 indicates those in the least deprived fifth, and 5 indicates those in the most deprived fifth. Interactive version of these data are provided (https:// multimorbidity-research-leeds.github.io/research-resources). CIF, cumulative incidence func- tion; IMD, Index of Multiple Deprivation; MI, myocardial infarction. (TIF) S5 Fig. Absolute riska of 11 non-fatal health outcomes occurring at least 2 months following study entry (sensitivity analyses) at 1 year and 5 years of follow-up and by age group, sex, and deprivationb among matched individuals without MI in England (N = 2,001,310). aCal- culated according to the standardised CIF, treating death without outcome as a competing risk and adjusted for nonlinear age using restricted cubic spline functions, sex, calendar year, and deprivation score. bDeprivation is measured using the IMD where 1 indicates those in the least deprived fifth, and 5 indicates those in the most deprived fifth. Interactive version of these data are provided (https://multimorbidity-research-leeds.github.io/research-resources). CIF, cumu- lative incidence function; IMD, Index of Multiple Deprivation; MI, myocardial infarction. (TIF) Acknowledgments We acknowledge the work by NHS Digital in providing access to these data for the purposes of our study and the staff in the Leeds Institute for Data Analytics DAT team, University of Leeds PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004343 February 15, 2024 21 / 26 PLOS MEDICINEHealth outcomes after myocardial infarction among the population of England involved in the data management and the secure and safe storage of data for this project. We would like to acknowledge the work by Dr Charlotte Sturley in enhancing our PPIE activities and establishing an ongoing PPIE group for individuals with cardiovascular disease and multi- ple long-term conditions to support future research, and that of Heart Voices for their help in disseminating our PPIE opportunities. Finally, we would like to acknowledge all the patients and carers who have taken the time to share their experiences of post-heart attack care for their valuable contributions to setting the direction and design of the research, their feedback, and their ongoing involvement in dissemination of the work. Author Contributions Conceptualization: Marlous Hall, Paul C. Lambert, Harry Hemingway, Chris P. Gale. Data curation: Marlous Hall. Formal analysis: Marlous Hall, Lesley Smith, Jianhua Wu, Chris Hayward, Jonathan A. Batty. Funding acquisition: Marlous Hall. Investigation: Marlous Hall. Methodology: Marlous Hall, Lesley Smith, Jianhua Wu, Jonathan A. Batty, Paul C. Lambert, Harry Hemingway. Project administration: Marlous Hall. Software: Chris Hayward. Supervision: Jianhua Wu, Paul C. Lambert, Harry Hemingway, Chris P. Gale. Validation: Marlous Hall. Visualization: Marlous Hall, Chris Hayward, Jonathan A. Batty. Writing – original draft: Marlous Hall, Lesley Smith. 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10.1371_journal.pmed.1004362	RESEARCH ARTICLE Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses 1☯, Wenqiang Zhang1☯, Li ZhangID Huijie CuiID Peijing Yan1, Mingshuang Tang1, Chao YangID Chenghan XiaoID Yuqin YaoID 4, Jiayuan LiID 2, Yanqiu Zou1, Yunjie Liu1, Ling ZhangID 3, Yanfang YangID 1, 1, Zhenmi Liu2, Chunxia Yang1, Xia Jiang1,5,6, Ben ZhangID 7 1☯, Yang Qu1, Zhengxing Xu1, Zhixin Tan1, 1, Yutong Wang1, Lin Chen1, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China, 2 Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China, 3 Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China, 4 Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China, 5 Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China, 6 Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden, 7 Hainan General Hospital and Hainan Affiliated Hospital, Hainan Medical University, Haikou, China; West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China OPEN ACCESS Citation: Cui H, Zhang W, Zhang L, Qu Y, Xu Z, Tan Z, et al. (2024) Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses. PLoS Med 21(3): e1004362. https://doi.org/ 10.1371/journal.pmed.1004362 ☯ These authors contributed equally to this work. * xiajiang@scu.edu.cn (XJ); benzhang@vip.163.com (BZ) Abstract Academic Editor: Aadel A Chaudhuri, Washington University in St Louis, UNITED STATES Background Received: June 19, 2023 Accepted: February 16, 2024 Published: March 15, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pmed.1004362 Copyright: © 2024 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting information files. The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifi- able factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses. Methods and findings We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analy- sis was repeated using the random-effects inverse-variance model with DerSimonian— Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subse- quent evidence grading criteria for significant associations in meta-analyses contained PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 1 / 33 PLOS MEDICINEFunding: The National Natural Science Foundation of China: U22A20359, 81874283, and 81673255, granted to BZ; the National Key R&D Program of China: 2022YFC3600604, granted to BZ; the Recruitment Program for Young Professionals of China, the Promotion Plan for Basic Medical Sciences and the Development Plan for Cutting- Edge Disciplines, Sichuan University, and other Projects from West China School of Public Health and West China Fourth Hospital, Sichuan University, granted to BZ. The National Natural Science Foundation of China for young scholars: 82204170, granted to XJ; the National Natural Science Foundation of China for young outstanding scholars (overseas), granted to XJ. The sponsors or funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: AASV, anti-neutrophil cytoplasm antibody associated vasculitide; ACEI, angiotensin converting enzyme inhibitor; BMI, body mass index; BPH, benign prostatic hyperplasia; CCB, calcium-channel blocker; CRP, C-reactive protein; DHA, docosahexaenoic acid; FA, favorable adiposity; FGF, fibroblast growth factor; HDL, high- density lipoprotein; HR, hazard ratio; IQR, interquartile range; IRR, incidence rate ratio; IV, instrumental variable; LCI, lower confidence interval; LDL, low-density lipoprotein; LTL, leukocyte telomere length; MR, mendelian randomization; mTOR, mammalian target of rapamycin; NSAID, nonsteroidal anti inflammatory drug; OR, odds ratio; PA, physical activity; PI, prediction interval; PSA, prostate-specific antigen; RR, risk ratio; SD, standard deviation; SLE, systemic lupus erythematosus; T2D, type 2 diabetes; UC, ulcerative colitis; UCI, upper confidence interval; UFA, unfavorable adiposity. Risk factors for prostate cancer sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogene- ity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, sug- gestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same expo- sure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and envi- ronmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Meth- odological limitation is that the evidence grading criteria could be expanded by considering more indices. Conclusions In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analy- sis and genetically estimated causality by MR analyses. In the absence of convincing over- lapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking. Author summary Why was this study done? • The incidence of prostate cancer is increasing with the growing trend of aging globally. • Effective preventions and interventions for prostate cancer require better understand- ings of its etiology. • The well-known risk factors for prostate cancer are age, ethnicity, and family history, but few modifiable factors have been firmly established. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 2 / 33 PLOS MEDICINERisk factors for prostate cancer What did the researchers do and find? • Our study extensively collected, evaluated, and compared the current observational and genetic evidence for various factors modifying the risk of prostate cancer based on meta-analyses and mendelian randomization (MR) studies. • Totally 123 observational associations (45 significant and 78 null) from 92 meta-analy- ses and 145 causal associations (55 significant and 90 null) from 64 MR studies were identified and categorized into lifestyle; diet and nutrition; anthropometric indices; bio- markers; clinical variables, diseases, and treatments; and environmental factors. • Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. • Consistent significant associations between meta-analysis and MR studies were found for physical activity, height, and smoking, which however were not robust. What do these findings mean? • Most included cohort studies were conducted in developed western countries, and hence the findings in this study are limited for mainly European descendants. • The comparison between observational associations by meta-analysis and genetically estimated causality by MR analyses does not provide robust evidence due to the lack of overlapping associations and high-quality evidence, especially in MR studies. • Evidence grading criteria for meta-analyses could be further improved by adding more indices such as magnitude of effect size and different levels of sample size. Introduction Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer-related death among men, and its incidence is increasing in older males with the growing trend of aging globally [1]. Effective early preventions and interventions for prostate cancer require bet- ter understandings to its etiology which represents a complex interplay between genetic sus- ceptibility and micro- and macro-environmental factors [2]. Observational studies have investigated and identified a plethora of factors associated with the risk of prostate cancer [3–5]. The well-known risk factors for prostate cancer are age, ethnicity, and family history, but few modifiable factors have been firmly established. Umbrella review aggregates evidence from published meta-analysis and structurally sum- marizes evidence strength to provide an inclusive overview on a given topic via a comprehen- sive assessment of sample size, strength and precision of the association, heterogeneity, and biases [6–8]. The earliest umbrella review on prostate cancer, to our knowledge, was published in 2016, focusing on diet, body size, and physical activity [9]. Other existing umbrella reviews, involving prostate cancer as one of the many health outcomes, were specifically limited to die- tary factors including folate [10], fish and ω-3 fatty acids [11], tomato and lycopene [12], and PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 3 / 33 PLOS MEDICINERisk factors for prostate cancer whole grain consumption [13]. Several important factors including lifestyle; environmental exposures; and preexisting clinical variables, diseases, and treatments, are often overlooked by existing umbrella reviews. In addition to observational studies, mendelian randomization (MR) studies leverages genetic variations as proxies for exposures to obtain unbiased effect estimates, minimizing the influence of reverse causation or confounding which is often found in epidemiological settings [14]. MR studies have been extensively conducted to explore potential causal risk factors for prostate cancer [15–18], part of which have been summarized and assessed in the systematic review of MR studies by Markozannes and colleagues [19], yet needing update by including newly published MR studies. Therefore, an updated comprehensive umbrella review on prostate cancer is needed. To ensure the evidence quality from observational studies, meta-analyses of prospective observa- tional studies are preferred as they clearly indicate temporal relationship between exposure and outcome and are thus less biased than retrospective studies [20]. Similarly, MR studies provide unbiased evidence because the genotypes are defined at conception bases on the ran- dom assortment of genes and thus not influenced by conventional confounders [21]. It could be beneficial to compare epidemiological studies informing association and MR studies sug- gesting causality and investigate their mutual corroboration or discrepancy, to gain mutually complementary insights on understanding the risk of prostate cancer. Therefore, the objective of this umbrella review is to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and MR studies, to bet- ter understand the etiology of prostate cancer. Methods Literature search and eligibility criteria This study is reported as per the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) guideline (S1 PRISMA Checklist). No preregistered study protocol is available. This umbrella review was initially planned to focus on evidence from observational studies, so the initial search was conducted on January 10, 2022 only for meta-analyses. An additional search for MR studies was later conducted on July 6, 2022, to include the important genetic evidence from MR studies. Upon request, the literature search for meta-analyses and MR studies was updated on September 9, 2023. Systematic literature search was conducted in PubMed, Embase, and Web of Science. A predefined comprehensive search strategy (S1 Text) was used to search all meta-analyses and MR studies evaluating various factors associated with prostate cancer risk from the inception of database to September 9, 2023. We also searched Cochrane Database of Systematic Reviews as a complementary source of meta-analyses. References of retrieved articles were then reviewed to identify additional studies. Following PRISMA [22], 2 researchers (HC and YQ) independently searched and screened related literature. The titles, abstracts, keywords, and full text of each study were reviewed for inclusion, and any ambiguity was resolved through discus- sion. Articles were included if they met the following inclusion criteria: (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further calculation. The exclusion criteria were as follows: (1) meta-analyses including only retrospective studies; (2) narrative reviews or reviews without data synthesis results or failing to provide sufficient data for calculation; and (3) the outcome of interest was the diagnosis, treatment, or prognosis. The inclusion criteria for MR studies were similar but relatively concise: evaluating the factors of any category associated with PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 4 / 33 PLOS MEDICINERisk factors for prostate cancer prostate cancer incidence using mendelian randomized analysis methods and providing effect estimates. Overlapping and outdated meta-analyses For the same exposure factor evaluated by more than one meta-analysis published in different years, we preferentially selected the most recent or updated one including the largest number of studies (cohorts or datasets) with the maximum of participants to represent the best avail- able evidence. The overlapping and outdated meta-analyses which were published earlier and contained fewer cohorts or datasets were thus excluded compared with selected one. For MR studies, we also selected the one which represented the best available evidence so far, taking into consideration the publication year, data source of both exposure and outcome, sample size, the proportion of variance (r2) explained by selected instrumental variables (IVs), and the study quality comprehensively. The selection details of meta-analyses and MR studies were presented in S1 and S2 Tables, respectively. Data extraction and synthesis A statistical analysis protocol in detail for this umbrella review was provided (S2 Text). In brief, in each included meta-analysis, qualified individual studies (cohort, case-cohort, or nested case-control study where exposure precedes the outcome) were selected, and relevant information were collected based on a predefined template: first author, publication year, study design, number of studies included, number of cases/population, ethnicity, exposure fac- tors, outcomes of prostate cancer, comparisons, and effect estimates of any type, i.e., maxi- mally adjusted hazard ratio (HR)/incidence rate ratio (IRR)/odds ratio (OR)/risk ratio (RR) with 95% confidence intervals, i.e., lower confidence interval (LCI) and upper confidence interval (UCI). Data extraction was conducted by 2 researchers (HC and YQ) separately and cross-check was performed to ensure correctness. Then, we repeated each meta-analysis based on extracted effect estimates, LCI, and UCI using the random-effects inverse-variance model with DerSimonian—Laird method. Heterogeneity between studies included in meta-analyses was represented using I square (I2) value and Cochrane’s Q P value [23]. I2 � 50% was consid- ered as no or small heterogeneity, and I2 > 50% large heterogeneity. Publication bias was eval- uated by using the Egger regression asymmetry test (significance threshold, P < 0.10) [24]. If the Egger’s P value was less than 0.1, we assumed the existence of publication bias. The 95% prediction interval (PI) estimated the middle 95% area of the predictive distribution and showed the range of true effects in future studies [25], reflecting the variation in the true effects across study settings. All statistical analyses were conducted with the use of Stata, version 14.0 (StataCorp), and R, version 3.3.0 (R Foundation for Statistical Computing). From MR studies, we extracted key information of exposure, outcome, sample size, number of IVs, the variance (r2) explained by IV, F statistics, and maximally adjusted effect estimates with 95% CI using the main analysis method, and no further calculation was needed for MR studies in this umbrella review. Quality assessment for included studies The online 16-item AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) checklist was used to assess methodological quality [26]. AMSTAR-2 considers the quality of the search, study inclusion and exclusion, description of individual studies, assessment of publication bias, heterogeneity, use of appropriate statistical methods, assessment of risk of bias in individ- ual studies, and reporting of sources of funding and conflicts of interest. The items were scored PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 5 / 33 PLOS MEDICINERisk factors for prostate cancer as No (0 point), Partial yes (0.5 point), or Yes (1 point). Both the total scores and critical item scores were calculated in our umbrella review [27]. For MR studies, quality assessment was performed with reference to the recently published STROBE-MR Statement (Strengthening the Reporting of Observational Studies in Epidemiol- ogy Using Mendelian Randomization) [28]. Briefly, the STROBE-MR checklist consists of 20 items that are grouped into sections Title and Abstract (item 1), Introduction (items 2 to 3), Methods (items 4 to 9), Results (items 10 to 13), Discussion (items 14 to 17), and Other Infor- mation (items 18 to 20). The checklist details were described elsewhere [28]. STROBE-MR puts emphasis on the transparent reporting of model assumptions assessment and sensitivity analyses, which also stands as a primary evaluation criterion in our review. Mendelian ran- domization assumptions regarding the reliability of IV (assumption 1) and absence of pleiotro- pic effects (assumption 2) were evaluated. Two researchers (HC and ZT) rated the methodological quality of meta-analyses and reporting quality of MR studies and evaluated the assumptions of MR studies. In the case of disagreements, a decision was reached by consulting a third investigator (WZ). Evidence grading criteria for associations from meta-analyses As shown in Table 1, the evidence credibility of statistically significant associations with pros- tate cancer was graded into 4 levels (convincing, highly suggestive, suggestive, and weak) based on precision of statistical significance, sample size, 95% PI, heterogeneity, and publica- tion bias, with references to existing umbrella reviews [7,11,29]. Specifically, convincing evi- dence, as the highest level with the most stringent threshold, required summary estimate P value <0.000001, large sample size (number of prostate cancer patients >1,000), no or small heterogeneity (I2 � 50%), no publication bias (Egger’s P � 0.10), the largest component study (i.e., with the largest weight in meta-analysis) reporting directionally consistent with the over- all estimate statistically significant association, and 95% PI excluding the null. Highly Table 1. Credibility assessment criteria for significant associations derived from meta-analyses of prospective observational studies and MR studies. Evidence grading for meta- analyses Convincing (I) Highly suggestive (II) Suggestive (III) Weak (IV) Evidence grading for MR studies Robust (I) Probable (II) Suggestive (III) Insufficient (IV) Detailed description Significant associations with P < 0.000001; number of cases >1,000; the study with the largest weight reporting nominally significant results in the same direction as the overall estimate; 95% prediction interval excluding the null; no or small heterogeneity (I2 � 50%); no evidence of publication bias (Egger’s P value � 0.10). Associations with P < 0.001; number of cases >1,000; no or small heterogeneity (I2 � 50%); no evidence of publication bias (Egger’s P value � 0.10). Associations with P < 0.05; number of cases >1,000; the presence of large heterogeneity (I2 > 50%) or evidence of publication bias (Egger’s P value < 0.10). Associations with P < 0.05; number of cases <1,000; the presence of large heterogeneity (I2 > 50%) and evidence of publication bias (Egger’s P value < 0.10). Detailed description Significant associations with P < 0.05 across all analysis methods with consistent direction. Significant associations with P < 0.05 in at least 1 analysis method with consistent direction. Significant associations with P < 0.05 in at least 1 analysis method with inconsistent directions. Significant associations with P < 0.05 based on 1 single analysis method (without sensitivity analysis). https://doi.org/10.1371/journal.pmed.1004362.t001 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 6 / 33 PLOS MEDICINERisk factors for prostate cancer suggestive evidence, with the largest component study requirement removed, required a loos- ened effect P value threshold of <0.001, large sample size (number of prostate cancer patients >1,000), no or small heterogeneity (I2 � 50%), no evidence of publication bias (Egger’s P � 0.10), and 95% PI excluding the null. Suggestive evidence required only statistical signifi- cance (P < 0.05), large sample size (number of prostate cancer patients >1,000), and allowed for the existence of either large heterogeneity (I2 > 50%) or publication bias (Egger’s P < 0.10). Lastly, if one association was reported based on a case number less than 1,000, it would be defined as weak evidence due to insufficient statistical power. Also, associations showing the presence of both large heterogeneity and publication bias (I2 > 50% and Egger’s P < 0.10) would be graded as weak. Null associations were not included for evidence evalua- tion in this present umbrella review. Evidence grading criteria for causal associations from MR studies We adopted and modified the evidence grading criteria categorized into robust, probable, sug- gestive, and insufficient proposed in the recently published MR review by Markozannes and colleagues [19]. The modified criteria excluded null associations and redefined the level of “insufficient” evidence. Briefly, robust evidence for causality was assigned based on nominally significant P value and directional concordant effect across all methods performed; probable evidence was assigned based on nominally significant P value in at least 1 method (main or sensitivity analyses) and concordant effect direction among all methods performed; suggestive evidence was assigned when at least 1 method had a nominally significant P value but the direction of the effect estimates differed between methods; insufficient evidence was assigned for significant associations based only on 1 main analysis while no sensitivity analysis was available (Table 1). Results Characteristics of included meta-analyses and summary on evidence grading The process of literature identification and selection as well as updated work was recorded in detail in Fig 1. The initial search on January 10, 2022 yielded a total of 6,349 articles, and approximately 360 meta-analyses containing overlapped ones reporting on the same expo- sure published in different years were identified after excluding unrelated or duplicated arti- cles. Then, 72 meta-analyses were selected for initial data synthesis. Updated search was conducted on September 9, 2023 upon request, yielding 1,015 newly published literature after the initial search, and 51 articles were included after excluding unrelated or duplicated articles. Then, 25 meta-analyses were selected for updated data synthesis, 5 of which replaced the previous ones. The selection of included meta-analyses was shown in S1 Table. Finally, in total 92 meta-analyses reporting 123 observational associations (Fig 2A) were included, cate- gorized into 6 major categories: lifestyle [3,4,30–40] (N = 17); diet and nutrition [41–69] (N = 44); anthropometric indices [70–74] (N = 5); biomarkers [48,61,75–80] (N = 12); clini- cal variables, diseases, and treatments [81–112] (N = 39); and environmental factors [38,113–117] (N = 6). Note that the total number of associations was 123 while there were totally 122 factors because both the inverse association of finasteride with total prostate can- cer and the positive association of finasteride with advanced prostate cancer were counted as 2 distinct associations. The median (interquartile range, IQR) of studies (datasets) included in meta-analyses was 7 (4.25, 13), ranging from 2 to 35. The median (IQR) of case numbers in meta-analysis was PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 7 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 1. Flowchart of literature search, inclusion, and results. MR, mendelian randomization. https://doi.org/10.1371/journal.pmed.1004362.g001 5,653 (2,735, 15,254), ranged from 20 to 118,077. The study design contained mostly cohort studies (N = 1,342, 95.7% of 1,403), with a small portion of nested case-control studies (N = 50, 3.6% of 1,403), case-cohort studies (N = 4, 0.2% of 1,403), and randomized controlled trials (N = 7, 0.5% of 1,403). In total 90 eligible meta-analyses were assessed using AMSTAR-2 tool. The median (IQR) of AMSTAR-2 total score was 13.5 (13, 14) points, and that for AMSTAR-2 critical item score was 6 (5.5, 6) points. For the 7 AMSTAR-2 critical domains, 29% (26/90) of the included meta- analyses established a priori a protocol for the review, 100% (90/90) performed a comprehen- sive literature search, 71% (64/90) provided a list of excluded studies with justification, 93% (84/90) used a satisfactory technique for assessing the risk of bias in individual studies, 100% (90/90) used the appropriate model for meta-analysis, 74% (67/90) discussed the impact of risk of bias in individual studies in the interpretation of the results of the review, and 87% (78/90) performed graphical or statistical tests for publication bias and discussed the likelihood and magnitude of impact of publication bias (Fig 3). Each AMSTAR-2 domain judgment for each outcome is available in S3 Table. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 8 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 2. Overall presentation of associations with the risk of prostate cancer. (A) Observational associations from meta-analyses (Meta). (B) Causal associations from MR studies. Numbers presented in the graphs are OR with 95% confidence intervals. Different colors indicate different categories; ¶ represents significant associations (P < 0.05). Metrics with * denoting the outcome was advanced, aggressive, high-grade, or lethal prostate cancer, and metrics with # denoting the outcome was non-advanced, non-aggressive, or localized prostate cancer in graph (A). Metrics with * denoting the outcome of MR studies was aggressive prostate cancer, and metrics with # denoting the outcome of MR studies was early-onset prostate cancer in graph (B). Note that the null associations of biomarkers (N = 58) in MR studies are not presented here considering the graph size. Abbreviations in meta-analyses: PA, physical activity; DHA, docosahexaenoic acids; EPA, eicosapentaenoic; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; T2D, type 2 diabetes; BPH, benign prostate hyperplasia; HIV, human immunodeficiency virus; AIDS, acquired immune deficiency syndrome; CD, Crohn’s disease; UC, ulcerative colitis; AASVs, anti-neutrophil cytoplasm antibody associated vasculitides; ACEI, angiotensin converting enzyme inhibitors; NSAID, nonsteroidal anti-inflammatory drug; CCB, calcium channel blockers. Abbreviations in MR studies: PA, physical activity; BMI, body mass index; UFA, unfavorable adiposity; FA, favorable adiposity; LTL, leukocyte telomere length; CCL2, Chemokine (C-C motif) ligand 2; CCL4, Chemokine (C-C motif) ligand 4; TG, triglyceride; IGF, insulin-like growth factor; LDL, low-density lipoprotein; HGF, hepatocyte growth factor; IL-1ra, IL-1 receptor antagonist; MUFAs, monounsaturated fatty acids; TOR1AIP1, Torsin-1A-interacting protein 1; IL-6, interleukin-6; ALT, alanine aminotransferase; IDO 1, Indoleamine 2,3-dioxygenase 1; PDGF-bb, platelet-derived growth factor BB; SCGF-β, stem cell growth factor-beta; TSH, thyroid-stimulating hormone; β-NGF, beta nerve growth factor; M.VLDL.TG, Triglycerides in medium VLDL; MSP, microseminoprotein-beta; CCB, calcium channel blockers; PCSK9, proprotein convertase subtilisin/kexin type 9; PPARG, peroxisome proliferator activated receptor γ; ABCC8, ATP binding cassette subfamily C member 8; GLP1R, glucagon-like peptide 1 receptor; ACE, angiotensin-converting enzyme; ADRB1, β-1 adrenergic receptor; NCC, sodium-chloride symporter; SBP, systolic blood pressure; DBP, diastolic blood pressure; MDD, major depressive disorder; SLE, systemic lupus erythematosus; IBD, inflammatory bowel disease; CD, Crohn’s disease; UC, ulcerative colitis; T2D, type 2 diabetes; HMG-CoA, 3-hydroxy-3-methylglutaryl coenzyme A; NPC1L1, Niemann-Pick C1-Like 1. MR, mendelian randomization; OR, odds ratio. https://doi.org/10.1371/journal.pmed.1004362.g002 In total 45 (of 123) significant associations (S1 Fig) were derived from 43 meta-analyses and subsequently graded, and the evidence grading details were elaborated in S4 Table. Among them, P values for summary effects were mostly between 0.001 and 0.05 (N = 27, 60% of 45) and between 0.001 and 0.000001 (N = 12, 27% of 45), while only 6 associations (N = 6, 13% of 45) had P values less than 0.000001. Only 3 associations (N = 3, 6.7% of 45) had case number of less than 1,000. Eleven associations (N = 11, 24% of 45) had 95% PI excluding the null. Twenty-three (N = 23, 51% of 45) associations reported presence of large heterogeneity (I2 > 50%) and 9 (N = 9, 20% of 45) showed significant publication bias. In summary, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses (Fig 4 and S5 Table). The remaining 78 associations were null and not graded. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 9 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 3. Quality assessment of meta-analyses using AMSTAR-2. The total number of meta-analyses included was 90. The items were scored as No (0 point), Partial yes (0.5 point), or Yes (1 point). Abbreviation: PI(E)CO, population, intervention or exposure, comparator, outcome. https://doi.org/10.1371/journal.pmed.1004362.g003 Additionally, subgroup analyses of whites and non-whites population were performed for 11 significant associations from 11 meta-analyses (S6 Table and S2 Fig). As shown in S6 Table, the datasets, i.e., individual studies, in non-white populations were very limited compared to those in white populations. Five of the factors (firefighter, calcium, dairy products, height, and aspirin) were assessed in only 1 dataset in the corresponding meta-analysis [40,45,46,72,118] and 4 of the factors in 2 datasets [35,49,57,100]. There were 3 datasets available for ulcerative colitis (UC) [89] and 4 datasets for current smoking [30] in non-white populations. The sub- group analyses results showed the significant effects remained largely consistent for white pop- ulation, while in non-white population, only the inverse associations of smoking and finasteride remained significant. In addition, total dairy products showed stronger effects in non-white population, though supported by only 1 study [119]. Results of meta-analyses in categories In total 17 lifestyle factors (of 123 total associations) were identified, of which 6 were signifi- cantly associated with prostate cancer (Fig 4 and S5 Table). Except for occupational physical activity reducing prostate cancer risk (OR = 0.87, 95% CI: 0.80, 0.94) as highly suggestive evi- dence, the remaining significant associations, including smoking (current smoking versus non-smoker, OR = 0.74, 95% CI: 0.68, 0.80), coffee (highest versus lowest, OR = 0.91, 95% CI: 0.84, 0.98), number of female sexual partners (highest versus lowest, OR = 1.40, 95% CI: 1.14, 1.70), age at first intercourse (highest versus lowest, OR = 0.85, 95% CI: 0.74, 0.99, for high- grade prostate cancer), and firefighter (ever-employment as a career firefighter versus general population, OR = 1.21, 95% CI: 1.11, 1.33) were all graded as suggestive. Null associations were found between prostate cancer and the following lifestyle factors: sleep duration (long or short), sedentary behavior, overall physical activity, green tea, black tea, alcohol, ejaculation frequency, shiftwork, whole body vibration, farming, and police. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 10 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 4. Forest plot of evidence grading for significant associations with the risk of prostate cancer in categories from meta-analyses. The statistical test to determine the P value in meta-analyses was the random-effects inverse-variance model with DerSimonian—Laird method. The pooled effect estimate OR of each association is represented by the green colored square and 95% CI by the horizontal lines. Metrics with * denoting the outcome was high-grade, aggressive, or advanced prostate cancer. PA, physical activity; CRP, C-reactive protein; T2D, type 2 diabetes; BPH, benign prostate hyperplasia; UC, ulcerative colitis; HIV, human immunodeficiency virus; AIDS, acquired immune deficiency syndrome; OR, odds ratio. https://doi.org/10.1371/journal.pmed.1004362.g004 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 11 / 33 PLOS MEDICINERisk factors for prostate cancer A total of 44 diet and nutritional factors (of 123 total associations) were included in this review, and 10 of them showed significant associations with prostate cancer (Fig 4 and S5 Table). Highly suggestive evidence was observed for sweetened beverage (highest versus low- est, OR = 1.18, 95% CI: 1.07, 1.31) and circulating 25-hydroxyvitamin D (high versus low, OR = 1.18, 95% CI: 1.07, 1.30). Suggestive evidence was observed for daidzein (highest versus lowest, OR = 0.75, 95% CI: 0.60, 0.93), selenium (highest versus lowest, OR = 0.67, 95% CI: 0.45, 0.99), total flavonoids (highest versus lowest, OR = 1.11, 95% CI: 1.02, 1.22), and 4 other factors with only marginal effect including total dairy products (highest versus lowest, OR = 1.05, 95% CI: 1.00, 1.09), processed meat (highest versus lowest, OR = 1.06, 95% CI: 1.02, 1.10), total calcium intake (per 400 mg/d, RR = 1.02, 95% CI: 1.01, 1.04), and soy consumption (highest versus lowest, OR = 0.90, 95% CI: 0.82, 0.99). Egg consumption (increase of 5 eggs, OR = 1.48, 95% CI: 1.01, 2.15) increasing high-grade prostate cancer risk was graded as weak evidence mainly due to the small number of patients (less than 1,000). Null associations with prostate cancer (N = 34) were found for docosahexaenoic acids (DHAs), eicosapentaenoic (EPA), dietary omega-3, genistein, equol, dietary lycopene, dietary phosphorus intake, dietary linoleic acid, dietary inflammatory index, Mediterranean diet, dietary folate intake, dietary vitamin E intake, supplemental vitamin E intake, total protein intake, animal protein intake, plant protein intake, dairy protein intake, cruciferous vegetable intake, total fish, zinc, raw tomato, total tomato, total nut intake, fruit, vegetable, vegetarian, pescatarian, red meat, cheese, butter, yogurt, ice cream, dietary folate intake, and total fat intake. Five anthropometric indices (of 123 total associations) were included (Fig 4 and S5 Table) and 4 including birth weight (per kg increase, OR = 1.02, 95% CI: 1.00, 1.05), height (per 10 cm increase, OR = 1.09, 95% CI: 1.06, 1.12), and fat mass (highest versus lowest, OR = 0.87, 95% CI: 0.76, 1.00) were significantly associated with total prostate cancer risk and adult weight gain with high-risk prostate cancer (highest versus lowest, OR = 1.15, 95% CI: 1.01, 1.32), all with small effect and graded as suggestive evidence. Body mass index (BMI) was not found to associate with prostate cancer according to the selected meta-analysis [70]. In total 12 biomarkers (of 123 total associations) were included, with 5 showing significant association with prostate cancer (Fig 4 and S5 Table). Total cholesterol level was associated with increased risk of high-grade prostate cancer (highest versus lowest, OR = 1.26, 95% CI: 1.09, 1.46), which was highly suggestive. C-reactive protein (CRP) (highest versus lowest quar- tiles, OR = 1.09, 95% CI: 1.03, 1.15), serum folate (highest versus lowest, OR = 1.21, 95% CI: 1.05, 1.39), tissue level linoleic acid (highest versus lowest, OR = 0.81, 95% CI: 0.67, 0.97), and blood α-tocopherol level (highest versus lowest, OR = 0.79, 95% CI: 0.68, 0.91) showed signifi- cant association and were all graded as suggestive. The rest of included biomarkers blood γ- tocopherol level, high-density lipoprotein (HDL), low-density lipoprotein (LDL), leptin, adi- ponectin, serum C-peptide concentration, and white blood cell count exhibited null associa- tion with prostate cancer. Totally 39 clinical variables, diseases, and treatments (of 123 total associations) were included in this review, with almost half significantly associated with prostate cancer risk and mostly graded as suggestive evidence (Fig 4 and S5 Table). Among the 18 significant associa- tions, 11 factors were associated with higher prostate cancer risk including melanoma (patients versus non-patients, OR = 1.24, 95% CI: 1.18, 1.30), acne in adolescence (patients versus non- patients, OR = 1.51, 95% CI: 1.19, 1.93), infertility (infertile versus fertile, OR = 1.49, 95% CI: 1.06, 2.09), prostatitis (patients versus non-patients, OR = 1.45, 95% CI: 1.13, 1.87), benign prostatic hyperplasia (BPH) (patients versus non-patients, OR = 1.41, 95% CI: 1.00, 1.99), vasectomy (treated versus non-treated, OR = 1.09, 95% CI: 1.04, 1.13), and finasteride with high-grade prostate cancer (users versus non-users, OR = 2.10, 95% CI: 1.85, 2.38), graded as suggestive evidence, and first-degree family breast cancer (patients versus non-patients, PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 12 / 33 PLOS MEDICINERisk factors for prostate cancer OR = 1.19, 95% CI: 1.12, 1.26), UC (patients versus non-patients, OR = 1.22, 95% CI: 1.05, 1.41), primary Sjo¨gren’s syndrome (patients versus non-patients, OR = 1.51, 95% CI: 1.02, 2.22), and androgenic alopecia for high-grade prostate cancer (patients versus non-patients, OR = 1.42, 95% CI: 1.02, 1.99) as weak evidence. In addition, 7 clinical variables, diseases, and treatments were inversely associated with prostate cancer risk, including type 2 diabetes (T2D) (patients versus non-patients, OR = 0.84, 95% CI: 0.79, 0.90), Parkinson’s disease (patients ver- sus non-patients, OR = 0.78, 95% CI: 0.64, 0.96), schizophrenia (patients versus non-patients, OR = 0.59, 95% CI: 0.46, 0.74), regular use of aspirin (patients versus non-patients, OR = 0.93, 95% CI: 0.88, 0.97), digoxin (patients versus non-patients, OR = 0.89, 95% CI: 0.80, 0.99), and finasteride (users versus non-users, OR = 0.70, 95% CI: 0.51, 0.96) graded as suggestive evi- dence except HIV/AIDS (patients versus non-patients, OR = 0.74, 95% CI: 0.60, 0.91) as weak evidence. Interestingly, opposite associations found in finasteride, which decreased risk of total prostate cancer but increased risk of high-grade prostate cancer, both as suggestive evi- dence. The remaining clinical variables, diseases, and treatments showing no significant asso- ciations with prostate cancer were hepatitis C, periodontitis, asthma, Crohn’s disease, rheumatoid arthritis, anti-neutrophil cytoplasm antibody associated vasculitides (AASVs), hypertension, obstructive sleep apnea, subclinical hypothyroidism, bariatric surgery, multiple sclerosis, cholelithiasis, metformin, statins, angiotensin converting enzyme inhibitors (ACEI), calcium-channel blockers (CCB), thiazolidinediones, sulfonylureas, insulin, cardiac glycoside, and nonsteroidal anti-inflammatory drug (NSAID). Six environmental factors (of 123 total associations) were identified in this review, with 2 factors significantly associated with prostate cancer risk (Fig 4 and S5 Table). Asbestos (exposed versus unexposed, OR = 1.14, 95% CI: 1.07, 1.21) and cobalt (exposed versus unex- posed, OR = 1.08, 95% CI: 1.04, 1.14) increasing the risk of prostate cancer were graded as highly suggestive and suggestive evidence, respectively. Cadmium, pesticides, green space, and arsenic exposure had no significant association with prostate cancer. Characteristics of included MR studies and summary on evidence grading results As shown in Fig 1, the initial search on July 6, 2022 yielded a total of 174 articles, approxi- mately 86 MR studies containing overlapped ones reporting on the same exposure published in different years were identified after excluding unrelated or duplicated articles, and then 43 were initially selected. Updated search on September 9, 2023 yielded 74 newly published litera- ture, and 32 articles were included after excluding unrelated or duplicated articles. Then, 27 were selected for updated data synthesis, 6 of which replaced the previous ones. The selection of included MR studies was shown in S2 Table. Finally, 64 MR studies investigated 145 associa- tions (Fig 2B) categorized into lifestyle [16,120–127] (N = 10); diet and nutrition [125,128] (N = 2); anthropometric indices [125,129,130] (N = 9); biomarkers [17,60,125,131–163] (N = 98); clinical variables, diseases, and treatments [93,163–176] (N = 26), and environmental factors (N = 0) (S7 Table). Particularly, over 200 biomarkers including amino acids and deriva- tive, fatty acids and derivatives, growth factors, inflammatory biomarkers, lipid metabolism biomarkers, methylations, other metabolites/biomarkers, steroids, and circulating leukocyte telomere length were well documented in the previous review [19], and hence only significant associations (N = 18) were selected and discussed in this present review. All studies used two- sample MR design, with European ancestry outcome data mostly from PRACTICAL (The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium) (N = 113, 78% of 145 total associations). The median (IQR) of number of IVs was 13.5 (4, 54.25), ranging from 1 to 663. All studies were in line with the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 13 / 33 PLOS MEDICINERisk factors for prostate cancer STROBE-MR, demonstrating good reporting quality. Concerning sensitivity analysis, there were 94 associations (65% of 145 total associations) reporting sensitivity analysis results. In total 55 significant causal associations (of 145 total associations) from MR studies were graded. Finally, 10 causal associations were assigned robust, 24 probable, 4 suggestive, and 17 insuffi- cient (Fig 5 and S7 Table). Results of MR studies in categories Ten lifestyle factors (of 145 total associations) were included, with 5 showing significant causal associations (Fig 5 and S7 Table). Robust evidence was assigned to morning chronotype (1 h earlier, OR = 0.71, 95% CI: 0.54, 0.94) and smoking initiation (1 standard deviation (SD) increase, OR = 0.91, 95% CI: 0.86, 0.97). Probable evidence was assigned to education attain- ment (per SD increase in genetically predicted years of education, OR = 1.10, 95% CI: 1.01, 1.21). Suggestive evidence was assigned to age of sexual initiation (older age, OR = 1.18, 95% CI: 1.01, 1.38). Insufficient evidence was observed for accelerator-measured physical activity (per SD increase, OR = 0.49, 95% CI: 0.33, 0.72), causally reducing prostate cancer risk. Remaining lifestyle factors, namely coffee consumption, alcohol, cannabis, short sleep dura- tion, and sedentary behavior demonstrated no causal relationship with prostate cancer. Only 2 diet and nutrition factors (of 145 total associations) including dairy products (milk intake) and dried fruit intake were identified and both showed no evidence of causality (S7 Table). In total 9 anthropometric indices (of 145 total associations) were identified, with 4 signifi- cant causally associated with prostate cancer (Fig 5 and S7 Table). Robust evidence was assigned to BMI (per SD, OR = 0.92, 95% CI: 0.85, 1.00), and insufficient evidence was assigned to height for high-grade prostate cancer (per SD, OR = 1.07, 95% CI: 1.01, 1.15) and puberty timing (later puberty, OR = 0.93, 95% CI: 0.88, 0.98). Probable evidence was assigned to unfavorable adiposity (UFA), which met the evidence criteria for probable though the P value for main analysis was larger than 0.05. While 5 factors including birth weight, waist cir- cumference, waist-hip ratio, favorable adiposity (FA), and total fat showed null causality with prostate cancer. A total of 98 biomarkers (of 145 total associations) were included, with 40 biomarkers sig- nificantly associated with prostate cancer (Fig 5 and S7 Table). Robust evidence was observed for circulating phosphorous (per SD, OR = 1.19, 95% CI: 1.09, 1.31), leukocyte telomere length (LTL) (long versus short, OR = 1.37, 95% CI: 1.25, 1.50), serum uric acid (per SD, OR = 1.12, 95% CI: 1.00, 1.26) increasing risk of prostate cancer, and alanine aminotransferase (per SD, OR = 0.43, 95% CI: 0.27, 0.68), albumin (per SD, OR = 0.79, 95% CI: 0.68, 0.91) reducing risk of prostate cancer. In addition, there were 19 probable (vitamin B12, transferrin saturation, alanine, Chemokine (C-C motif) ligand 2, Chemokine (C-C motif) ligand 4, C-X-C motif che- mokine ligand 9, triglyceride, insulin-like growth factor 1, LDL, bioavailable testosterone, free testosterone, hepatocyte growth factor, IL-1 receptor antagonist, Indoleamine 2,3-dioxygenase 1, platelet-derived growth factor BB, stem cell growth factor-beta, Class. Alphaproteobacteria, Order. Rhodospirillales, and Genus. Adlercreutzia), 3 suggestive (monounsaturated fatty acids, aspartate, and Genus. Coprobacter), and 13 insufficient associations (a1-acid glycopro- tein, Torsin-1A-interacting protein 1, pyruvate, lactate, creatinine, alanine, zinc, interleukin-6, serum iron, thyroid-stimulating hormone, beta nerve growth factor; triglycerides in medium VLDL, and microseminoprotein-beta). The remaining 58 biomarkers showed null association with prostate cancer. Totally 26 clinical variables, diseases, and treatments (of 145 total associations) were included, with 6 showing significant causal association with prostate cancer (Fig 5 and S7 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 14 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 5. Forest plot of evidence grading for significant associations with the risk of prostate cancer in categories from MR studies. The statistical test to determine the P value in MR study was the IVW regression analysis. The effect estimate OR of each association is represented by the blue colored square and 95% CI by the horizontal lines. Metrics with * denoting the outcome was high-grade, aggressive, or advanced prostate cancer. Metrics with # denoting the outcome was early-onset prostate cancer. Note that UFA meets the evidence criteria for probable though the P value for main analysis is larger than 0.05. SD, standard deviation; PA, physical activity; BMI, body mass index; LTL, leukocyte telomere length; CCL2, Chemokine (C-C motif) ligand 2; CCL4, Chemokine (C-C motif) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 15 / 33 PLOS MEDICINERisk factors for prostate cancer ligand 4; TG, triglyceride; IGF, insulin-like growth factor; LDL, low-density lipoprotein; HGF, hepatocyte growth factor; IL-1ra, IL-1 receptor antagonist; MUFAs, monounsaturated fatty acids; TOR1AIP1, Torsin-1A-interacting protein 1; UFA, unfavorable adiposity; IL-6, interleukin-6; ALT, alanine aminotransferase; IDO 1, Indoleamine 2,3-dioxygenase 1; PDGF-bb, platelet-derived growth factor BB; SCGF-β, stem cell growth factor-beta; TSH, thyroid- stimulating hormone; β-NGF, beta nerve growth factor; M.VLDL.TG, Triglycerides in medium VLDL; MSP, microseminoprotein-beta; CCB, calcium channel blockers; PPARG, peroxisome proliferator activated receptor γ; PCSK9, proprotein convertase subtilisin/kexin type 9; SLE, systemic lupus erythematosus; IVW, inverse variance weighted; MR, mendelian randomization; OR, odds ratio. https://doi.org/10.1371/journal.pmed.1004362.g005 Table). Robust evidence was assigned to PCSK9 inhibition (OR = 0.85, 95% CI: 0.76, 0.96) and hyperthyroidism (increased susceptibility, OR = 0.86, 95% CI: 0.79, 0.94) with relatively small sample size. Probable evidence was assigned to CCB (per SD, OR = 1.22, 95% CI: 1.06, 1.42), atrial fibrillation (increased susceptibility, OR = 0.96, 95% CI: 0.92, 0.99), and systemic lupus erythematosus (SLE) (increased susceptibility, OR = 0.98, 95% CI: 0.97, 0.99). Insufficient evi- dence was assigned to genetically proxied perturbation of PPARG (per SD, OR = 1.75, 95% CI: 1.07, 2.85). No significant causal association with prostate cancer was found for the following clinical variables, diseases, and treatments including inflammatory bowel disease, Crohn’s dis- ease, UC, heart failure, major depressive disorder, systolic blood pressure, diastolic blood pres- sure, hypothyroidism, schizophrenia, allergic disease, asthma, vitiligo, T2D, and 7 genetically proxied therapeutic inhibition of drug targets. Comparison between associations derived from meta-analyses and MR studies Taking evidence grading results into consideration, no factor showed notable effect on modi- fying prostate cancer risk with high-quality evidence (Fig 6). In total 26 overlapping factors investigated by both meta-analyses and MR studies were identified, and only 3 factors showed consistent significant associations, yet with no consistent robust evidence: physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94, highly suggestive; accelerator- measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72, insufficient), height (meta: OR = 1.09, 95% CI: 1.06, 1.12, suggestive; MR: OR = 1.07, 95% CI: 1.01, 1.15, insufficient), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80, suggestive; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97, robust). Eleven factors including total dairy product, birth weight, calcium, CRP, circulating 25-hydroxyvitamin D, and UC positively linked with pros- tate cancer and coffee, selenium, vitamin E, schizophrenia, and T2D inversely associated with prostate cancer showed null causal associations by MR studies. However, 3 factors with statisti- cally significant causal associations by MR studies were null in meta-analyses (LDL, zinc, and BMI). Another 9 factors were not significantly associated with prostate cancer neither in meta- analyses nor in MR studies (S8 Table). Except for the overlapping factors, comparison was lim- ited between meta-analyses and MR studies for other factors largely due to unavailability. For example, most of the dietary factors identified in meta-analyses were not suitable for conduct- ing MR studies due to lack of appropriate instrumental variables, whereas some factors found significant in MR studies did not have available meta-analyses (education attainment, morning chronotype, puberty timing, and many biomarkers). Discussion To the best of our knowledge, this large-scale umbrella review conducted a very comprehen- sive appraisal of the evidence strength of associations between various factors and the risk of developing prostate cancer, based on meta-analyses of prospective observational studies and MR studies. Collectively, 92 meta-analyses and 64 MR studies generated 268 associations with the risk of prostate cancer, covering 6 categories: lifestyle; diet and nutrition; anthropometric PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 16 / 33 PLOS MEDICINERisk factors for prostate cancer Fig 6. Comparison between meta-analyses and MR studies. The statistical test to determine the P value in meta-analyses was the random-effects inverse-variance model with DerSimonian—Laird method. The statistical test to determine the P value in MR study was the IVW regression analysis. The effect estimates OR from meta-analyses and MR studies are represented by the green and blue squares, respectively, and 95% CI by the horizontal lines. Metrics with * denoting the outcome was high-grade, aggressive, or advanced prostate cancer. NA, not available; SD, standard deviation; PA, physical activity; CRP, C-reactive protein; T2D, type 2 diabetes; UC, ulcerative colitis; LDL, low-density lipoprotein; BMI, body mass index; IVW, inverse variance weighted; MR, mendelian randomization; OR, odds ratio. https://doi.org/10.1371/journal.pmed.1004362.g006 indices; clinical variables, diseases, and treatments; biomarkers; and environmental factors. Further evidence grading on statistically significant associations according to respective pre- specified criteria was performed. Concerning meta-analyses, our results corroborate largely with previous findings mainly in the category of diet and nutrition [9], including sweetened beverage, vitamin D, folate, dairy product, processed meat, egg consumption increasing the risk of prostate cancer and selenium and soy consumption decreasing the risk. Compared with previous researches, this umbrella PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 17 / 33 PLOS MEDICINERisk factors for prostate cancer review has the strength of updated evidence and expanded categories of risk factors. The exist- ing umbrella review by Markozannes and colleagues in 2016 [9] was conducted based on liter- ature published up to April 30, 2013, while in our umbrella review all the included meta- analyses for data synthesis were published after 2014 except 2 articles [72,105], of which 57.6% (53/92) were published after 2020, presenting updated evidence for each factor. Secondly, the previous umbrella review studied associations of 23 foods, 31 nutrients, 8 indices of body size and 3 indices of physical activity, while our umbrella review greatly expanded the categories of risk factors by containing 6 categories covering lifestyle; diet and nutrition; anthropometric indices; clinical variables, diseases, and treatments; biomarkers; and environmental factors, bringing the total number of studied factors to 123. Furthermore, this umbrella review col- lected evidence of clinical variables, diseases, and treatments including preexisting diseases, medication, and surgery, which was often neglected in previous reviews. Diseases such as mel- anoma, acne in adolescence, UC, infertility, prostatitis, and BPH associated with higher pros- tate cancer risk indicated shared biological mechanisms such as hormone dependency, inflammation [177], and genetic susceptibility [88,92,97]. We could approach these associa- tions from the perspective of shared causal intermediary pathway or mechanisms to investigate the carcinogenesis of prostate cancer, which warrants further researches such as genetic, func- tional, and pharmaceutical studies. Apart from updated evidence and expanded categories, the unique strength of this umbrella review is the comparison between high-quality evidence from meta-analyses of prospective observational studies and MR studies. Integrating epidemiological evidence and MR causal inference, with the former providing the foundation for MR causal exploration while MR help- ing verify the causality in turn, provides useful insights in examining intrinsic relationships. In this umbrella review, however, the comparison between observational associations by meta- analysis and genetically estimated causality by MR does not provide robust evidence due to the lack of overlapping observations as well as the lack of high-quality evidence, especially in MR studies. First, concerning height, MR analyses on height provided insufficient evidence of its causal association with prostate cancer, in addition to inconsistent results from other identified MR studies [178–180], which is not very supportive of this association. Height is implicated in many biological pathways such as skeletal growth, fibroblast growth factor (FGF) signaling, WNT (Wingless/Integrated) signaling, regulation of β-catenin, mammalian target of rapamy- cin (mTOR) signaling [181], and associates with overall cancer risk and mortality [178]. A plausible mechanism involves dietary programming of the IGF-1, which plays an important role in the regulation of postnatal growth and is also associated with prepubertal growth in height [182]. Thus, the variations in the IGF-1 system might underlie associations of height with prostate cancers that are more likely to progress [183]. Still the causal mechanism of height in progressive prostate cancer needs further investigation. Smoking, albeit with consis- tent effect, should be taken prudently for the observed effect was moderate and mixed and that positive association in earlier years (before 1995) and with mortality collectively suggested a link to aggressive prostate cancer rather than indolent one [184]. Current smoking was believed to be associated with a lower likelihood of prostate-specific antigen (PSA) testing, and individuals with a smoking history were less likely to undergo prostate biopsy [185,186]. Con- sequently, the detection rate of prostate cancer could be relatively lower among participants in the PSA screening era. Another possible explanation is that smoking is the leading risk factor for death among males [187]. Smokers may die from smoking attributable diseases including cancers, cardiovascular diseases, and respiratory diseases before their diagnosis of prostate cancer. In addition, multiple inconsistent exposure categories for smoking such as current smoking, former smoking, and ever smoked, etc., might contribute to the varied results. To sum up, measures should be taken to help smokers to be more compliant with early cancer PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 18 / 33 PLOS MEDICINERisk factors for prostate cancer screening and to quit smoking [30]. Concerning physical activity, physical activity may be associated with cancer through several pathways related to oxidative stress, DNA methylation, telomere length, immune function, and gut microbiome [188]. Shorter duration aerobic physi- cal activity stimulates short-term increases in immunoglobulins, neutrophils, natural killer cells, cytotoxic T cells, and immature B cells, which over time enhance immunosurveillance [189]. Physical activity reduces adipose tissue and correcting metabolic abnormalities, which has been shown to reduce plasma insulin and increase insulin sensitivity and glucose metabo- lism, thereby lowering the risk of certain cancers [190]. In terms of cancer progression, physi- cal activity may predispose to biologically less aggressive tumors and may improve functional capacity to tolerate and complete cancer treatment, thereby slowing down cancer progression [191]. The results regarding physical activity in this umbrella review are not robust because accelerator-measured physical activity showed a protective effect on prostate cancer in MR but with very weak instrumental strength explaining only 0.1% of the variance. In addition, in meta-analyses occupational physical activity was graded as highly suggestive evidence but overall physical activity showed null associations with prostate cancer, possibly attributed to differed measurement. Several limitations should be noted in this umbrella review. First, missing literature may exist despite of exhaustive literature search, and some factors that were not assessed at the meta-analysis level or failed the inclusion criteria may be overlooked. Second, most cohort studies were conducted in developed western countries, and hence findings of this current study are limited mainly for European descendants. Despite subgroup analysis performed by ethnicity, it was greatly limited by sparsity of data on non-white populations. Effects of differ- ent risk factors on prostate cancer may vary between ethnicities, which may be attributed to diverse genetic backgrounds and lifestyles. Data on prostate cancer incidence in Asian coun- tries might be statistically biased by the immature implementation of early screening practice and national cancer registry [192]. As prostate cancer is expected to rise in developing coun- tries due to increased aging and popularity of PSA screening, data of non-white population are accumulating and await evaluation. Third, heterogenous effects based on prostate cancer clas- sifications suggest both pathological variation of prostate cancer and diverse effects of exposure factors. For instance, smoking was found to be inversely associated with total prostate cancer, but its effect on aggressive prostate cancer appeared to be the opposite in some literature [193]. Therefore, it is necessary to conduct more precise evaluations on associations with further characterizations considering the complex clinical and pathological nature of prostate cancer. Fourth, evidence grading criteria both for meta-analyses and MR studies could be refined, for example, considering magnitude of effect size and levels of sample size, which requires aca- demically sound innovation and collective effort from the broad science community. Some implications for next step of research can be derived from this umbrella review. First, the discrepancy that a fair number of factors explored in MR studies are not found in meta- analyses or observational studies should be noted. The accessibility of abundant resources in MR-base may permit analyses to be performed without careful consideration of the epidemio- logical evidence/background that are being made or the assumptions inherent in the approach [194]. Therefore, it is suggested that MR be performed based on properly and adequately eval- uating evidence provided by epidemiological studies. MR results that are not biologically sound or supported by observational studies should be interpreted with caution. Second, the identification of risk factors that are robustly associated with risk of prostate cancer avail tar- geted prevention strategies. Biomarkers identified in MR studies warrant further investigation, which may benefit future research on prostate cancer carcinogenesis, prevention, and screen- ing. Third, weak and insufficient evidence identified in this umbrella review warrant further investigations. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 19 / 33 PLOS MEDICINERisk factors for prostate cancer In summary, this umbrella review provides a comprehensive evaluation on risk factors associated with prostate cancer as well as large-scale comparison between observational asso- ciations by meta-analysis and genetically estimated causality by MR analyses. Though no robust association is identified due to the lack of overlapping robust evidence based on exist- ing literature, future researches are warranted to further our understanding on prostate can- cer risk. Supporting information S1 PRISMA Checklist. Prisma 2020 checklist. (DOCX) S1 Text. Search strategies. (DOCX) S2 Text. Statistical analysis protocol. (DOCX) S1 Fig. Forest plots of significant associations in meta-analyses. The effect estimates are pre- sented as risk ratios (RR) with 95% confidence intervals (95% CI). (PDF) S2 Fig. Forest plots of subgroup analyses according to ethnicity (white versus non-white). The 2 dashed line indicated the odds ratios derived from the common effect model (the loosely dashed line) and from random-effects model (the densely dashed line), respectively. W, white population; non-W, non-white population; CI, confidence interval. (PDF) S1 Table. Selection of meta-analyses. (XLSX) S2 Table. Selection of MR studies. IVs, instrumental variables; OR, odds ratio; CI, confidence interval; BMI, body mass index; IGF, insulin-like growth factor; IGFBP, IGF-binding protein; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; CCB, calcium channel blockers; PCSK9, proprotein convertase subtilisin/kexin type 9; SHBG, sex-hormone binding globulin. (XLSX) S3 Table. Details of AMSTAR-2 grading for quality of meta-analyses. Y: yes (1 point); PY: partial yes (0.5 point); N: no (0 point); * denoting the critical AMSTAR-2 items; critical item score = total score of 0 (N), 0.5 (PY), and 1 (Y) on the critical AMSTAR-2 items; total score = total score of 0 (N), 0.5 (PY), and 1 (Y) on all AMSTAR-2 items. (DOCX) S4 Table. Details of evidence grading for significant associations from meta-analyses. NA: not available; PI, prediction interval; PA, physical activity; CRP, C-reactive protein; T2D, type 2 diabetes; BPH, benign prostate hyperplasia; UC, ulcerative colitis; HIV, human immunodefi- ciency virus; AIDS, acquired immune deficiency syndrome. (DOCX) S5 Table. Basic characteristics of included meta-analyses and evidence grading results. The statistical test to determine the P value in meta-analyses was using the random-effects inverse- variance model with DerSimonian—Laird method. Metrics with * denoting advanced, aggres- sive, high-grade, or lethal prostate cancer, metrics with # denoting nonadvanced, PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 20 / 33 PLOS MEDICINERisk factors for prostate cancer nonaggressive, or localized prostate cancer. W, White; A, Asian; RR, risk ratio; OR, odds ratio; HR, hazard ratio; SIR, standard incidence ratio; SRRE, summary relative risk estimate; NR, not reported; NA, not available; PA, physical activity; DHA, docosahexaenoic acids; EPA, eico- sapentaenoic; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; T2D, type 2 diabetes; BPH, benign prostate hyperplasia; HIV, human immunodefi- ciency virus; AIDS, acquired immune deficiency syndrome; CD, Crohn’s disease; UC, ulcera- tive colitis; AASVs, anti-neutrophil cytoplasm antibody associated vasculitides; ACEI, angiotensin converting enzyme inhibitors; NSAID, nonsteroidal anti-inflammatory drug; CCB, calcium channel blockers. (DOCX) S6 Table. Subgroup analyses according to ethnicity (white versus non-white). N, number of datasets in the corresponding meta-analysis; OR, odds ratio; CI, confidence interval. Regular use of aspirin: users vs. non-users; Total calcium intake: per 400 mg/d; Coffee: highest vs. lowest; Current smoking: current smoking vs. non-smoker (never smokers plus former smokers); Daidzein: highest vs. lowest; Finasteride: users vs. non-users; Firefighter: ever employment as a career firefighter vs. general population; Height: per 10 cm increase; Soy consumption: highest vs. lowest; Total dairy products: highest vs. lowest; Ulcerative colitis: patients vs. non-patients. (DOCX) S7 Table. Basic characteristics of included MR studies and evidence grading results. The statistical test to determine the P value in MR study was the inverse variance weighted (IVW) regression analysis; § denoting the exposure population source was of Asian ancestry or mixed ancestry; * denoting the outcome of MR studies was aggressive prostate cancer; # denoting the outcome of MR studies was early-onset prostate cancer; ⸸ denoting the summary metric of this MR study was beta estimates. NA, not available; SD, standard deviation; PRACTICAL: The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome consortium; PA, physical activity; BMI, body mass index; UFA, unfavorable adipos- ity; FA, favorable adiposity; HbA1c, hemoglobin A1c; GST, glutathione s-transferase; SOD, superoxide dismutase; CAT, catalase; GPX, glutathione peroxidase; IL, interleukin; IL-1b, IL-1 beta; IL-1ra, IL-1 receptor antagonist; IL-2ra, IL-2 receptor alpha subunit; IL-6ra, IL-6 receptor subunit alpha; ALT, alanine aminotransferase; VEGF, vascular endothelial growth factor; IGF, insulin-like growth factor; IGFBP, IGF-binding protein; TOR1AIP1, Torsin-1A-interacting protein 1; MUFAs, monounsaturated fatty acids; AA, Arachidonic acid; ALA, α -linolenic acid; DHA, Docosahexaenoic acid; DPA, Docosapentaenoic acid; EPA, Eicosapentaenoic acid; LA, linoleic acid; OA, Oleic acid; PA, Palmitic acid; POA, Palmitoleic acid; SA, Stearic acid; CRP, C-reactive protein; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Lp(a), lipoprotein A; TG, triglyceride; apo A, apoprotein A; apo B, apoprotein B; VLDL, very low- density lipoprotein; S.HDL.TG, Triglycerides in small HDL; M.VLDL.TG, Triglycerides in medium VLDL; PDGF-bb, platelet-derived growth factor BB; β-NGF, beta nerve growth fac- tor; SCGF-β, stem cell growth factor-beta; HGF, hepatocyte growth factor; CCL2, Chemokine (C-C motif) ligand 2; CCL4, Chemokine (C-C motif) ligand 4; IDO 1, Indoleamine 2,3-dioxy- genase 1; MSP, microseminoprotein-beta; LTL, leukocyte telomere length; SHBG, sex-hor- mone binding globulin; TSH, thyroid-stimulating hormone; CCB, calcium channel blockers; PCSK9, proprotein convertase subtilisin/kexin type 9; PPARG, peroxisome proliferator acti- vated receptor γ; ABCC8, ATP binding cassette subfamily C member 8; GLP1R, glucagon-like peptide 1 receptor; ACE, angiotensin-converting enzyme; ADRB1, β-1 adrenergic receptor; NCC, sodium-chloride symporter; SBP, systolic blood pressure; DBP, diastolic blood pressure; MDD, major depressive disorder; SLE, systemic lupus erythematosus; IBD, inflammatory PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 21 / 33 PLOS MEDICINERisk factors for prostate cancer bowel disease; CD, Crohn’s disease; UC, ulcerative colitis; T2D, type 2 diabetes; HMG-CoA, 3-hydroxy-3-methylglutaryl coenzyme A; NPC1L1, Niemann-Pick C1-Like 1. (DOCX) S8 Table. Overall comparison between meta-analyses and MR studies. Metrics with * denot- ing the outcome was advanced, aggressive, high-grade, or lethal prostate cancer. Other null associations of biomarkers in MR studies were recorded in a previous review by Markozannes and colleagues (reference [19]). PA, physical activity; DHA, docosahexaenoic acids; EPA, eico- sapentaenoic; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; T2D, type 2 diabetes; BPH, benign prostate hyperplasia; HIV, human immunodefi- ciency virus; AIDS, acquired immune deficiency syndrome; CD, Crohn’s disease; UC, ulcera- tive colitis; AASVs, anti-neutrophil cytoplasm antibody associated vasculitides; ACEI, angiotensin converting enzyme inhibitors; NSAID, nonsteroidal anti-inflammatory drug; CCB, calcium channel blockers; TG, triglyceride; MUFAs, monounsaturated fatty acids; MDD, major depressive disorder; LTL, leukocyte telomere length; IGF, insulin-like growth factor; IGFBP, IGF-binding protein; TOR1AIP1, Torsin-1A-interacting protein 1; IL-6ra, IL-6 receptor subunit alpha; IDO 1, Indoleamine 2,3-dioxygenase 1; SCGF-β, stem cell growth fac- tor-beta; β-NGF, beta nerve growth factor; MSP, microseminoprotein-beta; ALT, alanine ami- notransferase; SLE, systemic lupus erythematosus; TSH, thyroid-stimulating hormone; PCSK9, proprotein convertase subtilisin/kexin type 9; PPARG, peroxisome proliferator acti- vated receptor γ; SHBG, sex-hormone binding globulin; S.HDL.TG, Triglycerides in small HDL; M.VLDL.TG, Triglycerides in medium VLDL; PDGF-bb, platelet-derived growth factor BB. (DOCX) Author Contributions Conceptualization: Xia Jiang, Ben Zhang. Data curation: Huijie Cui, Wenqiang Zhang, Li Zhang, Yang Qu. Formal analysis: Huijie Cui, Wenqiang Zhang, Li Zhang. Funding acquisition: Ling Zhang, Yanfang Yang, Yuqin Yao, Jiayuan Li, Zhenmi Liu, Chun- xia Yang, Ben Zhang. Investigation: Huijie Cui, Wenqiang Zhang, Li Zhang, Yang Qu. Resources: Ben Zhang. Software: Zhengxing Xu, Zhixin Tan, Peijing Yan, Mingshuang Tang, Chao Yang, Yutong Wang, Lin Chen, Chenghan Xiao, Yanqiu Zou, Yunjie Liu. Supervision: Ling Zhang, Yanfang Yang, Yuqin Yao, Jiayuan Li, Zhenmi Liu, Chunxia Yang, Xia Jiang, Ben Zhang. Visualization: Zhengxing Xu, Zhixin Tan, Peijing Yan, Mingshuang Tang, Chao Yang, Yutong Wang, Lin Chen, Chenghan Xiao, Yanqiu Zou, Yunjie Liu. Writing – original draft: Huijie Cui, Wenqiang Zhang, Li Zhang. Writing – review & editing: Huijie Cui, Wenqiang Zhang, Li Zhang, Yang Qu, Zhengxing Xu, Zhixin Tan, Peijing Yan, Mingshuang Tang, Chao Yang, Yutong Wang, Lin Chen, Chenghan Xiao, Yanqiu Zou, Yunjie Liu, Xia Jiang, Ben Zhang. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004362 March 15, 2024 22 / 33 PLOS MEDICINERisk factors for prostate cancer References 1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209–49. Epub 2021/02/05. https://doi.org/10.3322/caac.21660 PMID: 33538338. 2. Sandhu S, Moore CM, Chiong E, Beltran H, Bristow RG, Williams SG. Prostate cancer. Lancet. 2021; 398(10305):1075–90. Epub 2021/08/10. https://doi.org/10.1016/S0140-6736(21)00950-8 PMID: 34370973. 3. Benke IN, Leitzmann MF, Behrens G, Schmid D. 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10.1371_journal.pntd.0011960	RESEARCH ARTICLE Altered IL-7 signaling in CD4+ T cells from patients with visceral leishmaniasis Shashi Kumar1, Shashi Bhushan Chauhan2, Shreya Upadhyay1, Siddharth Sankar Singh3, Vimal Verma1, Rajiv Kumar4☯, Christian Engwerda5☯, Susanne Nyle´ n6☯, Shyam SundarID 1☯* 1 Department of Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi Uttar Pradesh India, 2 School of Medicine & Health Sciences, The George Washington University, Washington, Washington, United States of America, 3 University of Massachusetts Chan Medical School, Shrewsbury, Massachusetts, United States of America, 4 Centre of Experimental Medicine and Surgery, Banaras Hindu University, Varanasi, India, 5 QIMR Berghofer Medical Research Institute, Brisbane, Australia, 6 Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden ☯ These authors contributed equally to this work. * rajiv.kumar@bhu.ac.in (RK); susanne.nylen@ki.se (SN); drshyamsundar@hotmail.com (SS) a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 Abstract OPEN ACCESS Citation: Kumar S, Chauhan SB, Upadhyay S, Singh SS, Verma V, Kumar R, et al. (2024) Altered IL-7 signaling in CD4+ T cells from patients with visceral leishmaniasis. PLoS Negl Trop Dis 18(2): e0011960. https://doi.org/10.1371/journal. pntd.0011960 Editor: Abhay R Satoskar, Ohio State University, UNITED STATES Received: September 1, 2023 Accepted: February 1, 2024 Published: February 26, 2024 Copyright: © 2024 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting information files. Funding: This work is supported by National Institutes of Health-Tropical Medicine Research Centre Grant (grant no. 2U19AI074321) to SS, by Indian Council of Medical Research (grant no. 2020-9898) to RK and SS, by Banaras Hindu University- Institute of Eminence (IoE) grant to RK Background CD4+ T cells play a central role in control of L. donovani infection, through IFN-γ production required for activation of macrophages and killing of intracellular parasites. Impaired control of parasites can in part be explained by hampered CD4+ T cells effector functions in visceral leishmaniasis (VL) patients. In a recent studies that defined transcriptional signatures for CD4+ T cells from active VL patients, we found that expression of the IL-7 receptor alpha chain (IL-7RΑ; CD127) was downregulated, compared to CD4+ T cells from endemic con- trols (ECs). Since IL-7 signaling is critical for the survival and homeostatic maintenance of CD4+ T cells, we investigated this signaling pathway in VL patients, relative to ECs. Methods CD4+ T cells were enriched from peripheral blood collected from VL patients and EC sub- jects and expression of IL7 and IL7RA mRNA was measured by real time qPCR. IL-7 signal- ing potential and surface expression of CD127 and CD132 on CD4+ T cell was analyzed by multicolor flow cytometry. Plasma levels of soluble IL-7 and sIL-7Rα were measured by ELISA. Result Transcriptional profiling data sets generated previously from our group showed lower IL7RA mRNA expression in VL CD4+ T cells as compared to EC. A significant reduction was, how- ever not seen when assessing IL7RA mRNA by RT-qPCR. Yet, the levels of soluble IL-7Rα (sIL-7Rα) were reduced in plasma of VL patients compared to ECs. Furthermore, the levels of soluble IL-7 were higher in plasma from VL patients compared to ECs. Interestingly, expression of the IL-7Rα protein was higher on VL patient CD4+ T cells as compared to EC, with activated CD38+ CD4+ T cells showing higher surface expression of IL-7Rα compared PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 1 / 16 PLOS NEGLECTED TROPICAL DISEASESand SS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. IL-7 signaling in CD4+ T cells of VL patients to CD38- CD4+ T cells in VL patients. CD4+ T cells from VL patients had higher signaling potential baseline and after stimulation with recombinant human IL-7 (rhIL-7) compared to EC, as measured by phosphorylation of STAT5 (pSTAT5). Interestingly, it was the CD38 negative cells that had the highest level of pSTAT5 in VL patient CD4+ T cells after IL-7 stim- ulation. Thus, despite unaltered or potentially lowered IL7RA mRNA expression by CD4+ T cells from VL patients, the surface expression of the IL-7Rα was higher compared to EC and increased pSTAT5 was seen following exposure to rhIL-7. Accordingly, IL-7 signaling appears to be functional and even enhanced in VL CD4+ T cells and cannot explain the impaired effector function of VL CD4+ T cells. The enhanced plasma IL-7 may serve as part of homeostatic feedback mechanism regulating IL7RA expression in CD4+ T cells. Author summary In visceral leishmaniasis (VL), antigen specific CD4+ T cell responses are muted hindering the control of the Leishmania donovani infection. IL-7 signaling is crucial for CD4+ T cell survival and function, and gene expression analysis indicated that the IL-7 pathway could be altered in VL. Thus, we investigate if impaired IL-7 signaling could explain the loss of antigen specific T cell response in VL. Although we didn’t observe significant reduction of IL7RA mRNA by RT-qPCR, yet, the levels of soluble IL-7Rα (sIL-7Rα) were reduced in plasma of VL patients compared to ECs. Furthermore, the levels of soluble IL-7 were higher in plasma from VL patients compared to ECs and their CD4+ T cells exhibited heightened IL-7 receptor protein expression. Surprisingly, VL patient CD4+ T cells showed increased IL-7 signaling potential, as evidenced by higher phosphorylation of STAT5 upon IL-7 stimulation. While altered, the findings presented here do not attrib- uted the impaired effector function of VL CD4+ T cells to defective IL-7 signaling. We speculate that the elevated plasma IL-7 is part of a homeostatic feedback mechanism in response to the reduced IL7RA transcription in CD4+ T cells. Introduction Leishmaniasis are a parasitic disease caused by protozoan parasites of the Leishmania genus. All Leishmania spp are transmitted through the bite of infected female Phlebotomine sandflies. The disease can manifest in different ways, from life threating visceral leishmaniasis (VL) to localized cutaneous disease depending on the species of Leishmania involved. To date, around 20 different species of Leishmania have been identified. Each year, there are approximately 50 000–90 000 new cases of VL [1], with most cases coming from Brazil, Ethiopia, India, South Sudan, and Sudan. The most frequent manifestation of VL is anemia, and early symptoms may also include leucopenia [2,3]. Other clinical symptoms of VL include prolonged fever, enlarged spleen and liver, weight loss, and polyclonal hypergammaglobulinemia (IgG and IgM) [4]. CD4+ T-helper (Th) cells are central in orchestrating immune responses against Leishmania parasites. Specifically, T-bet+ CD4+ T cells (Th1 cells), play a crucial role in controlling Leish- mania infection, by production of IFN-γ leading to activation of macrophages and killing of intracellular parasites [5]. However, CD4+ Th cells also play a role in regulating the balance between pro-inflammatory and anti-inflammatory responses, and regulatory cytokines such as PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 2 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients IL-10 which are critical for controlling the immune response also inhibit macrophage func- tions and facilitate Leishmania infection [6,7]. IL-7 is produced by nonhematopoietic cells (e.g. stromal cells, bone marrow- mesenchymal stem cells, keratinocytes, neurons, epithelial cells, and hepatocytes) as well as dendritic cells, and plays a crucial role in supporting hematopoiesis [8]. T cells rely on this cytokine for their development, survival, and memory formation. Production of IL-7 is stimulated by various factors, including inflammation, tissue damage, and immune cell interactions [9]. IL-7 exerts its effects by binding to the IL-7 receptor (IL-7Rα), a heterodimer composed of a high-affinity CD127 (IL-7Rα) subunit and the common cytokine gamma-chain CD132. The latter is also used by other cytokines including IL-2, IL-4, IL-9, IL-15, and IL-21 [10]. Upon the binding of IL-7 to the IL-7Rα, a series of intracellular signaling events are triggered. The exact signaling pathway varies depending on cell type, but generally involves activation of Janus kinases (JAKs) and signal transducer and activator of transcription (STAT) proteins. The JAKs phos- phorylate tyrosine residues on IL-7Rα, creating docking sites for STAT5 and phosphorylation of the STAT5 protein, which then as a homodimer translocate to the nucleus to modulate gene expression, thus phosphorylated STAT5 (pSTAT5) is often used to assess IL-7 signaling capac- ity. The activity of IL-7 is tightly regulated to maintain immune cell homeostasis. Negative reg- ulators, such as suppressor of cytokine signaling (SOCS) proteins and protein inhibitors of activated STATs (PIAS), help to dampen IL-7 signaling and prevent excessive immune responses [11]. Increased expression of IL-7Rα on naive (TN) and memory (TM) T cells aids in the clearance of excess soluble IL-7 [12]. Once the peripheral T cell pool reaches a critical size, a balance is achieved between IL-7 consumption and production, preventing the survival of additional T cells and maintaining T cell homeostasis [12–14]. Administration of IL-7 can potentially enhance the function of immune cells and allow a larger lymphocyte pool to develop in vivo, and when used as an adjuvant in immunizations, IL-7 has been shown to improve long-term, antigen-specific T cell responses [15], However, dysregulation of the IL-7 pathway can contribute to the development of cancer [16]. In a previous studies, we observed a decrease in IL7RA expression in CD4+ T cells from individuals with VL compared to ECs [17,18], which suggested that IL-7 signaling could be impaired in VL patients. To gain a better understanding of the role of IL-7 in VL patients and if IL-7 played a role in VL pathogenesis and CD4+ T cell dysfunction, we analyzed mRNA and protein expression of IL-7 and IL-7Rα, and the ability of the IL-7 receptor to signal in PBMC and CD4+ T cells from VL patients and ECs. We found a divergence between the IL7RA mRNA and protein expression, with an increase in IL-7Rα surface protein on VL patient CD4+ T cells compared to ECs. The levels of IL-7 were higher, while the levels of soluble IL-7Rα were lower in VL patient serum, compared to ECs. Moreover, activation of VL patient PBMCs showed that IL-7 signaling was functional and even enhanced in VL patient CD4+ T cells. In conclusion, while our data show clear differ- ences between VL patients and ECs in regard to IL-7 and IL-7Rα levels, we cannot explain the impaired CD4+ T cell responses seen in VL patients by lack of IL-7 or its signaling capacity. Methods Ethics statements All experiments were performed in accordance with the Helsinki declaration for use of human subjects in research and approval from the ethical committee of Institute of Medical Science, Banaras Hindu University-India (ethical approval No. Dean/2019/EC/1001 Dated 18/01/ 2019). All the participants provided written informed consent and in case of children consent PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 3 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients was obtained from their parents or legal guardian. All subjects selected were human immuno- deficiency virus negative and above 12 years of age. Research subjects The following groups were included in this study: VL patients before treatment (VL, n = 128) and 30 days post treatment (VL D30) (n = 13) and endemic healthy control (ECs) subjects (n = 104) [19,20]. All donors were recruited from the Kala-Azar Medical Research Center (KAMRC), Muzaffarpur, Bihar India. The numbers of individuals indicated for each group are the total numbers included in the study, the number of donors included in each experiment is indicated in the figure legend. There was no intentional selection of the donors included in each experiment; this was based on the order of which the experiments were done and the patients available at the time. Diagnosis of VL was made based on clinical symptoms consistent with VL and detection of anti-leishmanial antibodies in serum by recombinant K39-test and/ or detection of amastigote in bone marrow/splenic aspirates by microscopy [21,22]. Clinical data from the patients are summarized in Table 1. ECs were recruited from people accompanying VL patients to the clinic. Venous blood was collected from the patients and controls into heparinized tubes. Plasma was separated by centrifugation at 770 g for 10 minutes and stored at -80˚C till further use. The plasma was replaced by PBS and PBMC were isolated by density gradient separation using Lymphoprep (STEMCELL Technologies). Details of all reagents used in this study are described in S1 Table. Table 1. Demographic and clinical information on study participants. Variables Age, Year Mean ± SD Median Sex, no Male Female Illness Duration, days Mean ± SD Median Haemoglobin level, mg ml-1 Mean ± SD Median WBC, x103 cells mm-3 Mean ± SD Median Splenic enlargement, cm Mean ± SD Median ND, Assay not done https://doi.org/10.1371/journal.pntd.0011960.t001 EC Group (n = 104) 33.6 ± 12.1 35.0 39 65 NA 14.26 ± 1.2 14.5 8941 ± 926 9020 NA VL D0 Group (n = 128) 32.5 ± 13.8 35.0 49 80 31.4 ± 23.5 30.0 8.8 ± 1.5 9.2 3338 ± 1497 3300 6.0 ± 2.7 7.0 VL D30 Group (n = 13) 24.0 ± 14.4 22.0 5 8 9.9 ± 1.4 10.3 7961 ± 2199 8800 0 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 4 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Gene expression for IL7RA and IL7, mRNA CD4+ cells were enriched from freshly isolated PBMCs by positive selection using anti-human CD4 magnetic Microbeads (Miltenyi Biotec) and MS columns according to the manufacturer’s protocol (Miltenyi Biotec). The enriched CD4+ cells were 99% CD4+ T cell as analyzed by FACS. After washing the cell pellets, they were stored in RLT buffer at -80˚C. Total RNA was isolated from both PBMCs and CD4+ cells using the Qiagen RNeasy mini kit following the manufacturer’s instructions. A high-capacity cDNA Reverse Transcription Kit (Thermo- Fisher) was used to reverse transcribe 1000 ng of RNA according to the manufacturer’s instructions. TaqMan-based gene expression assays were performed for IL7RA, IL7 mRNA targets and 18s ribosomal RNA (rRNA) using 7500 real-time PCR. For each donor, the mean cycle threshold value from duplicated qPCR tests was used to calculate the relative quantifica- tion (2-ΔCt) as follows: ð DCt ¼ Ct target gene Þ (cid:0) Ct 18S rRNA ð Þ DDCt ¼ DCt Sample ð Þ (cid:0) DCt ECmean ð Þ Expression ratio ¼ 2(cid:0) DDCt As indicated above, the 18S rRNA expression was for internal normalization of each sam- ple. The mean ΔCt of all EC samples (ECmean) was used to calculate the fold change between the individual sample and the ECmean, making the spread of samples within the VL and EC groups visible. For each amplification, 25 μg of cDNA was used, each amplification tube con- taining a mixture of 5 μl of cDNA (5 ng/μl), 1μl of primer/probe, 4 μl of MilliQ, and 10 μl of TaqMan master mix (Applied Biosystems, Foster City, CA, USA). Measurement of soluble IL-7 and IL-7Rα in plasma Plasma samples were thawed at the time of ELISA analysis and diluted two-fold with Assay diluent A (provided in the kit) and concentrations of IL-7 and sIL-7Rα were measured in duplicate using the IL-7 Human ELISA kit (invitrogen) and the Human CD127 ELISA kit (abcam), respectively, following the manufacturer’s protocol. The standard curves were gen- erated using recombinant protein provided by the manufacturer and a 4-parametric logistic regression in SoftMax Pro software (version 3.1.2) to calculate the concentrations of IL-7 and sIL-7Rα. Phenotypic expression of IL-7Rα by PBMC staining For analysis of surface expression, 5 x 105 PBMCs from VL and ECs were used. Briefly, PBMCs were washed with staining buffer (PBS, 5% heat inactivated fetal calf serum) and stained with viability dye Zombie aqua at room temperature for 20 minutes. After washing, surface staining was performed with fluorochrome labelled antibodies against CD3ε, CD4, CD127, CD25, CD45RA, CD185 (CXCR5), CD194 (CCR4), CD196 (CCR6), CD197 (CCR7), CD183 (CXCR3), CD38, and CD132, for 30 minutes at 4˚C in the dark. Following washing, the cells were re-suspended in staining buffer, and acquired on a flow cytometer (BD LSRFor- tessa) using FACS Diva software (version 8.0.2). Flow Jo version 10 software (Tree Star, BD) was used to analyze the FACS data. CD4+ T cell subsets were defined as Treg (CD25+, CD127-), Tem (CD45RA+/-, CCR7-), Th (Tem—CXCR5), Th17_Th22 (CCR6+, CCR4+), Th1_Th2 (Th—CCR6, CCR4+/-), Th1 (Th1_Th2—CCR4, CXCR3+), Th2 (Th1_Th2—CXCR3 CCR4+), Th9 (Th—CCR4, CCR6+) [23–26]. CD4+ T cell subsets were defined as previously PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 5 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients reported by us and others (References [18,23–26]), and the gating strategy employed was as we previously described (references [18,25]). Details of all antibodies used in this study are described in S2 Table. Signaling potential of IL-7 and IL-7Rα To assess STAT5 phosphorylation (pSTAT5), as indicative of IL-7Rα signaling capacity in CD4+ T cell subsets, freshly isolated heparinized whole blood (200μl) was first surface stained for 5 minutes at room temperature. Thereafter, the samples were stimulated with rhIL-7 (200 ng/ml) for 5 minutes or left unstimulated at 37˚C, 5% CO2. 200 ng/ml of rhIL-7 was sufficient to induce phosphorylation of STAT5 in maximum CD4+ T cells [27]. Phosphorylation of STAT5 was detected using BD Phosflow, according to manufacturer’s instructions. Briefly, the cells were fixed directly after completion with Lyse/Fix Buffer for 7 min at 37˚C in a water bath. After washing, the cells were gently vortexed to loosen and permeabilized by using chilled BD Phosflow Perm Buffer III for 30 minutes on ice. The cells were washed twice and stained for intracellular pSTAT5 for 60 minutes at room temperature in the dark, with gentle vortexing every 15 minutes. After washing, the cells were re-suspended in staining buffer acquired on flow cytometer (BD LSRFortessa) using FACS Diva software version 8.0.2. Statistical analysis Statistical analysis was performed using Excel (Microsoft) and GraphPad Prism 8.01 software (Graph Pad Software, La Jolla. CA, USA). Analysis of cellular assays and qPCR was performed using nonparametric Kruskal-Wallis test for multiple groups and with a post test to see between which groups differences exist and Mann-Whitney U-test for comparison between two groups. Wilcoxon signed-rank test was used to compare matched sample pairs. SPICE analysis was performed using SPICE version 5.3 (M. Roeder, Vaccine Research Centre, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, USA; http://exon.niaid.nih.gov) [28]. The data are presented as mean ± SEMs. P-values less than 0.05 were considered statistically significant. Outliers were defined by the ROUT method, alpha 0.05 and removed from analysis. Results Decrease in soluble sIL-7α and increase in IL-7 plasma protein levels in VL patient Aberrant expression of IL-7 and soluble IL-7Rα in plasma is indicative of pathological T cell immunity in chronic viral, inflammatory, and autoimmune diseases. Using data from tran- scriptional profiling [17] and NanoString mRNA expression analysis [18], we observed down- regulation of IL7RA mRNA in CD4+ T cells from patients with visceral leishmaniasis (VL) compared to endemic healthy individuals (ECs) (Fig 1A, extracted from [17] and as previously reported [18]). Lower IL7RA were also previously reported in VL CD4+ T cell pretreatment as compared to post treatment [18]. To confirm this finding, we analyzed the mRNA expression of IL7RA in CD4+ cells and PBMCs from VL patients using real-time qPCR. Our results did not show any significant difference in expression of IL7RA in PBMCs or CD4+ cells between VL patients and ECs (Fig 1B). However, in line with the transcriptional profiling data, when, we measured the levels of sIL-7Rα (CD127) in plasma we found that patients infected with L. donovani, both active infection (D-0) and 30 days post treatment (D-30) had significantly lower levels of sIL-7Rα compared to ECs (p<0.0001) (Fig 1C). Combined, the data indicate an aberrant expression of the IL-7 receptor in VL patients. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 6 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Fig 1. IL7RA expression in VL. A. Volcano plot of immune-related genes in peripheral blood CD4+ T cell data extracted from a previously published dataset [17]. Analysis of differentially expressed immune-related genes in peripheral blood CD4+ T cells between visceral leishmaniasis (VL, n = 12) patients prior to treatment (D0) and endemic controls (EC, n = 12) shows downregulation of IL7RA. B. Relative expression of IL7RA determined by RT-qPCR in PBMC and CD4+ T cells, as indicated, each dot represents one sample PBMC (EC n = 11, VL n = 13), CD4 (EC n = 11, VL n = 15). C. Soluble IL-7Rα plasma levels in EC (n = 8) and VL before (D-0, n = 13) and 30 days (D-30 n = 13) after initiation of drug treatment. Statistical significance was determined by Kruskal-Wallis with multiple comparison follow-up test for Fig 1C and are indicated as p<0.05; p<0.01; p<0.001; *p<0.0001. https://doi.org/10.1371/journal.pntd.0011960.g001 PBMC and CD4+ cells are not a major source of IL-7, and analysis of IL7 mRNA expression did not show any differences between IL7 mRNA between VL and EC cells (Fig 2A). Surpris- ingly, analysis of soluble IL-7 in plasma demonstrated that the IL-7 levels were significantly higher in active, VL compared to EC (p<0.001) (Fig 2B). Following treatment of VL, we observed a decrease in the level of soluble IL-7 in plasma (VL D0 (n = 14), 40.13 pg/ml ±19.79 SEM, VL D30 (n = 14) 19.8pg/ml ±12.85 SEM, with P<0.05). Fig 2. IL-7 expression and secretion following Leishmania donovani infection. A. Relative expression of IL7RA determined by RT-qPCR in PBMC and CD4+ T cells, as indicated, each dot represents one sample, PBMC (EC n = 11, VL n = 13), CD4 (EC n = 12, VL n = 13). Median range is depicted. B. IL-7 levels in the plasma of VL patients (n = 10), ECs (n = 10), as determined by ELISA. Statistical significance was determined by Mann-Whitney U-test for Fig 2A, and Kruskal-Wallis with multiple comparison follow-up test for Fig 2B and are indicated as p<0.05; p<0.01; p<0.001; **p<0.0001. https://doi.org/10.1371/journal.pntd.0011960.g002 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 7 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Fig 3. Surface protein expression of CD127 and CD132 on CD4+ T cells from endemic controls (ECs) and visceral leishmaniasis (VL) patients. A. Gating strategy for CD4+ T cell flow cytometry analysis. B. Percentage (top) and mean fluorescent intensity (MFI) (bottom) of CD127 on CD4+ T cells. C. Percentage (top) and MFI (bottom) of CD132 on CD4+ T cells. D. Percentage (top) and MFI (bottom) of CD38 on CD4+ T cells. Statistical significance between ECs (n = 11) and VL patients (n = 12) was determined by Mann-Whitney U-test in Fig 3B-D, and 3F are indicated as p<0.05; *p<0.01. E. Boolean-Gating (FlowJo), was used to define complex cell sub-population, and Simplified presentation of incredibly complex evaluations (SPICE) polyfunctionality analysis. SPICE was used to establish overlap in expression of CD38, CD127 and CD132 on CD4+ T cells. Pie charts represent the entire CD4+ T cell population expressing either CD38, CD127, CD132 or none of them. Data was generated from EC (n = 7) and VL patients (n = 7). F. Percentage and MFI of CD127, and CD132, expression on CD38+/- CD4 T+ cells of VL (n = 7) and EC (n = 7). https://doi.org/10.1371/journal.pntd.0011960.g003 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 8 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients The IL7 receptor is upregulated on VL CD4+ T cells compared to EC Next, we conducted an analysis of the surface expression of the IL-7 receptors (CD127 and CD132) on CD4+ T cell subsets (as defined in Fig 3A), and observed an upregulation of CD127 and CD132 on VL CD4+ T cells (Fig 3B and 3C). Additionally, we detected an increase in activated CD4+ T cells in VL patients based on the expression of CD38 (Fig 3D). The acti- vated CD38+ CD4+ T cells from VL patients expressed higher levels of IL-7Rα, as demon- strated by the higher MFI of CD127 compared to EC and by SPICE analysis (Fig 3D, 3E and 3F) and bar graph (S1 Fig). A detailed analysis of IL-7Rα expression on VL patient (D0) CD4+ T cells subsets (Fig 4B– 4H), (S2 Fig) showed that most Th subsets from VL patients express more, while Th2 and Th17 had similar levels of CD38, CD127 and CD132 compared to ECs. VL patient CD4+ T cells respond to IL-7 stimulation To test if IL-7 signaling was affected in VL patients we stimulated whole blood with rhIL-7 and measured phosphorylation of STAT5 (pSTAT5), as indicative of IL-7 signaling capacity (Fig 5). Increase in pSTAT5 was most evident in the CD4+ T cells, relative to other lymphocyte sub- sets (S3 Fig). Upon rhIL-7 stimulation pSTAT5 was more noticeable in VL patient as com- pared to EC CD4+ T cells, seen both as frequency of cells positive for pSTAT5 (Fig 5B and 5C) and MFI of pSTAT5 (Fig 5D). Baseline levels (unstimulated cells) of pSTAT5 were higher in VL patient CD4+ T cell compared to ECs (Fig 5D), in line with reported higher STAT5 mRNA levels in VL compared to EC CD4+ cells in our previous Nano-String mRNA expression analy- sis [18]. In VL CD4+ T cells, pSTAT5 frequency and MFI was increased in all the CD4+ Th cell sub- sets defined upon rhIL-7 stimulation (S4A and S4B Fig). Stimulation with rhIL-7 increased pSTAT5 in CD4+ T cell subsets from ECs, but never reached the levels observed in VL patient CD4+ T cells (S4A and S4B Fig). To test if IL-7 signaling was linked to activation of T cells, we next examined the IL-7 signaling potential in activated and non-activated CD4+ T cells using CD38 as a marker of activation (Fig 6A). In VL patient CD4+ T cells pSTAT5 staining was notably stronger in non-activated CD38- compared to activated CD38+ CD4+ T cells (Fig 6B and 6C), while no differences in pSTAT5 were seen between CD38- and CD38+ CD4+ T cells in ECs (Fig 6C and 6D). Discussion Lymphopenia and an inability to mount adequate T cell responses contribute to immunosup- pression and disease progression in VL patients. Deficiencies in IL-7 signaling have been linked to other chronic diseases such as HIV, and IL-7 therapy has been suggested to improve T cell survival in these patients [29]. Similar to observation made in HIV patients, previous studies by Chauhan et al. [18] and Kumar et al. [17] found downregulation of IL7RA (CD127) in T cells from VL patients compared to ECs [18]. We could not confirm the downregulation in the set of samples included in our analysis here, as no difference in mRNA expression of IL7RA between VL patient CD4+ T cells and PBMCs, relative to the same cell populations in ECs was observed. The lack of correlation between transcriptional data and RT-qPCR data, was unexpected but may be explained by the use of different individuals in the different assays combined with that the differences in RNA seq and Nanostring results seen between groups were not the most pronounced (logFC -1.39 and -0.719 respectively). However, our analysis of plasma showed less soluble IL-7Rα in the plasma of VL patients both before and 30 days post- drug treatment, compared to ECs, suggesting that the IL-7 pathway could be impaired during L. donovani infection. The precise biological role of soluble IL-7R (sIL-7Rα) remains unclear, PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 9 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Fig 4. Surface protein expression of CD127 and CD132 on CD4+ T cell subsets from endemic controls (ECs) and visceral leishmaniasis (VL) patients. A. Gating strategy for CD4+ T cell subsets. B-H. Boolean-Gating and Simplified presentation of incredibly complex evaluations (SPICE) polyfunctionality analysis. SPICE was used to establish overlap in expression of CD38, CD127 and CD132 on CD4+ T cell subsets in endemic controls (ECs) and visceral leishmaniasis (VL) patients; EC (n = 7), VL (n = 7). B. Treg cells C. Tem D. Th E. Th17_Th22 F. Th9 G. Th1 H. Th2. https://doi.org/10.1371/journal.pntd.0011960.g004 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 10 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Fig 5. IL-7-mediated activation of intracellular pSTAT5. A. Gating strategy for CD4+ T cells. B. Representative histograms of pSTAT5 in endemic controls (ECs) and visceral leishmaniasis (VL) patients following rhIL-7 treatment. C. Frequency of CD4+ T cells expressing pSTAT5 and D. Mean fluorescence intensity (MFI) of pSTAT5 in CD4+ T cell at baseline without (-) and after rhIL-7 stimulation (+) in EC (n = 8) and VL patients (n = 12). Statistical significance was determined by the Wilcoxon matched-pairs signed rank test between control and rhIL-7 stimulation in figure C-D, or Mann-Whitney U-test to compare EC (n = 8) and VL patients (n = 12) in Fig D. Statistically significant differences are indicated as P < 0.05; P < 0.01; P < 0.001; **P < 0.0001. https://doi.org/10.1371/journal.pntd.0011960.g005 but like the membrane-bound IL7Rα, the sIL-7Rα binds to IL-7 with comparable affinity and is suggested to inhibit IL-7 signaling [30,31]. The reduction in sIL-7Rα was accompanied by increased levels of IL-7 in plasma from VL patients (D0) compared to EC and VL post treat- ment (D30). Increased plasma level of IL-7 is a sign of lymphopenia [30,31], something fre- quently observed in VL patients [32] Higher IL-7 and less sIL-7Rα have also been seen in TB patients [27]. In these patients, the cell surface expression of IL-7Rα and the signaling capacity was reduced in T cells. Tran- scriptional downregulation of IL7RA in T cells that have received IL-7 signaling is a feed- back mechanism to prevent competition with T cells that have not yet received the signal [33]. While we found no significant reduction IL7RA mRNA in VL by qPCR, previous stud- ies reporting on transcriptional profiling of cells from VL patients found, in line with the observation made in TB patients, reduced IL7RA mRNA levels in CD4+ T cells from VL patients compared to ECs [17,18]. Interestingly, the surface expression of CD127 and CD132, was found to be increased in VL patients (D0) compared to ECs, this in contrast to TB patients, where IL-7Rα surface expression also was reduced. This finding led us to fur- ther investigate IL-7Rα surface expression and the signaling capacity of the receptor on CD4+ T cell subsets. Most CD4+ T cell subsets from VL patients had more CD127 and PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 11 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients Fig 6. pSTAT5 by CD38+ and CD38- CD4+ T cells. A-B. Gating Strategy for CD38+ and CD38- CD4+ T cells. C. Frequency of pSTAT5 expressing CD38+ and CD38- CD4+ T cells from endemic controls (EC) and visceral leishmaniasis (VL) patients after rhIL-7 stimulation. D. pSTAT5 mean fluorescence intensity (MFI) in CD38+ and CD38- CD4+ T cells upon rhIL-7 stimulation. Data was generated from EC (n = 8) and VL (n = 12) donor samples. Statistical differences were determined using comparison between two groups with a Wilcoxon matched-pairs signed rank test between control and rIL-7 stimulation and significant differences are indicated as P < 0.05; P < 0.01; P < 0.001; ***P < 0.0001. https://doi.org/10.1371/journal.pntd.0011960.g006 CD132 on their surface as compared to ECs. CD38, which is upregulated by inflammatory mediators [34], was used as an activation marker on CD4+ T cells (9, 20–22, 29, 31–40). More CD4+ T cells expressed CD38 in active VL compared to ECs. These activated (CD38+) cells expressed more IL-7Rα as compared to non-activated (CD38-) CD4+ T cells from VL patients. Impaired IL-7 signaling via the IL-7Rα, as measured by pSTAT5 levels in T cells has been observed in subjects with HIV infection and TB [35–37]. In contrast, pSTAT5 levels were higher in VL patient CD4+ T cells at baseline and after stimulation with rhIL-7, compared to EC CD4+ T cells. This finding was surprising since VL is characterized by lymphopenia and dysregulated CD4+ T cells, but is in accord with elevated IL-7Rα on the cell surface of VL patient CD4+ T cells, and previously reported upregulation of STAT5 mRNA in VL patient CD4+ T cells [18]. The increased IL-7 signaling may be a response to the lymphopenia to sup- port the survival of existing T cells in VL. While we show that additional rhIL-7 stimulation increased (already heightened) pSTAT5 in VL patient CD4+ T cells compared to EC, it is uncertain if manipulation of the IL-7 signaling pathway would improve cell survival in VL patients. When comparing pSTAT5 in CD38+ compared to CD38- CD4+ T cells, less pSTAT5 was seen in the CD38+ CD4+ T cell subset from VL patients, where the pSTAT5 levels were PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 12 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients similar in the two subsets in ECs, suggesting that there is a feedback mechanism to downregu- late IL-7 signaling upon activation. While not conclusive, our attempts to improve cell survival in 72-hour antigen stimulation assays by addition of rhIL-7 were ineffective on both EC (n = 5) and VL (n = 5) cells, measured as frequency of 7AAD, Annexin V positive lymphocytes, moreover the addition of rhIL-7 did not alter the levels of IFNγ in culture supernatants of antigen or superantigen stimulated cells. On the basis of these preliminary findings, we did not pursue these investigations further. However, further investigation into feedback mechanisms regulating IL-7 signaling could be of relevance to understand cell survival and death in lymphopenic conditions. Interestingly, it has been shown in mice that prolonged exposure to high IL-7 levels leads to IFN-γ triggered apoptosis in CD8 T cells, with cells having low affinity T cell receptor (TCR) engagement being particularly affected [38]. Furthermore, Rehti et al. have shown that high levels of IL-7 can prime both human T cells and B cells for Fas-mediated apoptosis in [39,40] With elevated expression and secretion of Fas/FasL [41] and IFN-γ [42] being a reported features in VL patients, the high IL-7 levels and the elevated IL-7 signaling assumed to promote cell survival and proliferation could potentially simultaneously prime the T cell for to Fas/FasL induced death. Thus, the lower pSTAT5 seen in VL CD38+ compared to CD38- CD4+ T cell could potentially be beneficial for the survival of effector T cells in VL. In conclusion, defects in IL-7Rα expression or IL-7 signaling were not evident in CD4+ T cells from VL patients. Instead, VL patient CD4+ T cells appeared to maintain an elevated expression of IL-7Rα and pSTAT5, as compared to ECs. Our data does not support impaired IL-7 signaling as an explanation for loss of CD4+ T cells during VL. We speculate that the IL- 7/IL-7Rα pathway may allow cells to survive longer but render them weakened and susceptible to apoptosis when actively engaged in the immune response. Supporting information S1 Fig. Supporting to Fig 3E. Frequency of CD4+ T cell expressing CD38, CD127 and/or CD132 as indicated on the Y axis. (TIF) S2 Fig. Supporting to Fig 4B–4H. Frequency of the gated CD4+T cells subsets expressing CD38, CD127 and/or CD132 as indicated on the Y axis. (TIF) S3 Fig. Gating of lymphocyte populations and representative pSTAT5 in CD4+ T cells and other lymphocytes upon rhIL-7 stimulation. pSTAT5 in CD3+ CD4+ T cells, CD3+ CD4- T cells and CD3- CD4- cells in EC and VL. (TIF) S4 Fig. pSTAT5 expression by CD4+ T cell subsets from endemic controls (EC) and visceral leishmaniasis (VL) patients. A. Merged CD4+ T cell samples were used to create t-distributed stochastic neighbor embedding (tSNE) plots showing CD38, STAT5 and pSTAT5 expression by CD4+ T cells from ECs and VL patients upon rhIL-7 stimulation. Each point represents one sin- gle cell and cells in the same cluster represents high similarity in phenotypic expression. FACS data, showing B. Frequency and C. Mean Fluorescence Intensity (MFI) of pSTAT5 in CD4+ T cell subsets identified as shown in Fig 4A. The heat map was rendered using the Morpheus tool, and the grid shows quantitative signaling upon rhIL-7 treatment in activated (CD38+) and non- activated (CD38-) CD4+ T cell subsets (columns) from ECs and VL patients (rows). (TIF) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011960 February 26, 2024 13 / 16 PLOS NEGLECTED TROPICAL DISEASESIL-7 signaling in CD4+ T cells of VL patients S1 Table. Reagent List. (DOCX) S2 Table. FACS Antibody. (DOCX) Acknowledgments We thank all volunteers and patients for their consent and participation in this study. We are also thankful to the KAMRC staff for their assistance in collection of clinical samples. SK would like to acknowledge Indian Council of Medical Research (ICMR) for providing him senior research fellowship. Author Contributions Conceptualization: Shashi Kumar, Rajiv Kumar, Christian Engwerda, Susanne Nyle´n, Shyam Sundar. 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10.1371_journal.pgph.0002891	RESEARCH ARTICLE HIV epidemiologic trends among occupational groups in Rakai, Uganda: A population-based longitudinal study, 1999–2016 1, Joseph Kagaayi2,3, Joseph Ssekasanvu1,2, Robert Ssekubugu2, Victor O. PopoolaID Grace Kigozi2, Anthony Ndyanabo2, Fred Nalugoda2, Larry W. Chang1,2,4, Tom Lutalo2, Aaron A. R. Tobian1,5, Donna Kabatesi6, Stella Alamo6, Lisa A. MillsID 6, Godfrey Kigozi2, Maria J. Wawer1,2, John SantelliID David Serwadda2,3, Justin Lessler1,9,10, M. Kate Grabowski1,2,5* 7, Ronald H. Gray1,2, Steven J. Reynolds4,8, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Popoola VO, Kagaayi J, Ssekasanvu J, Ssekubugu R, Kigozi G, Ndyanabo A, et al. (2024) HIV epidemiologic trends among occupational groups in Rakai, Uganda: A population-based longitudinal study, 1999–2016. PLOS Glob Public Health 4(2): e0002891. https://doi.org/10.1371/ journal.pgph.0002891 Editor: Siyan Yi, National University of Singapore, SINGAPORE Received: August 8, 2023 Accepted: January 12, 2024 Published: February 20, 2024 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: Data are included in supporting materials. Funding: This study was supported by the National Institute of Allergy and Infectious Diseases (grants R01AI110324, U01AI100031, and U01AI075115 to RHG, R01AI143333 to LWC, R01AI155080 and K01AI125086-01 to MKG), the National Institute of Mental Health (grants R01MH107275 to LWC and R01MH105313 to CK), the Eunice Kennedy Shriver National Institute of Child Health and Human 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America, 2 Rakai Health Sciences Program, Entebbe, Uganda, 3 Makerere University School of Public Health, Kampala, Uganda, 4 Department of Medicine, Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America, 5 Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America, 6 Division of Global HIV and TB, Centers for Disease Control and Prevention Uganda, Kampala, Uganda, 7 Department of Population and Family Health and Pediatrics, Columbia University, New York, New York, United States of America, 8 Laboratory of Immunoregulation, Division of Intramural Research, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America, 9 Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, North Carolina, United States of America, 10 Carolina Population Center, Chapel Hill, North Carolina, United States of America * mgrabow2@jhu.edu Abstract Certain occupations have been associated with heightened risk of HIV acquisition and spread in sub-Saharan Africa, including female bar and restaurant work and male transpor- tation work. However, data on changes in population prevalence of HIV infection and HIV incidence within occupations following mass scale-up of African HIV treatment and preven- tion programs is very limited. We evaluated prospective data collected between 1999 and 2016 from the Rakai Community Cohort Study, a longitudinal population-based study of 15- to 49-year-old persons in Uganda. Adjusted prevalence risk ratios for overall, treated, and untreated, prevalent HIV infection, and incidence rate ratios for HIV incidence with 95% con- fidence intervals were estimated using Poisson regression to assess changes in HIV out- comes by occupation. Analyses were stratified by gender. There were 33,866 participants, including 19,113 (56%) women. Overall, HIV seroprevalence declined in most occupational subgroups among men, but increased or remained mostly stable among women. In con- trast, prevalence of untreated HIV substantially declined between 1999 and 2016 in most occupations, irrespective of gender, including by 70% among men (12.3 to 4.2%; adjPRR = 0.30; 95%CI:0.23–0.41) and by 78% among women (14.7 to 4.0%; adjPRR = 0.22; 95% CI:0.18–0.27) working in agriculture, the most common self-reported primary occupation. Exceptions included men working in transportation. HIV incidence similarly declined in most occupations, but there were no reductions in incidence among female bar and restaurant workers, women working in local crafts, or men working in transportation. In summary, PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 1 / 18 PLOS GLOBAL PUBLIC HEALTHDevelopment (grants R01HD070769 and R01HD050180 to MJW), the Division of Intramural Research of the National Institute for Allergy and Infectious Diseases (to SJR), the Johns Hopkins University Center for AIDS Research (grant P30AI094189 to MKG), and the President’s Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (grant NU2GGH000817 to DS). The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: Drs. Wawer and Gray are paid consultants to the Rakai Health Sciences Program and serve on its Board of Directors. These arrangements have been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. HIV epidemiologic trends among occupational groups in Rakai, Uganda untreated HIV infection and HIV incidence have declined within most occupational groups in Uganda. However, women working in bars/restaurants and local crafts and men working in transportation continue to have a relatively high burden of untreated HIV and HIV incidence, and as such, should be considered priority populations for HIV programming. Introduction The scale-up of combination HIV treatment and prevention interventions (CHI) in sub-Saha- ran Africa has led to significant declines in HIV incidence [1–4]. However, rates of new HIV infection remain significantly above elimination thresholds in most countries [5,6]. Demo- graphic heterogeneities in population-level risk of HIV acquisition and onward transmission likely drive continued virus spread, but they remain poorly characterized. A detailed under- standing of such heterogeneities may facilitate targeted control efforts leading to further declines in HIV incidence and, ultimately, disease elimination. Decades-old data established a person’s occupation as a salient risk factor for HIV acquisi- tion in Africa. Occupations historically associated with increased HIV risk have included min- ing, bar work, truck driving, sex work, fishing, trading, and construction [3,4,7–10]. For example, a study of HIV risk in Uganda, conducted in 1992, prior to the availability of antire- troviral therapy (ART), found that bar and restaurant work, trading, and truck and taxi driving were associated with three times higher odds of HIV acquisition compared to agricultural work [4]. In southern Africa, truck driving, factory work, and mining have been strongly linked to higher HIV burden [10–12]. While historical studies have provided useful insights into HIV risk by occupation, there are very limited data comparatively assessing key HIV out- comes within occupational subgroups since the widespread rollout of HIV interventions in sub-Saharan Africa. Given that an individual’s occupation can be readily assessed in program- matic settings, understanding whether HIV burden currently varies by occupation may facili- tate efficient targeting of interventions. Here, we assessed the extent to which occupation-specific population prevalence of HIV and HIV incidence have changed since the implementation of combination HIV interventions (CHIs) including ART, using data from the Rakai Community Cohort Study (RCCS), a popu- lation-based HIV surveillance cohort in southern Uganda. We have previously measured trends in HIV prevalence and incidence in the RCCS and shown a 42% reduction in HIV inci- dence with ART rollout beginning in 2004 and VMMC scale-up beginning in 2007.13 How- ever, it remains unclear whether or not untreated HIV prevalence and incidence declines have occurred uniformly across occupational subgroups in this population. We hypothesized that while the burdens of HIV, untreated HIV, and HIV incidence have declined within all occupa- tions, heterogeneities in HIV outcomes by occupation persist. Methods Study population and procedures The Rakai Community Cohort Study (RCCS) is conducted by the Rakai Health Sciences Pro- gram and is an open, population-based census and cohort study including consenting individ- uals aged 15–49 years across 40 communities in southern Uganda [13]. Individuals are followed at ~18-month intervals. Briefly, the RCCS conducts a household census to enumerate all individuals who are residents in the household, irrespective of presence or absence in the PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 2 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda home at time of census, based on sex, age, and how long they have been resident in the com- munity. The census is followed by a survey of residents aged 15 to 49 years. All RCCS partici- pants provide written informed consent prior to interviews. Participant interviews provide self-reported data on socio-demographic characteristics, sexual behaviors, male circumcision status, and ART use. Two attempts are made to contact individuals who are censused and eligi- ble but who do not participate in the surveys. To determine individual participant HIV serostatus in RCCS, venous blood samples are obtained for HIV testing. Prior to October 2011, HIV testing used enzyme immunoassays (EIAs) with confirmation via western blot. Subsequently, a field-validated, parallel three-test, rapid HIV testing algorithm was introduced with demonstrated high sensitivity (>99.5%) and specificity (>99.5%). All rapid test positives in RCCS are confirmed by two EIAs, with western blot or PCR for discordant EIA results [14,15]. In this study, we included data from 12 consecutive RCCS survey rounds conducted between April 6, 1999, and September 2, 2016, collected from 30 continuously surveyed communities. The 12 surveys are herein denoted as Surveys 1 through 12: start and completion dates for each survey are included in S1 Table. Participation rates among census-eligible persons present in the community at the time of survey ranged from 74% to 98% (59%-66%, including those absent from the community) across survey rounds [16]. There were generally lower levels of participation in earlier survey rounds due to higher refusal rates. During the study period, par- ticipant retention (i.e., follow-up between consecutive survey rounds) decreased from 73% to 55% [16,17]. Loss to follow-up was due mostly to out-migration to non-eligible study communi- ties. When considering only participants who were resident in the community at time of survey (e.g. excluding non-eligible migrants), retention decreased over the analysis from 93% to 80%. For this study, RCCS data were accessed from December 15, 2018 through December 15, 2022. This study was approved by the Research and Ethics Committee of the Uganda Virus Research Institute and the Johns Hopkins School of Medicine Institutional Review Board. This study was also approved for the inclusion of children as ’research not involving greater than minimal risk’ with the permission of at least one parent. Measurement and classification of participant occupation Occupational data were collected as self-reported primary occupations at the time of RCCS interviews. Participants were asked, “What kind of work do you do, or what kind of activities keep you busy during an average day, whether you get money for them or not.” There were 23 occupational subgroups that participants could select from on the questionnaire, including “other.” Individuals who listed “other” were asked to provide occupational details as a free-text response. Free-text responses were reviewed and re-assigned into pre-existing categories, or new categories were created as needed. There were 36 self-reported primary occupations, which were subsequently aggregated into 15 primary occupational subgroups (S2 Table). Of these larger subgroups, eight among men (agriculture, trading, student, construction, civil ser- vice, causal labor, mechanic, transportation) and nine among women (agriculture, trading, student, bar/restaurant work, civil service, hairdressing, local crafts, tailoring/laundry, house- keeping) contained a median number of � 50 observations per survey across all surveys (S3A and S3B Table). These eight occupational subgroups among men and nine among women were the primary exposure units for all subsequent occupational analyses. Primary and secondary outcomes Our primary study outcomes were (1) prevalent HIV infection, (2) prevalent untreated HIV infection, and (3) incident HIV infection. We defined prevalent HIV infection as any HIV PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 3 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda infection in an individual (whether treated or untreated) and untreated HIV as HIV infection in an individual with HIV who did not self-report ART use at time of survey. We have previ- ously shown that self-reported ART use has high specificity (99%) and moderate sensitivity (77%) in this population, and that this does not substantially vary by self-reported occupation [18]. We note that the prevalence of untreated HIV infection in the overall population (includ- ing seronegative individuals and persons living with HIV) was measured as a surrogate mea- sure for population prevalence of viremia, which previous studies have shown is predictive of HIV incidence [16,19]. Incident HIV infection was defined as a first HIV seropositive test result in a person with a prior seronegative test result irrespective of HIV treatment status at first positive visit. The unit of analysis for HIV incidence was person-years of follow-up between surveys among persons who were initially HIV-seronegative and who contributed two consecutive survey visits or two visits with no more than one missing intervening survey. Incident infections were assumed to have occurred at the mid-point of the visit interval. Our secondary outcome was self-reported ART use among persons with HIV. Scale-up and measurement of combination HIV intervention coverage in Rakai During the analysis period, ART rollout in Uganda, including Rakai, was phased as follows: in 2004, ART was offered to persons with a CD4-T-cell count of <250 cells/mm3; in 2011, the CD4 T-cell criterion was raised to <350; and in 2013, it was further increased to <500 and ART was also offered to all individuals with HIV, regardless of CD4 T-cell count, if they were pregnant, in a serodiscordant relationship, or self-identified as a sex worker or fisherfolk. The prevalence of self-reported ART use had risen to 69% among all persons with HIV by 2016. In addition to ART, the Rakai Health Sciences Program has provided free VMMC since 2007 to adolescents and men aged 13 years or older [16]. The prevalence of male circumcision increased from 15% in 1999 to 59% by 2016 [16]. Impacts of universal HIV test and treat and pre-exposure prophylaxis were not assessed in this study as implementation of these programs occurred after the analysis period in 2017 and 2018, respectively. To assess changes in HIV incidence by occupation over calendar time, we divided the study period into pre-CHI (surveys 1–5; 1999–2004), early-CHI scale up (surveys 6–9; 2005–2011), and mature-CHI (surveys 10–12; 2011–2016) periods. Period-specific baselines were estab- lished as the first survey during each period, while the study baseline for individual partici- pants was defined as their first survey during the entire study period. Statistical analysis Demographic characteristics of participants at period-specific baselines were summarized using descriptive statistics, including median and interquartile ranges for continuous variables and frequencies and percentages for categorical variables. The prevalence of each primary occupation was estimated as the number of participants self-reporting that occupation, expressed as a proportion of all participants surveyed, and was stratified by sex. Self-reported ART use among participants with HIV was assessed during the early and mature-CHI periods and at the final study visit. Overall and untreated HIV prevalence were assessed at each of the 12 study visits and HIV incidence was estimated during the eleven inter-survey intervals over the 17-year analysis period. To evaluate changes in prevalence of untreated HIV infection and HIV incidence within occupational subgroups, we constructed log-binomial regression mod- els to estimate prevalence risk ratios (PRR) and Poisson regression models to estimate inci- dence rate ratios (IRR). Because our primary objective was to describe patterns of HIV infection within occupational subgroups as opposed to causal inference, PRRs and IRRs were PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 4 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda only adjusted for age and marital status to ensure demographic comparability across popula- tions. We calculated IRRs for HIV infection, comparing incidence rates during the pre-, early- , and late-CHI periods. All statistical analyses were performed in Stata version 15 and the R sta- tistical software (Version 3.6). Results Characteristics of study participants Overall, 33,866 individuals (including 19,113 (56%) women) participated, contributing to a total of 102,759 person visits. Of these participants, 17,840 women and 14,244 men who were HIV-seronegative at their first study visit contributed 57,912 and 49,403 person-years to the incidence cohort, respectively. S4 Table shows characteristics of the study population by sex at the first (baseline) study visit within the CHI periods. Among women, during the pre-CHI baseline visit, median age was 25 years (IQR: 20–34), 59% (2056/3474) were married, and the prevalence of untreated HIV was 16%. Median age at the late-CHI baseline visit for women was somewhat older at 28 years (IQR: 22–34), 60% (2265/3758) were married, and prevalence of untreated HIV was 9.1%. Among men, during the first pre-CHI baseline visit, median age was 26 years (IQR: 20–33), 56% (1418/2518) were married, 15% (374/2518) were circumcised, and the prevalence of untreated HIV was 8.1%. In comparison, median age at the late-CHI baseline visit for men was 27 years (IQR: 20–36), 52% (1524/2944) were married, 46% (1359/ 2944) were circumcised, and the prevalence of untreated HIV was 6.4%. Population prevalence of occupations over calendar time Fig 1 shows the proportion of participants in each occupational subgroup over calendar time stratified by gender (see also S5A and S5B Table). At the initial visit (1999–2000), the majority Fig 1. Prevalence of primary occupational subgroups by gender in the Rakai Community Cohort Study, 1999–2016. https://doi.org/10.1371/journal.pgph.0002891.g001 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 5 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda of women (61%) reported agriculture as their primary occupation. While agriculture remained the most commonly reported female occupation at the final visit (2015–16), its prevalence sig- nificantly declined to 40% (PRR = 0.66; 95%CI: 0.62–0.69) (Fig 1). Declines in agricultural work among women were accompanied by an increase in the average age within the occupa- tion (S1 Fig) and were predominately offset by the proportion of women who reported work- ing in trading (9.4% in 1999 vs.16% in 2016, PRR = 1.7; 95%CI: 1.49–1.91) and being a student (7.3% vs. 14%, PRR = 1.97; 95%CI: 1.72–2.27). Notably, no women or men reported sex work as a primary occupation, and very few people reported being unemployed (n<7 at all study vis- its; S6A and S6B Table). Men similarly reported agriculture and trading as their most common primary occupations (Fig 1). Between the first (1999–2000) and last (2015–2016) study visit, there was a decrease in the proportion of male participants reporting agriculture (39% vs. 29%, PRR = 0.74; 95%CI: 0.68–0.80), while a greater proportion reported being a student (13% vs. 22%, PRR = 1.74; 95% CI: 1.53–1.96), mechanic (2.5% vs. 5.6%, PRR = 2.29, 95%CI: 1.74–3.01), or working in trans- portation (1.9% vs. 4.7%, PRR = 2.42, 95%CI: 1.78–3.28). Trends in the prevalence of HIV, ART use, and untreated HIV within occupations The prevalence of HIV remained unchanged in most occupational groups among women (Table 1), but increased among women working in agriculture (adjPRR = 1.19; 95%CI: 1.04– 1.35) and decreased among hairdressers (adjPRR = 0.27; 95%CI: 0.18–0.41) and housekeepers (adjPRR = 0.68; 95%CI: 0.47–0.98). Among men, HIV prevalence decreased or trended down- wards in most occupational groups but non-significantly trended upwards among men work- ing in transportation (8.2% vs. 15.1%; adjPRR = 1.71; 95% CI: 0.64–4.58) and men working in casual labor (10.6% vs. 16.7%; adjPRR = 1.26; 95% CI: 0.58–2.73). The proportion of male and female participants with HIV self-reporting ART use increased over time among all occupational subgroups (Table 2A and 2B). During the late-CHI period and at the final study visit, levels of ART use were highest among women working in agricul- ture and lowest among female students. ART use was statistically significantly lower among female traders (adjPRR = 0.91; 95%CI: 0.83–0.98) and bar and restaurant workers (adjPRR = 0.87; 95%CI: 0.78–0.97) compared to women working in agriculture during the late CHI-period. Among men, ART use was highest among those working in civil service over the entire analysis period. During the late CHI period, ART use was statistically significantly lower among men working in trading (adjPRR = 0.91; 95%CI: 0.83–0.98) and male students (adjPRR = 0.59; 95%CI: 0.41–0.84) compared to men working in agriculture. Figs 2 and 3 show the prevalence of untreated HIV within occupational subgroups among men and women at each of the 12 survey visits, respectively. Significant declines in the preva- lence of untreated HIV were observed in nearly all occupational subgroups, irrespective of gender, with scale-up of ART use. Relative changes in untreated HIV prevalence between the first and final study visits are shown in Table 3 for each occupational subgroup by gender. The prevalence of untreated HIV significantly decreased within most occupations. For example, among women working in agriculture, prevalence of untreated HIV decreased from 14.7% to 4.0% (adjPRR = 0.22; 95%CI: 0.18–0.27), and among men, prevalence of untreated HIV decreased from 12.3% to 4.2% (adjPRR = 0.30, 95%CI: 0.23–0.41). Women working in bars and restaurants had among the highest HIV burdens across all occupational subgroups (Fig 3). The prevalence of untreated HIV significantly declined among female bar and restaurant workers from a high of 34.7% in 1999–2000 to 12.0% by 2015–2016 (adjPRR = 0.38; 95%CI: 0.25–0.58) (Table 3). However, these women had a 41.6% overall HIV seroprevalence at the PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 6 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Table 1. Changes in prevalence of HIV infection between RCCS survey visit 1 (1999–2000) and RCCS survey visit 12 (2015–2016) by primary occupational sub- group and gender of study participants. Occupational subgroup Agriculture Women N = 10,121 Unadjusted PRR (95% CI) Visit 12 (2015–2016), HIV prevalence, % (n/T) n = 6647 18.0 (481/ 2669) 1.23 (1.08– 1.40) Visit 1 (1999– 2000), HIV prevalence, % (n/T) n = 3474 14.7 (313/ 2128) Construction - - - Trading 21.5 (70/ 325) 19.2 (201/ 1048) 0.89 (0.70– 1.13) Casual labor - - - Civil service 11.2 (19/ 170) 10.8 (57/529) Student 2.4 (6/254) 3.1 (30/959) Mechanic Transportation - - - - Bar/Restaurant worker 34.7 (50/ 144) 41.6 (111/267) Local crafts 19.8 (20/ 101) 24.8 (34/137) Hairdressing 46.3 (19/41) 13.7 (41/300) Tailoring/ laundry 4.0 (2/50) 13.1 (16/122) Housekeeping 16.7 (38/ 227) 12.7 (69/545) Other occupations 17.6 (6/34) 26.8 (19/71) 0.96 (0.59– 1.57) 1.32 (0.56– 3.15) - - 1.20 (0.92– 1.56) 1.25 (0.77– 2.05) 0.30 (0.19– 0.46) 3.28 (0.78– 13.79) 0.76 (0.53– 1.09) 1.52 (0.66– 3.46) All occupations 15.6 (543/ 3474) 15.9 (1059/ 6647) 1.02 (0.93– 1.12) Men N = 7,876 adjPRR* (95% CI) adjPRR p-value Visit 1 (1999– 2000), HIV prevalence, % (n/T) n = 2,518 Visit 12 (2015– 2016), untreated HIV prevalence, % (n/ T) n = 5,358 Unadjusted PRR (95% CI) adjPRR** (95% CI) adjPRR p-value 1.19 (1.04– 1.35) - 0.82 (0.64– 1.05) - 0.92 (0.54– 1.58) 0.91 (0.37– 2.26) - - 1.22 (0.93– 1.61) 0.98 (0.59– 1.65) 0.27 (0.18– 0.41) 2.49 (0.58– 10.62) 0.68 (0.47– 0.98) 1.48 (0.63– 3.50) 0.95 (0.86– 1.04) 0.010 12.3 (120/975) 11.9 (183/1538) - 12.6 (36/285) 10.8 (54/500) 0.112 12.7 (51/401) 11.0 (83/756) - 10.6 (7/66) 16.7 (22/132) 0.772 10.4 (23/221) 6.1 (26/429) 0.837 0.6 (2/319) 0.5 (6/1178) - - 0.158 0.945 <0.001 0.218 0.040 9.7 (6/62) 4.6 (14/302) 8.2 (4/49) 15.1 (38/252) - - - - - - - - - - 0.97 (0.78– 1.20) 0.83 (0.67– 1.02) 0.081 0.86 (0.58– 1.27) 0.86 (0.62– 1.20) 1.57 (0.71– 3.50) 0.58 (0.34– 1.00) 0.64 (0.43– 0.94 0.59 (0.41– 0.84) 1.26 (0.58– 2.73) 0.50 (0.30– 0.83) 0.025 0.004 0.558 0.008 0.81 (0.17– 4.01) 0.59 (0.12– 2.99) 0.524 0.48 (0.19– 1.20) 1.85 (0.69– 4.95) 0.38 (0.15– 0.99) 1.71 (0.64– 4.58) 0.049 0.286 - - - - - - - - - - - - - - - 0.369 11.4 (16/140) 14.0 (38/271) 1.23 (0.71– 2.12) 1.15 (0.66– 1.99) 0.619 0.243 10.5 (265/ 2518) 8.7 (464/5358) 0.82 (0.71– 0.95) 0.72 (0.62– 0.83) <0.001 PRR = prevalence risk ratios; adjPRR = adjusted prevalence risk; Models adjusted for age and marital status of study participants. https://doi.org/10.1371/journal.pgph.0002891.t001 final study visit in 2016 and still maintained a three-fold higher burden of untreated HIV com- pared to women working in agriculture at the final versus initial visits (12.0% versus 4.0%). Women working in local crafts and in trading also continued to have a high prevalence of PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 7 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Table 2. a. Prevalence of self-reported ART use among women with HIV during the early and late-CHI periods and at the final study visit (Visit 12). b. Prevalence of self-reported ART use among men with HIV during the early and late-CHI periods and at the final study visit (Visit 12). Early–CHI (2004–2011) N = 3,352 Late–CHI (2011–2016) N = 2,695 Visit 12 N = 1,059 % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) Agriculture 24.9 (432/1734) Trading 25.5 (149/584) Casual labor Civil service - 19.5 (42/215) Student Bar/restaurant worker Local crafts 11.8 (2/17) 22.7 (65/287) 12.8 (12/94) Hairdressing 21.0 (22/105) Tailoring/laundry 23.7 (9/38) Housekeeping 12.4 (28/226) Other occupations 23.1 (12/52) Ref 1.02 (0.87– 1.20) - 0.78 (0.59– 1.04) 0.47 (0.13– 1.74) 0.91 (0.72– 1.14) 0.51 (0.30– 0.88) 0.84 (0.58– 1.23) 0.95 (0.53– 1.69) 0.50* (0.35– 0.71) 0.93 (0.56– 1.53) Ref 1.04 (0.89– 1.22) - 0.89 (0.68– 1.17) 1.31 (0.35– 4.92) 0.95 (0.76– 1.20) 0.57** (0.34– 0.95) 1.13 (0.76– 1.66) 1.07 (0.66– 1.75) 0.73* (0.52– 1.04) 0.82 (0.50– 1.33) 64.6 (811/1256) 56.9 (302/531 - 58.2 (85/146) 38.0 (19/50) 55.9 (160/286) 50.0 (32/64) 54.3 (51/94) 60.0 (18/30) 52.5 (94/179) 66.1 (39/59) Ref 0.88* (0.81– 0.96) - 0.90 (0.78– 1.04) 0.59* (0.41– 0.84) 0.87 (0.78– 0.97) 0.77 (0.60– 0.99) 0.84* (0.70– 1.02) 0.93 (0.69– 1.25) 0.81* (0.70– 0.94) 1.02 (0.85– 1.24) Ref 0.91 (0.83– 0.98) - 0.93 (0.81– 1.07) 0.83 (0.58– 1.20) 0.89 (0.80– 0.99) 0.82 (0.65– 1.05) 0.95 (0.79– 1.15) 0.99 (0.75– 1.31) 0.96 (0.83– 1.11) 1.04 (0.85– 1.27) 78.0 (375/481) 68.2 (137/201) - 70.2 (40/57) 53.3 (16/30) 71.2 (79/111) 58.8 (20/34) 65.9 (27/41) 75.0 (12/16) 63.8 (44/69) 79.0 (15/19) Ref 0.87 (0.79– 0.97) - 0.90 (0.76– 1.07) 0.68** (0.49– 0.96) 0.91 (0.80– 1.04) 0.76* (0.57– 1.00) 0.85 (0.67– 1.06) 0.96 (0.72– 1.28) 0.82 (0.68– 0.98) 1.01 (0.80– 1.28) Ref 0.90 (0.81– 0.99) - 0.93 (0.78– 1.11) 0.86 (0.60– 1.22) 0.93 (0.82– 1.06) 0.79 (0.60– 1.05) 0.92 (0.74– 1.15) 1.01 (0.75– 1.35) 0.90 (0.75– 1.08) 1.05 (0.82– 1.36) Early–CHI (2004–2011) N = 1,702 Late–CHI (2011–2016) N = 1,260 Visit 12 N = 464 % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) Agriculture Construction 21.2 (140/661) 7.8 (14/180) Trading 14.9 (47/316) Casual labor 21.7 (15/69) Civil service 24.5 (34/139) Student 33.3 (4/12) Mechanic 18.6 (11/59) Ref 0.37* (0.22– 0.62) 0.70 (0.52– 0.95) 1.03 (0.64– 1.64) 1.16 (0.83– 1.60) 1.57 (0.70– 3.55) 0.88 (0.51– 1.53) Ref 0.48*** (0.29– 0.80) 0.76* (0.57– 1.01) 1.26 (0.79– 2.02) 0.96 (0.69– 1.34) 11.83* (4.72– 29.68) 0.87 (0.54– 1.40) 54.4 (262/482) 41.0 (64/156) 42.0 (94/224) 36.9 (24/65) 60.6 (43/71) 35.7 (5/14) 43.3 (13/30) Ref 0.76* (0.62– 0.93) 0.77* (0.65– 0.92) 0.68 (0.49– 0.94) 1.11 (0.91– 1.37) 0.66 (0.32– 1.33) 0.80 (0.53– 1.21) Ref 0.86 (0.70– 1.06) 0.80* (0.67– 0.94) 0.70 (0.51– 0.96) 1.03 (0.84– 1.25) 1.20 (0.56– 2.55) 0.80 (0.52– 1.23) 64.5 (118/183) 55.6 (30/54) 57.8 (48/83) 59.1 (13/22) 84.6 (22/26) 50.0 (3/6) 50.0 (7/14) Ref 0.86 (0.66– 1.12) 0.90 (0.73– 1.11) 0.92 (0.64– 1.32) 1.31*** (1.08– 1.60) 0.78 (0.35– 1.74) 0.78 (0.45– 1.32) Ref 0.91 (0.69– 1.19) 0.91 (0.74– 1.12) 0.91 (0.64– 1.29) 1.21* (1.00– 1.48) 1.21 (0.49– 2.96) 0.82 (0.50– 1.34) (Continued ) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 8 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Table 2. (Continued) Early–CHI (2004–2011) N = 3,352 Late–CHI (2011–2016) N = 2,695 Visit 12 N = 1,059 % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) % self-reporting ART (n/T) Transportation 15.4 (18/117) Other occupations 16.1 (24/149) 0.73 (0.46– 1.14) 0.76 (0.51– 1.13) 1.01 (0.67– 1.52) 0.83 (0.57– 1.21) 42.9 (48/112) 50.9 (54/106) https://doi.org/10.1371/journal.pgph.0002891.t002 PRR (95% CI) 0.79 (0.63– 0.99) 0.94 (0.76– 1.15) adjPRR (95% CI) % self-reporting ART (n/T) PRR (95% CI) adjPRR (95% CI) 0.94 (0.76– 1.17) 1.01 (0.83– 1.24) 52.6 (20/38) 60.5 (23/38) 0.82 (0.59– 1.13) 0.94 (0.71– 1.24) 0.94 (0.70– 1.28) 0.99 (0.76– 1.29) untreated HIV compared to women in agriculture at the final visit (Table 3). Men working in transportation did not have significantly higher HIV prevalence than other male occupations at the initial visit (Table 3). However, we observed no declines in untreated HIV in this popula- tion over the analysis period, and by the final visit, they had the highest prevalence of untreated HIV among all male occupations at 7.1%. Changes in HIV incidence within occupations before and during scale-up of CHI programs Table 4 shows HIV incidence by occupation, gender, and calendar time. In the early CHI period, HIV incidence rates ranged from 0.4 to 2.3 per 100 person-years between occupational subgroups among women, and from 0.1 to 1.8 per 100 person-years among men. Between the early and late CHI periods, HIV incidence declined or trended downwards among most occu- pational subgroups. For example, among those working in agriculture, HIV incidence declined by 67% among men (adjIRR = 0.33; 95%CI: 0.21–0.54) and 38% among women (adjIRR = 0.62; 95%CI: 0.45–0.86). HIV incidence trends in most other occupations showed a decline, but Fig 2. Trends in HIV prevalence (overall and untreated) among men by primary occupational subgroup in the Rakai Community Cohort Study (RCCS), 1999–2016; Untreated prevalence and 95% confidence intervals are shown as solid lines; overall HIV prevalence is shown as dashed lines with 95% confidence bands in gray. Data are plotted at the calendar midpoint of each study visit. https://doi.org/10.1371/journal.pgph.0002891.g002 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 9 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Fig 3. Trends in HIV prevalence (overall and untreated) among women by primary occupational subgroup in the Rakai Community Cohort Study (RCCS), 1999–2016; Untreated prevalence and 95% confidence intervals are shown as solid lines; overall HIV prevalence is shown as dashed lines with 95% confidence bands in gray. Data are plotted at the calendar midpoint of each study visit. https://doi.org/10.1371/journal.pgph.0002891.g003 Table 3. Changes in prevalence of untreated HIV infection between RCCS survey visit 1 (1999–2000) and RCCS survey visit 12 (2015–2016) by primary occupa- tional subgroup and gender of study participants. Men N = 7,876 Unadjusted PRR (95% CI) adjPRR (95% CI) adjPRR p-value Occupational subgroup Women N = 10,121 Visit 1 (1999–2000), untreated HIV prevalence, % (n/T) n = 3474 14.7 (313/ 2128) Agriculture Unadjusted PRR (95% CI) adjPRR* (95% CI) adjPRR p-value Visit 12 (2015– 2016), untreated HIV prevalence, % (n/T) n = 6647 Visit 1 (1999– 2000), untreated HIV prevalence, % (n/T) n = 2,518 Visit 12 (2015– 2016), untreated HIV prevalence, % (n/T) n = 5,358 4.0 (106/2669) 0.27 (0.22–0.33) 0.22 (0.18–0.27) <0.001 12.3 (120/975) 4.2 (65/1538) 0.34 (0.26–0.46) 0.30 (0.23–0.41) <0.001 Construction - - - - - 12.6 (36/285) 4.8 (24/500) 0.38 (0.23–0.62 0.27 (0.17–0.43 <0.001 Trading 21.5 (70/325) 6.1 (64/1048) 0.28 (0.21–0.39) 0.09 (0.06–0.12) <0.001 12.7 (51/401) 4.6 (35/756) 0.36 (0.24–0.55) 0.27 (0.17–0.43) <0.001 Casual labor - - - - - 10.6 (7/66) 6.8 (9/132) 0.64 (0.25–1.65) 0.54 (0.22–1.35) 0.185 Civil service 11.2 (19/170) 3.2 (17/529) 0.29 (0.15–0.54) 0.07 (0.04–0.15) <0.001 10.4 (23/221) 0.9 (4/429) 0.09 (0.03–0.26) 0.09 (0.03–0.25) <0.001 Student Mechanic Transportation Bar/Restaurant worker 2.4 (6/254) 1.5 (14/959) 0.62 (0.24–1.59) 0.11 (0.04–0.30) <0.001 0.6 (2/319) 0.3 (3/1178) 0.41 (0.07–2.42) 0.23 (0.04–1.19) 0.079 - - - - - - - - - - 9.7 (6/62) 8.2 (4/49) 2.3 (7/302) 0.24 (0.08–0.69) 0.22 (0.07–0.67) 0.007 7.1 (18/252) 0.88 (0.31–2.47) 0.90 (0.30–2.73) 0.858 34.7 (50/144) 12.0 (32/267) 0.35 (0.23–0.51) 0.21 (0.14–0.31) <0.001 Local crafts 19.8 (20/101) 10.2 (14/137) 0.52 (0.27–0.97) 0.29 (0.14–0.58) <0.001 Hairdressing 46.3 (19/41) 4.7 (14/300) 0.10 (0.05–0.19) 0.01 (0.01–0.02) <0.001 Tailoring/ laundry 4.0 (2/50) 3.3 (4/122) 0.82 (0.16–4.33) 0.25 (0.05–1.35) 0.107 Housekeeping 16.7 (38/227) 4.6 (25/545) 0.27 (0.17–0.44) 0.11 (0.07–0.17) <0.001 - - - - - - - - - - - - - - - - - - - - - - - - - Other occupations All occupations 17.6 (6/34) 5.6 (4/71) 0.32 (0.10–1.06) 0.28 (0.08–1.02) 0.053 11.4 (16/140) 5.5 (15/271) 0.48 (0.25–0.95) 0.48 (0.25–0.96) 0.037 15.6 (543/ 3474) 4.4 (294/6647) 0.28 (0.25–0.32) 0.27 (0.24–0.31) <0.001 10.5 (265/2518) 3.4 (180/5358) 0.32 (0.27–0.38) 0.29 (0.24–0.35) <0.001 PRR = prevalence risk ratios; adjPRR = adjusted prevalence risk; Models adjusted for age and marital status of study participants. https://doi.org/10.1371/journal.pgph.0002891.t003 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 10 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Table 4. Incidence of HIV infection by primary occupational subgroup, sex, and CHI (combination HIV intervention) calendar period. Occupation Incidence rate per 100 py (n/py) Women (N = 17,840) IRR (95% CI) adjIRR (95%CI) Pre–CHI (1999–2004) Early–CHI (2004–2011) Late–CHI (2011–2016) Early–CHI vs. Pre- CHI (ref) Agriculture 1.1 (97/8490) 0.9 (129/ 13785) 0.7 (62/9515) 0.82 (0.63–1.07) Late–CHI vs. Pre- CHI (ref) 0.57 (0.41–0.79) Early–CHI vs. Pre- CHI (ref) 0.87 (0.67–1.13) Late–CHI vs. Pre- CHI (ref) 0.62* (0.45–0.86) Bar/restaurant worker 1.1 (4/365) 2.1 (17/813) 2.0 (12/605) 1.91 (0.64–5.69) 1.81 (0.58–5.66) 2.13 (0.72–6.36) 2.79 (0.85–9.19) Trading 1.4 (17/1222) 1.4 (49/3474) 0.7 (23/3117) 1.01 (0.58–1.77) Hairdressing 2.3 (3/133) 1.6 (10/624) 0.8 (6/774) 0.71 (0.19–2.62) Civil service 1.1 (9/851) 0.6 (14/2399) 0.3 (5/1849) 0.55 (0.24–1.28) Student 0.3 (2/623) 0.4 (6/1433) 0.7 (14/2059) 1.30 (0.26–6.48) Housekeeping 1.2 (6/496) 1.1 (15/1388) 0.9 (12/1278) 0.89 (0.35–2.32) Local crafts 1.3 (5/387) 2.3 (12/518) 1.6 (5/308) 1.79 (0.63–5.13) Tailoring/laundry 1.7 (2/121) 1.4 (5/355) 1.1 (3/261) 0.86 (0.16–4.47) Other occupations 1.1 (1/93) 0.0 (0/169) 1.2 (5/406) - All occupations 1.1 (146/ 12781) 1.0 (257/ 24958) 0.7 (147/ 20173) 0.90 (0.74–1.11) 0.53* (0.28–1.00) 0.34 (0.08–1.39) 0.26* (0.09–0.77) 2.12 (0.48–9.35) 0.78 (0.29–2.08) 1.25 (0.36–4.38) 0.70 (0.12–4.23) 1.14 (0.13–9.96) 0.64* (0.51–0.80) 1.19 (0.69–2.06) 0.73 (0.20–2.68) 0.76 (0.31–1.86) 1.23 (0.25–6.11) 1.0 (0.37–2.67) 1.88 (0.65–5.44) 0.94 (0.17–5.10) - 0.93 (0.76–1.14) 0.72 (0.38–1.37) 0.36 (0.09–1.43) 0.49 (0.13–1.78) 1.93 (0.44–8.36) 0.89 (0.30–2.59) 1.36 (0.38–4.91) 0.85 (0.15–4.84) 1.41 (0.16–12.48) 0.66* (0.53–0.83) Men (N = 14,244) IRR (95% CI) adjIRR (95%CI) Incidence rate per 100 py (n/py) Pre–CHI (1999–2004) Early–CHI (2004–2011) Late–CHI (2011–2016) Agriculture 1.4 (53/3894) 0.8 (62/8046) 0.4 (25/5834) Construction 1.6 (18/1128) 1.2 (28/2322) 0.5 (9/1690) Early–CHI vs. Pre- CHI (ref) 0.57* (0.39–0.82) 0.76 (0.42–1.37) Trading 0.8 (13/1541) 0.7 (26/3613) 0.5 (15/2746) 0.85 (0.44–1.67) Casual labor 1.2 (3/259) 1.6 (7/431) 0.6 (2/322) 1.40 (0.36–5.49) Civil service 0.7 (7/981) 0.6 (12/2121) 0.4 (6/1673) 0.79 (0.31–2.02) Student Mechanic 0.1 (1/955) 0.05 (1/1905) 0.1 (3/2840) 0.50 (0.03–8.03) 0.0 (0/228) 1.0 (8/780) 0.4 (4/901) - Late–CHI vs Pre-CHI (ref) 0.32* (0.20–0.51) 0.33* (0.15–0.75) 0.65 (0.31–1.37) 0.54 (0.09–3.25) 0.50 (0.17–1.50) 1.01 (0.11–9.71) - Early–CHI vs. Pre- CHI (ref) 0.58* (0.40–0.84) 0.79 (0.42–1.47) 0.89 (0.46–1.70) 1.67 (0.42–6.66) 0.78 (0.31–2.00) 0.51 (0.03–8.23) - Late–CHI vs Pre-CHI (ref) 0.33* (0.21–0.54) 0.35* (0.15–0.83) 0.69 (0.33–1.45) 0.68 (0.11–4.30) 0.49 (0.16–1.51) 0.44 (0.04–4.86) - Transportation 1.4 (4/287) 1.8 (21/1181) 1.2 (12/964) 1.28 (0.43–3.75) 0.89 (0.29–2.80) 1.33 (0.45–3.91) 1.10 (0.35–3.50) Other occupations 1.8 (10/546) 1.9 (21/1099) 0.9 (10/1115) All occupations 1.1 (109/ 9821) 0.9 (186/ 21498) 0.5 (86/ 18085) 1.04 (0.49–2.23) 0.78* (0.62–0.99) 0.49 (0.20–1.19) 0.43* (0.32–0.57) 1.06 (0.50–2.26) 0.79* (0.62–1.0) 0.50 (0.21–1.23) 0.44* (0.33–0.59) py = person years; IRR = incidence rate ratio; adjIRR = adjusted incidence rate ratio for age and marital status; IRR not presented for other occupations (women, Early- CHI) and mechanic (men) because there were no cases in the numerator and denominator respectively; CHI = combination HIV intervention *p<0.05. https://doi.org/10.1371/journal.pgph.0002891.t004 were not statistically significant. While HIV incidence did not decline among students, inci- dence in this population was low overall. HIV incidence rates also did not decline among men working in transportation, and women working in bars and restaurants or local crafts. S7 Table shows the adjusted relative risk of HIV acquisition by occupation during the late CHI period. Compared to women working in agriculture, female bar and restaurant workers had a three-fold higher rate of HIV incidence (adjIRR = 2.88; 95%CI: 1.51–5.49). Men working in transportation also had significantly higher HIV incidence compared to agricultural workers (adjIRR = 2.75; 95% CI: 1.37–5.50). Regardless of sex, students had a significantly lower risk of HIV acquisition compared to persons working in agriculture (men: adjIRR = 0.19; 95% CI: 0.05–0.73; women: adjIRR = 0.36; 95% CI: 0.18–0.72). PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 11 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Discussion In this population-based study, overall prevalence of HIV (treated and untreated) mostly declined among men, but remained stable or increased in most occupational subgroups among women. We also observed declining prevalence of untreated HIV and HIV incidence among most occupational subgroups with the scale up of HIV treatment and prevention pro- grams in Uganda. Among men and women working in agriculture, the most common self- reported primary occupation, prevalence of untreated HIV and HIV incidence declined by more than two-thirds. However, this downward trend was not always the case for other occu- pations. While women working in bars and restaurants made up a small proportion of the overall population, they had among the highest burdens of untreated HIV prior to HIV inter- vention scale-up, with no declines in HIV incidence over the analysis period. We also found no significant reduction in HIV incidence among male transportation workers. Moreover, both female bar and restaurant workers and male transportation workers had the highest prev- alence of untreated HIV at the final study visit. HIV incidence rates among women reporting student and crafting as primary occupations also showed no decrease following CHI scale-up, although students had a very low HIV burden overall. Taken together, these results suggest that members of traditionally high-risk occupations continue to experience elevated rates of HIV incidence and remain sub-optimally served by HIV programs. Other studies have reported high HIV prevalence among female bar workers in sub-Saha- ran Africa [20,21]. In this study, HIV prevalence among female bar and restaurant workers exceeded 40% with rising prevalence in recent years. While the prevalence of untreated HIV significantly declined in this population, it was three times higher than among women working in agriculture at the final study visit. The high burden of HIV among these women has been linked to female sex work, alcohol use, and mobility [22–24]. In a systematic review of socio- demographic characteristics and risk factors for HIV among female bar workers, high rural- to-urban mobility, transactional sex, and inconsistent condom use were common and associ- ated with financial needs and social marginalization [22]. Our results underscore that female bar and restaurant workers should be a priority population for African HIV treatment and prevention programs. While key population-based programs in Africa include female sex workers, and female bar workers are often engaged in sex work, not all women working in bars and restaurants at high risk of HIV classify themselves as sex workers [22]. Multi-level, social influence, and structural HIV prevention interventions targeting alcohol-serving estab- lishments, including enhanced sexually transmitted infection clinic services, portable health services, and peer education, have been shown to be effective in settings outside Africa, for reducing HIV risk and increasing treatment uptake [25,26]. Prior research has shown that men working in transportation are highly mobile and often engage in transactional sex [27–29]. We found that the prevalence of untreated HIV did not sig- nificantly decline in this occupational sub-group with the increasing availability of treatment and prevention. Prior research has linked male transportation workers, including truck drivers, to higher risk of HIV transmission [27], and has shown that men working in this occupation fre- quently engage with sex workers and women working in bars and restaurants [28,30]. Supplies of free condoms, roadside clinics, and free HIV testing services at truck stops are some HIV preven- tion interventions that have been targeted to male transportation workers [10,30]; however, levels of awareness and uptake of such services in this population have been low [10,31]. Adolescent girls and young women aged 15 to 24 years have a disproportionately high risk of HIV acquisition in Africa [32–35], but HIV risk was significantly lower among young peo- ple who list their occupation as “student” and who have higher education attainment, regard- less of sex [36–39]. During the study period, HIV prevalence declined in female students by PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 12 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda nearly 90%. Incidence of HIV remained stable for both male and female students, but com- pared to those in agriculture, students of both sexes had lower HIV incidence during the late- CHI period. Research from South Africa has shown that students tend to have smaller sexual networks and are less likely to report high-risk sexual behaviors compared to those not in school [37]. Lower HIV incidence and prevalence among female students have also been attributed to avoiding the consequences of unprotected sex and increased self-efficacy for negotiating safer sex with their partners [40]. Interventions that increase school enrollment of adolescent girls and young women may decrease sexual initiation, high-risk sexual behavior, and HIV risk [32]. Since the onset of the COVID-19 pandemic in Uganda during the spring of 2020, schools remained fully or partially closed until 2022. A review of adolescent sexual and reproductive health during the COVID-19 pandemic found an increase in teenage pregnancies and gender- based violence [41]. Given the strong protective effects of schooling on HIV acquisition, understanding the extent to which school closures impact HIV and other reproductive health outcomes, such as unplanned pregnancy, is an urgent public health priority. Earlier studies have established migration and mobility as a key risk factor for HIV acquisi- tion and transmission [23,42,43]. Overall, we found that the occupations which tend to have high mobility also had higher prevalence of untreated HIV and HIV incidence despite scale- up of HIV interventions. Both female bar and restaurant work and male transportation work are associated with increased mobility as well as high-risk sexual behaviors, including concur- rent sexual partnerships and inconsistent condom use [28,29,44]. Specialized service-delivery tailored to mobile populations, such as client-managed groups, adherence clubs, community drug distribution points, and multi-month prescriptions may reduce HIV burden in these populations [45–47]. The shifting distribution of the occupational makeup in our study population away from agriculture likely reflects the increasing urbanization happening across the African continent [48]. Little data exists on the impact of urbanization on HIV transmission; however, in sub- Saharan Africa, HIV prevalence and incidence have been reported to be higher in urban than in rural centers [49,50]. This has been attributed to factors such as relative affluence in urban centers, increased social interaction, and higher-risk behaviors such as transactional sex and concurrent sexual partnerships [51–53]. More research is needed to elucidate the impact of increasing urbanization on HIV transmission within African populations. Our study has important limitations. First, both occupation and ART use were self-reported and may be subject to bias. However, we have previously shown that self-reported ART use has high specificity and moderate sensitivity in this same study population, and does not sub- stantially vary by self-reported occupation [18]. Second, female sex work in Uganda is crimi- nalized and was likely substantially underreported in our survey [4]. Third, PEPFAR- supported key population HIV prevention programs began in this region in 2017, after the time of the analysis, and so their impact cannot be assessed. Given previously reported links between female sex work and bar work [21], our findings support PEPFAR’s ongoing focus on targeted HIV prevention and treatment to female sex workers. However, many bar workers do not identify as sex workers (none in this study), suggesting that they and other population sub- groups may merit additional programmatic consideration. Neither bar and restaurant workers nor male transportation workers are presently considered priority populations for HIV pro- gramming in Uganda. Fourth, while the longitudinal nature of this study is a strength, analysis of incident HIV infections were limited by a small number of events in some occupational sub- groups, which may have obscured significant trends. Additionally, non-differential non- response and loss to follow-up may have biased our results but in earlier studies from this same population, sensitivity analyses showed little to no impact of selection bias on incidence PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 13 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda estimates [16]. Lastly, because participants become aware of the risk of contracting HIV, their HIV status, and available treatments and prevention through their participation in the study, they may be more likely to take up and adhere to preventative measures or treatment, and so our results may not be generalizable to other populations. However, we expect the Hawthorne effect to be limited in this open cohort with substantial in- and out-migration. In summary, prevalence of untreated HIV infection and HIV incidence declined in most occupational subgroups following the mass scale-up of HIV prevention and treatment inter- ventions in rural southern Uganda. However, HIV burden remained relatively high in some occupations, including the traditionally high-risk occupations of female bar and restaurant work and male transportation work. HIV programs that meet the unique needs of these high- risk populations, which tend to be more mobile with higher levels of HIV-associated risk behaviors, may help achieve HIV epidemic control. Supporting information S1 Checklist. Inclusivity in global research. (DOCX) S2 Checklist. STROBE Statement—checklist of items that should be included in reports of observational studies. (DOCX) S1 Fig. Boxplots of age in years at each study visit, among RCCS agricultural workers. (TIF) S1 Table. Rakai Community Cohort Study (RCCS) survey start and end dates. (DOCX) S2 Table. Recategorization of 36 self-reported primary occupations into occupational sub- groups. (DOCX) S3 Table. A. Number of male observations at each study visit by primary occupational sub- group. B. Number of female observations in each primary occupational subgroup at each study visit. (DOCX) S4 Table. Characteristics of the study population at the baseline visit within each CHI cal- endar period by gender. (DOCX) S5 Table. A. Prevalence (%) of major occupations among women by visit. B. Prevalence (%) of major occupations among men by visit. (DOCX) S6 Table. A. Self-reported primary occupations by male RCCS study participants at each study visit. B. Self-reported primary occupations by female RCCS study participants at each study visit. (DOCX) S7 Table. Adjusted incidence rate ratios of HIV infection comparing all occupations vs. agriculture during the late-CHI period. (DOCX) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002891 February 20, 2024 14 / 18 PLOS GLOBAL PUBLIC HEALTHHIV epidemiologic trends among occupational groups in Rakai, Uganda Acknowledgments We thank the RCCS participants and many staff and investigators who have made this study possible over the years. Additionally, we thank the personnel at the Office of Cyberinfrastruc- ture and Computational Biology at the National Institute of Allergy and Infectious Diseases for data management support. Author Contributions Conceptualization: Victor O. Popoola, M. Kate Grabowski. Data curation: Victor O. Popoola, Joseph Kagaayi, Joseph Ssekasanvu, Robert Ssekubugu, Grace Kigozi, Anthony Ndyanabo, Fred Nalugoda, Larry W. Chang, Tom Lutalo, Aaron A. R. Tobian, Godfrey Kigozi, Ronald H. Gray, Steven J. Reynolds, David Serwadda. Formal analysis: Victor O. Popoola, Justin Lessler. Funding acquisition: Joseph Kagaayi, Maria J. Wawer, Ronald H. Gray. Investigation: Victor O. Popoola. Methodology: Victor O. Popoola, Justin Lessler, M. Kate Grabowski. Project administration: Joseph Kagaayi, Robert Ssekubugu, Grace Kigozi, Fred Nalugoda, Larry W. Chang, Donna Kabatesi, Stella Alamo, Lisa A. Mills, Maria J. Wawer, John San- telli, Ronald H. Gray, David Serwadda, M. Kate Grabowski. Software: Victor O. Popoola. Supervision: M. Kate Grabowski. Validation: Joseph Ssekasanvu. Writing – original draft: Victor O. Popoola, M. Kate Grabowski. Writing – review & editing: Victor O. Popoola, Joseph Kagaayi, Joseph Ssekasanvu, Robert Ssekubugu, Grace Kigozi, Anthony Ndyanabo, Fred Nalugoda, Larry W. Chang, Tom Lutalo, Aaron A. R. Tobian, Donna Kabatesi, Stella Alamo, Lisa A. Mills, Godfrey Kigozi, Maria J. Wawer, John Santelli, Ronald H. Gray, Steven J. Reynolds, David Serwadda, Justin Lessler, M. Kate Grabowski. References 1. Joshi K, Lessler J, Olawore O, Loevinsohn G, Bushey S, Tobian AAR, et al. 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10.1371_journal.pone.0295062	RESEARCH ARTICLE Chemobiosis reveals tardigrade tun formation is dependent on reversible cysteine oxidation Amanda L. Smythers1, Kara M. JosephID R. Crislip2, Brendin B. Flinn2, Meredith H. Daughtridge1, Evan R. StairID N. Mubarek1, Hailey C. Lewis1, Abel A. Salas1, Megan E. Hnilica1, Derrick R. J. KollingID Leslie M. HicksID 2, Hayden M. O’Dell2, Trace A. Clark2, Jessica 1, Saher 1* 2, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Smythers AL, Joseph KM, O’Dell HM, Clark TA, Crislip JR, Flinn BB, et al. (2024) Chemobiosis reveals tardigrade tun formation is dependent on reversible cysteine oxidation. PLoS ONE 19(1): e0295062. https://doi.org/10.1371/ journal.pone.0295062 Editor: Michael Klymkowsky, University of Colorado Boulder, UNITED STATES Received: July 7, 2023 Accepted: November 14, 2023 Published: January 17, 2024 Copyright: © 2024 Smythers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This research was supported by National Science Foundation grants awarded to L.M.H. (NSF-MCB 2149172) and D.R.J.K. (NSF-MCB 2149173). A.L.S. acknowledges funding from the North Carolina Space Grant. Marshall University students were also funded by a National Science Foundation (NSF) Grant (Award Nos. CHE1229498 and OIA1458952), the NASA West Virginia Space 1 Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America, 2 Department of Chemistry, Marshall University, Huntington, WV, United States of America lmhicks@unc.edu (LMH); kolling@marshall.edu (DRJK) Abstract Tardigrades, commonly known as ‘waterbears’, are eight-legged microscopic invertebrates renowned for their ability to withstand extreme stressors, including high osmotic pressure, freezing temperatures, and complete desiccation. Limb retraction and substantial decreases to their internal water stores results in the tun state, greatly increasing their ability to survive. Emergence from the tun state and/or activity regain follows stress removal, where resumption of life cycle occurs as if stasis never occurred. However, the mechanism (s) through which tardigrades initiate tun formation is yet to be uncovered. Herein, we use chemobiosis to demonstrate that tardigrade tun formation is mediated by reactive oxygen species (ROS). We further reveal that tuns are dependent on reversible cysteine oxidation, and that this reversible cysteine oxidation is facilitated by the release of intracellular reactive oxygen species (ROS). We provide the first empirical evidence of chemobiosis and map the initiation and survival of tardigrades via osmobiosis, chemobiosis, and cryobiosis. In vivo electron paramagnetic spectrometry suggests an intracellular release of reactive oxygen species following stress induction; when this release is quenched through the application of exogenous antioxidants, the tardigrades can no longer survive osmotic stress. Together, this work suggests a conserved dependence of reversible cysteine oxidation across distinct tardigrade cryptobioses. Introduction Tardigrades are a phylum of eight-legged microscopic invertebrates renowned for their remarkable ability to survive extreme environmental stressors [1–6]. This survival is rooted in their ability to initiate cryptobiosis, a physiological state wherein metabolism slows to near undetectable conditions, enabling long-term survival despite inhospitable conditions [7]. Although some eukaryotes and bacteria are capable of cryptobiosis, no eukaryotes are able to do so across the entirety of their lifespan, including as eggs, juveniles, and adults, or in response to such a broad range of stressors as tardigrades [8]. Thus tardigrades’ ability to PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 1 / 19 PLOS ONEGrant Consortium (Grant no. NNX15AK74A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Cysteine oxidation essential for tardigrade survival survive desiccation, freezing, oxygen starvation, fluctuating osmotic pressure, and ionizing radiation (via anhydrobiosis, non tun-forming cryobiosis, anoxybiosis, osmobiosis, and irradi- ation-induced dormancy, respectively) is matched by none [9]. Mechanisms of tardigrade survival are still poorly understood. While several cryptobiotes rely on trehalose synthesis as a protectant during dormancy, many tardigrade species produce low or undetectable levels of the disaccharide [10–14]. Tardigrades also lack highly conserved networks for stress regulation, including those that connect hypoxia, genotoxic stress, and oxi- dative stress to the conserved master regulator target of rapamycin (TOR) [15]. Instead, tardi- grades rely on several organism-specific proteins for stress protection, such as the damage suppressor protein (Dsup) that associates with nuclear DNA to protect from ionizing radiation [15]. Additionally, cytoplasmic-, secreted-, and mitochondrial- abundant heat soluble (CAHS, SAHS, and MAHS) proteins, collectively known as tardigrade disordered proteins (TDPs), possess sequences without conservation in other phyla and are essential for tardigrade survival to desiccation via anhydrobiosis [16]. Further, late embryogenesis abundant (LEA) proteins and heat shock proteins (HSP) are widely distributed throughout tardigrade cells, and many are upregulated during tardigrade anhydrobiosis suggesting an essential role in cryptobiosis [17–20]. The hallmark of most tardigrades undergoing cryptobiosis is their remarkable ability to shift into a shriveled anatomical state known as a tun, which they achieve by contracting their limbs, retract along their medial axis, and in the process decrease their internal water stores [21]. Upon exposure to favorable conditions, tardigrades rapidly distend and return to active metabolism. It has long been understood that tardigrades facilitate these anatomical transitions via active processes, yet the mechanism(s) through which tardigrades recognize environmental fluctuations and signal the transitions to enter and exit tuns remains largely unexplored [22]. Tardigrades rely on mitochondrial activity for desiccation survival; using chemical inhibitors to uncouple the mitochondrial electron transport chain prevents tuns from forming following exposure to anhydrobiotic conditions [23]. Further, the ATP-dependent differential phosphor- ylation of the AMP-activated protein kinase (AMPK) regulatory network following anhydro- biosis revealed that protein phosphatase 2A activation is essential to successfully induce tuns via anhydrobiosis [24–26]. Mitochondrial electron transport and AMPK regulation, both of which are highly conserved across taxa, engage in crosstalk with reactive oxygen species (ROS). In fact, mitochondrial ROS generation has been directly linked with AMPK activation in mouse embryonic fibroblasts, which is considered to be conserved across taxa [27–30]. When combined with increased accumulation of antioxidant enzymes and the redox homeo- stasis mediator glutathione following anhydrobiosis [31, 32], evidence suggests oxidative sig- naling may play a key regulatory role in initiating tun formation. Herein, novel evidence supporting reversible oxidation as an essential regulatory signal for cryptobiosis is demonstrated in the model tardigrade species, Hypsibius exemplaris. Tardi- grades are shown to respond to exogenously applied ROS by forming tuns in a dose-dependent manner that is inhibited when cysteine thiols are irreversibly blocked. Thiol-specific fluores- cent labeling enabled imaging via confocal fluorescence microscopy with quantification of reduced thiols accomplished through quantitative fluorescent assays. Rapid exposure to reduc- ing conditions causes tun release and death, indicating that the oxidation and reduction of cys- teine thiols is accomplished via highly regulated internal networks. Further, quantitative electron paramagnetic resonance spectrometry (EPR) spectroscopy reveals an intracellular release of ROS following stressor induction, that is fatal when blocked with exogenously applied antioxidants. Inhibition of voltage-dependent anion channel protein 2 generates tuns, suggesting ROS control of this ion transporter is likely implicated in tardigrade stress. Together, these data support reversible oxidative signaling as an indispensable regulatory PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 2 / 19 PLOS ONECysteine oxidation essential for tardigrade survival mechanism for tardigrade survival to adverse environments, with a conserved role across dis- tinct cryptobiosis. Materials and methods Tardigrade husbandry Cultures of H. exemplaris (Sciento; Manchester, UK) were reared in 1- or 2-L Erlenmeyer flasks on stationary-phase C. vulgaris. Culture medium was changed biweekly using a 40-μm Corning™ Sterile Cell Strainers (Thermo Fisher Scientific, Waltham, MA), which retains the tardigrades while allowing algae and waste to flow through. The filter is inverted and rinsed into the same flask with the addition of fresh deionized water. Tardigrades are fed weekly with C. vulgaris that was grown photoautotrophically under previously established protocols [23]. Cultures were maintained at room temperature on a 12:12-h light:dark cycle using a 7 W (630 lumens) LED lamp [33]. Chemobiosis induction and concentration dependence Tardigrades were transferred in minimal media to 35-mm plastic Petri dishes and dosed with concentrations of 750 μM, 1 mM, 2.5 mM, or 5 mM hydrogen peroxide (Thermo Fischer Sci- entific, Waltham, MA) for 12 h and tun formation was observed with hourly checks. There were 30 animals used per biological replicate, with 4 biological replicates per peroxide concen- tration. Tardigrades were manually counted every half hour for tun formation. Following tun formation, animals were transferred to new plates containing culturing media and monitored for recovery. Survival for each stressor was defined as tardigrades exhibiting controlled move- ment of limbs and/or body post-recovery treatment. Surviving tardigrades were counted after 24 h. Osmobiosis induction and concentration dependence Tardigrades (30 animals per replicate, 4 replicates per concentration/condition) were trans- ferred in minimal media (<100 μL) on 35-mm plastic Petri dishes and dosed with working concentrations of 50, 75, 100, and 150 mM CaCl2 (Amresco, Dallas, TX) or 225, 300, 450, or 600 mM sucrose (Millipore, Burlington, MA). Tardigrades were manually counted every half hour for tun formation; counting stopped once all conditions were 100% in tuns (12 h for CaCl2 and 6 h for sucrose) for 5 and 1 h, respectively, and tun formation was monitored. Ani- mals were transferred to new plates containing culturing media and monitored for recovery. Survival for each concentration was defined as tardigrades exhibiting controlled movement of limbs and/or body post-recovery treatment. Surviving tardigrades were counted after 24 h. Confocal bright field imaging Sample preparation. All tardigrades were imaged on microscope slides with tardigrades placed in 20 μL total volume droplets within 0.12 mm deep SecureSealTM Imaging Spacers (Thermo Fisher Scientific, Waltham, MA) to prevent crushing of tardigrades. Hydrated (active) tardigrades were dosed with 12.5% (v/v) methanol in water (Thermo Fisher Scientific, Waltham, MA) prior to microscopy in order to stop of movement of the tardigrades and allow for high resolution imaging without tardigrade fatalities. Methanol has been previously reported to successfully anesthetize Caenorhabditis elegans in a completely reversible manner and with minimal lethality [34]. In house tests reproducibly observed 100% tardigrade survival when dosed with 12.5% v/v methanol. For tardigrades in tuns, it was essential to keep the tardi- grades exposed to the tun inducing conditions (for example, tuns induced in 600 mM sucrose PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 3 / 19 PLOS ONECysteine oxidation essential for tardigrade survival were also imaged in 600 mM sucrose). Since tuns do not move, no methanol was needed. After animals were placed inside a spacer, the slides were sealed with a glass coverslip. Imaging. All images were obtained using a Leica SP5 TCSII (Wetzlar, Germany). 10 indi- vidual tardigrades were imaged for each condition (hydrated, all tun-forming stressors, and cryobiotes). Tardigrade sizes were estimated by measuring from the most anterior region to the most posterior region on ImageJ [35]. Cysteine blocking in chemobiosis Tardigrades (30 animals per sample, 4 replicates per condition) were collected in microcentri- fuge tubes were subjected to a 30-min incubation in 30 μM iodoacetamide (IAM) (Thermo Fisher Scientific, Waltham, MA), 30 μM N-ethylmaleimide (NEM) (Thermo Fisher Scientific, Walthman, MA), or water (control). Both IAM and NEM irreversibly bind to reduced cyste- ines. The 30 μM concentration was chosen because this exposure did not result in tardigrade fatalities in ideal (non-stressed) conditions after 24 h of monitoring for irreversible cysteine thiol binding. Following cysteine blocking, tardigrades were rinsed twice with 1 mL of deion- ized water to remove remaining blocking reagent and centrifuged at 10,000 xg for 3 min. Fol- lowing the second rinse, the supernate was removed, and tardigrades were transferred to 35-mm Petri dishes in less than 20 μL final volume. The dish was then filled with 750 μM per- oxide in culturing media, and the tardigrades were observed for tun formation after 12 h (cho- sen as earlier experiments show 100% of tardigrades in tuns after 12 h). Tardigrades exposed to NEM and peroxide were imaged using a Leica SP5 TCSII (Wetzlar, Germany). Tardigrades were added to microscope slides in 20 μL total volume droplets within 0.12 mm deep Secure- SealTM Imaging Spacers (Thermo Fisher Scientific, Waltham, MA). The concentration of per- oxide was maintained on the slide in order to prevent premature release from tuns. After animals were placed inside a spacer, the slides were sealed with a glass coverslip. Induction of cryobiosis Tardigrades were transferred in droplets of minimal media (<40 μL) on a 35-mm plastic Petri dish and placed in Thermo Fisher Scientific Nalgene ‘Mr. Frosty’ Freezing Container contain- ing 70% isopropyl alcohol (Thermo Fisher Scientific, Waltham, MA) at -80 C for 4 h. Mr. Frosty freezing containers limit cooling to -1˚C per minute. The tardigrades were left for 4 h as this ensured the temperature would have reached -80 C. Tardigrades were subsequently removed from the apparatus, thawed at room temp, and monitored for recovery. Repeated tri- als showed 100% recovery following 4 h freezing treatment. Cysteine blocking in cryobiosis Tardigrades (30 animals per sample, 4 replicates per condition) were collected in microcentri- fuge tubes were subjected to a 30-min incubation in 30 μM NEM or water (control). Following incubation, tardigrades were rinsed twice with 1 mL of deionized water to remove remaining blocking reagent and centrifuged at 10,000 xg for 3 min. Following the second rinse, the super- nate was removed, and tardigrades were transferred to 35-mm Petri dishes in less than 10 μL final volume and placed in a Thermo Fisher Scientific Nalgene ‘Mr. Frosty’ Freezing Container containing 70% isopropyl alcohol at -80 C for 4 h. Tardigrades were then removed from the freezer, allowed to thaw to room temperature, and counted for survival, where survival is defined by tardigrades exhibiting controlled movement of limbs and/or body. Imaging cryobiotic tardigrades is challenging as it is impossible to maintain below freezing conditions at the microscope. In house experimentation showed that if frozen tardigrades are put directly in chilled methanol (final concentration 12.5% v/v), their morphology did not PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 4 / 19 PLOS ONECysteine oxidation essential for tardigrade survival change once the ice thawed, as confirmed from viewing under a light microscope. To image cryobiotic tardigrades, 10 μL of thawed but cold 25% (v/v) methanol/water solution previously kept at -80C was added to the 10 μL frozen droplet containing bears (to reach a final methanol concentration of 12.5% v/v) within 0.12 mm deep SecureSealTM Imaging Spacers. Methanol blocking in osmo- and cryobiosis Methanol can be used to stop cell signaling and arrest cellular metabolism [36–40]. In order to determine if cell signaling is required for tun formation and tardigrade survival, we exposed tar- digrades to methanol before inducing cryptobioses by freezing, exposure to 600 mM sucrose, or exposure to 150 mM CaCl2. Tardigrades (30 per replicate, 4 replicates per condition) were incu- bated in 12.5% v/v methanol in a 35 mM Petri dish for 5 min before adding either 600 mM sucrose, 150 mM CaCl2, or freezing. Results were compared with those of non-methanol condi- tions to establish the reliance on cell signaling for cryptobiotic exposure. For cryobiosis, metha- nol exposed tardigrades were frozen in a Thermo Fisher Scientific Nalgene ‘Mr. Frosty’ Freezing Container containing 70% isopropyl alcohol at -80 C for 4 h, after which they were removed and allowed to thaw to room temperature. Tardigrades were monitored for survival and compared to tardigrades who underwent cryobiosis without methanol exposure. Quantification of reduced thiols Tardigrades (1000 animals per sample, 4 samples per condition) collected in microcentrifuge tubes. There were a total of 12 samples, including controls (active tardigrades, not in crypto- biosis), osmobiosis via 75 mM CaCl2, osmobiosis via 600 mM sucrose, and cryobiosis, all induced as described earlier. Following cryptobiote induction for each respective condition, proteins were immediately extracted. Tardigrade proteins were extracted using a modified acid extraction [41]. Tardigrades were incubated in concentrated trifluoroacetic acid (Sigma Aldrich, St. Louis, MO) for 40 min before neutralizing to approximately pH 7 with 2 M Tris (Thermo Fisher Scientific, Waltham, MA) in water. Proteins were precipitated in 70% (v/v) ethanol (Thermo Fisher Scientific) overnight at -20˚C before centrifuging at 3,220 xg for 1 h and collecting the resulting pellet. For those induced into cryobiosis, frozen samples were homogenized via a plastic pestle before being subjected to protein extraction. Following precipitation, protein pellets were resuspended in 4 M urea in PBS at pH 7.5 and quantified with a CB-X assay (G-Biosciences, St. Louis, MO) against bovine serum albumin standards. Proteins were enzymatically digested with 0.5 ug Trypsin Gold (Promega, Madison, WI) for 16 h, shaking at 850 RPM at 37˚C. Sam- ples were dried in a CentriVap (Labconco, Kansas City, MO) and subsequently resuspended in PBS at pH 7.5. Resuspended samples were subjected to reduced thiol quantitation using the Measure-iTTM Thiol Assay Kit (Thermo Fisher Scientific, Waltham, MA) against reduced glu- tathione following the manufacturer’s protocol. Briefly, the Measure-iTTM thiol quantitation reagent was diluted 1:100 in PBS. In a 96-well plate, 100 uL of the diluted thiol quantitation reagent were added to 10 uL of sample/standard. Samples were incubated at room temperature for 5 min before fluorescence was measured using a SpectraMax M5 (Molecular Devices, San Jose, CA) microplate reader with excitation/emission maxima at 490/520 nm. Thiol concentra- tions were normalized to total protein amounts in each sample. Statistical testing was per- formed using a Student’s t-test. Confocal fluorescence microscopy Tardigrades (either active or in tuns) were exposed to 30 μM fluorescein-5-maleimide (Sigma Aldrich, St. Louis, MO, USA) in 50 mM PBS, pH 7.2, in 12% (v/v) methanol for 2 h at room PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 5 / 19 PLOS ONECysteine oxidation essential for tardigrade survival temperature. Following incubation, tardigrades were centrifuged at 10,000 xg for 5 min, before removing the supernate and rinsing with 1 mL of 50 mM PBS, 12% methanol (v/v), pH 7.2. Brightfield contrast and fluorescence images were acquired on a Zeiss LSM710 confocal laser- scanning microscope using a PlanApo 40X objective lens. Brightfield and fluorescence images were collected using 561-nm and 480-nm lasers, respectively, on Zeiss ZEN software (Car Zeiss, INC. NY, USA). Forced tun emergence via reduction To determine if chemical reduction could induce reemergence from tuns, chemobiotic tardi- grades induced into tuns by 750 μM hydrogen peroxide were dosed with concentrated (14.3 M) β-mercaptoethanol (BME or 2-mercaptoethanol, Sigma Aldrich, St. Louis, MO). Tardi- grades were monitored using a dissection microscope to observe linearization, whereby tardi- grade limbs returned to erect positions and bodies increased to active tardigrade size. Tardigrades were counted manually to determine total reemergence, and survival was moni- tored after 24 h. Chemical inhibition of tardigrade tuns Tardigrades were plated one organism per well in a clear 96-well plate in 1-μL droplets of water. Tardigrades were incubated for 1 h with a working concentration of 100 μM of all inhib- itors from the SCREENWELL Redox Library (Enzo Life Sciences, Farmingdale, NY) and mon- itored for physiological changes. There were approximately 10 tardigrades monitored per inhibitor in the SCREENWELL library. Following this incubation, tardigrades were incubated in a working concentration of 75 mM CaCl2 and observed for tun formation after 3 h. Glutathione (reduced and oxidized), L-ergothioniene, and B-lapachone were studied using 30 tardigrades per sample in 4 replicates per inhibitor. Each was applied with a working con- centration of 75 mM and 0.1% DMSO to improve permeation. Following incubation for 1 h, tardigrades were treated in 75 mM CaCl2 and observed for tun formation after 3 h. Electron paramagnetic resonance spectroscopy Aliquots of the superoxide-reactive spin trap 1-hydroxy-3-methoxycarbonyl-2,2,5,5-tetra- methylpyrrolidine (CMH, 1 mM final concentration) (Enzo Life Sciences, Farmingdale, NY) were prepared in modified Krebs-HEPES buffer containing 99.01 mM NaCl, 4.69 mM KCl, 2.50 mM CaCl2�2H2O, 1.20 mM MgSO4�7H2O, 29.76 mM NaHCO3, 1.04 mM KH2PO4, 11.10 mM D+ glucose, 20.00 mM Na+-HEPES, 25 μM deferoxamine mesylate, and 5 μM diethyl- dithiocarboxamic acid ammonium salt (all reagents from either Thermo Fisher Scientific or Sigma Aldrich). CMH aliquots were stored at -20˚C until use. For each of 7 trials (stressed, n = 3; non-stressed, n = 4), a sample of 200 H. exemplaris tardigrades with minimal algae was collected and centrifuged at 13,400 xg. The resultant supernate was removed until the final vol- ume of culture sample remaining was 10 μL. For each trial, timing was begun upon transfer of an aliquot of CMH from storage to ice. Prior to adding CMH to samples, the CMH was held under nitrogen gas flow for five min to remove dissolved oxygen. Measurements were col- lected on an X-band EMXPlus Spectrometer (Bruker, Billerica, MA) with the following param- eters: sweep width, 100 G; center field, 3510 G; modulation amplitude, 1.000 G; receiver gain, 30; microwave power, 10.02 mW; number of scans, 4. Across trials, samples were time- matched with respect to transferring the CMH to ice (e.g., all samples received CMH at the same time post transfer, were placed into the instrument at the same time post transfer, and data was collected at the same time post transfer). Stressor solution or deionized water and CMH (1:1, v/v; 500 μM working concentration) were added 2 min prior to the collection of PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 6 / 19 PLOS ONECysteine oxidation essential for tardigrade survival data for the initial timepoint. Samples were wicked into 50 μL capillary tubes (Sigma Aldrich, St. Louis, MO). Data points were collected at 5 min intervals (beginning at 2 min post-addition of CMH/stressor) for 30 min. Signal intensity for all data points was determined using the dif- ference between the maximum intensity and minimum intensity of the CMH signal and then normalized to the number of animals. The rates of CMH oxidation between timepoints were determined using the rate of signal change between points on a signal intensity per-animal ver- sus time plot. Chemical inhibition with erastin To determine the role of VDAC2 on tardigrade tun formation, erastin inhibition was con- ducted using 3 replicates of 30 tardigrades, each tardigrade in an individual well of a 96-well plate. Tardigrades were incubated in a 100 nM working concentration of erastin (MedChem- Express, Monmouth Junction, NJ) in 0.1% DMSO overnight. After 20 h, tardigrades were observed under a dissecting light microscope and manually counted to determine the number of tuns formed. Results and discussion ROS exposure induces tun formation in tardigrades–direct evidence of chemobiosis Tardigrades survive multiple stressors that are prone to generating ROS in vivo, including des- iccation, extreme fluctuation in osmolarity, and freezing, among others [14, 42–45]. We there- fore hypothesized that ROS may play a crucial role in signaling tun induction in response to environmental fluctuations. This was explored using exogenously applied H2O2 and monitor- ing the physiological effect(s) on tardigrade morphology (Fig 1). Tardigrades exhibited a dose- dependent response to exogenous H2O2, with higher concentrations generating tuns within an hour of exposure. Tardigrades exposed to peroxide retracted their limbs and decreased their size to approximately 25% of their hydrated counterparts, thus indicating chemobiosis result- ing in tun formation had taken place (Fig 1A). To further explore this phenomenon, tardi- grade tun formation was monitored over time for varying concentrations. Measurements were conducted in 3 biological replicates, with a single replicate including 25–30 specimens. Time to tun formation was distinct across peroxide concentrations, with 5 mM H2O2 resulting in Fig 1. Peroxide induces tuns in a concentration dependent manner in Hypsibius exemplaris. A) Confocal images of tardigrades in the hydrated state as well as a H2O2– induced tun. Tuns are about 25% of the size of the hydrated tardigrades. B) The percent of tardigrades entering tuns following different concentrations of H2O2. Tardigrades were measured in three biological replicates, where a single replicate included 25–30 tardigrades. Error bars represent standard deviation. C) The percentage of tardigrades that reemerged from H2O2– induced tuns following reintroduction to distilled water. Error bars represent standard deviation. Only tardigrades that enter tuns are counted toward total survival. Statistical significance was determined by paired t-tests with Welch’s correction, where * indicates p < 0.05. https://doi.org/10.1371/journal.pone.0295062.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 7 / 19 PLOS ONECysteine oxidation essential for tardigrade survival tuns for 10% of the tardigrades by 90 min of exposure, while 750 μM H2O2 took at least 4 h to achieve the same result (Fig 1B). All tardigrades entered tuns following exposure to 750 μM H2O2 by 12 h, whereas it took only 8 h with 2.5 mM H2O2. Only 57% of tardigrades exposed to 5 mM H2O2 entered tuns; the remaining 43% entered a turgid, semi-translucent state indica- tive of tardigrade death. The lethality of higher ROS concentrations could indicate a risk of over-oxidation (perhaps in the form of irreversible oxidation, in contrast to reversible) in extreme conditions. This was further mirrored by a significantly decreased tardigrade viability at all concentrations >1 mM H2O2, with only 34% survival following exposure to 2.5 mM H2O2 and 16% following exposure to 5 mM H2O2, both determined by recovery of motility following emergence from tuns (Fig 1C). In contrast, concentrations of 1 mM and 750 μM H2O2 resulted in post-tun tardigrade viability of 57.4 and 70.0%, respectively. This is markedly lower than the survival of Echiniscus blumi exposed to H2O2, unsurprising due to the decreased stress survival of H. exemplaris frequently reported in literature [46, 47]. Despite the absence of desiccation or significant fluctuations in osmolarity, tardigrades still expelled internal water stores and initiated tun formation, resulting in an approximate 75% decrease in body length (Fig 1A). This suggests that water expulsion during tun formation is an active process rather than the result of spontaneous flux; this corroborates previous work in the tardigrade species Richtersius coronifer that reported an 87% volume reduction following anhydrobiosis that was dependent on mitochondrial activity [23]. This previously reported dependence on mitochondrial activity combined with the results reported herein suggests that the water expulsion may be mediated by oxidation or oxidative signaling. This process was quickly reversed when tuns were placed back in distilled water in the absence of exogenous ROS, suggesting that tardigrades are able to rapidly sense environmental changes to initiate anatomical transitions. Tardigrades’ ability to undergo chemobiosis has previously been pre- dicted, but the data presented here is the first empirical evidence of chemical-induced tun induction. H. exemplaris can be reproducibly induced into cryo- and osmobiosis While cryobiosis has been previously explored in H. exemplaris, osmobiosis required thorough empirical characterization. We mapped the induction and concentration of osmobiosis using two distinct osmolytes, CaCl2 and sucrose, chosen due to their ionic and non-ionic natures (Fig 2). Both stressors were applied in concentrations far exceeding expected physiological concentrations, as it was essential to induce a stress response that would prove fatal should the tardigrades not enter their resilient tun form. As observed with peroxide, tun inductions by CaCl2 and sucrose were highly concentration dependent. While 50 mM CaCl2 resulted in total tun formation in 12 h, 75 mM CaCl2 produced the same result in only 3.5 h (Fig 2A). Exposure to 100 and 150 mM CaCl2 resulted in 92% and 52% of tuns induced after 1 h, respectively; interestingly, these higher concentrations also resulted in tardigrade fatality before tun forma- tion could occur. Survival was high in all concentrations for those that induced tuns following CaCl2 exposure, with an average survival rate of 80–90% for all concentrations (Fig 2C). Sucrose also formed tuns in a concentration-dependent manner (Fig 2B); however, unlike per- oxide and CaCl2 exposure, higher concentrations did not result in tardigrade death prior to tun formation. This is consistent with osmobiosis mapped in the tardigrade species Ramazzot- tius oberhaeuseri, which had a higher tolerance to nonionic osmolytes in comparison to ionic [45]. All tardigrades exposed to 225 mM, 300 mM, 450 mM, and 600 mM sucrose formed tuns, with 100% of the tardigrades forming tuns at 5.5, 4, 2, and 0.5 h, respectively. Interestingly, the survival rate post-tun formation was not concentration dependent, with repeated trials produc- ing the same pattern of survival that is inconsistent with dose concentration (Fig 2D). While PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 8 / 19 PLOS ONECysteine oxidation essential for tardigrade survival Fig 2. Tardigrades can be induced via osmobiosis using both CaCl2 and sucrose. A) The percent of tardigrades entering tuns following different concentrations of CaCl2. Tardigrades were measured in three biological replicates, where a single replicate included 25–30 tardigrades. B) The percent of tardigrades entering tuns following different concentrations of sucrose. Tardigrades were measured in three biological replicates, where a single replicate included 25–30 tardigrades. C) The percentage of tardigrades that reemerged from CaCl2–induced tuns following reintroduction to aqueous media. D) The percentage of tardigrades that reemerged from sucrose–induced tuns following reintroduction to aqueous media. A-D) Error bars represent standard deviation. C-D) Statistical significance was determined by paired t-tests with Welch’s correction, where * indicates p < 0.05. E) Tardigrades imaged using a confocal microscope in their hydrated, CaCl2-induced, and sucrose-induced states. https://doi.org/10.1371/journal.pone.0295062.g002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 9 / 19 PLOS ONECysteine oxidation essential for tardigrade survival deviation in tardigrade size and/or age could generate stochasticity in the data, these differences would be expected to be random. The repeated findings of non-correlative survival rates suggest that this is a regulated phenomenon rather than random. Further investigation is needed before a conclusion can be made. Both CaCl2 and sucrose can reproducibly produce tuns with high rates of post-stressor sur- vival (Fig 2). Physiologically, there appears to be slight differences between CaCl2 and sucrose- induced tuns, with sucrose tuns appearing more compact (Fig 2E). This was reflected in vol- ume estimations, where sucrose induced tuns appear to have a slightly smaller overall volume than CaCl2 induced (S1 Fig). This could be due to the higher calculated osmotic pressure (π) in sucrose, with 15.17 bar compared to 9.59 bar; however, more investigation is needed to determine the precise cause. Interestingly, this tun formation and survival was not consistent across all osmolytes. Glucose and glycerol exhibited nearly total tun formation at 600 mM, but survival was less than 50%; in contrast, neither NaCl or MnCl2 formed reproducible tuns in repeated trials. This suggests that osmotic pressure alone is not the inducer of tun formation, and that intracellular regulatory mechanisms must have some control over tun initiation. Reversible cysteine oxidation is essential for tun formation ROS contribute to cellular homeostasis through rapid reversible oxidative signaling through which the oxidation of cysteine thiols is used to trigger protein responses [48]. Therefore, ROS induction of tun formation through the oxidation of cysteine thiols was examined by blocking reduced cysteine thiols in hydrated tardigrades with either iodoacetamide (IAM) or N-ethyl- maleimide (NEM), both of which irreversibly bind to reduced cysteine thiols and block oxida- tion [49]. Tardigrades were incubated in either 30 μM IAM or NEM (concentration chosen following titration series to ensure tardigrade survival) and exposed to 750 μM H2O2 for 12 h (Fig 3A). Experiment was conducted with 3 biological replicates, where each biological repli- cate included 30 specimens. While 100% of tardigrades formed tuns in the absence of Cys- blocking, only 7.6 and 4.0% of tardigrades blocked with IAM and NEM, respectively, formed tuns by 12 h. This dramatic decrease in tun formation indicates an essential role for cysteine oxidation in the mechanism and/or signaling of the process. While a small difference was observed in IAM compared to NEM, this difference was not statistically significant, and likely represents the slightly higher binding affinity and specificity of NEM compared to IAM [50, 51]. Tardigrades exposed to NEM before being exposed to peroxide showed decreased size compared to fully hydrated, and visible deformity with limbs extended rather than retracted (Fig 3B), suggesting that cysteine oxidation is necessary for limb retraction during tun formation. Whereas chemo- and osmobiosis form tuns, cryobiosis does not; it was therefore unclear whether or not oxidation would still be implicated in cryobiosis. To assess this phenomenon, cryobiosis was analyzed to ensure high survival rates, with a freezing rate of 1˚C/min resulting in 87% survival (S2A Fig). Cryobiotes imaged using a confocal microscope revealed a distinct morphology from osmo- or chemobiotes, with the tardigrades appearing more globular rather than the ordered, compact form of tuns (S2B Fig). To determine if cysteine oxidation was nec- essary for survival, tardigrades were exposed to 30 uM NEM prior to freezing. This resulted in tardigrades becoming more linear and distended during freezing, likely contributing to the lack of any survivors following reheating to room temperature (S2C Fig). Based on this data, it appears that cysteine oxidation is still necessary for cryptobiotic survival, even when the tun formation is not involved. In addition to blocking reversible oxidation of cysteine thiols using NEM or IAM, crypto- biosis can be blocked using 12.5% (v/v) methanol. Methanol quenching is a well-established PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 10 / 19 PLOS ONECysteine oxidation essential for tardigrade survival Fig 3. Blocking of cysteine residues prevents tun formation. A) Tardigrades exposed to either IAM or NEM, both of which bind irreversibly to reduced cysteines, significantly decreased tun formation in H2O2–exposed tardigrades. B) Confocal image of a NEM- blocked tardigrade following exposure to H2O2. C) Tardigrade proteins were extracted and digested with trypsin before fluorescent quantification of reduced cysteine thiols. Thiol concentration was normalized to protein concentration. A, C) Error bars represent standard deviation. Statistical significance was determined by paired t-tests with Welch’s correction, where * indicates p < 0.05. D) Tardigrades were labeled with 5-MF before imaging with a confocal microscope. To demonstrate the selectivity of the label, one set of tardigrades was blocked with NEM prior to 5-MF labeling, showing significantly diminished fluorescence compared to the unblocked tardigrade. https://doi.org/10.1371/journal.pone.0295062.g003 method to stop cell signaling events and arrest metabolism [36–40]. Previous work has used methanol to reversibly and non-lethally anesthetize C. elegans; [34] we therefore titrated meth- anol into tardigrades and determined that 12.5% (v/v) resulted in complete cessation of move- ment and 100% survival following methanol removal. Following methanol exposure, tun formation was inhibited by 91% and 99% in sucrose- and CaCl2-exposed samples, respectively, and cryobiote formation was inhibited by 100% (Table 1). If tun generation was the result of desiccation via passive osmosis, we would expect methanol-dosed tardigrades to still generate tuns following application of sucrose, CaCl2, and/or cryobiotic conditions. Thus, the blocking of tun formation by methanol gives further evidence that tun initiation is not the result of Table 1. Percentage of tardigrades that undergo either osmobiosis or cryobiosis following exposure to methanol. Biological replicates included 30 specimens each and 4 replicates. The sucrose and CaCl2 conditions were monitored for tun formation, whereas the cryobiotes were monitored for survival following freezing. None of the cryobiotic tardi- grades survived. Condition Sucrose CaCl2 Cryobiosis + Methanol Mean 9.01 0.47 0.00 SD 2.98 0.94 0.00 - Methanol Mean 100.0 100.0 87.48 SD 0 0 5.11 https://doi.org/10.1371/journal.pone.0295062.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 11 / 19 PLOS ONECysteine oxidation essential for tardigrade survival desiccation via passive osmosis; rather, it is a highly regulated phenomenon, likely mediated by signaling mechanisms that can be quenched with methanol. For quantitative assessment of thiol oxidation, reduced thiols were quantified from the tar- digrade proteome using a fluorescence-based quantitative thiol assay. Proteome digestion was essential to increase accessibility to internal cysteines that may be sterically hindered from binding to the fluorophore. To circumvent variation due to tardigrade size and/or age, quanti- fied cysteine thiols were normalized to quantified protein content. All cryptobioses exhibited lower concentrations of reduced cysteines (Fig 3C). Cryobiosis had the lowest of all stressors, with approximately half of the free cysteines compared to the control (Fig 3C). This suggests differential cysteine regulation across stressors. Further qualitative assessment of cysteine thiol oxidation was conducted using confocal microscopy with fluorescein-5-maleimide (5-MF), a labeling agent that irreversibly derivatizes cysteine thiols, across all inducible stressors (Fig 3D). Active tardigrades exhibited fluores- cence, with 5-MF localized near the tardigrade epidermis, whereas tuns and cryobiotes exhib- ited minimal fluorescence. This indicates that reduced cysteine thiols in the active, hydrated tardigrades were likely oxidized and inaccessible to 5-MF labeling once in the tun state. As with the thiol quantification assay, cryobiosis exhibited the least amount of fluorescence with 5-MF. Interestingly, the remaining cryptobiotes also have diminished fluorescence compared to the control, with much less of the body showing any fluorescent signal. Cysteines can rapidly undergo a variety of reversible oxidative modifications, including sul- phenylation, disulfide bond formation, glutathionylation, and S-nitrosylation, that often facili- tate rapid intracellular signaling [52, 53]. To determine the reversibility of tun-inducing cysteine oxidation, tuns induced by 12-h exposure to 750 μM H2O2 were exposed to the reduc- tant β-mercaptoethanol (BME). After 30 min of concentrated (14.3 M) BME exposure, 98% of tardigrades had reemerged with their bodies lengthened and all 8 legs clearly visible, with emer- gence visualized via microscopy beginning at 5 min. Interestingly, reductant-induced tun release was lethal to all tardigrades, indicating that native tun release must be a highly regulated physiological event. This could potentially occur through crosstalk with other post-translational modifications; the rapid recovery of H2O2-induced tuns following placement in distilled water (<1 h) likely precludes an upregulation in protein expression as a contributing factor. The reversibility of tuns using BME suggests that the cysteine modification required for tun forma- tion is reversible oxidation that can be controlled through exogenous ROS and reducing agents. Tardigrades may use an intricate network of ROS signals to initiate and survive tun formation Our results indicate that the reversible oxidation of cysteine thiols is essential for tun induction in H. exemplaris. The dose-dependence of tun formation in the presence of H2O2 suggests that either environmental ROS or the intracellular production of ROS in the presence of external stressors triggers tardigrades to enter cryptobiosis. To further assess intracellular ROS genera- tion in tun formation, tardigrades were screened using the SCREEN-WELL REDOX library, through which they were exposed to either antioxidants or oxidants, monitored for physiologi- cal changes, and then induced into tuns using CaCl2. Compounds that did not have a physio- logical effect alone but prevented tun formation following stressor introduction were examined further. In total, 84 compounds were screened, including many phenolic antioxi- dants, radical scavengers, and thiol-containing reducing agents. Following application of CaCl2, 72 of the compounds inhibited tun formation, with 15 inhibiting over 50% of tardi- grades (S1 Table). Three of the highest inhibiting compounds in this preliminary screen were selected for re-analysis across replicates: glutathione, L-ergothioneine, and β -lapachone. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 12 / 19 PLOS ONECysteine oxidation essential for tardigrade survival Fig 4. Exposure to exogenous antioxidants prior to osmotic stress decreases tardigrade survival. A) Structures of antioxidants. B) Three inhibitors were further characterized in replicates following the initial screening. Glutathione, L-ergothioneine, and B-lapachone all inhibited >50% of tuns from forming. Error bars indicate standard deviation. Statistical significance was determined by paired t-tests with Welch’s correction, where * indicates p < 0.05. https://doi.org/10.1371/journal.pone.0295062.g004 Glutathione was one of the largest inhibitors of tun formation, preventing tuns in ~68% of the tardigrades following exposure to CaCl2 (Fig 4). When oxidized glutathione was used instead, tun inhibition decreased to 22%, thus indicating that reduced glutathione has a signifi- cant effect on tun formation. This likely derives from the readily oxidized cysteine in glutathi- one where an influx of glutathione renders ROS unable to reach their target(s). Two other antioxidants were evaluated for their ability to prevent tun formation: L-ergothioneine, a sul- fur containing small molecule, and β -lapachone, a sulfur-free benzochromenone, both of which significantly inhibited tun formation, with 52% and 69% inhibition, respectively (Fig 4B). The ability to suppress tun formation with antioxidants provides evidence that intra- cellularly generated ROS mediate tardigrade survival in the presence of exogenous stressors. EPR spectroscopy was implemented to further delineate ROS accumulation following expo- sure to stress. EPR has previously been implemented to successfully measure the superoxide anion radical in the tardigrade Paramacrobiotus richtersi [54]. Hydrated tardigrades and those exposed to 600 mM sucrose were analyzed using the superoxide-sensitive spin probe CMH [55]. Superoxide is generated by electron leak at Complex I and Complex III of the mitochon- dria, the rate of which is increased under stress [56, 57]. CMH is a hydroxylamine probe that generates a stable nitroxide radical that can be detected via EPR (Fig 5A). Exposing CMH to hydrated and sucrose-exposed tardigrades thus enabled relative quantification of superoxide production over time, allowing us to compare the rate of accumulation as tardigrades entered their osmobiotic tun. Both hydrated and sucrose-exposed tardigrades started with overlapping rate of superoxide accumulation (Fig 5B). By 10 min, however, sucrose significantly increased in accumulation rate compared to the hydrated sample, with an approximately 25% increase in superoxide rate. This increase continued for the duration of the 30-min measurements, thus showing that the induction of osmobiosis releases intracellular ROS. Previous work has shown upregulation of antioxidant enzymes in the tardigrade species Paramacrobiotus richtersi, Paramacroiotus spatialis, and H. exemplaris following cryptobiosis [26, 31, 58]. With chemical treatment mirroring the effects of anhydrobiosis, it was observed that glutathione increases in H. exemplaris [26]. However, in P. spatialis, increases in ROS were observed in tardigrade storage cells with increases correlating to the amount of time in anhydrobiosis. Herein, we have observed increased rates of ROS production following stressor induction as well as the ability to block tuns from forming when induced in the presence of antioxidants above the physiological levels reported in the literature [26]. This suggests that there may be two phases of ROS accumulation: a “burst” that occurs upon stressor exposure that triggers the reversible oxidation of cysteine and leads to cryptobiosis, and a long-term accumulation that occurs following cryptobiosis induction that may maintain cryptobiosis PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 13 / 19 PLOS ONECysteine oxidation essential for tardigrade survival Fig 5. Tardigrades accumulate ROS following stress exposure. A) CMH forms a stable nitroxide radical following contact with superoxide that is quantifiable via EPR. B) Hydrated and sucrose exposed tardigrades were probed with CMH and measured with EPR every 5 min. Sucrose exposed tardigrades accumulate superoxide at significantly higher rates than non-stressed tardigrades, beginning at 10 min post stress introduction. Error bars indicate standard deviation. Statistical significance was determined by paired t-tests at each time point with Welch’s correction, where * indicates p < 0.05. C) The working hypothesis for tardigrade survival via ROS-mediated protein signaling. https://doi.org/10.1371/journal.pone.0295062.g005 [58]. This is further supported by the hydrogen peroxide data shown here, indicating that the removal of tun-inducing ROS results in tun emergence with high survival rates. The ability to block and/or perturb these functions with exogenous antioxidants as well as high concentra- tions of ROS show that this is likely a highly regulated process worthy of further investigation. We hypothesize that tardigrades sense external changes and stressors via the activation or inhibition of ion channels (Fig 5C). Mitochondrial ROS are modulated by ion fluxes (particu- larly calcium), which allosterically increase mitochondrial electron transport via complex II (Fig 5C). We therefore hypothesized that a critical regulatory protein in tardigrade survival would be voltage-gated anion channel 2 (VDAC2), a highly abundant mitochondrial channel responsible for the majority of calcium flux in and out of the mitochondria. To determine the influence of VDAC2 on tun formation and tardigrade survival, tardigrades were exposed to the VDAC2 inhibitor erastin and monitored for physiological changes. Following exposure to 100 nM of erastin, tardigrades began to form tuns within minutes. Within 24 h, 75.56% (SD: PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 14 / 19 PLOS ONECysteine oxidation essential for tardigrade survival 6.94) of the tardigrades had formed tuns. This suggests that the flux of calcium in and out of the mitochondria is likely implicated in tun formation and that VDAC2 closes to decrease flux in response to external stressors. Conclusion Tardigrades have long been renowned for their remarkable ability to survive adverse condi- tions, an achievement that has helped them to survive fluctuating environments since they evolved in the Cambrian era. However, the mechanisms used to sense and respond to external stressors in order to initiate survival strategies have not previously been described. We have revealed that tardigrade survival is dependent on reversibly oxidized cysteines coordinating the entrance and emergence from survival states in a highly regulated manner. Through imple- mentation of EPR and redox library screens, we have demonstrated that intracellular release of ROS is essential for tun formation. We have also characterized the initiation, emergence from, and dose-dependence of both osmobiosis and chemobiosis, shown here in H. exemplaris for the first time. The rapid induction of tardigrade tuns via osmobiosis, paired with the high sur- vival rates following emergence, makes it an easily implemented cryptobiotic state for further exploration of the mechanisms of tardigrade survival. Supporting information S1 Fig. Tardigrade volume estimation when hydrated (control), induced into tuns with 75 mM CaCl2, and induced into tuns with 600 mM sucrose. (TIF) S2 Fig. Tardigrades rely on cysteine oxidation to survive cryobiosis. A) The survival of tar- digrades that underwent cryobiosis for 24 h. Survival was determined by coordinated leg movements following return to room temperature. B) Confocal image of a tardigrade under- going cryobiosis. Note that cryobiosis does not facilitate tun induction. C) Confocal image of a tardigrade exposed to 30 uM NEM prior to undergoing cryobiosis. None of the NEM-blocked tardigrades survived cryobiosis. (TIF) S1 Table. Results of tardigrade inhibition study. Tardigrades were screened with the SCREENWELL Redox Library (Enzo Life Sciences) to determine detrimental effects to tun formation. Tardigrades were exposed to 100 uM (working concentration) of each compound and monitored for fatalities after 1 h. Following the hour, tardigrades were exposed to 75 mM CaCl2 for 3 h, before monitoring for tun formation. Percentage of inhibition refers to the num- ber of tardigrades (that were not deceased after 1 h) not in tuns following 3 h of CaCl2. (XLSX) Author Contributions Conceptualization: Amanda L. Smythers, Jessica R. Crislip, Derrick R. J. Kolling, Leslie M. Hicks. Data curation: Amanda L. Smythers, Kara M. Joseph, Leslie M. Hicks. Formal analysis: Amanda L. Smythers, Kara M. Joseph, Hayden M. O’Dell, Jessica R. Crislip, Brendin B. Flinn, Evan R. Stair. Funding acquisition: Derrick R. J. Kolling, Leslie M. Hicks. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295062 January 17, 2024 15 / 19 PLOS ONECysteine oxidation essential for tardigrade survival Investigation: Amanda L. Smythers, Hayden M. O’Dell, Trace A. Clark, Jessica R. Crislip, Brendin B. Flinn, Meredith H. Daughtridge, Evan R. Stair, Saher N. Mubarek, Hailey C. Lewis, Abel A. Salas, Megan E. Hnilica. Methodology: Amanda L. Smythers, Kara M. Joseph, Hayden M. O’Dell, Trace A. Clark, Jes- sica R. Crislip, Brendin B. Flinn, Evan R. Stair, Derrick R. J. Kolling, Leslie M. Hicks. Project administration: Derrick R. J. Kolling, Leslie M. Hicks. Resources: Hayden M. O’Dell, Trace A. Clark, Brendin B. Flinn, Derrick R. J. Kolling. Supervision: Derrick R. J. Kolling, Leslie M. Hicks. Validation: Kara M. Joseph, Hayden M. O’Dell, Trace A. Clark, Brendin B. Flinn, Meredith H. Daughtridge, Abel A. 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10.1371_journal.pgph.0002740	RESEARCH ARTICLE Impact of the COVID-19 pandemic and policy response on access to and utilization of reproductive, maternal, child and adolescent health services in Kenya, Uganda and Zambia Shiphrah Kuria-NdirituID John Kutna5, Dona Anyona1, Joyce MurerwaID 1, Sarah KaranjaID 2, Brenda Mubita3, Tonny KapsanduiID 4, 6 1, Laura FergusonID 1 Amref Health Africa, Headquarters, Nairobi, Kenya, 2 Kenya Medical Research Institute, Nairobi, Kenya, 3 Amref Health Africa in Zambia, Ndola, Zambia, 4 Amref Health Africa in Uganda, Kampala, Uganda, 5 Amref Health Africa in Kenya, Nairobi, Kenya, 6 Institute on Inequalities in Global Health, University of Southern California, Los Angeles, California, United States of America Shiphrah.Kuria@amref.org, shiphonk@yahoo.com Abstract Global health crises can negatively impact access to and utilisation of essential health ser- vices. Access to and utilisation of reproductive health services were already challenged in Sub-Saharan Africa with the COVID-19 pandemic further complicating the critical situation. This cross-sectional qualitative study aimed to assess the impact of the COVID-19 pan- demic and policy responses to it on the access to, and utilization of reproductive, maternal, child and adolescent health services in Kenya, Uganda, and Zambia. It sought to explore the perspectives of women of reproductive age (18–49), frontline health workers and gov- ernment representatives, all from geographies that are under-researched in this context. Using purposive sampling, key informant and in-depth interviews were carried out with 63 participants across the three countries between November 2020 and February 2021. The study population included women of reproductive age (18–49 years), front-line health ser- vice providers, and government representatives We established that COVID-19 and the pol- icy response to it affected access to and utilization of services in the three countries, the most affected being antenatal care, delivery, family planning, and immunization services. Women reported not accessing the health facilities for various reasons. Barriers to access and utilization of services cut across all the socioecological levels. Movement restrictions, particularly in Uganda where they were most severe, and fear of contracting COVID-19 at health facilities were the most reported barriers. Weak structures at community level and inadequate supply of commodities in health facilities exacerbated the situation. Mitigation factors were put in place at different levels. There is need to strengthen the health system, particularly the supply chain and to have services closer to the community to enhance access to and utilisation of services at all times and particularly during crises such as the Covid-19 pandemic. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kuria-Ndiritu S, Karanja S, Mubita B, Kapsandui T, Kutna J, Anyona D, et al. (2024) Impact of the COVID-19 pandemic and policy response on access to and utilization of reproductive, maternal, child and adolescent health services in Kenya, Uganda and Zambia. PLOS Glob Public Health 4(1): e0002740. https://doi.org/ 10.1371/journal.pgph.0002740 Editor: Marguerite Massinga Loembe, African Society of Laboratory Medicine, ETHIOPIA Received: May 30, 2022 Accepted: November 29, 2023 Published: January 25, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgph.0002740 Copyright: © 2024 Kuria-Ndiritu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 1 / 22 PLOS GLOBAL PUBLIC HEALTHData Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. The COVID-19 pandemic and response impact on access to and utilization of RMCAH services Introduction The Coronavirus disease 2019 (COVID-19) pandemic affected all sectors directly or indirectly and has had far reaching effects on already overburdened health systems in many countries with significant implications on health worldwide [1]. History has shown that pandemics limit access to healthcare, with preventive and reproductive healthcare being affected severely [2, 3]. Health services such as reproductive, maternal, child, and adolescent health (RMCAH) ser- vices, treatment for hypertension, diabetes and their complications faced numerous challenges during the COVID-19 crisis, being partially or completely disrupted in many countries [4, 5]. These challenges have had negative impacts on people’s health and quality of life, and put extra strains in the achievement of many global health indicators such as the sustainable develop- ment goals (SDGs) [6]. The pandemic occurred at a time when many Sub-Saharan African countries including Kenya, Uganda and Zambia were struggling to make progress towards the SDGs [6]. Maternal and child mortality and morbidity in these countries have remained high, due in part to weak health systems [7–10]. Many women do not have access to modern contraceptives; many ado- lescents and young people have unintended pregnancies, children, particularly the under- fives, continue to suffer high morbidity, most of which is preventable [8–10]. Numerous studies and reports have examined the impact of the COVID-19 pandemic on access to and utilization of RMCAH services, including emergency obstetric and new-born care, finding widespread disruptions in Africa [5, 6, 11]. Many health facilities faced challenges including diversion of resources to the COVID-19 response compromising the provision of regular services, leading to reduced access to and utilization of essential care [5, 11, 12]. Several studies reported a decline in antenatal and postnatal care utilization during the pandemic with fear of infection, transportation difficulties, and restrictions on movement being among the major contributors to this decline [11, 13, 14]. Access to family planning services and contra- ceptives was significantly affected, including with stock outs of contraceptives, reduced avail- ability of services, and limited mobility, which resulted in increased unmet need for contraception and a rise in unintended pregnancies [15, 16]. Routine immunization programs in many African countries experienced service disruptions, limited vaccine availability, and reduced healthcare-seeking behaviour, which led to a decline in immunization coverage, potentially increasing the risk of vaccine-preventable diseases [6, 11]. Adolescents across Africa experienced school closures, limited access to sexual and reproductive health services, disruptions in mental health support, and increased vulnerability to gender-based violence [17, 18]. All of this has contributed to adverse health outcomes, including an increase in mater- nal and child mortality rates [6, 11]. Beyond the pressure exerted on already strained health systems in Sub-Saharan Africa by COVID-19, there was also substantial social and economic disruption caused by the pandemic [5, 19, 20]. Similar to the experiences of previous epidemics such as HIV, SARS and Ebola, COVID-19 has exacerbated a range of pre-existing inequalities including those relating to socioeconomic status and gender [2, 19, 21]. The stringent mitigating interventions against the pandemic, such as lockdowns which increased barriers to access to healthcare, placed a dispro- portionate burden on vulnerable individuals, exposing them to poorer health and other out- comes [22, 23]. Many people reported loss of businesses and livelihoods worsening their economic situation, increasing income-related inequalities [20, 24]. Studies have shown mixed results, on the impact of the pandemic on the utilisation of RMCAH services, particularly in sub-Saharan Africa with some studies showing reduction while others did not show significant change [5, 25]. The pandemic disproportionately affected women and girls in many countries [2, 19]. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 2 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Kenya, Uganda and Zambia experienced many of the same challenges to access and utiliza- tion of RMNCAH services as described above. On the supply side, there was disruption of the supply chain [26]. On the demand side, movement restrictions, myths and misconceptions about COVID-19 were widespread among communities, and fear of contracting the disease stopped many people from accessing health services [5, 11, 14, 27]. Lockdown measures, restricted movement, and transportation challenges limited women’s ability to reach health- care facilities [11, 14]. Additionally, some of the measures instituted at the facility level to curb the spread of the virus affected the provision and uptake of services [17]. Vulnerable popula- tions, including rural communities and low-income households in Kenya, Uganda and Zam- bia were disproportionately affected by the COVID-19 pandemic as well as the associated policy measures instituted to control the spread of the virus [6, 28]. A variety of measures was put in place at various levels to improve access to essential health services [11, 14]. Efforts have been made to document the effects of the pandemic and the subsequent response on RMCAH and the extent and the quality of implementation of continuity of care guidelines and policies but there are still communities whose stories have not been heard and additional documentation is important. The objective of this study was to explore the perspec- tives of services users and providers as well as health policy-makers on the impact of the COVID-19 pandemic and the associated policy responses relevant to RMCAH services. Previous studies carried out in Kenya, Uganda and Zambia focussed on different geograph- ical areas from this study, and we did not find studies comparing the three countries. This study also explored the perspectives and experiences of women and healthcare workers (HCWs) on health seeking behaviour, access and utilization of RMCAH services during the COVID-19 pandemic, which provides valuable information for efforts to make health systems more resilient and responsive during the recovery phase and for future pandemics. Methods Study design and setting This was a cross-sectional qualitative study, comprising of interviews with different stakehold- ers. In Kenya, it was conducted in one referral hospital, two sub-county hospitals, three health centres (HC), and three dispensaries in Homa Bay County, in the Western part of the country. In Uganda, the study was conducted in the regional hospital, six district hospitals and selected three Health Centres in Lango sub-region, Northern region. In Zambia, it was conducted in one regional and referral Hospital, one Mission hospital, one rural hospital and 16 primary health Care (PHC) facilities in Ndola District of the Copper Belt Province. The study population included women of reproductive age (18 to 49 years old) living within the selected study areas since January 2020 who were either pregnant at the time of the study or had delivered at home or at the health facility during the COVID-19 pandemic; front-line RMCAH services providers from health facilities within the study areas and government rep- resentatives from the three countries. The study regions were purposively selected, and the eligibility criteria is shown in Table 1. Participants were purposively selected based on their suitability to provide the desired information. They included nine frontline health workers, nine women of reproductive age who had needed services (delivery, ANC or child health services) for their children and three government officials involved in policy implementation, per country. The research team worked closely with the community health volunteers to identify women of reproductive age. Government officials were from both the national level and the regional level. Health workers included doctors, nurses/midwives and clinical officers. The eligibility criteria allowed us to collect data from participants with varied backgrounds and experiences. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 3 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Table 1. Eligibility criteria for selecting the study areas and facilities. Kenya Zambia Uganda Study area Homa Bay County Ndola district of the Copper belt province Lango region in Northern Uganda Selection criteria Sampling of health facilities Sampling of health facilities • Amref Health Africa has an ongoing RMNCAH project in the region • High maternal mortality • High prevalence of COVID-19 The selected facilities were stratified to include facilities located in urban, rural and peri-urban areas. The facilities were further stratified to tertiary, secondary and primary health care facilities Stratified sampling technique was used to select 3 sub counties within Homabay County to represent Urban (Homabay Town Sub County), Peri-Urban (Rangwe Sub County) and Rural (Mbita Sub County). The health facilities were further stratified to Sub County Hospital, Health Centre and Dispensary in each of the selected sub-counties. Data was collected from 6 primary health care facilities and one Rural level and 2 mission hospitals. All the facilities offered similar maternal, neonatal and Child health services including labour and delivery services. . Thus Regional referral hospital (Lira), 2 general district hospitals, 2 health center (HC) IVs, 2 HC IIIs and 2 HC IIs. https://doi.org/10.1371/journal.pgph.0002740.t001 Theoretical framework The study was based on the social ecological model (SEM) which posits that human behaviour is influenced by the interaction with the environment and has five levels (intrapersonal/indi- vidual, interpersonal/community, and structural (physical and cultural] and policy) [29, 30]. We explored how factors in all the different SEM levels influenced access and utilisation of RMCAH services acting at the different levels of the SEM. Data collection methods In-depth interviews were conducted with women of reproductive age to explore women’s experiences and perspectives on their health seeking behaviour, access to and utilization of RMCAH services during the COVID-19 pandemic. Community health volunteers assisted the research team to mobilize the interviewees. The key informant interviews with front-line RMCAH service providers were conducted to assess their perspectives of the access to and uti- lization of RMCAH services in the context of the COVID-19 pandemic. Key informant inter- views were conducted with policy makers at the national and regional level. Those involved in either the development or the implementation of the policies were targeted; in each country the person in charge of RMCAH at either national or regional level was interviewed. The inter- views sought to understand the government officials’ views on how policies and guidelines relating to COVID-19 affected the access to and utilisation of RMCAH services. In Kenya and Zambia, the interviews were carried out face to face following the appropriate Ministry of Health’s COVID-19 prevention guidelines. However, in Uganda, telephone con- versations were carried out, because physical interactions were not allowed by the ministry of health. The interviews took about 30 to 45 minutes. There were six research assistants (RAs) in each country (total 18 RAs), all of whom had at least a bachelor’s degree in social sciences or public health, previous experience conducting qualitative interviews, previous experience working with or interviewing health care providers and knowledge of RMCAH services, and were residents of the county/region/district where the study was conducted. RAs were taken through three days of training and one day of pilot- ing the interview guides. In Kenya, interviews were conducted in English, Kiswahili and Dho- luo. In Uganda, all interviews were conducted in English, while in Zambia interviews were conducted in English and Bemba. All interviews conducted in local languages were transcribed and translated into English. The interview guides are attached as S1 Appendix. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 4 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Data management and analysis Qualitative data were transcribed and analysed thematically and iteratively. Content and the- matic analysis of transcripts was done with the codes being generated both inductively and deductively in each country. Coding and analysis were done manually across the three coun- tries. The defined codes were then organized and sorted by relevant themes for reporting. Sub- themes were allowed to emerge from the data through an iterative process and codes were refined as needed during the analysis. Written notes captured by the researchers during the key informant interviews were reviewed to complement the transcripts. Emerging trends were analysed according to the research objectives using a critical-interpretive approach. Tran- scripts are available as S2 Appendix. Data triangulation and verification was done by comparing responses from different respondents to identify similarity of themes and areas of (dis)agreement on issues. Ethical considerations Ethical approvals were received from an Ethics and Scientific Review Committee (ESRC) in each country. In Uganda approval was given by the Makerere University College of health sciences school of public health higher degrees’ research and ethics committee; Protocol 891 while in Kenya approval was by the Amref Ethics and Scientific Review Committee, approval number P853-2020 and in Zambia by the Tropical Disease Research Centre ethics Committee; FWA number 00003729. Approval from the National Commission of Science and Technology (NACOSTI) was obtained in Kenya, License No: BAHAMAS ABS/P/20/6877 and from the National Health Research Authority in Zambia; Ref No: NHRA00001/25/09/2020. The implementation of this research was in full com- pliance with human subjects’ ethical requirements, and informed consent was taken appropriately. We excluded girls aged 15 to 17 years due to the anticipated challenge in seeking consent for their participation from parents/guardians especially during the pandemic period. Results The results are organised according to the main themes. The first theme is the effect of the pan- demic and the associated policies on the access to and utilisation of RMCAH services. The sec- ond, more extensive theme explores the reasons for decreased access to and utilization of services by the levels of the SEM; closely related to this one another theme that emerged was the quality of care and the experiences of the women as they sought services. Finally, the inno- vations to address the arising challenges are explored. Effects of the COVID-19 pandemic and the resulting policy responses on access to and utilization of RMCAH services Across the three countries, HCWs observed a reduction in the number of clients attending ANC clinics, family planning, delivery services, immunization and child welfare clinics, partic- ularly during the initial periods of the COVID-19 pandemic. Children defaulted on vaccina- tions and the child welfare clinics as pregnant mothers missed their ANC visits. The reduction in utilisation of these services was attributed to the effects of COVID 19 such as fear of con- tracting the infection and to the policies towards mitigation of the pandemic. These policies included movement restriction, transport regulation and restrictions on service provision. “It (Covid -19) affected us by even the numbers of the people in that all services (RMCAH) offered lowered during the lockdown. . .. . ... Delivery services also dropped because of the same issues”. (KII HCW, Uganda) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 5 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services “The number (accessing and utilising family planning services) has gone down. . .At first it was around 40s and recently it is 28 [per month]”. (KII HCW, Kenya) “We had a reduction in family planning coverage from 45% to 35.8%. That is contraceptive prevalence rate under reproductive health. So, those are the effects on family planning services. (KII Health Official at the district, Uganda) “The numbers (of ANC clients) are not the way they used to be like way back. They have decreased”. Service provider (KII HCW, Zambia) “It (baby welfare clinic clients) has decreased because they opt to stay at home waiting for the immunization. . .. just a few come (for Youth friendly services) about three out of 50. Service provider (KII HCW, Zambia) In all countries, the pandemic led to late ANC bookings with some pregnant women start- ing ANC in their second and third trimesters. In Kenya, HCW0 reported that some women attended the first ANC visit and only returned for delivery. In Uganda, HCWs reported a decline in the number of new ANC clients with few women attending four or more ANC clin- ics. Clients in Kenya and Zambia reported booking the clinic late and reducing the number of visits. “COVID has really affected the turn up of clients. In the first days for example the expectant mothers would come for the first visit, from there you would see her during delivery.” (KII, HCW, Kenya) “I just thought of going to book for antenatal care at 4 months” (IDI, Pregnant Woman, Zambia) Barriers in accessing and utilising healthcare services during the COVID- 19 pandemic by socio-ecological levels As explored below, the reduction in uptake of RMCAH services was caused by barriers that cut across all the socio-ecological levels. At the individual level fear of contracting COVID-19, fear of being tested for COVID-19 and getting quarantined and general fears around some of the government measures were reported. At the interpersonal and community level, rumours and misconceptions were identified as barriers to access and utilisation of services. At the pol- icy and systems level, movement restrictions that resulted in transport challenges, costs associ- ated with access, and lack of supplies, commodities and services were the main barriers. Individual level barriers Fear of contracting COVID-19. Fear of contracting COVID-19 was reported across the three countries as one of the major barriers in utilization of health services especially at the onset of the pandemic. Health facilities were viewed as a risky place where COVID-19 could be contracted given they are where every sick person including those with COVID-19 go to seek treatment. “. . .. . .people are fearing to come to the clinic, they fear it’s the place where there is Covid and won’t come to access the service”. (KII~ HCW, Zambia) “I started (ANC) at 5 months; I am now at 7 months. I initially had fears that I would con- tract COVID” (IDI WRA, Kenya) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 6 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services “We are scared of contracting Covid 19 from the health facilities. I am only at the health facil- ity because I was in extreme pain, and I had no way out”. (IDI WRA, Zambia) Some women who delivered at health facilities reported being worried that they and their babies might contract COVID. Fear of being tested for COVID-19 and getting quarantined and general fears around some of the government measures. Fear of being tested for COVID-19 and consequently quarantined was also reported in Kenya as another barrier in the access to health services in the facilities, particularly during the onset of the COVID-19 pandemic. In the initial phase of the pandemic clients were screened for COVID-19 and if it was suspected they might be infected, they were taken into isolation/quarantine. “Something that makes them fear more especially when the temperatures are high that they will be taken to a quarantine centre. So people just prefer buying drugs from a pharmacy to going to the hospital.” (IDI~ WRA, Kenya) In Uganda, negative perceptions of some of the COVID-19 prevention measures, particu- larly the temperature screening using thermo-guns was reported to be a hindrance in access to health care services for some of the community members. “. . . a man who was bringing his children for immunization . . .. he said no I do not want to be gunned, that is an evil practice, so he went back.” (KII~ HCW, Uganda) Associated cost of accessing and utilising the services While most of the health services are free at the health facilities, it was reported that some cli- ents are increasingly financially constrained due to COVID-19 and unable to afford the small administration fees charged, or the record books required. “It’s getting harder and you have to buy a book while there is no money. For some people even twenty shillings (less than 0.2 USD) to buy a book becomes difficult to get because of Corona. So they don’t go to the hospital.” (IDI~ WRA, Kenya) In Zambia, some of the respondents indicated that they incurred costs, such as that of pho- tocopying the ANC card, which they felt were expensive given the tough economic times. The general effects of the pandemic such as economic hardships made it challenging to access and utilise health services “Life is hard, business is so slow in this COVID era, and my husband lost his job because of COVID-19. I wanted to deliver from the facility, but circumstances made me deliver from home. . .. . .. I had no money for a taxi. I delivered on my own in my house assisted by a volun- teer from the clinic.” (IDI_ Zambia) Interpersonal and community level barriers Misconceptions about COVID-19 at community level. There were misconceptions that health care workers had contracted Covid and could infect those coming to seek health care services. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 7 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services “The barrier was the thinking that all health workers had COVID and all those who were coming to the facility were sick with COVID.” (KII_HCW_, Uganda) Even when outreach services resumed, there was still reluctance of the community mem- bers to use the services for fear of contracting COVID-19. “we used to go for an assessment of nutrition in the communities but the challenge we are get- ting now, if we move to the community, they will run away because of the fear of the pan- demic. This is the same with immunization, it has reduced our coverage on immunization because people think we have taken them COVID-19 and run away.” (KII~ HCW, Uganda) Policy and systems level barriers Movement restrictions and transport challenges. A major barrier in the access to health services was the movement restrictions, which were introduced in all the tree countries. In Kenya, the government introduced a night curfew at 7pm in the initial phase of the COVID-19 pandemic, making it difficult to access health facilities at night as there were no transport ser- vices. Additionally, people feared arrests and other consequences of encounters with the law enforcement during the curfew hours. Movement restrictions were particularly severe in Uganda: the government introduced a lockdown and a curfew accompanied by transport restrictions in the early phase of the pandemic in March 2020. Lockdown was eased but rein- troduced in June 2021 when there was another wave of infections. The result was difficulties in access to health services not deemed emergencies including some RMCAH services. Some women had planned to deliver at the health facility but ended up delivering in the community due to the COVID-19 associated restrictions such as movement restrictions and night curfews. There was unclear information on the policies and their implementation, with some women reporting that their delivery at home was due to inadequate information on how to access health facilities during curfew hours. The women did not get clear information that in case of a heath emergency, they could be allowed to go to the facility despite the curfew. The women were uncertain if they would be victimised as they tried to get to the facility for emer- gencies. These circumstances led to women delivering at home despite having planned to deliver at the health facility. “. . . The challenge comes especially with the April 20th restriction of movement. . .. . .., labour does not follow the restrictions in those hours. It will come at midnight but then you are not supposed to move so what do you do? Do you deliver at home? Do you risk moving to the facil- ity? Are you going to be arrested by the police? So those people who don’t know their rights because if you are in labour you can pass the police roadblocks but there are others who don’t know so they would rather stay at home. . ...” (KII_, Kenya) “. . .if you look at most of our emergencies from all the data that we’ve. . ..., most emergencies happen in the evening or at night. . .. The curfew is at 7 p.m.; all taxis have gone home, every- body that you know with a motorbike or the boda boda fellows who could help you like before are all at home because of the fear of unknown.” (KII Kenya) “I delivered at home. . .it was late. . . around 8pm in the evening and you know the issue of transport even was a problem. By that time, you could not find a boda-boda (motorbike trans- port service) even if you searched for one. . . I failed to get transport, I sent for a traditional birth attendant who lives within [the community].” (IDI WRA, Uganda) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 8 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services “We accepted the curfews imposed by the government and we were kept indoors, I personally felt intimidated by these. . ...” Adolescent WRA, Zambia). “If we could have been told that there would be no problem on the way at night we would have just reached the hospital and delivered there. We would not have delivered at home or along the way.” (IDI_WRA, Kenya) In Uganda, the challenges in accessing transport, especially during the lockdown, resulted in birth complications, with some cases of maternal and neonatal mortalities being reported. Even in the cases of emergencies, the bureaucracy in securing transport permits from Resi- dent District Commissioners (RDCs) or District Health Officers (DHOs) was a challenge. As one of the HCW noted: “. . . You need to get a letter from either the RDC or the DHO in order to access the road to go where you were supposed to get the service from.” (KII~ HCW, Uganda). In Uganda health workers reported that during the lockdown women on short-term family planning methods such as pills and injections lacked transportation means to access health facilities leading to interruptions in family planning use and unintended pregnancies among young women. “When the lockdown started there was no transport from home to wherever they could get these services [family planning services] and majority never went back for refills or injections. As a result, women and young girls conceived and we lost some.” (KII_HCW Uganda) “Family planning was reduced, as you know family planning is not an emergency to some peo- ple, and then they would say aaah-aaaah. [Meaning no] all those services were reduced.” (KII, HCW, Uganda). In Zambia, movement restrictions mainly affected the availability and cost of public trans- port. Cost of transport was reported as a barrier to accessing and utilising services across all three countries due to the limited transport availability and the guidelines imposed on the sec- tor. This situation was particularly difficult for communities because of the tough economic environment resulting from the pandemic and the accompanying restrictions. “There is low access and utilization due to transports costs, sometimes these are the reasons why our women are delivering from home. They are having a hard time sourcing for money because of the pandemic”. Service provider Zambia) “. . . The issue that the vehicles have to carry the required number of persons; the Nissans carry 14, and now they are supposed to carry eight (8). There is that tendency of the vehicles to increase the rates. The motorbikes were supposed to carry one person.” (KII~ County health official, Kenya) With transport costs high, some clients choose to access lower level health facilities or phar- macies rather than travelling further to access larger health facilities with more capacity. “Transport costs are very high for them, remember that the government came with a policy that you have to space, somebody in a taxi has to pay double, boda-boda (motor bikes) are now very expensive to bring someone to a regional referral here. They will resort to small units or peripheral units, other clinics or drug shops calling them. So, by the time they are referred PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 9 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services here they are in bad state because they delayed as they were thinking of reducing cost by remaining home." (KII, HCW, Uganda) “The biggest challenge now is that at the regional referral, we are receiving these mothers late, and this is our biggest challenge. Sometimes they are in their dying moment and they have very bad complications. Sometimes the babies have already died, and that is the biggest challenge. . .” (KII~ Government Official, Uganda) Shortage of supplies, commodities and capacity to offer services Limited supplies, and commodities including medicines, vaccines and personal protective equipment was cited as a barrier to accessing health services across the three countries, even if clients could get to health facilities. “. . . there were short supplies of medical supplies because they could not enter the country. In some cases they [clients] would withdraw and say why am I going there when I am not getting the services I need.” (KII~ HCW, Zambia) “Sometimes people go to the health facility but they fail to get drugs, so going there again becomes difficult for them. Like there is a woman who told me that she took the child to (name) Health Centre but she did not get the BCG so she had to go to (name) facility. It dis- courages clients.” (IDI_WRA, Kenya) “The biggest challenge here is the issue of supplies, there are key things that must be there if you are to maintain some quality and those things are given to us by NMS (National Medical Stores) or MOH and if they do not give, then there you’re compromised and there is nothing much you can do. . .” (KII, HCW, Uganda) It was also reported by some key informants that the health care workers were not well pre- pared to offer services in the context of the pandemic. Although there were trainings to equip some HCWs, coverage was low. In addition to limited information and knowledge, personal protective equipment shortage was a challenge. “We are talking about 75 health workers–one per facility–which means we only cover 75 facil- ities but we have 283 health facilities so we need more trainings.” (KII, Kenya) “There was fear. You know when there are no guidelines. . .. . .. There was fear and getting PPEs was a problem. . .. . .. . . They don’t have PPEs. We saw in Kakamega people [HCWs] ran away. I don’t blame them because it is a disease which you don’t understand, you sort yourself out first.” (KII_, Kenya) “We need a full package of PPE for MCH because these are very important services that we cannot avoid. They should be treated like people in the treatment centres so that we can work with confidence.” (KII_HCW_TR, Uganda) Interruptions to service provision The HCWs reported that some services were actually stopped at some point due to directives from the respective authorities. “For OPD [Out Patient Department] services, what happened here at the beginning of COVID the unit was transformed into the COVID isolation centre and the OPD was PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 10 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services transferred to another place. This made people get lost while others feared coming because of COVID.” (KII_HCW, Uganda) “At some point we were told to stop (outreaches). I think that was before June [2020]. We didn’t have enough information regarding the pandemic . . .. . .. . .” (Service provider, Zambia) “. . ..the community outreaches that we used to have, it was first stopped and up to now, we are not doing that integrated outreach, we only do for immunization. In addition, this has just started of recent just like about 2 months ago but it had stopped for some 4 months during the total lockdown.” (HCW, Uganda). The HCWs were concerned about their safety and feared being infected with Covid. They made adjustments to reduce the number of clients coming to the facility for services thus compromising the access to and utilisation of RMNCAH services. They gave the clients return dates after longer periods. It was reported that in some instances, the HCWs actually declined to get close to the patients. . . .. now during the pandemic you are like “if I make them to begin coming like the way they used to come, they would have overcrowded and if they over crowd I may get the infection. Therefore, we would give them two or 3 months for them to come back; the worst was one month but it used not to be like that”. [HCW, Uganda] “Even in immunization, the ones who are getting the third doses, we tell them to come when their children are 6 months to minimize movements.” (KII_HCW, Kenya) The provision of youth friendly RMCAH services, which are critical for the provision of contraception to young people, was interrupted during the initial phases of the COVID-19 pandemic. In Uganda, at the onset of the pandemic, the facilities were not offering the youth friendly services as they were considered, by the government, non-emergency. “It has changed, as I said earlier youth are no longer coming to attend this clinic for the clear reason that majority of them think that they are not sick. . .so they don’t come to the clinic. What brings a person to a hospital (during the pandemic) should be an emergency you can- not avoid”. (KII~ HCW, Uganda) In Kenya, the youth friendly services also stopped even as the need for them increased as schools were closed and young people had more leisure time than usual. “Yes we have a room for the youths. . . With COVID-19 it stopped for a while. . . .. As we speak we have booked them for December. They used to have plays, songs and teachings. We reduced the activities due to COVID-19.” (KII~ HCW, Kenya) “I think the younger age adolescents suffered more because you even find that during this Covid period most of the school going adolescents conceived a lot and we have high numbers of students who are pregnant. . .” (KII HCW, Kenya) Restrictions within health facilities With regard to their experience attending ANCs during the COVID-19 pandemic, the WRA across the three countries reported that there were changes in service delivery in line with the COVID-19 guidelines and regulations. Those seeking services at the health facilities were PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 11 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services required to fully comply with the COVID-19 infection prevention measures such as hand washing/sanitization, wearing of face masks and social distancing. Failure to observe the COVID-19 guidelines, led to being denied access to services; clients were sent away from the health facilities. The cost of face masks was prohibitive to some. As such across the three coun- tries, those who did not have face masks were restricted from accessing and utilising health services. “Yes. . . We were told to put on the masks. Those without masks were sent back home”. (KII~ WRA, Zambia) “. . .When you go to the health facility [to deliver], you have to follow the routine (SOPs) for COVID, and if you did not follow the routine for COVID they do not work on you or else they delay to work on you.” (IDI WRA, Uganda). In Kenya and Zambia, the requirement to have face masks led to sharing of masks or using unsuitable substitutes, thus defeating the purpose of enforcing wearing of masks for COVID prevention. “Some of them can’t even manage a mere mask they would come and just put on a hanky it will keep on falling off from their face they pick it up. Sometimes the mothers themselves exchange masks because you put up a policy to say if you have not put on mask you will not be attended to. . ..” (KII~ HCW, Zambia) In Kenya, women reported that it was difficult to adhere to the COVID-19 preventive mea- sures such as maintaining social distance (due to space limitations within health facilities) and wearing a face mask during delivery (it was uncomfortable); only the nurses had their face masks on. They also reported minimal interactions with HCWs. People without masks were being arrested thus further discouraging those who could not afford them from going to the facilities. Quality of care Women reported different levels of satisfaction with the care they had received at health facili- ties. Women in Kenya and Zambia reported having had pleasant experiences though some others in Zambia, but from different locality, reported unpleasant ones. In Uganda, some women reported delays in being attended to for those who did not adhere to the COVID-19 prevention measures while some in Zambia reported that the services were fast. It was reported that even heath workers were afraid of offering services. “The nurse treated me well even if it was a weekend. I could not wear a face mask but the nurse had one on. She treated me with respect and I appreciate.” (IDI WRA, Kenya). “The way the health workers mistreat people at the facility also scares away people” (IDI~ WRA, Uganda) “With the service, we were sitting two per bench, they weighed the babies, and blood tests were done. The service was very fast.” (IDI Kawama HC, Zambia) “. . . I have no card; I am afraid they will shout at me for not having a card.” (IDI_WRA, Zambia) Lubuto HC PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 12 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services “. . .I went for immunization, and we stayed there for some period, and nurses were not there. Then afterward we heard rumours that they feared to attend to us because of COVID-19.” (IDI, HCW, Uganda) “I think there is need for more knowledge because lack of knowledge is what is causing fear. We encourage the members of staff to adhere to what they know about Covid 19. Through counselling and creating awareness of Covid 19, fear will constantly be dealt with and service delivery will continue normally”. (Service provider Zambia) When women were asked if they received sufficient information on COVID 19 and on the services they were receiving, they did not feel they received much information on the pan- demic, including from HCWs. “Not really, we were only encouraged to maintain social distance and to wear face mask as we were”. (WRA, Zambia) According to health workers, provision of information to clients was limited due to COVID; gathering in groups was discouraged which led to missing out on clients receiving information from the health workers and from fellow clients. Health care workers did not get sufficient training to equip them with the information they needed to share with the commu- nity members. “. . ... since we were not holding gatherings, health education of clients was not being done. Sometimes we did not respond to patients’ questions because we never wanted to get close to people with COVID like symptoms.” (HCW, Uganda.) “We didn’t have enough information regarding the pandemic and what to tell the commu- nity.” HCW Zambia Alignment and structural innovations to address challenges In a bid to make services accessible despite the challenges occasioned by the COVID pan- demic, various measures were put in place, some using the existing structures particularly at the community level while in other cases new approaches were created. The facilities worked with existing community structures and partners to reach the community. In Kenya, the HCWs reported using Community Health Volunteers (CHVs) to help track some of the clients who had defaulted on immunisations. To overcome curfew restriction challenges, authorities came up with letters to issue to the clients, transporters and relatives as they left facilities late after receiving services. In Zambia, CHVs as well as other cadres were used to track the pan- demic and to reach out to the community with information. “. . . In Homabay County we came up with an authority letter which we were giving every facility that when a motorbike drops a mother who is in labour at night then they are given to go home.” (KII, Kenya) “The management of COVID is within the structures that are already in existence; structures such as community health volunteers, health centres/health posts statistic health office, the PHO [public health office]. I know that at the PHO there is an incident management system that responds to the progress of the COVID disease.” (KII, Zambia) “We have the community based organizations that we were involving. We have NGOs that are working with the community. There are some women’s groups that were mounting PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 13 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services vehicles with the mega phones . . ...going from one place to another. . .., giving the messages. . . We also had our health promotion officer here and the county working . . .. in the county and sub-county.” (KII_ Kenya) “During the lockdown, there was an intervention done by RHITES-NORTH-LANGO (an NGO) in conjunction with MOH where we used to carry medicines to the community.” (HCW Uganda). “We have that bit of taking the drugs to the clients through the CHVs. They form a group . . .. . .. . . and choose one person who can collect for them the drugs or the CHVs.” (KII_ Kenya) In Kenya, healthcare providers emphasized use of long-acting reversible contraception methods as a strategy to alleviate the burden on healthcare facilities. For individuals who pre- ferred short-term contraceptive pills, a standard three-month dosage was typically provided. However, if individuals demonstrated stability and adequate understanding, the supply was extended to six months. “We are now stressing on long term family planning methods whereby we don’t interact with the patients soon. Even the short term like the pills, we are able to give many. We usually give 3 monthly, but we can go up to 6 if they are stable and educate them”. (KII HCW, Kenya) In Zambia, it was reported that the government had already implemented self-injectable contraceptives before the pandemic. This family planning method would have been an alterna- tive that would have enabled women to autonomously manage their contraceptive needs with- out the necessity of in-person visits to healthcare settings and thus would have alleviated congestion in healthcare facilities. However, the commodities were out of stock and hence did not work when needed most during the pandemic. ". . .The government also introduced a new self-injection for family planning so that mothers do not come to the clinic for this service. They inject themselves at home. . . However, that type of family planning is in short supply. We have not had it for eight months now. It’s not there." (KII, HCW, Zambia) Looking across all socio-ecological levels It is clear that a wide range of factors across all the socio-ecological levels influenced access to and utilization of RMCAH services, with some arising from the pandemic itself and others a result of government responses. All of these factors interacted to create a constrained environ- ment for both WRA and HCWs who had inadequate information and resources to respond appropriately. Table 2 below summarises the different barriers to access to and utilization of RMCAH services across the barriers the socio-ecological levels. Discussion Access to and utilisation RMCAH services Our findings indicate that accessing and utilising maternal health services such as antenatal care, facility deliveries and family planning services during the COVID-19 pandemic in Kenya, Uganda and Zambia were compromised and reduced. The reduction occurred through multiple pathways; they have been explored according to the SEM levels. Both the service PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 14 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Table 2. Barriers in accessing and utilising healthcare services during the COVID-19 pandemic by socio-ecologi- cal levels. SEM levels Barriers in accessing and utilising healthcare services during the COVID-19 Country reported Individual • Fear of contracting COVID-19 Kenya, Uganda and Zambia • Fear of being tested for COVID-19 and consequently Kenya quarantined • Fear of thermo-guns Uganda Interpersonal and community • Associated cost of accessing and utilising the services Kenya, Zambia, • Limited information and misconceptions about COVID-19 Zambia, Uganda at community level Policy and Systems • Movement restrictions and transport challenges • Shortage of supplies, commodities and capacity to offer services • Interruptions to service provision (some services were not being offered) • Restrictions within health facility (no mask, no service) Kenya, Uganda and Zambia Kenya, Uganda and Zambia Kenya, Uganda and Zambia Kenya, Uganda and Zambia https://doi.org/10.1371/journal.pgph.0002740.t002 providers and the clients corroborated the difficulties in accessing and utilising services, many of which were similar in the three countries. The main challenges were related to the policies that were introduced to mitigate the spread of the pandemic in all the three countries. At the individual level, fear of contracting the infection both by the WRA and the HCWs was reported. At the organisation level, limited ability to comply to the prevention guidelines was reported by many HCWs and WRA. Most of our findings corroborate the findings of other studies that have now been published as shown in the discussion that follows, even as we cover new geographies. Antenatal and delivery services. The reduced access to and utilisation of services found in our study is similar to findings by Kotlar et al., 2021 and Tadesse, 2020 [17, 31]. Kotler and colleagues did a scoping review of published evidence globally while Tadesse did his study in Ethiopia. They established decreased access to and utilization of antenatal care services due to COVID-19 pandemic and the resulting policies. A study done to assess the utilization of ante- natal care and facility deliveries among refugees in Kenya indicated decline in access to and utilisation of these services attributed to the fear of contracting COVID-19 and economic chal- lenges occasioned by the pandemic and associated policies [32]. A study in Uganda docu- mented reduced access and utilisation to child health services [11]. We belief that the reduction in access to and utilisation of care posed a great risk to clients. Although our study did not collect data on maternal mortality, Roberton et al. [33] estimated an 8.3–38.6% rise in maternal mortality per month due to interrupted maternal and child health services during the COVID-19 pandemic in 118 low- and middle-income countries (LMIC). While we are unable to substantiate any such rise quantitatively from our study, it is likely that some deaths resulted from the inability to access care as reported by some respon- dents in our study. The World Health Organisation has indicated a large discrepancy between estimated global excess mortality and reported deaths due to COVID-19 in 2023 [6]. Family planning services and youth friendly services. The findings in our study of reduced access to and utilisation of youth friendly services through which the majority of youths and adolescents access family planning services imply the possibility of the vulnerable being disproportionately affected. These findings are, similar to other studies in Africa and PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 15 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services beyond, which have shown the vulnerable were disproportionately affected by both the pan- demic and the mitigation policies, with many young women unable to access family planning services leading to increased unintended pregnancies as well as unsafe abortions [6, 15, 19]. Evidence indicates that there is high unmet need among married adolescents aged 15–19, and among women from rural areas, the poor, those with low education and those not exposed to mass media [34]. Though our study did not include adolescents aged below 18 years, the access challenges applied to clients of all ages accessing RMCAH in the area studied. By clients not accessing family planning services, then their need for modern contraceptive methods was not met. Khowaja and Shalwani, reported that there was reduced access to and utilization of family planning services in health facilities resulting from COVID-19 pandemic and policies imposed to curb spread of the disease in Pakistan [16]. According to UNICEF, disruption in the provi- sion of family planning services among young people, increased unintended pregnancies, childbirth complications, and child mortality [18]. Increased unmet need for modern family planning methods threatens the achievement of the SDG target 3.7. (ensure universal access to sexual and reproductive health-care services, including for family planning, information and education) which is tracked by the indicator 3.7.1, “Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods [35]. Similar to our findings on the interruption of provision of youth friendly services, Compact for Young People in Humanitarian Action and other studies found interruption of youth friendly services [11, 36]. Postnatal care services including immunization and child welfare clinics. Disruption in immunization services has been reported in other studies. Ninety-five percent of countries in South-East Asia and Western Pacific reported disruption of routine immunization service provision due to COVID-19; vaccinations had dropped by 39% by June 2020 [37]. The reduc- tion in access to and utilisation of child immunization services was caused by very similar chal- lenges as those recorded in our study: fears of infection, movement restrictions and limited healthcare access. Evidence shows that 28 out of 62 countries suspended house-to-house immunization during the pandemic [13]. A study in Uganda documented reduced access and utilisation of child health services [11]. In all the three countries reduced access to and utilisa- tion of child health services was reported in our study. Barriers to access and utilisation of RMCAH services by socio-ecological level. The pandemic reduced access due to fear of contracting COVID-19 while the measures put in place to curb spread of COVID-19 had an even greater impact with inadequate information exacerbating the situation. Personal/individual level Our study findings that WRA avoided going to facilities for fear both of the infection and of contravening the measures instituted to curb the spread of the pandemic are similar to other studies. Oluoch-Aridi et al. [14], reported that amongst women in informal settlements in Kenya some avoided facilities due to fear of contracting COVID-19 while Musoke et al, [11] reported fear as a reason for clients keeping away MCH services in Uganda. Interpersonal and community level The fears were made worse by the inadequate information that individuals had including rumours and misconceptions. Incorrect information including misconceptions and rumours have been reported in other studies. In Ghana myths and misconceptions, on the causes of the PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 16 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services disease and vulnerability to the disease were reported, including that the hot climate in Africa inhibited viral replication and transmission of the corona virus [38]. In Uganda, misconcep- tions that COVID-19 was a disease of the white and the elderly was reported [39]. In Zambia the misconceptions included that the disease had been introduced by foreigners with ulterior motives, that it was a political gimmick by the government, and that it was due to radiation from phones, which made people sceptical about measures such as testing for COVID-19 and wearing masks 27. It has been noted that misinformation posed a risk of vaccine hesitancy and continued spread of COVID-19 infection among pregnant women [36, 37]. Low levels of adherence to the preventive and control measures towards COVID-19 in the community have been reported due to limited information [7]. Disinformation has undermined trust, intensi- fied fear and led to inappropriate behaviour in respect to the control of the pandemic [40, 41]. Policy and systems level barriers Our findings indicate that in all the three countries, there were disruptions in the medical sup- plies including personal protective equipment, and interruptions in offering services which negatively affected the provision of services have been corroborated by other studies. Reports indicate that the pandemic disrupted the supply chain particularly in the low- and middle- income countries [26]. Unlike the findings in our study where the HCWs did not have ade- quate information on Covid, a study in Lusaka city and Chirundu international border town in Zambia reported that HCWs had correct knowledge on the disease including the prevention measures [27]. HCWs needed information to enable them offer services in the context of the pandemic. Limited information amongst the HCWs in our study might have hampered the quality of information and services available to community members through the health sys- tem thereby further disincentivizing attendance at services and affecting the quality of care of those who received services. Unsatisfactory experience by those receiving services could nega- tively affect the subsequent visits to the facilities. At the policy level, various some of the policies instituted to control the pandemic were a hindrance to access and utilisation of RMCAH services in all the three countries but access challenges due to movement restrictions appear to have been more pronounced in Uganda where the restrictions were stricter and in place for a longer time, compared to Kenya and Zambia. Many respondents from Uganda referred to lockdown and movement restrictions that severely interfered with access and utilisation of RMCAH services. These findings are cor- roborated by studies that have reported negative impact of the lockdown on access and utilisa- tion of MCH services in Uganda [11]. While it is very important to control pandemics, the effect of limiting access to essential services such as health and the economic impact of restric- tions on the population must be carefully considered. The “No Mask, No Service” policy in health services was a barrier since the masks were costly. The stay at home policy, additionally contributed to clients avoiding health facilities, as has also been found in other studies [11, 14]. Similar to findings in our study, inadequate infor- mation on the pandemic and on the policies and guidelines introduced to mitigate the spread of the infection at different levels, resulted in reduced access to and utilisation of services [42]. Quality of care and the right to health Wangamati and Sundby [43] observed that the already strained health system in Kenya, strug- gled to deliver quality care due to the increased demand occasioned by the pandemic. In our study, the women had different experiences while seeking services some of which raise con- cerns about the quality of care clients received and indicate impediment of the realisation of their right to health in the three countries. The requirement of social distancing led to limited PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 17 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services interaction with HCWs and the regular health education was either shortened or not done in some cases. The limited information indicates a challenge in the quality of services offered and might have contributed to women not having enough information for birth planning as some reported being surprised when labour came earlier than the expected due date. Birth and emergency preparedness is a critical aspect of ANC to ensure women get to health facility for skilled care during delivery or in case of an emergency. From our findings, the right to health for the clients who needed RMCAH services in the area was infringed in some instances. For example, failure to comply with the COVID-19 pre- vention guidelines (wearing of masks) led to denial of services for the poorest who could not afford to buy masks, thus exacerbating inequities. It is the role of the government to protect all citizens to the extent possible from infringement of the right to health, particularly the vulnera- ble. In this case, this could have been addressed by providing masks for the clients who did not have them instead of sending them away from the facilities. Women also reported having failed to go to facilities for delivery since they were not aware this would have been allowed despite the restriction movements. Providing accurate information on how to access essential services in the phase of this crisis that necessitated movement restrictions would have avoided these barriers to access. The existing community structures were pivotal in mitigating the negative effects of the pandemic on the access and utilisation of MCAH services. CHWs and other resource persons were used to reach clients at the community level and shows the importance of resilient community structures. Some of the experiences of living through the COVID-19 pandemic may have led to better quality of care; washing of hands is known to be an important strategy in infection prevention. The availability of hand washing infrastructure and the focus on the practice could mean better quality of care in regard to infection prevention. Experience in Zambia of faster services corre- lates to findings in Kenya where a study done amongst women living in the informal settle- ments in the Embakasi area in Nairobi City, Kenya where most women perceived improvements in quality of care due to short-waiting times and hygiene measures [14]. Financial hardships occasioned by the pandemic contributed to difficulties in access to and utilisation of services in our study, a finding substantiated by other studies that found these hardships to have increased in the three countries and negatively impacted healthcare seeking [14, 44]. These findings corroborate those of other researchers, adding to evidence that will be critical to consider while planning the recovery phase to support investing in resilient systems in order to avoid the system weaknesses that contributed to negative policy related ill effects experienced during the pandemic. Many mitigation efforts were implemented through the existing community structures such as the CHVs and community-based organisation. These structures need strengthening for sustainable access to and utilisation of RMCAH services rou- tinely and during crisis. Study limitations The main limitations of this study include covering a limited geographical area in each country and hence the results may not be generalisable. In addition, we excluded adolescents aged 15 to 17 years old due to the anticipated challenge in seeking consent for their participation from parents/guardians especially during the pandemic period, and so we did not document their experiences. However, though we cannot make deductions on the experience of this group, the information about the access challenges applied to all the clients. We had hoped to triangu- late our findings with routine health data but were unable to do so due to concerns around missingness across the study settings. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 18 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Conclusion The COVID-19 pandemic and the resulting policy responses negatively impacted access to and utilisation of most RMCAH services including ANC services, skilled deliveries, family planning, youth friendly services, and postnatal care services such as immunization and the child welfare clinics. There was a reduction in the number of clients seeking these RMCAH services. Lessons learnt can usefully inform pandemic recovery and future pandemic preparedness; from the findings of our study we highlight some lessons and recommendations. There is a clear need to strengthen the health system, including health services at the com- munity level to enhance access to and utilisation RMCAH services at all times including dur- ing crises that could limit movement. Government and other stakeholders must prioritise this agenda in a sustainable way. Our study has demonstrated the need to plan for emergencies considering how the different levels of the SEM interact to affect the lives of community mem- bers. Additionally, the study demonstrates the similarity of the challenges faced by the health systems in the region. It is key to ensure that all communities have well trained and resourced community resource persons including community health workers/volunteers and other com- munity leaders who would be able to get correct information promptly and be able to share with the community members in case of a crisis. Contingencies are required to ensure continuous service provision even in the context of a pandemic, which includes ensuring an adequate supply of necessary equipment such as PPE; provision of clear information and clear guidelines including on how those needing services could access them. Implementation of guidelines during emergencies must be monitored closely to mitigate the emerging unintended negative effects particularly on the health of the community. Supporting information S1 Appendix. Interview guides. (PDF) S2 Appendix. Transcripts. (ZIP) Acknowledgments All the CHVs who supported us in identifying appropriate study participants and all those who supported us in data collection and analysis, thus the consultants who coordinated data collection in the three countries; Kenya; Dr John Oyare and Mr. John Dave Molla Uganda; Mr. John Bosco Waswa Zambia; Mr. Tato Nyirenda Author Contributions Conceptualization: Shiphrah Kuria-Ndiritu, Sarah Karanja, Brenda Mubita, Tonny Kapsan- dui, John Kutna, Dona Anyona, Joyce Murerwa, Laura Ferguson. Data curation: Shiphrah Kuria-Ndiritu. Investigation: Shiphrah Kuria-Ndiritu. Methodology: Shiphrah Kuria-Ndiritu, Sarah Karanja, Laura Ferguson. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002740 January 25, 2024 19 / 22 PLOS GLOBAL PUBLIC HEALTHThe COVID-19 pandemic and response impact on access to and utilization of RMCAH services Project administration: Shiphrah Kuria-Ndiritu, Brenda Mubita, Tonny Kapsandui, John Kutna. Resources: Shiphrah Kuria-Ndiritu. 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10.1371_journal.pntd.0012027	RESEARCH ARTICLE Human myiasis in Sub-Saharan Africa: A systematic review Binta J. J. JallowID Jifeng Cai1,4, Jingjing Huang4, Fanming MengID 1, Goudja Gassara2, Ousman Bajinka1,3, Yifei Luo1, Mandie Liu1, 1,4* 1 Central South University, Department of Medical Parasitology, Changsha City, China, 2 Central South University, Department of Nutrition Science and Food Hygiene, Xiangya School of Public Health, Changsha City, China, 3 University of The Gambia, School of Medicine and Allied Health Science, Banjul City, Gambia, 4 Xinjiang Medical University, Department of Forensic Medicine, School of Basic Medical Sciences, Urumqi City, China * mengfanming1984@163.com Abstract a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Background Citation: Jallow BJJ, Gassara G, Bajinka O, Luo Y, Liu M, Cai J, et al. (2024) Human myiasis in Sub- Saharan Africa: A systematic review. PLoS Negl Trop Dis 18(3): e0012027. https://doi.org/10.1371/ journal.pntd.0012027 Editor: Nigel Beebe, University of Queensland & CSIRO Biosecurity Flagship, AUSTRALIA Human myiasis is a parasitic dipteran fly infestation that infects humans and vertebrates worldwide. However, the disease is endemic in Sub-Saharan Africa and Latin America. In Sub-Saharan Africa, it is under-reported and therefore its prevalence is unknown. This sys- tematic review aims to elucidate the prevalence of human myiasis, factors that influence the infection, and myiasis-causing fly species in SSA. The review also dwelled on the common myiasis types and treatment methods of human myiasis. Received: February 19, 2023 Accepted: February 27, 2024 Published: March 28, 2024 Copyright: © 2024 Jallow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are in the manuscript and its Supporting information files. Funding: This work is funded by The National Natural Science Foundation China (81901923; 32370554 to FM) and the Natural Science Foundation of Hunan province (2022JJ30693 to FM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Methods Here, we collect cases of human myiasis in Sub-Saharan Africa based on literature retrieved from PubMed, Google Scholar and Science Direct from 1959 to 2022. A total of 75 articles and 157 cases were included in the study. The recommendations of PRISMA 2020 were used for the realization of this systematic review. Results In total, 157 cases of human myiasis in SSA were reviewed. Eleven fly species (Cordylobia anthropophaga, Cordylobia rodhaini, Dermatobia hominis, Lucilia cuprina, Lucilia sericata, Oestrus ovis, Sarcophaga spp., Sarcophaga nodosa, Chrysomya megacephala, Chryso- mya chloropyga and Clogmia albipuntum) were found to cause human myiasis in SSA. Cor- dylobia anthropophaga was the most prevalent myiasis-causing species of the reported cases (n = 104, 66.2%). More than half of the reported cases were from travelers returning from SSA (n = 122, 77.7%). Cutaneous myiasis was the most common clinical presentation of the disease (n = 86, 54.7%). Females were more infected (n = 78, 49.6%) than males, and there was a higher infestation in adults than young children. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 1 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa Conclusion The findings of this study reveals that international travelers to Sub-Saharan Africa were mostly infested therefore, we recommend that both international travelers and natives of SSA be enlightened by public health officers about the disease and its risk factors at entry points in SSA and the community level respectively. Clinicians in Sub-Saharan Africa often misdiagnose the disease and most of them lack the expertise to properly identify larvae, so we recommend the extensive use of molecular identification methods instead. Author summary Human myiasis is a neglected tropical disease in the world especially in SSA. Human myiasis in SSA has infects patients from all dynamics especially those with underlying health issues like primary wounds. The disease can be fatal especially when it involves heavy infestation of the scalp (migratory myiasis) among young children. Subcutaneous myiasis of the eye and genitals can be devastating for patients and can lead to damage in these areas if the disease is misdiagnosed and larvae removal is delayed. The findings and recommendations in our study can be used by government officials in SSA to provide hos- pitals with state-of-the-art diagnostic tools, trained entomologists, and research funding to comprehensively study human myiasis. Public health officers at entry points in SSA should inform international travelers about the risk factors of the disease and common preventive measures. Clinicians in SSA should report more human myiasis cases to enable researchers to estimate the epidemiology and prevalence of the disease. The natives of SSA should be enlighten more on the symptoms and risk factors of the disease and encourage them to report to health facilities when they experience these symptoms. 1. Introduction Myiasis, coined from the Greek word ‘myia’ meaning fly, is the infestation of live or dead tis- sues of vertebrates (humans and animals) by immature stages (maggots) of dipteran flies [1,2]. The disease dates back to 1840 when it was first described by Hope [3] and is still considered a neglected disease in humans, especially in the tropical and sub-tropical regions in SSA, Asia, and Latin America [4]. The disease has a worldwide distribution and has been endemic in Latin America and SSA for years. However, with the increase in global travel, the disease has spread widely, especially in areas with warmer temperatures and high humidity [5]. Myiasis is more common in animals, such as sheep, rodents, and antelope, than humans because humans are accidental hosts. Furuncular myiasis is the most common myiasis reported from travelers returning from endemic regions and is usually caused by the human botfly, Dermatobia homi- nis in Latin America. In SSA, the tumbu fly or mango fly (Cordylobia anthropophaga) causes year-round infestation which could be dated back to 1904 [6], albeit most of the human myia- sis infestation in SSA are caused by this species [7]. The climate condition in SSA is suitable for the breeding of some fly species which makes most places to be endemic of them. Although human myiasis is endemic in SSA, the diversity and prevalence of myiasis-causing flies in SSA is still not clear to date. Human myiasis can be categorized depending on several factors. According to the host-par- asitic relationship (feeding relationship between larva and the host), myiasis can be divided PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 2 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa into obligatory myiasis, facultative myiasis, and accidental myiasis. In obligatory myiasis, fly larvae require living tissues for survival and to complete the immature stages of their life cycle. Facultative myiasis on the other hand is caused by free-living fly species (feeding on decaying organic matter and can opportunistically infest living tissues), their larvae do not require a liv- ing host to complete their life cycle. While accidental myiasis is a condition in which the larval stages of dipteran flies are accidentally ingested through contaminated food or water [8,9]. Additionally, human myiasis can further be classified into primary and secondary myiasis. When dipteran fly larvae invade healthy tissues or skin it will result in primary myiasis, and when these larvae colonize pre-existing wounds it will result in secondary myiasis [4,10]. According to the anatomical site or clinical presentation, myiasis can be cutaneous which involves the infestation of dermal and sub-dermal layers (tissues) of the skin (humans and ani- mals) or infest any part of the body (nose, eyes, scalp, breast, intestine, leg, urogenital, mouth, arms, and thighs) [11]. Cutaneous myiasis takes account the largest part of clinical presenta- tion in humans which could be categorized into migratory (creeping) myiasis, furuncular myiasis, and wound (traumatic) myiasis [Fig 1] [12]. Similarly, furuncular myiasis is the most common type reported from travelers from endemic regions, and is characterized by the for- mation of a painful inflammatory nodule with a central punctum on healthy or unbroken skin [1]. It is mainly caused by the tumbu fly or the botfly [13]. Wound (traumatic) myiasis is caused by dipteran fly larvae which colonize pre-existing wounds and enlarge them [8,10]. While migratory (creeping) myiasis is a condition in which dipteran fly larvae burrow in the subcutaneous tissues of the host and migrate, and often causes pruritic lesions within the host tissues. Theppote A., et al., 2020 presented a clinical presentation of cutaneous myiasis [12]. Compelling scientific literature have revealed that a significant rise in temperature or humidity will increase the growth and redistribution of most myiasis-causing flies, subse- quently increasing myiasis infestation in such regions [11,14]. Some factors that influence this dermatosis in SSA are high humidity and temperature (especially during the rainy season) [5]. Poverty, and poor hygiene also add to the vulnerability to human myiasis infestation. Rodents, antelopes, and pet animals, especially dogs, are the hosts of tumbu fly, making farmers, and pet keepers more vulnerable to C. anthropophaga infestation [15,16]. Skin-related diseases belong to the leading travel-related health problems reported. Currently, human myiasis is reported Fig 1. Life cycle of Cordylobia anthropophaga. 1. Adult female Cordylobia anthropophaga lay 100–300 eggs on the on wet clothing or faecal-contaminated soil. 2. Eggs catch to form 1st instar larva d. 3. 1st instar larva penetrates a host which is usually dog, or rodent, but could accidently penetrate a human host and develop to 3rd instar larva. 4. Larva leaves its host to the ground to pupate. 5. pupa metamorphosis to an adult fly. https://doi.org/10.1371/journal.pntd.0012027.g001 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 3 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa as one of the five most common travel-related skin diseases, which accounts for 7–12% of travel-related diseases globally [1,17]. Hence, an increase in international travel will drastically increase human myiasis infestation in both endemic and non-endemic regions. Human myiasis can affect people (tourists, businesspersons, etc.) traveling to endemic regions, especially in SSA, where it remains a burden and needs urgent attention. Both travel- ers and natives of SSA lack awareness of how to prevent themselves against human myiasis infestation which adds to the vulnerability of the disease. Wearing long garments to cover legs and hands, especially during the rainy season, and sleeping in bed nets would help prevent insect bites. Lying on the ground, hanging clothes on shady lines or bushes, and lack of ironing of garments or bedding after laundry should be avoided [17]. The use of some fly repellents is encouraged to avoid insect bites. Open wounds should be routinely dressed, good skincare and standard hygiene should be maintained to avoid human myiasis infestation [16]. Animal pets should be properly handled because some of these pets can be reservoirs for the disease and can add to the vulnerability of human myiasis infestation. This parasitological condition causes harm not only to humans but also to the livestock industry, accruing substantial economic losses for farmers [8,18]. Human myiasis is often mis- diagnosed as cellulitis, leishmaniasis, tungiasis, or furunculosis [19] which is a common prob- lem in diagnosing the disease. Therefore, properly extracting fly maggots, and sometimes the use of antibiotics becomes the gold standard for the treatment of human myiasis cases. Mor- phological identification is considered standard for larval identification in human myiasis cases, however, the use of molecular identification method has been being utilized globally. This method can differentiate closely related species and identify immature stages which could be an effective method in cases where traditional morphological method is ineffective [1,20,21]. Secondary bacterial superinfection and tetanus are some of the severe complications of human myiasis especially if larvae fragmentation occurs during removal [16,21,22]. Subse- quently, the need to systematically review the current literature on human myiasis in SSA is an important priority. This review aims to elucidate the prevalence of human myiasis, and high- light the most common myiasis-causing flies and areas where they are endemic in SSA. We wish to uncover the predisposing factors of human myiasis in SSA and highlight the most common clinical forms of myiasis. Our study will equally highlight the common extraction and treatment forms of human myiasis. Ecology and Life Cycle of Cordylobia anthropophaga The tumbu fly is the most common myiasis-causing fly in SSA. The adult tumbu fly is yellow- ish-brown in colour measuring 6–12 mm in length with two bands on the thoracic region and a brownish-black on the abdomen [23]. The female tumbu fly lays approximately 100–300 eggs on urine and faecal-contaminated soil or wet clothing (linens), especially clothes dried on shady lines and or bushes which are favourable oviposition locations [11]. When eggs hatch, the first instar larvae penetrate the host skin [24] and after 7–12 days, the second and third instar larvae of tumbu fly will be formed. These stages are characterized by a cuticular spine, spiracular plates, and peritrenes on both the anterior and posterior ends. The third instar larva of C. anthropophaga leaves the host to the ground to pupate and becomes an adult fly and this cycle is repeated [Fig 1] [25]. 2. Materials and methods 2.1 Search strategy According to the PRISMA Recommendations (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) of 2020 [26], a systematic review of the literature was carried out PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 4 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa in pairs. A literature search was conducted in Science Direct, PubMed, and Google scholar using the following search terms: human, myiasis, Sub-Saharan Africa (SSA), and case reports. Boolean operators (AND, OR) were used to combine search terms. We searched manually using the references of retrieved articles and thereby identified articles that were not retrieved from the database search. The search focused on studies conducted in all (48) countries in SSA and the search covered the years between 1959 and 2022 with no filter applied. Case reports from countries in SSA or those acquired from travel to SSA were considered. The following combined keywords were used for the search: 1. For PubMed, search terms were: human myiasis AND SSA AND case reports; 2. For Google Scholar, the search terms used were: human myiasis AND SSA AND case reports. 3. For Science Direct, search terms were: human myiasis AND SSA AND case reports. It was worth noting that papers were also retrieved using the search terms; human myiasis AND country name AND case reports for all countries in SSA and this was done for more thorough search. 2.2 Inclusion and exclusion criteria The inclusion criteria were: (1) original articles related to the topic of interest of this study; (2) any case report article from SSA with no language restriction and/or could be translated using Google translate; (3) relevant case reports from SSA found in review studies; (4) studies carried out in SSA; (5) studies published between 1959 to 2022. The exclusion criteria of this were: (1) articles that are not case reports and articles with case reports that are not from SSA; (2) articles with no author’s name and year of publication; (3) review studies that are with no relevant case reports; (4) unpublished studies; and (5) non- human studies. 2.3 Data extraction All duplicate articles were removed using the Endnote library. Three separate examiners car- ried out the first phase of the selection. It allowed us to delete certain selected studies. Thus, after a complete reading, we selected the studies that met the inclusion criteria. Discrepancies were resolved by discussion with a fourth reviewer. The following information was extracted from the articles selected; patient’s age, sex, country of origin, myiasis type, site of infection, country reported, number of patients, and country of infestation [Table 1]. The studies will be grouped according to the sub-regions SSA (West Africa, East Africa, Southern Africa, and Central Africa) based on the United Nations (UN) system classification. A protocol for this study has been upload on protocol.oi with a DOI of DOI: dx.doi.org/10.17504/. 2.4 Quality assessment The risk of bias was assessed by the Joanna Briggs Institute (JBI) checklist for Case Reports Critical Appraisal Tool [92]. Two reviewers independently assessed selected articles, and dis- crepancies were resolved by discussion or by the other reviewers. The assessment of the quality of the selected studies is presented in the S1 Table. The selected studies were homogeneous, and 73 of 75 studies were of high quality, one study was moderate and one study was below quality according to the JBI-MAStARI. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 5 / 17 PLOS NEGLECTED TROPICAL DISEASESTable 1. Summary of selected studies. Human myiasis in Sub-Saharan Africa Sex Fly species Site of infection Central Africa Country reported No. of patients Country of infestation Central Africa France Vienna Panama Korea Sri-lanka Britain Congo France Italy Morocco Morocco Morocco Country reported Sudan Myiasis type Patient, Age Furuncular 29 (average) Furuncular Furuncular Furuncular Cutaneous Cutaneous Cutaneous Glans (penis) Furuncular Furuncular Furuncular Furuncular Cutaneous 59 50 46 55 46 54 22 5 months 61 37 59 - M F M F M M M M M F M F - C. anthropophaga Various body parts C. rodhaini C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. rodhaini Scalps Thorax, back, lower lip Right lumbar, gluteal region Left buttock upper thigh Anterior chest a acromioclavicular joint Entire back Near the opening of urethra Right shoulder sub clavicular Chest & shoulder Scalp, left parietal temporal part, right flank, upper lip intranasal lesion and abdomen D. hominis leg East Africa Myiasis type Patient Age Sex Fly species Site of infection Furuncular 45 F C. anthropophaga Left thigh Cutaneous Furuncular Cutaneous Furuncular Furuncular Furuncular Furuncular Furuncular Furuncular Ocular Furuncular Furuncular Furuncular Cutaneous Furuncular Furuncular Cutaneous Cutaneous Cutaneous Cutaneous Cutaneous 31, 28 M, F C. anthropophaga Trunk penis lower leg, left buttock Germany 21 46 61 F F F 59, 58 52 42,5 M, F F M, M 22 56 59 - 33 - 30 57 - 52 38 55 26 26 F - M M M - M F M F M F F F C. anthropophaga C. anthropophaga Left finger Upper right arm Japan Sudan C. anthropophaga Left arm, abdomen, left, thigh Ethiopia C. rodhaini C. rodhaini C. rodhaini C. anthropophaga C. anthropophaga C. anthropophaga C. rodhaini C. anthropophaga C. anthropophaga C. anthropophaga C. rodhaini C. rodhaini C. rodhaini C. anthropophaga C. rodhaini C. anthropophaga C. rodhaini Left shoulder, abdomen Pubis Right leg Left buttock, top right side of head & maxilla Italy Italy Ethiopia Right gluteus Upper thigh, lower abdomen, lower back Right eye lid Scalp, right arm, left arm, torso Left leg Left thigh Left groin Right thigh All over the body - Left lower back Fore head Left upper arm Left upper arm Italy USA Italy Italy Korea - UK Australia Italy Germany China UK China Canada 16 1 1 1 1 1 1 1 1 1 1 1 1 No. of patients 1 2 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Ref [27] [28] [29] [30] [23] [31] [32] [33] [34] [35] [36] [37] Local infestation Cameroon Cameroon Central Africa Central Africa Cameroon Congo Local infestation Congo Cameroon Congo Cameroon Congo [38] Country of infestation Local infestation East Africa Uganda Local infestation Local infestation Uganda, Ethiopia Local infestation Kenya Ethiopia Kenya Uganda Uganda Tanzania Sierra Leone East Africa Ethiopia Tanzania Ethiopia Uganda Uganda Ethiopia Ref [39] [40] [41] [42] [43] [13] [13] [44] [17] [45] [46] [8] [1] [47] [48] [49] [50] [51] [52] [53] [20] [54] (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 6 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa Kenya [55] Tanzania Kenya No. of patients Country of infestation Cutaneous Furuncular Glans penis Cutaneous Furuncular Cutaneous Furuncular Furuncular Cutaneous Furuncular Cutaneous Furuncular Cutaneous Vulva Cutaneous Furuncular furuncular Ocular Furuncular Cutaneous Table 1. (Continued) Furuncular Furuncular Cutaneous 4month, 13month 26 26 F, - M M C. anthropophaga C. anthropophaga C. anthropophaga Aural flanks, chin, and fore legs, Lower trunk, forearm and right arm Fifth digit on foot Back (mid scapular region) West Africa Myiasis type Patient Age Sex Fly species Site of infection 17 42 10 - 45 29 - F F M F M F F 16, 17 M, F 48, 47, 14 M, F, F 32 30 45 F F 6 weeks Child C. rodhaini C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga C. rodhaini C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga Left breast Left buttocks penis Breast, upper & lower lips Limbs & trunk - On the thigh, below the breast Bilateral legs, ankle, left thigh, buttocks Back & nose, shoulder, wrist, back Thigh, left flank Right leg C. anthropophaga C. anthropophaga Aural flanks, chin, and fore legs Scalp, dorsal part of trunk, hands, leg 16 50 - 29 24 12 55 F M - F F F M M M -, F M F F F, F F, F C. anthropophaga C. anthropophaga D. hominis D. hominis C. anthropophaga C. anthropophaga C. anthropophaga Clogmia albipunctatum C. anthropophaga C. anthropophaga D. hominis C. anthropophaga C. anthropophaga C. anthropophaga C. anthropophaga vulva Sole of the left foot - buttocks Upper eye lid Lower lid of the right eye and forearm Trunks, arms and legs Nose - Right arm, thigh Upper lip Lower back Right breast Left flank, right thigh Breast, breast Nasopharyngeal Adult Cutaneous Furuncular Cutaneous Furuncular Cutaneous Cutaneous Cutaneous 27 11month, 10y 52 30 70 24, 23 34, 21 Ireland USA UK Country reported UK France Denmark Nigeria India US Israel USA Slovenia Britain Italy Vienna Gambia - Italy Nigeria Spain France Thailand USA UK Netherlands Nigeria UK USA Nigeria Italy Nigeria 2 1 1 1 1 1 28 1 1 2 2 3 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 Myiasis type Patient Age Sex Fly species Site of infection South Africa Country reported No. of patients - 22 &57 Ocular - Ocular 10 - 27 - M Infants F Sarcophaga spp & L. cuprina Various body part South Africa Oestrus ovis C. anthropophaga Oestrus ovis Right and left eye India Arms, buttocks, trunk South Africa eyes Greece 2 1 6 1 [56] [57] Ref [58] [28] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68] [11] [69] [70] [71] Ghana Senegal Senegal Local infestation Nigeria Sierra Leone Ghana Senegal Ghana Gambia Senegal Senegal Local infestation Senegal Senegal Local infestation Guinea Bissau [72] Cape Verde Ghana Nigeria Nigeria Gambia Local infestation Gambia Guinea Local infestation Senegal Local infestation Country of infestation Local infestation South Africa - South Africa [73] [74] [22] [75] [76] [77] [78] [79] [80] [81] [60] Ref [82] [83] [84] [85] (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 7 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa Table 1. (Continued) Furuncular 6-month Furuncular Wound or traumatic Cutaneous Furuncular Furuncular Cutaneous 39 51 28 57 38 F M M F F M 56 (average) M(14), F(11) C. anthropophaga Thighs, trunk Malawi C. anthropophaga L. cuprina Left eye, right thigh, left buttocks UK foot South Africa C. anthropophaga Upper arms, wrist & inner thigh Australia - C. anthropophaga L. cuprina, L. sericata, C. megacephala, C. chloropyga, Sarcophaga, 2UI Left shoulder, back, left arm, right thigh, popliteal fossa Right side of his scalp UK UK Different body parts South Africa 25 1 1 1 1 1 1 Local infestation Angola Local infestation South Africa South Africa South Africa Local infestation [19] [86] [87] [88] [89] [90] [91] https://doi.org/10.1371/journal.pntd.0012027.t001 3. Results There were 1,453 original articles identified in the three databases, of which 847 articles were retained after the deletion of duplicates. A selection based on title led to the exclusion of 650 articles. Abstracts of the remaining 197 articles were reviewed, excluding 116 more articles. Reading the full text of the remaining 81 articles allowed the exclusion of 10 articles. Finally, 71 articles which met the inclusion criteria were selected. A reverse search was performed on the 71 included articles by searching the terms in the references of the selected articles to iden- tify the articles which had not been initially selected and which fulfilled the inclusion criteria. Thus, other articles were identified and included, totaling 75 articles for this systematic review. The representative search design and number of eligible studies are shown in Fig 2. 3.1. Characteristics of included studies Table 1 summarized all included studies in this systematic review and were grouped by sub- regions. Thus, 13 studies were identified from Central Africa, 23 in East Africa, 28 in West Africa, and 11 in Southern Africa. There were 75 studies in total and the cases from these study included both males and females of different age groups. Some cases do not contain gen- der information. There were 7 classifications of human myiasis (cutaneous, furuncular, ocular, glancpenis, vulva, wound, and nasopharyngeal myiasis) detected in this study. The sample size ranged from 1 to 157 patients, and patients’ ages ranged from 6 weeks to 70 years (the average age is 17 years). 3.2. Evidence from reviewed studies Table 1 summarizes the different types of myiasis, fly species, and site of infection. This table also shows the countries where these infections were reported, the number of patients, and the country of infestation. Regarding myiasis types, cutaneous myiasis was described by more than half (54.7%) of the studies examined (n = 86). Furuncular myiasis was described by 38.2% of the articles retained (n = 60). Three (n = 03) articles reviewed genital myiasis (glans penis, and vulva) representing 1.2% and 0.6%, respectively. Very few studies (n = 4) described ocular myiasis (2.5%). In addi- tion, two (n = 2) (1.2%) studies reviewed had described nasopharyngeal myiasis and wound myiasis. There were 2 (1.2%) cases with un-identified myiasis type. Based on reviewed cases, a total of 11 fly species were found to cause human myiasis in SSA which included Cordylobia anthropophaga, Cordylobia rodhaini, Dermatobia hominis, Lucilia PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 8 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa Fig 2. Flowchart for the selection of studies based on the PRISMA 2020 guidelines. https://doi.org/10.1371/journal.pntd.0012027.g002 cuprina, Lucilia sericata, Oestrus ovis, Sarcophaga spp., Sarcophaga nodosa, Chrysomya megace- phala, Chrysomya chloropyga and Clogmia albipuntum. The obligatory cutaneous parasite C. anthropophaga, was the most commonly encountered fly species (n = 104), which accounted for 66% of the total cases. Lucilia cuprina, which is usually responsible for facultative myiasis, recorded 18 cases which accounts for 11% of the total cases. There were 16 cases of Cordylobia rodhaini which accounted for 10% of the cases detected. These three fly species were responsi- ble for the majority of the human myiasis cases in SSA. The other species detected during this study accounted for either 2% or 1% and accumulated to 12% of the total reported cases. The prevalence (percentage) of all the myiasis-causing flies from this study are shown in Fig 3. Anatomically, the most prevalent sites of infestation were from the lower abdomen down to the lower limbs (abdomen, genital regions, buttocks, both lower and upper thighs, both lower and upper legs, feet). The second most frequent infection sites found in this study were the thoracic and back region (thorax, chest, both shoulders, both breasts, both lower and upper arms including wrists, and upper back). However, the least common infested sites in this study were the head and neck regions (scalp, parietal and temporal lobes, intranasal lesions, fore- head, chin, both lower and upper eyelids, truck, nose, maxilla). Three (n = 3) of the studies indicated unspecified infestation sites (various both parts). Regarding patients’ gender, 78 out of 157 patients were female (49.6%). Males accounted for 38.9% (n = 61) of the studies, and 11.5% (n = 18) were un-identified gender. There were 35 (22.3%) cases reported in SSA with no travel history outside of this area. While 122 (77.7%) of the cases were infected in SSA but reported from countries out of SSA. 4. Discussion Human myiasis is a dermatological condition that can affect people from different areas. How- ever, the most common underlying factor for human myiasis infestation in this study is PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 9 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa Fig 3. Diversity and prevalence of human myiasis–causing fly species. https://doi.org/10.1371/journal.pntd.0012027.g003 international travel to endemic regions in SSA [42] [Table 1]. The cases infected and reported in SSA are those patients who are inhabitants (natives) of SSA, and were infected and diag- nosed in SSA without traveling out of SSA. While the cases reported outside SSA but are infected in SSA are travelers who visited SSA and got infected in SSA. When they returned to their countries (outside SSA) and got diagnosed, the cases were reported from their countries. With the increase in international travel, there is a higher risk of re-introducing some of these myiasis-causing flies into non-endemic regions of the world that were successful in eradicating these flies [93]. 4.1 Human myiasis from travelers visiting SSA In this study, cases were reported from different continents of the world ranging from Europe (mainly Italy) [81], North America (mainly USA) [13,62,79], Asia [20,52,61], North Africa [36–38] to Australia [49] however, North Africa was not included as part of SSA. More than half of the reported cases in this study came from Europe (mainly Italy) [28,40]. This could be because more European travelers visited SSA, also clinicians in these countries report more human myiasis cases due to the availability of entomologist compared to other regions of the world, especially in SSA. While in SSA, only a few countries like Nigeria, Ghana, and South Africa seem to have entomologists [71,74,91]. 4.2 Reported myiasis causing-species in SSA Cordylobia anthropophaga was the most common myiasis-causing fly species in SSA mainly in West Africa and this information is consistent with previously published literature [52] and the geo-climatic condition in SSA is a suitable breeding ground for this fly (Cordylobia anthropophaga). Cordylobia rodhaini was most prevalent in East Africa than other parts of SSA [42] however, this species have also been recorded from other parts of SSA which sug- gest that this fly can survive in different region of the world. The results show that L. cuprina was most prevalent in Southern Africa. This could be because most of the published litera- ture is from South Africa, and or because the cold weather in some parts of Southern Africa (especially South Africa) is a favourable condition for the breeding of this particular fly spe- cies [91]. Although the human botfly (D. hominis) is not endemic in SSA, there were PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 10 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa reported cases of D. hominis infestation with no travel history to an endemic region (e.g., Latin America) [38,71,72,78]. Although, there is no evidence for this proposition, this phe- nomenon could be due to climatic changes in SSA, studies on the temperature tolerance, epi- demiology, and occurrence pattern of the botfly (D. hominis) in SSA should be explored to establish the cause for this phenomenon. To enable proper dipteran fly larvae studies, there is a need to extract, identify, and preserve these larvae. Larvae are usually identified morpho- logically, but sometimes molecular identification is used. Preserving the larvae is very impor- tant, especially for molecular identification. Improper preservation can result in DNA degradation that can affect molecular analysis. For morphological identification, larvae are usually killed in warm water of >80˚C for 30 seconds to avoid larval decay. Larvae could also be immersed in normal saline or fix in 10% formaldehyde for morphological identification [8,20]. For molecular identification, larvae cannot be killed in hot water. Instead, larvae could be stored in 70%-95% ethanol for a short period or samples can be frozen to -20˚C or -80˚C for long-term preservation [9,23,44]. 4.3 Reported myiasis type in SSA The most common clinical manifestation of myiasis in humans is cutaneous myiasis and this is consistent with the results of this study [11]. As the larvae of Cordylobia species are laid in wet environments, they can invade exposed parts of the body (limbs, buttocks, genitals) and subsequently more of these areas would be infected [42]. Several fly species have been found to cause cutaneous myiasis in our study. Different parts of the body (ocular, genitals, nasopharyn- geal) could be infested by different fly species resulting in different clinical presentations. 4.4 Common identification and treatment (removal) for human myiasis Human myiasis is usually unpleasant to both patients and health workers, and it is not gener- ally fatal. However, the disease could be serious when it involves delicate parts of the body especially the scalp of young children [6]. Treatment normally involves the extraction of larvae and sometimes the use of antibiotics which has been evident in our study. There are three main larvae removal methods (manual, mechanical, and surgical extraction) described in pre- vious studies and all these extraction methods have been encountered in our study. Manual extraction is usually done with a gentle press on the furuncle with hands (fingers) and paraffin or ointments are usually applied on the central punctum to suffocate the larvae. Dehecq and colleauges [34] manually extracted two larvae from a 9-month-old boy and the lesions healed shortly after extraction [23,66]. There was 45.3% of the reviewed literature which used manual extraction to successfully remove larvae [9,17,30,33,36,37]. None of these cases reported larval fragmentation during the extraction process. In cases involving cutaneous tissues, larvae could be mechanically removed by using specialized devices to aid in larvae extraction. Only 12% of cases in this research used mechanical extraction. Pezzi M and colleauges [8] mechanically extracted 15 larvae from a 45-year-old man, however, 5 of the larvae were damaged. The patient was given a treatment of doxycycline for a week and recovered later [22,66,73]. Of the reported cases, 28% did not specify the extraction method used. Human myiasis can be very severe especially when subcutaneous tissues of the eyes, nose, ears, genitals, and scalp regions of the body are infested [94]. In such extreme cases involving delicate parts of the body, a sur- gical incision under local anaesthesia (lidocaine) is used to remove larvae. Georgalas I and col- leauges [85] used surgical incision to remove a larva from the sub retinal space from a 27-year- old female. Two months after removal, the patient was asymptomatic with visual acuity (VA) of 6/6. Out of the reported cases, 14.6% used surgical extraction to successfully remove larvae and all patients recovered well after incision [35,46,70,74,83]. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 11 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa When larvae are very difficult to detect in subcutaneous tissues, ultrasonography (USG) can be used to detect larvae by scanning the infested area. In such cases, surgical extraction could be used to remove larvae because when other extraction methods are used [83], larvae fragmentation can occur and it could lead to secondary infection or tetanus. Punch biopsy is also an uncommon but very effective method used for larvae detection in subcutaneous lesions [33,64]. Fundoscopy can be used to detect larvae especially with ocular myiasis, and larvae can also be removed through surgical incision [83,85]. Ultrasonography (USG), Punch biopsy, and Fundoscopy were all used in our study to detect larvae in subcutaneous tissues and procedures were successful. These procedures are recorded for use in patients with subcutaneous infestation. There is no published literature suggesting that sex is a predisposing factor to human myia- sis infestation. However, our results have shown that there were more females infected with the disease than males. This could be because more females reported to the hospital when they are sick. Observational and cross-sectional studies should be conducted to ascertain this proposition. 5. Limitations Some reported cases (published papers) of human myiasis from SSA could not be accessed due to lack of institutional access. Therefore, we conclude that the prevalence of human myiasis in SSA could be slightly under-reported. 6. Conclusion Human myiasis is a parasitological condition, which is under-reported and neglected in SSA. Therefore, the actual prevalence and epidemiology of this parasitic infestation is not enough to be estimated. Cases of D. hominis infestation in SSA with no travel history to Latin America have been reported, therefore, research to study the underlying reasons why this species is resurfacing in SSA should be explored. The molecular identification method should be exten- sively utilized to identify myiasis-causing flies for its importance in determining myiasis spe- cies diversity and epidemic studies. There should be research done to explore dipteran flies’ endemicity in SSA; and the relationship between their prevalence and climatic conditions in the sub-region. Both travelers and natives of SSA (tourists, business people, etc.) should be notified, and provided with adequate information about the prevention of human myiasis at entry points and at a community level in endemic regions. There is also a need to raise aware- ness for inhabitants, clinicians, and travelers to these regions about some of the symptoms and predisposing factors for human myiasis. Supporting information S1 Dataset. Metadata of the study. Sheet A-Number and Percentage of human myiasis-caus- ing fly species. Sheet B-Number and percentage of the Sex of infected patients. Sheet C-Num- ber and percentage of human myiasis types. Sheet D- Summary of selected studies. (XLSX) S1 Table. Risk of Bias of the Selected Studies by JBI-MAStARI. The items were collapsed into 8 quality-appraisal criteria (Q1-Were patient’s demographic characteristics clearly described? Q2-Was the patient’s history clearly described and presented as a timeline? Q3-Was the current clinical condition of the patient on presentation clearly described? Q4-Were diagnostic tests or assessment methods and the results clearly described? Q5-Was the intervention(s) or treatment procedure(s) clearly described? Q6-Was the post-intervention PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0012027 March 28, 2024 12 / 17 PLOS NEGLECTED TROPICAL DISEASESHuman myiasis in Sub-Saharan Africa clinical condition clearly described? Q7-Were adverse events (harms) or unanticipated events identified and described? Q8-Does the case report provide takeaway lessons?). JBI-MAStARI was used to assess risk of bias. Articles that scored between 1 and 2 were classified as low meth- odological quality, articles with scores between 3 and 4 were classified as moderate quality, and those with scores � 5 were classified as high quality. N: no, NA: not applicable, U: unclear, Y: yes. (DOCX) S2 Table. PRISMA checklist. (DOCX) Acknowledgments Mr Paul A Correa, Assistant Lecturer at the University of The Gambia (UTG) and PhD Candi- date in the department of Biosciences at Comsats University Islamabad (CUI), Pakistan, assis- tance with academic writing organizers like Mendeley and endnote 20. Author Contributions Conceptualization: Binta J. J. Jallow, Goudja Gassara, Ousman Bajinka, Yifei Luo, Mandie Liu. Data curation: Binta J. J. Jallow, Goudja Gassara, Ousman Bajinka, Yifei Luo, Mandie Liu, Jifeng Cai, Jingjing Huang, Fanming Meng. Formal analysis: Binta J. J. Jallow, Goudja Gassara. Funding acquisition: Fanming Meng. Methodology: Binta J. J. Jallow, Goudja Gassara. 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10.1371_journal.pone.0227835	RESEARCH ARTICLE An IL-18-centered inflammatory network as a biomarker for cerebral white matter injury Marie Altendahl1, Pauline Maillard2, Danielle Harvey3, Devyn Cotter1, Samantha Walters1, Amy Wolf1, Baljeet Singh2, Visesha Kakarla4, Ida Azizkhanian5, Sunil A. Sheth6, Guanxi Xiao4, Emily Fox1, Michelle You1, Mei Leng7, David Elashoff7, Joel H. Kramer1,8, Charlie Decarli2, Fanny Elahi1, Jason D. HinmanID 4* 1 Memory & Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America, 2 Department of Neurology and Center for Neurosciences, University of California, Davis, CA, United States of America, 3 Department of Public Health Sciences, University of California, Davis, CA, United States of America, 4 Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America, 5 School of Medicine, New York Medical College, Vahalla, NY, United States of America, 6 University of Texas Health McGovern School of Medicine, Department of Neurology, Houston, TX, United States of America, 7 Department of Medicine Statistics Core, Department of Medicine, University of California Los Angeles, Los Angeles, CA, United States of America, 8 Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America * jhinman@mednet.ucla.edu Abstract Chronic systemic sterile inflammation is implicated in the pathogenesis of cerebrovascular disease and white matter injury. Non-invasive blood markers for risk stratification and dis- section of inflammatory molecular substrates in vivo are lacking. We sought to identify whether an interconnected network of inflammatory biomarkers centered on IL-18 and all previously associated with white matter lesions could detect overt and antecedent white matter changes in two populations at risk for cerebral small vessel disease. In a cohort of 167 older adults (mean age: 76, SD 7.1, 83 females) that completed a cognitive battery, physical examination, and blood draw in parallel with MR imaging including DTI, we mea- sured cerebral white matter hyperintensities (WMH) and free water (FW). Concurrently, serum levels of a biologic network of inflammation molecules including MPO, GDF-15, RAGE, ST2, IL-18, and MCP-1 were measured. The ability of a log-transformed population mean-adjusted inflammatory composite score (ICS) to associate with MR variables was demonstrated in an age and total intracranial volume adjusted model. In this cohort, ICS was significantly associated with WMH (β = 0.222, p = 0.013), FW (β = 0.3, p = 0.01), and with the number of vascular risk factor diagnoses (r = 0.36, p<0.001). In a second cohort of 131 subjects presenting for the evaluation of acute neurologic deficits concerning for stroke, we used serum levels of 11 inflammatory biomarkers in an unbiased principal com- ponent analysis which identified a single factor significantly associated with WMH. This single factor was strongly correlated with the six component ICS identified in the first cohort and was associated with WMH in a generalized linear regression model adjusted for age and gender (p = 0.027) but not acute stroke. A network of inflammatory molecules driven by IL-18 is associated with overt and antecedent white matter injury resulting from a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Altendahl M, Maillard P, Harvey D, Cotter D, Walters S, Wolf A, et al. (2020) An IL-18- centered inflammatory network as a biomarker for cerebral white matter injury. PLoS ONE 15(1): e0227835. https://doi.org/10.1371/journal. pone.0227835 Editor: Niels Bergsland, University at Buffalo, UNITED STATES Received: September 27, 2019 Accepted: December 30, 2019 Published: January 24, 2020 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All serum biomarker values, imaging analysis results, and appropriate demographic data files are available via Open Science Framework: DOI 10.17605/OSF.IO/92ERQ. Funding: This study was supported by the following funding agencies: NIH AG062422 (UCSF), NIH AG010129 (UCD), AHA Grant #15CRP22900006 (UCLA), AHA Grant-in-Aid #16GRNT31080021 (UCLA), and the MarkVCID Consortium Project UH2/UH3 NS100608 (UCSF/ UCLA/UCD). Additional support provided by the PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 1 / 20 Medical Student Training in Aging Research Program (NIH T35AG026736) and the Lillian R. Gleitsman Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Dr. DeCarli serves as a consultant to Novartis Pharmaceuticals on a trial studying the safety of heart failure medication. The University of California has filed U.S. patent (16/ 487,332) application for: “Serologic assay for silent brain ischemia” for which Drs. Hinman and Xiao are co-inventors. This does not alter our adherence to PLOS ONE policies on sharing data and materials. IL-18-mediated inflammation and white matter injury cerebrovascular disease and may be a promising peripheral biomarker for vascular white matter injury. Introduction Cerebral small vessel disease (cSVD) is a leading contributor to vascular cognitive impairment and is estimated to cause 1/5th of strokes in older adults [1]. cSVD has been associated with global cognitive decline, decreased executive function and reduced processing speeds [2–4]. Individuals with poorer cardiovascular health have higher cross-sectional cSVD burden and accelerated disease progression [5, 6]. Early detection of those at risk for cSVD would allow patients to improve their vascular health and possibly slow the progression of cSVD and cogni- tive decline associated with poor vascular health [7, 8]. Currently, diagnosis and risk stratification of cSVD relies on imaging techniques such as quantification of high signal intensities, or white matter hyperintensities (WMH) on T2-FLAIR imaging. In the context of cSVD, WMH are thought to be evidence of irreversible white matter injury with axonal and myelin damage [9, 10]. Recent studies using diffusion ten- sor imaging (DTI), found that DTI-derived measures, including fractional anisotropy (FA) and extracellular free water (FW), constitute sensitive biomarkers of early-stage white matter injury resulting from cSVD that occurs in advance of the lasting tissue injury measured by WMH [11–13]. However, MRI scans are costly and not indicated in the absence of neurologic symptoms, therefore limiting the ability to prevent or intervene in early cSVD to prevent cog- nitive decline late in life. Therefore, efforts are needed to identify tools that are both easily accessible and reproducible to facilitate earlier diagnosis and identification of patients at risk for cSVD. At the cellular level, cSVD is hypothesized to result from endothelial dysfunction leading to subtle dysfunction of the blood brain barrier (BBB), resultant tissue damage, and progressive inflammatory responses within the brain [14–16]. High levels of chronic inflammation result- ing from systemic vascular risk factors such as hypertension and diabetes are proposed to exac- erbate cSVD by damaging cerebral endothelia. A number of systemic inflammatory indicators have been implicated in cSVD [17, 18], yet do not coalesce around a specific molecular path- way. In this study, we aimed to investigate the association of a biologically interconnected net- work of systemic inflammatory markers centered on the pleiotropic pro-inflammatory cytokine IL-18 with sCVD burden. IL-18 is associated with cardiovascular risk factors and dis- ease [19–21], increases the expression of cell adhesion molecules on endothelial cells [22, 23], and may serve as a central coordinator for pathogenic inflammatory signaling [23]. Therefore, our investigation centered on IL-18 and associated proteins and their cross-sectional correla- tion with traditional and advanced neuroimaging measures of white matter integrity in a cross-sectional cohort design involving two populations at risk for cSVD. In a community- based aging population referred for cognitive evaluation, we used concurrent blood samples and MRI to develop a composite measure of inflammatory markers (IL-18, MPO, GDF-15, RAGE, ST2, and MCP-1) [18, 24–27] and correlated this inflammatory composite score (ICS) with MRI indicators of white matter injury. In a second cohort of acutely ill neurologic patients presenting for evaluation of stroke, we used an unbiased principal components analy- sis on a larger set of serum markers to derive inflammatory factors correlated with ICS to and test their association with cSVD as measured by Fazekas scoring of WMH as further validation of the ICS as a biomarker. Our findings suggest that an IL-18-centered network of systemic PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 2 / 20 IL-18-mediated inflammation and white matter injury inflammation is associated with overt and antecedent white matter injury resulting from cere- brovascular disease and may be a promising biomarker of cSVD. Subjects/materials and methods MarkVCID study cohort Research involving human subjects was approved by the Institutional Review Boards of the University of California, San Francisco (IRB #17–22314) and University of California, Davis (IRB #215830–47) and was conducted in compliance with the Health Information Portability and Accountability Act. One hundred and sixty-seven (167) community-dwelling older adults with normal cognition or mild cognitive impairment (MCI) were recruited from the Univer- sity of California, San Francisco Memory and Aging Center or the Alzheimer’s Disease Center at University of California, Davis. Formal written consent including an estimation of capacity judged by study investigators was obtained and participants completed a baseline neuropsy- chological testing, neurological evaluation with a trained neurologist, Clinical Dementia Rate (CDR) completed with a study partner, and a blood draw. Blood samples were collected by peripheral vein venipuncture into serum-separating tubes, centrifuged immediately, processed for serum, aliquoted and stored at -80˚C. One hundred and ten (110) study participants com- pleted an MRI scan within six months of their baseline assessment, and a subset of 49 partici- pants completed DTI. Participants were included in this study if they were considered non- demented by a formal consensus panel with a CDR total score of 0.0 or 0.5. ASPIRE study cohort Research involving human subjects was approved by the Institutional Review Board of the University of California, Los Angeles (IRB # 14–001798) and was conducted in compliance with the Health Information Portability and Accountability Act. Formal written consent was obtained for all participants prior to the collection of blood samples. Capacity to provide con- sent was judged by study co-investigators based on the subject’s ability to articulate risks and benefits of participating after reviewing the consent form. Surrogate consent was approved by the IRB. Consecutive participants were patients presenting to the UCLA Emergency Depart- ment with symptoms concerning for stroke between December 2014 and June 2016 and offered to participate in the study. Study inclusion criteria were: onset of stroke symptoms within 8 hours of presentation (or within 2 hours of presentation if symptoms were present upon awakening); greater than 18 years of age and able to consent or had a suitable surrogate individual to consent on their behalf. Final clinical diagnosis was determined by a board-certi- fied vascular neurologist. Blood samples were collected by peripheral vein venipuncture into heparin-containing tubes. Samples were kept on ice and then centrifuged immediately at 13,000 x g for five minutes at 4˚C. The serum was collected and aliquoted into freezer vials for storage at -80˚C. Subjects with evidence of CNS infection, known CNS malignancy, or recent head trauma as a potential cause of neurologic symptoms were excluded. Protein interactions Tests for protein interactions among biomarkers was performed using the STRING database v11.0 (string-db.org) [28]. Multiple protein search tool was used to input GDF-15, MPO, ST2, IL-18, MCP-1, and RAGE. Settings for tests of interactions were: meaning of network edges = confidence; active interaction sources = all; minimum interaction score = medium confidence. A second shell of interactors limited to 5 was added for visual representation. Resulting analysis data were exported and are available via permanent web link (S1 File). PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 3 / 20 IL-18-mediated inflammation and white matter injury Luminex assay and composite score generation Serum levels of six markers of inflammation: myeloperoxidase (MPO), growth differentiation factor 15 (GDF-15), receptor for advanced glycation end products (RAGE), ST2, interleukin- 18 (IL-18), and monocyte chemoattractant protein-1 (MCP-1) were measured in duplicate using a custom assay run across two plates on the Luminex platform (R&D Systems) measur- ing a total of 15 analytes: TNF-α, IL-6, ST2, MCP-1, RAGE, GDF-15, IL-18, CXCL5, CXCL6, IGFBP-2, MPO, ITGB3, BDNF, FGF-23, IL-17. The manufacturer protocol was followed and antigen binding within the assay was measured on a Luminex 200 System and analyzed using Milliplex Analyst 5.1. Four markers (TNF-α, BDNF, FGF-23, IL-17) were removed prior to analysis due to missing data and/or high proportions of values less than the limits of detection. Data points with coefficient of variance greater than 0.15 were excluded. To create a variable that measures inflammation of the whole network, we calculated an inflammation composite score (ICS) by normalizing raw inflammatory marker concentrations (pg/mL) using a log transformation, then standardizing data into z-scores, and finally taking the average of the z- scores across all six inflammatory markers. z-score generation was performed independently for each cohort. MarkVCID cohort MRI acquisition Participants at the University of California, San Francisco completed MRI on a Siemens Trio 3T machine or Siemens Prisma 3T machine. T1, diffusion, and FLAIR sequences were col- lected. T1 acquisition: Volumetric MPRAGE sequences were used to acquire T1-weighted images of the entire brain (Sagittal slice orientation; slice thickness = 1.0 mm; slices per slab = 160; in-plane resolution = 1.0x1.0 mm; matrix = 240x256; TR = 2,300 ms; Trio: TE = 2.98 ms (Prisma: TE = 2.9); TI = 900 ms; flip angle = 9˚). Diffusion (Trio) parameters: TR/TE 8200/86 ms; B = 0 image and 64 directions at B = 2000 s/mm2; FOV 220×220 mm2 and 2.2 mm thick slices; matrix 100×100 with 60 slices yielding 2.2 mm3 isotropic voxels / (TR/TE 8000/109 ms; B = 0 image and 64 directions at B = 2000 s/mm2; FOV 220×220 mm2 and 2.2 mm thick slices; matrix 100×100 with 55 slices yielding 2.2 mm3 isotropic voxels). Diffusion (Prisma) parameters: FOV 220×220 mm2 and 2.0 mm slice thickness; matrix 110×110 with 69 slices yielding 2.0 mm3 isotropic voxels; B = 0 images with TR/TE 7080/72.20 ms; 96 directions at B = 2500 s/mm2, 48 directions at B = 1000 s/mm2, and 30 directions at B = 500 s/mm all with TR/TE 2420/72.20 ms. FLAIR (Trio) parameters: slice thickness = 1.00mm; slices per slab = 160; in-plane resolution = 0.98x0.98mm; matrix = 256x256; TR = 6000ms; TE = 388ms; TI = 2100ms; flip angle = 120˚. FLAIR (Prisma) parameters: slice thickness = 1.00mm; slices per slab = 176; in-plane resolution = 1.0x1.0mm; matrix = 256x256; TR = 5000ms; TE = 397ms; TI = 1800ms; flip angle = 120˚. All brain imaging at the University of California, Davis Imaging Research Center was per- formed on a 3T Siemens TIM Trio MRI System. Three sequences were used: an axial-oblique 3D T1 acquisition (FSPGR, TE: 2.9ms (min), TR: 2500ms (min), TI: 1100ms, flip angle: 7 degrees, slice thickness: 1mm, number of slices: 192, FOV: 256 x 256 mm, matrix size: 256 x 256, phase encoding direction: A/P), an axial-oblique 2D FLAIR Fast Spin Echo (TE: 90ms, TR: 9000ms, TI: 2500ms, flip Angle: 150 degrees, slice thickness: 1 mm interleaved, FOV: 256 x 256 mm, matrix size: 256 x 256, phase encoding direction: A/P, Options: Superior/Inferior saturation pulse On, 80 mm thick) and an axial-oblique 2D DTI sequence (Base sequence: Sin- gle-shot spin-echo echo planar imaging, TE: 101ms, TR: 9000ms, flip angle: 90 degrees, slice thickness: 2mm, FOV: 256 x 256 mm, matrix size: 128 x 128, phase encoding direction: P/A, Options: bandwidth: 1628Hz/Px, echo spacing: 0.7ms, EPI factor: 128). Diffusion weighted PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 4 / 20 IL-18-mediated inflammation and white matter injury images were generated using gradients applied in 60 directions, with total gradient diffusion sensitivity measured at b = 1000 s/mm2, and 5 volumes with b = 0 s/mm2. ASPIRE cohort MRI acquisition MRI was performed on either Siemens Avanto 1.5T or Siemens Trio 3T machines. Axial T2- weighted images were obtained continuously in 5-mm-thick sections with repetition time of 3800 milliseconds and time to echo of 116 milliseconds. The field of view was 220 mm, and the matrix was 384x384. Axial FLAIR images were obtained continuously in 5-mm-thick sections with repetition time of 9000 milliseconds and time to echo of 89 milliseconds. The field of view was 220 mm, and the matrix was 320x216. Axial diffusion-weighted images were obtained continuously in 5-mm-thick sections with repetition time of 5600 milliseconds and time to echo of 106 milliseconds. The field of view was 255 mm, and the matrix was 192x192. MarkVCID MRI processing We used DTI measures of free water content (FW), FW-corrected fractional anisotropy (FACOR) and FW-corrected mean diffusivity (MDCOR). Briefly, DTI images were first prepro- cessed using FSL software tools [29], including correction for eddy current-induced distor- tions and participant head movements. Individual uncorrected FA maps were co-registered to the FSL FA DTI template using linear and nonlinear transformations. Resulting transforma- tion parameters were inversed and applied to the FSL white-matter labels atlas to provide a mask of WM region in the native DTI space of the individual. For each individual, overall measures of mean FW, FACOR and MDCOR were computed by superimposing individual WM masks onto the respective individual DTI-derived maps and averaging values within these WM voxels. Segmentation of WMH, hippocampus and total cranial volume (TCV) were per- formed from FLAIR designed to enhance WMH segmentation [30] and T1-weighted images by automated procedures previously described and which demonstrates high inter-rater reli- ability [31–33]. For each individual, overall WMH burden and hippocampus volume were computed and normalized by TCV to account for differences in head volume. Resulting WMH burden was also log-transformed to normalize population variance. ASPIRE cohort Fazekas scoring Two blinded authors (I.A. and V.K.) evaluated WMH on axial T2-weighted FLAIR images using the modified Fazekas rating scale to measure hyperintensity burden in periventricular and deep white matter regions [34, 35]. The total Fazekas score (FS) was obtained by summing the scores from periventricular and deep white matter regions and the average score used in subsequent analysis. Statistical analyses All statistical analyses were conducted using SPSS (IBM Corp. Released 2013. IBM SPSS Statis- tics for Windows, Version 22.0. Armonk, NY: IBM Corp.) and SAS 9.4 (SAS Institute Inc.). Heat maps of ICS scores were generated in Prism (GraphPad). Means, standard deviations and frequencies are reported for the discovery and validation cohorts. Cohorts, including those with and without imaging, were compared using t-tests for continuous measures or Chi- square tests for categorical variables. Linear regression, controlling for age and total intracra- nial volume, was used to investigate the association of the inflammatory composite score (ICS) with measures of white matter integrity: WMH, FW, FACOR, and MDCOR. Volumetric WMH were log-transformed prior to analysis to better meet the assumptions of the regression model. PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 5 / 20 IL-18-mediated inflammation and white matter injury Standardized betas are reported. Correlation coefficients were estimated to assess the associa- tion between ICS and the total number of vascular risk factors. Principal component analysis was performed on the serum markers in the ASPIRE cohort to generate two main factors. Pearson correlations were calculated to assess the association of the principal components and the ICS score. Linear regressions models, controlling for age and gender, were used to evaluate the association of the serum principal components as well as the ICS itself on the outcome of Fazekas score. Results MarkVCID and ASPIRE cohort demographics Fig 1 describes the subject identification, sample collection, and imaging workflows for each cohort. MarkVCID participants had a mean age of 76.4 ± 7.1 years, mean education of 15.3 ± 3.8 years, and 83 (49.7%) participants identified as female. All participants were func- tionally intact with 111 participants having a CDR total score of 0.0 and 56 participants having a CDR total score of 0.5. Participants had an average WMH volume (ml) of 6.94 ± 9.8, FW of 0.23 ± 0.03, FACOR of 0.49 ± 0.08, and MDCOR of 0.54 ± 0.05. Overall, participants with brain imaging had better vascular and cognitive health (Table 1). ASPIRE participants had a mean age of 70.8 ± 1.2 years, 60 (45.8%) participants identified as female, and 10 (7.6%) participants had dementia. MarkVCID participants had an average ICS of 0.004 ± 0.56 and ASPIRE partici- pants had an average ICS of 0.000 ± 0.60. Individual marker data is shown in Table 2. In the MarkVCID cohort, inflammatory markers measured in participants with T2-FLAIR imaging (n = 110) did not significantly differ from subjects without imaging. Participants with DTI (n = 49) did significantly differ from those without imaging in measures of GDF-15 (t = 2.6, p = 0.01) and IL-18 (t = 2.7, p = 0.007); participants with DTI had significantly lower levels of GDF-15 and IL-18. Additional raw imaging and serum inflammatory data are available upon request. Although evaluated in different clinical settings, the MarkVCID and ASPIRE Cohorts had similar levels of vascular factors that increase the risk for cSVD. There were no statistical differ- ences between the cohorts in gender (p = 0.50), history of myocardial infarction (p = 0.15), Fig 1. Imaging and fluid analysis workflows in the MarkVCID and ASPIRE cohorts. Workflow diagram of the MarkVCID cohort of 167 subjects that underwent detailed cognitive evaluations, MRI including DTI, and serum collections (left). Workflow diagram of the ASPIRE cohort of 202 subjects presenting with acute neurologic symptoms who underwent MRI and concurrent serum collection (right). https://doi.org/10.1371/journal.pone.0227835.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 6 / 20 Table 1. MarkVCID and ASPIRE cohort demographics and vascular health history. IL-18-mediated inflammation and white matter injury MarkVCID Cohort Total FLAIR DTI ASPIRE Cohort Total FLAIR vs. None χ2 (p) DTI vs. None χ2 (p) Total Gender (F) CDR = 0 Dementia Stroke MI AFib HTN HCL Diabetes Age Education BMI Systolic BP Diastolic BP n (%) 167 (100) 83 (49.7) 111 (66.5) 0 (0) 30 (18.2) 13 (7.8) 18 (10.8) 107 (64.1) 105 (64.0) 50 (30.3) Mean (SD) 76.4 (7.1) 15.3 (3.8) 27.6 (5.6) 139 (15.6) 72.3 (7.71) 110 (65.8) 58 (52.7) 80 (72.7) 0 (0) 12 (11.1) 6 (5.5) 11 (10.0) 63 (57.3) 59 (54.6) 23 (21.1) 76.0 (6.9) 15.6 (3.6) 26.6 (5.3) 138 (15.9) 72.9 (7.8) 49 (29.3) 27 (55.1) 40 (81.6) 0 (0) 6 (12.2) 4 (8.2) 5 (10.2) 23 (46.9) 21 (43.7) 5 (10.4) 76.6 (7.0) 16.3 (3.0) 25.9 (4.9) 135 (17.7) 73.4 (8.7) 1.2 (0.33) 5.7 (0.024)� 10.5 (0.001)�� 2.4 (0.1) 0.2 (0.6) 6.5 (0.01)� 12.1 (0.001)�� 12.9 (0.001)�� t (p) 1.4 (0.2) -1.3 (0.1) 2.9 (0.004)�� 1.6 (0.10) -1.2 (0.2) 0.8 (0.40) 7.2 (0.007)�� 1.6 (0.20) 0.01 (0.9) 0.02 (0.9) 8.84 (0.003)�� 12.1 (0.001)�� 12.7 (0.001)�� t (p) -0.24 (0.8) 2.5 (0.01)� 2.6 (0.01)� 2.3 (0.02)� -1.1 (0.3) Total Gender (F) CDR = 0 Dementia Stroke MI AFib HTN HCL Diabetes Age Education BMI Systolic BP Diastolic BP n (%) 131 (100) 60 (45.8) N/A 10 (7.63) 51 (38.9) 5 (3.8) 17 (13.0) 69 (52.7) 45 (34.4) 30 (22.9) Mean (SD) 70.8 (1.2) N/A N/A 159.0 (2.9) 86.5 (1.5) Demographic and vascular health information for 167 MarkVCID Cohort participants and 131 ASPIRE Cohort participants. Chi squared and T-tests evaluated the group differences between MarkVCID Cohort participants with FLAIR imaging or DTI, and those without. Overall, participants with brain imaging had better vascular and cognitive health. Missing data for Mark VCID: Stroke (n = 2), HCL (n = 3), Diabetes (n = 2). F = Female, CDR = Clinical Dementia Rating, MI = Myocardial Infarction, AFib = Atrial Fibrillation, HTN = Hypertension, HCL = Hypercholesterolemia, BMI = Body Mass Index, BP = Blood Pressure https://doi.org/10.1371/journal.pone.0227835.t001 history of atrial fibrillation (p = 0.56), or history of diabetes (p = 0.17). As expected by partici- pant enrollment procedures, the ASPIRE Cohort participants had more strokes (p<0.0001), and higher rates of dementia (p = 0.0003). The MarkVCID cohort was older in age (p<0.0001) and had a higher proportion of participants with hypertension (p = 0.047) and hypercholester- olemia (p<0.0001). Protein interactions With independent evidence supporting a role for MPO, GDF-15, RAGE, ST2, IL-18, and MCP-1 in the development of white matter hyperintensities, we asked whether this group of validated biomarkers might be interconnected biologically. We performed STRING database Table 2. MarkVCID and ASPIRE cohort inflammation levels. MarkVCID Cohort (n = 167) Marker (pg/mL) IL-18 ST2 MPO MCP-1 GDF-15 RAGE Mean (SD) 363.4 (134.3) 13146.1 (6995.1) 111567.7 (103133.0) 300.8 (161.9) 1887.8 (1764.4) 1949.3 (958.9) ASPIRE Cohort (n = 131) Marker (pg/mL) IL-18 ST2 MPO MCP-1 GDF-15 RAGE Mean (SD) 397.6 (211.2) 22113.2 (33073.9) 254458.16 (394569.63) 457.5 (222.1) 2758.4 (3575.5) 2543.5 (1358.6) Inflammatory marker data measured on 167 participants in the MarkVCID Cohort and 131 participants in the ASPIRE Cohort. https://doi.org/10.1371/journal.pone.0227835.t002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 7 / 20 IL-18-mediated inflammation and white matter injury Fig 2. ICS components form an inflammatory network. STRING database query of the six ICS component analytes reveals a biologically interconnected network centered on IL-18 and highly related to inflammation (p-value for protein interactions = 0.00022). ICS component analytes are shown as colored nodes (bold) while first level interacting proteins are shown as white nodes. Line width reflects the strength of data support. https://doi.org/10.1371/journal.pone.0227835.g002 analysis of these six components to determine if they function in a biologic network. Using the 6 validated protein biomarkers with a first shell of interactors, we identified a biologic network centered on IL-18 that was enriched for protein-protein interactions (p = 2.14x10-8) (Fig 2). This network was enriched for 75 gene ontology terms including positive regulation of leuko- cyte activation (GO.0002696, FDR = 0.00064); positive regulation of inflammatory response (GO.0050729, FDR = 0.0012); cytokine receptor binding (GO.0005126, FDR = 0.0015); cyto- kine activity (GO.0005125, FDR = 0.0015), and extracellular region (GO.0005576, FDR = 0.00029). The major signaling pathways center on interleukin signaling and IL-18 is the most PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 8 / 20 IL-18-mediated inflammation and white matter injury connected node at the center of the network. Step-wise expansion of the network by known protein-protein interactions reveals a complex and tightly interconnected network that is strongly enriched for cytokine and immune system regulatory elements. Association of an IL-18 inflammatory network and white matter injury To determine how these interconnected biomarkers relate to vascular risk factors, white matter hyperintensities, and DTI measures of white matter injury, we used the means and standard deviations across the MarkVCID sample (n = 167) to create z-scores for each analyte for each participant. Z-scores for each analyte were averaged to generate an inflammatory composite score (ICS) for each subject. This approach reduces the impact of the relatively high population standard deviations common in biomarker studies. The z-scores for each ICS component ana- lyte for each individual subject are shown in Fig 3A demonstrating that cumulative ICS was not driven by one outperforming analyte but rather reflect a true composite of the interacting inflammatory network. ICS was significantly associated with white matter hyperintensities (logWMH) (Fig 3B; β = 0.222, p = 0.013) as well as DTI FW (Fig 3C; β = 0.3, p = 0.01) but not with DTI FACOR (β = 0.004, p = 0.98) or MDCOR (β = -0.2, p = 0.2). Spatial maps of average FW and WMH distributions on MRI in those subjects with low ICS (below median; upper panel) and high ICS (above median; middle panel) as well as the difference (lower panel) demonstrates the effect of high levels of IL-18 driven inflammation on subcortical white matter injury (Fig 4). The inflammatory composite score and vascular risk Vascular risk factors such as hypertension, hyperlipidemia, and diabetes increase the risk of developing white matter hyperintensities. Therefore, we reasoned that if ICS positively associ- ates with white matter injury by MRI, then ICS should also scale with the burden of cardiovas- cular risk factors. The number of vascular risk factors significantly correlates with ICS (0.36, p<0.001). Categorization of the MarkVCID cohort by number of vascular risk factor diagnoses compared to those with fewer vascular risk factor diagnoses reveals a step-wise increase in mean ICS (Fig 5). Notably, the magnitude of the difference in mean ICS values increases as the number of vascular factors increases. Table 3 shows the group differences between MarkVCID cohort subjects with specific vascular risk factor diagnoses and those without. Association of ICS with white matter injury in those at risk for stroke To confirm the ability of ICS to detect WMH, we utilized serum samples from the ASPIRE biomarker study of acute stroke (n = 202), a single center study designed to identify acute bio- markers for ischemic stroke. Acutely obtained MRI scans (n = 168) were independently evalu- ated using the modified Fazekas scoring method. In those subjects with acutely obtained serum samples (n = 131), the average mean modified Fazekas score was 2.50 ± 1.53. Serum samples were assayed for biomarker levels and a principal component analysis was performed on 11 serum markers described in the methods section. This PCA identified two factors with eigenvalues >1 that account for 53% of the variance (Fig 6A). In this independent cohort, Fac- tor 1 significantly correlated with ICS (r = 0.94, p<0.0001) (Fig 6B). The most significant con- tributors to Factor 1 were the log-normalized values of ST2, RAGE, GDF15, and IL-18, all core markers included in the ICS. In an age- and gender-adjusted generalized linear regression model, the addition of Factor 1 significantly improved the detection of WMH as measured by the average modified Fazekas score in this cohort (p = 0.0267). Due to the strong correlation between Factor 1 and ICS in this cohort, we also assessed associations between ICS and WMH. In bi-variate analysis, ICS significantly correlated with average modified Fazekas score (p<0.0001) (Fig 6C) highly PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 9 / 20 IL-18-mediated inflammation and white matter injury Fig 3. ICS correlates with MRI measures of cerebrovascular injury. Heatmap of z-scores for each of the individual analytes composing the ICS for each included subject in the MarkVCID cohort ordered left to right by ICS (average z-score of each analyte) (A). Scatter plot and regression line of logWMH vs. ICS (n = 110) (B). Scatter plot and regression line of free water vs. ICS (n = 49) (C). Red dashed lines indicate 95% confidence intervals. https://doi.org/10.1371/journal.pone.0227835.g003 similar to the relationship of ICS with volumetric WMH in the MarkVCID cohort. In an age- and gender-adjusted model, the association between ICS and Fazekas score was p = 0.083. Representative FLAIR images from ASPIRE subjects with low and high ICS demonstrate the association with WMH (Fig 6D). Other demographic factors available in the ASPIRE cohort including stroke, hypertension, diabetes, and obesity were excluded from the model as they did not demonstrate significant effects on Fazekas score. PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 10 / 20 IL-18-mediated inflammation and white matter injury PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 11 / 20 IL-18-mediated inflammation and white matter injury Fig 4. Association of ICS with overt and antecedent white matter injury. Average intensity maps of free water (FW) and frequency maps of white matter hyperintensities (WMH) of groups with low (upper) and high (middle) ICS groups dichotomized around median ICS. Lower panels illustrate voxel differences in FW and WMH between low and high ICS groups. https://doi.org/10.1371/journal.pone.0227835.g004 Discussion Cerebral small vessel disease contributes to dementia [36] and increases both the risk of stroke [37] and poor outcomes after stroke [38]. The progressive, silent development of cerebral small vessel disease necessitates the development of alternative methods for identifying those at risk. In population studies, mid-life vascular risk factors increase the risk of white matter injury resulting from cSVD [17]. However, the identification of biomarkers to assist in separat- ing those with concurrent vascular risk factors yet no brain injury from those with vascular risk factors who already have evidence of pathology from cerebral small vessel disease is critical to developing therapeutic strategies to stem this growing public health challenge. Advances in imaging techniques such as DTI free water are one approach to detecting a higher risk popula- tion [12]. Reliable fluid-based biomarkers for early detection are another approach with cer- tain advantages over imaging including accessibility, applicability, and the ability to test frequently enabling repeated measurements. Various proteomic and single molecule approaches for fluid biomarkers that associate with WMH have shown associations but lack an integrated conceptual framework that drives at disease pathogenesis. Here, we show that a bio- logically interconnected network of molecules reflecting a composite measure of inflammation is associated with T2/FLAIR white matter hyperintensities in both an aging community-based population and a population presenting for evaluation of acute neurologic symptoms. We also show that this composite measure of inflammation is associated with increases in DTI free water, further implicating an IL-18-centered inflammatory network in the disease process underlying cerebral small vessel disease. These data demonstrate a new, reproducible tool to identify those with and at risk for cSVD. Fig 5. ICS increases with vascular risk factors. Mean ICS in groups with one or more vascular risk factor diagnoses (red) vs. those with less vascular risk factor diagnoses (black). All group comparisons were statistically significant at adjusted p<0.008 except between those with 6 vascular risk factor diagnoses (n = 2). https://doi.org/10.1371/journal.pone.0227835.g005 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 12 / 20 IL-18-mediated inflammation and white matter injury Table 3. ICS associates with vascular risk factor diagnoses. Vascular Risk Factor Diagnosis Diff in Mean ICS Atrial fibrillation (n = 18/167) Stroke (n = 30/165) Hypertension (n = 107/167) Hyperlipidemia (n = 105/164) Diabetes (n = 50/165) Myocardial infarction (n = 13/167) 0.415 0.427 0.253 0.209 0.399 0.146 p-value 0.003 0.000 0.004 0.021 <0.001 0.364 Group differences between MarkVCID cohort subjects with specific vascular risk factor diagnoses. Missing data for Mark VCID: Hx Stroke (n = 2), Hx HCL (n = 3), Hx Diabetes (n = 2). https://doi.org/10.1371/journal.pone.0227835.t003 Existing data on purported serum and plasma biomarkers for cSVD strongly implicate inflammation in the cSVD disease process. In a study on 163 lacunar stroke patients and 183 hypertensive patients, patients with evidence of cSVD on brain MRI had significantly elevated levels of inflammatory markers: neopterin, sICAM-1 and sVCAM-1 [39]. Elevated levels of inflammation are associated with increased risk of major vascular events, infarct size, and death [14, 40, 41]. In the Framingham Study, men and women in the highest quartile of CRP levels at baseline had two to three times the risk of ischemic strokes compared to those in the lowest CRP quartile [42]. Similar increases in lacunar stroke risk were seen in subjects with ele- vated CRP in the SPS3 trial [43]. In this study, we selected MPO, GDF-15, RAGE, ST2, IL-18, and MCP-1 because research using each marker independently showed that the markers may be related to cSVD. Unlike previous studies, our team investigated the mechanistic relation- ship between MPO, GDF-15, RAGE, ST2, IL-18, and MCP-1 and discovered that the markers were related in a biological pathway. Via STRING database analysis, we identified a biologic network centered on IL-18. IL-18 is a pleotropic pro-inflammatory cytokine implicated in multiple autoimmune disor- ders [44], vascular disease [19–21], acute stroke [45], and can be both generated and have action within the CNS [46]. Within the brain, IL-18 is largely produced by neurons [47, 48] but can also be found in infiltrating immune cells after ischemia [49, 50]. Here, we propose that the action of this IL-18 inflammatory network is to damage cerebral small vessels at the blood-brain barrier interface. The role this pathway plays in regulating downstream white matter injury resulting from IL-18-mediated cerebral vessel injury is unknown. The action of IL-18 is tightly regulated by IL-18 binding protein (IL-18BP) [51] and in autoimmune diseases, the serum IL- 18/IL-18BP ratio is associated with disease severity [52–54]. Indeed, blocking the action of IL- 18 using recombinant human IL-18BP (Tadekinig Alfa) is in late stage clinical trials for a num- ber of autoimmune disorders [55, 56]. Future studies may consider measuring IL-18BP levels and/or targeting IL-18 as a novel therapeutic strategy for cerebral white matter injury. By identifying the biologic connectivity of previously reported inflammatory cytokines and molecules, we begin to apply a more rigorous systems biology approach to the identification of reliable fluid biomarkers for cerebral small vessel disease. Harnessing this biologic connectivity provided a clear advantage in this study as evidenced by a strong correlation of ICS with the results of an unbiased principal components analysis (PCA) in a distinct cohort at increased risk for cSVD. By using PCA to identify an independent association of a collection of biomark- ers (F1) that strongly correlates with our previously generated ICS in a different cohort, we functionally validate the use of the population mean-adjusted ICS to detect cSVD. Systemic vascular risk factors have long been associated with increased inflammation that can be indirectly or directly measured [57]. High-sensitivity CRP (hsCRP) is the best example PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 13 / 20 IL-18-mediated inflammation and white matter injury Fig 6. ICS is associated with white matter injury in those at risk for stroke. Scree plot of principal components analysis of data from ASPIRE cohort serum biomarker panel with two main factors (Factor 1 and Factor 2) driving variance (A). Scatter plot of Factor 1 values versus ICS in this cohort demonstrating a significant correlation (r = 0.94) (B). Scatter plot and regression line of modified Fazekas score and ICS for individual subjects (C). Red dashed lines indicate 95% confidence intervals. Representative T2/FLAIR MR images of ASPIRE subjects with low (left) or high (right) ICS scores (D). https://doi.org/10.1371/journal.pone.0227835.g006 of an indirect inflammatory marker associated with vascular risk, white matter hyperintensi- ties, and recurrent lacunar stroke. Vascular risk factors drive hsCRP levels upwards but pro- vide no pathogenic clues to the underlying disease process and therefore require a large study population to verify their association with a disease outcome [58]. A number of inflammatory pathways with more direct signaling cascades have been associated with vascular risk factors including IL-18. Here we show evidence that a composite inflammatory measure (ICS) steadily increases as the number of vascular risk factors increases and that this associates with measures of silent cerebrovascular injury. Therefore, ICS could add to a clinical evaluation of stroke and dementia risk by providing a numerical severity to an individual subject’s cerebral microvascu- lar injury and ongoing risk assessment [59]. Our cohorts lack sufficient data to determine the effect of vascular risk factor control on ICS. Presumably, sustained uncontrolled risk factors such as hypertension and diabetes would promote higher inflammatory composite scores PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 14 / 20 IL-18-mediated inflammation and white matter injury however this remains to be determined. Precisely how these molecules damage the cerebral endothelia and promote the development of white matter injury is unknown. The present data implicate a signaling pathway centered on IL-18 as a potential driver of cerebral endothelia damage. Future studies can systemically test how persistent elevations of inflammatory cyto- kines directly damage cerebral endothelia and lead to white matter damage. Advances in iso- lating endothelial exosomes [2] will likely prove helpful to elucidate these mechanisms. Establishing the extent to which these silent changes may be reversible seems particularly criti- cal to establish. DTI free water is an emerging MR metric that indirectly measures leakage of extracellular fluid into white matter and precedes the development of T2/FLAIR white matter hyperintensi- ties. DTI free water is associated with vascular risk factors including systolic blood pressure and arterial stiffness [11] and more recently has been shown to be associated with cognitive decline [13]. Exactly what DTI free water is measuring in tissue is unknown, however one hypothesis is that excess extracellular fluid results from blood-brain barrier leakage with leak- ing serum proteins damaging myelin and axons. Leakage of the blood-brain barrier is pro- posed to play a central role in the pathogenesis of cSVD with increased contrast-enhancement within T2/FLAIR white matter hyperintensities compared to normal appearing white matter using dynamic contrast enhanced MRI (DCE-MRI) techniques [60, 61]. In a population of recent lacunar stroke patients, blood-brain barrier leakage within white matter was also associ- ated with impaired cognition at 1 year. The present study provides a mechanistic link between a cocktail of markers of peripheral inflammation and blood-brain barrier leakage in relation- ship to cSVD by demonstrating clear cross-sectional relationships between ICS and DTI free water. Further studies will be needed to establish causality between ICS and BBB leakage using longitudinal measures of fluid biomarkers and imaging with DCE-MRI. In the presented imaging data from the MarkVCID cohort, we did not observe any clear regional pattern to the differences in either WMH or free water measures between those indi- viduals with high or low ICS. This finding suggests that the observed association between WMH and free water with ICS is independent of differences in blood pressure and flow between anterior and posterior circulations. This result is not surprising given that the whole brain vasculature is exposed to elevated systemic inflammatory signals and whatever changes are induced are likely distributed globally throughout the brain. Notably, we also did not see any association between ICS and clinical stroke in the ASPIRE cohort, indicating that the ele- vated levels of inflammation measured by ICS are specifically linked to cSVD rather than an overall increased risk of cerebrovascular disease. This study establishes a cross-sectional relationship between interconnected inflammatory molecules and MRI indicators of cerebral small vessel disease in two distinct populations. Lim- ited to cross-sectional relationships, it does not firmly establish that IL-18-mediated inflamma- tion is associated with cognitive decline nor with other conditions associated cSVD indicators, namely the risk of stroke and/or dementia. However, because ICS scales additively with increased vascular risk factors, which in turn are known to increase the risk of white matter hyperintensities and impaired cognition, we expect that longitudinal studies will demonstrate that ICS is predictive of longitudinal declines in cognition and/or the risk of future stroke. Additionally, we did not observe an association between ICS and other DTI metrics such as FA or MD. DTI free water and WMH are more directly linked on a continuum of white matter injury related to inflammation and blood-brain barrier leakage while FA and MD more directly reflect the integrity of axons within a functional tract. Moreover, the MarkVCID cohort is largely cognitive normal and with relatively healthy brains, and therefore lacks a wide range of FA/MD values. A further limitation of this study is the contrast in imaging methodol- ogies used in the varying cohorts. The lack of precise volumetric assessment of white matter PLOS ONE \| https://doi.org/10.1371/journal.pone.0227835 January 24, 2020 15 / 20 IL-18-mediated inflammation and white matter injury lesion volume in the ASPIRE cohort may underestimate the true burden of cSVD in this popu- lation. However, the ability of this set of inflammatory markers to retain its relationship with subjectively graded WMH in this population suggests that our approach in generating a popu- lation mean-adjusted composite inflammatory score may have broad generalizability as a bio- marker for cSVD in at-risk with different risk factor profiles and demographics. Conclusion Cerebral small vessel disease is provoked by cardiovascular risk factors through increased sys- temic sterile inflammation. This increase in systemic inflammation may be associated with a specific inflammatory pathway involving IL-18 signaling that can be targeted for therapeutic engagement. Fluid-based biomarkers to reliably identify those at risk for and with early indica- tors of cerebral small vessel disease resulting from inflammation can provide a widely accessi- ble method for risk assessment, monitoring, and therapeutic development. Supporting information S1 File. Permanent weblink to STRING database results for ICS components. (DOCX) Acknowledgments The authors are grateful to the support staff of the Memory and Aging Center and the UCLA Stroke Force for help in subject enrollment. Author Contributions Conceptualization: Marie Altendahl, Pauline Maillard, Sunil A. Sheth, Guanxi Xiao, Fanny Elahi, Jason D. Hinman. Data curation: Marie Altendahl, Devyn Cotter, Samantha Walters, Amy Wolf, Baljeet Singh, Visesha Kakarla, Ida Azizkhanian, Emily Fox, Mei Leng, Jason D. Hinman. Formal analysis: Marie Altendahl, Pauline Maillard, Danielle Harvey, Baljeet Singh, Visesha Kakarla, Ida Azizkhanian, Sunil A. Sheth, Mei Leng, David Elashoff, Jason D. Hinman. Funding acquisition: Sunil A. Sheth, Joel H. Kramer, Charlie Decarli, Fanny Elahi, Jason D. Hinman. Investigation: Marie Altendahl, Sunil A. Sheth, Emily Fox, Michelle You, Joel H. Kramer, Charlie Decarli, Fanny Elahi, Jason D. Hinman. Methodology: Pauline Maillard, Danielle Harvey, Guanxi Xiao, Mei Leng, David Elashoff, Joel H. Kramer, Fanny Elahi. Project administration: Devyn Cotter, Samantha Walters, Amy Wolf, Sunil A. Sheth, Emily Fox, Michelle You. Resources: Joel H. Kramer, Charlie Decarli. Software: Pauline Maillard, Charlie Decarli. Supervision: Sunil A. Sheth, Joel H. Kramer, Charlie Decarli, Jason D. Hinman. Validation: Danielle Harvey, Mei Leng, Jason D. Hinman. Visualization: Pauline Maillard, Baljeet Singh, Visesha Kakarla, Ida Azizkhanian, Charlie Dec- arli, Jason D. Hinman. 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10.1371_journal.pgph.0002467	RESEARCH ARTICLE Assessing the impact of the president’s emergency plan for AIDS relief on all-cause mortality Gary Gaumer1, Yiqun LuanID Monica JordanID 1, Clare L. HurleyID 1, Allyala Nandakumar1 1, Dhwani Hariharan1, William CrownID 1, Jennifer Kates2, 1 Institute for Global Health and Development, The Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts, United States of America, 2 Global Health & HIV Policy Program, KFF, Washington, District of Columbia, United States of America garygaumer@gmail.com Abstract This study estimated the impacts of PEPFAR on all-cause mortality (ACM) rates (deaths per 1,000 population) across PEPFAR recipient countries from 2004–2018. As PEPFAR moves into its 3rd decade, this study supplements the existing literature on PEPFAR ‘s overall effectiveness in saving lives by focusing impact estimates on the important subgroups of countries that received different intensities of aid, and provides estimates of impact for differ- ent phases of this 15-year period study. The study uses a country-level panel data set of 157 low- and middle-income countries (LMICs) from 1990–2018, including 90 PEPFAR recipient countries receiving bilateral aid from the U.S. government, employing difference- in-differences (DID) econometric models with several model specifications, including mod- els with differing baseline covariates, and models with yearly covariates including other donor spending and domestic health spending. Using five different model specifications, a 10–21% decline in ACM rates from 2004 to 2018 is attributed to PEPFAR presence in the group of 90 recipient countries. Declines are somewhat larger (15–25%) in those countries that are subject to PEPFAR’s country operational planning (COP) process, and where PEP- FAR per capita aid amounts are largest (17–27%). Across the 90 recipient countries we study, the average impact across models is estimated to be a 7.6% reduction in ACM in the first 5-year period (2004–2008), somewhat smaller in the second 5-year period (5.5%) and in the third 5-year period (4.7%). In COP countries the impacts show decreases in ACM of 7.4% in the first period attributed to PEPFAR, 7.7% reductions in the second, and 6.6% reductions in the third. PEPFAR presence is correlated with large declines in the ACM rate, and the overall life-saving results persisted over time. The effects of PEFAR on ACM have been large, suggesting the possibility of spillover life-saving impacts of PEPFAR program- ming beyond HIV disease alone. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Gaumer G, Luan Y, Hariharan D, Crown W, Kates J, Jordan M, et al. (2024) Assessing the impact of the president’s emergency plan for AIDS relief on all-cause mortality. PLOS Glob Public Health 4(1): e0002467. https://doi.org/10.1371/ journal.pgph.0002467 Editor: Julia´n Alfredo Ferna´ndez-Niño, Johns Hopkins University Bloomberg School of Public Health, COLOMBIA Received: April 14, 2023 Accepted: October 5, 2023 Published: January 18, 2024 Copyright: © 2024 Gaumer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Our data came from four publicly available datasets: World Bank’s World Development Indicators; U.S. government’s foreignassistance.gov database; OECD Creditor Reporting System database; and the Institute of Health Metrics and Evaluation GBD Result’s Tool. Funding: This report was produced in part with funding from the Bill and Melinda Gates Foundation (https://www.gatesfoundation.org/) to AN under grant INV-046299; as well as from PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 1 / 14 PLOS GLOBAL PUBLIC HEALTHPalladium International, LLC (https:// thepalladiumgroup.com/) to AN under subcontract number 217730-Brandeis-01; and Prime Contract number 2021-002516 from the Global Fund to Fight AIDS, Tuberculosis, and Malaria (https://www.theglobalfund.org/en/). Its contents are solely the responsibility of Brandeis University and do not necessarily represent the official views of the Bill and Melinda Gates Foundation, Palladium International or the Global Fund to Fight AIDS, Tuberculosis, and Malaria. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Assessing PEPFAR’s impacts on all-cause mortality Introduction The U.S. government, through the President’s Emergency Plan for AIDS Relief (PEPFAR), committed approximately $70 billion to address HIV/AIDS in low- and middle-income countries (LMICs) through bilateral efforts between 2004 and 2018 [1]. Services supported include the costs of antiretroviral therapy (ART), care and support for families, HIV testing, prevention services, voluntary male medical circumcision, and other services such as health worker training, operations management, and health system strengthening [2]. Initially, the PEPFAR program led to quick implementation using large, primarily vertical programs in a selected group of high need, “focus” countries, a group that has expanded over time. While PEPFAR has provided bilateral funding to more than 100 countries over the 2004–2018 period, funding has been concentrated in a smaller subset of approximately 30 high-need countries, mostly in Africa. This subset of countries is subjected to the annual preparation of operating plans, performance monitoring, and budget negotiations [3]. PEPFAR funding increased quickly in its initial years, from 2004 to 2010, and then largely plateaued through 2018. Research evidence has previously demonstrated that PEPFAR and other external health aid programs have saved millions of lives during the HIV epidemic. Early single-country studies of PEPFAR and other donor aid have documented substantial life-saving impacts including declines in all-cause mortality and increased life expectancy [4–6]. Multi-country PEPFAR impact studies evaluated PEPFAR “focus” countries, or PEPFAR recipients strati- fied by level of PEPFAR investment, and reported declines in the number of HIV-related deaths after 2003 [7–9]. Most recently a PEPFAR impact study [10] used panel data and a difference-in-difference (DID) design to assess PEPFAR’s impact on women and children in both “focus” and “non-focus” countries. That study found that funding from PEPFAR was associated with reductions in new HIV infections and HIV-related deaths among both women and children. Another study [11] found that between 2004 and 2013, 2.9 million HIV infections were averted with 11.6 million life years gained, and 9 million children were saved from becoming orphans. In the largest and most thorough field evaluation of PEPFAR, the National Academy of Sci- ences [12] reviewed literature and program records, and conducted extensive field interviews, and concluded that PEPFAR made significant contributions in scaling up testing, counseling, and a variety of prevention and treatment programs, in addition to saving lives and improving the quality of life for persons living with HIV(PLHIV) [12]. In this study, we seek to add to the body of knowledge of PEPFAR’s impact by providing an assessment over 15 years of the program, from 2004–2018, across subgroups of recipient coun- tries and over separate programmatic phases. We examine three overall research questions: 1. Over the 2004–2018 period, was PEPFAR’s presence in low-and middle-income countries (LMICs) associated with greater reductions in all-cause mortality (ACM) than would have been expected in the absence of PEPFAR? 2. Were there differences in the impact of PEPFAR across different phases of the 15-year period used in this assessment? 3. Are there variations in the impact of PEPFAR across countries arising from differences in the intensity of PEPFAR financial support, and/or due to the participation in the COP pro- cess involving careful planning and monitoring? PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 2 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Methods We assessed the impact of PEPFAR presence on the ACM rate (deaths per 1,000) by analyzing a 29-year panel dataset (1990–2018) of 157 low- and middle-income countries that included 90 PEPFAR countries and 67 countries in the control group. The ACM rate was selected as our outcome of interest to overcome issues in the reliability of using an HIV-specific outcome measure and to capture possible spillover impacts beyond HIV mortality [13]. Data sources include the World Bank’s World Development Indicators (WDI) [14], the U. S. government’s foreignassistance.gov database [15], the OECD Creditor Reporting System database [16], the United Nations Department of Economic and Social Affairs [17], and the Institute of Health Metrics and Evaluation (IHME) GBD Result’s Tool [18]. Cohorts Our PEPFAR recipient group included 90 countries that had received at least $1 million in PEPFAR support over the 2004–2018 period. Our control group included 49 low- and middle- income countries that had not received any PEPFAR support and 18 LMICs that received min- imal PEPFAR support (<$1M total or <$0.05 per capita cumulatively) between 2004 and 2018. Complete lists of countries and modeling results are provided in S1 Text and S1 Table Appendices. Ethics approval was not required for this study. Patients or the public were not involved in this research study. In addition to examining outcomes for all PEPFAR recipient countries taken together, we also estimated PEPFAR impacts on separate cohorts among the 90 recipient countries. These separate cohorts included tertiles (three equally sized cohorts) of countries with the highest, medium, and lowest PEPFAR spending (cumulative PEPFAR disbursements per capita from 2004 to 2018). A second set of cohorts were countries that were subject to annual country- operating plans (COP), and those countries that did not have COP status. We also examined impacts separately for three different five-year time periods of program operation (2004–2008, 2004–2013, 2004–2018), to assess the size and direction of PEPFAR impacts over time. These periods generally correspond to the three authorizing periods for the program. S2 Text in the Appendix provides more details on how we created PEPFAR country cohorts for analysis. Difference-in-differences models and covariates Difference-in-differences (DID) modeling is used to obtain PEPFAR impact estimates. DID controls for any unobserved differences between intervention and control groups that remain constant over time and is widely used in program evaluation research to provide program impact estimates [19] including previous evaluations of PEPFAR [7, 8]. The method can be used when pre and post data are available for countries that received PEPFAR funding and for those that did not (e.g. the control group). All of the DID models included three dummy vari- ables: (1) a dummy variable that captures the overall differences in the mean value of the depen- dent variable between the baseline period (pre-2004) and the follow-up period. This measures the time trend in the control group; (2) a dummy variable for PEPFAR countries and control group countries that measures differences at baseline; and (3) an interaction dummy variable between the first two dummy variables, which is interpreted as the impact of the PEPFAR pro- gram. See more information on the difference-in-difference method in S3 Text. To examine the consistency of impact estimates across alternative model specifications we estimated five models for each of our cohorts. Model 1 used no covariates other than the three DID dummy variables noted above. Model 2 included baseline (2004) values of population, GDP/capita, HIV prevalence rate, fertility rate, life expectancy at birth, percent population in urban areas, a dummy variable for whether the US provided HIV aid prior to 2004, a dummy PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 3 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality variable for whether the country was a low-income or a middle-income country, and second- ary school enrollment (% of gross). Model 3 added two additional baseline covariates: per cap- ita other donor (non-PEPFAR) spending on health, and per capita domestic spending on health which is calculated as the aggregate of domestic government spending and private spending on health, in per capita term. In models 1 through 3 our emphasis is on baseline covariates rather than time varying covariates, a deliberate decision made to avoid endogeneity problems. Causal inference regard- ing PEPFAR’s impact on all-cause mortality is derived using the Rubin potential outcomes framework [20]. This approach seeks to estimate the average treatment effect as the difference between the ACM outcomes for PEPFAR countries versus the outcomes that would have resulted had countries not been exposed to the PEPFAR program. The estimate of outcomes for the unexposed group of countries is derived from the control group. It is important that the control group be as similar as possible to the PEPFAR intervention group; balancing on baseline characteristics helps to achieve this. We estimate the causal parameter, the average treatment effect (ATE) of PEPFAR, using a differences-in-differences (DID) model. Models 4 and 5 include yearly health spending covariates to control for potential confound- ing from donor and local policy activities. Model 4 has baseline covariates and a yearly variable for annual other donor health spending per capita. Model 5 adds (to model 4) yearly domestic health spending which is a combination of domestic government spending and private spend- ing on health. The yearly health spending variables provide additional potential balancing of the PEPFAR and control group countries when estimating the impacts of PEPFAR. However, it is important to note that these variables could introduce endogeneity. In particular, changes in ACM and PEPFAR spending could also influence the annual spending variables added in models 4 and 5. For these, and other reasons, we conducted extensive specification testing on all of the models to better characterize their statistical robustness. We estimated logged and unlogged models for all five model specifications. These included the five main model specifi- cations, each of which was estimated for cohort groups compared to the control group coun- tries. We tested the normality of residuals for both the logged and unlogged models 4 and 5 using the Shapiro-Wilk test. The residuals for both sets of models were visually close to nor- mal, although statistical testing revealed that they deviated from normality. The logged models were not, however, clearly superior in terms of normality and may introduce potential prob- lems with retransformation bias when estimating program impacts in the presence of hetero- scedasticity in the original ACM units. For this reason, we report the unlogged model results in this paper and provide the logged versions of the models in the S1 Table. Logged and unlogged models were highly consistent in terms of signs and statistical significance of the PEPFAR program intervention variable. In models with time-varying covariates (models 4 and 5) we tested for potential endogene- ity using the Hausman test. The results indicate that time varying variables were, indeed, endogenous. The time-varying variables included in Model 4 resulted in somewhat smaller estimates of PEPFAR impacts on ACM. It is not clear how much of this difference was due to the extra control introduced by the time-varying variables, endogeneity bias, or both. How- ever, the results did not materially change the conclusion that PEPFAR has had very large and statistically significant, and persisting impacts on ACM throughout the 2004–2018 period. Only with Model 5 did we find results that suggested materially different policy conclusions than the models without time-varying covariates, although evidence of endogeneity suggests that these results should be interpreted with caution. Finally, the DID parallel trends assumption was tested for all models and found to be upheld. All model results and specification testing are reported in the S1 Table, S1 Fig, and Fig A in S2 Fig. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 4 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Adjusted R-square values for model 1 are 0.10–0.20, with the other models being in the 0.40–0.60 range and higher as more covariates are used. These statistics are reported in the S1 Table. Results Fig 1 shows the ACM trends in the five all-PEPFAR cohorts and the control group. After the introduction of PEPFAR (2004 and after) the trends for ACM in PEPFAR country cohorts are notably different than the control. ACM rate trends from 1990–2018 indicate that both PEP- FAR countries and control countries show a modest (and quite similar) decline in ACM from 1990 to the introduction of PEPFAR funding in 2004, followed by a more rapid decline in ACM for PEPFAR countries and a slight upward trend for the control group (Fig 1A). Across the three funding intensity cohorts based on cumulative PEPFAR aid per capita (high, medium, and low intensity) there is a particularly striking decline in ACM after 2004 in coun- tries where per capita aid has been largest (high intensity), although ACM rates decrease across all groups (Fig 1B). While the ACM trends throughout the baseline period (pre-2004) are downward sloping (lower death rates), the trends in PEPFAR cohorts continue to fall after 2004 while the control group trends become relatively flat after 2004. As required for DID validity, the down-sloping baseline trends in the control group are roughly parallel to the trends in the PEPFAR cohorts. Tests were done to confirm that the baseline trends in ACM for treatment and control groups were parallel. These test results, provided in S2 Fig materials, show that in all cohorts of the PEPFAR recipient countries, the baseline trends in PEPFAR and control countries are parallel. Table 1 describes the characteristics of the PEPFAR cohorts and the control group. Most of the PEPFAR program funding (97%) was provided to COP countries over the 2004–2018 period. Notably, 54 of the 90 PEPFAR countries received US aid for HIV prevention prior to 2004. PEPFAR countries at baseline were poorer; spent less on health; and populations were less educated, more rural, had higher fertility rates, and had a lower life expectancy at birth than the controls. COP countries and the high PEPFAR-spending intensity countries are nearly identical (all high spending countries are also COP countries). Table 2 provides DID impact estimates for each country PEPFAR cohort on ACM using the five model specifications. Generally, the impact estimates we report from the no-covariate specification (Model 1) and the models using baseline covariates (Models 2,3) are similar in sign, significance, and order of magnitude. Fig 1. Trends in all-cause mortality rates (per 1000) for PEPFAR and control group countries. Notes: ACM = all-cause mortality; COP = country operating plans; PEPFAR = President’s Emergency Plan for AIDS Relief. https://doi.org/10.1371/journal.pgph.0002467.g001 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 5 / 14 PLOS GLOBAL PUBLIC HEALTHTable 1. Characteristics of PEPFAR-recipient and control countries. All PEPFAR funded LMICs* COPs Non-COP other PEPFAR High intensity PEPFAR funding per capita group Middle intensity PEPFAR funding per capita group Low intensity PEPFAR funding per capita group Control group (LMICs)** Assessing PEPFAR’s impacts on all-cause mortality Number of countries 90 31 Total population (2018) in millions 5,609.5 2,680.3 $40,920.2 $39,783.7 59 2,929.2 $1,136.5 30 903.5 $38,800.0 30 441.5 $1,164.6 30 4,264.5 $955.6 67 860.2 $8.0 Cumulative PEPFAR disbursements, 2004–2018 (in millions of USD) Cumulative PEPFAR disbursements per capita, 2004– 2018 (in constant USD) Cumulative other donor health spending per capita (non- PEPFAR), 2004–2018 (in constant USD) Cumulative domestic health spending per capita, 2000–2016 (PPP, current international $) BL GDP per capita, PPP (constant 2011 international $) BL HIV prevalence (% of population ages 15–49) BL life expectancy at birth BL urban population (%) BL school enrollment, secondary (% gross) BL fertility rate (births per woman) Number of countries receiving US HIV aid before 2004 $3,094.2 $2,974.1 $120.1 $3,008.0 $77.3 $8.9 $0.4 $10,357.0 $3,948.4 $6,408.6 $4,416.8 $3,931.6 $2,008.6 $14,008.5 $365,733.0 $85,928.7 $279,804.3 $84,553.9 $129,107.2 $152,072.0 $588,474.8 $5,249.3 $3,555.5 $6,154.7 $3,579.1 $5,819.4 $6,293.9 $4,654.6 3.0% 7.0% 0.9% 61.1 41.7% 52.8% 4.0 54 55.1 33.8% 42.1% 4.4 25 64.2 45.9% 58.2% 3.8 29 7.4% 55.1 36.4% 40.3% 4.5 24 0.8% 64.1 41.0% 59.0% 3.7 16 0.8% 64.0 47.8% 58.6% 3.7 14 0.2% 71.1 58.0% 80.4% 2.6 2 Notes: BL = baseline (2004); COP = country operating plans; HIV = human immunodeficiency virus; LMIC = low- and middle-income countries; PEPFAR = President’s Emergency Plan for AIDS Relief; PPP = purchasing power parity; USD = US dollars. Baseline values are the country group average for 2004. Missing data were managed by interpolation from existing data. (See S4 Text) Excludes 18 minimally and sporadically PEPFAR-funded countries (<$1m cumulative funding through 2018, or less than $0.50 per capita cumulative funding through 2018) Includes 49 LMIC countries not funded by PEPFAR and 18 minimally and sporadically PEPFAR-funded countries (<$1m cumulative funding through 2018, or less than $0.50 per capita cumulative funding through 2018). https://doi.org/10.1371/journal.pgph.0002467.t001 Overall, we find that across all 90 PEPFAR recipient countries, the ACM rate declined by approximately 10–21% over the 2004–2018 period. The pattern of mortality effects of PEPFAR varies substantially across the cohort categories of countries. The 31 COP countries have larger reductions in ACM than the programmatic average (15–25%). We also find that PEPFAR’s impact was greater in countries with higher spending intensity. The 30 countries receiving the most aid per capita have the largest estimated PEPFAR impacts (17–27% reductions in ACM). The size of these estimated effects is very similar to the COP estimates, given the high overlap between the groups (25 countries are common to both groups). The 30 countries receiving the least PEPFAR funding have the lowest ACM rate reductions (6–14%). The middle grouping of 30 countries achieved a reduction in between the high and low groups (7–21%). Impact estimates are higher in models using the baseline covariates, and lower for the mod- els where yearly spending variables are included in the model (models 4 and 5). The inclusion of the non-financial baseline covariates plus “yearly other donor spending per capita” (Model 4) shows the same general pattern as the models with only baseline PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 6 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Table 2. DID estimates of PEPFAR impact on all-cause mortality (ACM) rates in PEPFAR-recipient countries, 2004–2018. All-Cause Mortality Rate (deaths per, 1000) All PEPFAR COP PEPFAR Non-COP other PEPFAR High program spending intensity Medium program spending intensity Low program spending intensity Mean ACM in PEPFAR countries (2004) Model 1. DID model with no covariates, 1990–2018 Model 2. DID model with non-financial BL covariates only, 1990–2018a Model 3. DID model with non-financial and financial BL covariates, 1990–2018b Model 4. DID model with non-financial BL covariates and yearly other donor spending on health covariate, 2002– 2018 Model 5. DID model with non-financial BL covariates and yearly other donor spending and domestic spending on health covariates, 2002–2016 Approximate % reduction in ACMc 10.5 -2.090 -2.206* 12.6 -2.883* -3.086* 9.4 -1.674* -1.758* 12.3 -3.081* -3.373* 9.7 -1.942* -2.003* 9.5 -1.247* -1.329* -2.157* -3.036* -1.709* -3.324* -1.952* -1.281* -1.814* -2.809* -1.302* -2.986* -1.324* -1.208 -1.072* -1.854* -0.629* -2.035* -0.638 -0.527 10.2– 21.0% 14.7– 24.5% 6.7–18.7% 16.5–27.4% 6.6–20.6% 5.5–14.0% Notes: ACM = All-cause mortality (rate of deaths per 1,000); BL = baseline (2004); COP = country operating plans; DID difference-in-difference; PEPFAR = President’s Emergency Plan for AIDS Relief. a Baseline (2004) non-financial covariates include HIV prevalence rate (% of population ages 15–49), GDP per capita (constant USD), population size, percent of urban population, secondary school enrollment (% gross), life expectancy at birth, fertility rate (births per woman), whether the country was a low-income or a middle-income country (dummy), and whether the country received HIV aid from the U.S. prior to 2004 (dummy). b Baseline (2004) financial covariates include other donor health spending and domestic health spending. c For each model the percent is calculated by dividing the coefficient by the mean of ACM in 2004 as shown in the first row of the table. The range is formed by taking the lowest and highest percentages across the five models p < 0.001 p < 0.01 p< 0.05. https://doi.org/10.1371/journal.pgph.0002467.t002 covariates, with slightly smaller PEPFAR impacts. Model 5, where the yearly domestic per cap- ita health spending covariate is added to Model 4, the results show the same general pattern of significant impact coefficients, but the impact estimates are much lower than the other models. Model 5 also has two cohorts (medium and low-spending countries) where the negative ACM coefficients were too small to be statistically significant. Full model results are presented in S1 Table. Table 3 shows the DID model estimates of PEPFAR impact on ACM for 3 time periods: 2004–2008, 2004–2013, and 2004–2018, generally corresponding with the three different authorizing periods of the program. The same five models are estimated here, but for only two cohorts: all 90 recipient countries taken together, and COP countries. We used these estimates from the five models to calculate the average incremental impact for each time period, which is shown on the table. The incremental impact for the second 5-year period is computed by subtracting the average impact in the first period (2004–2008) from the estimated impact in the second period (2004–2013). The table supports three conclusions: (1) the longer PEPFAR operates in countries, the larger the overall program impact on ACM. In each cohort the effect of PEPFAR on lives saved is larger if the program lasts 10 years, rather than just 5; and is larger if the program lasts 15 years, compared to 10; (2) for all 90 countries taken together the reduction in ACM per addi- tional year of PEPFAR presence gets somewhat smaller per time period the longer the program operates. The highest incremental returns appear in the first 5-year phase, and progressively PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 7 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Table 3. Estimates of PEPFAR impact on all-cause mortality (deaths per 1,000), cumulative 5-year estimates. All-Cause Mortality Rate All PEPFAR COP PEPFAR Model 1. DID model with no covariates, 1990–2018 Model 2. DID model with non-financial BL covariates only, 1990–2018a Model 3. DID model with non-financial and financial BL covariates, 1990–2018b Model 4. DID model with non-financial BL covariates and yearly other donor spending on health covariate, 2002–2018 Model 5. DID model with non-financial BL covariates and yearly other donor spending and domestic spending on health covariates, 2002–2016 Approximate % reduction in ACMc 2004– 2008 -0.945 -1.081* -1.027*** -0.721* 2004– 2013 -1.567* -1.677* -1.629* -1.294* 2004–2018 -2.090* -2.206* -2.157* -1.814* 2004– 2008 -0.996* -1.205 -1.152* -0.961* 2004– 2013 -2.016* -2.207* -2.157* -1.939* 2004–2018 -2.883* -3.086* -3.036* -2.809* -0.221 -0.691* -1.072* -0.340 -1.215 -1.854* 2.1–10.3% 6.6–16.0% 10.9% 10.2– 21.0% 2.7–9.6% 9.6–17.5% 14.7– 24.5% Incremental averaged 7.6% 5.5% 4.7% 7.4% 7.7% 6.6% Notes: ACM = All-cause mortality (rate of deaths per 1,000); BL = baseline (2004); COP = country operating plans; DID = difference-in-difference; PEPFAR = President’s Emergency Plan for AIDS Relief. a Baseline (2004) non-financial covariates include HIV prevalence rate (% of population ages 15–49), GDP per capita (constant USD), population size, percent of urban population, secondary school enrollment (% gross), life expectancy at birth, fertility rate (births per woman), whether the country was a low-income or a middle-income country (dummy), and whether the country received HIV aid from the U.S. prior to 2004 (dummy). b Baseline (2004) financial covariates include other donor health spending and domestic health spending. c For each model the percent is calculated by dividing the coefficient by the mean of ACM in 2004 (10.5 for ALL, and 12.6 for COP). The range is formed by taking the lowest and highest percentages across the five models d The incremental impact for the first period is the average impact across the 5 models for 2004–2008. The incremental impact for the second 5-year period is computed by subtracting the average impact across models in the first period (2004–2008) from the estimated average impact across models in the second period (2004–2013). The third period incremental impact is computed by subtracting the average impact across models in the second period (2004–2013) from the estimated average impact across models in the third period (2004–2018) p < 0.001 p < 0.01 p< 0.05. https://doi.org/10.1371/journal.pgph.0002467.t003 become somewhat smaller for the second and third phases. (3) The diminishing incremental effect sizes over time are less pronounced for COP countries than others. For All PEPFAR countries taken together over the period 2004–2008, the average impact across all five models is an average estimated reduction in ACM by -7.6%. In the second 5-year period the average (across models) incremental impact of PEPFAR was– 5.5%. And in the third period the average incremental impact is somewhat smaller at -4.7%. In COP countries the story from Table 3 is somewhat different. In the first 5-year period COP countries achieved an average (across models) of a -7.4% reduction in ACM–somewhat comparable to the estimate of -7.6% for all PEPFAR countries taken together. In the second period (2009–2013) the average increment across models shows a somewhat larger impact (-7.7%) in the COP countries. And, in the third period (2014–2018) the incremental ACM impact decreased to -6.6%. In Table 3 the impact coefficients for Model 5 are insignificant for the initial period (2004– 2008) for both the All Country and COP cohorts. Also, in Table 2, the Model 5 coefficients were too small to be statistically significant for the Medium and Low Spending cohorts. This model has all non -financial baseline covariates, as well as three time-varying covariates that combines three types of non-PEPFAR health spending (per capita levels of other donor health spending, domestic government health spending, and private health spending). As noted PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 8 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality earlier, specification revealed that the time-varying covariates in both 4 and 5 were endoge- nous. As a consequence, these results should be viewed with caution. Discussion Our results show estimated impacts of PEPFAR on the ACM rate during the program’s first 15 years of operation (2004–2018) in 90 recipient LMICs. Several programmatic decisions and other policy choices most certainly influenced these impacts. The PEPFAR program was authorized in 2003 and began operating at some scale in 2004 in some LMIC countries. Over time, the specific operational services were expanded, as was the list of recipient countries. Some countries received more aid, some less; some countries were required to participate in a rigorous annual planning and performance monitoring process (the Country Operational Plan, or COP, process), while others were not. While we cannot say what the impact of these programmatic choices were, the analyses of population and temporal segments suggest a pattern of impacts. Our results clearly show that the COP countries, where the epidemic needs were greatest, where ACM rates were highest at baseline, and where the vast majority of funds were expended, were able to achieve the largest PEPFAR impacts on ACM. Across the 5 models the estimates of COP country impacts were reductions in ACM of 14.7–24.5%, while the other 59 non-COP countries achieved reductions in ACM of only 6.7–18.7% over the same 2004–2018 period. The magnitude of our impact estimates on ACM rates is large and consistent with earlier research studies. Daschle & Frist [9] reported a reduction of about 33% in ACM in their assess- ment of 25 countries with highest HIV prevalence between 2004 and 2016. This is somewhat larger than our estimates for COP and highly funded countries. Similarly, Bendavid et al. [7] found a 10.5% annual decrease in the number of HIV deaths in 12 “focus” countries between 2004 and 2007. Although an inexact comparison, the estimated impact is only slightly higher than our estimates for the 2004–2008 period in COP countries (7.4%). Our analysis also shows that the impact of PEPFAR presence on ACM is subject to tempo- ral patterns. Across all the 90 recipient countries we study, ACM declined on average (the Table 3 average across the 5 models) from about 7.6% in the first 5-year period we study, to 5.5% in the second period, and 4.7% in the third 5-year period. The COP countries show simi- lar impacts in the first 5-year period with a 7.4% reduction, followed by a slightly larger impact in the second period with a 7.7% reduction, and a smaller decline to a 6.6% reduction in the last period. This pattern of declines in PEPFAR impact in the later periods mirrors the con- verging patterns of ACM across the control and PEPFAR recipient countries seen in Fig 1. This pattern of slight declining impact of PEPFAR in the third period may suggest that the program enabled recipient countries to quickly begin to save and extend lives, but further increments of improvements vis a´ vis the control group have been more challenging to achieve in the third period. A second possibility is that this results from PEPFAR’s changes in strategy over time (e.g., shifting from an “emergency strategy” in the first five years to a “building sus- tainability strategy” in the second five years, and then moving to the “accountable control” of the epidemic period after 2014) [3]. Finally, the earlier years correspond to rapid increases in PEPFAR spending from 2004 to 2009 and then a plateauing after that point, which could influ- ence the magnitude of the impact across the study periods [1]. It is important to recognize the limitations of the DID estimates we make here. While we have been able to estimate models that control for levels of other donor health sector support (model 4) and domestic health spending (model 5) we have not been able to include local country programs or changes in policy for domestic health systems that might have been taken to combat the HIV epidemic. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 9 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality While we believe that the size of PEPFAR program impacts are generally consistent with other studies, it is certainly possible that our DID estimates unintentionally capture the impacts of some country specific health policies and programs that were introduced after 2003 that have also saved lives. The models do suggest that when other donor and domestic spend- ing are controlled (model 5) the PEPFAR impact estimates are lower. Unfortunately, these donor and domestic spending variables are clearly endogenous, and their coefficients may be the results of endogeneity. Our research examines the impacts of “PEPFAR presence” across cohorts of countries, and across time periods. More research is needed to better understand the linkages between pro- gram characteristics and lives saved. Specifically, PEPFAR intervened by providing resources to implementing partners, who in turn offered products and services to people on the ground. Country PEPFAR programs are somewhat different, and the DID estimates are not able to identify which program characteristics (other than PEPFAR presence, and which PEPFAR cohort) are driving the life-saving results. There are likely other programmatic factors affecting impact which would improve policy-maker’s understanding about what works best to save lives. Such programmatic factors may include choices about facility siting location of services for testing and treatment, the extent of country leadership support of public information cam- paigns about risk factors and public health measures, and other factors. Country programs are also different in terms of how budgets are allocated. Understanding more about the links between resources, services, outputs and outcomes would help to create confidence in “how PEPFAR saved lives,” and it would also help program staff understand how the overall budget allocation might be better distributed to “get more bang for the buck.” These budget variances can be used to analyze the incremental contribution of spending more on preven- tion, testing, care and treatment, or other areas related to broader HIV efforts. Finally, the magnitude of the impact on ACM suggests that PEPFAR may have had positive spillover effects on ACM beyond HIV disease alone. This is plausible, given that PEPFAR has invested significantly in the recipient countries’ health systems, including the workforce and supply chains, (estimated to be more than $1 billion per year) [2]. It also seems likely that the expansion of health facility capacities, including in rural areas, may well have introduced many families to professional health services for the first time and may have contributed to ACM impacts captured in the data that are not related to HIV. Our results (not shown here) show sizable health spillover effects beyond HIV. Bendavid (2016) also mentions possible spill- over effects [21]. Future research could explore this further and seek to quantify this spillover impact. Strengths and limitations of the study Despite the consistency of our findings across model specifications, this study does have some limitations. The quasi-experimental design offers a convenient method for obtaining estimates with a large panel dataset, and the use of baseline and other covariates helps to adjust estimates for differences between recipient and control countries. Despite these adjustments, the control group is not ideal and the use of some minimally supported countries in the control group may make our PEPFAR impact estimates somewhat conservative in magnitude. While we con- trol for baseline differences and yearly “other donor health spending per capita” and “domestic health spending per capita” it is still possible that the effect of PEPFAR is being systematically over- or underestimated. Our model specifications appear reasonably stable and consistent, but there is always a risk of attributing unwarranted differences and changes in ACM to PEPFAR’s presence. We have included in models 4 and 5 time-varying covariates of non PEFAR health spending, hoping to PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 10 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality control for potentially confounding influences of other programs and initiatives aiming to control the epidemic. The estimates of PEPFAR influence in these specifications are consis- tent with other models, though the PEPFAR impacts are, in some cases, somewhat smaller, particularly in Model 5. But, the Hausman test for endogeneity confirmed the problem in Models 4 and 5, making it impossible to say whether the differences in size of impact esti- mates in these models, particularly Model 5, are a result of endogeneity issues or something else. Another important limitation is the inability to separate the PEPFAR effects of spending intensity from COP status. Unfortunately, there is a very high overlap between these two cate- gories (e.g., PEPFAR demands very intense country annual planning where it provides larger sums of money). Finally, the DID estimates presented here do not tell us about which specific features of pro- gramming are most effective in saving lives. Estimates of cohort Impacts are attributed to “PEPFAR’s presence” only. Many contributing characteristics of country-specific program- ming and budget allocations would provide richer data, and more refined knowledge about what is saving lives. Despite these limitations, we believe that our estimates of PEPFAR’s impact on ACM are properly attributed to PEPFAR due to four factors: (1) the general size of ACM reductions are similar to the prior literature, particularly, Bendavid (2012) [8]; (2) the general consis- tency in the size of our estimates of effects of PEPFAR on ACM using different model speci- fications; and (3) the steps taken in our research approach to estimate patterns of PEPFAR effects across subgroups of countries with intense country planning (or not) and countries with high-, medium- and low-aid levels. These results show patterns of PEPFAR impacts we would expect to see if the PEPFAR program was working as intended. As expected, places with higher program spending have larger program effects on ACM and countries that are required to conduct rigorous annual planning and budget scrutiny (COP) have bigger impacts on ACM. (4) We also estimated all models using a double log transformation, and report results in the S1 Table materials. The estimates are quite similar and the patterns across models and cohorts are also similar to the non-log results. The log models suggest that PEPFAR has reduced ACM rates across all 90 country recipients by approximately 15– 17% over the 15-year interval, somewhat higher (approximately 23–26%) in country seg- ments where cumulative spending has been higher and/or where intensive planning has occurred, and somewhat lower (approximately 11–14%) in the other non-COP PEPFAR recipients. The log models also suggest that the life-saving impacts of the program remain substantial in the most recent, 2014–2018 period (4.9% annual reductions in all PEPFAR countries, and 7.9% in the COP countries). Conclusions PEPFAR’s substantial response to the HIV/AIDS epidemic beginning in late 2003 continues to bring accessible, free, and modern tools of medicine and public health to LMIC countries fighting HIV. Using a panel data set starting in 1990, and across the program’s operations dur- ing 2004–2018 in 90 recipient countries, we estimate that PEPFAR has reduced ACM rates by 10–21% over the 15-year interval, and somewhat higher in country segments where cumula- tive spending has been higher and/or where intensive planning has occurred. Over the course of the first 15 years of PEPFAR operation, the life-saving impacts of the program remain very substantial in the most recent, 2014–2018 period (4.7% decrease in all PEPFAR countries over the period, and 6.6% in the COP countries). These findings suggest that continued support from PEPFAR would achieve further gains over time. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 11 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Supporting information S1 Text. Country list by groups. (DOCX) S2 Text. Cohorts of PEPFAR countries created for analysis. (DOCX) S3 Text. Difference-in-difference (DID) methodology. (DOCX) S4 Text. Missingness. (DOCX) S1 Table. Regression results–Tables A—O. Table A in S1 Table. Summary of PEPFAR impact by country cohort from estimation of five logged models. Table B in S1 Table. Summary of adjusted R-squares of five logged and unlogged models (level) on PEPFAR impact by country cohort. Table C in S1 Table. Summary of PEPFAR impact over three periods from estimation of five logged models. Table D in S1 Table. Full model results for "All PEPFAR" group vs con- trol: unlogged models. Table E in S1 Table. Full model results for "All PEPFAR" group vs con- trol: logged models. Table F in S1 Table. Full model results for "COP-PEPFAR" group vs control: unlogged models. Table G in S1 Table. Full model results for "COP-PEPFAR" group vs control: logged models. Table H in S1 Table. Full model results for "Other PEPFAR" group vs control: unlogged models. Table I in S1 Table. Full model results for "Other PEPFAR" group vs control: logged models. Table J in S1 Table. Full model results for "High intensity PEPFAR" group vs control: unlogged models. Table K in S1 Table. Full model results for "High intensity PEPFAR" group vs control: logged models. Table L in S1 Table. Full model results for "Medium intensity PEPFAR" group vs control: unlogged models. Table M in S1 Table. Full model results for "Medium intensity PEPFAR" group vs control: logged models. Table N in S1 Table. Full model results for "Low intensity PEPFAR" group vs control: unlogged models. Table O in S1 Table. Full model results for “Low intensity PEPFAR” group vs control: logged models. (DOCX) S1 Fig. Test the normal distribution of residuals derived from logged and unlogged Model 4 and Model 5—Figs A—H. Fig A in S1 Fig. Residuals from unlogged Model 4 on all PEPFAR countries. Fig B in S1 Fig. Residuals from logged Model 4 on all PEPFAR countries. Fig C in S1 Fig. Residuals from unlogged Model 4 on COP countries. Fig D in S1 Fig. Residuals from logged Model 4 on COP countries. Fig E in S1 Fig. Residuals from unlogged Model 5 on all PEPFAR countries. Fig F in S1 Fig. Residuals from logged Model 5 on all PEPFAR countries. Fig G in S1 Fig. Residuals from unlogged Model 5 on COP countries. Fig H in S1 Fig. Residuals from logged Model 5 on COP countries. (DOCX) S2 Fig. Test the parallel assumption of ACM by country cohort. Fig A in S2 Fig. All PEPFAR countries versus control countries. Fig B in S2 Fig. COP countries versus control countries. Fig C in S2 Fig. Non-COP PEPFAR countries versus control countries. Fig D in S2 Fig. High intensity countries versus control countries. Fig E in S2 Fig. Medium intensity countries versus control countries. Fig F in S2 Fig. Low intensity countries versus control countries. (DOCX) PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 12 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality Acknowledgments The authors thank Yara Halasa-Rappel, Brandeis University, and Adam Wexler and Stephanie Oum, KFF, Washington DC, for data retrieval and dataset preparation. We also thank the edi- tor and reviewers of the manuscript for thoughtful concerns and suggestions. Author Contributions Conceptualization: Gary Gaumer, Jennifer Kates, Allyala Nandakumar. Data curation: Gary Gaumer, William Crown, Allyala Nandakumar. Formal analysis: Yiqun Luan, Dhwani Hariharan, William Crown, Jennifer Kates. Funding acquisition: Allyala Nandakumar. Methodology: Gary Gaumer, William Crown, Jennifer Kates. Project administration: Jennifer Kates, Monica Jordan. Supervision: Monica Jordan, Allyala Nandakumar. Validation: Gary Gaumer. Writing – original draft: Gary Gaumer, Dhwani Hariharan, Jennifer Kates, Monica Jordan, Allyala Nandakumar. Writing – review & editing: Gary Gaumer, Yiqun Luan, Dhwani Hariharan, William Crown, Jennifer Kates, Monica Jordan, Clare L. Hurley. References 1. Kates J, Carbaugh A, Isbell M. Kaiser Family Foundation Global Health Policy. Key Issues and Ques- tions for PEPFAR’s Future. 2021. [cited 2021 Dec 20]. Available: https://www.kff.org/report-section/ key-issues-and-questions-for-pepfars-future-issue-brief/. 2. Kaiser Family Foundation (KFF). The U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). 2022. [cited 2022 Aug 30]. Available: https://www.kff.org/global-health-policy/fact-sheet/the-u-s- presidents-emergency-plan-for-aids-relief-pepfar/. 3. US Department of State. Annual Reports to Congress on the President’s Emergency Plan for AIDS Relief. 2021. [cited 2022 May 5]. Available: https://www.state.gov/annual-reports-to-congress-on-the- presidents-emergency-plan-for-aids-relief/. 4. Jahn A, Floyd S, Crampin AC, Mwaungulu F, Mvula H, Munthali F, et al. Population-level effect of HIV on adult mortality and early evidence of reversal after introduction of antiretroviral therapy in Malawi. Lancet. 2008; 371(1924):1603–1611. https://doi.org/10.1016/S0140-6736(08)60693-5 PMID: 18468544 5. Mermin J, Were W, Ekwaru JP, Moore D, Downing R, Behumbiize P, et al. Mortality in HIV-infected Ugandan adults receiving antiretroviral treatment and survival of their HIV-uninfected children: A pro- spective cohort study. AIDS. 2008; 371(9614):752–759. https://doi.org/10.1016/S0140-6736(08) 60345-1 PMID: 18313504 6. Stoneburner R, Montagu D, Pervalhac C, Fidzani B, Gill W, Kennedy G, et al. Declines in adult HIV mor- tality in Botswana, 2003–2005: Evidence for an impact of ARV therapy programs. 16th International AIDS Conference. Toronto, 2006. 7. Bendavid E, Bhattacharya J. The President’s Emergency Plan for AIDS Relief in Africa: An evaluation of outcomes. Ann Intern Med. 2009; 150(10):688–695. https://doi.org/10.7326/0003-4819-150-10- 200905190-00117 PMID: 19349625 8. Bendavid E, Holmes CB, Bhattacharya J, Miller G. HIV development assistance and adult mortality in Africa. JAMA Intern Med. 2012; 307(19):2060–2067. https://doi.org/10.1001/jama.2012.2001 PMID: 22665105 9. Daschle T, Frist W, Wald C, Birx D, Gerson M, Storella MC, et al. 15 Years of PEPFAR: Advancing Stra- tegic Health Diplomacy In. Washington, DC: Bipartisan Policy Center; 2018. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0002467 January 18, 2024 13 / 14 PLOS GLOBAL PUBLIC HEALTHAssessing PEPFAR’s impacts on all-cause mortality 10. Kim Y. The effectiveness of PEPFAR’s funding for women and children with HIV/AIDS. Int J Health Plann Manage. 2019; 34(1):e896–e916. https://doi.org/10.1002/hpm.2706 PMID: 30451315 11. Heaton LM, Bouey PD, Fu J, Stover J, Fowler TB, Lyerla R, et al. Estimating the impact of the US Presi- dent’s Emergency Plan for AIDS Relief on HIV treatment and prevention programmes in Africa. Sex Transm Infect. 2015; 91(8):615–620. https://doi.org/10.1136/sextrans-2014-051991 PMID: 26056389 12. Institute of Medicine. Evaluation of PEPFAR. Washington, DC: The National Academies Press. 2013. [cited 2021 Dec 20]. Available: https://www.nap.edu/catalog/18256/evaluation-of-pepfar. 13. Over M. PEPFAR Might Be Saving Millions of Lives–But We Don’t Have Evidence Yet. Center for Global Development (CGD). 2009. 14. World Bank. The World Bank Development Indicators. 2021. [cited 2023 Jan 25]. Available: https:// datatopics.worldbank.org/world-development-indicators/. 15. US Department of State, USAID. 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10.1371_journal.ppat.1012031	RESEARCH ARTICLE Candida albicans translocation through the intestinal epithelial barrier is promoted by fungal zinc acquisition and limited by NFκB- mediated barrier protection Jakob L. Sprague1, Tim B. Schille1,2, Stefanie Allert1, Verena Tru¨ mper1, Adrian Lier1, Peter Großmann3, Emily L. Priest4, Antzela Tsavou4, Gianni Panagiotou2,3,5, Julian R. Naglik4, Duncan Wilson6, Sascha Scha¨ uble3, Lydia Kasper1☯, Bernhard HubeID 1,2,5☯* 1 Department of Microbial Pathogenicity Mechanisms, Hans-Kno¨ll-Institute, Jena, Germany, 2 Cluster of Excellence Balance of the Microverse, Friedrich-Schiller-University Jena, Jena, Germany, 3 Department of Microbiome Dynamics, Hans-Kno¨ll-Institute, Jena, Germany, 4 Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, United Kingdom, 5 Institute of Microbiology, Friedrich-Schiller-University Jena, Jena, Germany, 6 Medical Research Council, Centre for Medical Mycology at the University of Exeter, Exeter, United Kingdom ☯ These authors contributed equally to this work. * bernhard.hube@leibniz-hki.de Abstract The opportunistic fungal pathogen Candida albicans thrives on human mucosal surfaces as a harmless commensal, but frequently causes infections under certain predisposing condi- tions. Translocation across the intestinal barrier into the bloodstream by intestine-colonizing C. albicans cells serves as the main source of disseminated candidiasis. However, the host and microbial mechanisms behind this process remain unclear. In this study we identified fungal and host factors specifically involved in infection of intestinal epithelial cells (IECs) using dual-RNA sequencing. Our data suggest that host-cell damage mediated by the pep- tide toxin candidalysin-encoding gene ECE1 facilitates fungal zinc acquisition. This in turn is crucial for the full virulence potential of C. albicans during infection. IECs in turn exhibit a fila- mentation- and damage-specific response to C. albicans infection, including NFκB, MAPK, and TNF signaling. NFκB activation by IECs limits candidalysin-mediated host-cell damage and mediates maintenance of the intestinal barrier and cell-cell junctions to further restrict fungal translocation. This is the first study to show that candidalysin-mediated damage is necessary for C. albicans nutrient acquisition during infection and to explain how IECs coun- teract damage and limit fungal translocation via NFκB-mediated maintenance of the intesti- nal barrier. Author summary Candida albicans populations colonizing the intestine serve as the main source for sys- temic infections. Though normally commensal, under certain conditions, C. albicans can a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Sprague JL, Schille TB, Allert S, Tru¨mper V, Lier A, Großmann P, et al. (2024) Candida albicans translocation through the intestinal epithelial barrier is promoted by fungal zinc acquisition and limited by NFκB-mediated barrier protection. PLoS Pathog 20(3): e1012031. https:// doi.org/10.1371/journal.ppat.1012031 Editor: Teresa R. O’Meara, University of Michigan, UNITED STATES Received: July 20, 2023 Accepted: February 6, 2024 Published: March 1, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.ppat.1012031 Copyright: © 2024 Sprague et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Programming code and data necessary to generate plots shown in this manuscript were deposited at Github: https:// PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 1 / 29 PLOS PATHOGENSgithub.com/SchSascha/Cal_Translocation. Raw sequencing data is submitted under project accession number GSE237496 to the GEO gene accession omnibus. Funding: JLS, AL, LK, BH, SaS, PG, and GP were supported by the German Research Foundation (Deutsche Forschungsgemeinschaft – DFG) within the Collaborative Research Centre (CRC)/ Transregio (TRR) 124 “FungiNet” projects C1 and INF (DFG project number 210879364). TBS was supported by the DFG under Germany’s Excellence Strategy – EXC 2051 – Project ID 390713860. VT was supported by the German Federal Ministry of Education and Research (BMBF) within the funding program Photonics Research Germany, Leibniz Center for Photonics in Infection Research (LPI) (subproject LPI-BT2; contract number 13N15705). GP was also supported by the BMBF within the funding project PerMiCCion (project ID: 01KD2101A). JRN was supported by grants from the Wellcome Trust (214229_Z_18_Z) and National Institutes of Health (DE022550). DW was supported by a Wellcome Trust Senior Research Fellowship (214317/Z/18/Z), the MRC Centre for Medical Mycology at the University of Exeter (MR/ N006364/2 and MR/V033417/1), and the NIHR Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. NFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation translocate across the intestine and into the bloodstream, leading to systemic candidiasis. Here we dissect the fungal and host activities involved in this process. We find that dam- age to host cells, which supports efficient translocation, is associated with active acquisi- tion of host-cell zinc by C. albicans. At the same time, intestinal epithelial cells foster barrier integrity to limit fungal translocation independently of host damage. Introduction The majority of life-threatening invasive Candida infections are caused by C. albicans [1–3]. The WHO have acknowledged the global threat posed by fungal pathogens and recently pub- lished a priority list which included C. albicans in the critical priority group [4]. C. albicans normally exists as a commensal member of the mycobiota. Within a healthy host the resident microbiota, epithelial barriers, and the host immune system keep C. albicans commensal and prevent translocation over the intestinal barrier into the blood stream [5–7]. The fungus can, however, transition to a pathogenic state under certain predisposing conditions [5]. In fact, systemic infections arise from endogenous C. albicans populations within the gastrointestinal (GI) tract and require translocation across the intestinal epithelium into the bloodstream [6,8– 10]. Translocation events occur in patients suffering from a variety of predisposing conditions, such as long-term use of broad-spectrum antibiotics or immunosuppression, and the resulting infections are challenging to diagnose, difficult to treat effectively, and associated with high mortality rates [2]. However, the specific fungal factors and host mechanisms that contribute to fungal translocation still remain largely undefined. In an in vitro model of cultured intestinal epithelial cells, C. albicans-mediated damage due to the hypha-associated fungal cytolytic toxin candidalysin was described as a major mecha- nism of fungal translocation via a transcellular route [11]. In line with this, fungal genes that are necessary for hyphal growth or delivery of candidalysin were needed for the full transloca- tion capacity [11,12]. Nevertheless, low-level translocation was possible in strains lacking can- didalysin, suggesting that further, yet uncharacterized factors and mechanisms contribute to translocation. The host response to C. albicans infection has been extensively described in oral epithelial cells (OEC). C. albicans infection activates NFκB and c-Jun-based MAPK signaling pathways in OECs independent of fungal morphology [13]. A second MAPK signaling phase involving MKP1 and c-Fos is triggered by the activation of epidermal growth factor receptor (EGFR) by candidalysin-mediated host-cell damage [13–15]. This innate immune response results in the production of cytokines like IL-1β or IL-8 [15,16]. Independent of NFκB and MAPK signaling, C. albicans also induces PI3K/Akt signaling during infection of OECs [17]. The response of IECs to C. albicans infection is less well characterized. IECs respond via MAPK and TNF signal- ing as well as activation of NFκB which protects from fungal-mediated host-cell damage [18]. Dual-species RNA sequencing has proven to be a powerful tool for identifying the time-resolved infection-specific activities of C. albicans and host cells during their interaction [19–22]. In this study, we therefore used dual-species transcriptional profiling to investigate the molecular dynamics upon interaction of C. albicans with IECs and to determine which fungal and epithelial processes contribute to IEC damage and fungal translocation. We found that the candidalysin-encoding gene ECE1 is required for zinc acquisition during invasion of host cells. This indicates that C. albicans-induced host-cell damage supports the acquisition of host micronutrients, in this case zinc, during infection. We also show that ECE1-dependent host damage and subsequent fungal translocation are limited by NFκB- PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 2 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation mediated maintenance of the epithelial barrier. Consequently, NFκB activation by IECs limits the pathogenic potential of C. albicans and helps to protect epithelial barrier integrity. Results Transcriptional adaptation of C. albicans to IEC infection includes dynamic metabolic shifts and indicates scavenging of host zinc To characterize the interaction of C. albicans with intestinal epithelial cells (IECs) from early fungal adhesion to initial invasion (up to 6 h) and later translocation and damage phases (12– 24 h), we conducted dual-species RNA sequencing of C. albicans-infected IECs over a 24 h time course. Intestinal epithelial cells (C2BBe1) were seeded and differentiated for 12 d after reaching confluency to form a polarized cell layer [23]. Fungal and host RNA was isolated from C. albicans and IECs alone as well as from co-incubated samples. Samples were taken before infection (0 h), at 45 min (0.75 h), 3 h, 6 h, 12 h, and 24 h. C. albicans showed a clear time-dependent transcriptional response with distinct clusters for each time point (Fig 1A). C. albicans samples cultured with and without IECs showed similar transcriptional responses and clustered together, indicating the fungal response in this experimental model was largely inde- pendent of the presence of host cells (Fig 1A). To explore the transcriptional changes in C. albicans that are specific for the presence of IECs, we identified infection-specific differentially expressed genes (DEGs). For that, we com- pared fungal transcript levels in the presence vs absence of IECs at each respective time point (S1 Table). Using these DEGs, we performed functional enrichment analysis of gene ontology terms (GO; biological process category; S2 Table and Fig 1B) to identify the molecular patterns of infection. We first looked at stress- or virulence-related functional categories that were enriched upon infection. In the earliest stages of infection, at 45 min, the enrichment analysis detected the term “response to chemical” which contained the stress-related catalase-encoding gene, CAT1, and the thioredoxin-encoding gene, TRR1 (S2 Table and Fig 1B). Decreased mRNA levels of these genes in infected samples compared to medium-only controls (S1 Table) suggests that C. albicans does not experience significant stress upon early contact to IECs. In late infection stages, however, the category “detoxification” was significantly overrepresented among infec- tion-specific DEGs with increased transcript levels during infection (Fig 1B). These included many genes encoding predicted membrane transporters like the putative ABC transporter, CDR4, and the putative MFS transporter, QDR3 (S2 Table). This indicates increased stress related to the host or to host-cell damage. The majority of enriched GO terms did not contain classical virulence-associated categories and did not include genes related to fungal filamentation or virulence. Only few enriched terms, including some related to lipid metabolism and cell adhesion, fall into these categories. Genes involved in lipid metabolism were significantly overrepresented at 6 h among the infec- tion-specific DEGs (Fig 1B). These included many genes needed for ergosterol biosynthesis, which showed increased expression during infection of IECs (S1 and S2 Tables). As ergosterol biosynthesis is important for filamentation, this increased expression may indicate infection- specific surface changes in invading hyphae [24]. Enrichment of the category “cell adhesion” at 12 h similarly points to infection-specific surface changes (Fig 1B). This category includes DEGs coding for adhesins, cell surface proteins and cell wall integrity-related genes, such as EAP1, ALS1, PRA1, HWP1, XOG1, MNT2 and PMT1 (S1 and S2 Tables). Most infection-specific changes were, however, due to metabolic adaptations of C. albicans. At time points of initial hypha formation prior to substantial host cell invasion (3 h) but also during host cell invasion and damage (12 h and 24 h) [11], the enrichment analysis revealed PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 3 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Fig 1. C. albicans shows transcriptional changes specific to the presence of IECs during infection. (A) Principal component analysis for WT (SC5314) C. albicans RNA sequencing data. Samples clustered together depending on the time point regardless of the presence of IECs (enterocytes). (B) GO terms significantly overrepresented among infection-specific C. albicans DEGs (positive regulation of filamentous growth of a population of unicellular organisms in response to chemical stimulus; biological process involved in interspecies interaction between organisms). Gene ratio represents the proportion of genes within each category that were significantly differentially expressed during infection compared to C. albicans cells only. The color scale represents the mean Log2(fold-change) of significant DEGs within each category. Red indicates that most DEGs showed increased expression during infection, blue indicates most DEGs showed decreased expression, and grey indicates a mix of DEGs with increased and decreased expression within the category. Dots are only shown for significantly enriched categories (overrepresentation analysis, Benjamin-Hochberg adjusted p-value � 0.05, S2 Table). (C) Gene expression of zinc-related genes comparing C. albicans cell during infection of IECs to those cultured in medium only. The color scale represents the Log2(fold-change) for each gene during infection of IECs compared to medium only, with red representing higher expression during infection and blue representing lower expression during infection. Genes for the zinc transporters ZRT101, ZRT2, ZRT3 and ZRC1; the zinc-scavenging protein PRA1; and the transcription factor ZAP1 (CSR1) were less expressed during infection of IECs. An asterisk indicates the time points with a statistically significant difference in gene expression (DESeq2 p < 0.05). The mean MRN values are given in S1 Table. https://doi.org/10.1371/journal.ppat.1012031.g001 adaptations in the central fungal carbon metabolism in categories for carbohydrate transmem- brane transport, the glyoxylate cycle, and monocarboxylic acid metabolism were enriched at 3 h (Fig 1B). These categories cover glucose transporter genes like HGT2 and HGT12, which are known to be repressed by high glucose levels [25], and genes involved in utilization of non-glu- cose carbon sources via the glyoxylate cycle and fatty acid beta oxidation, like ICL1, MLS1, FOX2, and PEX5 (S2 Table). Lower transcript levels of these genes during infection (S1 Table) indicates that the presence of IECs provides glucose to infecting C. albicans cells even before substantial fungal invasion. In addition, there was an infection-specific increase in transla- tional activity at 3 h, with higher transcript levels of genes within the categories for ribonucleo- protein biogenesis and snRNA metabolic processes (Fig 1B). This indicates infection-specific re-organization of central cellular processes at an early time point. At 12 h, genes falling under the term “pyruvate metabolic process” were also significantly enriched, including genes relevant for glycolysis (ENO1, CDC19, TDH3, and FBA2) and for PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 4 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation the pyruvate dehydrogenase complex (PDA1, PDB1, PDX1, and LAT1) (Fig 1B and S2 Table). The lower transcript levels of these genes points to reduced glucose availability during these later stages of IEC infection (Fig 1B and S1 Table). Finally, at 24 h, the term “monosaccharide catabolism” was overrepresented among the infection-specific DEGs. This suggests that non- glucose C6 carbon sources are available specifically during the host-cell damage phase of infec- tion (Fig 1B). These genes included some involved in galactose metabolism (GAL1, 7,10) and glycolysis (PFK1, 2) which were more highly expressed in the presence of IECs (S1 and S2 Tables). Translation-related processes were again enriched during infection at the 24 h time point (Fig 1B). Potentially, this reflects an adaptation to the more complex environment when C. albicans gains access to the host cell content. Taken together, our data indicate that C. albicans dynamically adapts to the high availability of its preferred carbon source glucose at 3 h, to glucose limitation at 12 h, and the availability of alternative carbon sources upon host-cell damage at 24 h. Importantly, not only did our analysis pick up infection-specific changes in macronutrient metabolism, but also adaptation to micronutrient availability. At 12 h, the term “zinc ion trans- port” was significantly overrepresented among the infection-specific DEGs (Fig 1B). This term contained the zinc transporter genes ZRT101, ZRT2, ZRT3, and ZRC1, which all had signifi- cantly reduced transcript levels during IEC infection. The zincophore gene PRA1 and the zinc homeostasis regulator gene ZAP1 (CSR1) showed the same pattern (Fig 1C) [26]. While mRNA levels of these zinc-related genes increased over time in both infecting C. albicans and C. albicans in medium only, their levels were higher in the absence of IECs, especially at later stages of infection (S1 Fig). The presence of their substrates strongly down-regulates the tran- scription of genes involved in micronutrient acquisition, like those for zinc [27–29]. Together, our data therefore indicate a stronger starvation for zinc when C. albicans is grown in the absence of IECs. In contrast, other metal-related terms were not overrepresented except for copper ion transport at initial infection stages (45 min) (Fig 1B). In fact, increasing transcript levels of iron-metabolic genes, like the high-affinity iron permease genes FTR1 and FTR2, in both infection and control conditions indicated a general iron starvation response at later time points. Unlike for zinc, this was not rescued by the presence of IECs (S1 Table). Overall, our transcriptome analysis indicates that C. albicans adapts its metabolism and sur- face to the dynamic environmental changes during experimental IEC infection, including acquisition of zinc from the host cells. Zinc acquisition during infection of IECs is ECE1-dependent Zinc ion transport was a significantly overrepresented category among infection-specific DEGs during the later stages of invasion and host-cell damage (Fig 1B). This was not the case for genes involved in the transport of other metals or micronutrients, so we hypothe- sized that zinc may be of particular importance. To determine whether zinc acquisition plays a role during C. albicans infection of IECs, we utilized gene deletion mutants for the zinc importer genes ZRT101 and ZRT2, the zincophore gene PRA1, and the intracellular zinc transporter gene ZRC1. Zrt101, Zrt2, and Pra1 are involved in zinc acquisition and are upregulated by low zinc [27,28]; in contrast, Zrc1 is involved in zinc detoxification, although its transcriptional regulation has not been investigated. Loss of ZRT101, ZRT2, and PRA1 had no impact on adhesion to, invasion of, or translocation through IECs by zinc-pre-starved C. albicans cells (Fig 2A–2C). The strain lacking ZRC1, however, showed decreased invasion and strongly impaired translocation (Fig 2B and 2C). The dramatic decrease in translocation ability (~10-fold) is unlikely to be solely due to the decreased hypha formation (~1.5-fold) of this strain (S2 Fig). In contrast to these results, we found PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 5 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Fig 2. Loss of zinc acquisition genes in C. albicans decreases damage potential by impairing growth during infection of IECs. (A) Loss of zinc acquisition and storage genes had no effect on the adhesion of C. albicans to C2BBe1:HT29-MTX intestinal epithelial cells after 3 h. (B) Zinc acquisition and storage genes did not significantly affect the percentage of invasive hyphae after 6 h co-incubation with C2BBe1:HT29-MTX cells. The invasion of zrc1Δ/Δ was reduced, though not to a statistically significant degree. (C) Loss of ZRC1 significantly impaired translocation of C. albicans cells through C2BBe1:HT29-MTX intestinal epithelial cells grown on a porous membrane after 24 h. (D) The host-cell damage of the zrt101Δ/Δ, zrc1Δ/Δ, pra1Δ/Δ, and zrt101ΔΔ/zrt2ΔΔ strains was significantly reduced after 24 h of infection. The severe damage impairment could be rescued with exogenous addition of 25 μM ZnSO4 during infection for zrt101ΔΔ/zrt2ΔΔ (P < 0.0001), but not for zrc1Δ/Δ (P > 0.9999). LDH release was adjusted by subtracting the release from uninfected and untreated host cells. (E) The addition of exogenous zinc during infection of C2BBe1:HT29-MTX cells increases the fungal growth of the zrt101ΔΔ/zrt2ΔΔ strain to a level similar to that of the WT (BWP17) strain. Fungal hyphae (blue) were stained with calclofluor white (scale bar = 100 μm). All values are shown as the mean with standard deviation. Invasion (B) and translocation (C) data were compared using a one-way analysis of variance (ANOVA) and the host-cell damage (D) data were compared using a two-way ANOVA. Statistical significance was determined with a post-hoc Dunnett’s multiple comparisons test. Statistical significance: , P � 0.05; *, P � 0.001; *, P � 0.0001. https://doi.org/10.1371/journal.ppat.1012031.g002 that loss of ZRT101, PRA1, and ZRC1 results in significantly decreased damage potential, and an almost complete absence of host-cell damage after deletion of both ZRT101 and ZRT2 (Fig 2D). The host-cell damage defect of this zinc uptake impaired mutant (zrt101ΔΔ/ zrt2ΔΔ), but not of the zinc detoxification defective mutant (zrc1Δ/Δ) was rescued by addi- tion of 25 μM ZnSO4, a zinc concentration known to promote C. albicans growth while not being toxic (Fig 2D) [28]. This restoration of host-cell damage is likely due to increased growth and filamentation as assessed by microscopy images showing that without addition of zinc there is little-to-no fungal growth for the zrt101ΔΔ/zrt2ΔΔ strain (Fig 2E). Therefore, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 6 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Fig 3. Loss of ECE1 increases the transcriptional zinc starvation response during infection of IECs. Fold change in gene expression in C. albicans WT (BWP17) and ece1Δ/Δ strains during co-incubation with C2BBe1 cells for (A) ZRT101, (B) ZRT2, (C) PRA1, (D) ZRT3, and (E) ZRC1. Fold changes are calculated as the normalized gene expression at each time point compared to the respective 0 h control samples. (F) Fold change in normalized gene expression of the ece1Δ/Δ strain compared to WT(BWP17) at 24 h in medium only or during infection of IECs. Gene expression was analyzed by q-RT-PCR and is normalized to ACT1 as a housekeeping gene. All values are shown as the mean with standard deviation. All the ratio data from q-RT-PCR experiments were log-transformed before performing a two-tailed, paired t-test. Statistical significance: , P � 0.05. https://doi.org/10.1371/journal.ppat.1012031.g003 efficient zinc acquisition by C. albicans appears necessary to fully damage IECs, likely by fostering its growth and filamentation. We have shown that C. albicans experiences more intense late-stage zinc starvation in the absence of host cells–at time points at which damage of the IECs takes place in infected sam- ples (Fig 1C) [11]. We therefore hypothesized that C. albicans acquires zinc from host cells during infection of the intestinal epithelium and that host-cell damage enables this zinc acqui- sition. The fungal peptide toxin candidalysin, encoded by the ECE1 gene, is the main damag- ing factor of C. albicans [11,14]. To further investigate the interplay between host-cell damage and zinc acquisition and the involvement of candidalysin in this process, we compared the transcriptional zinc starvation response of wild-type (WT, BWP17) C. albicans to an ece1Δ/Δ strain. The strain lacking ECE1 showed significantly higher transcript levels of ZRT2, PRA1, and ZRT3 after 24 h of infection of IECs, consistent with more severe zinc starvation (Fig 3B, 3C, 3D and 3F). A similar pattern was seen for ZRT101 mRNA levels, though the difference was not statistically significant (Fig 3A and 3F). When cultured in the absence of host cells, the WT (BWP17) and ece1Δ/Δ strains showed no significant differences in transcript levels of zinc-related genes (Fig 3F and S3A–S3E Fig). The addition of exogenous zinc during co-incu- bation with IECs did not affect the host-cell damage, fungal translocation, or fungal mass of the WT(BWP17) or ece1Δ/Δ strains during infection of IECs (S4A–S4C Fig). The differences in the transcriptional zinc starvation response between the WT (BWP17) and ece1Δ/Δ strains after 24 h were reduced with addition of zinc, though there was still a trend towards increased transcript levels in the ece1Δ/Δ strain (S4D Fig). These data indicate, that ECE1 is necessary for zinc acquisition during interaction with IECs. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 7 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation IEC response to C. albicans infection by NFκB, MAPK, and TNF signaling and damage-dependent c-Fos/IL-8 induction Similar to C. albicans, IECs showed a time-dependent transcriptional response with patterns reflecting the different stages of infection (Figs 1A and 5A). The transcriptomes of infected host cells differed from those cultured without C. albicans at all time points, showing a clear infec- tion-specific response (Fig 4A). The time-dependent response of IECs presented as a drastic increase in the number of infection-specific DEGs after 12 and 24 h–time points at which C. albicans hyphae damage the epithelium and translocate (S1 Table) [11]. KEGG (S2 Table and Fig 4B) enrichment analysis was performed on the infection-specific IEC DEGs. Similar to C. albicans, there were few significant infection-specific pathways at the 45 min time point (over- representation analysis, adjusted p � 0.05; Fig 4B). Components of the JNK and p38 MAPK pathway were induced upon early infection, including the c-Jun N-terminal kinase (JNK) gene MAPK10 as well as the transcription factor AP-1 components FOS, JUN and JUNB. FOS tran- script levels remained high during infection at all later time points (S1 and S2 Tables). Soon after initial hypha formation at 3 h, we observed an infection-specific increase in mRNA levels of epithelial genes involved in oxidative phosphorylation (Fig 4B). Specifically, transcript levels were higher for mitochondrial genes (ND1, 2, 4, 5, 6; ND4L; COX1, 2, 3; ATP6, 8; CYB), which has previously been observed in vaginal epithelial cells [19] (S1 and S2 Tables). After 6 h when invasion of host cells occurs, and until the final time point at 24 h, genes involved in innate immune responses were significantly overrepresented in the infec- tion-specific DEGs. MAPK, TNF, NFκB, and IL-17 signaling pathways were overrepresented due to genes with increasing transcript levels during IEC infection (Fig 4B). Genes involved in the classical MAP kinase pathway (including growth factor genes EFNA1, EREG, AREG, TGFA; calcium voltage-gated channel CACNA genes; receptor tyrosine kinase genes NGFR, EPHA2, FGRF1; protein kinase C gene PRKCB; protein tyrosine phosphatase and mitogen- activated protein kinase phosphatase genes PTPN7, DUSP2, 4, 5, 6, 10; NFKB2) had increased transcript levels upon C. albicans infection (S1 and S2 Tables). Genes encoding chemokines (CXCL1, 2, 3, 8, 17), inflammatory cytokines (IL1B, IL11, IL12A, IL27, IL32 CCL20), as well as genes for intracellular signaling proteins (GADD45A, B; FOS; FOSL1; FOSB; JUN; A20) showed increased transcript levels upon C. albicans infection (S1 and S2 Tables). The late- stage induction of innate immune signaling pathways is consistent with data previously pub- lished for IECs [18]. The majority of infection-specific host DEGs only appear after fungal invasion and result- ing host-cell damage. In comparison, vaginal epithelial cells show a uniform early response to various fungal species, but the response diverges at the later time points and is connected to damage potential [19]. We sought to determine whether the IEC DEGs at later time points reflect a general response to fungi or rather a specific response to C. albicans filamentation and damage. To determine which of the host responses identified were filamentation- or damage- specific, we extended our transcriptome analysis to compare IECs infected with C. albicans WT, non-damaging (ece1Δ/Δ), and non-filamentous and non-damaging (efg1ΔΔ/cph1ΔΔ) strains. The two mutant strains used (ece1Δ/Δ and efg1ΔΔ/cph1ΔΔ) were constructed in differ- ent genetic backgrounds, therefore the gene expression data was always compared to the corre- sponding WT strains (BWP17 and SC5314, respectively) to minimize the effect of strain- specific, genetic differences on the host response [30]. The set of genes that were only differen- tially expressed in response to the efg1ΔΔ/cph1ΔΔ strain was then categorized as a filamenta- tion-specific response. Genes that were differentially expressed in response to both the efg1ΔΔ/ cph1ΔΔ and ece1Δ/Δ strains, were considered to constitute a damage-specific response. The filamentation-specific response of IECs to C. albicans did not consist of many genes, though it PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 8 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Fig 4. The transcriptional response of IECs to C. albicans infection is largely damage- and filamentation-independent. (A) Principal component analysis for IEC RNA sequencing data. Samples from IECs (enterocytes) cultured in medium only cluster together across all time points and separately from those co-incubated with C. albicans. (B) KEGG categories significantly overrepresented among infection- specific DEGs in IECs. Gene ratio represents the proportion of genes within each category that were significantly differentially expressed during infection compared to IECs only. The color scale represents the mean Log2(fold-change) of significant DEGs within each category. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 9 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Red indicates that most DEGs showed increased expression during infection, blue indicates most DEGs showed decreased expression, and grey indicates a mix of DEGs with increased and decreased expression within the category. Dots are shown only for significantly enriched categories (overrepresentation analysis, Benjamin-Hochberg adjusted p-value � 0.05, S2 Table). (C) Significantly differentially expressed genes between ece1Δ/Δ and efg1ΔΔ/cph1ΔΔ C. albicans strains compared to their respective WT strains during infection of IECs. The color scale represents the Log2(fold-change) for each gene during infection in the mutant strain compared to the respective WT strain, with red representing higher expression in the mutant strain and blue representing lower expression in the mutant strain. An asterisk indicates the genes with a statistically significant difference (DESeq2 p < 0.05). The mean MRN values are given in S1 Table. (D) Release of IL-8 by C2BBe1 cells infected with C. albicans strains possessing varying damage potentials. The non-damaging efg1ΔΔ/cph1ΔΔ and ece1Δ/Δ strains induced less IL-8 release, though only significantly for the efg1ΔΔ/cph1ΔΔ strains. Conversely, the high-damaging cht2Δ/Δ strains induced significantly more IL-8 release. (E) NFκB activation as measured by the DNA-binding activity of the p65 transcription factor. Infection with all C. albicans strains increased the DNA-binding activity of p65 compared to the uninfected C2BBe1 cells, though not to a statistically significant degree for the efg1ΔΔ/cph1ΔΔ and ece1Δ/Δ strains. All values are shown as the mean with standard deviation. The IL-8 (D) and NFκB activation (E) data were compared using a one-way ANOVA with a post-hoc Sˇida´k’s multiple comparisons test. Statistical significance: , P � 0.05; , P � 0.01; , P � 0.001. https://doi.org/10.1371/journal.ppat.1012031.g004 included mitochondria-associated genes (COX7B, ND2, CYTB, ND4L) and some encoding heat-shock proteins (HSP90AA1 and HSP90AA6P), all with lower transcript levels in the non- filamentous mutant as compared to the WT (SC5314) (Fig 4C and S5 Fig). The damage-specific response of IECs comprises even fewer genes, which in most cases showed lower transcript levels in IECs infected with the ece1Δ/Δ and efg1ΔΔ/cph1ΔΔ mutants than with the respective WT strains. According to these data, IECs respond to C. albicans-medi- ated damage via upregulation of EGR1 and EGR2, the FOS genes FOSL1 and FOSB as well as the IL-8-encoding gene CXCL8 (Fig 4C and S5 Fig). c-Fos is a major component of the oral epi- thelial cell (OEC) response to C. albicans filamentation and damage [13, 14]. Similarly, EGR1 induction as well as IL-8 secretion are known responses to C. albicans infection in OECs [31]. To test how conserved the response of IECs and OECs to C. albicans and fungus-mediated damage are, we performed western blotting for the OEC response components EphA2 and EGFR (receptors), the transcription factor c-Fos, the phosphatase MKP1 and the kinases Akt and p38 [13,16,17,32]. We observed an infection-specific increase in phosphorylation of EphA2 and Akt, as well as an infection-specific increase in c-Fos protein levels. c-Fos signals were con- sistently reduced in the ece1Δ/Δ mutant. While there was also decreased phosphorylation of EphA2 for the ece1Δ/Δ mutant compared to the WT, this was not consistent for all replicates (S6A and S6B Fig). Thus, we see a partial conservation in the damage response during C. albi- cans infection of IECs compared to OECs, with ECE1-dependent induction of c-Fos and poten- tially phosphorylation of EphA2, but no EGFR or MKP1 phosphorylation and no change in the phosphorylation of p38 or Akt in the absence of ECE1 compared to the WT [14–16,33,34]. To verify that production of IL-8 in IECs is filamentation- and damage-dependent, we mea- sured IL-8 secretion in response to C. albicans infections. We compared the WT with the non- filamenting efg1ΔΔ/cph1ΔΔ strain, the non-damaging ece1Δ/Δ strain, and a previously identi- fied strain (cht2Δ/Δ) with increased damage of IECs [11] (Fig 4D). Indeed, the efg1ΔΔ/cph1ΔΔ and ece1Δ/Δ mutants elicited less IL-8 secretion while cht2Δ/Δ induced more IL-8 secretion than their respective WT strains. Overall these data show that while the response of IECs to C. albicans infection is largely independent of hypha formation and fungal-mediated damage, there is a damage-specific aspect via c-Fos induction and IL-8 secretion. Intestinal epithelial cells limit fungal damage and translocation via NFκB activation Most of the pathways overrepresented during infection with WT C. albicans were also found during infection with the non-damaging and non-filamentous strains (S2 Table), again sug- gesting that the IEC transcriptional response to C. albicans is largely independent of fungal filamentation or damage. Among other signaling pathways that were triggered by the initiation PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 10 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation of fungal-mediated damage (Fig 4B), DEGs were enriched in NFκB signaling after infection with all four C. albicans strains (S2 Table). Previous research has shown that C. albicans- induced NFκB activation in IECs limits the damage potential during infection [18]. We con- firmed the C. albicans-induced NFκB activation in IECs and additionally showed that infec- tion with C. albicans strains with high or low damage potential and even with a non- filamentous mutant elicit similar activation of NFκB by IECs (Fig 4E). This suggests that NFκB activation is a general epithelial response to the presence of C. albicans (Fig 4E). To determine whether the previously described NFκB-dependent limitation of IEC damage is predominantly host- or fungal-driven, IECs were infected with the non-filamentous efg1ΔΔ/ cph1ΔΔ and non-damaging ece1Δ/Δ strains in the presence and absence of the potent NFκB activation inhibitor quinazoline (QNZ) [18, 35]. In agreement with previous data, both tested WT strains elicited significantly more host-cell damage when NFκB activation was blocked (Fig 5A) [18]. In contrast, there was no statistically significant increase in damage caused by the strains lacking either ECE1 or both EFG1 and CPH1 (Fig 5A). Treatment of IECs with a DMSO vehicle control had no significant effect on either the damage potential or fungal trans- location for the WT (BWP17) and ece1Δ/Δ strains (S7A and S7B Fig). As host-cell damage of IECs is tightly linked to fungal translocation, the translocation rates of C. albicans in the presence of QNZ were also measured [11]. As expected from our host-cell damage data, blocking NFκB activation significantly increased the number of translocated fun- gal CFUs for both WT strains compared to the untreated controls (Fig 5B). The non-filamen- tous efg1ΔΔ/cph1ΔΔ strain showed no increase in its translocation rate, which was expected given that this strain cannot form hyphae or damage the host cells (Fig 5A and 5B) [11,36]. Surprisingly, despite showing no significant increase in host-cell damage upon QNZ-treat- ment [<50% of untreated WT (BWP17)], the ece1Δ/Δ strain showed a significant increase in translocation comparable to that of the WT (BWP17) under normal infection conditions (Fig 5B). This increased translocation in the absence of increased host-cell damage was confirmed for the ece1Δ/Δ strain with the use of another NFκB inhibitor with a different mode of action that directly inhibits the DNA-binding activity of the p65 subunit [37,38] (S8A and S8B Fig). These data together indicate that NFκB can limit C. albicans translocation, at least in part, independently of host-cell damage. We have previously shown that C. albicans translocation across IECs occurs mainly via a transcellular route and is associated with filamentation- and candidalysin-dependent necrotic host-cell damage [11]. The increased translocation of the ece1Δ/Δ strain without any increased host-cell damage upon NFκB inhibition suggests that NFκB activation likely limits a non-dam- aging route for translocation (Fig 5B). To test this hypothesis, the transepithelial electrical resistance (TEER) of IECs was measured with and without QNZ treatment as a metric for bar- rier integrity. After 24 h, IECs infected with both WT strains showed decreased barrier integ- rity upon QNZ treatment, though not to a statistically significant degree (Fig 5C) [WT (SC5314), P = 0.2466; WT (BWP17), P = 0.0824]. These results match the increased host-cell damage and translocation data for both strains. There was also a significant decrease in the barrier integrity for IECs infected with both, the efg1ΔΔ/cph1ΔΔ and ece1Δ/Δ strains upon QNZ treatment, again pointing to a host damage-independent effect of NFκB activation on translocation (Fig 5C). This was also confirmed for the WT and ece1Δ/Δ strains using the sec- ond NFκB inhibitor (S8A–S8C Fig). Non-damaging routes of fungal translocation that still reduce barrier integrity can involve induction of host-cell apoptosis or the use of paracellular routes with degradation of cell-cell connections such as tight junctions or adherens junctions [11]. As the NFκB signaling pathway can also induce the expression of anti-apoptotic genes and we observed an overrepresentation of infection-specific DEGs for IECs involved in apoptosis at 12 h when fungal-mediated PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 11 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Fig 5. NFκB activation limits ECE1-dependent damage and paracellular translocation. (A) Inhibition of NFκB activation using the high affinity NFκB activation inhibitor quinazoline (QNZ) increases the damage potential of C. albicans wildtype, but not for the non-filamentous efg1ΔΔ/cph1ΔΔ strain or the non-damaging ece1Δ/Δ strain towards C2BBe1 cells after 24 h. LDH release was adjusted by subtracting the release from uninfected and untreated host cells. (B) Inhibition of NFκB activation increases fungal translocation of C. albicans after 24 h across intestinal epithelial cells independent of damage potential. (C) Blocking NFκB activation significantly increases the breakdown of barrier integrity after 24 h of the intestinal epithelial cell layer for efg1ΔΔ/cph1ΔΔ and ece1Δ/ Δ, with a similar but not significant effect on both WT strains. (D) E-cadherin protein levels normalized to GAPDH and presented relative to levels in untreated C2BBe1 cells. QNZ treatment further increased degradation of E-cadherin during infection with WT (SC5314) and ece1Δ/Δ. (E) Fluorescent labeling PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 12 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation of E-cadherin during C. albicans infection with and without QNZ treatment (scale bar = 50 μm). Inhibition of NFκB activation decreased the organization of E-cadherin at IEC borders upon infection with the ece1Δ/Δ and both WT strains. Representative pictures for each strain and treatment condition are shown from 3 biological replicates with median score values in the inset for each strain and condition. All values are shown as the mean with standard deviation. Host- cell damage (A), fungal translocation (B), and barrier integrity (C) data were compared using a one-way ANOVA. Statistical significance for host-cell damage (A) and fungal translocation (B) data was determined with a post-hoc Tukey’s multiple comparisons test, while significance for the barrier integrity (C) data was determined using a post-hoc Sˇida´k’s multiple comparisons test. Statistical significance: , P � 0.01; ***, P � 0.0001. https://doi.org/10.1371/journal.ppat.1012031.g005 damage begins (Fig 4B) [39]. We therefore tested whether blocking NFκB activation during infection with C. albicans increases apoptosis or other types of programmed cell death in IECs. IECs were left untreated or treated with QNZ, a pan-caspase inhibitor (Z-VAD), or both, and were then infected with different C. albicans strains. Treatment with Z-VAD alone had no sig- nificant effects on host cells damage, loss of barrier integrity, or fungal translocation (S9A–S9C Fig). Similarly for IECs with blocked NFκB activation, addition of Z-VAD led to no significant differences with any of the strains tested (S8A–S8C Fig). Together, these data suggest that the ability of IECs to limit cellular damage and translocation through NFκB activation is depen- dent on the filamentation and damage potential of C. albicans itself, and does not rely on increased apoptosis or other forms of programmed cell death. To finally test whether the decreased barrier integrity during infection with the ece1Δ/Δ strain was associated with increased degradation of cell-cell junction proteins consistent with increased paracellular translocation, epithelial barriers were stained for E-cadherin 24 h after infection with and without QNZ treatment. Micrographs from three biological replicates were then scored by a blinded observer for the consistency of the cell-cell borders and overall orga- nization of the E-cadherin staining. Non-inhibitor treated uninfected and efg1ΔΔ/cph1ΔΔ- infected IECs showed a uniform staining of E-cadherin, indicating an intact host cell layer (Fig 5C and 5E). In contrast, untreated cells infected with either WT strain or the ece1Δ/Δ strain showed a weaker staining (Fig 5E). Inhibitor treatment further decreased the E-cadherin signal in IECs infected with either of the two WT strains, as expected from the inhibitor- dependent increase in damage (Fig 5A). Importantly, the ece1Δ/Δ strain showed less intact host cells and decreased fluorescence upon NFκB activation inhibition, while the host cell layer was largely unchanged for the uninfected and efg1ΔΔ/cph1ΔΔ-infected IECs when treated with QNZ (Fig 5E). These data indicate that NFκB limits paracellular translocation by stabiliz- ing cell-cell junction proteins like E-cadherin. They also suggest that, upon NFκB inhibition, the physical presence of hyphae can break down intercellular barrier function even in the absence of toxin-mediated damage. To further investigate the effects of NFκB inhibition on cell-cell junction proteins at a molecu- lar level, we measured the junction proteins E-cadherin and claudin-1 by western blotting for infected IECs with and without QNZ treatment (S10A Fig). Western blotting showed that the total protein levels of E-cadherin and claudin-1 (normalized to GAPDH protein levels) in C. albi- cans infected IECs were largely unaffected by NFκB inhibition (Figs 5D and S10B Fig). There was no consistent difference in E-cadherin levels upon inhibitor treatment during infection with the WT (BWP17) or efg1ΔΔ/cph1ΔΔ strains. However, relative E-cadherin levels upon treatment were reduced for IECs infected with the WT (SC5314) and ece1Δ/Δ strains, though not to a statis- tically significant degree (Fig 5D). The same trend was true for the ece1Δ/Δ and efg1ΔΔ/cph1ΔΔ strains for claudin-1 (S10B Fig). Though the decrease in both E-cadherin and claudin-1 for the ece1Δ/Δ strain was not significant, it did correlate with the decrease in barrier integrity (Fig 5C) and is also consistent with the organization of the cell-cell junction proteins (Fig 5E). In summary, these data show that NFκB activation limits ECE1-mediated host-cell damage and independently limits fungal translocation. It does do by maintaining cell-cell junctions and the epithelial barrier integrity in the absence of direct fungal-mediated host-cell damage. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 13 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Discussion In this study, we explored fungal and host factors that contribute to C. albicans translocation across intestinal epithelial cell layers, a process which precedes systemic infections. Dual-spe- cies RNA sequencing of C. albicans and human cells revealed the contribution of fungal zinc acquisition to host-cell damage. We confirmed a partially conserved damage-specific response of IECs to C. albicans infection and dissected the role of NFκB activation in limiting fungal- mediated damage and translocation. The C. albicans infection-specific transcriptional response was largely time dependent, with most DEGs appearing at the later stages of IEC infection. This was expected as the time points chosen are associated with different stages of epithelial infection: from adhesion, hypha forma- tion, and invasion to host-cell damage and fungal translocation [11]. There was no enrichment of known filamentation- or virulence-related terms at time points later than 45 min. This is likely due to the fact that the expression of many C. albicans virulence factors is associated with the yeast-to-hypha transition [40,41]. In our experimental setup, there were strong trig- gers for filamentation in both the infected and medium-only control samples, and hyphae were present at all experimental time points from 3 to 24 h. Transcriptional changes due to the switch from YPD to DMEM medium may account for some similarities in the transcriptional profiles between the infected and control samples at the earlier timepoints. However, the simi- lar pre-culture conditions have been shown previously to play no major role in the gene expression pattern of C. albicans already after 30 min [42]. At around 6 h when epithelial inva- sion occurs, we however observed an infection-specific increase in transcript levels for genes involved in ergosterol biosynthesis. This may be due to subtle physiological changes that sup- port invasion and damage of IECs, as ergosterol biosynthesis is linked to filamentation in C. albicans [43]. This is followed by infection-specific changes in the transcript levels of cell adhe- sion genes at 12 h. These data suggest that the interaction with IECs induces C. albicans hyphae that are physiologically distinct to hyphae grown without any contact to host cells. Taken together, our data along with that of previous studies indicate that while many different envi- ronmental conditions trigger morphologically similar hyphae in C. albicans, their precise molecular compositions depend on the growth conditions in which they form [44,45]. The majority of infection-specific changes in the fungal transcriptome were metabolic in nature. Our data show faster glucose consumption during IEC infection, indicated by decreased transcript levels of glycolysis genes after 12 h. This was unsurprising as both fungus and host compete for glucose during infection. In line with this, previous data showed glucose levels were lower during IEC infection by C. albicans after 12 h compared to the fungus grown in medium alone [46]. C. albicans further adjusted its metabolism during infection by altering the transcript levels of genes involved in sugar transport and in amino acid metabolism. After 24 h, when a large portion of the epithelial cells was damaged, there were further fungal meta- bolic adaptations, especially increased mRNA levels of genes involved in glycolysis and galac- tose metabolism. As transcription of galactose-metabolic GAL genes is induced in the absence of glucose [47], this may suggest that C. albicans gains access to new non-glucose carbon sources which are released due to extensive host-cell damage. It has been hypothesized that ECE1/candidalysin-mediated cytolysis may be necessary for nutrient acquisition from host cells during fungal infection [48,49]. Our study provides evi- dence in support of this hypothesis by showing an ECE1-dependent alleviation of zinc starva- tion during epithelial invasion. This suggests that invasion may contribute to zinc acquisition from the host. Zinc is a vital micronutrient for both pathogen and host alike. The availability of zinc for C. albicans within the host is limited during systemic infection and oropharyngeal candidiasis due to nutritional immunity [50–53]. Depending on the host niche, C. albicans can PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 14 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation also encounter high and potentially toxic levels of zinc, such as following phagocytosis by immune cells. While C. albicans is limited for zinc during systemic infection, the fungus does not experience severe zinc starvation during infection of IECs [50,51]. Our observations indicate that C. albicans faces a nutritionally complex environment dur- ing IEC infection. In general, the transcript levels of the zinc uptake genes ZRT101, ZRT2, and PRA1 were higher in medium alone than during IEC co-culture. This shows that C. albicans experiences less zinc starvation in presence of IECs and suggests that it can access zinc from the host. Deletion of either component of the Pra1/Zrt101 zincophore system significantly reduced the capacity of C. albicans to damage IECs. The zrt2Δ/Δ mutant was unaffected, and the zrt101ΔΔ/zrt2ΔΔ, which is defective in both known zinc uptake pathways, caused no dam- age. This suggests that C. albicans primarily utilizes the Pra1/Zrt101 system to acquire zinc from IECs, and that the Zrt2 importer can play an important role in the absence of a functional zincophore. In line with this, zinc supplementation of the media fully restored growth and damage potential of zinc uptake-defective strains. In the absence of exogenous zinc supplementation, the only zinc source for C. albicans is the host epithelium itself. In line with this, preventing host cell cytolysis by ECE1 deletion resulted in a greatly amplified zinc starvation response, which could again be partially allevi- ated by exogenous zinc supplementation. These results also fit well with clinical manifestations of candidemia. While the human gastrointestinal tract normally contains high levels of zinc in various complexed forms, zinc deficiency is recognized as a significant risk factor for patients in the pediatric intensive care unit, and oral zinc supplementation is recommended as a pro- phylaxis [54–56]. Imbalanced zinc homeostasis within the intestine is also associated with microbial dysbiosis and other intestinal diseases that impair the barrier function of the intesti- nal epithelium, such as inflammatory bowel disease, irritable bowel syndrome and colorectal cancer [57]. In our in vitro models of IEC infection, loss of ECE1 did not affect fungal growth per se and thus addition of exogenous zinc was not sufficient to restore normal host-cell dam- age or fungal translocation despite the partially alleviated starvation response. This could be due to increased sensitivity of our transcriptional analysis in our experimental model or com- pensatory activities in zinc mobilization of fungal cells that are sufficient to allow for normal growth. Additionally, most fungal material in our models grows atop the epithelial cells and not invasively. Thus, more complex model systems for prolonged intestinal epithelial invasion would be necessary to determine the contribution of Ece1 to nutrient acquisition from the host, especially the physiological relevance of ECE1-mediated zinc acquisition. Interestingly, zrc1Δ/Δ exhibited the most severe defect in invasive growth and IEC translo- cation; it also caused much less damage than the WT, which was not reversed upon zinc sup- plementation. The primary function of Zrc1 is to detoxify fungal cytoplasmic zinc via compartmentalization within vesicular-like structures called zincosomes [28]. This, together with the reduced expression of zinc uptake genes by C. albicans on IECs suggests that the fun- gus requires either Zrt101/Pra1 or Zrt2 to capture zinc from the host, but that once internal- ized, the zinc detoxification system is crucial for invasion, host-cell damage, and fungal translocation. The epithelial infection-specific transcriptional response was also dependent on the time point, though to a lesser degree than that of C. albicans. We observed increased transcript lev- els of mitochondria-associated genes. As this has been shown previously in vaginal epithelial cells as well, it indicates a potentially conserved early epithelial response to fungal infections via mitochondrial signaling [19]. The infection-specific host response at later time points was characterized by MAPK, TNF, and NFκB signaling consistent with earlier findings [18]. Using our data, we were able to dissect the conserved specificity of this response in a time-resolved manner and to identify additional aspects of the IEC response and its influence on the PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 15 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation interaction with C. albicans. At 12 h when host-cell damage and fungal translocation begin, our data show that there is a limited damage- and filamentation-specific transcriptional response to C. albicans infection. We could, however, show that IECs possess a damage-spe- cific response to C. albicans partially conserved with other epithelial cell types [13–17]. Specifi- cally, IECs show increased expression of FOSB, FOSL1, and CXCL8 in the presence of filamentation and candidalysin. Furthermore, IECs show induction of c-Fos and increased phosphorylation of EphA2 and Akt, though this was largely independent of host-cell damage. This indicates that IECs possess a conserved, albeit reduced, damage-specific response to C. albicans infection compared to OECs. This is further exemplified by IL-8 secretion of IECs which correlates with the damage potential of the infecting C. albicans strain, but is much lower overall as compared to oral and vaginal epithelial cells [19,31]. Our data show that the pathways induced in IECs are largely independent of C. albicans host-cell damage and filamentation. Activation of the NFκB signaling pathway in IECs was common for all C. albicans strains tested, and it has already been shown to limit the damage potential of C. albicans [18]. We show that this effect on host-cell damage is dependent on the ECE1 gene, as inhibiting NFκB activation had no significant effect on the damage potential of the ece1Δ/Δ strain. Blocking NFκB activation also increased fungal translocation, though this was independent of ECE1, suggesting that NFκB activation limits fungal-mediated damage and fungal translocation via separate mechanisms. We first hypothesized that NFκB activation could limit fungal-mediated damage by inducing expression of anti-apoptosis genes and pre- venting programmed cell death [39]. However, a pan-caspase inhibitor showed no effect on the damage potential of C. albicans and did not counteract the increased host-cell damage when NFκB activation was inhibited. These data suggest that NFκB activation upon C. albicans infection does not prevent apoptosis or other programmed cell death pathways. The specific mechanism by which NFκB signaling limits fungal-mediated damage remains undetermined, but may involve recently described membrane repair mechanisms of epithelial cells [58,59]. We found increased translocation upon inhibition of NFκB activation to be associated with increased breakdown of the epithelial barrier integrity, independent of fungal host-cell damage potential. This contrasts with an earlier study in which NFκB inhibition did not influence IEC barrier breakdown by C. albicans [18]. This discrepancy could be attributed to the different experimental conditions between the previous study and ours. While in our study, there were low remaining levels of transepithelial electrical resistance detected after 24 h of C. albicans infection in the absence of an NFκB inhibitor, no transepithelial electrical resistance measure- ment could be detected at this time point in the previous study [18]. This could be due to dif- ferent differentiation times for the IECs, cell culture media, pore sizes of the transwell membranes, or the MOIs used for infection. Our results rather suggest that NFκB activation may limit fungal translocation via the paracellular route [6]. We found the protective function of NFκB signaling on the barrier integrity during C. albi- cans infection to be associated with reduced degradation of cell-cell junction proteins. Under normal experimental conditions, C. albicans strains that filament and produce sufficient amounts of candidalysin are likely able to overcome this and translocate following necrotic host-cell death [11]. Strains with low-damage potential, like ece1Δ/Δ, are unable to take this route and are thus significantly diminished in their ability to translocate. In this study, we used immortalized intestinal cell lines as a model to dissect the specific cellular mechanisms behind fungal translocation. Future experiments with more complex in vitro model systems that incorporate more physiological factors, like organ-on-chip or intestinal organoids [60–62], will help to understand these interactions better. Nevertheless, our findings provide mechanis- tic insights into how conditions or treatments that alter activation of NFκB could put patients at risk for systemic candidiasis, such as those undergoing treatment with steroids [1,2,39,63]. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 16 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Host-cell damage and efficient translocation across the intestinal epithelium by C. albicans are dependent on candidalysin, though both processes also require other fungal factors [11,12]. Our data connect zinc acquisition from intestinal epithelial cells to fungal growth and host-cell damage during infection, and reveal a limited damage-dependent response of intesti- nal epithelial cells to C. albicans. Activation of NFκB signaling in intestinal epithelial cells increased the barrier integrity during infection, which limited fungal translocation especially for C. albicans strains with low damage potential. This suggests that host defense mechanisms at the intestinal epithelium limit paracellular translocation, forcing C. albicans towards the damage-mediated transcellular path to overcome the intestinal barrier. Materials and methods Candida albicans strains and growth conditions C. albicans strains used in this study are shown in Table 1. The WT strains SC5314 and BWP17+CIp30 are referred to as WT (SC5314) and WT (BWP17), respectively. Strains were routinely cultivated on/in YPD agar/broth (1% yeast extract, 2% peptone, 2% D-glucose with or without 1.5% agar) at 30˚C. Overnight (O/N) cultures were cultured for 16 h in YPD broth at 30˚C with shaking at 180 rpm unless otherwise specified. Cells were then washed twice with phosphate-buffered saline (PBS) and the cell number was adjusted. For experiments with prior zinc starvation, C. albicans strains were grown for 24 h in YPD liquid medium supplemented with 1 mM ZnSO4 at 30˚C with shaking at 180 rpm. The OD600 was then adjusted to 0.5 in SD liquid medium supplemented with 1 mM ZnSO4 and incubated at 30˚C for 24 h with shaking at 180 rpm. The cells were then washed twice with 1 mM EDTA, washed twice with water, and then diluted to an OD600 of 0.5 in Zn-free SD liquid medium in acid-washed, plastic flasks. The fungal strains were then incubated for another 24 h at 30˚C with shaking at 180 rpm. Culture of intestinal cells The intestinal epithelial Caco-2 brush border expressing 1 cell line (C2BBe1; ATCC, CRL2102) [68] and the human intestinal goblet cell line (HT29-MTX; ATCC, HTB-38; CLS, Lot No. 13B021) were routinely cultivated in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco, Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (FBS) (Bio&Sell), 10μg/ Table 1. C. albicans strains used in this study. C. albicans strains SC5314 BWP17+CIp30 SN250 efg1ΔΔ/cph1ΔΔ ece1Δ/Δ zrt101Δ/Δ zrt2Δ/Δ zrc1Δ/Δ pra1Δ/Δ zrt101ΔΔ/zrt2ΔΔ cht2Δ/Δ Parental strain SC5314 BWP17 BWP17 BWP17 BWP17 BWP17 BWP17 SN125 Relevant genotype Source wild type, clinical isolate ura3::λimm434/ura3::λimm434 his1::hisG/his1::hisG arg4::hisG/arg4:: hisG +CIp30 Noble wild type (leu2::CdHIS1/leu2::CmLEU2) efg1::FRT/efg1::FRT cph1::FRT/cph1::FRT ece1::HIS1/ece1::ARG4 RPS1/rps1::URA3 zrt101::HIS1/zrt101::ARG4 +CIp10 zrt2::HIS1/zrt2::ARG4 +CIp10 zrc1::HIS1/zrc1::ARG4 +CIp10 pra1::HIS1/pra1::ARG4 +CIp10 zrt1::HIS1/zrt1::ARG4 zrt2::FRT/zrt2::FRT +CIp10 cht2::HIS1/cht2::LEU2 [64] [65] [66] [67] [14] [27] [28] [28] [27] [28] [66] https://doi.org/10.1371/journal.ppat.1012031.t001 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 17 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation ml Holotransferrin (Calbiochem, Merck), and 1% non-essential amino acids (Gibco, Thermo Fisher Scientific) at 37˚C with 5% CO2 for no longer than 15 passages. C2BBe1 cells were seeded in 6-well plates at a concentration of 5×105 cells/well for RNA isolation. C2BBe1 and HT29-MTX cells were seeded in 96-well plates and transwell inserts (polycarbonate membrane with 5 μm pores; Corning) at a 70:30 ratio (C2BBe1:HT29-MTX) and a total concentration of 2×104 cells/well or insert for damage and translocation assays, respectively. For adhesion and invasion assays, C2BBe1 and HT29-MTX cells were seeded in 24-well plates with coverslips at a 70:30 ratio (C2BBe1:HT29-MTX) at a total concentration of 1×105 cells/well. All well plates and transwell inserts were coated with collagen I (10 μg/ml for 2 h at room temperature; Thermo Fisher Scientific) and maintained for 12 d at 100% confluency for differentiation with regular medium exchange before infection. Just prior to infection with C. albicans, the medium was removed and fresh DMEM without FBS, Holotransferrin, or 1% non-essential amino acids was added to the cells. RNA isolation C2BBe1 cells were cultured for 12 d in collagen-coated 6-well plates. To prepare samples for dual-RNA sequencing and qPCR, the differentiated intestinal epithelial cells (IECs) were infected with 2×106 Candida cells/well and incubated at 37˚C and 5% CO2 for 0.75, 3, 6, 12, or 24 h in serum-free DMEM medium. For qPCR samples with added zinc, the fungal and C22Be1 cells were co-incubated in serum-free DMEM with 25 μM ZnSO4 for 24 h. In parallel, C2BBe1 and C. albicans cells were cultured individually in 6-well plates in serum-free DMEM medium. C. albicans cells from the O/N cultures were collected as uninfected controls for the fungus (0 h C. albicans), and uninfected IECs were harvested as well (0 h IECs). At each respective time point, the medium was removed, unadhered Candida cells were washed away, and 650 μl of RLT buffer (RNeasy Minikit; Qiagen) was added to each well. The plates were frozen with liquid nitrogen and thawed at room temperature (RT). Each well was scraped and the suspensions were centrifuged for 8 min (20,000×g) to pellet the Candida cells. The super- natant was removed and the host RNA was isolated using the RNeasy Minikit (Qiagen) according to the manufacturer’s instructions. The fungal RNA was isolated from the pellet using a freeze-thaw method described previously [69]. RNA concentrations were measured using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific) and the quality was controlled using a 2100 Bioanalyzer (Agilent Technologies). Fungal RNA used for q-RT-PCR was isolated as described previously with minor modifica- tions [69]. Briefly, fungal pellets were resuspended in AE buffer (50 mM Na-acetate pH 5.3, 10 mM EDTA) with 0.5% SDS and transferred to screw cap tubes with acid-washed glass beads. An equal volume of phenol/chloroform/isoamylalcohol (25:24:1) was added and the fungal cells were lysed using a FastPrep-24 5GMP Biomedicals. AE buffer was added and the samples were centrifuged for 10 min (20,000×g, 4˚C). The aqueous phase was transferred to a PLG- heavy tube (QuantaBio), re-extracted with an equal volume of phenol/chloroform/isoamylal- cohol, and centrifuged for 5 min (20,000×g, 4˚C). The RNA was then precipitated overnight at -20˚C with pure ethanol and sodium acetate. The RNA concentration was measured using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific) and samples were stored at -70˚C. Fungal gDNA isolation Differentiated C2BBe1:HT29-MTX cells in 24-well plates were infected with 4×105 C. albicans cells and incubated at 37˚C with 5% CO2 for 24 h in serum-free DMEM with or without 25 μM ZnSO4. The medium was removed, the host cells were lysed with 350 μl RLT buffer PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 18 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation (RNeasy Minikit: Qiagen), and the fungal cells were spun down. Fungal gDNA was isolated as previously published with the following modifications [70]. The fungal pellet was washed with water once. Equal volumes of lysis buffer (2% Triton X-100; 1% SDS; 100 mM NaCl; 10 mM Tris, pH 8; 1 mM EDTA) and phenol:chloroform:isoamylalcohol (25:24:1) were added and the samples were transferred to tubes with acid-washed glass beads. The fungal cells were dis- rupted using a FastPrep-24 5G (MP Biomedicals). Half the total volume of TE buffer (10 mM Tris; 1 mM EDTA; pH 7.5) was added. Nucleic acid was precipitated from the aqueous phase and resuspended in 400 μl TE buffer following RNase A treatment (10 mg/ml; Sigma) for 30 min at 37˚C. gDNA was precipitated in pure ethanol, washed and resuspended in 25 μl nucle- ase-free water. cDNA synthesis and q-RT-PCR Isolated RNA (1μg) was treated with DNase I (Baseline-ZERO DNase, Lucigen) according to the manufacturer’s instructions and then transcribed into complementary DNA using 50μM of oligo (dT)12-18 primer (Invitrogen), 150 U of Superscript III Reverse Transcriptase (Invitro- gen), and 10 U RNase OUT (Invitrogen). The cDNA was then diluted 1:5 and used for q- RT-PCR with the GoTaq qPCR Master Mix (Promega) in a CFX96 thermocycler (BioRad). The expression levels were normalized against the C. albicans ACT1 gene. For fungal gDNA samples, EFB1 was amplified from 1μl of template with qPCR Master Mix (Promega) in a CFX Opus Real-Time PCR System (BioRad). All primers used are listed in Table 2. The relative gDNA for each sample was calculated relative to the untreated WT (BWP17) for the respective biological replicate. RNA sequencing and transcriptional analysis Individual C. albicans and human C2BBe1 samples were combined for dual-RNA sequencing for uninfected isolated samples for time course data. Both C. albicans and Homo sapiens reads were present in infected samples. C2BBe1 reads were present in infection data for mutant vs. WT comparison. Library preparation and RNA sequencing were carried out at Eurofins Genomics GmbH (Ebersberg, Germany) using the Illumina HiSeq 2500 platform (time course analysis) or Table 2. Primers used for q-RT-PCR. Name ZRT101 fwd ZRT101 rev ZRT2 fwd ZRT2 rev ZRC1 fwd ZRC1 rev PRA1 fwd PRA1 rev ACT1 fwd ACT1 rev ZRT3 fwd ZRT3 rev EFB1 fwd EFB1 rev https://doi.org/10.1371/journal.ppat.1012031.t002 Sequence 5’ to 3’ TCGAAGGTTTGGCTTTGTCT CTCATGAGCAACATTCCCAA CAACTACCAATTGGGCCAGA GCTCCCCAACACATGACAAA TTTAGTACGTAAAGCCCTGAG TCTTGGTCTTGGTCTTGTTCT CATTACGCTGACACTTATGAGG ATGTGTGTGGCAATGCAGGT TCAGACCAGCTGATTTAGGTTTG GTGAACAATGGATGGACCAG AGGGGGATTATCTCAACCTTT CCACCAAATGAAACACTACTACC CGAAATGGAAGGTTTGACTTGG ACAGCAGCTTGTAAGTCATCC Gene ZRT101 ZRT101 ZRT2 ZRT2 ZRC1 ZRC1 PRA1 PRA1 ACT1 ACT1 ZRT3 ZRT3 EFB1 EFB1 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 19 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Illumina HiSeq 4000 platform (C. albicans mutant to WT comparisons). For library prepara- tion, after poly(A) enrichment, mRNA was fragmented and random-primed cDNA synthesis was performed followed by adaptor ligation and adaptor-specific PCR amplification. Single sequence reads of 50 bp were produced. Preprocessing of raw reads including quality control and gene abundance estimation was done with the GEO2RNaseq pipeline (v0.100.3) [71] in R (version 3.6.3). Quality analysis was done with FastQC (v0.11.5) before and after trimming. Read-quality trimming was done with Trimmomatic (v0.36). Reads were rRNA-filtered using SortMeRNA (v2.1) with a single rRNA database combining all rRNA databases shipped with SortMeRNA. Reference annotation was created by extracting and combining exon features from corresponding annotation files. Reads were mapped against the joined reference genomes of H. sapiens (Ensembl_GRCh38) and C. albicans (C_albicans_SC5314_version_A22) using HiSat2 (v2.1.0, single end mode). Gene abundance estimation was done with featureCounts (v2.0.1) in single-end mode with default parameters. MultiQC version 1.7 was finally used to summarize and assess the quality of the output of FastQC, Trimmomatic, HiSat, featureCounts and SAMtools. The count matri- ces with gene abundance data without and with median-of-ratios normalization (MRN) were extracted. Raw files are accessible under the Gene Expression Omnibus accession number GSE237496 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237496) [72]. Differential gene expression was analyzed using GEO2RNaseq. Pairwise tests were per- formed using four statistical tools (DESeq2 v1.26.0) to report p values and multiple testing cor- rected p values using the false-discovery rate method q = FDR(p). Mean MRN values were computed per test per group including corresponding log2 of fold-changes. Gene expression differences were considered significant if they were reported significant by DESeq2 (q � 0.05) and \|log2(fold-change[MRN based])\| � 1. Functional enrichment analysis was performed using overrepresentation analysis. Gene ontology (Biological process) for C. albicans was performed by parsing data gene lists to the web application of FungiDB for gene annotation and subsequent analysis using default param- eters (https://fungidb.org) [73]. Enrichment for human reads against KEGG pathways were computed using g:Profiler from within R (package gprofiler2, v0.2.1). Enriched terms were dis- carded if background size was below 3 or above 1000 genes. A bash script was used to call the Revigo web app for removing redundant terms (reduction factor = 0.5, removal of obsolete terms = yes) [74]. KEGG terms were further filtered to exclude coincidental significant hits that were not relevant for the current study (virus-related categories, “Organismal systems” except “Immune system”, “Human diseases”, “Drug development”). Programming code and data necessary to generate plots shown in this manuscript were deposited at Github: https:// github.com/SchSascha/Cal_Translocation. Quantification of adhesion, invasion, and hyphal length Differentiated C2BBe1:HT29-MTX cells in 24-well plates with glass coverslips were infected with 1×105 C. albicans cells and incubated at 37˚C with 5% CO2 for 1 h (adhesion) or 6 h (inva- sion). For adhesion, samples were fixed with 4% formaldehyde and washed twice with PBS. Adherent C. albicans cells were stained with 10 μg/ml Calcofluor white (CFW) (Fluorescent Brightener, Sigma-Aldrich) in 100 mM TRIS-HCl (pH 9.5) for 30 min at RT. After washing with water, samples were mounted on a glass slide with mounting medium (ProLong Gold Antifade, Invitrogen) and imaged. For each replicate, 12 representative images were counted and the mean number of adherent cells in a defined area was calculated from four independent replicates. For quantification of invasion, extracellular C. albicans hyphae were stained with CFW as stated above. The host cells were then permeabilized with 0.5% Triton X-100 for 5 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 20 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation min and washed with PBS. Intracellular C. albicans hyphae were first labelled with 20–25 μg/ ml rabbit anti-C. albicans antibody (Acris) for 3 h at 4˚C. After washing with PBS, 4 μg/ml goat anti-rabbit antibody conjugated to AlexaFluor 488 was added and incubated at 37˚C for 1 h. The samples were washed with PBS and mounted as mentioned above. The number of inva- sive hyphae was determined from 100 hyphae counted per sample. All samples were imaged using an Axio Observer fluorescence microscope (Zeiss). To quantify hyphal length, 2×104 C. albicans cells were seeded per well in a 96-well plate in DMEM and incubated for 3 h at 37˚C with 5% CO2. Images were taken using a Cell Discoverer 7 microscope (Zeiss) and the hyphal length was measured using Zeiss Zen3.4 (blue edition). Quantification of host cytotoxicity (LDH assay) Differentiated C2BBe1:HT29-MTX cells in 96-well plates were infected with 8×104 C. albicans cells and incubated at 37˚C with 5% CO2 for 24 h. Epithelial damage was quantified by release of lactate dehydrogenase (LDH) from IECs. LDH concentrations were measured using a cyto- toxicity detection kit (Roche) according to the manufacturer’s instructions and LDH from rab- bit muscle (Roche) was used to generate a standard curve. The baseline LDH concentration of uninfected and untreated IECs was subtracted and the corrected LDH release is shown as a concentration (ng/ml) present in the supernatant unless otherwise stated. In vitro translocation assay Differentiated C2BBe1 cells or a mix of C2BBe1:HT29-MTX cells in transwell inserts were infected with 1×105 C. albicans cells and incubated at 37˚C with 5% CO2. Supernatant from the upper compartment was removed 24 hpi and used for LDH measurement as described above. The lower compartment was treated with 20 U/ml zymolyase (Amsbio) for 2 h at 37˚C and 5% CO2 to detach translocated hyphae. The zymolyase-treated hyphae were then plated on YPD agar, incubated at 30˚C for 2 d, and the colony forming units (CFUs) were counted. For experiments with NFκB inhibition, C2BBe1 cells were treated with DMEM with 2.5 μM 6-Amino-4-(4-phenoxyphenylethylamino)quinazoline (QNZ) (EVP4593; Sigma-Aldrich) or either 2.5 or 5 μM N-(6-benzoyl-1H-benzo[d]imidazol-2-yl)-2-(1-(thieno[3,2-d]pyrimidin- 4-yl)piperidin-4-yl)thiazole-4-carboxamide (SC75741) (MedChemExpress) in the lower com- partment for 1 h prior to infection at 37˚C with 5% CO2. IECs were then infected as above. After 24 h the trans-epithelial electrical resistance (TEER) was measured using a volt-ohm meter (EVOM2, World Precision Instruments), supernatants from the upper compartment were collected for LDH measurements, the lower compartment was treated as above to mea- sure CFUs, and the transwell membranes were removed for staining. IECs were fixed with His- tofix (Roth) for 10 min at 37˚C then washed with PBS. The cells were permeabilized with 0.5% TritonX-100 for 10 min at RT and washed with PBS. Cells were blocked with 2% BSA for 10 min at 37˚C, washed with PBS, and incubated with a primary mouse anti-E-cadherin antibody (10 μg/ml) (BD Biosciences) for 4 h at 4˚C, then washed with 2% BSA. Cells were then stained with a secondary goat anti-mouse antibody conjugated to AlexaFluor 488 (10 μg/ml) (Invitro- gen). Samples were washed with PBS, mounted on glass slides, and images with an Axio Observer fluorescence microscope (Zeiss). Samples were prepared from 3 independent biolog- ical replicates and 3 images were taken per strain per condition for each replicate. Images were randomized and scored by a blinded observer. Scores ranged from 1 to 5, with a score of 1 rep- resenting a sample with consistent, organized staining of E-cadherin at cell-cell borders throughout the sample. A score of 5 was assigned to samples with no visible staining of E-cad- herin at cell-cell junctions. For experiments using the pan-caspase inhibitor (Z-VAD), host PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 21 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation cells in transwell inserts were treated for 1 h prior to infection with 25 μM of the inhibitor alone or in combination with 2.5 μM of QNZ as described above. For experiments with added zinc, the number of fungal cells was adjusted in either DMEM or DMEM + 25 μM ZnSO4. Differentiated C2BBe1:HT29-MTX cells in transwell inserts were infected as above, with either DMEM or DMEM + 25 μM ZnSO4 in the transwell insert and DMEM in the lower compartment. After 24 h incubation at 37˚C with 5% CO2, the translo- cated fungal CFUs were plated as described above. Immunoblotting For detection of junction proteins with and without NFκB inhibition, C2BBe1 cells were infected as mentioned for the in vitro translocation assay with and without the NFκB inhibitor QNZ and incubated for 24 h at 37˚C with 5% CO2. The transwell membranes were then removed, the host cells were scraped off, and spun down for 5 min at 4˚C at 500×g. The pellets were lysed in RIPA lysis buffer (VWR) supplemented with a protease inhibitor mix (Roche) and Benzonase (Millipore) and chilled for 15 min on ice. The samples were spun down for 15 min at 4˚C at 18,000×g. The supernatants were collected and stored at -70˚C. The protein con- centrations of total protein extracts were measured by a Lowry protein assay kit (Bio-Rad Lab- oratories GmbH). 25 μg of each sample was used for a standard 12% SDS polyacrylamide gel electrophoresis (SDS-PAGE) at 30 mA and 200 V. Therefore, lysates were supplemented with SDS sample buffer (50 mM Tris/HCl, 2% (v/v) glycerol, 2% (v/v) β-mercaptoethanol, 1.6% (w/ v) SDS, 0.004% (w/v) Serva Blue G-250, pH 6.8) and boiled for 10 min at 95˚C. After SDS-PAGE with subsequent blotting of the samples onto an Amersham Protran 0.45 μm nitrocellulose membrane at 1.8 mA/cm2 and 25 V, blots were incubated with 5% (w/v) milk dissolved in TBS (50 mM Tris/HCl, 140 mM NaCl, pH 7.2) for 1 h at room temperature. For specific, immunologic labeling, primary antibodies against E-Cadherin (1:1000, MAB1838, R&D Systems), Claudin-1 D5H1D (1:1000, 13255, Cell Signaling Technology), and GAPDH D16H11 (1:1000, 5174, Cell Signaling Technology) were used in 5% (w/v) milk/TBS for 3 h at room temperature or 16–48 h at 4˚C. After 3 times washing in TBS-T (TBS supplemented with 0.05% (v/v) Tween-20), membranes were incubated for 1.5–3 h with IRDye 800CW donkey anti-mouse or donkey anti-rabbit (1:10000, 925–32212 and 925–32213, LI-COR GmbH) as secondary antibodies, respectively. Immunological detections were carried out using an Odys- sey M imaging system (LI-COR GmbH). Blots were analyzed by Image Studio Lite Version 5.0 (LI-COR GmbH). For detection of immune signaling proteins, C2BBe1 cells were seeded in 6-well plates and infected with WT (BWP17) and ece1Δ/Δ C. albicans as described above for RNA isolation experiments. After 6 h of infection, tissue culture plates were placed on ice, the medium was removed, and the cells were washed with ice-cold PBS. Cells were lysed with 120 μL of RIPA buffer (25 mM Tris-HCl pH 7.4, 150 mM NaCl, 1% Nonidet P-40 (NP-40), 1 mM EDTA and 5% glycerol) supplemented with protease and phosphatase inhibitors (1:100 dilution) (Sigma Aldrich). Adherent cells were then scraped, transferred into pre-cooled microfuge tubes and incubated on ice for 30 min. Lysates were clarified by centrifugation at 13,300×g at 4˚C for 10 min. The protein extract concentration was measured using a bicinchoninic acid assay (BCA) (Thermo Fisher Scientific) according to the manufacturer’s instructions. Proteins were resolved by electrophoresis on 20% SDS-PAGE gels. Following electrophoresis, proteins were transferred onto nitrocellulose membranes (Bio-Rad). Membranes were blocked in 1× Tris- buffered saline (TBS; Severn Biotech) containing 0.001% Tween 20 (Acros Organics) and 5% skimmed milk powder (Sainsbury’s). After washing once with TBST, membranes were incu- bated with primary antibody (Table 3) with gentle agitation overnight at 4˚C. The following PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 22 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Table 3. Detection antibodies for immune signaling proteins. Antibody α-actin (clone C4) p-DUSP1/MKP1 (S359) Total p38 Total EGFR Total Akt Total-EphA2 p-p38 (T180/Y182) p-Akt (Ser473) p-EGFR (Tyr1068) p-EphA2 (S897) c-Fos Peroxidase-conjugate AffiniPure anti-mouse IgG Peroxidase-conjugate AffiniPure anti-rabbit IgG https://doi.org/10.1371/journal.ppat.1012031.t003 Species Mouse Rabbit Rabbit Rabbit Rabbit Rabbit Rabbit Rabbit Rabbit Rabbit Rabbit Goat Goat Dilution 1:10,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:1,000 1:20,000 1:20,000 Company Merck Millipore Catalogue Number MAB1501 Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling Cell Signaling 2857 8690 4267 9272 6997 4511 9271 3777 6347 2250 Jackson ImmunoReasearch Jackson ImmunoReasearch 115-035-062 115-035-003 day, membranes were washed 3 times for 5 min with TBST. Membranes were subsequently incubated with rabbit or mouse secondary antibody (Thermo Fisher Scientific) for 1 h at room temperature and then washed 6 times for 5 min with TBST. Finally, the proteins were detected using Immobilon western Chemiluminescent HRP Substrate (Merck Millipore) and developed with an Odyssey Fc Imaging System (LI-COR). Human α-actin was used as a loading control. Statistical analyses All experiments were performed with at least three biological replicates. Data were analyzed using Prism 9.4 (GraphPad Software). All data used to generate the graphs is provided as source data (S3 Table). Supporting information S1 Fig. C. albicans zinc-associated genes are more highly expressed in the absence of IECs. Expression of the genes involved in zinc transport (ZRT101, ZRT2, ZRT3, ZRC1), zinc scav- enging (PRA1), and regulation of zinc acquisition genes (ZAP1). Log2(fold-change) compares infected samples to the yeast pre-culture conditions on the left and medium-only samples to the yeast pre-culture conditions on the right. Asterisks indicate time points with significantly expression changes (DESeq2 p < 0.05). (TIF) S2 Fig. Loss of ZRC1 significantly impairs hyphal growth in C. albicans. Loss of ZRT101, ZRT2, or PRA1 did not significantly impact hypha formation. However, loss of ZRC1 signifi- cantly decreased hyphal length in C. albicans in cell culture medium after 6 h. All values are shown as the mean with standard deviation. Data were compared using a one-way ANOVA with a post-hoc Dunnett’s multiple comparisons test. Statistical significance: , P � 0.05. (TIF) S3 Fig. Loss of ECE1 has no effect on the transcriptional zinc starvation response in medium only. Fold change in gene expression in C. albicans WT (BWP17) and ece1Δ/Δ strains during incubation in cell culture medium for (A) ZRT101, (B) ZRT2, (C) PRA1, (D) ZRT3, and (E) ZRC1. All values are shown as the mean with standard deviation. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 23 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation S4 Fig. Addition of exogenous zinc alleviates the transcriptional zinc starvation response in the absence of ECE1 but does not affect host-cell damage, fungal translocation, or fungal load. (A) Host-cell damage in the absence or presence of 25 μM exogenous ZnSO4 for the WT mutant (same data presented in Fig 2D) and the ece1Δ/Δ strain. (B) Fungal translocation of the WT(BWP17) and ece1Δ/Δ strains with or without the addition of 25 μM ZnSO4. (C) Relative quantification of fungal gDNA during infection of IECs. All samples are compared to the WT (BWP17) without added zinc. (D) Fold change in normalized gene expression of the ece1Δ/Δ strain compared to WT(BWP17) at 24 h during infection of IECs with addition of 25 μM ZnSO4. Gene expression was normalized to ACT1 as a housekeeping gene. All values are shown as the mean with standard deviation. (TIF) S5 Fig. Transcriptional response of IECs to infection with non-damaging and non-filamen- tous C. albicans. Genes differentially expressed when comparing the non-damaging ece1Δ/Δ and the non-filamentous efg1ΔΔ/cph1ΔΔ strains to their respective WT strains (ece1Δ/Δ com- pared to WT (BWP17) and efg1ΔΔ/cph1ΔΔ compared to WT (SC5314)). The data are shown as the Log2(fold-change) of infected cells with the different strains compared to uninfected IECs. Asterisks indicate genes with statistically significant differences in expression (DESeq2 p < 0.05). (TIFF) S6 Fig. Western blot detection of immune signaling proteins for IECs infected with WT and ece1Δ/Δ. (A) Confluent, differentiated C2BBe1 cells were infected with WT (BWP17) and ece1Δ/Δ C. albicans for 6 h and the protein content was sampled. Proteins involved in the dam- age response of oral epithelial cells were detected with ACT1 serving as a control. n.d. = not determined. (B) Protein levels normalized to actin. For p38, MKP1, EPHA2, EGFR, and AKT the normalized protein level for the phosphorylated protein is presented relative to the total respective protein level. (TIF) S7 Fig. DMSO vehicle control has no effect on host damage or translocation of C. albicans. C2BBe1 cells were treated with a DMSO vehicle control and infected with the WT or ece1Δ/Δ C. albicans strains. There were no significant changes in (A) host cell damage or (B) fungal translocation. All values are shown as the mean with standard deviation. (TIF) S8 Fig. Treatment of IECs with another NFκB inhibitor increases host cell damage, loss of barrier integrity, and fungal translocation. (A) Inhibition of NFκB activation using the NFκB inhibitor SC75741 at concentrations of either 2.5 or 5 μM increased the damage of WT (BWP17) C. albicans, but not for the ece1Δ/Δ strains. LDH release was adjusted by subtracting the release from uninfected and untreated host cells. (B) NFκB inhibition using either concen- tration also decreased the barrier integrity during infection with both WT and ece1Δ/Δ strains. (C) Fungal translocation was significantly increased during infection with both WT and ece1Δ/Δ C. albicans upon inhibition of NFκB using both concentrations of SC75741. These results match those obtained with the high-affinity NFκB inhibitor quinazoline. All values are shown as the mean with standard deviation. Host-cell damage (A), barrier integrity (B), and fungal translocation (C) data were compared using a one-way ANOVA with a post-hoc Sˇida´k’s multiple comparisons test. Statistical significance: , P � 0.05; , P � 0.01; , P � 0.001; *, P � 0.0001. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 24 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation S9 Fig. Treatment with a pan-caspase inhibitor does not prevent increased virulence upon inhibition of NFκB activation. Treatment with a pan-caspase inhibitor (Z-VAD) had no sig- nificant effect on (A) host-cell damage, (B) barrier integrity, or (C) fungal translocation when used alone or in combination with the NFκB inhibitor quinazoline (QNZ). All values are shown as the mean with standard deviation. Host-cell damage (A), barrier integrity (B), and fungal translocation (C) data were compared using a one-way ANOVA with a post-hoc Sˇida´k’s multiple comparisons test. Statistical significance: , P � 0.05; *, P � 0.001; **, P � 0.0001. (TIF) S10 Fig. Western blot detection of cell-cell junction proteins for IECs during C. albicans infection and NFκB inhibition. Confluent, differentiated C2BBe1 cells were infected with WT ece1Δ/Δ, and efg1ΔΔ/cph1ΔΔ C. albicans for 24 h. Samples were either untreated or treated with an NFκB activation inhibitor (QNZ) and the protein content was sampled. (A) Proteins that make up tight and adherens junctions (E-cadherin and claudin-1) were detected with GAPDH serving as a control. (B) Claudin-1 protein levels normalized to GAPDH and pre- sented relative to levels in untreated C2BBe1 cells. QNZ treatment further increased degrada- tion of claudin-1, even during infection with efg1ΔΔ/cph1ΔΔ and ece1Δ/Δ. All values are shown as the mean with standard deviation. (TIF) S1 Table. Differential expression data during C. albicans-interaction. Infection-specific DEGs of C. albicans and the host at different time points of infection and host DEGs during infection with either the ece1Δ/Δ or efg1ΔΔ/cph1ΔΔ strains compared to infection with the WT as stated in the Description sheet. (XLSX) S2 Table. Overrepresentation analysis of DEGs. Enrichment analysis for C. albicans and host DEGs from both the IEC infection time course and mutant comparison RNA sequencing data- sets as stated in the Description sheet. (XLSX) S3 Table. Source data. All data used to generate graphs for main text and supplementary fig- ures. (XLSX) Acknowledgments We would like to thank all members of the Microbial Pathogenicity Mechanisms department for their valuable feedback and fruitful discussions, in particular Simone Schiele and Julia Mantke for their technical assistance in performing the hyphal length assay and NFκB ELISA, respectively. We thank Thomas Beder, Thomas Wolf, and Sascha Brunke for their initial analy- ses of the transcriptomic data. We thank Ilse Jacobsen and Nicole Engert-Ellenberger for their help with taking samples for the dual-RNA sequencing. We additionally thank Sascha Brunke for a critical reading of the manuscript. Author Contributions Conceptualization: Jakob L. Sprague, Gianni Panagiotou, Julian R. Naglik, Duncan Wilson, Lydia Kasper, Bernhard Hube. Data curation: Jakob L. Sprague, Tim B. Schille, Stefanie Allert, Verena Tru¨mper, Adrian Lier, Peter Großmann, Emily L. Priest, Antzela Tsavou, Sascha Scha¨uble, Lydia Kasper. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1012031 March 1, 2024 25 / 29 PLOS PATHOGENSNFκB-mediated barrier protection and fungal zinc acquisition during intestinal translocation Formal analysis: Tim B. Schille, Verena Tru¨mper, Peter Großmann, Sascha Scha¨uble. Funding acquisition: Bernhard Hube. Investigation: Jakob L. Sprague, Lydia Kasper, Bernhard Hube. Methodology: Jakob L. Sprague, Tim B. Schille, Stefanie Allert, Verena Tru¨mper, Adrian Lier, Emily L. Priest, Antzela Tsavou, Sascha Scha¨uble. Project administration: Jakob L. Sprague, Gianni Panagiotou, Julian R. Naglik, Duncan Wil- son, Sascha Scha¨uble, Lydia Kasper, Bernhard Hube. Resources: Bernhard Hube. Software: Peter Großmann, Sascha Scha¨uble. 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10.1371_journal.pntd.0011973	RESEARCH ARTICLE Parasites and microorganisms associated with the snakes collected for the “festa Dei serpari” in Cocullo, Italy Jairo Alfonso Mendoza-Roldan1, Livia Perles1, Ernesto Filippi2, Nicole Szafranski3, Gianpaolo Montinaro4, Mariaelisa Carbonara1, Riccardo Scalera5, Pedro Paulo de Abreu Teles1, Julia Walochnik6, Domenico OtrantoID 1,7* 1 Department of Veterinary Medicine, University of Bari, Valenzano, Italy, 2 Biologist consultant for the Cocullo municipality, Rome, Italy, 3 College of Veterinary Medicine, Department of Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, United States, 4 RIFCON GmbH, Goldbeckstrasse 13, Hirschberg, Germany, 5 IUCN/SSC Invasive Species Specialist Group, Rome, Italy, 6 Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria, 7 Department of Veterinary Clinical Sciences, City University of Hong Kong, Hong Kong, SAR China * domenico.otranto@uniba.it Abstract While in much of the Western world snakes are feared, in the small, rural, mountainous town of Cocullo, in the middle of central Italy, snakes are annually collected and celebrated in a sacro-profane ritual. Every 1st of May, Serpari (snake catchers) capture and showcase doz- ens of non-venomous snakes to celebrate the ritual of San Domenico. In order to detect potential zoonotic pathogens within this unique epidemiological context, parasites and micro- organisms of snakes harvested for the “festa dei serpari” ritual were investigated. Snakes (n = 112) were examined and ectoparasites collected, as well as blood and feces sampled. Ectoparasites were identified morpho-molecularly, and coprological examination conducted through direct smear and flotation. Molecular screenings were performed to identify parasites and microorganisms in collected samples (i.e., Mesostigmata mites, Anaplasma/Ehrlichia spp., Rickettsia spp., Borrelia burgdorferi sensu lato, Coxiella burnetii, Babesia/Theileria spp., Cryptosporidium spp., Giardia spp., Leishmania spp. and helminths). Overall, 28.5% (32/112) of snakes were molecularly positive for at least one parasite and/or microorganism. Endosym- biont Wolbachia bacteria were identified from Macronyssidae mites and zoonotic vector- borne pathogens (e.g., Rickettsia, Leishmania), as well as orally transmitted pathogens (i.e., Cryptosporidium, Giardia, Proteus vulgaris, Pseudomonas), were detected from blood and feces. Thus, given the central role of the snakes in the tradition of Cocullo, surveys of their parasitic fauna and associated zoonotic pathogens may aid to generate conservation policies to benefit the human-snake interactions, whilst preserving the cultural patrimony of this event. Author summary The “festa dei serpari” is a unique sacro-profane ritual held each 1st of May in the small town of Cocullo, Central Italy. In this ceremony, dozens of non-venomous free-ranging a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mendoza-Roldan JA, Perles L, Filippi E, Szafranski N, Montinaro G, Carbonara M, et al. (2024) Parasites and microorganisms associated with the snakes collected for the “festa Dei serpari” in Cocullo, Italy. PLoS Negl Trop Dis 18(2): e0011973. https://doi.org/10.1371/journal. pntd.0011973 Editor: Marcelo Larami Santoro, Instituto Butantan, BRAZIL Received: November 15, 2023 Accepted: February 6, 2024 Published: February 21, 2024 Copyright: © 2024 Mendoza-Roldan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Representative sequences were deposited in GenBank (https:// www.ncbi.nlm.nih.gov/genbank) (accession number OR753376 for gltA; OR755903 to OQ630505 for 16S rRNA; OQ632771 to OQ632773, OR771463 to OR771475 and OR771476-OR771477 for 18S rRNA; OQ672452, OR758867, OR761977, OR761978 and for OR763078 cox1). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 1 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Funding: D.O. and J.A.M.R. were partially supported by EU funding within the NextGenerationEU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors report there are no competing interests to declare. snakes are captured by serpari (snake catchers) and showcased to thousands of pilgrims and tourists. Therefore, we aimed to assess the parasites and microorganisms of snakes within this unique epidemiological context to identify potential zoonotic pathogens. Snakes were examined and ectoparasites, blood and feces were collected, and morpho- molecular studies were performed. Overall, 28.5% (32/112) of snakes were positive for at least one parasite and/or microorganism. We identified new records of Mesostigmata mites as well as Leishmania tarentolae for the first time in Italian snakes. Importantly, we detected zoonotic microorganisms such as Rickettsia sp. in Aesculapian snake, as well as orally transmitted pathogens (i.e., Cryptosporidium, Giardia, Pseudomonas sp., Proteus vulgaris) from blood and feces of four species of snakes. Thus, snakes handled in this tra- dition may play a role in the zoonotic transmission of pathogens, given the contact with humans during this unique event. Introduction Snakes’ (Serpentes: Squamata) perception and interaction with human societies can be con- trasting, generating fear and negative feelings (e.g., disgust, repulsion; [1,2]), being merely tol- erated or even used for food or economic sources (i.e., snake charming) [3], or considered as new companion animals [4,5]. One of the major threats on snake conservation is the anthropo- genic pressure, directly implicated in the decline of snake populations [6,7]. Similarly, habitat and biodiversity loss and climate change represent main threats on snake populations [8–11]. Important threats are also represented by the predation by domestic carnivores (i.e., dogs and cats) and the high density of some wild ungulates, such as wild boars [12–14], as well as the emergence of pathogens (e.g., Ophidiomyces ophidiicola) within wild populations of snakes [15]. Both factors above are connected, as many pathogens are transmitted by predation, with snakes being intermediate or definitive hosts of parasites, some of which are zoonotic [16]. The relationships and uses by human communities of reptiles is also known as ethnoherpe- tology [17], which studies the importance of reptiles in different ecological, economic, and cul- tural contexts [2]. Further investigations were conducted toward integrating One-Health parasitological approaches with ethnoherpetology (i.e., ethnoherpetoparasitology), which allowed to identify the microorganisms and parasites that these animals harbor, as well as the potential risk of zoonotic transmission to snake charmers and vendors in the souks of Marra- kech, Morocco [3]. All of the above was assessed in a place where snakes are feared but highly tolerated, given their cultural and economic importance. In Italy, these slithery animals are part of socio-cultural and religious aspects of the coun- try’s history. One of the most ancient and iconic ethnoherpetological rituals across Europe, known as the “festa dei serpari” (also called the ritual of San Domenico), is performed in the small mountainous town of Cocullo, central Apennine (Abruzzo, central Italy) [6,18,19]. This mixed Catholic and pagan ceremony has been performed for centuries during the first days of May, with little to no alterations, consisting on placing four-lined snakes (i.e., E. quatuorli- neata) on top of the statue of San Domenico [20]. Soon after, the snake-adorned statue is taken through the small town, in a religious procession with thousands of onlookers in attendance. During the main event, other species of snakes (e.g., western whip snake—Hierophis viridifla- vus, Aesculapian snake—Zamenis longissimus, juvenile specimens of E. quatuorlineata) are handled by “serpari” (i.e., people that capture and handle the snakes) for thousands of pilgrims and tourists to photograph or interact with them [18,21]. In order to have a good number of snakes for the festival, the “serpari” are formally authorized by relevant authorities to capture PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 2 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy snakes alive in the surroundings of the Cocullo municipality from the 19th of March till the 30th of April, after which they are obliged to release them in the same capture sites, within three days following the main event. In Italy, most parasitological studies on snakes have been focused on Cryptosporidium, hel- minths and ectoparasites in exotic/pet snakes [22–24], with few investigations focused on wild species [25]. In the same context, the introduction of exotic parasites (e.g., Renifer aniarum) in grass snakes (Natrix natrix) and of snake fungal disease (SFD) caused by O. ophidiicola in dice snakes (Natrix tessellata) stresses the importance of monitoring the health status of wild popu- lations of animals by accurate risk assessment [26–28]. In addition, aside from salmonellosis [29,30], zoonotic parasites of reptiles [16,31], including Reptile Vector-Borne diseases (RVBDs; [32]) have gained interest of the scientific community. Indeed, wild snakes are senti- nels for zoonotic agents as they are reservoirs of a plethora of pathogens, playing a role in the life cycle of helminths (i.e., cestodes, nematodes and trematodes), pentastomids and vector- borne pathogens [16]. Ticks such as Ixodes ricinus have been collected from wild four-lined snakes (Elaphe quatuorlineata) from southern Italy, that tested positive for Mediterranean spotted-fever (Rickettsia helvetica), but not for Lyme disease (Borrelia burgdorferi sensu lato [33–35]). Other zoonotic parasites have been identified in free-ranging snakes, such as Spiro- metra erinaceieuropaei, Mesocestoides and Raillietiella [25,36]. Moreover, efforts have been car- ried out to assess possible emerging pathogens that could be a threat to the snake populations, such as bacteria [37], and the devastating keratinophilic fungus O. ophidiicola [27]. Consider- ing all the above, the present study aimed to investigate parasites and microorganisms associ- ated to snakes collected for the “festa dei serpari” ritual, as well as to identify potential zoonotic pathogens that these animals may harbor. Methods Ethics statement The study was conducted in accordance with all applicable international, national, and/or institutional guidelines for the care and use of animals. One of the major goals was to monitor for threats that could negatively impact the health of snakes due to their handling and exposure to humans within the ritual, whilst continuing to preserve the cultural patrimony of this event. Protocols of snake sampling, handling, and capture by Serpari, as well as by scientific commit- tee, is allowed under the National authorizations (National law DPR 357/97). The permit was granted by the Italian Ministry of Environment (n. 16271/2023 PNM and 79052/2023 PNM). Animal examination and sampling Given its cultural, religious, and historical importance, the ritual is accredited by local authori- ties, which in recent years have worked on increasing the awareness of the importance of con- servation of the snake populations to perpetuate the traditional ritual. Since 2010, all “serpari” must declare captured snakes to a scientific committee established specifically for the purpose of the event by the major of the town (EF; GM). The scientific committee is in charge of the operations to record all captured animals along with their biometric data (i.e., weight, snout- vent length measures), and other details, such as sex, age class (juvenile, subadult, adult), and site of capture. The sex of snakes was determined by probing, using round-ended metallic probes, or palpating the hemipenes. Additionally, animals are marked with a subcutaneous Passive Integrated Transponder (PIT) tag, which allows to recognize animals in case of recap- tures. Moreover, the scientific committee includes a veterinarian that performs clinical exams on all animals. This initiative has improved the welfare conditions of the collected snakes, as well as educated the “serpari” on animal husbandry. The added value of the presence of the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 3 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 1. Map of the town of Cocullo, Abruzzo, Central Italy, where the ritual of “festa dei serpari” is celebrated. L’Aquila province is evidenced in light green. Red circles indicate the municipality of Cocullo, as well as the town square where the statue of San Domenico is covered in snakes. Map prepared using QGIS software—Buenos Aires version (link of the XYZ tile: https://gdg.sc.egov.usda.gov/GDGOrder.aspx). https://doi.org/10.1371/journal.pntd.0011973.g001 scientific committee has permitted scientists to improve the knowledge of the distribution of different snake species at local and regional scale and to assess the snake population dynamics, accounting for more than 1300 animals examined to date [27]. Snakes captured for the “Festa dei serpari” event in Cocullo, Abruzzo (Fig 1) were examined the 29th and 30th of April 2023, as part of the annual monitoring program performed by the local authorities and a scientific committee (EF, GM). Animals were examined according to the protocols of the monitoring program described elsewhere [27]. After these procedures, animals were clinically assessed and examined for ectoparasites and when found they were removed by scarification methods and stored in 70% ethanol (Fig 2). Blood samples (~100 μl to 1 ml; Fig 3A) were drawn from all animals using the ventral coccygeal vein. Blood was divided between Whatman FTA Cards and 1.5 ml Eppendorf tubes which were later stored at -20˚C. Blood smears were performed from all animals and then assessed for the presence of hemoparasites [38] using Diff-Quik stain [39], and later evaluated using an optical microscope (LEICA DM LB2, Germany). Cloacal swabs (Fig 3B) were performed from all animals and stored at -20˚C. Whenever possible, fecal samples were collected and stored in 1.5 ml Eppendorf tubes at 4˚C. Ectoparasite processing and identification Ectoparasites were slide-mounted in Hoyer’s medium [40] and identified using dichotomous keys [41–43], as well as original species descriptions [42,44] were used for morphological iden- tification of Mesostigmata mites. To assess the parasitic load of mites, descriptive statistics was calculated using Quantitative Parasitology software, version 3.0 [45]. Prevalence, mean PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 4 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 2. Ectoparasite collection from snakes. a) mite collection from the gular region from a western whip-tail snake (H. viridiflavus); b) Macronyssidae mites in the gular area of a whip-tail snake (H. viridiflavus). https://doi.org/10.1371/journal.pntd.0011973.g002 Fig 3. Blood and fecal sampling of snakes. a) blood draw from the ventral coccygeal vein from a four-lined snake (E. quatuorlineata); b) cloacal swab from a four-lined snake (E. quatuorlineata). https://doi.org/10.1371/journal.pntd.0011973.g003 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 5 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy abundance (i.e., number of mites per total number of hosts) and mean intensity (i.e., number of mites per number of infested hosts) were calculated. Coprological studies Fecal samples were stored at 4˚C and analyzed within 48 hours. Due to the low volume obtained per individual snake (~50 μl), all samples were only analyzed microscopically through direct smear (using saline solution) to observe motile protozoa, helminths, acanthocephalans, and pentastomids, as well as a flotation test with a low-density solution was performed (satu- rated ZnCl solution, specific gravity 1350) [23]. Molecular screening DNA of mites was extracted via lysis using the guanidine isothiocyanate protocol (GT) [46]. This protocol was adapted to avoid mite destruction, which allowed the preservation of a voucher for morphological evaluation [33]. Extractions were performed from individual mites. DNA was extracted from individual blood samples (n = 112), feces (n = 38), and cloacal swabs (n = 101) using commercial kits (QIAamp DNA Mini Kit and DNeasy PowerSoil kit, Qiagen, Hilden, Germany), according to the manufacturer’s instructions. PCRs of the mites were performed to confirm species identity using two molecular markers: Cytochrome Oxidase subunit 1 (cox1; primers Cox1 LCO1490 and HCO2198), that amplifies 680 bp fragment [47], and primers for the 18S rRNA gene (18S+ and 18S−, respectively), which amplify a fragment of 480 bp of the V4 region [48]. Cycling conditions for both PCRs were initial denaturation at 94˚C for 1 min, then 30 cycles of 20 s at 94˚C, 50˚C for 30 s and 72˚C for 1 min and 30 s, with a final extension of 72˚C for 7 min. DNA extracted from mites, blood, feces, and cloacal swabs was analyzed for the detection of different microorganisms and parasites through cPCR and qPCR protocols (Table 1). All cPCR products were examined on 2% agarose gel stained with GelRed (VWR International PBI, Milan, Italy) and visualized on a GelLogic 100 gel documentation system (Kodak, New York, USA). Amplicons were then purified and sequenced in both directions using the same PCR primers, by the Big Dye Terminator version 3.1 chemistry in a 3130 Genetic Analyzer (Applied Bio-systems, Foster City, CA, USA). Sequences were edited and analyzed using Gen- eious Prime software version 9.0 (Biomatters Ltd., Auckland, New Zealand) [49] and com- pared with those available in the GenBank database by the Basic Local Alignment Search Tool (BLAST; http://blast.ncbi.nlm.nih.gov/Blast.cgi) for species identification. Additionally, fecal samples and cloacal swabs were tested using a multiplex (5plex) qPCR for Blastocystis hominis, Cryptosporidium spp., Dientamoeba fragilis, Entamoeba histolytica, and Giardia duodenalis assemblages A and B [62]. In order to have sequences of the positive samples, nested PCRs were performed for Giardia spp. and Cryptosporidium spp. as follows. For Giardia, a nested PCR amplifying a partial sequence of the triosephosphate isomerase (tpi) gene (532 bp) was used that detects all known assemblages [63, 64]. For Cryptosporidium spp., a nPCR targeting a fragment of the 18S rRNA gene was run [65]. Phylogenetic analyses Mite 18S rRNA and Rickettsial gltA, were separately aligned against those closely related spe- cies available from GenBank database using the ClustalW application within MEGA7 software [66]. The Akaike Information Criterion (AIC) option in MEGA7 was used to establish the best nucleotide substitution model adapted to each sequence alignment. Tamura 3-parameter model with invariant sites (I) [66] was used to generate the gltA trees and Tamura 3-parameter model with invariant sites (I) and discrete Gamma distribution (G) for 18S rRNA of mites, PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 6 / 25 PLOS NEGLECTED TROPICAL DISEASESTable 1. Pathogens screened in this study by conventional (c) and quantitative (q) PCR, with target genes, primers, probes nucleotide sequences and fragment length. Zoonotic pathogens of snakes of the “festa dei serpari” in Italy Fragment length (bp) References Pathogens Target gene Primers cPCR Anaplasma/Ehrlichia spp. 16S rRNA Borrelia burgdorferi sensu lato Flagellin Rickettsia spp. gltA Spotted Fever Group Rickettsiae ompA Coxiella burnetii IS1111a Babesia/Theileria spp. 18S rRNA Cestodes/Nematodes Leishmania spp. cox1 ITS1 EHR-16SD EHR-16SR FLA1 FLA2 CS-78F CS-323R Rr190.70F Rr190.701R Trans-1 Trans-2 RLB-F RLB-R JB3 JB4.5 L5.8S LITSR Sequence (50−30) GGTACCYACAGAAGAAGTCC TAGCACTCATCGTTTACAGC AGAGCAACTTACAGACGAAATTAAT CAAGTCTATTTTGGAAAGCACCTAA GCAAGTATCGGTGAGGATGTAAT GCTTCCTTAAAATTCAATAAATCAGGAT ATGGCGAATATTTCTCCAAAA GTTCCGTTAATGGCAGCATCT TATGTATCCACCGTAGCCAGT CCCAACAACACCTCCTTATTC TCTTCGATCCCCTAACTTTC TTTTTTGGGCATCCTGAGGTTTAT TAAAGAAAGAACATAATGAAAATG TGATACCACTTATCGCACTT CTGGATCATTT-TCCGATG Trypanosomatidae 18S rRNA 18SN1F GGATAACAAAGG AGCAGCCTCTA 18SN1R CTCCACACT TTG GTTCTTGATTGA qPCR Leishmania spp. kinetoplast LEISH-1 AACTTTTCTGGTCCTCCGGGTAG LEISH-2 ACCCCCAGTTTCCCGCC Probe 6-FAM-AAAAATGGGTGCAGAAAT-MGB 345 482 401 632 687 400 320 332 120 [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] GAGGTAGTGACAAGAAATAACAATA 460–520 Duplex Leishmania ITS1 L.i.t. -ITS1-F GCAGTAAAAAAAAGGCCG 150 [60] L.i.t. -ITS1-R CGGCTCACATAACGTGTCGCG Probe L.t. Probe L.i. 6-FAM-CACGCCGCGTATACAAAAACAC-MGB VIC-TAACGCACCGCCTATACAAAAGCA-MGB Giardia duodenalis SSU Giardia-80F GACGGCTCAGGACAACGGTT 62 [61] Giardia-127R TTGCCAGCGGTGTCCG Giardi-105 Fam-50- CCCGCGGCGGTCCCTGCTAG-30-Tamra https://doi.org/10.1371/journal.pntd.0011973.t001 and Kimura 2-parameter model with invariant sites (I) and discrete Gamma distribution (G) for 18S rRNA of Leishmania. Maximum likelihood (ML) phylogenetic inference was used with 2000 bootstrap replicates to generate the phylogenetic tree in MEGA7. Homologous sequences of 18S rRNA for Ixodes ricinus tick (Z74479) were used as outgroup to root the trees, as well as for Rickettsia including the gltA sequences from Rickettsia belli and Rickettsia canadensis (AB297809), and the 18S rRNA sequence of sequence of Trypanosoma brucei (XR_002989995). Results Overall, 112 snakes were examined and screened representing two families and five species (Table 2). Only two animals were not marked with PIT tags because of their small body size (one Coronella girondica, one Zamenis longissimus). Most individuals were apparently healthy, with three animals having some type of external lesion (Table 2). Of the 101 blood smears performed and examined, none of them had visible hemoparasites. Additionally, ectoparasites were collected from 10.7% of snakes (12/112) being all of them mites (n = 46) of the Mesostigmata order. Species of mites, their sex and snake hosts, as well as PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 7 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Observations Abscesses of masses in their dorsal subcutaneous area (2) Skin lesions compatible scale loses due to trauma (1) Table 2. Species of snakes (scientific and common names) sampled, sex and clinical observations. Family Colubridae Species Common name Coronella girondica Southern smooth snake Elaphe quatuorlineata Four-lined snake Hierophis viridiflavus Western whip snake Zamenis longissimus Aesculapian snake Natricidae Total Natrix helvetica Grass snake https://doi.org/10.1371/journal.pntd.0011973.t002 n 1 66 28 15 2 112 Sex Female (1) Male (47) Female (19) Male (20) Female (8) Male (20) Female (8) Female (2) Male (87) Female (38) infestation rates are summarized in Table 3. Namely, mites were identified as Hemilaelaps piger (Berlese, 1918) (Ixodorhynchidae: Mesostigmata) in two H. viridiflavus, one of them also co-infested with the second species identified, Ophionyssus sp. (Macronissydae: Mesostig- mata). The latter mite species was identified in the remaining 11 infested snakes (Table 3). Mites were found mainly in the gular area (Fig 2B). Hemilaelaps piger were all females (n = 5) and some of them bearing eggs (Fig 4A). Mor- phologically, mites displayed features such as three pairs of sternal setae (Fig 4B) and one pair of metasternal setae. The species identified in this study belongs to the piger group, given that all coxae I bear two strong bifid spurs (Fig 4C), and coxae II and III have a single bifid spur and a simple seta. Observed females had dorsal shield bearing 34–36 setae. The sclerotized part of the sternal shield was short, arched and very broad, the anal shield is within a cribrum (Fig 4D), and the anal opening is in the anterior part of the anal shield. Both 18S gene sequences obtained herein had nucleotide identity of 99.2% with sequences of Hemilaelaps triangulus from captive snakes of Mexico (i.e., MT163322, MT163324, MT163324). The remaining mites (n = 41) were all identified as Ophionyssus sp. (Fig 5A), most of them females (n = 32), characterized by having less than three pore pairs on sternal shield (Fig 5B). Also, the Genu III have 10 setae, and the epigynal shield is surrounded by the genital setae inserted on the integument (Fig 5B). Moreover, females had a dorsal shield divided in a large anterior and a small pygidial shield (Fig 5C) but differed in not having mesonotal scutae (Fig 5D) and having 9 pair of setae in the podonotal shield. Additionally, sequences obtained from 18S (n = 14) had high nucleotide similarities (99.7%) with Ophionyssus natricis from captive Table 3. Mite species, snake host, biological stage (nymph = N; male = M; female = F), infestation rates and sequence accession numbers (AN). Mites Species Snake species N snakes n and sex of Prevalence Mean Intensity Mean Abundance Sequence AN Hemilaelaps piger Ophionyssus sp. Hierophis viridiflavus Elaphe quatuorlineata 2* 3 mites 5F 1.8% (2/112) 2.5 (95% CI: 1–2.5) 0.37 (95% CI: 0.16–0.79), 18S rRNA: OR771478, OR771479 2M; 1F 9.8% (11/112) 3.73 (95% CI: 2–6.55) 0.04 (95% CI: 0.0–0.16) 18S rRNA: OR771465 Hierophis viridiflavus Zamenis longissimus 1 8* 1N; 3M; 32F 2F Total 12 1N; 5M;35F (46) one snake co-infested https://doi.org/10.1371/journal.pntd.0011973.t003 18S rRNA: OR771463 cox1: OR763078 18S rRNA OR771469 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 8 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 4. Morphological features of Hemilaelaps piger females. a) egg in the idiosoma of a female (red arrow); b) Three pairs of sternal setae (red arrows) and one pair of metasternal setae (black arrow); c) coxae I bear two strong bifid spurs (red arrows) typical of the piger group; d) the anal shield within a cribrum. Scale bars: 200μm (a); 100μm (b); 50μm (c,d). https://doi.org/10.1371/journal.pntd.0011973.g004 snakes of Italy (OP752167). On the other hand, cox1 sequences (n = 15) had low homology (85.8%) with those of O. natricis from captive snakes in Mexico (i.e., MT154424, MT154425). The 18S rRNA ML tree clustered the 13 generated sequences of Ophionyssus sp. with the avail- able sequences of Ophionyssus natricis, with high bootstrap values (99%). On the other hand, the two sequences of H. piger clustered together with sequences of H. triangulus and Ixodor- hynchus leptodeirae (Fig 6). Representative sequences herein generated were deposited in Gen- Bank (Table 3). Furthermore, of the 38 fecal samples collected from different snake species (i.e., 16 H. viridi- flavus, 10 E. quatuorlineata, 2 Natrix helvetica, and 10 Z. longissimus), 39.4% (15/38) scored positive for parasites, with 26.3% (10/38) positive at direct fecal examination, and 13.1% (5/38) through flotation test, being only two positive for both tests (i.e., two H. viridiflavus; Table 4). Briefly, ciliates (i.e., Nyctotherus sp.) were observed in two H. viridiflavus and Coccidia (Fig 7A) were detected in two H. viridiflavus. Helminth eggs (i.e., Capillarid, Strongyle, Oxyurid, Trematoda, Cestoda; Fig 7B and 7C) were detected in 2 H. viridiflavus and one E. quatuorli- neata. One H. viridiflavus had a Trombiculidae mite larva (Table 4; Fig 7D). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 9 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 5. Morphological features of Ophionyssus sp. a) dorsal view of female Ophionyssus sp; b) pore pairs on sternal shield (black arrows); c) dorsal shield divided in a large anterior podonotal shield (red arrow) and a small pygidial shield (black arrow); d) absence of mesonotal scutae in between the podonotal and pygidial shield. Scale bars: 200μm (a,c); 50μm (b, d). https://doi.org/10.1371/journal.pntd.0011973.g005 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 10 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Overall, 28.5% (32/112) of snakes were molecularly positive for at least one microorganism or parasite (Table 5). Three Ophionyssus sp. of three different snake hosts (i.e., E. quatuorli- neata, H. viridiflavus, Z. longissimus) were positive for the endosymbiont Wolbachia bacteria, similar to that detected in Dermanyssus gallinae from Japan (LC710644–97.62%) and from Spinturnix mites collected in bats from Thailand (KP165044–98.88%). In addition, one Z. long- issimus snake was positive for Rickettsia (gltA gene) in blood, with high nucleotide identity (100%) to Rickettsia aeschlimannii detected in human blood from Kenya (RQB050057). Phylo- genetic inference clustered the sequence of Rickettsia sp. generated in this study with Rickettsia aeschlimannii available in Genbank (Fig 8). On the other hand, Leishmania tarentolae was detected in cloacal swabs from two snakes (i.e., E. quatuorlineata—Ct 31,8; H. viridiflavus—Ct Fig 6. Maximum-likelihood phylogenetic trees of 18S rRNA genes of Mesostigmata mites. Bootstrap values (>40%) are shown near the nodes. Ixodes ricinus was used as outgroup. Scale bar indicates nucleotide substitution per site. Sequences of this study are in bold. https://doi.org/10.1371/journal.pntd.0011973.g006 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 11 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Table 4. Parasitic forms observed through direct and/or flotation fecal tests with species of snake hosts. Snake ID Species Sex Direct fecal test Flotation fecal test CS007 CS011 CS021 CS025 CS043 CS047 CS051 CS078 CS097 CS098 CS100 CS102 CS104 Zamenis longissimus Hierophis viridiflavus Zamenis longissimus Elaphe quatuorlineata Hierophis viridiflavus Hierophis viridiflavus Elaphe quatuorlineata Hierophis viridiflavus Hierophis viridiflavus Hierophis viridiflavus Hierophis viridiflavus Elaphe quatuorlineata Hierophis viridiflavus M M M F M M F M F M F M M Flagellates Coccidia Capillarid eggs Kalicephalus-type eggs Trematode egg Acanthocephalan egg Flagellates Flagellates Flagellates Flagellates Flagellates - Flagellates Capillarid eggs Trombiculidae mite larva - - Nyctotherus sp. - Coccidia - - - - - Nyctotherus sp. - Capillarid eggs Cestode eggs Sporulated Eimeria Oxyurid eggs - https://doi.org/10.1371/journal.pntd.0011973.t004 31,65), whereas two Leishmania sp. 18S rRNA sequences were retrieved from the blood of snakes (i.e., E. quatuorlineata, H. viridiflavus) different from those positive in cloacal swabs. Phylogenetic inference, despite being discretely informative, clustered both sequences from this study with those of Sauroleishmania species, as well as clustering all the Leishmania sub- clade ones apart (Fig 9). Furthermore, various fecal pathogens of zoonotic potential were detected by the 5plex qPCR, with high CT values (Table 5). In brief, Blastocystis spp. and Cryptosporidium spp. were detected in two species of snakes (i.e., E. quatuorlineata, H. viridiflavus), whilst Ent- amoeba spp. were detected in one H. viridiflavus, and Giardia assemblage B in two other spe- cies of snakes (i.e., E. quatuorlineata, Z. longissimus). The nPCRs for Cryptosporidium spp. and Giardia spp. confirmed the two Cryptosporidum spp., but did not give specific bands for Giardia spp. However, several sequences of bacteria were obtained from feces and cloacal swabs by sequencing un-specific bands of the two nPCRs (i.e., Achromobacter xylosoxidans, Citrobacter braakii, Citrobacter freundii, Pseudomonas sp., Pseudomonas brenneri, Stenotro- phomonas) and protozoa (i.e., Alveolate, Coccidia, Heteromita globosa), one of the positive snakes was also positive to protozoa in the coprological tests (i.e., CS011—H. viridiflavus; Table 3). Additionally, cox1 sequences from the cloacal swab were obtained for two nema- todes, one from an E. quatuorlineata similar to Oswaldocruzia filiformis from Russia (OQ346357–87.96%), and one from Z. longissimus similar to Rhabdias kafunata from China (OP605735–89.46). Moreover, a sequence was retrieved from a N. natrix with high homology to Proteus vulgaris from wastewater facilities in Canada (CP054157–99.18%; Table 5). Repre- sentative sequences herein generated were deposited in GenBank (accession number OR753376 for gltA; OR755903 to OQ630505 for 16S rRNA; OQ632771 to OQ632773, OR771463 to OR771475 and OR771476-OR771477 for 18S rRNA; OQ672452, OR758867, OR761977, OR761978 and for OR763078 cox1). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 12 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 7. Parasitic forms found in coprological exams H. viridiflavus. a) sporulated Eimeria sp.; b) Capillarid egg; c) Taeniid egg; d) pseudoparasite Trombiculidae mite larva from. Scale bars: 100μm (a); 200μm (b,d); 20μm (c). https://doi.org/10.1371/journal.pntd.0011973.g007 Discussion Ecto- and endoparasites were identified using a morpho-molecular approach from four of the five screened species of snakes collected for the “festa dei serpari” ritual. While most of the identified parasites are specific of reptiles and non-pathogenic (i.e., mites, helminths and pro- tozoa), others transmitted by ticks (e.g., Rickettsia), as well as through fecal-oral transmission (i.e., Cryptosporidium spp., Giardia; Pseudomonas, Proteus vulgaris) have a zoonotic potential. The surveillance performed in this study with the local authorities, allowed to evaluate the par- asitic fauna of free-ranging native snakes, which has been until now scarcely investigated or tackled in the Italian ophidic fauna. The species composition of snake population (i.e., E. quatuorlineata, H. viridiflavus, Z. long- issimus) is typical of the surroundings of Cocullo municipality, as already observed in previous screenings [27]. On the other hand, the absence of ticks in the snake population from this study may be due to the collection of the snakes during the early spring, where ophidians are less exposed to larvae and nymphs of I. ricinus [67]. Indeed, this tick species is most prevalent PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 13 / 25 PLOS NEGLECTED TROPICAL DISEASESTable 5. Molecular identification of vector-borne and fecal pathogens detected in snakes. Zoonotic pathogens of snakes of the “festa dei serpari” in Italy Vector-Borne Pathogen Fecal pathogen cPCR (16S rRNA) cPCR (gltA) cPCR (18S rRNA) dqPCR (ITS1) cPCR (cox1) cPCR—nPCR (18S rRNA) 5plex-qPCR Species of snake (Infected/total) Coronella girondica (0/1) Elaphe quatuorlineata (11/66) (1)* Wolbachia sp.—LC710644 (97.62%) (1)* Leishmania tarentolae - KC205986 (100%) (1)* Leishmania tarentolae (Ct 31,8) (1)* Oswaldocruzia filiformis - OQ346357 (87.96%) Hierophis viridiflavus (12/ 28) (1)** Wolbachia sp.—LC710644 (97.40%) (1)* Leishmania tarentolae - KC205986 (100%) (1)* Leishmania tarentolae (Ct 31,65) Natrix helvetica (1/2) Zamenis longissimus (8/15) (1) Wolbachia sp.—KP165044 (98.88%) (1)* Rickettsia aeschlimannii— RQB050057 (100%) (1)** Proteus vulgaris— CP054157 (99.18%) (1)* Rhabdias kafunata— OP605735 (89.46%) (1)* Blastocystis (2)* Cryptosporidium (1)* Giardia assemblage B (2)* Blastocystis (2)* Cryptosporidium (1) Entamoeba (2)* Giardia assemblage B (1)* Alveolate FJ410512 (95.82%) (1)* Citrobacter freundii CP026235 (99.23%) (1)* Pseudomonas sp. CP043060 (93.75%) (1)* Achromobacter xylosoxidans HE798385 (97.77%) (1)* Coccidia sp. MH590231 (88.7%) (1)* Heteromita globosa - LC764482 (99.58%) (1)** Pseudomonas brenneri LT629800 (98.65%) (1) Pseudomonas brenneri LT629800 (97.32%) (1)* Stenotrophomonas sp. CP109812 (99.77%) (1)*** Citrobacter braakii CP113163 (98.08%) Total (32/112) (3/112) (1/112) (2/112) (2/112) (3/112) (10/112) (11/112) Blood; Mite; Swab; **Feces https://doi.org/10.1371/journal.pntd.0011973.t005 in woody areas of central Italy and was previously recorded in four-lined snakes from southern Italy [33]. In addition, immature stages of I. ricinus may prefer other reptile species that are more abundant and fossorial, such as Podarcis or Lacerta lizards [68,69]. The Ixodorhynchidae mites identified herein (H. piger) were described from an unknown snake collected in Florence (central Italy) [44]. Probably, H. viridiflavus is new host for this mite species, as well as Cocullo PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 14 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 8. Maximum-likelihood phylogenetic trees of gltA genes of Rickettsia spp. Bootstrap values (>40%) are shown near the nodes. Rickettsia belli, Rickettsia canadensis were used as outgroups. Scale bar indicates nucleotide substitution per site. Sequences of this study are in bold. https://doi.org/10.1371/journal.pntd.0011973.g008 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 15 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Fig 9. Maximum-likelihood phylogenetic trees of 18S rRNA genes of Leishmania spp. Bootstrap values (>40%) are shown near the nodes. Trypanosoma brucei and Trypanosoma evansi were used as outgroups. Scale bar indicates nucleotide substitution per site. Sequences of this study are in bold. https://doi.org/10.1371/journal.pntd.0011973.g009 a new locality. The Ophionyssus species herein identified differs from the common snake mite, O. natricis, which is usually found in captive snakes around the world and may feed also on humans [70]. Moreover, the identified mite specimens morphologically differed from the 16 known species of Ophionyssus [42,43], with low homology in the cox1 nucleotide sequences compared to those of O. natricis deposited in Genbank. Given all the above, further studies are required to elucidate if the Ophionyssus species found is in fact a new species. Nonetheless, H. viridiflavus and E. quatuorlineata represent new hosts for Ophionyssus mites, as well as the Abruzzo region a new locality for Macronyssidae mites of snakes. Although all of the tested mites were negative for pathogens, the molecular detection of Wolbachia sp. in Ophionyssus sp. supports a previous finding in O. natricis from captive Boa constrictor [24], as well as in Ornithonyssus bursa [71]. The finding of Wolbachia in female mites, but not males, may be due to the role this endosymbiont bacterium displays in the reproduction of mites, through male-killing, feminization, and parthenogenesis [72,73]. Furthermore, the diversity and prevalence of the endoparasitic fauna of snakes recorded in this study (39.4%) were higher than that reported for captive snakes from Poland (i.e., 13.7%; [74]) and Italy (i.e., 10.5%; [23]), and lower from another survey conducted also in Italy (56.8%; [75]). Nevertheless, this study represents the first coprological survey of free-raging snakes in the Italian peninsula, without the need of euthanizing or working with recently dead/killed snakes [25,76,77]. Coccidia identified were morphologically similar with Eimeria [78], of mild pathogenicity, commonly observed in wild snakes. In addition, snakes were found infected with helminth eggs similar to Kalicephalus, Rhabdias and Strongyloides [79], as well as oxyurids and Capillaria eggs [80], with the latter being generally found in healthy ani- mals. Overall, the findings of the above parasites may derive from the predation attitude of PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 16 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy snakes [81] under different ecological contexts, therefore representing spurious parasites [82], rather than host specific ones. The possibility of having spurious parasites is higher in wild snakes than in those kept in captivity (e.g., 1.4%, 4/283; [82]), given that they actively feed on small preys. Given all the above, future studies are warranted to better distinguish real parasitic fauna of free-ranging snakes from spurious or free-living parasites. Importantly, molecular screening allowed to identify potentially zoonotic parasites and microorganisms, highlighting the need of an integrative approach using morpho-molecular techniques to assess wild animal populations under a One-Health perspective. For the zoonotic protozoa identified with the 5-plex qPCR, confirmatory sequences could only be retrieved for Cryptosporidium, but they were too short for reliable species identification. Zoonotic species, such as Cryptosporidium muris, Cryptosporidium parvum and Cryptosporidium tyzzeri were already identified in captive snakes [83]. Indeed, zoonotic Cryptosporidium species are spurious parasites in snakes, where resistant oocysts may contaminate the environment. Given that the diet of both species of snakes (i.e., E. quatuorlineata, H. viridiflavus) found positive for Cryptosporidium may include also rodents [84,85], further attempts should establish whether the captured snakes of Cocullo harbor zoonotic Cryptosporidium species or Cryptosporidium serpentis. The latter species may cause asymptomatic or chronic infections, being highly pathogenic, infectious, and irrespon- sive to therapeutic treatment [86]. The 5-plex qPCR may have low specificity and limited dis- criminatory power for the highly diverse protozoa species in snakes’ feces [62,87]. Moreover, mixed infections with related species of protozoa result in close melting curves hindering the detection of targeted species. Nonetheless, specific investigations on zoonotic pathogens in snakes are warranted considering that G. duodenalis is an important food and waterborne pathogen [88] as well as E. histolytica. The latter has never been reported in snakes [89], which typically host a highly pathogenic reptilian protozoan, Entamoeba invadens, causing necrotic enteritis and hepatitis [90], as well as the less pathogenic species Entamoeba ranarum [90]. Certainly, the integrative approach using morphological and molecular tools further permitted the identification of nematodes belonging to the genera Oswaldocruzia and Rhabdias from clo- acal swabs, when fecal samples were not collected [91,92]. These genera of nematodes are potentially pathogenic to snakes and should be actively surveyed to assess their deleterious effects on the free-raging snake populations, also considering the zoonotic potential and emer- gence of snake-associated human parasites, such as Ophidascaris robertsi neural larva migrans [93]. While it was not possible to perform a definitive identification of zoonotic parasites, the sequences obtained from fecal and cloacal swabs allowed the detection of zoonotic bacteria (i.e., A. xylosoxidans, C. freundii, P. vulgaris, Pseudomonas), which were already detected in snakes, that may act both as reservoirs and spreaders [94,95]. As the bacteria above may be opportunistic pathogens of humans and multi-drug resistant strains have been identified [96], correct biosafety measures should be applied during and after the “festa dei serpari” event. This is mainly due to the fact that, when handled, free-ranging snakes defecate as a defense mecha- nism [97,98], therefore increasing the risks of contamination with pathogens such as Salmo- nella [29,99]. Regarding zoonotic vector-borne pathogens, the detection of R. aeschlimannii in Z. longis- simus blood suggests exposure to tick bites, further corroborating the potential role of rep- tiles as reservoirs for Rickettisa spp. [100,101]. Again, molecular positivity for species of the spotted fever group (i.e., Rickettsia monacensis, Rickettsia helvetica) was reported in lizards from Italy [35] and snakes (i.e., Rickettsia asiatica) from Morocco [3]. As R. aeschlimannii and other rickettsiae (i.e., R. monacensis, Rickettsia massiliae) are considered as emerging human pathogens [102], further studies should be conducted to verify the occurrence of this PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 17 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy species of Rickettsia, previously detected in Algeria from Hyalomma aegyptium ticks col- lected from tortoises [103]. The retrieval of the reptile-associated L. tarentolae from snakes in Italy represents new hosts (i.e., E. quatuorlineata, H. viridiflavus) and broadens its geographical distribution north- wards, near the Lazio region where it was previously detected in human blood donors and sand flies [104]. Prior surveys from Italy yielded positive molecular results in species of lizards and geckos for L. infantum and L. tarentolae, in urban, peri-urban areas and dog shelters [105]. In addition, L. tarentolae was detected for the first time from cloacal swabs, however, attempts to isolate Leishmania spp. from snakes are warranted given that, thus far L. tarentolae has been isolated only from geckos of the Mediterranean basin [106,107]. To further address the epidemiological picture of Leishmania, entomological surveys are pivotal to describe the species composition of sand flies and address if there is also a sympatric occurrence of both L. tarentolae and L. infantum in the surrounding of the Cocullo municipality, given that L. taren- tolae can potentially infect mammals (i.e., humans and dogs) [106]. Given all of the above, the population of snakes around Cocullo may be in part under a “positive” human-snake relationship, where traditional beliefs impact directly on reptile con- servation. This, effect has been already studied in other cultural contexts for reptiles such as water monitor lizards in the surroundings of a small village in northern India [108]. As in Cocullo, villagers from the small town of Chak Manik have many beliefs (i.e., protecting the marshlands for their Gods and for the village to thrive), that indirectly have a positive effect on the vulnerable population of reptiles, being mutually beneficial for both the villagers and the reptile species [108]. Although highly subjective, overall health status and condition of the screened snakes was established as “apparently healthy”. Compared to the previous study, where 23 animals had some type of dermal abnormality [27], the three snakes that were herein reported as having skin lesions, were all probably signs of healed trauma or infection. How- ever, given that it was not possible to discard fungal granuloma, annual screening of the col- lected snake population of Cocullo, assessing fungal, bacterial, and parasitic infections, should be encouraged to have a well-established and consistent surveillance program that will allow for rapid detection of harmful and zoonotic pathogens. Accordingly, results from this study will aid to create strategies to prevent zoonotic transmission of pathogens. Indeed, alongside the established snakes’ population monitoring efforts, pathogens surveillance using a multi- sectorial approach should be also performed annually to assess zoonotic pathogen emergence and their dynamics within the capture population of snakes, that are after released in the envi- ronment [109]. Data generated from the present study will be useful for local and national authorities to formulate proper prevention policies specific for the serpari as well as for tourist and pilgrims that participate in the event. For example, education and training of serpari on proper husbandry and protective measures when capturing and handling snakes will aid to reduce the risk of transmission of pathogens to this group of people that are at higher risk, as they are in contact with snakes for over a month [110]. Indeed, educating serpari on proper husbandry and procedures such as quarantine of snakes and the proper use of personal protec- tive equipment (PPE), may reduce the risk of transmission from snake to snake, as well to humans [111]. On the other hand, coordinated policies with local authorities during the event are important to minimize the risk of oral-fecal transmission of pathogens from snakes to tourists. These prevention strategies should be focused on providing hand hygienization/disin- fection places, as well as information on why and how to wash their hand after handling a wild animal [112]. Using a One-Health approach to monitor the snake population and reduce the risk of zoonotic transmission will thereby contribute to the conservation of the snakes and the perpetuation of the tradition. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 18 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Conclusion Data presented here demonstrate that using an ethnoherpetoparasitological framework to assess the collected ophidian population prior to the annual celebration of the “festa dei ser- pari” ritual in Cocullo, is a useful approach that allowed for the assessment of the health of the handled snakes, as well as the risks of transmission of zoonotic pathogens present in wild pop- ulations. Although snake collected for the ritual harbored reptile-specific and non-pathogenic mites, helminths, and protozoa, the presence of zoonotic pathogens should not be disregarded. This is the case with vector-borne pathogens (e.g., Rickettsia, Leishmania), as well as opportu- nistic zoonotic pathogens (i.e., Cryptosporidium, Giardia, A. xylosoxidans, C. freundii, P. vulga- ris, Pseudomonas) present in the feces of these scaley animals. Thus, snakes collected and showcased in the “festa dei serpari” are optimal sentinels and bioindicators of environmental and ophidian population health, as well as reservoirs of microorganisms that should be con- trolled through proper biosafety measures when handled by serpari to avoid the risk of zoo- notic transmission. Effective public health policies within this unique epidemiological context are advocated, while promoting targeted conservation initiatives, education, and biosafety measures. Supporting information S1 Video. Festa dei Serpati ritual. Every 1st of may for more than 500 hundred years, the statue of San Domenico is covered with four-lined snakes (Elaphe quatuorlineata), followed by a procession through the small town of Cocullo, Italy. (MP4) Acknowledgments Authors thank Giada Annoscia (UniBa) for her technical work in molecular biology in the lab- oratories of de Department of Veterinary Medicine of the university of Bari. Authors would like to thank the Serpari, the local community, authorities, and the veterinarian Pasqualino Piro for supporting and rendering possible this study. Authors would like to also thank, Rifcon GmbH for sponsoring since 2016 the professional terraria to house captured reptiles. Authors would like to also acknowledge the Major and the local community of Cocullo for supporting and allowing this annual survey. Author Contributions Conceptualization: Jairo Alfonso Mendoza-Roldan, Ernesto Filippi, Gianpaolo Montinaro, Domenico Otranto. Data curation: Jairo Alfonso Mendoza-Roldan, Ernesto Filippi, Gianpaolo Montinaro, Ric- cardo Scalera, Domenico Otranto. Formal analysis: Jairo Alfonso Mendoza-Roldan, Livia Perles, Nicole Szafranski, Mariaelisa Carbonara, Pedro Paulo de Abreu Teles, Julia Walochnik, Domenico Otranto. Funding acquisition: Domenico Otranto. Investigation: Jairo Alfonso Mendoza-Roldan, Livia Perles, Ernesto Filippi, Nicole Szafranski, Gianpaolo Montinaro, Mariaelisa Carbonara, Riccardo Scalera, Pedro Paulo de Abreu Teles, Julia Walochnik, Domenico Otranto. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011973 February 21, 2024 19 / 25 PLOS NEGLECTED TROPICAL DISEASESZoonotic pathogens of snakes of the “festa dei serpari” in Italy Methodology: Jairo Alfonso Mendoza-Roldan, Livia Perles, Ernesto Filippi, Nicole Szafranski, Gianpaolo Montinaro, Mariaelisa Carbonara, Riccardo Scalera, Pedro Paulo de Abreu Teles, Julia Walochnik, Domenico Otranto. Project administration: Jairo Alfonso Mendoza-Roldan, Ernesto Filippi, Gianpaolo Monti- naro, Domenico Otranto. Resources: Julia Walochnik. Software: Jairo Alfonso Mendoza-Roldan, Livia Perles. Supervision: Jairo Alfonso Mendoza-Roldan, Domenico Otranto. Validation: Jairo Alfonso Mendoza-Roldan, Livia Perles, Mariaelisa Carbonara, Pedro Paulo de Abreu Teles, Julia Walochnik. Visualization: Jairo Alfonso Mendoza-Roldan, Riccardo Scalera, Julia Walochnik. Writing – original draft: Jairo Alfonso Mendoza-Roldan, Livia Perles, Nicole Szafranski, Mar- iaelisa Carbonara, Pedro Paulo de Abreu Teles, Domenico Otranto. Writing – review & editing: Jairo Alfonso Mendoza-Roldan, Livia Perles, Ernesto Filippi, Nicole Szafranski, Gianpaolo Montinaro, Mariaelisa Carbonara, Riccardo Scalera, Pedro Paulo de Abreu Teles, Julia Walochnik, Domenico Otranto. References 1. Alves R, Silva VN, Trovão DM, Oliveira JV, Mourão JS, Dias TL, et al. 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10.1371_journal.pone.0295207	RESEARCH ARTICLE A novel method for linguistic steganography by English translation using attention mechanism and probability distribution theory YiQing LinID 1, ZhongHua Wang2 1 School of Foreign Languages, Xi’an Shiyou University, Xi’an, China, 2 Xi’an Aeronautics Computing Technique Research Institute, AVIC, Xi’an, China tnhlwbvzehpx129@yahoo.com Abstract To enhance our ability to model long-range semantical dependencies, we introduce a novel approach for linguistic steganography through English translation. This method leverages attention mechanisms and probability distribution theory, known as NMT-stega (Neural Machine Translation-steganography). Specifically, to optimize translation accuracy and make full use of valuable source text information, we employ an attention-based NMT model as our translation technique. To address potential issues related to the degradation of text quality due to secret information embedding, we have devised a dynamic word pick policy based on probability variance. This policy adaptively constructs an alternative set and dynamically adjusts embedding capacity at each time step, guided by variance thresholds. Additionally, we have incorporated prior knowledge into the model by introducing a hyper- parameter that balances the contributions of the source and target text when predicting the embedded words. Extensive ablation experiments and comparative analyses, conducted on a large-scale Chinese-English corpus, validate the effectiveness of the proposed method across several critical aspects, including embedding rate, text quality, anti-steganography, and semantical distance. Notably, our numerical results demonstrate that the NMT-stega method outperforms alternative approaches in anti-steganography tasks, achieving the highest scores in two steganalysis models, NFZ-WDA (with score of 53) and LS-CNN (with score of 56.4). This underscores the superiority of NMT-stega in the anti-steganography attack task. Furthermore, even when generating longer sentences, with average lengths reaching 47 words, our method maintains strong semantical relationships, as evidenced by a semantic distance of 87.916. Moreover, we evaluate the proposed method using two met- rics, Bilingual Evaluation Understudy and Perplexity, and achieve impressive scores of 42.103 and 23.592, respectively, highlighting its exceptional performance in the machine translation task. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Lin Y, Wang Z (2024) A novel method for linguistic steganography by English translation using attention mechanism and probability distribution theory. PLoS ONE 19(1): e0295207. https://doi.org/10.1371/journal.pone.0295207 Editor: Toqeer Mahmood, National Textile University, PAKISTAN Received: August 3, 2023 Accepted: November 16, 2023 Published: January 2, 2024 Copyright: © 2024 Lin, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting information files. Funding: The General Research Project of Higher Education Teaching Reform under Grant SJGY20220659. The funders play a crucial role in data collection and analysis. Competing interests: The authors have declared that no competing interests exist. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 1 / 23 PLOS ONEN/A 1. Introduction Linguistic steganography refers to the technique of concealing secret information within text, making it difficult for people to perceive its presence. Unlike other forms of steganography, linguistic steganography focuses on utilizing language and semantical features to hide infor- mation [1]. In the field of linguistic steganography, information can be concealed by altering word order, using specific grammar structures, or incorporating unusual vocabulary in the text. The hidden information may include encrypted messages, secret instructions, or any other content that needs to be kept confidential [2]. The applications of linguistic steganogra- phy are diverse and have far-reaching implications in the fields of information security and network security. Some of these applications include: 1. Covert Communication: Linguistic steganography can be used to covertly exchange sensi- tive information between parties. By concealing data within seemingly innocuous text, it offers a level of discretion that can be vital in various situations, including intelligence oper- ations and secure corporate communications. 2. Privacy Preservation: Linguistic steganography can be a means of preserving the privacy of communications. In cases where individuals or organizations need to protect their data from unauthorized access or surveillance, this technique allows for discreet information exchange. 3. Censorship Evasion: In regions with strict censorship policies, linguistic steganography can serve as a way to bypass content restrictions. By hiding information within seemingly innocuous text, individuals can share and access information that would otherwise be prohibited. 4. Secure Communication: Linguistic steganography can facilitate secure communication by embedding encrypted messages within carrier text. This method enhances the confidential- ity of the information being transmitted, making it harder for eavesdroppers to intercept and understand the content. 5. Security Protocols: Some security protocols and systems utilize linguistic steganography to embed digital watermarks or additional security features within textual documents to pre- vent forgery or unauthorized access. Linguistic steganography offers a unique and versatile approach to data security, providing a covert channel for the exchange of sensitive information, all while maintaining the appear- ance of regular text. As technology continues to advance, the development and analysis of lin- guistic steganography techniques play a crucial role in ensuring the confidentiality and integrity of digital communications [3]. In the early stages of linguistic steganography, modification-based techniques were com- monly used, such as synonym substitution, introducing spelling errors, syntactic transforma- tions, and semantical operations, to embed secret information [4, 5]. However, these methods heavily relied on complex syntactic or semantical analysis, making it challenging to achieve high accuracy [6]. In addition, attackers could potentially detect the modifications through comparisons, resulting in lower security [7]. Moreover, due to the limited redundancy in the text, these techniques had a smaller embedding capacity, and even minor text alterations could lead to semantical anomalies or grammatical errors. In response to these issues, researchers have proposed non-modification-based stegano- graphic methods, where carrier texts are obtained or generated under the guidance of secret information [8]. These carrier-based methods aim to find a series of texts that match the secret PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 2 / 23 PLOS ONEN/A information, for instance, by selecting several texts from a large corpus using the mapping function. On the other hand, generative linguistic steganography relies on specific statistical patterns or language models to automatically produce steganographic texts [9]. Early genera- tive steganography often relied on grammar rules, such as context-free grammars or sentence templates. TEXTTO was one of the earliest methods [10], designed with sentence templates composed of word groups. Based on the syntactic features of the sentences, these templates were filled to generate the carrier texts. Other early studies, like NICETEXT [11] and Mimicry [12], utilized grammar rules to generate carrier texts. The key difference of these methods from modification-based linguistic steganography is that they do not require the original texts, making it difficult for attackers to detect by comparison. Although these methods have a high embedding rate, they lack consideration of semantical information, resulting in carrier texts with no contextual relevance, leading to lower security. To address the issue of semantical irrelevance, some generative steganography models using statistical language models emerged, such as using n-gram models [13] or Markov chains [14] to model semantical features. Due to the difficulty of semantical modeling, some statistical models are applied to specific genres such as short jokes [15], emails [16], and poetry [17]. To improve text fluency, Guo et al. [18] used n-gram models to generate alternative carrier text, which was then manually edited and polished. This method calculates the conditional proba- bility p(xi\|x1, x2,. . ., xi-1), where xi is the i-th word, to determine the word at each moment. To address data sparsity and excessive parameters, both n-gram models and Markov models introduce the Markov assumption, assuming that each word is only related to the previous sev- eral words, i.e., p(xi\|x1, x2,. . ., xi-1) � p(xi\|xi-n+1, xi-n+2,. . ., xi-1). However, according to the Mar- kov assumption, as the word distance increases, the semantical relevance between words decreases, resulting in weak semantical relevance between sentences in the entire text [19]. In recent years, neural networks have provided new solutions for modeling long-range semantical dependencies in text. Sun et al. [20] combined the Encoder-Decoder architecture of RNN (Recurrent Neural Network) with grammatical templates to generate hidden Chinese poetry, improving the long-range semantical relevance between generated carrier text. Cao et al. [21] used Long Short-Term Memory network (LSTM) to select encoded carrier words from a specific word library that matches the secret message. Yang et al. [22] proposed an RNN-Stega algorithm for information hiding, achieving state-of-the-art performance in embedding capacity and text quality. The algorithm first encodes alternative words based on conditional probability distributions and then picks words that match the current secret mes- sage bitstream for embedding. As traditional models have limited long-term memory com- pared to RNN and LSTM, deep learning-based models have gradually replaced traditional linguistic steganography models [23]. However, RNN memory units also have certain limita- tions: As the vocabulary grows, previous semantical information is eventually ignored, result- ing in weaker semantical consistency in the entire text [24]. Therefore, how to maintain semantical relevance when generating long or multiple sentences of carrier text remains a chal- lenge in linguistic steganography. This paper combines the neural machine translation (NMT) model with steganography models to introduce the NMT-Stega model. The proposed NMT-Stega model generates the carrier text y of target language based on the source text x and previously generated target words. During text generation, the source text provides useful information for semantical rele- vance between sentences. For example, when generating the word yn+1, NMT-Stega considers not only the previously generated words y1, y2,. . ., yn but also the corresponding source words x1, x2,. . ., xm. Furthermore, the model uses an attention-based hyper-parameter to equilibrate the impact of the source text and the target text on the target word. In order to further enhance the quality of hidden text, this paper introduces a word pick policy to construct an alternative PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 3 / 23 PLOS ONEN/A set. Experimental results demonstrate that NMT-Stega has the capability to generate multiple semantically related lengthy sentences. Additionally, NMT-Stega exhibits excellent perfor- mance in anti-steganography experiments. The following three aspects are where our contributions lie: 1. Proposed NMT-Stega method addresses the issue of decreased semantical relevance as the length of generated sentences increases. It adopts an Encoder-Decoder architecture fused with attention mechanism to dynamically embed the secret information during text genera- tion. Each target word’s pick takes into consideration both the source text and previously generated target text to maintain semantical coherence with distant words. 2. To mitigate the degradation of text quality caused by embedding secret information, a dynamic word pick policy based on probability variance is designed, allowing adaptive con- struction of the alternative set and dynamic adjustment of embedding capacity at each time step. 3. Attention hyper-parameters are introduced to study their effects on the embedding capacity and text quality, providing insights into the interplay between different attention parame- ters and variance thresholds. 4. The article makes a significant contribution by proposing a novel method that combines attention mechanisms and probability theory for linguistic steganography. This approach could potentially improve the security and quality of steganographic text, which is a valu- able contribution to the field. 2. Materials and methods 2.1 Background 2.1.1 Linguistic steganography by machine translation. Translation-based Steganogra- phy (TBS) is a technique that leverages the variability of different translations produced by multiple translators for the same source text. Initially proposed by Beltra´n et al. [25], the Lost in Translation (LiT) model utilizes this characteristic to hide information within the translated sentences. Each translator’s output is encoded using Huffman coding, and the sentences that correspond to the encoding of the secret message bits are selected as the final carrier texts. An improved version of LiT, known as LiJtT (Lost in just the Translation), was later intro- duced by Zidenberg et al. [26]. Unlike LiT, LiJtT directly encodes the generated translated sen- tences. Each sentence is transformed into a hash value based on a secret key, and the sentences that match the least significant bit (LSB) of the secret message bits are chosen as the carrier texts. Both LiT and LiJtT require the involvement of multiple translators, and the embedding capacity of the model depends on the diversity of the generated translations [27]. However, if there are no LSB(Least Significant Bit) matches with the secret message bits in any translation at a given moment, the embedding process fails. To address this issue, Meng et al. [28] pro- posed LinL (Lost in n-best List), which utilizes a statistical machine translation (SMT) model to obtain the n-best translations for a given source sentence. For example, when n = 1, given a source sentence, the translated sentence ^t is the sentence that maximizes the conditional probability p(t\|s), i.e.: PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 ^t ¼ arg max pðtjsÞ t ð1Þ 4 / 23 PLOS ONEAccording to Bayes’ formula: ^t ¼ arg max pðtÞpðsjtÞ t N/A ð2Þ In which, p(t) represents the language model of the target text, and p(s\|t) represents the translation model trained with a bilingual corpus. LinL employs an n-best search algorithm to select the n-best translated sentences and encodes them for information embedding. Compared to LiT and LiJtT, LinL demonstrates improved robustness and higher embedding capacity. Existing TBS models are based on SMT models. One problem with SMT is that it uses the Markov assumption to compute the language model, which assumes that the generation of the next word depends only on a few preceding words. As a result, the quality of the generated translation text is relatively poor [19]. 2.1.2 NMT. Recently, NMT has demonstrated outstanding performance in various machine translation tasks such as English-German and English-French translations [29, 30]. Unlike SMT, NMT consists of an Encoder-Decoder architecture. The Encoder encodes the source sentence into a fixed-length vector, which is then fed into the Decoder to generate the translation in the target language [23], as shown in Fig 1. Let x = (x1, x2,. . ., xm) represent the current input source sentence to the Encoder, where m is the number of words in x. Let y = (y1, y2,. . ., yn) be the final output target sentence from the Decoder, where n is the number of words in y. At time step t, the NMT model predicts the tar- get word yt by calculating the conditional probability as follows: ð p ytjy1; y2; . . .; yt (cid:0) 1; x Þ ¼ softmax g htð Þ ð Þ ð3Þ Here, ht is the recursive hidden state computed according to (4), and g(�) is the transforma- tion function that converts ht into a word vector, with the word vector’s dimension equal to the size of the vocabulary. ht ¼ f ht(cid:0) 1; yt(cid:0) 1 ð s:t: h1 ¼ f c; y0 ð Þ; t � 2 Þ; t ¼ 1 ð4Þ Where c is the representation of the source sentence obtained from the Encoder, and f(�) is a non-linear function, which can be an RNN, LSTM, GRU (Gate Recurrent Unit), or Trans- former. Each sentence is represented with a start symbol <SOS> and an end symbol <EOS>, with y0 = <SOS>. Due to NMT’s superior performance across translation tasks compared to SMT, it has gained significant concern. The attention mechanism enables the model to focus on specific words in the source sentence when generating target words, significantly improving the quality of the generated translation text [31], this is also one of the sources of inspiration for this paper. 2.2 Proposed method In order to obtain high-quality steganographic text with inter-sentence semantical correlation, this paper proposes a NMT-based steganographic model called NMT-Stega. To fully leverage useful information from the source text during the generation process, an attention mecha- nism is employed. Traditional TBS method based on SMT generates multiple translation sen- tences from the source sentence, encodes each sentence, and finally selects the one corresponding to the secret information as the final target sentence. In contrast, NMT-Stega dynamically selects generated words based on the secret information during the target sen- tence generation process, achieving the goal of embedding secret information. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 5 / 23 PLOS ONEN/A Fig 1. NMT model. https://doi.org/10.1371/journal.pone.0295207.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 6 / 23 PLOS ONEN/A Since the selected words are not always the most probable ones, embedding process may lead to a decrease in the quality of the generated text to some extent. The main focus of this paper is to maintain inter-sentence semantics during the information embedding process while minimizing the quality degradation caused by word pick. 2.2.1 General architecture. To improve translation accuracy, this paper adopts an NMT model based on attention mechanism as the translation model. Integrating attention mecha- nism into the Encoder-Decoder structure allows the model to dynamically focus on specific parts of the input, thereby enhancing the efficiency of natural language processing (NLP) tasks. For instance, in machine translation, the attention mechanism can find highly relevant source language words when predicting the output value yt. The architecture of the proposed NMT-Stega model is illustrated in Figs 2 and 3. Next, the working principles and roles of the encoder and decoder will be introduced separately. Let x ! ¼ ðx1; x2; . . .; xmÞ represent the current input sentence to the encoder, where m is !. The hidden layer h can take the form of RNN, LSTM, or GRU. In the number of words in x this paper, bidirectional LSTM (Bi-LSTM) is used as the hidden unit, which can better capture contextual information within sentences, as it compresses not only the information preceding the current word but also the information following it. In other words, each word xt can be ! represented as ht, which is a fusion of the forward hidden state h t and the backward hidden state h t: ! h ! t ¼ fLSTMðxt; h t(cid:0) 1Þ h t ¼ fLSTMðxt; h tþ1Þ ð5Þ ð6Þ Fig 2. Encoder of NMT-Stega model. https://doi.org/10.1371/journal.pone.0295207.g002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 7 / 23 PLOS ONE N/A Fig 3. Decoder of NMT-Stega model. https://doi.org/10.1371/journal.pone.0295207.g003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 8 / 23 PLOS ONE!T ht ¼ ½ h T t ; h t �T N/A ð7Þ The decoder’s final output is y = (y1, y2,. . ., yn), where n is the number of words in the target sentence y. The probability distribution for predicting the current word yt can be represented as: � (cid:0) ! p ytjy1; y2; . . .; yt(cid:0) 1; x ¼ MLPðyt(cid:0) 1; st; ctÞ ð8Þ where MLP is a multi-layer perceptron component, st is the output state of the hidden layer, which is given by: where, ct is another hidden state besides ht, computed as follows: st ¼ fLSTMðst(cid:0) 1; yt(cid:0) 1; ctÞ ct ¼ Xm i¼1 ^ai thi where ^ai t is the attention weight, calculated as follows: t ¼ softmaxðai ^ai tÞ ð9Þ ð10Þ ð11Þ Where ai by: t represents the reference weight of the current word yt on the source word xi, given ai t ¼ aðst(cid:0) 1; htÞ ð12Þ The component a is a feedforward neural network trained together with the other parts of the NMT model. 2.2.2 Dynamic word pick policy. Applying the proposed word pick policy to the obtained probability distribution allows us to generate an alternative set, and the effectiveness of the word pick policy directly determines the quality of elements in the alternative set. In this experiment, we determine whether a word can enter the alternative set by setting a limit on the variance of the probability distribution. First, we need to select the top-8 words with the highest probabilities from the obtained probability distribution. The probability value ranked first is denoted as p1, corresponding to the target word target_word1 generated at time t in the case of no embedding. We then calcu- late the variance values var(n) (n = 2, 4, 8) for the top-8 words, specifically: varðnÞ ¼ 1 n Xn i¼1 ðpi (cid:0) 1 n Xn j¼1 pjÞ2 < ε ð13Þ where pi represents the probability value of the i-th word wi at time t, sorted in descending order of probability, and ε is the variance threshold. If the variance of the top-8 words’ proba- bilities satisfies the threshold condition, i.e., var(8) is less than ε, then all 8 words are added to the alternative set. Otherwise, we reduce n to 4 and check if var(4) is less than ε. If it satisfies the condition, we add the first 4 words to the alternative set. If not, we further reduce n to 2 and check if var(2) is less than ε. If it satisfies the condition, the current alternative set consists of 2 words. If not, we skip the embedding at the current position and directly output the word with the highest probability. Different values of ε will produce different alternative sets. The impact of ε on the model will be detailed in the experimental section. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 9 / 23 PLOS ONEN/A The proposed word pick policy allows the number of words in the alternative set at each time step to be potentially different. In other words, the embedding capacity at each time step is automatically adjusted based on the current conditional probability distribution, making it an adaptive secret information embedding policy. 2.2.3. Our attention-based hyper-parameter adjustment. In our model, the probability distribution of the target word at time t depends on both the source text and the already gener- ated target text. The calculation formula is as follows: ð p ytjy1; y2; . . .; yt(cid:0) 1; x1; x2; . . .; xn Þ ¼ MLPða � Ct; StÞ ð14Þ Where, α is the attention hyper-parameter, and it satisfies α2[0, 1]. MLP represents a multi- layer perceptron. During model training, α is set to 1. During generation, α is adjusted to con- trol the degree of dependency between the target sentence and the source sentence. When α = 0, the generation of the word at time t only depends on the already generated target sentence, which represents the traditional generative linguistic steganography method. Due to the lack of dependency on the source text, the generated target sentences lack semantical relevance. As α increases, the embedding of the secret word at time t gradually depends on the source text. When α = 1, the dependency of the secret word on both the source and target sentences becomes consistent. In this paper, we manually pick different attention parameter weights to test the model’s text generation quality and steganography ability. Different weights will yield different proba- bility distributions, thereby changing the word pick in the alternative set. Embedding and extraction algorithms. The main idea of the embedding algorithm is to construct a alternative set based on the probability distribution generated by the language model and then select the word corresponding to the current moment’s secret information as the final embedded word. The specific embedding process is shown in Algorithm 1. Algorithm 1. Secret information embedding algorithm. Input: Secret information bitstream B; Source text C; Beam size bs; Variance threshold ε; Weights α, β. Output: Target embedded word. Procedure: Step 1: Data preprocessing and model training. Step 2: While B is not empty do: Step 3: Read a sentence from the source text C. Step 4: If not at the end of the sentence then: Step 5: Calculate the probability distribution of the next word using the model according to (14). Step 6: End if. Step 7: For bs = 8; bs > 0, do: Step 8: If var(bs) < ε then: Step 9: Add these bs words to the alternative set. Step 10: Else: Step 11: bs = bs-2. Step 12: End if. Step 13: End for. Step 14: Build a binary tree based on the probability distribution of alternative words in the alternative set and encode the alternative words. Step 15: Generate the target word corresponding to the binary code that matches the current secret information bit. Step 16: End while. Step 17: Return the generated target embedded word. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 10 / 23 PLOS ONEN/A For example, assuming a secret bit stream B = {01011011. . .}, and the source text is He said he hopes that the two sides will further strengthen their exchanges and cooperation (In Chinese). The alternative set size is 2, and the current secret bit is 0. Then, we select the word with the highest probability as the embedded word. If the next bit is 1, we choose the word with the sec- ond-highest probability as the embedded word. Based on the secret bit stream, the final embedded sentence obtained will be He said he hopes that the two sides will further strengthen their exchanges and cooperation (In English). The process of secret information extraction is similar to embedding. The receiver shares the source text and the same NMT model with the sender. Then, the same method is used to build the alternative set, encode alternative words, and compare the received stego-text with alternative words to extract the corresponding secret information bits. The specific extraction algorithm is shown in Algorithm 2. Algorithm 2. Secret information extraction algorithm. Input: Target carrier text; Source text C; Beam size bs; Variance threshold ε; Weights α, β. Output: Secret information bitstream B. Procedure: Step 1: Data preprocessing and model training. Step 2: Read a sentence from the source text C. Step 3: If not at the end of the sentence then: Step 4: Calculate the probability distribution of the next word using the model according to (14). Step 5: End if. Step 6: For bs = 8; bs > 0, do: Step 7: If var(bs) < ε then: Step 8: Add these bs words to the alternative set. Step 9: Else: Step 10: bs = bs-2. Step 11: End if. Step 12: End for. Step 13: Build a binary tree based on the probability distribution of alternative words in the alternative set and encode alternative words. Step 14: Compare the received hidden sentence with alternative words, extract the corresponding secret information bits. Step 15: Add the extracted bits to B. Step 16: Return the secret information bitstream B. 3. Experiments and results This paper conducted a series of experiments to test the embedding rate, text quality, and secu- rity of the generated carrier text. In addition, a comparison with other generative linguistic ste- ganography models was performed to assess the model’s performance in preserving text semantics. 3.1 Data acquisition, processing and experimental setup The proposed model used a parallel Chinese-English corpus obtained from the public news website as the dataset, comprising 1,252,977 news sentences. The maximum and average sen- tence lengths were 98 and 34, respectively. The dataset was split into training, validation, and testing sets with an 8:1:1 ratio. Before model training, data preprocessing was performed, including removing special symbols, website links, and numerical characters. All test experiments in this paper were carried out on the Ubuntu 18.4 operating system with a GX2080Ti GPU (128GB) and CUDA 10.0. The model was implemented using Pytorch and Python 3.8. The hyper-parameters of the model were set as follows: encoder and decoder PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 11 / 23 PLOS ONEN/A Fig 4. The conditional probability variance histogram of top-2. https://doi.org/10.1371/journal.pone.0295207.g004 with 6 stacked layers, each layer using an 8-head attention mechanism; word embedding dimension set to 512; dropout regularization (dropout rate = 0.2) during pretraining to avoid overfitting; Adam optimization algorithm with an initial learning rate of 0.0003. Additionally, a batch size of 64 and 75 iterations were used. To ensure the quality of the generated carrier text, setting a reasonable variance threshold is essential. This paper first calculated the variance distributions of the top-2, 4, and 8 words without embedding, and their corresponding histograms are shown in Figs 4–6. Based on the obtained histograms, manual threshold values were selected: 0.07, 0.072, 0.074, 0.076 for the top-2; 0.07, 0.08, 0.09, 0.1 for the top-4; 0.045, 0.05, 0.055, 0.06 for the top- 8. The impact of variance thresholds on evaluation metrics will be further demonstrated in the following experiments. 3.2 Evaluation metrics In machine translation systems, BLEU (Bilingual Evaluation Understudy) [32] and PPL (Per- plexity) [33] are commonly used to evaluate text quality. BLEU is a metric that measures the similarity between machine translation and professional translation. Higher BLEU values indi- cate higher translation quality, and its calculation is as follows: BLEUN ¼ bðC; SÞ expð XN n¼1 wn log CPnðC; SÞÞ Where b(C, S) is a penalty factor: bðC; SÞ ¼ ( 1; ls � lc ls lc; lc � ls e1(cid:0) ð15Þ ð16Þ Where, ls is the length of the reference sentence, and lc is the length of the evaluated sentence. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 12 / 23 PLOS ONEN/A Fig 5. The conditional probability variance histogram of top-4. https://doi.org/10.1371/journal.pone.0295207.g005 CPn(C,S) represents the precision of generated translation C compared to reference translation S: X X CPnðC; SÞ ¼ i k min ðhkðciÞ; max hkðsijÞÞ X hkðciÞ X i k ð17Þ Fig 6. The conditional probability variance histogram of top-8. https://doi.org/10.1371/journal.pone.0295207.g006 PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 13 / 23 PLOS ONEN/A Table 1. Example target texts generated with different attention HYPER-parameter. (A) α 1 It is not the level and number of people that we have seen. Translation 0.9 There has been a long time, but not the level and number of people we have seen now. 0.8 There is always a problem, but not the level and numbers that we have seen now. 0.7 However, it is not the case that we have seen in the level and number of people. 0.6 It is not enough to see that the current level of work. (B) α 1 During the passage of typhoon disaster, we found in the sky and some trees on the ground were found damaged by road safety. Translation 0.9 During the passage of typhoon, certain trees on the slope were posed as a safety hazard in August last year. 0.8 During the passage of typhoon, some trees on the slope were once burning and posed a risk of fallen trees. 0.7 During the passage of typhoon course, there were many trees on the slope of Sheungyiu last August and posed a challenge to road safety. 0.6 During the course of typhoon, it was forbidden to grow down on the ground and posed a chance to prevent the spread of land. (C) α 1 The best way is to encourage enterprises of the two countries to explore areas and content of cooperation, and the government has given positive support. Translation 0.9 The best way is to encourage the companies of both sides to explore areas and content of cooperation, and the government should give positive support. 0.8 The best way is to encourage the enterprises of the two countries to explore areas and content of cooperation, and to offer positive support. 0.7 The best way to promote cooperation is to encourage enterprises of the two sides to explore new ways to expand cooperation and to give them positive support. 0.6 The best way to promote cooperation is to encourage enterprises to discuss ways to expand cooperation. https://doi.org/10.1371/journal.pone.0295207.t001 PPL is a metric used to evaluate the quality of language model. It treats the language model as a probability distribution over sentences or paragraphs, representing the probability of gen- erating a sentence in the text. A smaller PPL indicates a better-trained model, and its calcula- tion is as follows: PPL ¼ 2(cid:0) 1 N XN i¼1 log PðsiÞ ð18Þ Where si represents the i-th generated sentence, N is the total number of sentences, and P(si) is the probability of si calculated by the language model. In this paper, BLEU was used to assess text quality, while PPL was used to evaluate the statistical performance of the generated text. 3.3 Ablation experiment Table I presents example target texts generated with different values of the attention hyper- parameter α. From Table 1, it can be observed that sentences generated under different attention hyper- parameters are different, but they share similar semantical attributes. Therefore, further research on the relationship between attention hyper-parameters and model performance is necessary. This section will discuss the impact of different α values on the model’s embedding rate and the quality of the generated text, using bs = 2 as an example. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 14 / 23 PLOS ONEN/A Fig 7. Influence of different α and ε on bpw. https://doi.org/10.1371/journal.pone.0295207.g007 3.3.1 The influence of α on the embedding rate. As shown in Fig 7, as α increases, the embedding rate bpw (bits per word) of different models decreases. This is because α reflects the dependency level of the current generated word yt on the source sentence. When α decreases from 1 to 0.6, the constraints on the current word yt are reduced, which expands the selectable range of alternative set elements and ultimately increases the size of the alternative set. Thus, reducing the value of α can increase the model’s embedding capacity. 3.3.2 The Influence of α on the PPL. Fig 8 reveals that as α increases, PPL also increases, which is opposite to bpw. This indicates that appropriately reducing the value of α not only increases the model’s embedding capacity but also reduces the complexity of the language model. This is because when the dependency of yt on the source sentence is reduced, the gen- eration of yt relies more on the already generated parts. It is these parts that provide more use- ful information, making the distribution of the final generated sentences closer to the true distribution of the target text. 3.3.3 The influence of α on the BLEU. Fig 9 shows that BLEU decreases as α decreases, further confirming that changing the value of α affects the dependency of yt on the source sentence. Table 2 presents the experimental results of PPL, BLEU, and bpw under different variance thresholds ε and attention hyper-parameters α, with varying bs values. From Table 2, it can be concluded that as α decreases, both PPL and BLEU decrease, while bpw increases. Moreover, increasing bs and variance threshold ε also increases the model’s embedding capacity and complexity, with BLEU decreasing. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 15 / 23 PLOS ONEN/A Fig 8. Influence of different α and ε on PPL. https://doi.org/10.1371/journal.pone.0295207.g008 When the embedding rate is too high, the quality of the generated carrier text may decline due to the impact on the quality of alternative set elements. For example, given the source text: An area of approximately 330 hectares of foreshore and seabed are affected by the works as required in the gazette today April 14 (In Chinese), a high embedding rate may produce the carrier text: The scope of the service is well affected by the three stages of the project, which is scheduled for today April 15 in 2004 (In English). Reducing the embedding rate results in better carrier text such as An area of approximately 330 hectares of foreshore and seabed are affected by the works as required in the gazette today April 14 (In English). 3.4 Contrast experiment This study compares three generative linguistic steganography models, including two different Markov-based steganography models [34, 35] and one RNN-based generative text steganogra- phy model, i.e., RNN-stega [22]. Specifically, literature [34] utilizes Markov models and Huff- man coding to embed secret information by analyzing the statistical features of text. It first establishes a Markov model of the text and then employs Huffman coding to embed the secret information into the text, minimizing its impact. This method emphasizes the efficient con- cealment of information while preserving the naturalness of the text. By utilizing the Markov model, it can better maintain the statistical characteristics of the text. On the other hand, litera- ture [35] focuses on a language model based on Markov chains, modifying the order of text to embed secret information. It utilizes the properties of Markov chains to embed information into the text and reconstructs the Markov chain during extraction to retrieve hidden PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 16 / 23 PLOS ONEN/A Fig 9. Influence of different α and ε on BLEU. https://doi.org/10.1371/journal.pone.0295207.g009 information. This approach emphasizes achieving information concealment within the text by imitating the characteristics of natural text to enhance security. As for the introduction of RNN-stega, please refer to the introduction section. We utilize two linguistic steganalysis tools, namely NFZ-WDA [36] and LS-CNN [37], to evaluate the security of the carrier texts. NFZ-WDA is a specialized steganalysis model for detecting neural network-based steganography that uses n-gram algorithms to identify statisti- cal feature variations between the carrier texts and reference texts. NFZ-WDA is a specialized steganalysis model based on the observation that all authors leave distinct inherent traces of vocabulary use in their written texts, which can be recognized and used for authorship analy- sis. By analyzing the distribution of intrinsic words within the text using the n-gram algorithm, the inherent word usage style within the text can be estimated to detect statistical feature changes between the carrier text and the reference text. On the other hand, LS-CNN first uses the word embedding layer to extract the semantic and syntactic features of words, and then learns sentence features using rectangular convolutional kernels of different sizes to capture complex long-text dependencies and detect distribution differences between the carrier text and the reference text. In this study, conventional training methods were not employed, where steganalysis tools are trained separately for different bs. As real-world carrier texts are often a mixture of various PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 17 / 23 PLOS ONETable 2. Influence of different parameters on bwp, BLEU and PPL. (A) (B) (C) (D) ε 0.07 0.072 0.074 0.076 0.07 0.08 0.09 0.1 0.045 0.05 0.055 0.06 ε 0.07 0.072 0.074 0.076 0.07 0.08 0.09 0.1 0.045 0.05 0.055 0.06 ε 0.07 0.072 0.074 0.076 0.07 0.08 0.09 0.1 0.045 0.05 0.055 0.06 ε α 1 α 1 α 1 bs 2 4 8 bs 2 4 8 bs 2 4 8 bs α bpw 0.663 0.715 0.758 0.758 0.778 1.059 1.120 1.174 0.841 0.981 1.104 1.320 bpw 0.770 0.815 0.843 0.866 1.180 1.265 1.297 1.390 1.243 1.395 1.457 1.650 bpw 0.787 0.835 0.867 0.896 1.199 1.301 1.332 1.446 1.265 1.452 1.518 1.762 bpw PPL 55.860 57.915 59.482 62.499 63.507 67.658 72.800 75.913 69.731 73.123 79.709 81.373 PPL 54.399 55.437 57.985 58.031 63.046 63.268 68.503 73.664 64.391 72.113 74.640 79.044 PPL 50.169 52.201 55.249 57.267 59.267 60.397 62.409 67.547 62.408 71.735 72.801 73.345 PPL N/A BLEU 23.592 23.286 23.370 23.216 23.516 23.202 23.316 23.050 23.368 23.131 22.898 22.468 BLEU 23.207 22.962 22.900 23.074 23.213 22.921 23.034 22.833 22.970 22.669 22.699 22.236 BLEU 22.339 22.271 22.151 22.273 22.245 22.144 22.143 21.813 22.269 21.803 21.674 21.196 BLEU (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 18 / 23 PLOS ONETable 2. (Continued) (E) 0.07 0.072 0.074 0.076 0.07 0.08 0.09 0.1 0.045 0.05 0.055 0.06 ε 0.07 0.072 0.074 0.076 0.07 0.08 0.09 0.1 0.045 0.05 0.055 0.06 1 α 1 2 4 8 bs 2 4 8 https://doi.org/10.1371/journal.pone.0295207.t002 0.824 0.883 0.924 0.949 1.265 1.393 1.488 1.572 1.352 1.604 1.803 1.988 bpw 0.896 0.923 0.944 0.948 1.398 1.553 1.678 1.775 1.548 1.878 2.102 2.211 44.105 45.148 45.167 47.144 52.200 52.270 55.270 62.448 51.329 59.586 63.176 65.112 PPL 42.103 43.144 45.138 45.193 45.177 50.258 50.327 52.443 44.348 46.549 50.751 55.065 N/A 20.883 20.815 20.751 20.589 20.932 20.804 20.669 20.272 20.810 20.516 20.095 19.777 BLEU 18.774 18.678 18.697 18.583 18.700 18.515 18.108 18.063 18.510 18.080 18.054 17.519 data, we aim to approximate real-world scenarios and train a single steganalysis model with the mixed carrier texts of different bs. For instance, when training LS-CNN to detect carrier texts, the training dataset contains a total of 15,000 carrier sentences generated from various payload bs, attention parameters α, and variance thresholds ε. Furthermore, to validate the long-range semantical correlations between the generated car- rier sentences, we computed the semantical distances and the average sentence lengths of the generated carrier sentences. Semantical distance is a metric used to measure the semantic simi- larity between texts. In steganalysis, it is often employed to compare the semantical similarity between carrier text and reference text. If the embedded secret information alters the semanti- cal content of the text, the semantical distance may increase. As a steganalysis metric, it helps detect whether a text contains hidden information. A larger semantic distance may indicate an increased difference between texts, possibly due to steganographic operations. Average sen- tence length is the mean length of sentences in a text. In steganalysis, this metric is typically used to detect changes in the text. When secret information is embedded in a text, the average sentence length may change because the embedded information can lead to certain sentences becoming shorter or longer. By comparing the average sentence length between carrier and reference text, changes can be detected, hinting at potential steganographic operations in the text. The semantical distance was computed using OpenAI GPT model [38]. The steganalysis accuracy and semantical distance results for different models are shown in Table 3. PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 19 / 23 PLOS ONEN/A Table 3. Comparison of experimental results under different experimental configurations. Note: # indicates that smaller values are better, and " indicates that larger values are better. Method Markov [34] Markov [35] RNN-stega [22] NMT-stega ε N/A N/A N/A N/A N/A N/A N/A N/A N/A 0.045 0.05 0.055 0.06 0.045 0.05 0.055 0.06 bpw 1 2 3 1 2 3 1 2 3 0.841 0.981 1.104 1.320 1.548 1.878 2.102 2.211 https://doi.org/10.1371/journal.pone.0295207.t003 NFZ-WDA# LS-CNN# Semantical distance# Average length" 82.5 80.5 82.5 88.5 84.5 82 76.5 73 64.5 53 53.5 53.5 56 55.5 52.5 57.5 55.5 95.2 94.8 95 94.9 96.6 96.8 80.5 88 89.8 56.5 58.2 59 61.5 53.4 55 59.7 65.5 354.317 355.293 368.158 329.113 360.997 373.532 280.612 324.685 331.454 87.916 89.941 93.218 94.301 92.019 96.428 106.440 113.629 17 17 23 19 20 25 26 28 31 46 46 46 46 47 47 47 47 PPL# 493.992 577.628 585.311 294.578 486.043 531.080 44.080 67.915 136.542 69.731 73.123 79.709 81.373 44.348 46.549 50.751 55.065 BLEU" 0.994 0.731 0.863 1.681 0.973 0.607 10.362 8.041 5.679 23.368 23.131 22.898 22.468 18.510 18.08 18.054 17.519 Table 3 indicates that the proposed model outperforms the other three models in terms of anti-steganography attacks and preserving semantical relationships between sentences. Com- pared to RNN-stega, the detection accuracy of NFZ-WDA and LS-CNN on the carrier texts generated by our model reduced by 21.5% and 28.3%, respectively, at the same embedding rate. This indicates that our model exhibits higher security and better resistance against mali- cious attacks. The proposed NMT-stega model generates carrier text with a maximum average sentence length of 47, whereas the previous methods had a maximum sentence length of only 31. This indicates that our method introduces more significant changes in sentence structure, resulting in longer sentences. Additionally, the minimum semantical distance between sen- tences reached 87.916, which is significantly lower than the minimum semantical distance of the previous methods. This suggests that the generated carrier texts exhibit higher semantical similarity between sentences, making them more natural and preserving more of the original semantical information compared to the previous methods. In summary, our method can effectively maintain semantical relationships between long sentences, even when generating lengthy text. Moreover, combining Tables 2 and 3, it can be seen that our method achieved the best PPL of 42.103 and a BLEU score of 23.592. Compared to the previous best method, RNN-stega, these represent improvements of 1.977 and 13.23, respectively. In conclusion, the proposed method excels at the machine translation task while effectively resisting steganalysis attacks. 3.5 Efficiency analysis The storage space for this method primarily comes from the encoder-decoder structure, occupying approximately 182MB of memory. Other components, such as the alternative set, require about 1.4MB of space, while the memory usage for intermediate variables is almost negligible. During the training phase, the neural network consumes approximately 7.3 hours. On average, during the inference phase, the time required to complete one pass of the PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 20 / 23 PLOS ONEN/A neural network is around 268ms. Constructing the binary tree takes about 17ms, resulting in an estimated time of about 311ms for embedding information in a single sentence and approximately 304ms for extracting information from a single sentence. The reason that the secret information embedding algorithm consumes more time compared to the extraction algorithm is that it needs to generate the carrier text word by word. In contrast, the extrac- tion algorithm primarily involves comparing the received carrier sentence with alternative words for information extraction, which tends to be a faster process as it doesn’t entail text generation but only involves comparison and matching operations. Considering the superi- ority of this method in the anti-steganography attacks and maintaining inter-sentence semantics, these results demonstrate an acceptable performance level in the sensitive field of linguistic steganography. 4. Conclusion In this paper, we combine NMT model with linguistic steganography model, resulting in the NMT-stega model. Firstly, we adopt an attention-fused Encoder-Decoder architecture to dynamically embed secret information during the text generation process. As the generated text needs to maintain semantical connections with distant words, the pick of each target text takes into account both the source text and the previously generated target words. Secondly, the proposed word pick policy based on probability variance allows for varying the number of words in the alternative set at each time step. This adaptive secret information embedding pol- icy adjusts the embedding capacity based on the current conditional probability distribution. Finally, we analyze the impact of the proposed hyper-parameter based on prior knowledge on the embedding rate and text generation quality of the model. The achievements of this study provide researchers with intriguing directions and visions for future work. Here is a discussion: 1. Enhancing NMT-stega Model Performance: Future work can focus on improving the per- formance of the NMT-stega model to increase the embedding rate and text generation qual- ity. This can be achieved through more complex encoder-decoder architectures, refined attention mechanisms, and advanced word selection strategies. 2. Exploring Diverse Applications: The success of the NMT-stega model’s application can be extended to various fields. Researchers can explore how this technology can be used to improve steganalysis, enhance information security, or advance other applications of steganography. 3. Increasing Model Robustness: Robustness is a key concern in the field of linguistic stegano- graphy. Future research can concentrate on developing more robust NMT-stega models to withstand various text processing and analysis attacks. 4. Advancements in Steganalysis and Detection: As linguistic steganography continues to evolve, steganalysis and detection techniques need to keep pace. Researchers can explore new methods and algorithms to improve the detection capabilities of linguistic steganography. Supporting information S1 File. (RAR) PLOS ONE \| https://doi.org/10.1371/journal.pone.0295207 January 2, 2024 21 / 23 PLOS ONEN/A Author Contributions Conceptualization: YiQing Lin. Data curation: YiQing Lin, ZhongHua Wang. Funding acquisition: YiQing Lin. Project administration: YiQing Lin, ZhongHua Wang. Visualization: ZhongHua Wang. Writing – original draft: YiQing Lin, ZhongHua Wang. Writing – review & editing: ZhongHua Wang. References 1. Xiang L, Wang R, Yang Z, et al. Generative Linguistic Steganography: A Comprehensive Review[J]. 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10.1371_journal.pone.0292082	RESEARCH ARTICLE Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm Qiuyan Wang1, Haibing DongID 1, Haoyue Huang2 1 School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China, 2 School of Electrical Engineering, University of South China, Hengyang, China 2005001481@hnit.edu.cn Abstract An essential industrial application is the examination of surface flaws in hot-rolled steel strips. While automatic visual inspection tools must meet strict real-time performance criteria for inspecting hot-rolled steel strips, their capabilities are constrained by the accuracy and processing speed of the algorithm used to identify defects. To solve the problems of poor detection accuracy, low detection efficiency, and unsuitability of low computing power plat- forms of the hot-rolled strip surface defect detection algorithm The Swin-Transformer- YOLOv5 model based on the improved one-stage detector is proposed. By employing GhostNet, the model’s lightweight design, and guaranteed detection accuracy are both achieved. The C3 module introduces Swin-Transformer to address the issues of cluttered backdrops of defect photos and easily confused defect categories. With the addition of the CoordAttention module, the model’s capacity to extract defective features is improved, and its performance keeps getting better. The issue of huge differences in different scales and poor detection of small flaws is resolved by employing BiFPN for feature fusion, and the detector’s capacity to adapt to targets of different scales is improved. The experimental results demonstrate that the improved Swin-Transformer-Yolov5 model significantly outper- forms the industry-standard target detection algorithms, and the model’s mAP value still improves by 8.39% over the original model while reducing the number of parameters, GFLOPs, and weight by 36.6%, 40.0%, and 34.7%, respectively. The model is better suited for use on low-arithmetic platforms as a result. 1 Introduction Hot-rolled steel is frequently used in sectors like pipeline construction, mechanical construc- tion, and automobile structural steel. Utilizing hot-rolled strip may have inner damage that affects the mechanical properties and corrosion resistance of the strip in addition to surface imperfections, which affect the strip’s appearance. It is crucial to inspect the surface and select the unqualified strips as a result [1–4]. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Wang Q, Dong H, Huang H (2024) Swin- Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm. PLoS ONE 19(1): e0292082. https://doi.org/ 10.1371/journal.pone.0292082 Editor: Ahmed Mancy Mosa, Al Mansour University College-Baghdad-Iraq, IRAQ Received: May 18, 2023 Accepted: September 12, 2023 Published: January 25, 2024 Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset, code and models are publicly available at https://github. com/donghaibing2005001481/YOLOv5s. Funding: This work was supported by the National College Students’ innovation and entrepreneurship training program under Grant No. 202211528034. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Common automatic vision inspection instruments first identify defective areas from the normal background [5]. The detected defects are then identified and marked. The inspection PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 1 / 14 PLOS ONESteel strips surface defect detection algorithm of hot-rolled steel strips has high requirements for real-time performance, but automatic vision inspection instruments are limited by the accuracy and time efficiency of the algorithm during defect detection. Therefore, the focus of this paper is to reduce the computational over- head of the algorithm while retaining a high accuracy rate. At the same time, the environment at a steel mill site is complex [6]. The suboptimal imaging environment requires detection algorithms that can withstand large intra-class variations and small rapier distances [7]. Eter- nal continuous image streams require algorithms that balance accuracy with computational complexity [8]. Industry and academia have been trying to figure out how to solve this prob- lem, including hardware upgrades and algorithm optimization. Although the hardware archi- tecture based on server scaling or ASIC acceleration has gained some progress [9–11]. But due to the limitations of Moore’s Law, it is difficult to get a significant breakthrough in hardware in a short period [12]. Therefore, it is very important to obtain performance improvements through algorithmic optimization. Therefore, this paper presents the Swin-Transformer-YOLOv5 model based on a one-stage detector improvement, which enhances classification accuracy and detection speed of flaws, and the smaller computational size simplifies the deployment of the model on platforms with lower arithmetic capability. 2 Literature review In recent years, with the popularity of artificial intelligence, machine learning has been heavily applied to steel strip surface defect detection. At present, machine learning usually classifies the steel strip surface defect detection task as a binary classification problem, i.e., with or with- out defects. The current steel strip surface defect detection methods are classified into three main categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning has made rapid progress in steel surface defects compared to unsuper- vised and reinforcement learning. Ghorai et al. ranked first among all feature classifier combi- nations by fusing the performance of VVRKFA (classifier) with one-level Haar features [13]. Liu et al. used a two-layer feed-forward neural network to reduce the defect detection task to a binary classification problem, but the large number of parameters of the neural network required a significant computational overhead [14]. Haq M A et al developed CNN based auto- mated weed detection system using UAV imagery. The developed model showed an overall accuracy after rigorous hyperparameter tuning for weed detection, significantly higher than previously reported studies [15]. Cha et al. proposed a deep CNN to detect steel surface defects with higher robustness with reduced computational overhead by tailoring model parameters for convolution and sub-sampling in a convolutional neural network (CNN) [16]. Moreover, the Faster R-CNN de-signed by the team further improves the real-time performance of the detection system [17]. Haq et al proposed Principal Component-based Convolution Neural Network (PCCNN) approach using CNN to detect intrusions, achieving greater precision based on deep learning [18]. YOLO enables CNN-based detection methods to be applied to real-time industrial scenarios by treating the bi-classification task as a regression problem. In the field of single-stage target detection, two classical algorithms, SSD and YOLO, have been widely used for surface defect detection. SSD is simple and efficient, suitable for small target detection, and has a good ability to handle the category imbalance problem. For example, Li et al. proposed a MobileNet-SSD-based method for detecting defects on the sealing surface of containers in filling lines, which simplified the parameters of the detection model [19]. Jawa- harlalnehru et al proposed target object detection from unmanned aerial vehicle (uav) images based on improved yolo algorithm. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 2 / 14 PLOS ONESteel strips surface defect detection algorithm accuracy [20]. The YOLO series algorithm uses a multi-scale training and prediction strategy to detect targets of different sizes and shapes. It also employs target classification and regres- sion techniques with faster detection speed and lower computational cost, which is signifi- cantly advanced in the field of strip steel surface defect detection. For example, Li et al applied an improved YOLO network to the detection of surface defects in cold-rolled strip steel and achieved high accuracy and detection precision [21]. However, it is difficult to detect defects with an area of less than 10 square millimeters. Kou et al. developed an end-to-end defect detection model based on YOLO-V3 [22]. Although the model’s FPS is higher than ours, our mAP is higher than theirs while the model is lighter and more suitable for deployment on mobile platforms. Li et al proposed an improved algorithm based on YOLOv4 [23]. Transformer has achieved great success in natural language processing in recent years [24– 26]. With the advent of Vision Transformer (ViT), Transformer has surpassed most CNN- based approaches on various computer vision tasks. Vision Transformer works by dividing the input image into two-dimensional Patches, which are then mapped into one-dimensional vec- tor sequences by a trainable linear mapping matrix. Finally, the one-dimensional vector sequence is fed into the standard Transformer architecture to learn the available feature repre- sentations. Also, Transformer has some applications in the field of target detection. For exam- ple, Zhang et al. proposed utilizing Vision Transformer with the ability to model remote dependencies using a self-attentive mechanism for image recognition [27]. However, it is found that ViT maintains the same feature resolution in all feature extraction stages and com- putes the dependencies among the pixels within the whole image, which leads to the problem that ViT cannot extract multi-level representation of the image and has high computational complexity. Therefore, an attempt is made to explore the Swin Transformer [28] as the basis for designing the network by introducing the local idea and hierarchical structure into the Transformer to solve the above problems. 3 Proposed methods The YOLOv5s algorithm is the lightest version of YOLOv5 (version 5.0) [29]. The algorithm for detecting surface imperfections in hot-rolled steel strips is developed using migration learning on the YOLOv5s pre-trained model [30]. The algorithm is made up of the following parts: Input, Backbone, Neck, and Head. The input is a 640 × 640 × 3 image, which is then processed using Mosaic data enhancement, adaptive image filling, and preprocessing opera- tions. The Backbone network is a high-performance classifier network that is used to extract generic features from the target. Its primary structural components are a slicing structure (Focus), a convolution module (Conv), a bottleneck (C3), and pyramidal pooling (SPP). Path aggregation and feature pyramid (FPN) are used in the Neck network structure (PAN). The head is mainly used for the final detection part, which applies anchor frames on the feature map and generates the final output volume with class probabilities, object scores, and enclos- ing frames. By swapping out the Conv in the Backbone and Neck of the YOLOv5s with GhostConv and the C3 module in the Backbone with the GhostBottleneck module, the Swin- Transformer-YOLOv5 algorithm proposed in this paper makes the model lighter. The Swin- Transformer is fused into the C3 module, and the C3STR module is utilized in place of the Neck C3 module. The CoordAttention module is positioned in the last layer of the backbone network, and the method of using PANet feature fusion in YOLOv5 is modified to using BiFPN for feature fusion. This leads to a lightweight design and better overall model perfor- mance by enabling the network precision to be increased and more features to be merged with little to no additional computational overhead. Fig 1 depicts the Swin-Transformer- YOLOv5 network structure. PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 3 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 1. Network structure of Swin-Transformer-YOLOv5. https://doi.org/10.1371/journal.pone.0292082.g001 3.1 Replace the Yolo5 Backbone and Neck with GhostNet Rich redundant features in the YOLOv5s model increase the generalization of the model but also create the issue of computational hit; therefore, the light weight of the model is achieved by introducing GhostNet in order to ensure the thorough interpretation of the input by the model [31]. Fig 2 depicts the Ghost module’s structural layout. The specific process of GhostNet is Y 0 ¼ X∗f 0 yij ¼ Fijðy0 iÞ; i 2 1; 2; . . . ; m; j 2 1; 2; . . . ; s ð1Þ ð2Þ The first step is the convolution operation, X is the input feature map, Y0 is the output m feature maps, and f0 is the convolution kernel of size K×K. The second operation is the linear Fig 2. The structure of the Ghost module. https://doi.org/10.1371/journal.pone.0292082.g002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 4 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 3. The structure of the GhostBottleneck module. https://doi.org/10.1371/journal.pone.0292082.g003 operation Fij for each feature map y0 Y12,. . .,Yms] are obtained, and after calculation, it can be concluded that the ordinary convolu- tion operation is about s times of Ghost module. i in Y0, and finally n = ms output feature maps Y = [Y11, The GhostBottleneck module, which consists of two Ghost Modules and replaces the features provided by regular convolution with features produced by a straightforward linear transforma- tion, significantly reduces the model complexity. When Stride = 1, the backbone is made up of two Ghost Modules (GMs), where the first GM increases the number of channels and the second GM decreases the number of channels to match the number of input channels, and the remaining edge component is identical to ResNet. Since S = 1, the input feature layer’s height and width are not compressed, and its purpose is to increase the network’s depth. When Stride is 2, a Deepwise convolution with a stride of 2 is added in the backbone region between the two GMs, which can condense the feature map’s height and breadth to make it only half as large as the input. To guar- antee that the Add operation may be aligned, a deep separable convolution with a step size of 2 × 2 and a standard convolution of 1 × 1 are additionally added to the residual edge part. The purpose of S = 2 is to modify the geometry of the input feature layer by compressing the height and width of the input feature layer. Fig 3 depicts the GhostBottleneck module’s structural layout. The Conv in the Backbone and Neck of the YOLOv5s is replaced with GhostConv to make the entire model lighter, and the C3 module in the Backbone is replaced with GhostBottleneck to speed up computation. 3.2 C3 module incorporating Swin-Transformer After several convolution processes, the high-level feature map loses the majority of the target feature information that a small target in the image should have as the network structure is PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 5 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 4. The structure of the C3STR. https://doi.org/10.1371/journal.pone.0292082.g004 deepened. Therefore, in the feature fusion part, we borrow the idea of the Swin-Transformer and embed it into the C3 module, replacing the four C3 models in Neck [32]. By introducing some discrete parameters of the Transformer and enhancing the semantic information and feature representation of small targets using the help of the window self-attention module. Fig 4 depicts the C3STR with a Swin-Transformer. Swin-Transformer features are learned by moving the window. Moving the window improves efficiency because self-attention is computed within the window, so as long as the window size remains constant, the computational complexity of self-attention remains con- stant, and the total computational complexity is a linear multiple of the image size, reducing the length and computational complexity of the sequence. The capacity to interact between two neighboring windows is enabled by the shifting operation at the same time, creating a cross- window connection between the upper and lower levels and thus enabling global modeling. The calculation process of the multi-headed self-attentive mechanism is as follows: AttentionðQ; K; VÞ ¼ SoftMax � � QKT p þ B ffiffiffi d V ð3Þ Attention denotes attention and SoftMax denotes the normalization function. Where Q, K, and V are query, key, and value matrices; d is the query/key dimension; and B is a smaller- sized bias matrix. Introducing B can effect significant improvement. The C3STR module controls the computational region in each window by dividing the local window to achieve cross-window information interaction, lowering the sequence length and computational complexity as compared to the Multi-head Self-Attention in the conven- tional Transformer. 3.3 CoordAttention module The application of attention mechanisms is hampered by the fact that lightweight networks cannot afford the higher computational overhead [33]. At the same time, the attention mecha- nism improves the model’s accuracy while ignoring location information, which is critical to the network. CoordAttention, as an attention mechanism designed for lightweight networks, is a good solution to the above problem [34]. Coordinate information embedding and coordi- nate attention generation are the two components of Coor-dAttention. The global pooling is transformed into a pair of one-dimensional feature encodings in information embedding, allowing the CoordAttention module to capture remote spatial interactions with location information. Long-range dependencies are captured along one spatial direction during atten- tion generation, while precise location information is retained along the other. The module acquires a good global sensory field and encodes precise location information, which improves the module’s ability to capture regions of interest. The module is simple, flexible, and efficient, PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 6 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 5. The structure of the CoordAttention module. https://doi.org/10.1371/journal.pone.0292082.g005 and its placement at the backbone network’s final layer can improve network accuracy while incurring little computational overhead. Fig 5 depicts the CoordAttention module’s structure. 3.4 Replace PANet with BiFPN The semantics of the features change from low latitude to high dimensional as the network lay- ers deepen. Each layer of the network will cause a certain degree of feature loss, and the seman- tic information can be enriched by fusing the features of different layers. The feature fusion method of YOLOv5 using PANet is changed in this paper to feature fusion using BiFPN to construct a feature pyramid, and the semantic features extracted from the backbone network are fused top-down using efficient bi-directional cross-scale connectivity and weighted feature map fusion. The shallow network can contain clearer location information due to larger reso- lution, and the deep network can contain more high-dimensional semantic information due to the large sensory field [35, 36]. More features can be fused without increasing the cost by adding lateral connections between the original input and output nodes of the same feature. The improved YOLOv5s network will extract features at various scales from the backbone net- work’s GhostBottleneck2 and GhostBottleneck3 layers, which will then be fed into the BiFPN network. Fig 6 depicts the BiFPN structure diagram. 4 Experiments 4.1 Datasets The dataset used for the experiments is a dataset of hot-rolled strip surface defects published by Northeastern University: the dataset includes six typical hot-rolled strip surface defects, which are rolled-in scale, patches, crazing, pitted surface, inclusion, and scratches [37]. The database contains a total of 1800 grayscale images, with 300 images per category. Using PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 7 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 6. The structure of the BiFPN. https://doi.org/10.1371/journal.pone.0292082.g006 LabelImg software, the dataset was divided into training datasets, validation datasets, and test datasets according to 7:2:1. Fig 7 depicts the six typical defects. The label box center point coordinates (x, y), width, and height of the hot-rolled steel strip surface defect dataset are normalized. Fig 8(A) shows the distribution of the center coordinates of the label. From the figure, it can be seen that the center coordinates are distributed almost anywhere in the image, which indicates that the defects are uniformly distributed anywhere in the image. Fig 8(B) shows the height and width distribution of the label, and it can be seen that the dark part is mainly distributed in the lower left corner of the image, although it is also dis- tributed in all other positions. This indicates that the defects to be detected are mainly small and medium targets, although there are also some large targets. 4.2 Training details The experimental environment in this paper is based on the Windows 10 operating system, with AMD R7 4800H CPU, NVIDIA GeForce RTX2060 GPU with 6 GB video memory, 16 GB RAM, PyTorch 1.12.1 as the deep learning framework, Python version 3.8.13, and CUDA version 11.7. This training is performed using migration learning by loading pre-trained weight parame- ters on the hot-rolled strip surface defect dataset and continuing the training 300 times. The PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 8 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 7. The six typical defects. https://doi.org/10.1371/journal.pone.0292082.g007 input image size is 640 × 640, the Batch_size is set to 16, the initial learning rate is 0.01, the momentum is 0.937, the weight decay is set to 0.0005, and the Epoch is set to 300. The model learning rate will gradually become larger during the first three Epoch training runs; after three Epochs, the learning rate will become 0.01, and with each Epoch iteration, the learning rate will gradually become smaller to prevent the model from overfitting while ensuring the deep temperature of the model. 5 Results and discussions 5.1 Experimental results and ablation study Table 1 shows the specific parameters of each model and the results of the ablation experi- ments. Analysis of the experimental results shows that our model has a 36.6% decrease in Fig 8. (a) Analysis of the center point of the callout box; (b) Analysis of the size of the callout box. https://doi.org/10.1371/journal.pone.0292082.g008 PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 9 / 14 PLOS ONESteel strips surface defect detection algorithm Table 1. Specific parameters of each model and ablation experiments. Method YOLOv5s(baseline) + GhostNet + C3STR + CoordAtt +BiFPN +CoordAtt+BiFPN + GhostNet+ C3STR +GhostNet+CoordAtt+BiFPN Swin-Transformer-YOLOV5 https://doi.org/10.1371/journal.pone.0292082.t001 Parameters(M) GFLOPs Weight(M) mAP(%) 7.08 4.17 7.29 7.46 7.00 7.18 4.37 4.28 4.49 16.5 9.3 17.0 17.0 16.4 16.6 9.8 9.4 9.9 14.4 8.7 15.0 15.2 16.3 14.6 9.2 8.9 9.4 69.1 69.5 73.2 71.4 70.6 73.0 72.5 72.6 74.9 FPS 52.1 50.7 47.5 51.2 51.4 50.6 45.9 48.3 43.2 parameters, a 40.0% decrease in GFLOPs, a 34.7% decrease in weight, and an 8.39% increase in mAP compared to the YOLOv5s model. This is primarily due to the fact that GhostNet gener- ates features using simple linear transformation rather than normal convolution, which signifi- cantly reduces model complexity while also reducing model accuracy to some extent. Although there is a small increase in parameters, GFLOPs, and weight when the CoordAttention module is added after Backbone, the mAP improves by 3.3%, demonstrating that the addition of Coor- dAttention improves the model’s ability to extract defective features and significantly increases the overall model accuracy improvement. Changing the PANet in the Neck of the YOLOv5s to BiFPN improves the mAP by 2.2% compared to the YOLOv5s model with a small reduction in parameters and GFLOPs and a slight increase in weight. This suggests that by fusing features of different scales, BiFPN improves the detector’s ability to adjust to targets of different scales, as well as the problem of poor detection ability for defects with large scale variations and small defects. By replacing the C3 module in Yolov5’s Neck with the C3STR module incorporating the Swin-Transformer, the mAP improved by 5.9% with a slight increase in parameters, GFLOPs, and weight. This suggests that the addition of the Swin-Transformer improves the problem of cluttered backgrounds of defect images and easy defect type confusion. The mAP of YOLOv5s and our model for detecting surface defects in six types of hot rolled strips are shown in Table 2. Table 2 shows that our model improved the mAP of rolled-in scale, patches, crazing, pitted surface, inclusion, and scratches by 21.0%, 6.9%, 12.7%, 10.9%, 5.5%, and 1.25% when compared to the YOLOv5s model. For YOLOv5s poor detection results of rolled-in scale, crazing has a large improvement. According to the above analysis, the addition of GhostNet effectively reduces the model’s parameters, GFLOPs, and Weight. The addition of Swin-Transformer, BiFPN, and CoordAt- tention significantly improves the model’s mAP. Fig 9(A) and 9(B) show some of the detection results of the YOLOv5s model and our model. According to the comparison plots, YOLOv5s has a problem with missed detection, especially for small targets, whereas our model improves the detection rate in this regard. 5.2 Comparison of different algorithms In this paper, SSD, Faster R-CNN, YOLOv3, YOLOv4, Retina-Net, and Swin-Transformer- YOLOv5 were selected for performance comparison, and the test results were derived from Table 2. The effect of Swin-Transformer-YOLOV5 and YOLOV5S on 6 types of defects detection. Method YOLOv5s OURS rolled-in scale patches 49.9 60.4 85.3 91.2 crazing 35.8 40.3 pitted surface inclusion scratches 77.4 85.8 78.2 82.5 88.0 89.1 https://doi.org/10.1371/journal.pone.0292082.t002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 10 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 9. (a) Detection effect of YOLOv5s; (b) Detection effect of Swin-Transformer-YOLOv5. https://doi.org/10.1371/journal.pone.0292082.g009 the same dataset. From Table 3, we can find that the mAP of our model was much higher than that of its counterparts, YOLOv3 and YOLOv4, which may be due to the poorer capture of small defects by YOLOv3. Meanwhile, mAP was significantly ahead of SSD and Faster R-CNN models and slightly ahead of Retina-Net, due to the fact that our model combines different lev- els of features and background effects of the images. However, extracting more complex fea- tures also had a negative impact in terms of FPS. In general, the performance of Swin- Transformer-YOLOv5 in this paper was excellent. For the six hot-rolled strip surface defects, Swin-Transformer-YOLOv5 outperformed the five models except Retina-Net on all six defects. Compared with the Retina-Net model, only two categories of crazing and inclusion lag, which indicate that there is room for further optimization of the model. In terms of detec- tion speed, Swin-Transformer-YOLOv5 achieved an FPS of 43.2, which indicates that the model can satisfy the real-time detection of surface defects in hot-rolled strips. The performance evaluation matrix of different algorithms is shown in Fig 10. Compared with Faster-RCNN and SSD, our model had a huge advantage in both mAP and FPS dimen- sions. YOLOv4 and Retina-Net with similar processing speed were lower than our model in mAP. YOLOv3, which has a faster processing speed, lagged behind our model substantially in mAP. This shows that the performance of Swin-Transformer-YOLOv5 in this paper was excellent. Table 3. Performance of different algorithms. Types rolled-in scale Patches Crazing pitted surface Inclusion Scratches mAP FPS SSD 70.8 61.9 30.3 39.1 50.0 51.1 51.0 31.5 Faster-RCNN YOLOv3 YOLOv4 YOLOv5 Retina-Net OURS 54.5 75.3 25.0 73.6 65.1 81.1 62.4 23.8 30.8 82.5 21.4 77.0 62.1 83.2 58.2 60.7 38.7 87.9 15.6 68.6 72.3 82.0 61.7 44.7 49.9 85.3 35.8 77.4 78.2 88.0 69.1 52.1 43.5 91.1 45.9 74.7 84.2 81.6 70.2 47.8 60.4 91.2 40.3 85.8 82.5 89.1 74.9 43.2 https://doi.org/10.1371/journal.pone.0292082.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 11 / 14 PLOS ONESteel strips surface defect detection algorithm Fig 10. Performance evaluation matrix for different algorithms. https://doi.org/10.1371/journal.pone.0292082.g010 6 Conclusions In this paper, an algorithm named Swin-Transformer-YOLOv5 is designed for detect surface defects on hot rolled strip steel. Swin-Transformer, GhostNet, CoordAttention, and BiFPN are some of the modern computer vision techniques that are combined in Swin-Transformer- YOLOv5. To address the issue of a large number of parameters and computation, GhostConv and GhostBottleneck were proposed to be utilized in Backbone, and GhostConv was used in Neck to keep the model lightweight. The improved YOLOv5 network is more suited for real- world industrial applications since it features a lighter network concept and lower demanding hardware. It was proposed that the C3 module be used in conjunction with the Swin-Trans- former at Neck to address the issues of cluttered defect image backdrops and simple defect type confusion. The usage of BiFPN rather than PANet was suggested to increase the detector’s ability to adjust to targets of varied scales by fusing characteristics of multiple scales, which would address the difficulties of high variance in defect scales and poor detection of minor defects. The model’s capacity to extract defective features was enhanced by the CoordAttention module. The improved model obtained a 74.9% mAP with a 36.6% decrease in parameters, a 40.0% decrease in GFLOPs, a 34.7% decrease in weight, an 8.39% improvement over the base- line, and an FPS of 43.2 when tested on the dataset. The experimental results show that the improved network can achieve better detection with fewer parameters while retaining the potential for real-time monitoring. There is still potential for development in terms of detec- tion speed and efficiency even though the enhanced model performs better than the majority of the current target detection methods. In the next study, the model will introduce a richer dataset to strengthen its generalization capability and improve the model’s real-time PLOS ONE \| https://doi.org/10.1371/journal.pone.0292082 January 25, 2024 12 / 14 PLOS ONESteel strips surface defect detection algorithm monitoring capability. Meanwhile, the network structure is further optimized to enhance the extraction of features and improve the detection speed and accuracy of the network. Study how to deploy the model on mobile, and refine and improve the model in real defect detection applications. Author Contributions Data curation: Haoyue Huang. Formal analysis: Qiuyan Wang. Funding acquisition: Haibing Dong. Investigation: Haoyue Huang. Methodology: Qiuyan Wang, Haibing Dong, Haoyue Huang. Supervision: Haibing Dong. Validation: Qiuyan Wang, Haibing Dong, Haoyue Huang. Writing – original draft: Qiuyan Wang. Writing – review & editing: Haibing Dong. References 1. Hao Z, Wang Z, Bai D, Tao B, Tong X, Chen B. Intelligent detection of steel defects based on improved split attention networks. Frontiers in Bioengineering and Biotechnology. 2022; 9, 1478. https://doi.org/ 10.3389/fbioe.2021.810876 PMID: 35096796 2. Wen X, Shan J, He Y, Song K. Steel surface defect recognition: A survey. 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10.1371_journal.pntd.0011919	RESEARCH ARTICLE Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling Rebecca A. Butler1, Mona Papeş2, James T. Vogt3, Dave J. Paulsen1, Christopher Crowe3, Rebecca T. Trout FryxellID 1 1 Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, Tennessee, United States of America, 2 Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America, 3 United States Department of Agriculture Forest Service, Southern Research Station, Knoxville, Tennessee, United States of America * rbutle25@vols.utk.edu (RAB); rfryxell@utk.edu (RTTF) Abstract Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tick- borne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014–2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, veg- etation indices, evapotranspiration, temperature, and gross primary productivity. By con- trast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Flor- ida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appala- chians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and moni- toring areas with unsuitable tick habitat for potential missed encounters. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Butler RA, Papeş M, Vogt JT, Paulsen DJ, Crowe C, Trout Fryxell RT (2024) Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling. PLoS Negl Trop Dis 18(2): e0011919. https://doi.org/ 10.1371/journal.pntd.0011919 Editor: A´lvaro Acosta-Serrano, University of Notre Dame, UNITED STATES Received: June 6, 2023 Accepted: January 14, 2024 Published: February 14, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pntd.0011919 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: Base layers for each map are publicly available from the U.S. census (https://www.census.gov/geographies/mapping- files/time-series/geo/carto-boundary-file.html). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 1 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Environmental spatial data used in environmental niche modeling were collected from EarthExplorer (http://earthexplorer.usgs.gov) and EarthData (http://earthdata.nasa.gov/). Tick encounter data can be accessed at Dryad (https://doi.org/10.5061/ dryad.v41ns1s3n). Funding: The Forest Inventory and Analysis Program of the Forest Service, United States Department of Agriculture providing funding for the collection and submission of the ticks used in the study; procured by JTV and RTTF. JTV and CC are employed by the USDA Forest Service Southern Research Station. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Author summary The study highlights the significance of passive tick surveillance data collected by Forest Inventory and Analysis crews in providing valuable information into the presence and distribution of ticks in the southeastern United States. We used ecological niche modeling and generalized linear models to assess the geographic regions and temporal periods asso- ciated with where and when ticks are likely to be encountered. In the region, ticks remain active throughout the year with their distribution influenced by climatic and topographi- cal factors. Of interest, maximum temperature was a significant environmental variable for all three species suggesting that distributions may be altered as the climate warms. Ele- vation and landcover were important variables for both Amblyomma americanum and Dermacentor variabilis, whereas Ixodes scapularis populations were correlated with evapo- transpiration, vegetation indices, and soil organic matter. The research also identified new tick occurrence records providing data in a region with minimal infrastructure for tick surveillance, but with many ticks and tick-borne diseases. Continued long-term passive surveillance with collaborations with academic and government partnerships will help monitor tick distribution changes resulting from landscape and temperature changes which affect public health risks. Introduction The geographic distribution of human-tick encounters for medically important tick species is rapidly increasing in the United States (US) [1,2]. Range expansion for commonly encoun- tered tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapu- laris L.) has resulted in an upsurge of tick-borne disease cases in the last two decades including spotted fever group rickettsiosis, anaplasmosis, ehrlichiosis, alpha-gal syndrome, Powassan virus, and Lyme disease [3–7]. Current distribution range maps for these tick species are commonly based on known occurrences at the continental or county level which is important for surveillance and manage- ment of ticks for regional and county health departments [8,9]. These range maps or distribu- tion maps are often based on administrative landmarks (county boundaries) rather than tick and host biological patterns because these maps encompass large spatial areas with environ- mental factors imprecise for the species’ distribution [10]. Environmental or ecological niche models (ENMs) create maps with estimated distributions based on interactions of the species with environmental variables in time and space [11]. Recent ENMs for A. americanum pre- dicted this species to inhabit regions in the northern, southeastern, and western regions of the US and Mexico, as well as in the Midwest from eastern Texas to Kansas, Oklahoma, and Mis- souri [12–14]. Similarly, D. variabilis was predicted to occur in parts of Canada and Mexico, as well as northern, southern, and midwestern regions of the US including regions in California [15]. Predictions for I. scapularis were distributed throughout regions in the eastern and mid- western US [16]. Understanding the effects that environmental variables have on each tick spe- cies distribution is vital because of the recent and predicted impacts of climate and land-use change on their population dynamics [17–20]. Ticks spend the majority of their lives in the environment compared to time spent on hosts; thus, understanding how the environment influences tick populations will lead to increased understanding of ticks and their associated pathogens which can lead to effective management strategies [21]. For example, soil properties such as percent litter coverage and soil moisture are associated with A. americanum abundance [22,23]. Additionally, land management PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 2 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States decisions (e.g., burning) were associated with decreased immature I. scapularis [24] and A. americanum populations [25,26]. Specifically prescribed burns reduce tick abundance by fac- tors such as heat exposure or decrease in soil moisture [26]. Climatic variables (e.g., tempera- ture, vapor pressure, and precipitation) have also been associated with A. americanum, D. variabilis, and I. scapularis (e.g., [27,28]. Landscape variables associated with forests (e.g., land cover, primary productivity) are important for host populations and likely regulate infesting tick populations [e.g., 29,30]. For example, normalized difference vegetation index (NDVI), a measure of greenness, has been associated with the abundance of A. americanum [31]. Know- ing if an environmental variable is associated with human-tick encounters can be an important surveillance tool for monitoring and a potential management tool for controlling host species. As tick populations are expanding in a rapidly changing environment, we collaborated with the Southern Research Station’s Forest Inventory and Analysis (FIA) Program of the Forest Service, U.S. Department of Agriculture (USDA) to evaluate the environmental conditions that could increase likelihood of encountering ticks. We chose to work with FIA because for- esters are known to collect data at a variety of sites with varying environmental conditions and their forest crews are consistently exposed to ticks in the environment. Specifically, foresters collected encountered ticks while working on sites around the southeastern US. We used eight years (2014–2021) of tick encounter data to create environmental niche and generalized linear models to understand where and when ticks are likely to be encountered. Here we test the hypothesis that temporal, climatic, physiographic, and soil variables are reliable predictors of human-tick encounters for A. americanum, D. variabilis, and I. scapularis in the southeastern U.S. Materials and methods Tick encounter data Ticks were collected from forest crews employed by FIA. Passive tick collections were opportunistic and occurred when crews worked at plots in the southeastern U.S. between 2014 and 2021. Every year 1/5, 1/7, or 1/10 of the total plots, which are spatially distributed throughout each state (2,000 to 4,800 forest plots per state), are sampled. Crew members visit a single plot each day to inventory each site which takes an entire day. Ticks encoun- tered that day were placed into a single vial containing 80% ethanol and labeled with the date, forestry crew identification number, and GPS coordinates where the crew was work- ing [32]. Encountered ticks were sent to the University of Tennessee, Knoxville Medical and Veterinary Entomology laboratory where they were identified to species and life stage using taxonomic keys [33–36]. Statistical analysis Generalized linear models were created from (PROC GLIMMIX) in Statistical Analysis Soft- ware (SAS, ver. 9.4, Cary, North Carolina) with two-tailed hypotheses (α = 0.05) to analyze how season affects the presence of each tick species together and by life stage. Date of tick col- lection was transformed into a categorical season variable (winter, spring, summer, and autumn) based on solstice or equinox to account for daylength. Season was used to determine the probability to detect tick presence for each species separately in the binary logit models. Odds ratios and their 95% confidence intervals were calculated for independent variables that were successful at predicting the presence of each tick species. Relative tick encounters, similar to human-tick encounter phenology, were graphed by summing tick abundance for each month per years collected and transformed as a percentage. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 3 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Environmental niche modeling of potential suitability for ticks FIA crews used hand-held GPS receivers to record the latitude and longitude of each FIA plot. The coordinates used for niche modeling are within 1.6 km (1 mile) of the actual encounter sites because (1) many of the plots are located on private lands and these exact locations are kept confidential (https://www.fia.fs.usda.gov/tools-data/spatial/Policy/default.asp) and (2) ticks may have been encountered traveling to or from plots or while working at the plot. We coarsened the spatial resolution of the model to account for differences between plots and uncertainty of collecting locations. We used these approximate locations as human-tick encounter data to train environmental niche models from 258 sites where A. americanum were encountered, 90 sites where D. variabilis were encountered, and 36 sites where I. scapu- laris were encountered. We used 20 environmental rasters (gridded data) as predictor variables in niche models acquired from EarthData (https://earthdata.nasa.gov/) or EarthExplorer (http://earthexplorer. usgs.gov) using NAD 1983 geographic coordinate system (Table 1). Each encounter was matched temporally to raster values with the same or nearest date available. We averaged ras- ters from every date each tick was collected from human hosts to encompass all environmental conditions when tick species were active on human hosts (FIA crews). To compensate for potential spatial sampling errors of ticks, for ticks collected while walking to and from plots and GPS recorded outside property boundaries, we aggregated (coarsened) the original spatial Table 1. Spatial variables used in environmental niche modeling for common ticks in the southeastern US. Environmental Variable Elevationa Gross primary productivityb Net primary productivity b Leaf area indexb Land coverb Land surface temperatureb Evapotranspirationb Vegetation indices b Precipitationb Vapor pressureb Minimum temperatureb Maximum temperatureb Burned areab Living aboveground biomassb Living belowground biomassb Leaf Litterb Soil organic matterb Dead aboveground biomassb Dead belowground biomassb Hydrologic soil groupb Definition Height above sea level Total amount of carbon produced by plants during photosynthesis The difference between carbon dioxide vegetation intakes during photosynthesis and the amount released during respiration Quantification of total canopy greenness Surface contents of within the visible landscape Earth surface temperature at a particular location All forms of evaporation and transpiration A measure of greenness Condensation of atmospheric water vapor affected by gravitational pull Point at which equilibrium pressure is acquired in a closed container Lowest temperature recorded over a given amount of time Highest temperature recorded over a given amount of time Surfaces which have been affected by burn scar from fire Living vegetation above the soil Living vegetation above below the soil Decomposing plant material on the forest floor surface Portion of the soil consisting of decomposing plant or animal tissue Dead vegetation above the soil Dead vegetation below the soil Index of the rate that water infiltrates a soil Reference [37] [38] [39] [40] [41] [42] [39] [43] [44] [45] [46] a Data were collected from http://earthexplorer.usgs.gov b Data were collected from http://earthdata.nasa.gov/ https://doi.org/10.1371/journal.pntd.0011919.t001 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 4 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States resolution of rasters from 500 meters to 4.8 km. All raster processing steps were completed in ESRI ArcMap 10.7. Environmental niche models were fitted using human-tick encounter data and environ- mental variables in the maximum entropy algorithm Maxent (Version 3.4.0) that estimates species’ potential geographic distributions [47]. Maxent was selected due to its robustness in handling presence-only model fitting with limited occurrences [48]. We removed duplicate presence records and ran independent models for each tick species with cross-validation and five replicates, each based on a maximum of 5,000 iterations. For each model, the algorithm selected 10,000 random background samples (or pseudo-absences) to contrast their environ- mental conditions to those at presence locations. A 10% training presence threshold (allowing 10% of training presence data to be predicted unsuitable) was applied to convert the model outputs of continuous probability of suitability to binary maps of suitable-unsuitable values. Because Maxent is a stable machine-learning platform for controlling correlated variables, environmental raster images were not assessed for autocorrelation beforehand but left to the algorithm to discern [49] (S1 Table). Highly correlated environmental variables do not impact model performance when model transfer is not being used in Maxent [50]. Due to lower sam- ple size for I. scapularis only the linear, quadratic, and product features were used in Maxent. Additionally, categorical variables (land cover) were removed from the environmental niche model for I. scapularis because the variable contribution was not balanced. For A. americanum and D. variabilis an addition of the hinge feature was used to build a piecewise linear exponent because there were more encounters (i.e., larger presence dataset) available for model training [47]. To quantify model performance we used area under the curve (AUC) of the receiver operat- ing characteristics; AUC � 0.9 indicate excellent models, 0.8–0.9 good, 0.7–0.8 fair, 0.6–0.7 poor, and < 0.6 failed models [51]. We also observed the omission error for each model at a 10% training threshold. In addition, we overlaid an U.S. ecoregions boundaries shapefile to evaluate risk based on regional similarities or differences (e.g., geology, soils, climate, vegeta- tion) of distinct geographic areas [52–56]. Independent model testing To test each species niche model in addition to the internal cross-validation of Maxent algo- rithm, we used targeted field work and an independent dataset of previously reported tick rec- ords for each tick species by county. We selected three USDA Southern Research Station Experimental Research Forests near the University of Tennessee, Knoxville to confirm the presence or absence of human-tick encounters in this area in 2021. The three experimental for- ests included Coweeta, Blue Valley, and Bent Creek in western North Carolina and northern Georgia in southern Appalachian Mountains. Each collection site in western North Carolina is representative of forests previously disturbed by logging and mining [57–59]. The Coweeta Hydrologic Laboratory has an area of 1,600 ha and is dominated by mixed-oak forests with understories of noncontinuous mountain laurel and rhododendron [60]. Soils in this region are comprised of Inceptosols and Ultisols [58]. The Blue Valley experimental forest has an area of 526 ha dominated by eastern white pine and stand of oak-hickory in the Blue Ridge Moun- tains [58]. This area contains acidic, infertile, well drained soils with vegetation primarily con- sisting of buckberry shrubs [58]. Bent Creek experimental station is comprised of 2,550-ha of upland hardwood forests primarily dominated by oak-hickory stands and understory vegeta- tion of rhododendron [58]. The Asheville Basin region contains soils with low organic matter contents, clay layers, and reduced fertility whereas the Mountain Highland are Inceptisols and acidic [58]. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 5 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States We provided vials to crews to collect encountered ticks, but we also conducted active sur- veillance in these forests. We set dry ice traps for overnight trapping once monthly (March- June 2021) at five different sites within each forest [61]. The use of dry ice traps were deter- mined by rough terrain at the collection site and limited funding, and the timing of our sur- veillance was informed by prior knowledge that most ticks are active March through June in the region. Collected ticks were stored in ethanol filled vials, then identified to species and sex as described above. Additionally, we compared county level records with our predicted distributions. County level records for I. scapularis, A. americanum, and D. variabilis were downloaded on May 14, 2022, from the Center for Disease Control (https://www.cdc.gov/ticks/surveillance/ TickSurveillanceData.html). True positives, used to calculate sensitivity, were identified as counties with available tick records and predicted suitable by the niche models. Here, we con- sidered the CDC datasets to be the standard, so false positive counties were those predicted suitable but lacking tick presences, indicating model commission error, and false negative counties were those with known tick presences but predicted unsuitable by the niche models, representing omission error. Although the true distribution for each tick species may not be fully represented due to differences in collection, the CDC datasets represent the most compre- hensive comparison for our models because they include surveillance by ArboNET or litera- ture published each year throughout the Southeast. Sensitivity tests were used to determine the proportion of true positives and were calculated in SAS using PROC FREQ. County-level vali- dation of models calibrated with environmental variables with 4.8 km resolution and GPS rec- ords of ticks represents a compromise between the need to validate the models and the lack of higher resolution presence data readily available for each species. Results A total of 1,901 ticks were collected and submitted to us during the study period from 384 sites: 1,720 (90.5%) A. americanum from 258 sites, 136 (7.2%) D. variabilis from 90 sites, and 45 (2.3%) I. scapularis from 36 sites. Of the A. americanum records, 669 were adults, 851 were nymphs, and 200 were larvae. There were 135 D. variabilis adults and one D. variabilis nymph. Finally, there were 45 I. scapularis adults. Ticks were primarily collected and submitted by crews in Kentucky, South Carolina, and Tennessee (Table 2). Table 2. Total number of ticks encountered and submitted by Forest Inventory and Analysis Program crews of the Forest Service, US Department of Agriculture. Total number of tick-encounter sites are separated by state in the southeastern United States (2014–2021). In total there were 1720 Amblyomma americanum (200 larvae, 851 nymph, 310 males, and 359 females), 136 Dermacentor variabilis (1 nymph, 73 females, and 62 males) and 45 Ixodes scapularis (27 females and 18 males). State Alabama Arkansas Florida Georgia Kentucky Louisiana Virginia Mississippi North Carolina South Carolina Tennessee Total Amblyomma americanum 35 (1 site) Dermacentor variabilis 17 (1 site) Ixodes scapularis 1 (1 site) 15 (13 sites) 160 (31 sites) 4 (3 sites) 1,030 (121 sites) 3 (3 sites) 7 (4 sites) 1 (1 site) 9 (3 sites) 35 (26 sites) 421 (52 sites) 1,720 (258 sites) 5 (5 sites) 3 (3 sites) 0 (0 sites) 61 (40 sites) 3 (3 sites) 0 (0 sites) 0 (0 sites) 0 (0 sites) 13 (11 sites) 34 (27 sites) 136 (90 sites) 2 (1 site) 15 (13 sites) 1 (1 site) 12 (7 sites) 3 (3 sites) 0 (0 sites) 0 (0 sites) 0 (0 sites) 8 (7 sites) 3 (3 sites) 45 (36 sites) https://doi.org/10.1371/journal.pntd.0011919.t002 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 6 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Statistical analysis Season was significantly associated with presence for all three species: A. americanum (F3, 349 = 13.69; p < 0.0001), D. variabilis (F3, 349 = 6.07; p = 0.0005), and I. scapularis (F3, 349 = 22.14; p < 0.0001). The likelihood of A. americanum being present was highest in spring compared to any other season, and higher in summer compared to autumn and winter. Amblyomma americanum larvae were most likely to be present on human hosts in summer months, nymphs were more likely to be present in spring compared to autumn months, and adults were more likely to be present in the spring compared to any other season. Dermacentor varia- bilis adult encounters were greater in summer months compared to spring and winter months; a single nymph was collected in the spring. Finally, encounters of adult I. scapularis were more likely to occur in autumn and winter months compared to spring and summer months. A detailed breakdown of each species monthly percent encounter throughout the eight-year study period is shown in Fig 1. Fig 1. Relative activity of three species of ticks in southeastern United States. Percent monthly encounter for Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis encountered by Forestry Inventory and Analysis crews in the southeastern United States between 2014 and 2021. https://doi.org/10.1371/journal.pntd.0011919.g001 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 7 / 23 PLOS NEGLECTED TROPICAL DISEASESTable 3. Model evaluation and variable contribution to environmental niche models generated with Maxent maximum entropy algorithm for ticks encountered by Forestry Inventory and Analysis (FIA) foresters from 2014–2021. Values for each variable represent that variables percent contribution to each model, bolded values were those identified as significant contributing variables to the model (greater than 0.1 or 10%). Mapping tick encounters in the southeastern United States Variable AUC values Elevation Landcover Vapor pressure Precipitation Maximum temperature Minimum temperature Dead belowground biomass Evapotranspiration Gross primary productivity Soil organic matter Vegetation indices Net primary productivity Hydrologic soil group Land surface temperature Litter Living belowground biomass Living aboveground biomass Leaf area index Burned area Dead aboveground biomass Amblyomma americanum model 2 model 1 model 3 0.85 0.80 model 1 0.87 Dermacentor variabilis model 2 0.83 16.7 19.6 15.4 6.7 14.5 4.5 1.8 0.9 1.2 3.1 0 5.1 5.8 4.1 0.2 0.1 0.1 0 0 0 Variables Contributing to One or More Model 20.2 16.4 15.4 5.7 7 11.2 0.4 2.4 0.1 2.5 0 15.3 16.7 13 6.2 14.7 6.9 0.4 1 0.4 4.4 0 24.9 21 10.2 11.4 16.2 1.3 3.5 3.3 1.5 0.3 0.8 Variables Assessed, but Not Contributing to a Model 2.6 7.4 7.4 0.6 0.6 0 0 0 0 6.4 8.5 5.5 0.2 0.4 0 0.1 0 0 2.4 2.6 0 0.2 0.4 0 0 0 0 0.85 21 19.2 11.6 9.7 17.8 1 7.2 1.8 1.7 0 0.5 1.6 5 0.5 0.3 1 0 0 0 0 model 3 0.86 16 28.8 9 10.5 10.5 1.5 11.7 3.2 1.9 0.2 0.1 1.4 4.1 0.1 0.1 0.9 0 0 0 0 Ixodes scapularis model 1 0.82 7.7 - 0 0 11.1 9.3 2.4 11.9 11.9 24.2 11.6 2 - 2 3 0 0.1 2.8 0 0 https://doi.org/10.1371/journal.pntd.0011919.t003 Environmental niche modeling of potential suitability for ticks Lambda file results for all environmental variables in our ENM’s can be found in (S2 Table). Three of the five replicate niche models for A. americanum had AUC values greater than 0.8 (Table 3). We averaged the raster outputs (probability of suitability) of the three Maxent mod- els to create a single map of potential suitability for A. americanum (Fig 2). Model 2 had the highest AUC and elevation, landcover, minimum temperature, and vapor pressure were the best predictors of habitat suitability (Table 3). In models 1 and 3, the same three contributing variables as for model 1 predicted environmental suitability except maximum temperature that had a higher contribution than minimum temperature in these two models. Resulting models indicate potential suitable environments where A. americanum are likely to be encoun- tered; thus, crews working in forests throughout Tennessee, Kentucky, northern Florida, Arkansas, North Carolina, and eastern Virginia have an increased likelihood of encountering A. americanum (Fig 2). Three D. variabilis models had AUC values greater than 0.8 (Table 2) so we averaged the outputs of the three models to generate the predicted suitability map for D. variabilis (Fig 3). High contribution variables to all three models included elevation, landcover, and maximum temperature. Differences between models were represented by precipitation and vapor pres- sure in the first model, vapor pressure in the second model, and dead belowground biomass and precipitation in the third model (Table 2). Areas of potential suitability and thus concern PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 8 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Fig 2. Potential distribution estimated with an environmental niche model for Amblyomma americanum. The model was based on tick encounters collected by Forest Inventory and Analysis Program of the Forest Service, US Department of Agriculture foresters in the southeastern United States (2014–2021). Landcover, elevation, maximum temperature, minimum temperature, and vapor pressure contributed the most to predicting this tick’s geographic suitability. The following publically available link was used as the base layer of the map (https://www.census.gov/ geographies/mapping-files/time-series/geo/carto-boundary-file.html). https://doi.org/10.1371/journal.pntd.0011919.g002 for crews encountering D. variabilis included regions throughout Tennessee and Kentucky, northern North Carolina, Alabama, Arkansas, and eastern Virginia (Fig 3). Only one model for I. scapularis had an AUC value greater than 0.8 (Table 2). The high contribution environmental variables were evapotranspiration, gross primary productivity, maximum temperature, vegetation indices, and soil organic matter (Table 3). The area of potential suitability for I. scapularis included all ecoregions in the southeastern US; however, there was a reduced risk to encounter I. scapularis ticks in southern Florida, eastern Tennessee and Mississippi, and western Arkansas (Fig 4). Independent model testing We tested each resulting niche model with data from passive and active surveillance at three Experimental Research Forests and by comparing predicted suitability maps with published or available county-level tick records. Passive and active tick collections at these targeted forests resulted in a total of five D. variabilis provided by a camper in the area, but the location of PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 9 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Fig 3. Potential distribution estimated with an environmental niche model for Dermacentor variabilis. The model was based on tick encounters collected by Forest Inventory and Analysis Program of the Forest Service, US Department of Agriculture foresters in the southeastern United States (2014–2021). Variables landcover, elevation, precipitation, vapor pressure, and dead belowground biomass contributed the most to predicting this tick’s geographic suitability. The following publically available link was used as the base layer of the map (https://www.census.gov/ geographies/mapping-files/time-series/geo/carto-boundary-file.html). https://doi.org/10.1371/journal.pntd.0011919.g003 encounter was not recorded. There were no ticks collected in any of the 15 traps that were placed in the three experimental research forests, nor were ticks encountered on the individual walking to and from each site. The CDC tick surveillance datasets indicated D. variabilis to be reported but there were no county records for A. americanum and I. scapularis in Macon County North Carolina where The Coweeta and Blue Valley Experimental Forests are located. Similarly, CDC surveillance data showed that D. variabilis was established but there were no county records for A. americanum and I. scapularis in Buncombe County North Carolina where The Bent Creek Experimental Forest is located. For all three tick species, our potential distribution maps suggested suitable habitats were present in regions scattered throughout Macon County; however, suitable habitats were found scattered throughout Buncombe County for D. variabilis and I. scapularis but not for A. americanum. Our models correctly pre- dicted sites where dry ice traps were located to be environmentally unsuitable for all three tick PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 10 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Fig 4. Potential distribution estimated with an environmental niche model for Ixodes scapularis. The model was based on tick encounters collected by Forest Inventory and Analysis Program of the Forest Service, US Department of Agriculture foresters in the southeastern United States (2014–2021). Variables soil organic matter, vegetation indices, maximum temperature, gross primary productivity, and evapotranspiration contributed the most to predicting this tick’s geographic suitability. The following publically available link was used as the base layer of the map (https://www. census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html). https://doi.org/10.1371/journal.pntd.0011919.g004 species; these absences and unsuitable sites were found in a landscape of predicted suitability by ENMs (Fig 5). Overall, the CDC website identified 390 I. scapularis-positive counties and 674 no county records, 718 D. variabilis-positive counties and 346 no county records, and 561 A. americanum -positive counties and 503 no county records. When we compared our resulting maps at the county level to the previously recorded data, sensitivity values and associated confidence inter- vals for A. americanum were 0.9465 (0.9279–0.9651), for D. variabilis were 0.8175 (0.7893– 0.8458), and for I. scapularis were 0.9956 (0.9905–1.00). This indicates that the models pre- dicted suitable habitats (~encounter areas) similar to those reported by others. There was no observed difference between county status (established, reported, or new county record) and the number of A. americanum or D. variabilis collected from foresters. Counties noted as established in the CDC database for I. scapularis were found to have multiple ticks collected compared to reported or new county records. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 11 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Fig 5. Maps indicating unsuitable habitat for sites used as active surveillance with dry ice traps for tick species (a) Amblyomma americanum, (b) Dermacentor variabilis, and (c) Ixodes scapularis in Coweeta, Blue Valley, and Bent Creek experimental forests in western North Carolina and northern Georgia in southern Appalachian Mountains (2021). Eco regions included Piedmont (45), Blue ridge (66), Ridge and Valley (67), Southwestern Appalachians (68), and Central Appalachians (69). The following publically available link was used as the base layer of the map (https:// www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html). https://doi.org/10.1371/journal.pntd.0011919.g005 Additionally, as a part of the project, we report several new county records for each species. New county records for A. americanum included: Kemper (MS), Madison (FL), Taylor (FL), Gilchrist (FL), Williamsburg (SC), Clarendon (SC), Lexington (SC), Edgefield (SC), Saluda (SC), Newberry (SC), Fairfield (SC), York (SC), Cherokee (SC), Anderson (SC), Halifax (NC), Franklin (NC), Amherst (VA), Henderson (KY), Hancock (KY), Trimble (KY), Kenton (KY), Scott (KY), Carter (KY), Johnson (KY), Perry (KY), Knox (KY), Jackson (KY), Lincoln (KY), Marion (KY), Casey (KY), Adair (KY), Russell (KY), Cumberland (KY), Monroe (KY), Pike (KY) (Fig 6). New county records for D. variabilis included: Pickens (SC), Wayne (TN), Law- rence (TN), Polk (TN), Daviess (KY), Hancock (KY), Butler (KY), Larue (KY), Green (KY), Taylor (KY), Cumberland (KY), Anderson (KY), Knox (KY), Clay (KY), Anderson (KY), Trimble (KY), Carter (KY), and Johnson (KY) (Fig 6). New county records for I. scapularis included: West Feliciana (LA), Dade (GA), Union (TN), Simpson (KY), Nelson (KY), Rock- castle (KY), Leslie (KY) (Fig 6). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 12 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Fig 6. Sites of tick species collected by foresters in the Forest Inventory and Analysis Program of the Forest Service, US Department of Agriculture foresters in the southeastern United States (2014–2021). The following publically available link was used as the base layer of the map (https://www.census.gov/geographies/mapping-files/ time-series/geo/carto-boundary-file.html). https://doi.org/10.1371/journal.pntd.0011919.g006 Discussion Three-host ticks reside in the environment for the majority of their two- to three-year life cycle, leaving them susceptible to biotic and abiotic factors [62]. Consequently, ecological niches and habitat suitability often differ for each species spatially and temporally in a rapidly changing environment [63]. In this study, we used ecological niche and generalized linear models to describe geographic regions (where) and temporal periods (when) that represent a risk for humans to tick encounters in the southeastern US. We identified environmental vari- ables associated with the human-tick encounters of three common human biting tick species, which are important indicators for tick encounters and could be targeted for management. The only common environmental variable significantly contributing to the human-tick encounters of all three species was maximum temperature, which supports the expectation that tick distributions will change in warming climates [64–66]. Additionally, long-term pas- sive surveillance is an important tool for obtaining broad-extent occurrence records which can be used to monitor geographic distribution shifts of ticks and public health risk [67,68]. This dataset highlights the potential power of passive tick surveillance by trained scientists from fed- eral agencies to generate accurate distributional information. Amblyomma americanum According to many published papers and citizen scientist reports, the most abundant and encountered tick in the southeastern US is A. americanum and the species is most likely to be PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 13 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States encountered in the spring [69,70]. The likelihood of encountering A. americanum at a site was determined by five environmental variables (elevation, landcover, maximum temperature or minimum temperature, and vapor pressure). Previously, others reported variables such as pre- cipitation, vapor pressure, diurnal range, and temperature as important contributors to pre- dicting A. americanum suitability and potential range [17,12,14]. Our environmental niche model highlighted regions at higher risk of encountering A. americanum (Fig 2) that align with the Interior Plateau, Interior River Valleys and Hills, Ozark Highlands, Boston Moun- tains, Arkansas Valley, Ouachita Mountains, northern region of the Southeastern Plains, northeastern Piedmont, Northern Southern Coastal Plain, Southwestern Appalachians, and western regions of the Central Appalachian ecoregions. These ecoregions suitable for A. ameri- canum contain upland hardwood forests, grassy plateaus, and low elevations in between the Appalachian Mountains and regions in the Southern Plains [71]. A similar pattern to our results was observed based on ticks collected from wildlife hosts in Florida where A. ameri- canum was more likely to be present in the northern region of the Southern Coastal Plain [72]. Importantly, our results also predicted when and where A. americanum may not be encountered: fall and winter months and locations typically thought to have standing water. Less suitable areas were comprised of wetlands and bottomland hardwoods (e.g., Mississippi Alluvial plain, Southern Florida Coastal Plain), in agreement with previous reports [14,73]. These areas have flat plains with wet soils, marshlands, and swampy land cover [74]. Ticks were less likely to be found in the Southeastern Plains, a region that has historically experi- enced various land use changes such as plantation forestry and agriculture; these land manage- ment decisions could have impacts on tick abundance [75,76]. The Southeastern Plains in Tennessee are known to be occupied by A. americanum and their associated tickborne diseases such as human ehrlichiosis and spotted fever group Rickettsioses [77,78]. Similar to our ENM, A. americanum populations are less likely to occur in regions with high elevations such as in the Smoky Mountains [17]. Importantly, because A. americanum was associated with both elevation and temperature variables, it is possible to investigate these relationships in more detail as higher elevations begin to warm. Future models could predict that, as areas become warmer, more tick encounters will occur at higher elevations where this species was previously absent [17]. Dermacentor variabilis Foresters frequently encountered D. variabilis adults in summer months, as previously reported [79]. Six environmental variables contributed to the environmental niche models with consistent variables including elevation and landcover; and the remaining four variables (maximum temperature, precipitation, vapor pressure, and dead belowground biomass) varied in contribution for each model. Similarly, previous research found precipitation, temperature, and elevation associated with D. variabilis [15]. Ticks were most likely to be present through- out Kentucky and Tennessee, and the predicted suitability was fragmented in northern Arkan- sas, North Carolina, and Virginia. Like our A. americanum model, the model for D. variabilis predicted suitable areas in ecoregions with upland hardwood forests and fewer extreme eleva- tion changes (e.g., Ozark Highlands, Boston Mountains, Arkansas Valley, Interior Plateau, Interior River Valleys and Hills, Southwestern Appalachians, Piedmont, Mississippi Valley Loess Plain, Ridge and Valley, and the Central Appalachians) [71]. Prior to the range expansion of A. americanum, D. variabilis was the most commonly encountered tick in southeastern US [14,80–82]. Areas where D. variabilis was least likely to be encountered included more southern areas of the study region such as the Southern Coastal Plain and Southeastern Plains, typically characterized as subtropical, low-elevation, sandy, and PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 14 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States areas with marshlands in Florida [71]. Historically D. variabilis were reported throughout the Midwest and Southeast but not regions in the upper Northeast and Southwest [83]. Recently, D. variabilis encounters were predicted by warm temperatures and low precipitation, thus cli- mate change could improve environmental conditions for this species, increasing their range into the upper Northeast US [15]. The Southeastern Plains region is characterized by drought-prone and nutrient-poor soils, with most land use practices reversing to industrial forests which has resulted in extreme clear cutting [84]. These conditions could lead to unsuitable environmental conditions for this tick species due to its association with precipitation, landcover, and vapor pressure. Of interest is the potential role that dead belowground biomass has on D. variabilis because this variable can be manipulated with land and forest management decisions. Intense forest harvesting methods could increase dead belowground biomass due to remaining root structures underground [85]. Dead belowground biomass in the form of dead roots has the ability to hold large amounts of moisture [86] for water storage [87]; which could be important for adult D. varia- bilis survival because they possess greater survival in dry environments compared to other ixo- did tick species [88]. Immature D. variabilis often quest below leaf litter [89] making them difficult to collect with active surveillance and easier to collect with passive surveillance, from small sized hosts [59,69,77,90]. Ixodes scapularis Historical encounters of I. scapularis in the Southeast are receiving more attention as recent reports are confirming more I. scapularis in the region [91]. This study confirms these more recent publications, with adult I. scapularis being encountered in the fall and winter and nearly the entire environmentally suitable region [16,92,93]. Although adult I. scapularis were encountered less frequently than D. variabilis and A. americanum, their predicted geographic range was larger. Specifically, I. scapularis was predicted to be present in all southeastern states and all ecoregions except the floodplains around the Mississippi River (Mississippi Alluvial Plains and Mississippi Valley Loess Plains). The environmental variable landcover was removed from our I. scapularis model because the available presence records did not represent well the variation in landcover categories within the model. Environmental variables that con- tributed to this ENM included evapotranspiration, gross primary productivity, maximum tem- perature, vegetation indices, and soil organic matter. Interestingly, the antagonistic effects of transpiration and relative humidity indicate that I. scapularis could withstand questing when evapotranspiration is high because it is buffered by drastic changes in relative humidity in for- ests [94]. Vegetation indices contributing to adult I. scapularis populations when they quest in colder months is likely associated with plant dormancy traits [95]. Previous research found variables vapor pressure, elevation, forest cover, isothermality, and temperature associated with their ENMs [16,96]. Similar to our I. scapularis model, ticks were less likely to be found at high elevations in the Appalachian Mountains or the Blue Ridge, or southern Florida or the Southern Florida Coastal Plain, and the Louisiana Coast or the Mississippi Alluvial Plain ecor- egions [16,71]. In these ecoregions it could be difficult for I. scapularis to survive off host because they are dominated by wetland habitats [71]. Studies of immature I. scapularis reported southern populations questing in leaf litter while genetically distinct northern populations questing higher on the vegetation, out of leaf litter [91,97]. These behavioral and genetic differences might also be reflected in our ecological niches because variables associated with questing and surviving in an environment (e.g., tem- perature, evapotranspiration, vegetation indices, etc.) contributed to model calibration. Importantly, the I. scapularis submitted by foresters in this study were adults and were not PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 15 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States genotyped. Follow up studies should compare the ecological niches of these genetically and behaviorally distinct populations. Model testing Our models could predict the presence of each tick species but had difficulties predicting the absence of each tick species. This was confirmed with sensitivity and specificity testing as well as conducting targeted surveillance. These results are to be expected because the Maxent algo- rithm was only trained with presence records that had latitude and longitude coordinates (GPS records). Presence records are readily available for this study because some crews were more vigilant tick collectors and absences could be due to lack of reporting and/or submitting. Nevertheless, our models were able to sufficiently predict tick absence when compared to cur- rent literature and active surveillance. Here, few encounters occurred in areas near the foothills of the Appalachian Mountains and our active surveillance supported that as well. Campers in the Bent Creek area helped us confirm D. variabilis, indicating ticks may be present in the region, but the likelihood of encounters at these sites is rare compared to other locations, a finding that is similar to other studies in the area [98]. These data support the overall findings that higher elevation areas are often unsuitable tick habitat, which could explain the decreased number of human-tick encounters in high elevation areas like Appalachia [99–101]. Previous research documented that air temperature and density decrease as elevation increases [102]. A possible future research direction could be to monitor these sites as temperature and precipita- tion change in response to climate change [103], because all three tick species in this study were associated with temperature and none were solely dependent on elevation. Utilizing com- munity science and passive surveillance, this study identified 60 new county records for tick occurrence in eight states. Data generated from this research could be an important tool for raising awareness of increased tick species distributions and used for tick avoidance and man- agement regimes. Potential for sampling bias All three species were absent on human hosts in areas bordering the Mississippi river, coast- lines around Louisiana, and southern Florida. This resulted in us reporting that ticks were absent from areas comprising wetlands, analogous to previous studies [99,104]. The absence of ticks on human hosts in these areas could also indicate sampling bias because fewer records were submitted by forest crews working in these areas. This is a known sampling limitation of this study: all data presented here are dependent on forestry crews submitting ticks. Future research should focus on confirming these findings by monitoring ticks in wetland habitats of these regions. Sensitivity and specificity tests indicated our niche models were efficient in their ability to detect known presences of all three species but had poor abilities in detecting a tick absence. Regardless, our relative activity and niche models can be used as a first approximation of risk for encountering ticks and their pathogens, as well as helping with tick management by forestry personnel. Land management Our results indicated that the burned area variable was not a good predictor of human-tick encounters; which suggests that burned locations are not a suitable habitat for tick encounters. Prescribed burning may remove tick and mammal habitats that promote the abundance of tick populations [105], but there are several studies in the Southeast that both support and reject these findings. Previously, prescribed burns reduced the abundance of ticks and the prevalence of tickborne pathogens in an area [106,107] which may be due to increased soil PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 16 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States temperatures, decreased soil moisture, or destruction of tick habitats [108,109]. Conversely, a weak negative association between prescribed burns and the number of ticks in an area could be due to increased host use of burned areas [110] or little impact on tick species that quest and dwell within the soil [111]. Others propose that the methods for controlled burns, such as season, burning intensity, and frequency could be important factors for managing ticks in pre- scribed burns [112]. Future research to address the potential of controlled burns in large land management areas to reduce recreational and work-related tick encounters is warranted. Tick encounter data can be accessed at Dryad (https://doi.org/10.5061/dryad.v41ns1s3n). [113]. Conclusions Passive tick surveillance data provided by FIA crews were valuable in delivering descriptions for when and where ticks were likely to be encountered and contributed to supporting our hypothesis. Data confirmed that ticks are active year-round in the southern US and habitats suitable for encounters can be explained by climatic and topographical features. Regions in the Southeast that were least suitable for all three tick species included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions for all tick spe- cies included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Temporal periods associated with encountering each tick species varied; for example, I. scapularis was associated with cold seasons (autumn and winter) whereas D. variabilis and A. americanum were associated with warm seasons (spring and sum- mer). This research provides important information for isolating regions for management of ticks when they are more seasonally active. This study also helps forestry managers alert field crews about tick activity and new county records of tick species. Additionally, this study pro- vides crews with the opportunity to test management decisions against tick encounters. Future studies will aim to combine forest management strategies at high-risk geographic regions to understand associations on human-tick encounters. Supporting information S1 Table. Correlation values for environmental variables that contributed 10% or more to each environmental niche model. (DOCX) S2 Table. Lambda file results for environmental variables in each Maxent environmental niche model. (DOCX) Acknowledgments We thank the Forest Inventory and Analysis Program of the Forest Service, United States Department of Agriculture for organizing the collection of ticks in this study. This manuscript was prepared in part by United States government employees as part of their official duties and therefore is in the public domain. We also want to thank Jennifer Chandler, Corey Day, and Katy Smith for the initial manuscript review. The Department of Entomology and Plant Pathology at the University of Tennessee helped support RAB’s graduate stipend and tuition. Author Contributions Conceptualization: Rebecca T. Trout Fryxell. Data curation: Rebecca A. Butler, Dave J. Paulsen, Rebecca T. Trout Fryxell. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011919 February 14, 2024 17 / 23 PLOS NEGLECTED TROPICAL DISEASESMapping tick encounters in the southeastern United States Formal analysis: Rebecca A. Butler, Mona Papeş. Funding acquisition: James T. Vogt. Methodology: Rebecca A. Butler, Mona Papeş, James T. Vogt, Dave J. Paulsen, Christopher Crowe, Rebecca T. Trout Fryxell. Project administration: James T. Vogt, Rebecca T. Trout Fryxell. Resources: James T. Vogt. Supervision: Mona Papeş, James T. Vogt, Rebecca T. Trout Fryxell. Validation: Rebecca A. Butler, Christopher Crowe, Rebecca T. Trout Fryxell. Visualization: Rebecca A. Butler, Mona Papeş, Rebecca T. Trout Fryxell. 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10.1371_journal.pntd.0011948	RESEARCH ARTICLE Longitudinal analysis of post-acute chikungunya-associated arthralgia in children and adults: A prospective cohort study in Managua, Nicaragua (2014–2018) Colin M. Warnes1, Fausto Andres Bustos Carrillo1,2, Jose Victor Zambrana3, Brenda Lopez Mercado3, Sonia Arguello3, Oscarlette Ampie´ 3, Damaris Collado3, Nery Sanchez3, Sergio Ojeda3, Guillermina Kuan3,4, Aubree Gordon5, Angel Balmaseda3,6, Eva HarrisID 1* 1 Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America, 2 Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America, 3 Sustainable Sciences Institute, Managua, Nicaragua, 4 Centro de Salud So´ crates Flores Vivas, Ministerio de Salud, Managua, Nicaragua, 5 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, United States of America, 6 Laboratorio Nacional de Virologı´a, Centro Nacional de Diagno´stico y Referencia, Ministerio de Salud, Managua, Nicaragua a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS * eharris@berkeley.edu Citation: Warnes CM, Bustos Carrillo FA, Zambrana JV, Lopez Mercado B, Arguello S, Ampie´ O, et al. (2024) Longitudinal analysis of post-acute chikungunya-associated arthralgia in children and adults: A prospective cohort study in Managua, Nicaragua (2014–2018). PLoS Negl Trop Dis 18(2): e0011948. https://doi.org/10.1371/journal. pntd.0011948 Editor: Nur Faeza Abu Kassim, Universiti Sains Malaysia, MALAYSIA Received: May 12, 2023 Accepted: January 27, 2024 Published: February 28, 2024 Copyright: © 2024 Warnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Individual data for reproducing figures may be shared with outside investigators following UC Berkeley IRB approval, as data may contain potentially identifying patient information. Consent documents enable data sharing via collaboration but not public access to patient data. Please contact the UC Berkeley Committee for the Protection of Human Subjects at ophs@berkeley.edu or 510-642-7461 to arrange for data access. The materials and data used in this Abstract Chikungunya can result in debilitating arthralgia, often presenting as acute, self-limited pain, but occasionally manifesting chronically. Little is known about differences in chikungunya- associated arthralgia comparing children to adults over time. To characterize long-term chi- kungunya-associated arthralgia, we recruited 770 patients (105 0–4 years old [y/o], 200 5–9 y/o, 307 10–15 y/o, and 158 16+ y/o) with symptomatic chikungunya virus infections in Managua, Nicaragua, during two consecutive chikungunya epidemics (2014–2015). Partici- pants were assessed at ~15 days and 1, 3, 6, 12, and 18 months post-fever onset. Following clinical guidelines, we defined participants by their last reported instance of arthralgia as acute (�10 days post-fever onset), interim (>10 and <90 days), or chronic (�90 days) cases. We observed a high prevalence of arthralgia (80–95%) across all ages over the study period. Overall, the odds of acute arthralgia increased in an age-dependent manner, with the lowest odds of arthralgia in the 0–4 y/o group (odds ratio [OR]: 0.27, 95% confi- dence interval [CI]: 0.14–0.51) and the highest odds of arthralgia in the 16+ y/o participants (OR: 4.91, 95% CI: 1.42–30.95) relative to 10–15 y/o participants. Females had higher odds of acute arthralgia than males (OR: 1.63, 95% CI: 1.01–2.65) across all ages. We found that 23–36% of pediatric and 53% of adult participants reported an instance of post-acute arthralgia. Children exhibited the highest prevalence of post-acute polyarthralgia in their legs, followed by the hands and torso – a pattern not seen among adult participants. Further, we observed pediatric chikungunya presenting in two distinct phases: the acute phase and the subsequent interim/chronic phases. Thus, differences in the presentation of arthralgia were observed across age, sex, and disease phase in this longitudinal chikungunya cohort. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 1 / 18 PLOS NEGLECTED TROPICAL DISEASESstudy are covered by standard data and material transfer agreements. Code used in this paper is available on GitHub at https://github.com/ colinwarnes/Post-AcuteChikungunyaArthralgia.git. Funding: This study was funded by NIH grants P01AI106695 (to E.H.), U19AI118610 (to E.H.), and R01AI099631 (to A.B.), and all authors were partially supported by these grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Analysis of post-acute chikungunya-associated arthralgia in children and adults Our results elucidate the long-term burden of chikungunya-associated arthralgia among pediatric and adult populations. Author summary Upon its emergence in the Americas in late 2013, chikungunya virus spread rapidly, lead- ing to >2 million suspected autochthonous chikungunya cases between 2014–2015. Much of what we know about chikungunya is derived from adult populations, leading to gaps in guidelines to treat pediatric chikungunya cases. To address these gaps, we assembled a large cohort of both pediatric (n = 612) and adult (n = 158) laboratory-confirmed (n = 682) or clinically/epidemiologically probable (n = 88) chikungunya cases from two dis- tinct epidemics in 2014 and 2015 in Managua, Nicaragua, followed these patients over a two-year timeframe, and analyzed chikungunya-associated arthralgia using rigorous sta- tistical approaches. Our analysis demonstrates that the pediatric (0–15 years old [y/o]) population faces a previously unappreciated high burden of post-acute chikungunya-asso- ciated arthralgia. Further, we observed that post-acute arthralgia presents differently between pediatric and adult cases (16+ y/o). The difference between the two groups was evident when comparing distribution of polyarthralgia across the body parts and when analyzing the persistence of arthralgia >10 days post-fever onset. Using detailed longitu- dinal data, our findings provide insight into long-term chikungunya-associated arthralgia across age, sex, body parts, and the different phases of chikungunya. We believe these findings will inform clinical guidelines regarding chikungunya-associated arthralgia across all ages. Introduction Symptomatic infection with chikungunya virus (CHIKV), a mosquito-borne alphavirus, typi- cally presents with high fever, rash, headache, myalgia, and debilitating arthralgia, which can be acute or chronic. The name chikungunya is derived from the Kimakonde word kungunyala, meaning “to become contorted” [1]. Chikungunya is increasingly recognized as causing mor- tality, particularly among older persons with chronic underlying conditions [2,3]. Current management protocols call for the use of nonsteroidal and steroidal anti-inflammatory drugs and creams, active physical therapy, and psychological treatment in specific cases [4]. Safety and immunogenicity Phase 1, 2, and 3 clinical trials in 2023 have shown promising results – highlighting recent advances in live-attenuated, inactivated, and virus-like particle- based chikungunya vaccine research [5] and culminating in the first accelerated approval of a live-attenuated CHIKV vaccine by the FDA in late 2023 [6]. Chikungunya is often described as having three phases: acute, post-acute/subacute/interim, and chronic [4]. The acute symptoms last 7–10 days post-symptom onset, per the US Centers for Disease Control and Prevention (CDC) [7]. Acute chikungunya can present nonspecifi- cally, complicating differential diagnosis [8]. Though most clinical manifestations subside after the acute phase, chronic arthralgia has been reported up to 6 years post-infection [9]. Chronic chikungunya is often defined as chikungunya-associated sequelae >3 months post- symptom onset [4,10–13]. The period between acute and chronic phases has been described as the subacute or post-acute period, which we call the interim phase [4,10,12,14–17]. Being over 45 years old (y/o) and female are considered risk factors for developing chronic chikungunya- PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 2 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults associated symptoms and sequelae, though the underlying etiology is unknown [4,10,14]. Despite recent studies on the short- and long-term presentation of chikungunya in adults [4,7,10,13,18–21], the literature is limited regarding the pediatric experience of chikungunya- associated arthralgia over time [14,15], a particular problem as children are considered a vul- nerable population that experiences high morbidity in the acute phase of disease [22,23]. In Managua, Nicaragua, CHIKV was first detected in July 2014 – following the introduction of CHIKV into the Americas in late 2013 [24] – with autochthonous transmission observed in September 2014 [25]. CHIKV caused two epidemics in Managua: a moderately sized epidemic in 2014–2015 and a much larger epidemic in 2015–2016. Both epidemics were caused by the Asian lineage, resulting in 61.1 and 218.1 infections per 1,000 person-years, respectively [23,24,26]. We assessed the presence of arthralgia for >18 months post-fever onset among a cohort of chikungunya cases that was recruited from the Pediatric Dengue Cohort Study (PDCS), a longitudinal cohort of ~3,700 children in Managua, as well as adults who received medical care from the study’s health facility, the Health Center So´crates Flores Vivas (HCSFV). The PDCS, originally designed to study dengue virus infections in children in Nica- ragua, was expanded in 2014 to study CHIKV infections and in 2015 to study Zika virus (ZIKV) infections [22,27,28]. All three viruses are spread by the same vector species Aedes aegypti mosquitoes. As CHIKV was first introduced into the study area in 2014, the PDCS cap- tured chikungunya cases without previous immunity to CHIKV that could have affected the presentation and duration of illness. In this study, we quantified differences between adult and pediatric cases. We also charac- terized the presentation of arthralgia over time and between the sexes to fill gaps in our knowl- edge of long-term pediatric arthralgia. Using our cohort, we demonstrate the age dependence of arthralgia occurrence, define the frequency of pediatric arthralgia over time, and describe the characteristics of the acute, interim, and chronic phases of chikungunya. Methods Ethics statement This study was approved by the Institutional Review Boards of the University of California, Berkeley, and the Nicaraguan Ministry of Health. Participants aged 18 and older and parents or legal representatives of participating children (0–17 y/o) provided written informed con- sent. Children 6–17 y/o provided verbal assent. Study recruitment All participants were recruited from the HCSFV during the 2014 and 2015 chikungunya epi- demics in Managua. During enrollment, study personnel administered informed consent and recruited participants based on laboratory-confirmed or clinically/epidemiologically probable presentation of chikungunya. Individuals aged 6 months and older were considered for the study. Inclusion criteria for the study were as follows: 1) a laboratory-confirmed or clinically/ epidemiologically probable presentation of chikungunya, 2) having been attended to at the HCSFV, 3) residing in the HCSFV catchment area during the study period, 4) having provided written informed consent themselves or through a legal representative if <18 y/o, and 5) hav- ing provided verbal assent if between 6 and 17 y/o [3,4]. All pediatric patients between 2–14 y/o were active participants in the PDCS [29], and all participants were recruited from the HCSFV upon fulfillment of inclusion criteria. All participants were identified as chikungunya cases by study physicians. Diagnostic crite- ria used during the 2014–2015 chikungunya epidemics were based on clinical standards for evaluating suspected arboviral infections that circulate in Managua. These criteria consisted of PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 3 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults the World Health Organization’s case definition for chikungunya cases [30]. Suspected chi- kungunya cases were assessed for 1) undifferentiated fever or 2) fever or feverishness and 2 or more of the following: headache, muscle pain, joint pain, retro-orbital pain, rash, hemorrhagic manifestations, and leukopenia [22]. Suspected cases were confirmed as CHIKV infections if 1) CHIKV real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results were positive 2) seroconversion was observed using an anti-CHIKV immunoglobulin M enzyme- linked immunosorbent assay (ELISA) using paired acute and convalescent samples and/or 3) seroconversion was detected by Inhibition ELISA in paired acute and convalescent samples [23]. Of suspected cases, 682 were confirmed as positive by rRT-PCR, and 53 were confirmed as positive by seroconversion. Thirty-five participants were recruited beyond the acute phase after being diagnosed as clinically/epidemiologically probable chikungunya cases at the HCSFV; they were included in the long-term follow-up and analysis. Laboratory diagnosis was not conducted for these patients as they lacked acute-phase serum samples. Such participants were included in the analysis because they were judged by our study’s physicians to be clinically/epidemiologically probable chikungunya cases, as during the chikungunya outbreaks, there was very low trans- mission of other arboviral diseases. Participants above the age of 55 were excluded from this study due to low sample sizes. Fol- low-up visit data were considered up to a maximum of 625 days post-fever onset, due to low sample sizes beyond day 625. Of the 811 participants eligible for the study, 41 were excluded due to disqualifying, missing, or incomplete data, bringing the size of the analytic population to 770 individuals. Parallel Zika cohort Participants from a longitudinal pediatric cohort of laboratory-confirmed and symptomatic ZIKV infections (n = 161), recruited from the PDCS during the 2016 ZIKV epidemic after the CHIKV epidemics, followed similar inclusion criteria but with clinical and laboratory diagnos- tic methods for Zika [31]. These Zika cases were monitored for ~18 months for the occurrence of arthralgia. The Zika cohort study ran parallel to the chikungunya cohort study and involved the same study recruitment procedures and study design. Participants in the Zika cohort study were diagnosed based on established PDCS clinical criteria for suspected Zika illness, and all were confirmed via rRT-PCR [31]. Our comparison of the two cohorts was conducted with the consideration that Zika may manifest similarly to chikungunya in the acute phase of dis- ease, but it has not been shown to contribute to chronic arthralgia [8,32]. Moreover, we have previously shown that among PDCS participants who report to the HCSFV and who meet our testing definition, the percentage of arthralgia among Zika and non-Zika cases is not signifi- cantly different (prevalence difference = 1.5%, p = 0.57)[31]. This suggests that Zika cases are a good comparison for understanding the baseline levels of pediatric arthralgia in our study setting. Study design Participants were clinically evaluated upon enrollment and followed up at approximately 15 days and 1, 3, 6, 12, and 18 months post-fever onset. Follow-up evaluations were conducted at the participants’ residence (home visits) or at the medical center, depending on the specific cir- cumstances. At each study follow-up visit, study medical professionals administered a ques- tionnaire to the participant or legal guardian and then conducted a physical examination that queried for arthralgia (S1 Appendix) [31]. Arthralgia was defined as verbal or physical indica- tions of joint pain, discomfort, or inflammation. The occurrence of arthralgia was assessed PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 4 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults across the neck, shoulders, back, hips, elbows, wrists, hands, knees, ankles, and feet. The study period, covering both chikungunya epidemics and the follow-up visits, extended from Septem- ber 2014 to January 2018. Pediatric participants 2–14 y/o were co-enrolled in the PDCS [29]. If participants demonstrated either persistent or apparent symptoms of disease, they were referred to seek further care at the medical center. Participants who required pharmacological treatment were recommended for non-steroidal anti-inflammatory drugs, mainly acetamino- phen and ibuprofen. All participants requiring medical attention received care at the HCSFV throughout the study. Phases of chikungunya Participants with chikungunya were classified as experiencing acute, interim, or chronic arthralgia. Acute cases were defined as experiencing illness strictly within the first 10 days post-fever onset based on CDC and World Health Organization (WHO) guidelines [7,13]. Acute chikungunya was initially stratified by both �15 days and �10 days post-fever onset in our analysis, with no meaningful difference in results. Chronic chikungunya cases were defined as experiencing chikungunya-associated signs or symptoms �90 days post-fever onset based on WHO and French guidelines from studies of outbreaks on Re´union Island [4,13]. The interim cases covered the period from 11–89 days post-fever onset [4,10]. Collectively, the interim and chronic cases constitute the post-acute phase of chikungunya, defined as any report of arthralgia >10 days post-fever onset. For our analysis, participants who experienced arthralgia in multiple chikungunya phases were analyzed by the last phase in which they reported arthralgia. Statistical methods and data analysis Participants were stratified into four age ranges: 0–4, 5–9, 10–15, and 16+ y/o based on estab- lished PDCS protocols and the Nicaraguan Ministry of Health. Adults were grouped into one range, 16+, due to the small sample size that prohibited further stratification. When directly comparing pediatric and adult cases, the pediatric group was condensed to ages �15 and the adult group consisted of ages 16+. We used logistic regression to estimate odds ratios (ORs) for binary outcomes, such as the presence or absence of arthralgia. We considered both unadjusted models and models adjusted for sex and age. In our analysis modeling the relationship between the three phases of disease (i.e., acute, interim, and chronic), we adjusted for age and sex based on supporting evidence from the literature and our own analyses [10]. Generalized additive models (GAM), semi- parametric extensions of generalized linear models (GLM), were used to visualize the non-lin- ear relationship between different variables such as age, chikungunya-associated arthralgia across different parts of the body, and phases of chikungunya disease [33]. GAMs were pre- ferred over GLMs due to the addition of smoothing functions that better estimate the func- tional relationships between the outcome and associated explanatory variables. GAMs relax GLMs’ assumption of linearity, allowing for the estimation of non-linear trends [33]. Pearson’s Chi-squared test was used to compare the proportion of participants with arthral- gia across age groups and sex [34]. Survival analyses were used to describe the percentage of participants reporting arthralgia over time, accounting for right censoring and stratified by age, sex, and diagnosis. Survival data were visualized using Kaplan-Meier (KM) curves, which plot the probability of an outcome not occurring across the study period [35]. KM survival curves were evaluated using the log-rank test, with the null hypothesis proposing that the plot- ted survival curves are similar. We evaluated group trends in the KM survival curves using the log rank test for trend [36]. We omitted confidence intervals in the visualization of the KM PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 5 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults survival curves to improve graphical representation of the data, and we used the p-value from the log-rank test to quantify statistical differences between group-specific survival curves. Wei- bull survival models were used to calculate the hazard ratio (HR) of experiencing the outcome (presence or absence of arthralgia) over the study period, compared to a reference group [36]. Survival analyses were utilized only when observing the data beginning from the interim phase of disease (>10 days post-fever onset) until study cessation, as the proportional hazard assumption was not satisfied during the acute phase. Survival analyses were conducted using the survival, survminer, and eha R packages and visualized in base R [36–38]. Agglomerative hierarchical clustering was used to construct a co-occurrence dendrogram of participants’ signs and symptoms [39]. The Ward method was used to create groups with mini- mal variance, and the Manhattan distance was used to determine the underlying distance matrix. The cophenetic distance correlation coefficient was calculated to measure the similarity between the original distance and the cophenetic distance [39]. The higher the cophenetic dis- tance correlation coefficient is the more appropriately the dendrogram represents a hierarchical structure present in the original data. Analyses were conducted using the base stats package, and dendrograms were visualized using base R graphics and the dendextend package [40]. For a sensitivity analysis, we used KM estimates to evaluate any differences in reported arthralgia occurrence between the rRT-PCR-confirmed and clinically/epidemiologically prob- able groups. Further, to evaluate whether baseline arthralgia among pediatric individuals might confound our results, we conducted a comparison between our pediatric chikungunya cases and the pediatric Zika cases. We compared the reported arthralgia occurrence beyond the acute phase of disease using KM estimates. Data were analyzed using R version 4.1.1 within the RStudio (2021.09.0, Build 351) inte- grated development environment [41]. Data management was conducted using the core tidy- verse packages along with base R, lubridate, reshape2, broom, epitools, plyr, and tableone packages [41–47]. Two-dimensional plots were visualized using base R graphics and the ggplot2 R package, supplemented with the scales package to express percentages [48,49]. Tables were created using Microsoft Word 2021 (v16.56). Results Participant characteristics Participants consisted of 770 chikungunya cases; 682 (88.6%) were positive by real-time RT-PCR, and 88 (11.4%) were clinically/epidemiologically probable cases. Study participants’ median age was 11 y/o (interquartile range: 7–14) (S1 Fig). We enrolled 394 (51.2%) females and 376 (48.8%) males. There were 612 pediatric (0–15 y/o) and 158 adults (16+ y/o) cases (Table 1). During the chikungunya epidemics, there were very few cases of dengue – which can present similarly as chikungunya – detected in the PDCS, limiting the number of incor- rectly classified chikungunya cases based on clinical/epidemiological criteria. Age-based differences in chikungunya-associated arthralgia Overall, 88% of pediatric and 98% of adult participants reported arthralgia during the study period (Table 2). We observed that the prevalence of arthralgia increased in an age-dependent manner (Fig 1A) but decreased over time since fever onset for all age groups (Fig 1B). KM plots demonstrated an age-dependent increase in the percentage of participants who reported post-acute arthralgia across ~1.5 years of follow-up time (Fig 2A). The pediatric groups reported ~30% of their total instances of arthralgia occurring beyond the acute phase, showing the substantial burden of chikungunya-associated arthralgia among children (Table 3). Cru- cially, a large percentage of pediatric participants (0–4 y/o: 18.6%; 5–9 y/o: 23.2%; 10–14 y/o: PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 6 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults Table 1. Characteristics of the participants in the chikungunya prospective cohort study in Managua, Nicaragua (2014–2018). Variable Age, median [IQR] Age (years) Sex, n (%) Female Male Confirmation method, n (%) Real-time RT-PCR-positive Serologically confirmed or clinically/epidemiologically probable Epidemic, n (%) Epidemic 1 (2014–15) Epidemic 2 (2015–2016) Age range (years), n (%) 0–4 5–9 10–15 16+ 16–25 26–35 36–45 46–55 Data (n = 770) a 11 [7.0, 14.0] 394 (51.2) 376 (48.8) 682 (88.6) 88 (11.4) 207 (26.9) 563 (73.1) 105 (13.6) 200 (26.0) 307 (39.9) 158 (20.5) 71 (44.9) 38 (24.1) 32 (20.3) 17 (10.9) a Visits after day 625 and participants >55 years of age were excluded. https://doi.org/10.1371/journal.pntd.0011948.t001 32.5%) reported 2+ visits with symptoms of arthralgia (Table 4). The adult group reported 43.4% of their total instances of arthralgia in the post-acute phase, a significantly higher pro- portion compared to all other age groups (p-value < 0.05) (Table 3). Adults reported the high- est percentage (55.1%) of individuals with 2+ visits with symptoms of arthralgia (Table 4). Age-dependent trends in reported arthralgia occurrence were supported by KM estimates (Fig 2A), ORs (Table 2), and HRs (S1 Table). Table 2. Risk factors for acute chikungunya-associated arthralgia in Managua, Nicaragua (2014–2018). Category Reported Arthralgia Odds Ratio (95% CI a) No; n (%) Yes; n (%) Age range (years) 0-4 5-9 10-15 16+ Sex Female Male Total b 23 (22.6) 30 (15.2) 22 (7.3) 2 (1.6) 31 (8.3) 46 (12.9) 79 (77.5) 168 (84.8) 280 (92.7) 125 (98.4) 341 (91.7) 311 (87.1) All participants 77 (10.6) 652 (89.4) a CI = Confidence Interval b Some participants were omitted due to missing acute-phase data, n = 41 https://doi.org/10.1371/journal.pntd.0011948.t002 0.27 (0.14, 0.51) 0.44 (0.24, 0.78) - 4.91 (1.42, 30.95) 1.63 (1.01, 2.65) - - PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 7 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults Fig 1. Prevalence of post-chikungunya-associated arthralgia over time, stratified by age range in years in Managua, Nicaragua (2014–2018). Age trends for the prevalence of post-chikungunya-associated arthralgia depicted using a generalized additive model. A 95% confidence band is shown around the mean trend (A). The prevalence of arthralgia measured across days since fever onset and stratified by age range is depicted using a generalized additive model. Distributed marks at the top indicate the density of patient responses by day since fever onset. Participants were considered as having either acute (<10 days), interim (>10 and <90 days), or chronic (>90 days) disease (B). https://doi.org/10.1371/journal.pntd.0011948.g001 Clinical presentation of post-acute chikungunya-associated polyarthralgia Clustering analyses demonstrated that polyarthralgia co-occurred in three general areas, con- stituting distinct clusters of localized arthralgia: the legs (knees, ankles, and feet), hands (wrists and hands), and torso/elbows (neck, shoulders, back, hips, and elbows) (Fig 3A). The most fundamental difference in polyarthralgia co-occurrence was the division of the hand and leg areas, which clustered together, from the torso/elbows, which formed its own cluster. The prevalence of arthralgia increased across age for each of the three clusters identified through hierarchical clustering (Fig 3B). The prevalence of polyarthralgia was highest in the legs (28.5%), followed by the hands (21.1%) and the torso/elbows (13.5%) (S2 Table). The pro- portion of polyarthralgia between distinct body parts was significantly different when Fig 2. Kaplan-Meier plot showing the proportion of participants reporting arthralgia over time in years in Managua, Nicaragua (2014–2018). A Kaplan-Meier graph plotting the proportion of participants not reporting arthralgia (y-axis) against days since fever onset (x-axis). Ticks correspond to censoring events. Panels show the distribution of participants beginning 10 days post-fever onset and ending at the last reported data point based on the exclusion criteria (< 625 days post-fever onset), stratified by age range (A) and sex (B). The p-values were calculated using the log-rank test. https://doi.org/10.1371/journal.pntd.0011948.g002 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 8 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults Table 3. Prevalence of reports of arthralgia in the acute and post-acute phase in Managua, Nicaragua (2014– 2018). Age range Acute, n (%) Post-acute, n (%) 0-4 5-9 10-15 16+ 73 (73.7) 158 (67.8) 275 (68.8) 124 (56.6) 26 (26.3) 75 (32.2) 125 (31.3) 95 (43.4) a Some participants were omitted due to missing acute data, n = 41 https://doi.org/10.1371/journal.pntd.0011948.t003 Total a 99 233 400 219 combining all age groups (S2 Table). Specifically, among pediatric participants (�15 y/o), the difference between localized polyarthralgia in the hands and legs was significantly different (p- value < 0.01), while this difference was not observed in adults (>15 y/o, p-value = 0.43), indi- cating distinct presentation of polyarthralgia between the two groups. For each cluster, the prevalence of polyarthralgia increased substantially and linearly across age; linearity is particu- larly evident after age 20 (Fig 3B). Such trends represent averages across the underlying body parts, which we also characterized. In general, the age-specific prevalence of arthralgia for the individual body parts resembled the trends of the corresponding cluster (Fig 3C), supporting the clustering analysis and extending its results across age. Sex-based differences in chikungunya-associated arthralgia Over 85% of both males and females reported arthralgia over the study period. Over 18 months of follow-up, females experienced significantly higher odds of arthralgia (OR: 1.63 [95% CI: 1.01–2.65]) than males (Table 2). Beyond the acute phase of disease, a significantly higher pro- portion of females experienced arthralgia compared to males (p < 0.001; log rank test) (Fig 2B). During this same time period, the hazard for females experiencing arthralgia was also sig- nificantly higher compared to males (HR: 2.27 [95% CI: 1.49–3.46]) (S1 Table), both among children (HR: 1.97 [95% CI: 1.18–3.28]) and adults (HR: 2.20 [95% CI: 1.11–4.35]). Sex-based differences in arthralgia occurrence were most apparent within the first six months of follow- up, and this difference remained relatively unchanged until study cessation (Fig 2B). Alto- gether, these data suggest distinct, sex-based experiences of arthralgia that are especially pro- nounced beyond the acute phase. Differences across the acute, interim, and chronic phases We then evaluated the proportion of acute, interim, and chronic arthralgia cases, defined by the last instance of reported arthralgia, across continuous age. We observed that as age increased, the proportion of acute cases decreased while the proportion of interim or chronic Table 4. Proportion of visits with reports of arthralgia in Managua, Nicaragua (2014–2018). Age range 0-4 5-9 10-15 16+ Number of visits with reported arthralgia a 0, n (%) 1, n (%) 23 (22.5) 30 (15.2) 22 (8.3) 2 (1.6) 60 (58.8) 122 (61.6) 182 (60.3) 55 (43.7) 2+, n (%) 19 (18.6) 46 (23.2) 98 (32.5) 70 (55.1) a Some participants were omitted due to missing acute data, n = 41 https://doi.org/10.1371/journal.pntd.0011948.t004 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 9 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults Fig 3. Reported polyarthralgia beyond the acute phase of chikungunya associated-arthralgia by body part and age in year in Managua, Nicaragua (2014–2018). Cluster dendrogram depicting the relationship between occurrence of polyarthralgia across the different body parts, with the y-axis representing the underlying cluster distance calculated using the Manhattan distance method. The cophenetic distance correlation coefficient is 0.95; the higher the cophenetic distance correlation coefficient is, the more appropriately the dendrogram represents a hierarchical structure present in the original data (A). Age trends of the prevalence of arthralgia among clustered body groups (B) and individual body parts (C), including the 95% confidence intervals, visualized using shading corresponding to each respective color group and depicted using a generalized additive model. https://doi.org/10.1371/journal.pntd.0011948.g003 Fig 4. Age trends in years for the percentage of chikungunya-associated arthralgia cases in each defined phase in Managua, Nicaragua (2014–2018). Participants were considered as acute (<10 days), interim (>10 days and <90 days), or chronic (>90 days) phase arthralgia cases (A) or considered as either acute (<10 days) or post-acute (>10 days) phase arthralgia cases (B), based on their last instance of arthralgia. Graphs include the 95% confidence intervals, visualized using shading corresponding to each respective color group and depicted using a generalized additive model. The y-axis reflects, out of all participants with reported arthralgia, what percent had their last instance of arthralgia in each given phase. https://doi.org/10.1371/journal.pntd.0011948.g004 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 10 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults cases increased (Fig 4A). Among younger participants (<15 y/o), ~20% were considered chronic and ~10% interim cases, with ~55% considered acute cases and a subset reporting no arthralgia (~15%) (S3 Table). A high proportion of the older participants were considered interim (26.0%) and chronic cases (29.1%), while fewer were defined as acute cases (43.3%) or did not report arthralgia (1.6%) (S3 Table). At ~18 years of age, the proportion that experi- enced interim or chronic arthralgia increased up to 50%, with the percentage increasing dra- matically as age increased (Fig 4A). Indeed, by age 30, the proportion of chronic and interim cases was approximately 90%. When comparing pediatric (<15 y/o) and adult (>15 y/o) par- ticipants, we observed that adults had a significantly higher proportion of interim cases (p- value < 0.01) but not chronic cases (p-value = 0.06). This difference is driven by the signifi- cantly lower proportion of post-acute pediatric cases being defined as interim cases (29.4%) rather than chronic cases (70.6%, p-value < 0.01), a difference not observed between interim (47.1%) and chronic (52.9%) cases among adults (p-value = 0.67). Thus, differences in the pre- sentation of arthralgia between the pediatric and adult groups were primarily driven by the occurrence of arthralgia during the interim period. Association between the interim and chronic phases The age-specific trends of interim and chronic arthralgia cases were broadly similar to each other in children, but the trends appeared to differ more among adults (Fig 4A and S3 Table). To examine this more thoroughly, we used logistic regression to quantify the association between having either acute or interim arthralgia (the exposure variables) and later developing chronic arthralgia (the outcome variable). Among pediatric (0–15 y/o) participants, having interim arthralgia was significantly associated with exhibiting chronic arthralgia, though there was no evidence that experiencing acute arthralgia was associated with developing chronic arthralgia (Table 5). Similar results were obtained after adjusting for sex and continuous age. However, among adults, the association between interim arthralgia and chronic arthralgia was not statistically significant in either unadjusted or adjusted models. As the regression results strengthened our finding that the interim and chronic phases were only similar in the pediatric group, we analyzed the proportion of acute and post-acute Table 5. Odds of progressing to chronic chikungunya-associated arthralgia after an initial presentation with only acute or interim chikungunya-associated arthralgia in Managua, Nicaragua (2014–2018). Category All ages Acute arthralgia c Interim arthralgia 0-15 y/o Acute arthralgia Interim arthralgia 16+ y/o Acute arthralgia Interim arthralgia Odds ratio (95% CI a) Unadjusted 1.65 (0.96, 3.01) 2.31 (1.50, 3.55) 1.44 (0.83, 2.65) 2.12 (1.24, 3.57) 0.92 (-1.62, 5.86) 0.14 (-0.99, 1.31) Adjusted b 1.36 (0.78, 2.50) 1.95 (1.19, 3.14) 1.27 (0.72, 2.36) 2.06 (1.20, 3.47) 1.08 (-1.57, 6.04) 0.50 (-0.78, 1.87) a CI = Confidence Interval b Adjusted for age and sex c Participants were considered as having either acute (�10 days), interim (>10 and <90 days), or chronic (�90 days) disease https://doi.org/10.1371/journal.pntd.0011948.t005 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 11 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults Fig 5. Kaplan-Meier plot demonstrating the proportion of participants not experiencing arthralgia by diagnostic method and cohort. A Kaplan-Meier graph plotting the proportion of participants not reporting arthralgia (y-axis) against days since fever onset (x-axis). Ticks correspond to censoring events. Panels show the distribution of participants beginning 10 days post-fever onset and ending at the last reported data point based on the exclusion criteria (<625 days post-fever onset), stratified by cohort (A) or CHIKV infection diagnostic method (B). The p-values were calculated using the log-rank test. https://doi.org/10.1371/journal.pntd.0011948.g005 (combining the interim and chronic cases) arthralgia cases over continuous age (Fig 4B). The proportion of post-acute cases increased linearly with continuous age, ranging from 25% to 45% among the youngest ages (0–15 y/o) and overtaking the proportion of acute cases around age 20 y/o before reaching ~100% by the age of 35 y/o (Fig 4B). Further, we observed a signifi- cant difference between the proportion of pediatric and adult patients with post-acute arthral- gia (p-value < 0.01). In sum, our analysis suggests that a child experiencing interim arthralgia is also likely to experience chronic arthralgia; however, having interim arthralgia as an adult is not associated with chronic pain. Sensitivity analysis As a final analysis, we examined whether the confirmation method and baseline levels of pedi- atric arthralgia impacted our major results. Real-time RT-PCR-confirmed participants and serologically confirmed and clinically/epidemiologically probable participants showed no dif- ferences in reported arthralgia occurrence over time (Fig 5A). However, the chikungunya cohort reported a significantly higher percentage of arthralgia occurrence beyond the acute phase of disease compared to the parallel Zika pediatric cohort, consistent with known clinical differences between chikungunya and Zika. This demonstrates that the levels of arthralgia observed in the cohort were distinct from baseline pediatric arthralgia (Fig 5B). Discussion We describe risk factors and characteristics of long-term arthralgia among 770 chikungunya cases in a prospective cohort in Managua, Nicaragua. We find that pediatric chikungunya cases are most vulnerable to chikungunya-associated arthralgia during the acute phase, but they do exhibit a meaningfully high prevalence of arthralgia occurring beyond the acute phase of disease. Further, we demonstrate an age-dependent increase in the prevalence of arthralgia from infancy throughout adulthood. While the association between age and arthralgia has been described in adults [18], we present one of the first analysis of longitudinal pediatric chi- kungunya-associated arthralgia [15]. The highest prevalence of post-acute pediatric polyar- thralgia was reported in the legs, followed by the hands and torso/elbows; no significant differences among body parts was observed in adults, though the sample size of adults was PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 12 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults limited. Finally, when comparing the three phases of chikungunya described in literature, we observed a strong similarity between interim and chronic arthralgia phases in pediatric cases (�15 y/o), but not in adult cases (>15 y/o). Our findings provide new insights into chikungu- nya-associated arthralgia in both pediatric and adult cases that could be used to improve clini- cal guidelines. There are limited longitudinal data on the age-specific changes in the risk of chronic arthralgia due to chikungunya, particularly in Latin American populations. Our regression model demonstrated that a substantial ~77.5–92.7% of pediatric cases reported arthralgia, and of the total arthralgia reported among pediatric cases, ~26.3–31.3% occurred beyond the acute phase, demonstrating that many pediatric cases are vulnerable to long-term arthralgia. Fur- ther, we observed that the proportion of acute, interim, and chronic cases were approximately equal around age 30–35, whereas the transitional age decreased to approximately 20 years when the interim and chronic phases were considered together. Notably, age 20 is far below the threshold of 45 years commonly used in clinical guidelines to demarcate risk for post-acute arthralgia in chikungunya cases. Thus, an opportunity exists for guidelines [4,10,17] to decrease the threshold age to capture more patients, thereby leading to improved case manage- ment and patient outcomes. These observations highlight the burden of chronic, chikungu- nya-associated arthralgia beginning at a younger age than often described in the literature [10,17,18]. Importantly, although quality-of-life research in children with chikungunya is lack- ing, several studies have demonstrated the negative effect of arthralgia on the livelihood of adult cases [50,51]. In a study conducted on La Re´union Island in 2012, adult cases reported arthralgia, discomfort, and depression up to six years after acute chikungunya, and 48% of the chikungunya patients declared moderate to intense pain compared to 16% of non-chikungu- nya cases [9]. If clinical guidelines do not fully account for the hidden burden of chikungunya in children and young adults, these groups will be at heightened risk of being neglected, might not receive appropriate medical management, and will suffer negative effects on their activity and education, particularly in resource-limited settings [14,15]. We observed that polyarthralgia in the legs was most pronounced in children. Thus, exami- nation of the legs may serve to diagnose post-acute arthralgia better than other body parts in children. Further, we note that arthralgia in the elbow clusters with arthralgia in the neck, back, shoulders, and hips, despite the proximity of elbows to the hands and wrists. Little is known about how arthralgia in the joints clusters and manifests post-acutely in children [10,15]. Understanding the unique presentation of polyarthralgia among different age groups can facilitate identification of chikungunya, particularly among young children with a limited capacity to detail their pain. The relatively high prevalence of post-acute polyarthralgia sug- gests long-term follow-up should be conducted for both children and adults. Our results extend the observed trend in the literature that older females (50+ y/o) experi- ence arthralgia more often than older males [18,52] to children, as we found that pediatric females (0–15 y/o) had significantly higher odds and post-acute hazards of arthralgia when compared to males. Hormonal and immunological differences between females and males has been hypothesized [53] to explain the higher prevalence of arthritic diseases among females, though similarities between chikungunya-associated arthralgia and rheumatoid arthritis are debated [11,54]. Altogether, our results and the literature suggest that across all ages, sex is an important risk factor for arthralgia. Consequently, it is critical for clinical guidelines to empha- size the risk and management of chikungunya-associated arthralgia for females of any age. The WHO recommends that the chronic phase of chikungunya be defined as 12 weeks post-symptom onset [13], with little acknowledgment of the interim phase. Here, we observed distinct trends and presentation of arthralgia in pediatric participants between the first 10 days post-fever onset (acute phase) and thereafter (interim and chronic phases). To our knowledge, PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 13 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults no analysis to date has described the prevalence of arthralgia in each phase of chikungunya from childhood to adulthood. Furthermore, the divergence between the acute and interim/ chronic phases we observed was particularly evident between age groups. This finding suggests that although interim-phase arthralgia in adults is self-limiting, interim-phase arthralgia in children differs from adults and can be utilized as an indicator for chronic arthralgia. Vairo et al. [16] and the French Infectious Diseases Society [4] have previously highlighted the simi- larities between the interim and chronic phases. It has been noted that earlier and more suc- cessful clearance of CHIKV during the acute phase results in protection from chronic chikungunya [55,56]. Further, early management of acute inflammation (by use of steroidal and non-steroidal anti-inflammatory drugs) has been shown to decrease the risk of chronic inflammation [57]. Interim arthralgia might be linked to poor viral clearance and constitutes a higher risk for long-term symptoms. If guidelines explicitly associate pediatric interim arthral- gia with greater odds of chronic arthralgia, medical professionals could identify cases with ear- lier signs of arthralgia as being at risk for prolonged arthralgia and ensure long-term follow-up to best manage pediatric cases. Our study has several limitations. Our results are based in part on reports of polyarthralgia from pediatric patients, which might introduce misclassification bias due to subjectivity when clinically probing for signs of arthralgia. However, our data is collected by physicians who individually have over 10 years’ of experience in diagnosing arthralgia and related conditions in young children due to the high burden of arboviral disease in Managua [31]. Further, our study is limited by the small sample size of adult participants, which was by design as we aimed to primarily characterize children so as to fill gaps in the literature. We were unable to conduct comprehensive analyses on the severity of arthralgia and changes in pain across body parts over time due to limitations in our study questionnaire. Finally, the generalizability of our findings may be limited to the Asian CHIKV lineage, due to the previously described vari- ability in clinical presentations of chikungunya across CHIKV lineages [18,24]. Overall, our results provide new insights into chikungunya-associated arthralgia and dem- onstrate the high prevalence of arthralgia in pediatric cases, both in the acute and post-acute phases. We observe a strong age-prevalence trend for arthralgia and suggest improvements for the pediatric definition of the phases of chikungunya. Overall, our results inform chikungunya clinical guidelines for short-term and long-term care in both pediatric and adult populations. Supporting information S1 Table. Hazards for chikungunya-associated arthralgia >10 days post-fever onset in Managua, Nicaragua (2014–2018). (DOCX) S2 Table. Prevalence of post-acute polyarthralgia by age and body part in Managua, Nica- ragua (2014–2018). (DOCX) S3 Table. Prevalence of each phase of chikungunya-associated arthralgia by age in Mana- gua, Nicaragua (2014–2018). (DOCX) S1 Fig. Density plot of participants stratified by age range in years in Managua, Nicaragua (2014–2018). The percent of participants in this study across age are depicted using an age- density plot. Colors correspond to the age-ranges defined within the study (0–4, 5–9, 10–15, and 16+ years old). (TIF) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011948 February 28, 2024 14 / 18 PLOS NEGLECTED TROPICAL DISEASESAnalysis of post-acute chikungunya-associated arthralgia in children and adults S1 Appendix. Questionnaire for the Retrospective and Prospective Study of Clinical, Viro- logical, and Immunological Characteristics of Chikungunya Cases in Nicaragua. (DOCX) Acknowledgments We thank the study participants for taking time out of their lives to help us understand more about the presentation of chikungunya. This study was made possible by the dedicated clinical, laboratory, and data scientists at the Sustainable Sciences Institute and Centro de Salud So´cra- tes Flores Vivas in Managua, Nicaragua. Their passion for improving our understanding of neglected tropical diseases benefits at-risk communities across the globe. We thank Victoria Warnes for her support. This work was made possible by the R language and RStudio teams that provide flexible and comprehensive open-source data analysis technology. Author Contributions Conceptualization: Colin M. Warnes, Fausto Andres Bustos Carrillo, Eva Harris. Data curation: Jose Victor Zambrana, Brenda Lopez Mercado, Sonia Arguello. Formal analysis: Colin M. Warnes. Funding acquisition: Angel Balmaseda, Eva Harris. Investigation: Colin M. Warnes, Fausto Andres Bustos Carrillo, Oscarlette Ampie´, Damaris Collado. Project administration: Nery Sanchez, Sergio Ojeda, Guillermina Kuan, Aubree Gordon, Angel Balmaseda, Eva Harris. Supervision: Fausto Andres Bustos Carrillo, Eva Harris. Visualization: Colin M. Warnes. Writing – original draft: Colin M. Warnes, Fausto Andres Bustos Carrillo, Eva Harris. Writing – review & editing: Colin M. Warnes, Fausto Andres Bustos Carrillo, Aubree Gor- don, Eva Harris. References 1. Lumsden WHR. An epidemic of virus disease in Southern Province, Tanganyika territory, in 1952–1953 II. General description and epidemiology. Trans R Soc Trop Med Hyg. 1955 Jan 1; 49(1):33–57. 2. Suhrbier A. Rheumatic manifestations of chikungunya: emerging concepts and interventions. Nat Rev Rheumatol. 2019 Oct; 15(10):597–611. https://doi.org/10.1038/s41584-019-0276-9 PMID: 31481759 3. Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. 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10.3389_fnbeh.2023.1111908	OPEN ACCESS EDITED BY Daniela Schulz, Bo˘gaziçi University, Türkiye REVIEWED BY Eric Horstick, West Virginia University, United States Moriel Zelikowsky, The University of Utah, United States Keegan Dohm, University of Utah, United States, in collaboration with reviewer MZ CORRESPONDENCE Kay M. Tye tye@salk.edu Cewu Lu lucewu@sjtu.edu.cn †These authors have contributed equally to this work and share ﬁrst authorship SPECIALTY SECTION This article was submitted to Individual and Social Behaviors, a section of the journal Frontiers in Behavioral Neuroscience RECEIVED 01 December 2022 ACCEPTED 21 February 2023 PUBLISHED 30 May 2023 CITATION Chen Z, Zhang R, Fang H-S, Zhang YE, Bal A, Zhou H, Rock RR, Padilla-Coreano N, Keyes LR, Zhu H, Li Y-L, Komiyama T, Tye KM and Lu C (2023) AlphaTracker: a multi-animal tracking and behavioral analysis tool. Front. Behav. Neurosci. 17:1111908. doi: 10.3389/fnbeh.2023.1111908 COPYRIGHT © 2023 Chen, Zhang, Fang, Zhang, Bal, Zhou, Rock, Padilla-Coreano, Keyes, Zhu, Li, Komiyama, Tye and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Methods PUBLISHED 30 May 2023 DOI 10.3389/fnbeh.2023.1111908 AlphaTracker: a multi-animal tracking and behavioral analysis tool Zexin Chen1†, Ruihan Zhang2,3†, Hao-Shu Fang1†, Yu E. Zhang4,5, Aneesh Bal6,7, Haowen Zhou2, Rachel R. Rock7, Nancy Padilla-Coreano7,8, Laurel R. Keyes7,9, Haoyi Zhu1, Yong-Lu Li1, Takaki Komiyama5, Kay M. Tye7,9 and Cewu Lu1,10* 1Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China, 2Zhiyuan College, Shanghai Jiao Tong University, Shanghai, China, 3Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Department of Neurobiology, Center for Neural Circuits and Behavior, University of California, San Diego, La Jolla, CA, United States, 5Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States, 6Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, United States, 7Salk Institute for Biological Studies, La Jolla, CA, United States, 8Department of Neuroscience, University of Florida, Gainesville, FL, United States, 9Howard Hughes Medical Institute, The Salk Institute, La Jolla, CA, United States, 10Shanghai Artiﬁcial Intelligence Laboratory, Shanghai, China tool to elevate behavioral Computer vision has emerged as a powerful research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics. KEYWORDS neuroscience, computer vision, animal behavior, animal tracking, behavioral clustering 1. Introduction 1.1. Development of the protocol The study of animal behavior can be dated back to the nineteenth century when most researchers focused on observing natural behaviors (Darwin, 1872; Tinbergen, 1963). While reductionist behavioral paradigms are still widely used to study speciﬁc aspects of behavior in a controlled manner, allowing animals to freely explore spaces and to exhibit complex behaviors greatly expands our understanding of system neuroscience (Kabra et al., 2013; Wiltschko et al., 2015; Mathis et al., 2018; Pereira et al., 2020a; Padilla-Coreano et al., 2022). Ethological behavioral research challenges our ability to quantify behavior and draw statistically meaningful conclusions with traditional tracking methods and manual annotations (Berman, 2018). Social behavior is even more challenging due to the diﬃculty for a human to observe multiple animals simultaneously. Traditional animal tracking software suﬀers from noisy prediction of animal poses and confusion between multiple, seemingly identical animals. In addition, there remains a large gap between tracking animal keypoints and the quantiﬁcation and understanding of observed behaviors. Frontiers in Behavioral Neuroscience 01 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 Here we present a pipeline that allows reliable tracking of multiple near-identical mice and the subsequent behavioral analysis via unsupervised methods. AlphaTracker enables multi-animal tracking of videos recorded via a webcam, rendering this tool convenient and aﬀordable for the laboratory setting. To facilitate accessibility to AlphaTracker for individuals without access to a GPU, we also provide a Google Colab version. We also provide an unsupervised behavioral clustering algorithm for the unbiased identiﬁcation of behavioral motifs, the results of which can be further inspected with customized functions in a Jupyter notebook and a web-based user interface. 1.2. Applications of the method AlphaTracker demonstrates excellent accuracy in diverse backgrounds and setups. Our test cases include both wild- type C57BL/6 black mice and mice with optical ﬁbers and in vivo recording head stages, the keypoints of which are often occluded in keypoint tracking for existing software. AlphaTracker demonstrates robust performance with various backgrounds including home cages, metal operant conditioning chambers, and open ﬁelds. Our tracking algorithm shows better accuracy and precision than that of two diﬀerent humans annotating the same dataset. It supports not only top-view cameras, but also cameras installed at an angle, and low-resolution webcams (e.g., 675 p), making simultaneous monitoring of many animals ﬁnancially tractable. AlphaTracker shows robust performance in tracking multiple animals and identifying social behavior. Traditional methods of attaching markers or changing fur colors can aﬀect the natural behavior of animals and thus confound the research. Our toolbox was developed speciﬁcally for markerless tracking in multi-animal paradigms. Such simultaneous observation of multiple freely- behaving animals makes it a handy tool for social behavioral neuroscience research. Our unsupervised behavioral clustering bridges the gap between current keypoint-tracking techniques and the challenge of behavior comprehension. Clusters identiﬁed by our behavioral clustering algorithm correspond greatly to human assignment (Adjusted Rand Index of 0.201186, random assignment has the ARI of 0.003451). We identiﬁed seven individual behaviors: walking, digging, sniﬃng, rearing, face grooming, and body turning, grooming, and nine social behaviors including following, chasing, anogenital sniﬃng, face sniﬃng, and social rearing. We envision our clustering algorithm, with proper training, demonstrating extended application in tracking other animals such as marmosets, ﬁsh, and humans with proper training. 2. Materials and methods 2.1. Materials 2.1.1. Software • Anaconda: a free and open-source distribution of the Python programming language. AlphaTracker is written in Python 3.8 and is not compatible with Python 2. • AlphaTracker: an actively maintained toolbox freely available at: https://github.com/MVIG-SJTU/AlphaTracker. Instructions in this paper are based on this version. Recently, we provided a sister version of our package on GitHub (https://github.com/Tyelab/AlphaTracker2) which is more friendly to Windows users as it provides a Python wrapper for the DarkNet in the YOLO package, the original version of which is a C-based toolbox that must be compiled on Linux systems. With the goal of oﬀering real-time tracking, this version also adds some ﬂexibility in processing speeds by oﬀering options for lighter-weight networks like Mobile Net in place of the ResNet backbone with the goal of oﬀering real-time tracking. • PyTorch: an open-source software library for Deep Learning. Our toolbox has been tested on PyTorch 1.8.0. • Nvidia Driver: a driver software with a version higher than 450 is required to run our model on a computer with Nvidia GPU card, available at: https://www.nvidia.com/download/ index.aspx. • Jupyter Notebook: a web-based interactive platform available at: https://jupyter.org/install. computing • Data annotation toolbox Sloth: an open-source software for labeling keypoints and identities of objects, provided as part of our toolbox. 2.1.2. Hardware • Computer: Windows and Linux all can be used for labeling data, performing behavioral clustering, and evaluating trained tracking models. For training the tracking model, desktops/cloud servers with GPU access are required. We recommend > = 32 GB of RAM on the system for CPU analysis. • GPU: GPU is required for training the tracking model. We recommend having a GPU with > = 8 GB memory, such as the NVIDIA GeForce 1,080 or 2,080. Alternatively, our toolbox can also be used on cloud computing services with GPU support (e.g., Google Cloud/Amazon Web Services). • Camera: Our toolbox supports both color and grayscale videos, and even infrared light. Though we demonstrate decent performance on images with low resolution, we recommend cameras with a resolution of > =1,080 p for the best performance. We used Logitech C930e cameras for data acquisition in this paper. Users either 2.1.3. Equipment setup can on Google Colab. We run AlphaTracker access avoid server) restarting Colab. to install our recommend locally reinstallation locally toolbox or users with GPU lab upon (local desktop or of dependency • Operating system: Linux (Ubuntu 16.04 LTS, 18.04 LTS), or Windows (10) (Windows only supports applying the tracking model, but not training the neural network-based model). For GPU support, NVIDIA drivers should be installed (see the previous subsection for details). Windows users double-click the downloaded .exe ﬁle to install it. Linux users ﬁrst navigate to the Frontiers in Behavioral Neuroscience 02 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 folder with the “.run” ﬁle after downloading, open the terminal and type the following command in the terminal: 1. cd path/of/driver 2. chmod +x ./Nvidia-driver-name.run 3. sudo ./Nvidia-driver-name.run We recommend both Linux and Windows users install Anaconda for managing packages and environments because Anaconda supports multiple environments with diﬀerent versions of Python and supporting libraries. This avoids version mismatch with existing packages and libraries of the operating system. Follow the instructions at https://docs.anaconda.com/anaconda/install/ to install Anaconda. Windows users should install Git Bash by downloading from https://git-scm.com/downloads. Open the “.exe” ﬁle named “Git Bash” and run all commands within Git Bash. 2.2. Algorithm The tracking component of the pipeline (AlphaTracker) is adapted from AlphaPose (Fang et al., 2017, 2022), a human pose estimation and tracking library that provides superior performance in both accuracy and eﬃciency. The algorithm consists of three steps: animal detection, keypoint estimation, and identity (ID) tracking across frames (Figure 1). First, the algorithm detects the positions of animals in each frame with YOLOv3 (Redmon and Farhadi, 2018) which is a state- of-the-art convolutional neural network designed to detect objects at a high inference speed. Next, individual animals are cropped out with the bounding box output from YOLOv3. The cropped individual images are fed into Squeeze-and-Excitation Networks (SENet) (Hu et al., 2017) which estimates keypoint positions. For our mouse dataset, we chose the snout, tail base and two ears as our four keypoints. The outputs from SENet include x and y coordinates as well as a conﬁdence score which indicates the reliability of each identiﬁed keypoint. Finally, the algorithm tracks each animal across frames. This presents a signiﬁcant challenge for many platforms as animals of the same genetic lines often look alike. Traditional Re-ID methods previously implemented (Chen et al., 2018; Ristani and Tomasi, 2018; Feng et al., 2019) tend to fail since such methods typically rely on diﬀerences in the appearance of tracked animals. In our pipeline, we propose a novel target association method that captures hierarchical visual information to keep track of the identities of nearly identical animals across frames. We deﬁne a descriptor for the position and orientation of the animal from the set of bounding boxes around the entire animal and individual body parts. The similarity score of pairs of descriptors in adjacent frames is calculated according to formula 1. In formula 1, Dt is the descriptor of animal i at frame t, i boxt i is the bounding box of animal i at frame t predicted by the convolutional neural network. Pt ik is the box that wraps the k-th body point of animal i at frame t. Intersection Overlap Union (IOU) is deﬁned by formula 2. After sorting the descriptor similarities in descending order, the descriptors between two adjacent frames with the highest similarity are matched and assigned with the same tracking ID. Across frames, descriptors for dyads are matched with the second-highest similarity score. This procedure is repeated until no animals are left unmatched. In some cases, the predictions of bounding boxes and body points may not be accurate due to either tracking errors or occlusion. When the users correct the position of keypoints in one frame, we apply Kalman ﬁltering (Kalman, 1960) to model the motion characterized by velocity and acceleration. We then modify the keypoint position predictions in consecutive frames to ensure consistency across time. Our behavioral clustering classiﬁes mouse behavior with an unsupervised hierarchical clustering algorithm (Wiltschko et al., 2015; Nilsson et al., 2020) for the following reasons: (1) Animal behavioral taxonomy is intrinsically hierarchical in structure. (2) It allows intuitive re-organization of results once the linkage matrix is computed. In our method, we ﬁrst extract the features of animal behaviors based on the temporal dynamics of poses captured within a 15-frame time window. The 15-frame time window is chosen here since sub-second actions of animals have mean duration ± s.d. = 425 ± 726 ms (Wiltschko et al., 2020). Such features include biologically distinct features such as body length and displacement. When analyzing social behavior, we set one mouse as the reference and calculate the relative motion of the non-reference mouse. Here, users can assign diﬀerent weights to each feature to reﬂect feature importance in behavioral clustering. We next apply an agglomerative hierarchical clustering algorithm (Ward, 1963) to cluster clips based on the similarity between their features. Finally, a customized web-based UI allows easy inspection and modiﬁcation of clustering results. 2.3. Methods Our protocol consists of several stages: installation, preparing training the tracking model training datasets (Section 2.3.2), (Section 2.3.3), running the tracking model (Section 2.3.4), inspecting the tracking results with UI (Section 2.3.7), behavioral clustering (Section 2.3.6), and inspecting clustering result (section 2.3.7). Users looking for a quick test of our toolbox can skip the training stages (Sections 2.3.2, 2.3.3) and use the pretrained tracking model directly (Section 2.3.4). We also have a tutorial for our Colab version in Section 2.3.8. Sim(Dt i , Dt+1 i ) = IOU(boxt i , boxt+1 j ) + 1 n n X k=1 IOU(Pt ik, Pt+1 jk IOU(box1, box2) = AreaOfOverlap(box1, box2) AreaOfUnion(box1, box2) ) (1) 2.3.1. Installation 1. Download the toolbox via the command line. Users can specify a working directory and install it by running the following in the Git Bash terminal. (2) 1. cd path/of/interest Frontiers in Behavioral Neuroscience 03 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 1 AlphaTracker architecture and pipeline. The AlphaTracker architecture consists of three main components: object detection using YOLO, pose estimation using Single Animal Pose Estimation (SAPE), and identity tracking based on intersection over union (IOU) with error correction via Kalman ﬁlter. It outputs bounding boxes and user-deﬁned keypoints for each detected animal, along with conﬁdence scores for these predictions. 2. git clone https://github.com/MVIG-SJTU/ AlphaTracker.git 3. cd AlphaTracker 2. Users can install our toolbox with either command line or via a coding-free GUI (Figure 2). Users can run the following commands to install our toolbox via the command line. Note that Windows users should ﬁrst check out the “Windows” branch before the actual installation. Our toolbox creates an Anaconda virtual environment to manage Python dependencies. 1. git checkout windows # Windows users only 2. bash scripts/install.sh To use our GUI for installation, users need to download a GUI named “main_ui” from https://github.com/MVIG-SJTU/ AlphaTracker/releases and save it inside the AlphaTracker folder. Users should: (1) Right-click the GUI app and choose “Properties,” (2) Check the “allow execution” or “allow run as a program” options under the “Permission” tab, (3) Open the main GUI by double-clicking the icon, then (4) Click the “Install” button to run the installation automatically. A video tutorial is available at: https://youtu.be/fQ1bSoAkV5o. 2.3.2. Training dataset preparation (e.g., animal species, lighting For users hoping to train the model using their own condition), we parameters include an image annotation toolkit to allow customized annotation of training datasets. This toolkit was adapted from an open-source tool Sloth and can be found under the directory ./Tracking/TheAnnotationTool/. This tool has only been tested under Windows. We have also provided a demo training dataset 600 annotated frames of two unmarked mice interacting in a home cage. Users can download this folder from https://drive.google. com/ﬁle/d/1TYIXYYIkDDQQ6KRPqforrup_rtS0YetR/view?usp= sharing and proceed to the next section for training. 1. Pick representative frames from input videos (> =200 frames are recommended) and save these frames as a folder called “im.” Place the folder under the folder “json.” These frames should be as distinct from each other as possible to cover the posture space. Models that learn from the entire space generalize better during the actual implementation. 2. Click json/clickme.bat to create a new json ﬁle named multi_person.json under the folder json/. Move the newly generated JSON ﬁle into the directory json/im/. 3. Go back to the directory tool and click tool/start.bat. Select the multi_person.json ﬁle and click “Open” to load all the images. 4. To meet the input speciﬁcations of AlphaTracker, strictly follow the proceeding instructions for image annotation: First, choose the “Face” option to generate a red bounding box around the animal of interest on the image. Your deﬁnition of a bounding box should be consistent (e.g., if you include the tail in the bounding box, always do so). We recommend only including the tail base for mice because tails are highly ﬂexible and extend to a large area. Next, choose the “point” option to label keypoints for that animal. If you have multiple keypoints, it is critical to follow the same annotation order for all the animals (e.g., snout −→ left ear −→ right ear −→ tail base). If you have multiple animals, repeat the process for another animal only after you are completely done with the current animal (i.e., bounding box −→ all the keypoints) because the order matters. 5. If there is a mistake, you should ﬁrst select the image on the bottom left of the UI, click the wrongly labeled coordinates, and press “delete.” Make sure to delete all the subsequent coordinates for this frame as well and redo the annotation because the order of annotation is important for our algorithm. 6. Once you are done, press the “save” button to save the JSON ﬁle before exiting the program. Rename and move the entire “im/” folder (images and the JSON ﬁle) to a safer storage location for later use. As a double-check, the generated JSON ﬁle should have the same structure as in Figure 3. Frontiers in Behavioral Neuroscience 04 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 2 AlphaTracker UI. (Left) The AlphaTracker user interface provides six functionalities, including installing/uninstalling, training the tracking model, running the tracking and clustering models, and examining and correcting tracking and clustering results. (Right) The installation involves verifying the presence of Anaconda, installing required packages, setting up YOLO, and downloading the model weights and demo data. FIGURE 3 Example annotation JSON ﬁle generated by Sloth. The example shows annotations for two frames, represented by blue boxes. Each frame depicts two mice, one of which is highlighted in a yellow box. The yellow-boxed mouse is annotated with a bounding box for the body and four keypoints, which correspond to the head, left ear, right ear, and tail base respectively. 2.3.3. Training the tracking model 1. Users either use our code-free GUI to specify the parameters or modify the settings directly. If using the code-free GUI, click the “Train” button on the main GUI. Select the image folder with the training images and the JSON annotation ﬁle in the prompt window. Modify the parameters in the “training” tab. Users can hover their mouse cursor over each parameter for detailed explanations. A video tutorial is available at: https://youtu.be/txjrZiVS4Eo. If modifying the setting directly, specify the parameters in the conﬁguration ﬁle ./Tracking/AlphaTracker/setting.py. Important parameters are as follows: • json_ﬁle_list. List of paths to the corresponding annotation JSON ﬁle. • num_mouse. A list of the maximum number of animals that may appear in each of the corresponding image folders. • exp_name. Name for the current project. • num_pose. The number of keypoints for each animal. This must be consistent within the project. If users videos with diﬀerent numbers of keypoints have can set up individual projects per to consistent within each project. animal, keep the keypoint number they • image_root_list. List of paths to the directories of annotated frames. Depending on the training results, users may need to modify hyperparameters related to training. For example, users can lower the learning rate and increase the number of epochs. Frontiers in Behavioral Neuroscience 05 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 4 User interface for training the tracking model. The user interface for training the tracking model requires several inputs from the user. The inputs include the path to the labeled images (“ImageDir”), a JSON label ﬁle (“Label”), the number of mice in the images (“num_mouse”), the experiment name (“exp_name”), and pose pairs for deﬁning the connections between keypoints to represent the skeleton (“pose_pairs”). Other adjustable parameters such as the learning rate (“sppe_lr,” “yolo_lr”) and batch size (“sppe_batchSize,” “yolo_batchSize”) can be modiﬁed as needed. However, over-reducing the learning rate may deteriorate tracking quality. Some hyperparameters are explained as follows. ﬁle. This implicitly calls the pretrained model. In case users do not have a video ready to use, we also provide a test video at: ./Tracking/AlphaTracker/data/demo.mp4. • sppe_lr. Learning rate for the pose estimation module. Default: 1e-4. • sppe_epoch. Training epochs for the pose estimation module. You might need to set a large number when training from scratch. • sppe_batchSize. Batch size for pose estimation. If you encounter an out-of-memory (OOM) error, you may need to reduce the batch size. 1. Users can set the parameters for tracking by either specifying the parameters in ./Tracking/AlphaTracker/setting.py or using the code-free UI. If using the GUI, click the “Tracking” button on the main GUI and select a video ﬁle. Modify the parameters on the “tracking” page. Users can hover the mouse cursor over each parameter for detailed explanations. A video tutorial is available at: https://youtu.be/t2skgohliAc. Important parameters are as follows: 2. Train the AlphaTracker model by either clicking the “start” button on the training page after specifying all the parameters (if using the GUI, Figure 4) or by running the following command in the command line. 1. cd path/of/interest/AlphaTracker/ Tracking/AlphaTracker/ 2. conda activate alphaTracker 3. python train.py 2.3.4. Running the tracking model If users have not trained their own models, they can use our pretrained model by setting exp_name=demo in the conﬁguration • video_full_path. Path to the video • start_frame. Index of the start frame of the video • end_frame. Index of the last frame of the video • max_pid_id_setting. Number of mice in the video • result_folder. Path to the folder for saving the results • vis_track_result. Whether results by overlaying the video. • exp_name. our use to demo. pretrained model, users this Project name. set If to visualize tracking the predicted keypoints on the want to parameter 2. Users can start the “Start” the tracking process by either clicking the tracking page the on of button Frontiers in Behavioral Neuroscience 06 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 5 User interface for running the tracking model. The UI for running the tracking model requires an input video (“Video”) and the name of the trained model (“exp_name_track”). Users can specify the frame interval to be tracked by setting the start and end frames (“start_frame,” “end_frame”), and indicate the maximum number of mice expected in the frames (“max_pid_id_setting”). The results will be saved in the speciﬁed result folder (“result_folder”). (Figure 5) or by GUI command line. running the following in the the original video (e.g., demo.mp4). Specify the frame rate of the imported video. The default frame rate is 25.0 fps. 1. conda activate alphatracker 2. python track.py 2.3.5. Tracking result inspection Users can inspect and modify the tracking results with (Figure 6, Supplementary Video 6). We a browser-based UI recommend Google Chrome as the default browser for using the UI. Pre-installed Python3 is required as Python scripts are called by the backend of the UI. 1. Users can start the UI by clicking the “Results” button on the main GUI and clicking the “Start” button on the next page. A video tutorial is available at: https://youtu.be/9Ksb04s8mm4. Alternatively, users can run the following in the command line. 1. cd UI/ 2. python setup.py 2. A window should now appear in the user’s browser. Click html/ and select curate.html. Click the “Click Here” button to upload the JSON tracking result (e.g., alphapose-results-forvis- tracked.json). Click the second “Click Here” button to upload the users to browse The video player allows the videos with overlaying tracked keypoints and identities indicated by colors. Users can jump to frames of interest or scan through videos frame by frame. We provide speed control to allow ﬂexible navigation within each video. The timeline visualizes the position of diﬀerent keypoints. An abrupt change in keypoint position often suggests an error in tracking. 3. If the detected keypoints show large jitters, this indicates that the SPPE (single perspective pose estimator) model may not be properly trained. Users can return to the training stage, modify the learning rate and the number of training epochs, or provide more training data, and retrain the network. 4. Users can correct small errors such as mislabeled identities and mislabeled keypoints. To correct mislabeled keypoints, users can pause at the relevant frame(s) and drag each mislabeled keypoint to the correct position. To correct mislabeled identities, users can exchange the identities between two mice. Since errors are likely to persist after the newly corrected frame, users can select a time interval by clicking “IN” at the start of the time interval (typically, this is the frame being just modiﬁed) and “OUT” at the end of the interval. Click “curate” to update the prediction for all the frames in the interval. Frontiers in Behavioral Neuroscience 07 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 6 The UI for AlphaTracker allows for inspection and correction of tracking errors. The UI consists of four areas: Area I is a navigation bar with icons for navigating between different interfaces (1, 2), help function (3), undoing/redoing actions (6, 7), exporting results (4), and starting new sessions (5); The operation area (Area II) in the UI allows the user to view and edit the overlaid skeletons on the video using the cursor. It also provides the option to navigate between frames (2), initiate curation (3) and identity reassignment (3), take notes (5), and toggle the timeline’s time indicator (6). Area III is a playback control panel with options for playing/pausing videos (1), displaying time and frame information (2, 3), controlling playback speed (4, 6), and specifying the interval for curation (5); Area IV is a timeline displaying the progress of the video (1), and keypoint locations over time (2). Detailed instructions for using the UI can be found on GitHub. 5. After ﬁnishing modifying the tracking results, users can export the current clip information as a local JSON ﬁle by clicking the “export” icon. 2.3.6. Behavioral clustering AlphaTracker allows the analysis of both individual and social behavior. Here, using videos of two interacting mice, we demonstrate the ability of AlphaTracker to track animals in both scenarios. We consider clips with 15 frames (500 ms) as the unit for mouse behavior because previous research has shown that fast mouse pose dynamics can be grouped into meaningful blocks lasting 200–900 ms sub-second timescale (Wiltschko et al., 2015). For computing social features, we ﬁrst rotate and move the poses such that the body of the reference mouse at the middle frame of the clip lies on the positive x axis. Figure 7 illustrates the deﬁnition of several features. 1. The success of behavioral clustering depends on the weights assigned to each feature. Users can assign higher weights to features of interest. Users either use the GUI as described in the “tracking section” or in the ./BehavioralClustering/setting.py. Deﬁnitions of the features are listed below. the parameters set • body_length, body length of the reference mouse. • body_change_sin, change in body direction of the reference mouse between adjacent frames. • left_ear, distance between the snout and the left ear keypoints of the reference mouse. • left_ear_cos, angle between the snout-left ear vector and the body vector(cos) of the reference mouse. • left_ear_sin, angle between the vector and the body vector(sin) of mouse. the snout-left ear reference • right_ear, distance between the snout and the right ear keypoints of the reference mouse. • right_ear_cos, angle between the snout-right ear vector and the body vector(cos) of the reference mouse. • right_ear_sin, angle between the snout-right ear vector and the body vector(sin) of the reference mouse. • displace_rho, displacement between adjacent frames of the reference mouse. • displace_sin, direction of displacement between adjacent frames(sin) of the reference mouse. • displace_cos, direction of displacement between adjacent frames(cos) of the reference mouse. • body_length_TO, body length of the non-reference mouse. • body_change_sin_TO, change in body direction between adjacent frames of the non-reference mouse. • left_ear_TO, distance between the snout and the left ear keypoints of the non-reference mouse. Frontiers in Behavioral Neuroscience 08 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 7 Features for clustering individual and social behavior. Individual behavioral clustering depends on both static features in individual frames and dynamic features across frames. Social behavioral clustering also depends on additional social features. The deﬁnition of example features is depicted in the diagram. Common features include distances between keypoints and the angle between two vectors. (Blue, green: skeleton of mouse poses. Red: distances between two points or angles between two vectors. Gray: reference coordinates). • left_ear_cos_TO, angle between the snout-left ear vector and the body vector (cos) of the non-reference mouse. • left_ear_sin_TO, angle between the snout-left ear vector and the body vector (sin) of the non-reference mouse. • right_ear_TO, distance between the snout and the right ear keypoints of the non-reference mouse. • right_ear_cos_TO, angle between the snout-right ear vector and the body vector(cos) of the non-reference mouse. • right_ear_sin_TO, angle between the snout-right ear vector and the body vector(sin) of the non-reference mouse. • displace_rho_TO, displacement between adjacent frames of the non-reference mouse. • displace_sin_TO, direction of displacement between adjacent frames(sin) of the non-reference mouse. • displace_cos_TO, direction of displacement between adjacent frames(cos) of the non-reference mouse. • two_body_sin, angle between two body vectors(sin). • two_body_cos, angle between two body vectors(cos). • two_head_sin, angle between two head vectors(sin). • two_head_cos, angle between two head vectors(cos). • TM_nose_RM_tail_rho, distance between the tail base of the reference mouse and the snout of the non-reference mouse. • TM_nose_RM_tail_sin, direction of the tail base of the reference mouse—the snout of the non-reference mouse vector(sin). • TM_nose_RM_tail_cos, direction of the tail base of the reference mouse—the snout of the non-reference mouse vector (cos). • RM_nose_TM_tail_rho, distance between the snout of the reference mouse and the tail base of the non-reference mouse. • RM_nose_TM_tail_sin, direction of the reference mouse—the tail base of the non-reference mouse vector(sin). the snout of • RM_nose_TM_tail_cos, direction of the reference mouse—the tail base of the non-reference mouse vector(cos). the snout of • nose_nose_rho, distance between the two snouts. • nose_nose_sin, direction of the snout-snout vector(sin). • nose_nose_cos, direction of the snout-snout vector(cos). 2. (Optional) Users can deﬁne new features for clustering. We provide ﬁve intermediate variables to facilitate the computation of new features. Each variable is a NumPy array with the shape of (number_of_clip, number_of_frames_in_one_clip, number_of_key_point, 3): • pose_clips, keypoints of the reference mouse. • pose_clips_align, keypoints of the target mouse aligned to its middle frame. • poseTheOther_clips, keypoints of the non-reference mouse. Frontiers in Behavioral Neuroscience 09 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 • poseTheOther_clips_alignSelf, keypoints of reference mouse frame. aligned to itself the non- in the middle • poseTheOther_clips_alignToOther, keypoints of the non- reference mouse aligned to the reference mouse in the middle frame. Each new feature should be deﬁned in ./ﬀt_utils.py: 1. new Feature = np.ones (pose_clips.shape[1]) 2. if ’newFeatureName’ in feature_clips_dict: 3. feature_clips_dict[’newFeatureName’]. append(newFeature) 4. else: 5. feature_clips_dict[’newFeatureName’] = [newFeature] Next, the weight and normalization method of the new feature should be deﬁned in ./utils_ﬁle/setting.py: 1. self.cluster_arg = [ 2. ’thred’:30, 3. ’name’:’all_twoMice’, 4. ’evaluation_metric’:’Adjusted Rand index’, 5. ’features_arg’:[ 6. # add the setting of the new feature here 7. ’feat_key’:’newFeatureName’,’weight’: 4,’norm’:’zscore_all’, 8. #......(original features)..... 9. ’feat_key’:’body_length’,’weight’:1, ’norm’:’zscore_all’, ] ] are the settings clustering behavioral for individual If 3. Specify you the distance threshold to be larger than the cage diameter, and features to 0. clustering. change the weight behavior, behavior social for set • imgdir, path to frames generated by the tracking model. interest from videos by running Alternatively, you can set here and generate frames ./BehavioralCluster/0_video2image.py. path to • tracked_json, a directory of corresponding tracking the results. • videodir, path to the original videos (required if you do not have the generated frames). • start_frame, a list of starting frames for each video. • end_frame, a list of ﬁnishing frames for each video. A number larger than the total frame number will be treated as the ending frame of the video. • mice_num, number of animals in each frame. • joint_num, number of keypoints per animal. • three, threshold for deﬁning clusters based on the threshold to any dendrogram. Users number on their ﬁrst this variable trial, based on the generated dendrogram, and rerun the clustering script. can set redeﬁne this • video_name_suﬃx, suﬃx for the generated videos with raw videos, aligned images, feature distribution, and UMAP shown together. • result_folder, the result folder for saving important intermediate results for inspection. 4. Run behavioral clustering with the following commands: 1. cd ./BehavioralClustering/ 2. python fft_main_sep_twoMiceInteract.py 5. Inspect clustering results by generating the following plots with ./BehavioralClustering/Evaluation/Analysis.ipynb. Detailed instructions are included in this Jupyter Notebook. This Jupyter Notebook will generate the following plots to help determine the clustering quality with the chosen features and feature weights. Users should try diﬀerent clustering thresholds and check the dendrogram and feature heatmaps in order to identify the optimal threshold to use. • Dendrogram. The dendrogram is based on a linkage algorithm. clustering the dendrogram below the user- their to calculated matrix The branches of deﬁned threshold cluster assignment. indicate colored the are by • Timeline. The timeline plot displays cluster assignments for each clip, with their color matching the cluster assignment as in the dendrogram. the • Feature heatmap. The feature heatmap visualizes the relative strength of each feature in the cluster. • UMAP. The UMAP shows the topological structure of all the clips in the feature space. Each dot represents one cluster, colored by their cluster assignment. • Mutual information plots. The mutual information information between each plot quantiﬁes the mutual feature and the cluster assignment. Note: Larger mutual information suggests the feature is a strong marker of the cluster. • Similarity matrix between clusters. Note: Clusters with a high similarity score are hard to diﬀerentiate. • Representative skeleton for each skeleton. Cluster skeletons visualize the representative pose and its temporal evolution for each cluster. 6. Besides these analysis plots, users can also inspect generated videos saved at self.gen_video_folder as speciﬁed in setting.py with feature and cluster assignment. 7. Once the optimal threshold is identiﬁed, users should set the threshold in the setting.py and rerun the algorithm. This will the clustering UI. save the correct cluster information for 2.3.7. Clustering UI We provide a Clustering UI for inspecting the clustering results (Figure 8). 1. Open the clustering UI following the same instruction as the tracking UI. Choose cluster.html. Upload the JSON ﬁle that Frontiers in Behavioral Neuroscience 10 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 8 The UI for AlphaTracker provides the ability to inspect and correct clustering errors. Area I is a navigation bar with options for navigation (1, 2), help (3), undoing/redoing actions (6, 7), result export (4), and starting new sessions (5). Area II displays the video (1) and the dendrogram (2) of clustering results, along with frame navigation (3). Double-clicking a node in the dendrogram highlights all corresponding frames in the timeline in cyan. Double-clicking a node in the dendrogramwould highlight all the frames belonging to that cluster in the timeline. Right-clicking allows for cluster and clip manipulation (move, rename, delete). Merging of clusters is available in region 4, with the ability to record rationale in region 5. Area III contains a playback control panel with options to play/pause video (1), display time, frame, and clip information (2, 3, 8), control playback speed (4, 5), choose video (6), and set the level to expand/collapse in the dendrogram (7). Area IV displays the progress bar (1) and cluster assignments for each clip encoded by color (2). Detailed instructions for using the UI can be found on GitHub. contains the clip information (clips_info.json) generated in the behavioral clustering step. Import the original video and specify the frame rate. Upload the JSON ﬁle that contains the cluster structure (e.g., Z_all_twoMice.json). 2. Play the video and inspect the cluster assignment for each clip. Users can examine the dendrogram by expanding and collapsing the tree structure. The branches of the dendrogram can be merged and moved to modify the cluster assignment. A detailed explanation is provided in our GitHub manual. 2.3.8. Tracking with Google Colab In addition to the desktop version, we also provide a Colab notebook for training and tracking. Users looking for a quick test of AlphaTracker can open this notebook https://colab.research. google.com/drive/1wYBAj3kjLMe6uir3TJVfWRAJNHtjCaPG and simply run through all blocks following the instructions. If users would like to train their own model, we provide another https://colab.research.google.com/drive/ 1bGUo3eMWIfzXiFWCvNrNiTOzhSsTsVHV. notebook 1. Open the Colab notebook and save a copy to your personal Google Drive. 2. Click Runtime and then change the runtime type to “GPU.” Run the “Install” section to connect to your Google Drive. Your Google Drive will now be mounted at /content/drive/MyDrive. Run the next block to download AlphaTracker and ﬁnish installation. 3. Upload your annotated training datasets to Google Drive and set variables such as “image_root_list,” “json_ﬁle_path,” “number_of_animals,” “number_of_poses,” “video_path” in the “Setting” section. 4. Run the training code block if you would like to train the model with your own datasets. 5. Run the tracking code block to perform training on the videos you listed in setting.py or the default demo video. Once this step is complete, in order to inspect the tracking results, you can go to the result folder as speciﬁed in setting.py. 3. Results 3.1. Anticipated tracking results To quantify AlphaTracker’s performance and compare it to SLEAP and DeepLabCut, we conducted experiments using a mouse dataset where. Trained human annotators labeled the bounding box and four keypoints (snout, left ear, right ear, and tail base) for each mouse in each frame. Our customization of the DeepLabCut default model includes the following modiﬁcations: enabling automatic computation of the PAF graph, utilization of the box tracker, and setting the maximum number of iterations to Frontiers in Behavioral Neuroscience 11 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 TABLE 1 AlphaTracker demonstrated superior performance in tracking both two mice in a home cage and four mice in an operant chamber. Two mice home cage Four mice operant chamber MOTA MOTP AlphaTracker DeepLabCut SLEAP 82.2 40.6 73.6 86.2 77.6 76.7 mAP 87.2 14.1 26.8 MOTA MOTP mAP 84.0 71.4 77.9 87.2 86.5 86.5 85.6 68.0 83.9 AlphaTracker, DeepLabCut, and SLEAP were evaluated on two datasets: two mice in a home cage and four mice in an operant chamber. Each model was trained on 600 annotated frames and evaluated on 200 frames with human-labeled ground truth. The evaluation results show that AlphaTracker outperforms DeepLabCut and SLEAP in both datasets, achieving higher keypoint detection accuracy (mAP) and tracking consistency (MOTA and MOTP). FIGURE 9 AlphaTracker surpasses the performance of both DeepLabCut and SLEAP in tracking two mice in a home cage. (A–C) Show the Average Precision (AP), Multi-Object Tracking Precision (MOTP), and Multi-Object Tracking Accuracy (MOTA) metrics for different keypoints of AlphaTracker results. The metrics were evaluated for different amounts of training frames (25, 50, 100, 200, 300, and 600 frames) using a 200-frame evaluation dataset. Different colors represent different keypoints including snouts (blue), ears (yellow) and tailbases (green) and the total metrics (red). (D–F) Show the evaluation results for AlphaTracker (red), SLEAP (green), and DeepLabCut (blue), with connected dots representing the total metric and the unconnected dots representing the metric for individual body parts. 100,000. Our customization of the SLEAP default model includes several modiﬁcations to improve its tracking performance. Firstly, we used the bottom-up model and set the tracker mode as “ﬂow”. Secondly, we implemented culling with an IoU threshold of 0.8. Thirdly, we utilized the instance similarity method and the greedy matching method. Fourthly, we set the elapsed window to 5 and used a robust quantile of similarity scores of 0.95. Fifthly, we applied post-tracking break connection to improve tracking continuity. Finally, we adjusted the minimum and maximum rotation angles to -180 and 180 degrees, respectively. We used the standard CLEAR MOT metrics [Average Precision (AP), Multiple Object Tracking Accuracy (MOTA), and Multiple Object Tracking Precision (MOTP)] (Bernardin and Stiefelhagen, 2008), and evaluated the performance using the open-source Poseval tool (Pishchulin et al., 2015) AP assesses the accuracy of object detection by computing precision and recall values. MOTA evaluates three types of errors: missed objects in a sequence, false positives, and mismatches. MOTP calculates the average total position error for matched object-hypothesis pairs across all frames. The evaluation was performed using the open-source Poseval tool (Pishchulin et al., 2015). To adapt the MOT metrics for mouse tracking, we modiﬁed the threshold for distinguishing matched keypoints from mismatched keypoints to be 5% of the bounding box’s diagonal. We evaluated AlphaTracker’s performance on a dataset with two mice interacting in a home cage, recorded at a resolution of 1,920 × 1,080 p. Results shown in Table 1 indicate that AlphaTracker outperformed SLEAP and DeepLabCut in terms of mAP, MOTA, and MOTP when trained with 600 frames and tested on 200 held-out frames (Supplementary Video 1). Furthermore, AlphaTracker demonstrate consistent performance across all four keypoints, while SLEAP and DeepLabCut showed signiﬁcant variance, as shown in Figure 9. Furthermore, AlphaTracker showed high performance with only 50 frames of training data, achieving an mAP higher than 0.7 (Figure 9). Frontiers in Behavioral Neuroscience 12 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 10 AlphaTracker identiﬁes clusters for both individual and social behavior. Hierarchical clustering was performed on 15-frame clips (500 ms duration) generated from videos of interacting dyads. The dendrogram of the clustering results is shown in (A) for individual behavior and (C) for social behavior. Each leaf on the dendrogram represents a single clip, and their relative distance reﬂects their similarity in the feature space. The different colors and numbers indicate the assigned cluster for each individual clip. Example skeletons in (B, D) provide a visual representation of the typical movement in each cluster. The pose of the reference mouse is displayed in red, while the non-reference mouse’s pose is displayed in green. The movement in a 15-frame clip is illustrated by plotting a skeleton representation, with colors ranging from dark to light to denote each individual frame. The skeletons have been rotated to align the pose of the reference mouse in the central frame with the negative x-axis direction. operant chamber evaluation in a metal (Supplementary Video 2) evaluated AlphaTracker’s performance Moreover, we identical-looking animals using four in tracking multiple scenario. C57/BL6 mice that showed Our in all AlphaTracker outperformed SLEAP and DeepLabCut metrics also tested AlphaTracker on mice with head implants, a common scenario in neuroscience research, and demonstrated its robust performance (Supplementary Video 3). This highlights AlphaTracker’s potential for studying naturalistic social group dynamics in common neuroscience settings. Supplementary Video 2). We (Table 1, It’s worth mentioning that the four mice operant chamber dataset was collected with low-quality webcams at a resolution of 540 p. AlphaTracker demonstrated robust performance in tracking animals in these videos (Table 1), making it an attractive solution for large-scale animal behavior studies as it enables the monitoring of multiple cages using low-cost webcams, greatly reducing the overall cost. 3.2. Anticipated behavioral clustering results The behavioral clustering component of AlphaTracker enables the clustering of both individual behavior and social interaction in an unsupervised manner. Here, we analyze a total of 4,661 clips for individual behavioral clustering and 2,356 clips for social behavioral clustering collected from four videos (Figure 10, Supplementary Videos 4, 5). Our algorithm can capture the following individual behaviors: walking, digging, sniﬃng, rearing, turning, face grooming, and body grooming, and social behaviors: following, chasing, anogenital sniﬃng, face sniﬃng, and social rearing. To evaluate the importance of each feature in clustering, we calculate the mutual information between features and cluster assignment (Figure 11), with the expectation that higher mutual information indicates that the feature may represent a unique characterization of a given cluster. For example, distances between two mice are a strong indicator of social clusters, while related to the head such as head length and nose-left ear distance stand out among other individual features, indicating the salience of the head in of many behaviors like rearing, digging and turning (Figure 11). The identiﬁed behavioral clusters allow users to visualize the temporal dynamics of animal behavior. This opens up the opportunity for associative analysis between changes in behavior motifs with experimental factors like optogenetic stimulus, drug administration, environmental changes, and manipulation in a social hierarchy. To validate the performance of our behavioral clustering algorithm, we compared its output to the ground 488 truth of human annotation. A human scorer was trained to categorically annotate behaviors. We used the Adjusted Rand Index (ARI) to measure the similarity of the class assignment between the algorithm and human scorer. ARI scores range from -1 to 1, Frontiers in Behavioral Neuroscience 13 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 FIGURE 11 Behavioral clusters can be differentiated by unique combinations of features. (A, C) Illustrate the heatmaps of averaged feature values utilized for individual and social behavioral clusters, respectively. M1 refers to the reference mouse and M2 refers to the non-reference mouse in the dyad. (B, D) Show the mutual information between cluster assignment and each feature for individual and social behavioral clusters, respectively. A higher mutual information score indicates that the feature plays a crucial role in distinguishing speciﬁc clusters. with negative values indicating independent labels, positive values indicating close agreement with ground truth labels, and values close to zero indicating random label assignments. dataset, and hyper-parameter settings. Using our default settings and example data (about 6000 images), it takes approximately 2 hours to train the YOLO detector and the pose estimation model. We evaluated the algorithm’s performance on datasets of diﬀerent sizes. The small dataset consisted of ﬁve videos, including two videos with human-annotated ground truth, with a total of 1345 clips. The large dataset included two additional videos, with a total of 3034 clips. The results presented in Table 2 suggest that both datasets perform signiﬁcantly better as compared to randomly assigning clips to each cluster. Moreover, the performance of the model further improved when given a larger clustering dataset, likely due to better coverage of the continuous input space. 3.3. Timing Installation time for AlphaTracker is highly dependent on the installation method selected and users’ Internet speed. We estimate that it will take a user between 10–30 minutes to download and install the package, pre-trained model, demo data, and all dependencies on Linux. On Windows, installation may take about 2 minutes less since Windows does not support YOLO training when using the C-based darknet toolbox. The training time for AlphaTracker (including YOLO and pose estimation) is highly dependent on hardware performance, The tracking time for AlphaTracker (including detection, pose estimation, and tracking) is also highly dependent on hardware performance, dataset, and hyperparameter settings. Using our default settings and demo video (about 7 minutes), tracking takes approximately 2 hours. The time required for behavior clustering varies according to the features selected. Using keypoint-based features takes approximately 10 minutes. When using the UI to inspect the results, the main time cost is spent on loading data, which typically takes about 1–2 minutes. These time estimates are for a server with 72 Intel(R) Xeon(R) Gold 6150 CPU @ 2.70GHz, and 393 GB of RAM, running an Ubuntu 18.04.5 LTS system, using a 2080-Ti GPU. CPU times or Windows times are also noted where appropriate. 4. Discussion In this paper, we introduce AlphaTracker, a robust machine- learning pipeline that accurately tracks and estimates the poses of multiple unmarked animals. AlphaTracker also includes a feature for discovering behavioral patterns through unsupervised Frontiers in Behavioral Neuroscience 14 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 TABLE 2 The results of AlphaTracker’s clustering algorithm are consistent with human judgement. Large dataset (3,034 clips) Small dataset (1,345 clips) Random assignment Adjusted rand index 0.201186 0.186533 0.003451 The accuracy of AlphaTracker’s clustering algorithm was evaluated by comparing its output with human annotations for individual clips (500 ms). The annotations included individual behaviors: walking, digging, sniﬃng, rearing, turning, face grooming, and body grooming, and nine social behaviors: following, chasing, anogenital sniﬃng, face sniﬃng, and social rearing. The similarity of the class assignments was measured using the Adjusted Rand Index (ARI). AlphaTracker showed signiﬁcantly higher ARI compared to random assignment, demonstrating its consistency with human judgement. Additionally, using a relatively small dataset of 1,345 clips, AlphaTracker was able to accurately capture most of the cluster assignments, further highlighting its eﬃciency and eﬀectiveness. clustering and a user-friendly interface for visualizing and proofreading results. Our pipeline is available on GitHub for educational use and is user-friendly for non-programmers. Users can model and analyze animal behaviors in a matter of hours with a GPU. Our aim is to provide the research community with a powerful tool for high-throughput behavioral analysis. tracking approaches Traditional multi-animal require heuristics to resolve animal identities, such as artiﬁcial colored markers (EthoVision, Noldus) and bleach-marking with fur patterns (Ohayon et al., 2013). These methods require performing procedures on animals that could aﬀect their natural behavior. Animal tracking has beneﬁted greatly from advances in pose estimation, such as DeepLabCut, a software package that can reliably track human-deﬁned unique keypoints (Mathis and Mathis, 2020). A recent algorithm, Moseq, has made progress on automated behavioral identiﬁcation by using a depth camera and unsupervised learning theory (Wiltschko et al., 2015). And, SimBA presents an open-source package with a graphical interface and workﬂow that uses pose-estimation to create supervised machine learning (Nilsson et al., 2020). However, these tools have not been eﬀective in tracking multiple identical animals. In recent years, other tools for multiple animal tracking have emerged. As an example, SLEAP is a full-featured general-purpose multi-animal pose tracking framework tested on a diverse array of datasets representative of common social behavioral monitoring setups and designed for ﬂexibility (Pereira et al., 2020b). Our model outperforms these tools in keypoint detection accuracy and multi-animal identiﬁcation consistency which is critical for studying social behavior. aﬀected by occlusions or animals temporarily leaving the frame. To address this, we have provided a curation UI for users to correct misidentiﬁcation and mislabeling. Secondly, the clustering algorithm does not work well for heterogeneous videos, such as those with diﬀerent imaging angles, animal sizes and scaling factors. In these cases, the algorithm will produce clusters speciﬁc to each condition, rather than uniform behavior patterns. We greatly facilitating envision AlphaTracker systems neuroscience research, as it premiered in Padilla-Coreano et al. (2022). In that paper, AlphaTracker played a key role in furthering research studying the role of the medial prefrontal cortex in regulating social hierarchy. Besides this paper, there has been a recent increase in the study of the neural mechanisms behind behaviors such as social dominance, mating behavior, and maternal behavior. To fully understand these behaviors, it is important to have reliable and eﬃcient methods of tracking social interactions and quantifying behavioral patterns with minimal bias. Human annotation performed by multiple researchers from biases in subjective behavior annotation and intensive labor. AlphaTracker which is designed for reducing biases and elevating eﬃciency holds great potential in accelerating this ﬁeld. suﬀers Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors. Ethics statement The animal study was reviewed and approved by IACUC Salk Institute and MIT. Author contributions ZC, RZ, and H-SF contributed to the conceptualization. ZC, RZ, HZhu, HZho, and YZ wrote the code. RZ and NP-C collected the mouse data. RR and H-SF labeled groundtruth data. RZ, H-SF, ZC, HZhu, YZ, NP-C, and HZho wrote the manuscript with input from all authors. CL and KT supervised the project. All authors contributed to the article and approved the submitted version. Our AlphaTracker model has two main limitations. Firstly, it was designed for tracking mice from a top view, and its adaptation to other animals and environments requires expert tuning and adaptation. To make this process easier, we have created a tutorial on annotating new data and model training. Typically, 200 annotated frames yield satisfactory performance in new settings. The second limitation is the hardware requirement for a GPU for model training. To overcome this, we oﬀer a Google Colab version of AlphaTracker. However, the free Colab version may time out during long training sessions, and requires packages to be reinstalled and connection to Google Drive for ﬁle storage each time it is used. Users may also encounter challenges that are common to all models of this kind. Firstly, keypoint detection accuracy may be Funding KT is an HHMI Investigator and the Wylie Vale Professor at the Salk Institute for Biological Studies and this work was supported by ﬁnance from the JPB Foundation, the Dolby Family Fund, R01-MH115920 (NIMH), and Pioneer Award DP1-AT009925 (NCCIH). NP-C was supported by the Simons Center for the Social Brain, the Ford Foundation, L’Oreal For Women in Science, the Burroughs Wellcome Fund, and K99 MH124435-01. CL was supported by the AI Institute, SJTU, the Shanghai Qi Zhi Institute, and the Meta Technology Group. This work was also supported by the National Key R&D Program of China (No. 2021ZD0110704), Shanghai Municipal Science and Technology Frontiers in Behavioral Neuroscience 15 frontiersin.org Chen et al. 10.3389/fnbeh.2023.1111908 Major Project (2021SHZDZX0102), Shanghai Qi Zhi Institute, and Shanghai Science and Technology Commission (21511101200). Publisher’s note Acknowledgments We would thank Talmo Pereira and Liezl Maree for their help with troubleshooting the SLEAP model. We would also like to thank Mackenzie Mathis and Lauer Jessy for their help with troubleshooting the DLC model. are article claims expressed in this aﬃliated organizations, or All the authors and do not necessarily represent solely those those of of the publisher, their the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, endorsed by the is not guaranteed or publisher. those of Conﬂict of interest Supplementary material The authors declare that the research was conducted in the absence of any commercial or ﬁnancial relationships that could be construed as a potential conﬂict of interest. The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnbeh.2023. 1111908/full#supplementary-material References Berman, G. J. (2018). 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10.1371_journal.pdig.0000481	RESEARCH ARTICLE Barriers and facilitators to parents’ engagement with and perceived impact of a childhood obesity app: A mixed-methods study Madison Milne-IvesID Timothy Boey6, Alison Potter3, Wendy Lawrence7, Michelle Helena van Velthoven8, Edward MeinertID 1,2, Em Rahman3, Hannah Bradwell4, Rebecca Baines5, 1,2,9,10* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Milne-Ives M, Rahman E, Bradwell H, Baines R, Boey T, Potter A, et al. (2024) Barriers and facilitators to parents’ engagement with and perceived impact of a childhood obesity app: A mixed-methods study. PLOS Digit Health 3(3): e0000481. https://doi.org/10.1371/journal. pdig.0000481 Editor: Haleh Ayatollahi, Iran University of Medical Sciences, IRAN (ISLAMIC REPUBLIC OF) Received: November 9, 2023 Accepted: February 28, 2024 Published: March 27, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pdig.0000481 Copyright: © 2024 Milne-Ives et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Deidentified individual participant data has been deposited in the Open Science Framework (OSF) repository 1 Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, 2 Centre for Health Technology, School of Nursing and Midwifery, University of Plymouth, Plymouth United Kingdom, 3 South East School of Public Health, Workforce Training and Education Directorate, NHS England, United Kingdom, 4 School of Nursing and Midwifery, Faculty of Health, University of Plymouth, Plymouth, United Kingdom, 5 Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, United Kingdom, 6 School of Medicine, University of Liverpool, Liverpool, United Kingdom, 7 Primary Care, Population Science and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, 8 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom, 9 Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom, 10 Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom * edward.meinert@newcastle.ac.uk Abstract Childhood obesity is a growing global health concern. Although mobile health apps have the potential to deliver behavioural interventions, their impact is commonly limited by a lack of sufficient engagement. The purpose of this study was to explore barriers and facilitators to engagement with a family-focused app and its perceived impact on motivation, self-efficacy, and behaviour. Parents with at least one child under 18 and healthcare professionals work- ing with children were recruited; all participants were allocated to use the NoObesity app over a 6-month period. The mixed-methods design was based on the Non-adoption, Aban- donment, Scale-Up, Spread, and Sustainability and Reach, Effectiveness, Adoption, Imple- mentation, and Maintenance frameworks. Qualitative and quantitative data were gathered through semi-structured interviews, questionnaires, and app use data (logins and in-app self-reported data). 35 parents were included in the final analysis; quantitative results were analysed descriptively and thematic analysis was conducted on the qualitative data. Key barriers to engagement were boredom, forgetting, and usability issues and key barriers to potential impact on behaviours were accessibility, lack of motivation, and family characteris- tics. Novelty, gamification features, reminders, goal setting, progress monitoring and feed- back, and suggestions for healthy foods and activities were key facilitators to engagement with the app and behaviours. A key observation was that intervention strategies could help address many motivation and capability barriers, but there was a gap in strategies address- ing opportunity barriers. Without incorporating strategies that successfully mitigate barriers in all three determinants of behaviour, an intervention is unlikely to be successful. We PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 1 / 25 PLOS DIGITAL HEALTH(DOI: 10.17605/OSF.IO/RUQ4G). The dataset does not include the full demographic data collected to avoid indirect identification of participants. Following the guidance in Hrynaszkiewicz et al. (2010), we have included only 3 indirect identifiers: age, gender, and ethnicity. Two participants did not consent for their data to be made available for future studies, so that data has been removed from the available dataset. Funding: MMI and EM received research grant funding for this study from the former Health Education England, which is now the South East School of Public Health, Workforce Training and Education Directorate, NHS England (grant reference number: AM1000393). EM and MMI are supported by the NIHR Newcastle Biomedical Research Council (BRC). The views expressed in the paper belong to the authors and not necessarily those of the South East School of Public Health, NHS England, the University of Plymouth, the University of Liverpool, the University of Southampton, the University of Oxford, Newcastle University, the NIHR Newcastle BRC, or Imperial College London. The funding body had no editorial control and was not involved in the decision to submit the article for publication. Competing interests: ER and AP are employees of the former Health Education England (now the South East School of Public Health, Workforce Training and Education Directorate, NHS England) and were involved in the development of the NoObesity apps. ER and AP contributed qualitative data and reviewed the final manuscript prior to submission, but the academic authors retained editorial control. MMI and EM received research grant funding for this study from the former Health Education England, which is now the South East School of Public Health, Workforce Training and Education Directorate, NHS England (grant reference number: AM1000393). EM and MMI are supported by the NIHR Newcastle Biomedical Research Council (BRC). The views expressed in the paper belong to the authors and not necessarily those of the South East School of Public Health, NHS England, the University of Plymouth, the University of Liverpool, the University of Southampton, the University of Oxford, Newcastle University, the NIHR Newcastle BRC, or Imperial College London. The funding body had no editorial control and was not involved in the decision to submit the article for publication. The other authors have declared that no competing interests exist. Factors influencing parents’ app use and perceived impact highlight key recommendations for developers to consider when designing the features and implementation of digital health interventions. Trial Registration: ClinicalTrials.gov (NCT05261555). Author summary Childhood obesity is a public health concern worldwide, but healthcare services lack the capacity to provide support and advice for all families on strategies for how it can be pre- vented or mitigated. This study explores what factors influence families’ engagement with a mobile health app for childhood obesity prevention and their perceptions of its impact on their physical activity and eating behaviours. We found that novelty and interactivity– making the app fun and interesting–were key features that helped motivate families to keep using the app. Features of the app that enabled families set goals, keep track of their progress, and get suggestions for activities and healthy eating helped support their motiva- tion and belief in their ability to engage in healthier behaviours. Beyond motivation, many families faced barriers related to their opportunity to engage in healthy behaviours such as lack of time, safe outdoor spaces, or affordable healthy food options. These findings highlighted the importance of understanding what prevents people from engaging with digital health interventions and the target behaviour change so that we can design inter- ventions that help mitigate those barriers. Introduction Childhood obesity is a global concern affecting around 1 in 5 children worldwide [1]. Being overweight can negatively impact physical and mental health [2–8] and strain healthcare resources [9]. Determinants of childhood obesity are complex, including structural inequalities and genetics [10–12], but individuals can influence behavioural contributors like diet and activity [7,13–15]. The scope of the problem of childhood obesity in the UK—over a third of 10–11 year old children are overweight or obese [16]—requires support beyond the delivery capacity of clinical services. Mobile apps are a promising tool to support behaviour change because of their ubiquity [17–20], but their impact is commonly limited by low engagement [21–26]. Previous studies have supported the potential benefit of mobile apps for weight manage- ment for adults and children [18,27–33], especially for highly engaged users [27], but many interventions lack robust evidence of a long-term impact on weight [19,31,34–36]. It is essen- tial that mobile health apps achieve sufficient user engagement to deliver impact [27,28,37]. Strategies for engaging users with digital interventions for childhood obesity are being exam- ined [38], but further understanding of how to mitigate barriers to engagement is needed [39– 42]. Behaviour Change Techniques (BCTs), the “smallest active ingredients” of interventions that can act to influence behaviour [43], are increasingly being incorporated into mobile health apps. Understanding how individual and contextual factors influence theoretical strategies, like BCTs, that aim to support engagement and behaviour change is necessary to optimise their behavioural and health impacts [44]. The purpose of this study was to gain insight into the context behind how and why parents engaged and disengaged with an app for childhood obesity, how it influenced their families’ behaviour, and how potential improvements could be incorporated. Specifically, the study aimed to 1) generate barriers and facilitators that PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 2 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact influence engagement with the app and 2) to evaluate the app’s impact on parents in terms of perceived motivation, self-efficacy, weight-related health behaviours, and communication with healthcare professionals (HCPs) [45]. Methods Study design We conducted a mixed-methods interventional study based on a Phase 1 implementation sci- ence design (Fig 1). Phase 1 studies focus on generating evidence around the implementation strategies and other factors that could influence acceptability, effectiveness, and successful adoption [46,47]. This is a critical step in the evaluative process, as it informs intervention refinement to improve impact in later efficacy- and effectiveness-oriented evaluations. The pri- mary analysis examined qualitative data about barriers and facilitators to users’ engagement with the app and its impact on motivation, self-efficacy, and behaviours. To improve the credi- bility of the qualitative results, they were triangulated with quantitative survey and app use data [45,48]. Three theoretical frameworks informed the study: the Reach Effectiveness Adoption Imple- mentation Maintenance (RE-AIM) framework [49], the Non-adoption, Abandonment, Scale- up, Spread and Sustainability (NASSS) framework [50], and the Capability, Opportunity, Motivation—Behaviour (COM-B) model [51,52]. RE-AIM and NASSS informed the data col- lection plan, to capture a comprehensive set of individual- and system-level factors (Table 1 [49,50,53,54]). The COM-B model was used to analyse how various factors influenced engage- ment and behaviour [55–59]. The SRQR [60] (S1 Table) and TREND [61] checklists (S2 Table) were used to ensure completeness of reporting. Intervention The NoObesity app was developed by Health Education England (HEE) to prevent and man- age childhood obesity. The term HEE is used as that was the organisation at the time of the study, but the organisation has since been incorporated into the South East School of Public Health, Workforce Training and Education Directorate, NHS England. The target audience included families wanting behavioural support (regardless of weight) and HCPs working with families [62]. It was not based on a particular behavioural theory but was co-produced with HCPs [62] and included features aligned with several BCTs (see S3 Table for a full list of Fig 1. Study logic diagram. https://doi.org/10.1371/journal.pdig.0000481.g001 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 3 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Table 1. Alignment of RE-AIM and NASSS frameworks (based on [49,50,53,54]). NASSS domains RE-AIM dimensions Key considerations Key outcomes Domain 1: The condition or illness Characteristics of users (clinical, socio- cultural, etc.) N/A • Domain 1 emphasises the importance of understanding contextual factors and how they influence implementation • Sample characteristics (demographics) • Barriers and facilitators • Both focus on key issues of usability, acceptability, and other factors associated with individual use • RE-AIM captures issue of whether the intervention was delivered as intended • Both emphasise the importance of evidence to support adoption of intervention (safety, positive impact on intended outcomes) • NASSS considers impact of intervention for suppliers as well as users, and how that factors into the intervention’s sustainability • RE-AIM ‘adoption’ also relates to the potential added value • NASSS includes adoption by patients, staff, and setting, which is captured separately in RE-AIM (‘Reach’ and ‘Adoption’) • Key questions around adoption: how much, how long, why, why not, what is required? • Domains 5 and 6 both related to various elements of the adoption and maintenance of the intervention, so have been analysed independently from the RE-AIM framework • System Usability Scale • Key themes about usability, acceptability, and app features • Barriers and facilitators • Suggestions for improvement • Key themes about motivation and perceived impact on self-efficacy and behaviours • Ratings of app motivation strategies • Self-reported success at goals (in app and survey) • Self-reported self-efficacy (in app and survey) • Participation and drop-out data • Key themes about adoption of HCP link and impact of name on adoption • Ratings of HCP link and app name • Key themes from qualitative interview with HEE representatives Domain 2: The technology Features of technology associated with usability, acceptability, trustworthiness, and sustainability Implementation Fidelity of intervention delivery (system), use of intervention strategies (user) Domain 3: The value proposition Evidence of impact and value, for users and suppliers (incl. safety, efficacy, benefit, affordability) Effectiveness Impact on intended outcomes Adoption Number, proportion, and representativeness of places/people willing to adopt intervention (system) Reach Number, proportion, and representativeness participants (user) N/A N/A Domain 4: The adopter system Adoption (and continued use) by staff and users; reasons for non-adoption / abandonment; assumptions about use (capability, opportunity) Domain 5: The organization Organization’s capacity and readiness for adoption and scale-up; recognition of need for and availability of dedicated resources; disruption to routines; work to implement Domain 6: The wider context Political, economic, regulatory, professional, sociocultural factors Domain 7: Embedding and adaptation over time Feasibility of ongoing adaptation; organisational resilience (willingness to reflect and adapt) https://doi.org/10.1371/journal.pdig.0000481.t001 Maintenance Institutionalisation of intervention in routine processes (system), long- term impact on outcomes (user) • Both capture institutional / organisational maintenance • RE-AIM also captures individual maintenance • In this study, the organisational aspects of ongoing maintenance were closely aligned with key themes in Domains 5 and 6, and were grouped with them in the analysis • Self-reported intention to continue use of app • Self-reported length of use (if discontinued) • App use data from system (length of use, most recent use date) features, previously published in [45]). For families, the app helps them set behavioural goals to improve healthy eating and physical activity (e.g. cut down on snacks, walk to school, get the kids outside), track progress on their goals, and learn about healthy behaviours together through games and suggestions. For HCPs, the app provides training and tools for how to communicate effectively with families about childhood obesity (Fig 2). At the time of the study, the app was publicly available in the Apple App Store and Google Play store and pro- moted on HEE’s website. Sample and recruitment From March to August 2020, a convenience sample of parents and HCPs living anywhere in the UK was recruited using social media advertising (Google Adwords, Instagram Ads) [63]. Participants self-selected to use the app in their daily lives over a 6-month intervention period PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 4 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Fig 2. NoObesity Family and Professional app features. https://doi.org/10.1371/journal.pdig.0000481.g002 based on the following inclusion criteria: (1) a parent/guardian (with at least one child any age under 18 years) OR a HCP working with families, (2) willing and able to use the app, (3) fluent in English, and (4) no previous use of the app. Individuals who were unable or unwilling to provide informed consent, had hearing impairments, or were known to the research team were excluded. Eligibility was not limited by weight to reflect real-world app use and the broad target audience. Informed consent was collected from all participants on enrolment using a Qualtrics survey. We also recruited two employees from HEE (ER, AP) who helped develop NoObesity to capture organisational-level factors influencing adoption and sustainability (Table 1). Stratified random sampling (based on gender, ethnicity, and income) was used to invite a subset of 20 participants for semi-structured interviews (SSIs) from the 61 participants who completed the demographic questionnaire. Interviewed participants and those included in the quantitative data analysis (completed all questionnaires and passed an attention check ques- tion) received a £100 (USD$139) Amazon voucher. Procedure Ethical approval was obtained from the University of Oxford Medical Sciences Interdivisional Research Ethics Committee (R62092/RE001) and the University of Plymouth’s Faculty Research Ethics and Integrity Committee (19/20-1316). Thirty-minute SSIs were conducted on Microsoft Teams (S1 Text) and transcribed by a third-party company (Rev). To mitigate response bias, participants were informed that the interviewer was independent from HEE and had not been involved in intervention development. Three questionnaires were adminis- tered online using Qualtrics: consent, demographics based on OxIS 2019 [64] (September 2020), and the final questionnaire at the end of the 6-month intervention period (April 2021), which examined usability, use, and impact on motivation, self-efficacy, and behaviour and included attention check questions (S1 Text). App use data was collected in June 2021. Outcomes The primary outcome—barriers and facilitators to engagement and health behaviours—was explored through the qualitative SSIs. Secondary outcomes (Table 2) were examined through PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 5 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Table 2. Quantitative secondary outcome measures. Outcome Outcome measure Engagement with the app App-captured dates of first and last logins Engagement with the study Usability Motivation Self-efficacy Quantitative self-reported app use duration Uptake (number of participants recruited) Dropout rates (number of participants completing each stage of the data collection) System Usability Scale (SUS, scale: 0–100) [65]; completed by parents twice—once for themselves and once from their children’s perspective Quantitative ratings of the impact of specific features on motivation (5-point Likert scale, see Table 3 for details) Bandura’s self-efficacy scale [66] (confidence in ability to do goal activities; scale: 0–100) In-app self-reported confidence in ability to achieve goals (6 star rating scale) Perceived impact on behaviour Quantitative questionnaire items about physical activity and healthy eating behaviours (5-point Likert scale, see Table 3 for details) In-app self-reported goal progress data Opinions about app features Quantitative questionnaire items about app features (5-point Likert scale, see Table 3 for details) https://doi.org/10.1371/journal.pdig.0000481.t002 qualitative interviews, questionnaire, and app use data (including self-reported data and objec- tive login data captured via the app admin portal). Analysis Qualitative data was analysed using a codebook thematic analysis approach [67,68] in Dedoose (version 9.0.17) [69]. To improve credibility—the alignment of participants’ feedback with our representation of their feedback—four authors engaged with the data (first independently, then collaboratively) over a prolonged period. This ensured that individual preconceptions were challenged and alternative interpretations considered [70]. An initial list of codes was generated inductively by one author based on the transcripts, which were coded at the seman- tic level (interpreting only the explicit meaning of the participants’ words) [71]. This codebook was provided to three independent authors who deductively coded the data (generating addi- tional codes if needed). Codings then evolved through intensive collaboration between the authors. Themes and sub-themes were generated by one author as topic summaries [68] and then discussed and refined by all coders (S4 Table). From the initial thematic framework, factors influencing engagement and perceived moti- vation, self-efficacy, and behaviours were deductively mapped to the COM-B model (S5 Table) [51,52,72]. A codebook for the mapping was created using the COM-B components [52] and the Theoretical Domains Framework (TDF) [73], which captures more detailed subthemes of the COM-B components (Table 3) and examples from the literature to guide coding in a digital health context (S6 Table) [58,59]. These factors are the main outcomes discussed. Quantitative survey and app use data were analysed using descriptive statistics. Where possible, these out- comes were triangulated with qualitative data. Results Sample characteristics Consented participants included 225 parents/guardians and 6 HCPs. 61 parents completed the demographic survey; 15 of whom were interviewed. Almost 40% of parents (85/225) com- pleted the final questionnaire, but only 16% (35/225) met the criteria to be included in the PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 6 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Table 3. COM-B and TDF components and definitions [52,73]. COM-B Definition [52] Physical capability Physical skill, strength or stamina TDF Skills Definition [73] An ability or proficiency acquired through practice Psycho- logical capability Knowledge or psychological skills, strength or stamina to engage in the necessary mental processes Knowledge An awareness of the existence of something Memory, attention, and decision processes The ability to retain information, focus selectively on aspects of the environment and choose between two or more alternatives Behavioural regulation Anything aimed at managing or changing objectively observed or measured actions Physical opportunity Opportunity afforded by the environment involving time, resources, locations, cues, physical ‘affordance’ Environ- mental context and resources Social opportunity Reflective motivation Opportunity afforded by interpersonal influences, social cues and cultural norms that influence the way that we think about things, e.g. the words and concepts that make up our language Reflective processes involving plans (self- conscious intentions) and evaluations (beliefs about what is good and bad) Social / professional role and identity A coherent set of behaviours and displayed personal qualities of an individual in a social or work setting Social influences Beliefs about capabilities Optimism Beliefs about consequences Intentions Goals Any circumstance of a person’s situation or environment that discourages or encourages the development of skills and abilities, independence, social competence and adaptive behaviour Those interpersonal processes that can cause individuals to change their thoughts, feelings, or behaviours Acceptance of the truth, reality or validity about an ability, talent or facility that a person can put to constructive use The confidence that things will happen for the best or that desired goals will be attained Acceptance of the truth, reality, or validity about outcomes of a behaviour in a given situation A conscious decision to perform a behaviour or a resolve to act in a certain way Mental representations of outcomes or end states that an individual wants to achieve Increasing the probability of a response by arranging a dependent relationship, or contingency, between the response and a given stimulus A complex reaction pattern, involving experiential, behavioural, and physiological elements, by which the individual attempts to deal with a personally significant matter or event Automatic motivation Automatic processes involving emotional reactions, desires (wants and needs), impulses, inhibitions, drive states and reflex responses Reinforce- ment Emotion https://doi.org/10.1371/journal.pdig.0000481.t003 quantitative analysis: completing demographic and final surveys and passing an attention check. No HCPs completed the study (Fig 3). Most participants were female and white, with a mean age of 39 years (SD 6.0; Table 4). Of the participants invited for interview via stratified random sampling, 8 responded and 5 completed the interviews. After a second round of strati- fied random sampling, all eligible participants were invited to reach a sample of 15 interviewed participants. Participants who did not complete follow-up assessments may still have been using the app, as it was publicly available and usage was not controlled. Engagement with the intervention (adoption and use) A large sample was initially recruited (n = 225), suggesting that there was interest in the app, but there was substantial drop-out from the study and discontinued app use (captured via self- report and app login data). Although most interviewed participants reported no plans to stop using NoObesity, only half of final survey respondents (19/35, 54%) reported still using the app and of the participants from whom app use data was collected (n = 26), only a third (9/26, 34%) had recently logged in by or after the end of the study (April 2021). On average, there were 5.1 months between account setup and most recent login (95% CI: 3.9–6.3, range: 0.0– PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 7 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Fig 3. Participant flow diagram (HCPs: healthcare professionals; HEE: Health Education England). https://doi.org/10.1371/journal.pdig.0000481.g003 10.4 months). This section examines the key capability, opportunity, and motivational factors identified from the thematic analysis as influencing engagement with the intervention (S5 Table). These could be grouped into two themes: motivation and usability (Figs 4–5). Factors influencing motivation to engage Most of the factors that influenced participants’ motivation to engage related to the automatic and reflective motivation components of COM-B, although several overlapped with psycho- logical capability (eg. TDF domain: ‘memory, attention, decision processes, and knowledge’) and physical opportunity (eg. TDF domain: ‘environmental context’ offered by the app). Nov- elty and variety were key for engagement and demonstrate this overlap; without something new to explore, participants felt bored, had reduced intentions to open the app, and needed increased cognitive effort to overcome these barriers (Box 1). Participants all valued feedback, but several found the app’s feedback lacking or unhelpful. Visualisations of progress were sug- gested to help support motivation and more interactivity and gamification (e.g. daily Box 1. Sample of participant quotes about factors affecting engagement with the app “Once you’ve gone through the sections it has, then there’s nothing else you can really do with it. So there’s not much interaction . . . to keep you engaged.” (ppt 2) “Another reason why I didn’t really do it is because. . . it feels quite repetitive having to click . . . Tuesday, Wednesday, Thursday, Friday [to record family progress] for every single challenge.” (ppt 3) “Had there been some new content on there, it would have encouraged me to come back more and more and go, "Oh, what more can I learn?"” (ppt 7) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 8 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Table 4. Participant characteristics of the sample who completed the final quantitative survey (n = 35) and inter- viewed sample (n = 15). Number (%) final survey Number (%) interviewed Characteristic Gender Female Male Age (parent/guardian) 30–39 40–49 50–59 Ethnicity White Asian Black Highest completed qualification Bachelor’s degree or higher Lower than Bachelor’s degree Annual household income before tax £12,500–19,999 £20,000–29,999 £30,000–39,999 £40,000–49,999 £50,000–59,999 £60,000–69,999 £70,000–79,999 � £80,000 Prefer not to say Employment Working full time (30 hours a week or more) Working part time (8–29 hours a week) 31 (89) 4 (11) 20 (57) 12 (34) 3 (9) 32 (91) 2 (6) 1 (3) 23 (66) 12 (34) 3 (9) 5 (14) 7 (20) 5 (14) 4 (11) 2 (6) 1 (3) 6 (17) 2 (6) 12 (34) 15 (43) Doing housework, looking after children or other persons 4 (11) Student Permanently sick or disabled Location A country village A small city or town The suburbs or outskirts of a big city A big city Number of adults in household One Two Three or more Number of kids in household One Two Three Four 2 (6) 2 (6) 5 (14) 24 (69) 4 (11) 2 (6) 3 (9) 31 (89) 1 (3) 11 (31) 16 (46) 6 (17) 2 (6) Not all interviewed participants completed the final quantitative survey https://doi.org/10.1371/journal.pdig.0000481.t004 14 (93) 1 (7) 11 (73) 3 (20) 1 (7) 12 (80) 1 (7) 2 (13) 8 (53) 7 (47) 1 (7) 2 (13) 5 (33) 2 (13) 2 (13) 0 (0) 0 (0) 1 (7) 2 (13) 3 (20) 7 (47) 3 (20) 1 (7) 1 (7) 2 (13) 10 (67) 1 (7) 2 (13) 2 (13) 13 (87) 0 (0) 8 (53) 5 (33) 2 (13) 0 (0) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 9 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Fig 4. Summary of factors identified in the thematic analysis and how they were associated with engagement (Note: + and—signs refer to whether the factor had a positive or negative influence on the outcome). https://doi.org/10.1371/journal.pdig.0000481.g004 challenges, tangible rewards, and friendly competition) to help engage children. Opinions about notifications were mixed: many participants appreciated the reminder, but several found them annoying or not delivered at appropriate times. A couple participants did not like the app’s aesthetic and felt this hindered use. Fig 5. Summary of key factors for engagement mapped to the COM-B components. https://doi.org/10.1371/journal.pdig.0000481.g005 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 10 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Positivity and a focus on behaviour rather than weight were important; some parents highlighted that while they might track calories or weight for themselves, they felt uncomfort- able doing so for their children. This was also reflected in opinions of the name “NoObesity;” although most participants did not personally feel strongly about it, they thought it had nega- tive connotations and that a more positive focus on health behaviours could improve new user engagement. The survey results supported this; participants reported somewhat disliking the name (M = 2.6/5, 95% CI: 2.2–3.0). Factors associated with usability Factors associated with ability to engage were primarily related to psychological capability and physical opportunity. Overall, interviewees found the app “quite easy to use,” in line with SUS scores (M = 70.3/100, 95% CI: 63.8–76.8), although ratings were slightly lower when parents completed the SUS from their child(ren)’s perspective (M = 65.1/100, 95% CI: 58.3–71.9). Bar- riers were primarily related to the technology (app freezing) and a lack of clarity and guidance in the app. Several parents reported not understanding the target audience, how to report goal progress as a family unit if only some of the family members had successfully achieved the goal, or how the app’s reward system worked, which hindered its potential impact on motiva- tion (Box 2). Box 2. Participant quotes about factors affecting usability “Because of the iconography and how it looks, I think it’s quite easy to use. It’s quite self- explanatory.” (ppt 4) “It could’ve possibly done with an introduction page to start with.” (ppt 6) “There’s a lot of external links. I think you could quite easily get lost looking and looking for things.” (ppt 13) “Two of us have gone out for a walk, but two of us haven’t. Do I score it [as having com- pleted the family goal], do I not?” (ppt 11) Perceived impact on behaviours Perceived impact on health behaviours was mixed and often conflicting; most participants reported some progress on their goals but many said that the app did not have an impact on their behaviour (Box 3). In-app self-reported goal progress indicated that families were suc- cessful at achieving any particular goal for a mean of 7.5 weeks (95% CI: 5.5–9.5), a median of 3 weeks, and a mode of 0 weeks (n = 27). Factors relating to all three COM-B components were associated with the perceived impact on families’ motivation, self-efficacy, and health behaviours (Figs 6–7). Facilitators for behaviours Factors relating to perceived impact on motivation and self-efficacy were mostly related to motivation and psychological capability. Many participants found goal setting and progress recording motivating because it increased their awareness of their current behaviour and pro- vided a sense of accountability (Box 4). Having “achievable, attainable” goals, “being able to PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 11 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Box 3. Participant quotes about perceived impact of the app “The healthier meals . . . last week we were able to tick off three or four, but I know we did more than that, it was just the actual ones I ticked off.” (ppt 15) “[I] don’t necessarily think there were any changes. Let’s say, we live very close to school, so it’s easiest to walk. So that thing that I put in as our first goal about walking to school every day. We were going to use it every day, anyway.” (ppt 4) input data,” and getting ideas for goal setting, physical activity, and healthy eating supported self-efficacy; which was largely aligned with the quantitative ratings of impact of app features on motivation, which were highest for ‘doing something together as a family’, ‘suggestions for activities and healthy eating’, and ‘goal setting’ (Table 5). Other facilitators included prompts and notifications and useful feedback (from app or a linked HCP). Median self-efficacy scores on the survey were around 70% (70/100) (range: 20–100%), similar to the mean confidence (out of 6 stars) users self-reported when setting goals on the app (3.9/6 stars, ~65%). Fig 6. Summary of factors identified in the thematic analysis associated with the app’s perceived impact. https://doi.org/10.1371/journal.pdig.0000481.g006 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 12 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Fig 7. Summary of key factors for perceived impact mapped to the COM-B components. https://doi.org/10.1371/journal.pdig.0000481.g007 Barriers to behaviours Most behavioural barriers were associated with physical opportunity, including Covid-19 restrictions, time, weather, accessibility, and affordability (Box 5). There were also barriers relating to social opportunity (children’s willingness to try new foods and sibling teasing) and physical and psychological capability (barriers relating to children’s age and skills). Some par- ticipants suggested potential capability barriers, such as not knowing how to cook a healthy meal, but few experienced these barriers themselves. A lack of motivation was also mentioned; sometimes related to physical opportunity barriers (e.g. outdoor activity in bad weather), but not always with a reason given. Box 4. Participant quotes about factors affecting motivation and self-efficacy “This kind of brings it forward for you to actually acknowledge what you’re doing and what you’re not doing, and to help you to correct it. So it was like, I’m not going to have a snack today because I want to say that I managed to not have a snack today.” (ppt 2) “I would have liked to be able to see trends. . . it’s nice to have a look at your graph, isn’t it?” (ppt 1) “If you’ve downloaded it and it’s a family challenge, and it’s telling you to do it all together, it does make it easier, I think” (ppt 3) “I thought the useful links were good . . . because if you’re not an outdoorsy family, you might not even know where to start” (ppt 8) “Without [feedback from a HCP], it was completely lacking. [The app] didn’t give me feed- back on whether my goals were good ideas. It didn’t give me feedback on how well I was doing meeting my goals.” (ppt 1) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 13 / 25 PLOS DIGITAL HEALTHTable 5. Participant ratings of the app and its features’ perceived impact (n = 35). Factors influencing parents’ app use and perceived impact Item App’s support for behaviour Set goals Achieve goals Eat healthier Be more active Perceived impact of app on motivation Eat healthier Be more active Perceived impact of motivation strategy Doing something together as a family Suggestions of activities Suggestions for healthier eating Goal-setting Self-monitoring Points / trophiesa Notificationsa Feedback about current behaviour Games Family photo Linking to a HCP Opinion of features Points / trophies Self-monitoring Information feedback Opinion on healthcare provider link Level of comfort Perceived usefulness Likelihood of linking aSample was missing one data point (n = 34) https://doi.org/10.1371/journal.pdig.0000481.t005 Mean (/5) St. dev. 95% CI Likert scale points meaning (1–5) 3�5 3�5 3�4 3�3 2�7 2�6 3�2 2�9 2�8 2�8 2�7 2�7 2�3 2�2 2�2 1�9 1�6 3�5 2�7 2�6 3�1 2�8 2�5 1�4 1�0 1�3 1�3 1�1 1�1 1�3 1�2 1�0 1�3 1�1 1�3 1�3 0�9 1�2 1�3 0�9 1�1 1�0 1�1 1�4 1�4 1�4 3�0–4�0 3�1–3�8 3�0–3�8 2�9–3�8 2�3–3�0 2�2–3�0 2�8–3�6 2�5–3�3 2�5–3�2 2�4–3�3 2�4–3�1 2�2–3�1 1�9–2�8 1�9–2�5 1�8–2�6 1�5–2�3 1�3–1�9 3�1–3�8 2�4–3�1 2�3–3�0 2�6–3�6 2�4–3�3 2�0–2�9 Strongly disagree—strongly agree Strongly disagree—strongly agree Strongly disagree—strongly agree Strongly disagree—strongly agree Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Not at all—extremely effective Dislike a great deal—like a great deal Not at all—extremely useful Not at all—extremely useful Extremely uncomfortable—extremely comfortable Not at all—extremely useful Extremely unlikely—extremely likely Box 5. Participant quotes about factors affecting behaviours “We don’t have outside space for us. So, it’s an effort to do it every day.” (ppt 1) “Some of the ideas were good, but there’s just nowhere around here that we’d be able to do them.” (ppt 10) “Our time is very limited. I go out to work and I’m not back till half five/six. So once we’ve had a family meal it’s dark, the children aren’t wanting to go back out. I’m not really want- ing to go back out. It’s cold.” (ppt 15) “It’s just easier to buy junk food, it’s cheaper and easier to put. . . something in the oven rather than cook from scratch. And some people might not necessarily have the skills.” (ppt 8) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 14 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Perceived impact on communication No HCPs completed the study and no participants connected with a HCP via the app (most had not seen a HCP during the intervention period because of Covid-19). Hypothetically, most parents felt comfortable communicating via the app and that the HCP link could provide useful feedback and accountability (Box 6), although a couple raised concerns about the app not reflecting true behaviour, bothering the HCP, discomfort discussing a sensitive topic, or data security. Box 6. Participant quotes about factors affecting communication with HCPs “I would have appreciated a real person saying, ‘well, that’s a good goal, or that’s a bit wishy-washy. Perhaps you could make that better’.” (ppt 1) “It would be good because I think for me personally, if I’m accountable for something then, somebody checking in on you and going to look at what progress you’ve made. . .it would make me more determined to do it” (ppt 8) “I don’t really feel I’ve got a relationship with my GP. So for me, I think I felt a bit reluctant about doing that just because I don’t know them.” (ppt 11) Organisational factors Interviews with HEE employees identified organisational-level factors that could influence the app’s sustainability. The app’s focus fits within UK priority areas around childhood obesity and digital-first healthcare. Its holistic approach, combining workforce development and service delivery, spans the mandate of several governmental bodies (since the interviews, HEE itself has been incorporated into NHS England) [74]. The HEE employees felt that this could be a benefit, as inter-agency collaboration could provide access to more expertise, but that it creates potential ambiguity around ownership, which could be a risk for longer-term maintenance. Discussion Main findings This study used the COM-B model to explore factors associated with engagement and the per- ceived impact of a digital health app for childhood obesity and the NASSS and RE-AIM frame- works to generate insights for its implementation. Facilitators of engagement included novelty and variety, gamification and feedback, and a clear and positive tone. In terms of health behav- iours, app features including BCTs [43] such as goal setting, problem solving (suggestions for goals), self-monitoring, feedback on behaviour, and instruction on how to perform the behav- iour (suggestions for healthy foods and activities) helped support motivation and capability. Key barriers—including accessibility (e.g. Covid-19 restrictions, weather, affordability, and availability) and family characteristics—were largely related to opportunity, highlighting a gap in the app’s engagement and behaviour change strategies. Recommendations in the context of existing evidence There are many digital health interventions that have been developed and evaluated to target childhood obesity, but while studies often assess user perceptions and acceptability, there is PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 15 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact limited investigation of factors influencing engagement with mobile health apps for childhood obesity [31,75]. A recent evaluation of the Aim2Be app for childhood obesity identified differ- ent patterns of engagement and associated them with demographic characteristics, but not user perceptions [76], and another conducted a formative co-design process with parents to identify preferences for engagement and barriers to the health behaviours, which were largely similar to those identified here; however, these were not analysed in depth in that paper [77]. For this reason, we examine our findings in comparison to factors influencing engagement with digital health interventions more generally. All three COM-B components are important for digital health intervention engagement and impact [78–85]. Facilitators for engagement—a simple visually-appealing interface, feed- back and visualisations of progress, guidance, customisation, reminders, access to a HCP, and positive messaging—were aligned with previous recommendations in the literature [59,86– 89]. Several factors, including common engagement strategies like gamification [90–92], noti- fications [82,93], and competition [94], could be barriers or facilitators. For example, in-app points and trophies alone may not be sufficiently meaningful rewards to support motivation [92] and notifications’ impact can depend on their timing, frequency, and personalisation [82,93,95,96]. Friendly competition could help engage children by increasing fun and motiva- tion [59]; however, parents were concerned about teasing and negative self-image. Social com- parison can be demotivating [94], but this could be mitigated through team-based competition that de-emphasises the individual [42,97]. For childhood obesity, enabling intergroup family- team-based competition could help support engagement and focus on positive behaviours rather than weight. Key facilitators for behavioural impact included awareness of behaviour, accountability, motivation, prompts, suggestions, family characteristics, and accessibility. Features that support a family focus [97], goal-setting, progress recording, positive messaging, feedback, and suggestions and inspiration could help support perceived motivation and self-efficacy in this context, in line with meta-analytic evidence in adults [98]. The relationship between motivation and accountability has been previously observed [99,100] and theoretically linked to adherence [101]. Accountability has the potential to help support behaviours not done for enjoyment [101,102], so more active involvement of a linked HCP with families via the app may have helped increase engagement with the intervention and behavioural goals; however, this may not work for everyone, as some participants worried that their app-reported progress did not reflect real behaviour and did not want to feel judged. Key structural- and individual-level barriers were also largely aligned with existing evidence [81,85,89,103,104]. Structural inequities can hinder weight management and contribute to feelings of stigma or blame that reduce motivation [105,106]; mitigating these barriers (e.g. by highlighting affordable healthy meal options or locating free resources personalised to the users’ location) will be necessary to enable positive health behaviours. Individual-level barriers were more aligned with motivation than capability [51], perhaps because families less aware of weight issues may have been less likely to use an app called “NoObesity.” As a key facilitator for engagement was the app’s family focus, we recommend that developers consider the psy- chology behind their users’ motivation to engage and how they can frame their intervention as something enjoyable rather than necessary but onerous [107]. Overall, we recommend that digital intervention designers research specific barriers (especially opportunity barriers) for their target populations and behaviours to identify the most appropriate means of mitigating them in that context. Table 6 highlights additional recommendations for intervention develop- ment, through the lens of the NASSS and RE-AIM implementation frameworks. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 16 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact Table 6. Summary of implications for implementing similar interventions in the future. NASSS domains Domain 1: The condition or illness RE-AIM dimensions N/A Domain 2: The technology Implementation Domain 3: The value proposition Effectiveness Domain 4: The adopter system Adoption Reach Domain 5: The organisation N/A Domain 6: The wider context Domain 7: Embedding and adaptation over time Maintenance https://doi.org/10.1371/journal.pdig.0000481.t006 Insights for designing interventions for successful implementation • Social determinants of health—such as income, environment, employment, etc.—are important factors that affect families’ abilities to engage in healthy weight-related behaviours. • Digital health interventions should account for these contextual differences in users by incorporating advice and behavioural suggestions that meet their varying abilities and financial and environmental circumstances. • For example, this could include providing advice or recipes on how to eat healthy on a tight budget and including suggestions of physical activities that can be engaged in within a house if outdoor spaces are unavailable or inaccessible. • The importance of novelty as a factor to support engagement raises a potential issue for not-for-profit organisations that do not have capacity or funding for regular updates and uploading of new content for an intervention. • These types of limitations should be accounted for in the intervention design, for example, by setting up the intervention to provide content periodically over the course of the intervention or by enabling users to generate content (where appropriate). • Specific implementation strategies should be developed in collaboration with the target population, to ensure that the intervention meets their needs and circumstances. • To have a strong case to support public health interventions, evidence of positive impact is key—on an individual level, users should be able to see evidence of impact for themselves (eg. through progress monitoring and feedback from the intervention, or prompted self-reflection); on an organisational level, there needs to be significant evidence of positive impact to support widespread adoption. • Evaluations of effectiveness should include assessments of engagement as a prerequisite for impact and should include end-user perspectives on what key outcomes they consider desirable (e.g. weight, mood, perceived energy, ability to do certain activities). • Given the high demand on HCPs’ time and changes in practices and routines associated with implementation, perceived value, familiarity, and ease of use will be essential to clinical adoption [83]. • User-centred design processes should include a variety of clinical stakeholders, including those who would be directly engaging with the intervention and those responsible for managing adoption and implementation processes [108]. • This will be needed to identify and address challenges to adoption, whether they are related to perceived value and desirability of the intervention or structural barriers such as its lack of integration into existing routines • Social media and app store advertisement is unlikely to be sufficient to reach the target population in many cases; to increase reach, organisations should diversify dissemination by engaging with community groups and settings, specific to their target population’s clinical and demographic characteristics. • The use of user-centred design with the target population could also improve reach by ensuring that, as well as being aware of the intervention, the target population expects that it will add value. • Within governmental and healthcare organisations, political changes could provide a potential challenge for continuity and responsibility of interventions; solutions for such issues will be case dependent, but should be considered from the outset to ensure there is a plan for sustainability and maintenance. • Digital health interventions that aim to change behaviour contributing to health outcomes should use technical and social features that facilitate the incorporation of these behaviours into regular routines (see recommendations for features above). Strengths, limitations, and future research Strengths of this study included the use of mixed methods, theoretical frameworks (which guided the investigation of a comprehensive set of factors influencing adoption and implemen- tation), and independent and collaborative coding by several authors for a rigorous thematic analysis. Due to time and resource limitations, mapping to the COM-B model was only com- pleted by one author, which limits the robustness of that aspect of the analysis. The main limitation was the sample; high dropout and non-representative demographics [109,110] created potential bias. Stratified random sampling was used to improve the diversity of the SSIs, but the limited overall sample and the need to invite all eligible participants to com- plete 15 interviews meant that interviewees’ perspectives are likely to represent a particular set of lived experiences, reducing generalisability. The convenience method of sampling via social media also introduces potential bias, as participants who respond are likely to be those with PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 17 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact the strongest opinions about the intervention and the reach of the recruitment materials may not reflect a representative sample of the population. Random sampling could reduce this potential bias, although it may not reflect real-world engagement. Alternate methods of recruitment may have more success in increasing the sample size; for example, recruiting fami- lies and HCPs via schools, children’s centres, primary care centres, and other community orga- nisations. A larger-scale study with a more representative sample will be needed to evaluate the impact of the intervention on health behaviour change and childhood obesity. We also rec- ommend that future studies capture demographic data immediately after consent, to enable analysis of participant characteristics in the initial and final samples. Other limitations included the exclusion of children (to facilitate data collection and ethical approval) and the monetary reward for participants who completed the study (which might have incentivised app engagement). Data about the age of the children was not collected in the demographic data, but this was highlighted as an important factor in the qualitative analysis for consideration in future design and evaluation. Additional factors that could influence engagement and perceived impact are initial behavioural- and weight-related characteristics; these were not assessed as weight was not an inclusion criteria but may be an important factor to consider in future research. The lack of data from HCPs meant we could not evaluate their perceptions of the interven- tion or their reasons for not participating in the study. The intervention period took place dur- ing the Covid-19 pandemic; unusually high demands on HCP’s time might have precluded engagement with the study and intervention, but lack of participation could also be related to a lack of awareness of the study or HCP perceptions of the app. This will be an important area for future investigation, as previous research in digital health has suggested that integrating human support can improve engagement and is important for childhood obesity management [111,112]. Our ability to triangulate qualitative and quantitative data was also limited; the app use data that the system could record was relatively minimal and did not enable detailed examination of the frequency, intensity, time, or type of engagement (first and last login only, rather than more detailed data capturing number of logins or use of intervention features) [48]. The app is being redesigned based on theoretical frameworks and the findings of this study; this process will also improve its ability to capture app usage data. The limitations described here will be addressed in future larger-scale efficacy and effectiveness studies. Conclusions The growing prevalence of childhood obesity and the overwhelming demand for healthcare resources more generally has resulted in a need for easily accessible interventions to empower families to manage weight-related health. The benefits of digital health interventions are often limited by insufficient engagement, so understanding facilitators and barriers to engagement is essential. This paper highlights how the use of a theoretical behavioural framework can clar- ify key barriers to engagement with a digital health intervention and its target behaviour(s) and suggest mitigations. We recommend that digital intervention designers incorporate inter- activity, novel content and suggestions, goal setting and progress monitoring, feedback and accountability, reminders, guidance on how to use the app, personalisation, and a positive and visually-appealing design. The caveat is that developers must identify whether these suggested intervention strategies align with engagement barriers and patient or other factors in their par- ticular context; if strategies address capability and motivation barriers but not opportunity bar- riers, the intervention is unlikely to succeed. In terms of implementation, the NASSS and RE-AIM domains highlight key, interconnected factors that can influence the success of an PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000481 March 27, 2024 18 / 25 PLOS DIGITAL HEALTHFactors influencing parents’ app use and perceived impact intervention—particularly important is the need to develop and demonstrate a value proposi- tion that meets the needs and circumstances of the target users and clinical adopting systems, which can best be executed by adopting user-centred design practices to ensure that solutions to potential barriers are incorporated from early stages in development. Supporting information S1 Text. Semi-structured interview guide and study questionnaires. (DOCX) S1 Table. SRQR checklist. (DOCX) S2 Table. TREND checklist. (DOC) S3 Table. NoObesity Family and Professional apps functional overview (adapted from pro- tocol) [45]. (DOCX) S4 Table. Thematic framework. (DOCX) S5 Table. Thematic framework mapping factors to COM-B. (DOCX) S6 Table. COM-B and TDF coding framework with digital health examples. (DOCX) Author Contributions Conceptualization: Em Rahman, Alison Potter, Wendy Lawrence, Michelle Helena van Velthoven, Edward Meinert. Formal analysis: Madison Milne-Ives, Hannah Bradwell, Rebecca Baines, Timothy Boey. Funding acquisition: Em Rahman. Investigation: Madison Milne-Ives. Methodology: Em Rahman, Alison Potter, Wendy Lawrence, Michelle Helena van Velthoven, Edward Meinert. Supervision: Edward Meinert. Visualization: Madison Milne-Ives. Writing – original draft: Madison Milne-Ives. Writing – review & editing: Madison Milne-Ives, Em Rahman, Hannah Bradwell, Rebecca Baines, Timothy Boey, Alison Potter, Wendy Lawrence, Michelle Helena van Velthoven, Edward Meinert. References 1. Obesity and overweight. 9 Jun 2021 [cited 12 Apr 2022]. In: World Health Organisation [Internet]. 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10.1371_journal.pclm.0000243	RESEARCH ARTICLE Impacts of climate change on human health in humanitarian settings: Evidence gaps and future research needs Lachlan McIverID Carol DevineID Emmanuel DewezID 1, Emma Beavon2, Alexandra Malm1, Amr Awad1, Angela Uyen3, 4, Caroline Vouˆ te5, Le´ o Tremblay4, Louisa Baxter6, Juan 1, Maria GuevaraID 1 7, Monica RullID 1 Me´decins Sans Frontières Operational Centre Geneva, Geneva, Switzerland, 2 Burnet Institute, Melbourne, Australia, 3 Me´decins Sans Frontières Operational Centre Brussels, Brussels, Belgium, 4 Me´decins Sans Frontières, Toronto, Canada, 5 Me´decins Sans Frontières Operational Centre Amsterdam, Amsterdam, Netherlands, 6 Me´ decins Sans Frontières Operational Centre Barcelona, Barcelona, Spain, 7 Me´decins Sans Frontières International, Geneva, Switzerland a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 lachlan.mciver@geneva.msf.org Abstract OPEN ACCESS Citation: McIver L, Beavon E, Malm A, Awad A, Uyen A, Devine C, et al. (2024) Impacts of climate change on human health in humanitarian settings: Evidence gaps and future research needs. PLOS Clim 3(3): e0000243. https://doi.org/10.1371/ journal.pclm.0000243 Editor: Jamie Males, PLOS Climate, UNITED KINGDOM Received: May 5, 2023 Accepted: February 13, 2024 Published: March 6, 2024 Copyright: © 2024 McIver et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Survey data available via the Open Science Framework repository. The relevant DOI is: DOI 10.17605/OSF.IO/7QR8C. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. This mixed-methods study focuses on the evidence of the health impacts of climate change on populations affected by humanitarian crises, presented from the perspective of Me´ decins Sans Frontières (MSF)–the world’s largest emergency humanitarian medical organisation. The Sixth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC) was used as the basis of a narrative review, with evidence gaps highlighted and additional literature identified relevant to climate-sensitive diseases and health problems under- reported in–or absent from–the latest IPCC report. An internal survey of MSF headquarters staff was also undertaken to evaluate the perceived frequency and severity of such prob- lems in settings where MSF works. The findings of the survey demonstrate some discrepan- cies between the health problems that appear most prominently in the IPCC Sixth Assessment Report and those that are most relevant to humanitarian settings. These find- ings should be used to guide the direction of future research, evidence-based adaptations and mitigation efforts to avoid the worst impacts of climate change on the health of the world’s most vulnerable populations. Introduction The impacts of climate change on human health have proven to be–and are expected to remain–mostly detrimental [1, 2]. The latest summary of evidence by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report (AR6) makes clear that, over- all, these impacts are severe, widespread, generally underestimated and worsening over time [3]. Multiple categories of climate-sensitive health problems are outlined in the report, includ- ing vector-borne diseases; water- and food-borne diseases; heat stress; zoonoses; food insecu- rity and malnutrition; air pollution; hydrometeorological disasters and mental health impacts PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 1 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings [3, 4]. The issue is largely one of amplification of existing problems, rather than the introduc- tion of new problems per se, although the contribution of climate change as a driver of emerg- ing infectious diseases, for example, is explicitly acknowledged in AR6 [2, 3]. This phenomenon of amplification, or exacerbation, of existing health problems, can be seen directly, for example as heatwaves increase morbidity and mortality in people with cardiovas- cular disease and diabetes [5, 6] as well as indirectly, for example the expansion of habitats suit- able for mosquitoes that spread diseases such as dengue fever [7, 8], and the impact of more severe droughts on food insecurity and malnutrition [9, 10]. The proportion of annual global deaths estimated to be due to ‘climate-sensitive diseases’ is 69.9% and the cumulative burden of these impacts is in the order of millions, if not tens of millions, of deaths per year [4]. The IPCC authors make clear that the health consequences of climate change are being unevenly distributed, with certain populations–particularly people at the extremes of age, those living in poverty, people with pre-existing conditions and communities in geographically vulnerable locations (such as low-lying coastal areas)–already suffering a disproportionate bur- den of climate-change-related health problems [4, 11]. This inequitable distribution of detri- mental health consequences will almost certainly continue for the foreseeable future, with the added injustice that for many such populations, while they are among the first and hardest hit by climate change, they are also those who have contributed least to causing the problem (i.e. via greenhouse gas emissions) [3, 12, 13]. What is largely lacking from AR6, however, is consideration of populations affected by humanitarian emergencies. The interaction between climate change and displacement is described in some detail [4], but the specific and combined health impacts of climate change on individuals and communities already affected by crises such as war, famine, epidemics and disasters are notable by their absence in the IPCC literature. This is far more reflective of an absence of evidence than evidence of absence–a gap that this review paper is intended to help address. This mixed-methods study focuses on populations affected by humanitarian emergencies. For the purposes of this paper, people in ‘humanitarian settings’ are considered to be those affected by extreme poverty, armed conflict, epidemics, pandemics, disasters (hydrometeoro- logical and others) and exclusion from healthcare. They are, generally speaking, populations that are vulnerable in one or more ways, and thus in need of assistance–beyond what can typi- cally be provided by governments in times of crisis–to meet their health needs, which are often multiple, severe and intersecting or overlapping. Working with such communities requires special considerations in terms of their health risk profiles (e.g. high rates of maternal and child mortality; low rates of vaccination coverage; food and/or nutrition insecurity; lack of access to improved water, sanitation and hygiene (WASH) facilities; exposure to tropical diseases; experience of physical and/or psychological trauma; and lack of access to care for chronic conditions). The particular challenges involved in trying to provide care for such populations include (but are not limited to): limited resources (of all kinds); obstacles related to geography, transport, access, utilities and/or secu- rity; political instability and/or armed conflict; weak, limited or absent infrastructure; fragile supply chains; low levels of trust in the health system; lack of support from governments and/ or other health and humanitarian actors; and constraining, excluding or harmful state policies. The abovementioned factors contribute to the relative scarcity of health research and evi- dence specific to such populations. So too do the colonial legacies of Western-centred research frameworks, neglect of Indigenous knowledge and the de-prioritisation of knowledge genera- tion from the so-called ‘global South’ [14, 15]. These, in turn, limit the amount of published lit- erature that is available to be reviewed, whether to inform evidence-based practice and policy implementation, or to include in summaries of evidence such as the IPCC assessment reports. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 2 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings The purpose of this study is thus to identify some of the most important gaps in the litera- ture regarding the impacts of climate change on human health in humanitarian settings–par- ticularly the evidence summarised in AR6. The findings may then guide further research specifically addressing the needs of populations affected by humanitarian emergencies, includ- ing the unique vulnerabilities of such groups, and help determine the most urgent and appro- priate adaptation strategies for different kinds of humanitarian settings. This study is presented from the perspective of a collection of authors, most of whom are employed by Me´decins Sans Frontières (MSF–Doctors Without Borders)–the world’s largest emergency humanitarian medical organisation. MSF has been providing medical care to largely neglected populations in challenging contexts for almost fifty years. The majority of MSF projects are in sub-Saharan Africa (55%), followed by the Middle East and North African region (20%), Asia-Pacific (11%), Americas (7%), Europe and Central Asia (4%), with 2% ‘Other’. Out of these (59%) are in settings of armed conflict, internal instability or post-conflict situations. Methods Ethics statement Approval for this study was provided by the medical department and operational research unit at MSF Operational Centre Geneva. This study was comprised of two main methodologies. The first was a narrative review, whereby the relevant literature on the health impacts of climate change was synthesised, with a particular focus on humanitarian settings. This technique was considered most appropriate, given the simultaneous assumptions that a) the principal source of up-to-date information on the health impacts of climate change would be the IPCC AR6 [3]; b) additional references would be required to attempt to encompass the evidence specific to the populations of interest (i.e. people affected by humanitarian emergencies); and c) even these sources combined would be unlikely to accurately, comprehensively or appropriately reflect the unique considerations specific to humanitarian settings. These assumptions, and the objectives of this paper to appraise existing literature and identify priorities for further research, made the narrative review method the most logical format in this instance [16]. A list of the diseases and other health problems sensitive to climate (hereunder referred to as climate-sensitive diseases–CSDs) explicitly mentioned in IPCC AR6 was used as the starting point. It must be noted that many such problems–for example heat, air pollution and hydro- meteorological disasters–are not technically ‘diseases’, but in order to align with the prevailing literature, and in the absence of a more accurate alternative, the term ‘climate-sensitive dis- eases’ has been used here. To the aforementioned list, several additional CSDs were added that were known (based on annual reports) or suspected (based on previous literature reviews) to occur in MSF projects, for a total of 46 CSDs (see Table 1). A search for titles and abstracts was conducted via PubMed, with each CSD entered as a distinct term (including logical alterna- tives, such as ‘antibiotic resistance’ in addition to ‘antimicrobial resistance), with the additional terms ‘weather’, ‘environment’, ‘climate’ and ‘climate change’ all included via the ‘and/or’ function. Abstracts were then reviewed, as well as full-text articles where necessary, to identify those articles that appeared to include information most relevant to the topic (i.e. climate-sen- sitivity of specific CSDs) and study populations (i.e. people affected by humanitarian emergen- cies). Further references were then added by ‘snowballing’, where deemed appropriate. The second methodology employed was a voluntary, anonymous survey of staff in the med- ical and operations departments across MSF’s headquarters (the International Office and Operational Centres that together oversee all of MSF projects in over seventy countries). This PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 3 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings Category B Category C External evidence: Patients treated in Ebola Treatment Units tents in Liberia and Sierra Leone during the 2014–2016 epidemic had higher odds of fatality if the average environmental temperature was above 27˚C during their stay than those below [50]. Large fluctuations in climate may increase the population of bats infected with Ebola increasing risk of spillover into human population [51]. Increased risk of Ebola spillover events with modelling taking into account climate change and population growth including more areas of northern, eastern and southern Africa not yet impacted by outbreaks [52]. Ebola Evidence mentioned in IPCC: Worsening animal health due to changing disease distributions including zoonotic diseases, reduction in feed quality and deforestation. This leads to increased antimicrobial use in livestock and other animal health practices leading to increased AMR (pg 233, 1076, 1381) [4, 17, 18]. Additional external evidence: Increased AMR associated with increasing local temperatures and population density for common pathogens across the US [19]. European countries with an increased ambient minimum temperature of 10˚C had faster AMR growth compared with countries with cooler temperatures over a 10-year period [20]. 30-country European observational study found Carbapenem-resistance Pseudomonas aeruginosa associated with increased temperature change during summer [21]. 1˚C increase in regional ambient temperature in regions across China positively associated with higher prevalence of AMR for carbapenem resistance Klebsiella pneumoniae and Pseudomonas aeruginosa [22] and E.coli antibiotic resistance [23]. These links were strongest in areas with fewer health facilities and higher perceived corruption and lower income. Meta-analysis looking at links between increased temperature and AMR in aquaculture from mostly low- and middle-income countries. Increased multi-antibiotic resistance in aquaculture bacteria correlated with increased temperature and also measures of AMR from human bacteria [24]. Table 1. CSDs included in study. Category A Air pollution Allergies (excluding reactive airways disease) Antimicrobial resistance Anthrax Cancer Cardiovascular disease Cholera Cold-related illness Conflict Dengue Diarrhoeal diseases Displacement Emerging infectious diseases Heat-related illness Hydrometeorological disasters Japanese encephalitis virus Leptospirosis Lyme disease Malaria Malnutrition Mental health Respiratory diseases (non- infectious) Respiratory infections (excluding measles & tuberculosis) Rift Valley fever Schistosomiasis Tick-borne diseases (excluding Lyme disease) Tularaemia Typhoid fever West Nile virus Zika Chagas disease Human African trypanosomiasis (HAT) Evidence mentioned in IPCC: Increasing range of triatomines into Southern USA and projected to continue further north. (pg 1969) [25] Changes in transmission and distribution of Chagas disease in Central and South America (low confidence evidence). (pg1717) [26] Additional external evidence: Using IPCC climate change projections modelling showed possible decreasing exposure to the Venezuelan population to triatomines [27]. Increased areas for potential transmission of Chagas disease in Chile particularly Central and Northern regions with projected climate change scenarios [28]. Geographical range of triatomines in Chile are likely to extend into previously unaffected areas under some climate change projections [29]. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 Rising temperatures in the Zambezi Valley have been linked to a reduction in tsetse flies, however such increases in temperature may increase numbers in cooler areas of Zimbabwe previously unaffected [53]. Under climate change models tsetse fly distribution are likely to move into highland areas of Kenya potentially exposing a new large population of people to risk of HAT [54]. Modelling using tsetse fly catch data and increasing local temperatures, tsetse fly populations are predicted to decrease in lower elevation regions but increase in higher elevation, previously cooler, regions [55]. Modelling to predict distribution of three species of tsetse flies under predicted climate change conditions demonstrated reductions in habitable area however potential movement into areas previously protected from HAT [56]. In Zimbabwe, habitat fragmentation and rising temperatures create conditions leading to higher populations of older tsetse flies, increases the rate of infection and risk of disease [57]. Modelling of temperature impacts on vector ecology predicted 46–77 million more people could be exposed to HAT risk by 2090 [58]. (Continued ) 4 / 18 PLOS CLIMATETable 1. (Continued) Category A Hepatitis A & E Leishmaniasis Meningitis Snakebite Health impacts of climate change in humanitarian settings Category B Category C Evidence mentioned in IPCC: High rainfall, warm temperatures and drought increase risk of gastrointestinal infection and waterborne diseases. No specific reference to Hepatitis A and E only as a water-borne disease (WBD) [4]. Additional external evidence: High rainfall (>90th percentile) associated with an increase in cases of hepatitis A across Spain between 2010 and 2014 lasting for 2 weeks post rain event [30]. Increased risk of hepatitis A after severe flood event in four cities of Anhui province in China [31]. In a Brazilian municipality cases of hepatitis A increased by nearly 300% in the three months after flood events within urban areas on floodplains over a 2 year period [32]. Evidence mentioned in IPCC: Leishmaniasis prevalence increase in Central and South America due to higher temperatures increasing the areas suitable for vectors along higher frequency climate related weather events (pg. 1699, 1722) [26] Additional external evidence: Increased areas suitable for leishmaniasis vector and reservoirs in Iran when modelled for climate change scenarios [33]. Increasing incidence of cutaneous leishmaniasis predicted in regions of Palestine including Gaza strip and the North West Bank using future projected climate change scenarios [34]. Leishmaniasis incidence higher in regions of Iran with higher rainfall, humidity, evapotranspiration and soil moisture [35]. In French Guiana increases in incidence of leishmaniasis 2 months after a decrease in rainfall [36]. Evidence mentioned in IPCC: In the western Sahel region with the highest burden of bacterial meningitis is predicted to have increase meningitis case with rising temperatures. (pg. 1375) [18]. Additional external evidence: Drivers of meningitis disease in Democratic Republic of Congo are sensitive to changes in climate [37]. Global ecological study found strong association between meningitis incidence and increased temperature variability [38]. Meningococcal meningitis in the Sahel is sensitive to climate with periods of low rainfall and El Niño coinciding with peaks in incidence [39]. Low rainfall, high temperatures and increased aerosols are predictive of meningitis outbreaks in Nigeria [40]. Evidence mentioned in IPCC: Snakebites more likely to occur in Costa Rica at higher temperatures (pg. 1699) [26]. Additional external evidence: In Sri Lanka snakebite incidence increases with low humidity and likely to increase with climate change [41]. Increased incidence of snakebites requiring medical evacuation in Israel with increased temperature and lower humidity [42]. Increased snakebite incidence correlated with water scarcity and desertification as well as lower Human Development Index (HDI) in Brazilian state of Ceara´ [43]. In areas of Colombia with marked dry seasons, snakebite incidence increased with increased rainfall. No increase with rainfall in other regions [44]. Lassa Lymphatic filariasis Some models suggests an increased Lassa spillover potential in West Africa attributable to a large extent to climate change [59]. Models from Nigeria also point to a substantial effect of climate in explaining Lassa fever occurrence and incidence patterns across Nigeria [60]. The number of people exposed to Lassa virus could increase by hundreds of million in Central and East Africa using modelling of projected climate, population and land use changes [61]. Studies of field populations of Culex mosquitoes have shown that increases in temperature are likely to accelerate mosquito development [62]. Modelling of climate projections predicts the range of risk for lymphatic filariasis infection to increase and could increase the population exposed to between 1.65 to 1.86 billion people [63]. Predicted rising sea levels are likely to increase the area of saline and brackish water in coastal regions thereby increasing the density of mosquito vectors including Culex mosquitoes [64]. Marburg Reduced temperature and rainfall seasonality in Uganda are important environmental variables for predicting increased risk of Marburg virus disease outbreaks [65]. Measles Measles cases in Ondo state, Nigeria linked to periods with higher human thermal comfort indices and low rainfall [66]. Both hot and cold temperatures resulted in decreases in the incidence of measles, and low relative humidity is a risk factor of measles morbidity in Guangzhou, China [67]. Experiencing drought at 12 months of age negatively associated with receiving a measles vaccine in Rwanda, Democratic Republic of the Congo, Ghana, and Malawi [68]. (Continued ) PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 5 / 18 PLOS CLIMATETable 1. (Continued) Category A Stroke Tuberculosis Health impacts of climate change in humanitarian settings Category B Category C Melioidosis An association between rainfall events and cases of melioidosis was found in Darwin, Australia between 1990 and 2013 [69]. Higher rainfall correlated with melioidosis case numbers over a 20-year period in Torres Strait Islands region, Australia [70]. Case clusters of melioidosis reported following extreme weather events in Sri Lanka and Australia [71, 72]. Monkeypox (MPX) Incidence of MPX positively associated with temperature as well as primary forest and economic well-being in Democratic Republic of the Congo (DRC) [73]. Projected shift of regions with suitability for MPX transmission to regions previously unaffected in DRC, Uganda, Kenya, Tanzania, Cameroon, Gabon and Equatorial Guinea with modelling using IPCC projected climate change scenarios [74]. Evidence mentioned in IPCC: Stroke hospitalisation increases in response to higher ambient temperatures (pg. 1073) [4] Increased incidence of stroke linked to heat in some countries in Africa (pg.1377) [18] Additional external evidence: Systematic review and meta-analysis examining impact of ambient heat on cardiovascular disease found positive association between incidence of cardiovascular disease mortality, with strongest risk in stroke and CHD. Risk increases with heat exposure for women, 65+ population, tropical climates and LMICs [45]. Across 22 East Asian cities extreme heat was associated with increase mortality due to stroke from 1972 to 2015. The burden attributable to heat increasing under modelled climate change scenarios [46]. Temperature related deaths due to ischaemic stroke projected to increase in Beijing under climate change models of approximately 100% by 2090 [47]. Evidence mentioned in IPCC: Higher proportions of climate-related infections such as tuberculosis in Indigenous populations compared with non-Indigenous e.g. Torres Strait (pg. 1054) [4]. Tuberculosis contributes 6.5% of deaths and 6% of DALYs due to climate-sensitive diseases (pg. 1060) [4]. For people living with HIV and reduced lung function due to tuberculosis infection could increase their risk from extreme heat (pg.1375) [18]. Additional external evidence: Systematic review of association between climate variable and tuberculosis risk factors found positive associations between TB risk factors and climate change including HIV, diabetes, undernutrition, overcrowding and poverty [48]. Systematic review of relationship between meteorological factors and TB showed increased risk of TB correlated with precipitation, temperature and humidity in populations in subtropical climate and with low and middle Human Development Index [49] https://doi.org/10.1371/journal.pclm.0000243.t001 group was targeted based on their roles and responsibilities in designing and managing proj- ects and providing technical and strategic support for the organisation’s medical activities. The survey was limited to headquarters staff in order to try and ensure a moderate to high level of experience working in MSF settings, as per the focus of the questions. Survey respondents were asked to estimate, on a five-point qualitative scale, how frequent and severe they perceived the 46 CSDs to be in settings where MSF works. The precise ques- tions asked were ‘How frequently is the problem of XXX seen or managed in MSF settings?’ and ‘How severe is the problem of XXX in MSF settings?’. The introductory material at the beginning of the survey explained that respondents were being requested to reply based on their own knowledge and experience, however subjective. The question of ‘severity’ was left deliberately open to interpretation, given the heterogeneous nature of the CSDs (from rare dis- eases to large-scale disasters) and the professional backgrounds of the survey respondents. The survey link was shared via email and completed responses were downloaded for analy- sis in Microsoft Excel once the survey deadline had passed. The survey responses were then aggregated, with a median score (between 0 and 4) generated for both perceived frequency and severity for each of the 46 CSDs. The results of these analyses were thus used to represent the estimated relative frequency and severity of each CSD. An additional, overall, subjective, qualitative approximation of each CSD’s ‘relevance’ to humanitarian settings was generated by PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 6 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings multiplying the average frequency score of each CSD by its average severity score. The survey also included an option of ‘I don’t know’ for the estimated frequency and severity of each CSD, in order to identify potential information and/or knowledge gaps within the organisation. As the survey was completely anonymous, and the results aggregated prior to analysis, no identifiable data was requested or collected. Findings The full list of 46 CSDs included in this study is provided in Table 1, where they are divided into three categories. The first (Category A) is of CSDs for whom the evidence of climate-sen- sitivity is strong, and which have been described as such in the IPCC AR6. The second (Cate- gory B) is of CSDs for whom the evidence of climate-sensitivity in the literature is moderate, but which appears to have been under-reported in AR6. The third (Category C) is of CSDs for which there is some evidence of climate-sensitivity, or at least plausible links, but which are not mentioned at all in AR6. The latter category was identified based on the authors’ collective experience, familiarity with the relevant literature and internal discussions within MSF. Of note, the papers cited in Category C differ substantially in scope and methodology, from obser- vational studies to experimental modelling. The key points cited from those papers therefore include different types of data, from epidemiological studies to estimates mapping potential future vulnerabilities, and are referred to variously in those papers as results, discussion and/or conclusions. The most relevant evidence identified in the literature review has been summa- rised for the respective CSDs in Categories B and C in Table 1. The aforementioned anonymous survey provided to MSF headquarters medical and opera- tions staff yielded 30 responses. The results of the average estimated frequency and severity (each on a scale of 0–4) of the 46 CSDs, as they were perceived by respondents to occur as problems in MSF settings, is displayed in Fig 1. Fig 1. Perceived frequency and severity of CSDs in MSF settings. https://doi.org/10.1371/journal.pclm.0000243.g001 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 7 / 18 PLOS CLIMATETable 2. Perceived overall relevance of CSDs to MSF (in terms of product of estimated frequency and severity). Health impacts of climate change in humanitarian settings CSDs in descending order from highest (top left) to lowest (bottom right) perceived relevance Respiratory diseases (non-infectious) Heat-related illness Emerging infectious diseases Lymphatic filariasis Malaria Measles Malnutrition Diarrhoeal diseases Mental health Displacement Cholera Tuberculosis Respiratory infections (excluding measles & tuberculosis) Ebola Conflict Antimicrobial resistance Hydrometeorological disasters Water pollution Meningitis https://doi.org/10.1371/journal.pclm.0000243.t002 Cardiovascular disease Typhoid fever Snakebite Dengue Leishmaniasis Hepatitis A & E Schistosomiasis Cancer Lassa Stroke Chagas Zika Allergies (excluding reactive airways disease) Leptospirosis Rift Valley fever Tick-borne diseases (excluding Lyme disease) Air pollution West Nile virus Monkeypox Tularaemia Japanese encephalitis virus Human African trypanosomiasis Marburg Cold-related illness Lyme disease Anthrax Melioidosis A complementary, yet distinct, perspective on the relevance of these CSDs to MSF settings can be discerned from Table 2, which lists the same 46 CSDs in descending order with respect to the product of their estimated frequency and severity–that is, the average frequency score from the survey multiplied by the average severity score. The CSDs at the top of the list may thus be considered, for this purpose, those perceived to be ‘most relevant’ at present to the MSF staff participating in the survey, with those at the bottom correspondingly perceived to be the ‘least relevant’ at present. An additional, valuable insight is provided in Fig 2, which displays the proportion (expressed as a percentage) of survey respondents who answered ‘I don’t know’ regarding the estimated frequency and severity of each CSD in contexts where MSF works. Fig 2 is displayed with the ‘best known’ CSDs at the extreme left of the x axis, with the overall level of perceived knowledge regarding the frequency and/or severity of the CSDs (specifically in such settings) decreasing towards the right of the x axis. Comparison of Table 2 and Fig 2 highlights an important phenomenon, namely the signifi- cant overlap between those CSDs considered to be of relatively low relevance (as represented by the product of their average estimated frequency and severity scores) and those CSDs with a high proportion of respondents who stated they didn’t know how frequent and/or severe these problems are in settings where MSF works. Consideration of the above findings together suggests that there may be some value in grouping the 46 CSDs into five categories, as outlined in Table 3. Discussion This study demonstrates that there are some important evidence gaps with respect to the health impacts of climate change on people affected by humanitarian crises. Unfortunately, this is but a small part of a wider problem: the ‘overlooking’ of the needs of vulnerable popula- tions. The phenomenon highlighted in this paper, whereby issues most relevant to high- income countries are over-represented in the literature, and those most relevant to low- income countries are under-represented, is effectively ubiquitous in academia. However, it is especially poignant for this topic, when one considers that the majority of the research being PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 8 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings Fig 2. Percentage of survey respondents who answered ‘I don’t know’ when asked to estimate the frequency and severity of each CSD in MSF settings. https://doi.org/10.1371/journal.pclm.0000243.g002 Table 3. Categories of CSDs. Category CSDs that are highly relevant to MSF settings and are well described in AR6 with appropriate and sufficient supporting evidence CSDs that are less relevant (or relatively irrelevant) to MSF settings but are nevertheless well described in AR6 CSDs that are relevant to MSF settings but are mentioned only briefly or indirectly in AR6 CSDs that are relevant to MSF settings but are not mentioned at all in AR6 Example CSDs Significance Malaria Cholera Malnutrition Anthrax Lyme disease West Nile virus Meningitis Snakebite Leishmaniasis Measles Ebola Human African trypanosomiasis This group represents the diseases most relevant to MSF with the strongest evidence of their climate sensitivity This group represents a possible form of information bias, whereby CSDs more relevant to high-income countries (e.g. Europe, North America) are relatively over-represented in the IPCC review This group likely represents a combination of bias and evidence gaps, whereby evidence does exist regarding the climate sensitivity of these diseases but the relevant information is not adequately reflected in the IPCC review This group represents a significant evidence gap, with possible bias, whereby the climate sensitivity of the diseases is biologically plausible or proven but the current evidence is limited or speculative CSDs that are potentially relevant to MSF settings but about which the level of knowledge (at least regarding estimated frequency and severity) within the organisation is currently low Melioidosis Air pollution Tick-borne diseases This group represents a potential knowledge gap for MSF staff, who may be unaware of the burden, climate-sensitivity and/or risk of these diseases https://doi.org/10.1371/journal.pclm.0000243.t003 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 9 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings generated on climate change and health originates from those countries who are both among the largest emitters of greenhouse gases and those with the greatest capacity to take action against climate change. Many of the populations most affected by climate change, including in terms of health impacts, are already experiencing multiple hardships, such as poverty, violence and displace- ment, that all have detrimental effects on health. The addition of the burden of climate-related health problems, coupled with the paucity of research on how these manifest, and how such impacts can be minimised, is clearly unjust, particularly considering the negligible contribu- tion that such populations have made to the problem of climate change itself. Of course, evidence requires research, research requires data, and data is often poor quality, incomplete or entirely absent in humanitarian contexts. The reasons for this are multiple, including discrimination and/or marginalisation of these specific populations; weak or failed health systems, lack of infrastructure (e.g. to generate meteorological and epidemiological information); lack of resources (human, financial, other); and the challenges of access due to conflict, geography, etcetera [75]. Incomplete, inaccurate and fragmented data has contributed to negative outcomes in previous disaster responses [76], and disruptions to health systems compromise the accuracy and efficacy of dissemination and analysis of health information [77]. However, while these obstacles may be understandable, that does not mean they are acceptable. When it comes to the inclusion–or absence–of such context-specific evidence in the IPCC reports, it must be acknowledged that the complex process of reviewing, synthesising and reporting of evidence in the Assessment Reports requires substantial time and effort, involving strict deadlines. This means that research findings published in the period immediately pre- ceding launch of an Assessment Report are usually not included until the next AR cycle, sev- eral years later. This study also suggests that some significant knowledge gaps may exist within the humani- tarian community–at least as it is represented by the small sample of MSF headquarters staff who participated in the survey–with respect to the burden of some specific climate-sensitive diseases. The majority of MSF’s work has historically been oriented towards acute emergencies such as epidemics, disasters and conflict, meaning that diseases and other climate-sensitive health problems such as malaria, cholera and malnutrition are those most familiar to MSF staff. Issues that are relatively familiar to many MSF staff extend to otherwise rare diseases such as the viral haemorrhagic fevers Ebola and Marburg, which were identified by survey respondents as infrequent but severe (see Fig 1). However, large-scale health problems such as air pollution and heat stress, whose burdens (in terms of illness and death) are already enor- mous, and expected to increase due to climate change [78], may become more prominent in humanitarian settings over time and thus represent a different type of emergency that is more chronic in nature. In parallel, climate-sensitive infectious diseases such as melioidosis, whose global burden is thought to be significantly under-estimated at present (but may in fact be greater than several other, better-known diseases such as dengue fever, leptospirosis and schis- tosomiasis, all of which are included in this review) [79], may evolve from being almost unknown in humanitarian settings to one that is increasingly recognised and treated as epide- miological data and diagnostic capacities improve. It is not only the health impacts of climate change themselves that are important to better understand through further research, including in humanitarian settings, but the strategies required to minimise those impacts. Analysis of adaptation strategies to protect the health of populations affected by humanitarian crises was outside the scope of this project, but this is an urgent priority for the research community. Ideally, this research should be conducted in part- nership with the populations affected and, where useful and feasible, in collaboration with PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 10 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings relevant humanitarian actors. Addressing these gaps and identifying the most promising oper- ational adaptations to protect human health in humanitarian settings is an established priority for MSF and was the principal reason for conducting this study. Such adaptation measures must be not only evidence-based, but acceptable and appropri- ate. This must include consideration and anticipation of the harmful effects that may result. The phenomenon of ‘maladaptation’ can be seen, for example, in attempts to address climate- change-related food insecurity through altered agricultural practices, which can lead to unin- tended negative consequences such as increased exposure to snakebite [80]. No discussion of the health impacts of climate change is complete without reiteration of the fundamental importance of mitigation. No amount of research, evidence or adaptation will enable humanity to avoid the worst impacts of anthropogenic climate change. This is only pos- sible through immediate, evidence-based and sustained actions to slow and halt carbon emis- sions and draw down previously emitted carbon from the atmosphere. MSF has committed itself to ambitious carbon reduction targets, in line with the Paris Agreements, to attempt to demonstrate a ‘best practice’ approach, become a more responsible humanitarian actor and adhere to the Hippocratic principle of Primum non nocere (First, do no harm). Strengths and limitations The authors collectively have decades of experience dealing with the majority of the above- mentioned health problems. However, the organisational scope and collective expertise of the authors may certainly be considered skewed–perhaps even biased–towards low- and middle- income countries, resource-constrained environments and vulnerable populations. Whether this may be considered a strength or limitation of the paper is open to interpretation. What is a clear limitation of this study, apart from the lack of published literature specific to the populations of interest already highlighted in the paper, is the internal survey. This was a highly subjective tool, whose results–including the semi-quantitative analyses presented in this paper–should be interpreted with caution. This issue of subjectivity is particularly pertinent in relation to the strategy of leaving the definition of ‘severity’ (of climate sensitive ‘diseases’) open to interpretation by survey respondents. The logic underpinning this decision was that MSF staff have expertise across a wide range of interdisciplinary areas, including not only medical specialty domains but technical, operational and logistical areas such as water, sanita- tion, hygiene, energy, transport, supply chain, finance and human resource management. For many of the CSDs included in this review, it may be assumed that what one headquarters staff member considers a ‘severe’ problem (for example, an Ebola outbreak) would be similarly viewed by other colleagues from their distinct areas of technical expertise. However, this would not always be the case. The level to which an MSF headquarters staff member may con- sider a problem to be severe would be significantly influenced by their professional profile and training, operational experience and previous exposure to the specific issue in question. A dis- ease that is ‘severe’ from a medical perspective is not necessarily a severe logistical challenge, and vice versa. The purpose of the survey was thus to capture, in the broadest possible sense, the level of concern with which such senior staff viewed each of these CSDs, in order that their collective expertise could inform the interpretation of the responses and resulting recommen- dations. To that end, it was decided that attempting to offer an a priori definition of ‘severity’ would influence–and thus potentially inhibit–the variety and richness of possible responses from the broad expertise of staff at MSF headquarters. The survey was shared only with staff in the medical and operations departments at MSF’s International Office and Operational Centres. This decision was based on the assumption that these staff would have a reasonable depth and breadth of experience across a variety of MSF PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 11 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings settings, and would be at least somewhat familiar with the majority of the CSDs included in the survey. Such decisions and assumptions come with obvious risks related to bias, including from excluding non-headquarters-based staff, and having no exclusion criteria for respondents based on their perceived or measured knowledge and experience. Only 30 of those headquar- ters staff responded to the survey, limiting the extent to which the results could be analysed and/or generalised at larger scales. Nevertheless, the authors feel that these results do provide useful indicators as to the required direction of future climate change and health research, and priorities for knowledge-sharing and awareness-raising within MSF and the wider humanitar- ian community. It must be acknowledged that the list of CSDs included in this study is far from exhaustive. There is increasing evidence that diseases of significant global importance, such as human immunodeficiency virus (HIV), may have altered transmission in connection to climate- linked phenomena such as drought [81]. Many other emerging or re-emerging diseases, such as Crimean-Congo haemorrhagic fever [82], have also been demonstrated to be sensitive to meteorological and environmental variables. The list of climate-sensitive diseases, while per- haps not infinite, is certainly lengthy. A final emphasis must be placed on the urgent challenge of establishing more accurate esti- mates of the global morbidity and mortality related to climate change. The official WHO figure of approximately 250,000 deaths per year for the period 2030–2050 is explicitly limited to only four categories of CSD: malnutrition, malaria, diarrhoeal disease and heat stress [83]. As this study demonstrates, and other authors have highlighted [84], the true scale and burden of the problem is being severely underestimated at present–likely by orders of magnitude–and this must be a priority area for further research. Conclusions As evidence regarding the health impacts of climate change continues to grow, the IPCC Assess- ment Reports remain the ‘gold standard’ sources of expertly synthesised information on this complex topic. However, there are important gaps in the IPCC’s latest Assessment Report, and in the evidence that was available for inclusion within it, particularly in relation to people affected by humanitarian emergencies. Such populations, which are already burdened by multi- ple layers of health vulnerabilities, are being forced to suffer further due to the lack of evidence available to inform efforts to address their particular health needs, including adaptation mea- sures to protect against the effects of climate change. There are also knowledge gaps within the humanitarian sector which are similarly important to address through research and advocacy. It cannot be the responsibility of the people affected by humanitarian crises, nor humanitar- ian actors alone, to address these evidence gaps and put in place the measures required to min- imise the harmful effects of climate change on the health of these populations. It is essential that the scientific community collaborates with government and non-government organisa- tions to address the evidence gaps and identify the most appropriate strategies to protect the health of the people most in need. The voices of people affected by humanitarian emergencies, and the evidence regarding the impacts of climate change that they are experiencing, including on their health, must be more accurately and comprehensively included in future IPCC reports, as well as the global policy agenda. Acknowledgments The authors, almost all of whom are current employees of MSF, are grateful to all colleagues who completed the internal anonymous survey mentioned herein. It is intended that this PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000243 March 6, 2024 12 / 18 PLOS CLIMATEHealth impacts of climate change in humanitarian settings article provides a robust perspective from multiple authors affiliated with MSF, but this should not be assumed or portrayed to represent an official position of MSF as an international organisation. Author Contributions Conceptualization: Lachlan McIver, Juan Emmanuel Dewez, Monica Rull. Data curation: Lachlan McIver, Emma Beavon. Formal analysis: Lachlan McIver, Emma Beavon. Investigation: Lachlan McIver. Methodology: Lachlan McIver, Emma Beavon. Project administration: Lachlan McIver. Writing – original draft: Lachlan McIver, Emma Beavon, Alexandra Malm, Amr Awad, Angela Uyen, Carol Devine, Caroline Vouˆte, Le´o Tremblay, Louisa Baxter, Juan Emmanuel Dewez, Maria Guevara, Monica Rull. Writing – review & editing: Lachlan McIver. References 1. 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10.1371_journal.pdig.0000457	RESEARCH ARTICLE Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use Hannah Albrechta1, Georgia R. Goodman1,2,3, Elizabeth Oginni1, Yassir Mohamed1, Krishna Venkatasubramanian4, Arlen Dumas4, Stephanie Carreiro5, Jasper S. Lee1,3, Tiffany R. Glynn1,2,3, Conall O’Cleirigh1,3, Kenneth H. Mayer1,6, Celia B. Fisher7, Peter R. ChaiID 1,2,8,9* 1 The Fenway Institute, Fenway Health, Boston, Massachusetts, United States of America, 2 Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America, 3 Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 4 Department of Computer Science and Statistics, The University of Rhode Island, Kingston, Rhode Island, United States of America, 5 Department of Emergency Medicine, University of Massachusetts Chan Medical School, 6 Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America, 7 Center for Ethics Education, Fordham University, New York City, New York, United States of America, 8 Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts, United States of America, 9 The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America * pchai@bwh.harvard.edu Abstract Once-daily oral HIV pre-exposure prophylaxis (PrEP) is an effective strategy to prevent HIV, but is highly dependent on adherence. Men who have sex with men (MSM) who use sub- stances face unique challenges maintaining PrEP adherence. Digital pill systems (DPS) allow for real-time adherence measurement through ingestible sensors. Integration of DPS technology with other digital health tools, such as digital phenotyping, may improve under- standing of nonadherence triggers and development of personalized adherence interven- tions based on ingestion behavior. This study explored the willingness of MSM with substance use to share digital phenotypic data and interact with ancillary systems in the con- text of DPS-measured PrEP adherence. Adult MSM on PrEP with substance use were recruited through a social networking app. Participants were introduced to DPS technology and completed an assessment to measure willingness to participate in DPS-based PrEP adherence research, contribute digital phenotyping data, and interact with ancillary systems in the context of DPS-based research. Medical mistrust, daily worry about PrEP adherence, and substance use were also assessed. Participants who identified as cisgender male and were willing to participate in DPS-based research (N = 131) were included in this subsample analysis. Most were White (76.3%) and non-Hispanic (77.9%). Participants who reported daily PrEP adherence worry had 3.7 times greater odds (95% CI: 1.03, 13.4) of willingness to share biometric data via a wearable device paired to the DPS. Participants with daily PrEP adherence worry were more likely to be willing to share smartphone data (p = 0.006) and receive text messages surrounding their daily activities (p = 0.003), compared to those a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Albrechta H, Goodman GR, Oginni E, Mohamed Y, Venkatasubramanian K, Dumas A, et al. (2024) Acceptance of digital phenotyping linked to a digital pill system to measure PrEP adherence among men who have sex with men with substance use. PLOS Digit Health 3(2): e0000457. https://doi.org/10.1371/journal.pdig.0000457 Editor: Haleh Ayatollahi, Iran University of Medical Sciences, IRAN (ISLAMIC REPUBLIC OF) Received: August 17, 2023 Accepted: February 1, 2024 Published: February 22, 2024 Copyright: © 2024 Albrechta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data from this study has been made available as supplementary information. Funding: This work was supported by the National Institutes of Health (K23DA044874 to PRC, DP2DA056107 to PRC and KV, P30AI060354 to CO and KM, T32AI007433 to TRG, and R25DA03196 to CBF and PRC). The funders had no role in study design, data collection and PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 1 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. with less worry. MSM with substance use disorder, who worried about PrEP adherence, were willing to use DPS technology and share data required for digital phenotyping in the context of PrEP adherence measurement. Efforts to address medical mistrust can increase advantages of this technology for HIV prevention. Author summary Oral medications for HIV pre-exposure prophylaxis (PrEP) are highly efficacious in pre- venting HIV infection, but efficacy is closely linked with adherence. Despite the availabil- ity of PrEP, measuring adherence and responding to nonadherence events remains difficult. One possible strategy to measure PrEP adherence is using a digital pill system (DPS) that activates directly in the stomach and reports adherence events. Integrating contextual markers like smartphone digital phenotyping may enhance behavioral inter- ventions that leverage DPS adherence data to provide PrEP adherence support. Here, we conducted a survey study through a social networking website to understand perceptions of the DPS and linked digital phenotyping among MSM with substance use on PrEP. We found that the degree of substance use did not mediate willingness to participate in research using digital phenotyping and the DPS. Individuals who worried more about PrEP adherence were more willilng to interact with the DPS and digital phenotyping techniques. Introduction Once-daily oral pre-exposure chemoprophylaxis (PrEP) is highly efficacious in preventing human immunodeficiency virus (HIV) acquisition when adherence is maintained [1]. Follow- ing results from multiple clinical trials, tenofovir disoproxil fumarate/emtricitabine (TDF/ FTC) was recommended by the World Health Organization (WHO) and the United States (US) Centers for Disease Control and Prevention (CDC) for use as oral PrEP in 2012 [2]. Over the past decade, PrEP has become widely recognized as a key pillar of the strategy to end the HIV epidemic and is recommended for populations at risk of HIV acquisition. Among indi- viduals at risk, men who have sex with men (MSM) experience disproportionate HIV expo- sure, particularly given common comorbidities of mental health, stigma, and trauma [3]. Additionally, substance use among MSM has been independently associated with an increased risk of HIV acquisition and PrEP nonadherence [4]. Despite the recent success of long-acting injectable cabotegravir as PrEP [5], there remains a need to develop strategies to assess and improve oral PrEP adherence, especially among MSM who may not qualify or be unable to access injectable PrEP. Given the importance of initiating and maintaining PrEP use for HIV prevention efforts, several tools have been developed to measure adherence [6]. These include both indirect meth- ods that infer medication ingestion events (e.g., self-report, pharmacy refill records, smart pill bottles) and direct methods (e.g., directly observed therapy, video-assisted observed therapy, and measurement of drug levels in biological matrices) [7]. Another tool that allows for direct measurement of adherence is a digital pill system (DPS), which provides confirmation of the presence of an ingested medication in the stomach. The FDA-cleared DPS (etectRx, Gaines- ville, FL) comprises a standard gelatin capsule with an integrated radiofrequency emitter that over-encapsulates PrEP. Upon ingestion, gastric chloride ions activate the radiofrequency PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 2 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills emitter, transmitting a prespecified radiofrequency signal to an off-body wearable device (Reader), which stores and forwards ingestion data to a smartphone app, where DPS users and clinical or research teams can view real-time adherence data [8]. This system can also serve as a platform for the delivery of tailored adherence interventions, which can be directly informed by changes in detected PrEP adherence patterns over time [9]. Previous qualitative work demonstrated that MSM with substance use are accepting of DPS technology, willing to operate it in the real world to measure PrEP adherence, and perceive value in having on-demand access to PrEP adherence data [8,10]. Additionally, a recent study surveyed a national sample of MSM on PrEP who use substances to understand broader per- ceptions of the DPS and willingness to interact with the system for PrEP adherence measure- ment [11]. The results were congruent with previous qualitative work demonstrating the willingness of MSM on PrEP who use substances to interact with the DPS. Participants also described an interest in accessing their adherence data on demand, and those with greater worry surrounding their PrEP adherence were statistically significantly more willing to inter- act with the DPS. The current investigation builds off of previous research by exploring the willingness of MSM who use substances to engage with ancillary devices and systems, and to share smartphone data, in the context of DPS-based research. One advantage of DPS technology lies in its ability to capture detailed daily patterns of inges- tions. Such patterns of medication adherence behavior can form the basis of systems that seek to measure the context in which ingestions occur [12]. The increasing ubiquity of smartphone own- ership and use of wearable, health-related devices [13] presents an additional opportunity to col- lect and leverage passive device data (e.g., battery life, accelerometry, and global positioning system [GPS] data) to identify digital traits that may be indicative of changes in adherence behav- iors, such as adherence. Digital phenotyping–the practice of aggregating large amounts of passive smartphone and wearable data–has been demonstrated to indicate exacerbations of mental health and pain among individuals with mental illnesses and acute bony fractures [14–16] and has been used to track and monitor changes in the health status of surgical care patients [17]. Applied to DPS-measured PrEP adherence, digital phenotyping may contribute detailed insights to contextualize ingestion events and potentially anticipate situations in which nonad- herence may occur. While the combination of digital phenotyping and DPS-based adherence data has the potential to deliver real-time tailored adherence interventions in the future, this has not yet been tested empirically. Despite demonstrated acceptance of DPS among substance using MSM, the addition of data from ancillary devices like smartphones or wearable devices may be perceived as a further encroachment of privacy, especially among a population that experiences heightened stigma surrounding sexual orientation, substance use and HIV risk. This investigation sought to examine the association between willingness to share digital phe- notypic data and interact with ancillary devices and systems in the context PrEP adherence DPS-based research, and daily PrEP worry, medical mistrust, and degree of substance use, among HIV-negative MSM with substance use. Methods Study design This was a one-time cross-section online sampling-based survey of a national sample. Please see Fig 1 below for a graphical representation of the study design and methods. Participants The eligibility criteria for the parent study were as follows: (1) 18 years or older; (2) cisgender or transgender MSM; (3) self-reported HIV-negative; (4) currently on PrEP; (5) self-reported PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 3 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills Fig 1. Study design and methods. * Of 715 ineligible individuals, 343 were ineligible for more than one reason. Reasons for ineligibility included: not �18 years old (n = 2), not cisgender or transgender male (n = 79), does not have sex with cisgender or transgender males (n = 37), not HIV-negative (n = 101), not on PrEP (n = 367), not sexually active in the last three months (n = 67), and CAGE-AID score <2 (n = 562). ** Of 18 participants who did not pass all validity checks, 1 participant failed to pass more than one validity check. Reasons for not passing all validity checks included: age and date of birth did not match (n = 15), home zip code and home state did not match (n = 2), and IP address did not confirm current location in the US (n = 2). https://doi.org/10.1371/journal.pdig.0000457.g001 sexually active in the past 3 months; (6) score of two or higher on the CAGE Questions Adapted to Include Drug Use (CAGE-AID) [18]; and (7) current user of the Grindr social net- working app. Procedures Participants were recruited through an advertisement partnership with Grindr (West Holly- wood, CA), a popular social network site that caters to gay, bisexual and transgender people. The study advertisement was delivered to 1,000,000 active US Grindr users via an inbox message, which was active for 24 hours in January 2022. The study advertisement was paid for by the study team via the Fordham University Research Ethics Training Institute (NIH R25DA031608). The study team was composed of cisgender heterosexual and sexual minority people trained in research surrounding technologies and HIV treatment/prevention. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 4 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills No members of the study team have commercial interests in digital pill systems or the digital phenotyping techniques described in this manuscript. Grindr was not involved in the design or conduct of the study or data analysis. Individuals who clicked on the study advertisement were linked to an eligibility screener via a computer-assisted self-interviewing (CASI) secure platform (Qualtrics, Provo UT), fol- lowed by a CAPTCHA validation question. Eligible individuals were presented with a fact sheet containing detailed study information, including a description of the study, study contact information, and an overview of study objectives and potential risks. After independently reviewing the fact sheet, participants documented their informed consent by selecting “I agree to participate.” Participants were provided with the option to download and save the fact sheet for future reference. Participants completed a cross-sectional quantitative assessment via a computer-assisted self-interviewing (CASI) secure platform (Qualtrics, Provo UT), which lasted approximately 30–60 minutes. The study team conducted several manual validity checks following survey completion (i.e., a confirmed match between age and date of birth, the validity of US home zip code, match between zip code and home state, and IP address indicated location within the US) to confirm eligibility for remuneration. Anonymized survey responses were stored on the secure Qualtrics platform after survey completion, and all validated anonymized datasets were exported, password-protected, and stored on a HIPAA-compliant Dropbox Business folder accessible only to study staff. All study staff were trained in data management and quality assurance protocols prior to the onset of the study. Of the parent sample (N = 157), only those who reported at least slight willingness to partic- ipate in DPS-based research and self-identified as cisgender males were included in the sub- sample (N = 131). Individuals who self-identified as transgender were excluded from the subsample (n = 6) due to the small sample size and potential for significantly different experi- ences with the medical system and HIV risk factors, as compared to cisgender MSM. The Fen- way Community Health Institutional Review Board (IRB) reviewed and approved all study procedures. Measures The quantitative assessment included an eligibility pre-screener, an overview of the DPS tech- nology–including images of the DPS components, and a video (recorded by PRC) explaining system functionality–followed by survey questions as detailed below. Sociodemographics Participants reported age, race, ethnicity, gender, sexual orientation, education, annual income, and geographic region (i.e., US census region). Participants also indicated their PrEP adherence over an average week in the past three months (i.e., PrEP adherence), and how long they have been taking PrEP (i.e., PrEP duration). Willingness to participate in DPS-based research After viewing a series of informative images and a video explaining how the DPS works, partic- ipants were asked to rate their willingness to participate in future DPS-based research studies on a 5-point Likert scale (1 = not at all, 2 = slightly, 3 = moderately, 4 = very, 5 = extremely willing). Those who indicated at least slight willingness to participate in future DPS-based research were included in the final subsample. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 5 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills Willingness to contribute digital phenotyping data and interact with ancillary systems in the context of DPS-based research We assessed participants’ willingness to contribute smartphone data (e.g., geographic location, battery level, text messaging, frequency of use of the app connected to the DPS); self-collected blood work (finger prick) in the context of DPS-based research; share biometric information (e.g., physiologic vital signs) during PrEP use via a wearable device paired to the DPS; and will- ingness to receive text messages asking about substance use, sexual activity, general daily activ- ities, and location. Participants rated their willingness for each of the above items on a five- point Likert scale (1 = not at all, 2 = slightly, 3 = moderately, 4 = very, 5 = extremely willing), which was then collapsed into two categories for analysis (1 and 2 = “slightly or not willing”; and 3, 4 and 5 = “willing or extremely willing”). Medical mistrust Degree of mistrust in research and medical settings was measured via an adapted, 6-item ver- sion of the Group-Based Medical Mistrust Scale (GBMMS), which has been demonstrated as a reliable and valid measure for assessing research mistrust among American adults [19]. The GBMMS is comprised of six questions scored using a 5-point Likert scale (1 = strongly dis- agree, 5 = strongly agree). Items are summed to calculate a cumulative medical mistrust score, with higher scores indicating greater mistrust (range: 6–25) [19]. Substance use As part of the eligibility screener, participants completed the CAGE Questions Adapted to Include Drug Use (CAGE-AID), which has been previously demonstrated as reliable and valid measure [18,20]. The CAGE-AID comprises four yes/no questions about substance use (i.e., perceived need to cut down on substance use, annoyance when substance use is criticized by others, feelings of guilt about substance use, and use of substances first thing in the morning). “Yes” responses are scored as 1 and “No” responses are scored as 0. Items are summed for a total score (possible range: 0–4), with higher total scores indicating greater potentially prob- lematic substance use, and scores �2 considered clinically significant. Participants were cate- gorized into three groups based on CAGE-AID score (i.e., 2, 3, and 4). Daily PrEP worry Participants reported their degree of daily worry about PrEP adherence on a single question via a 5-point Likert scale (1 = not at all, 2 = slightly, 3 = moderately, 4 = very, 5 = extremely willing). Responses were collapsed into two categories for analysis (1 and 2 = “slightly or not worried”; and 3, 4 and 5 = “worried or extremely worried”). Data analysis Descriptive statistics were generated for sociodemographic variables. A multivariable logistic regression model was used to measure the association between each of the outcome variables (i.e., willingness to share smartphone data; self-collected blood work in the context of DPS- based research; use a wearable device paired to the DPS to collect biometric information dur- ing PrEP use; and to receive text messages asking about substance use, sexual activity, general daily activities, and locations) and independent variables of interest (i.e., daily PrEP worry, medical mistrust (GBMMS), and substance use (CAGE-AID)). A multivariate logistic regres- sion model was used due to medical mistrust confounding the association between the out- come variables and the predicator variable of daily PrEP worry. We also assessed for a PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 6 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills potential confounding effect by the following covariates: age, education level, race/ethnicity, PrEP adherence, and PrEP duration. After assessing for a potential confounding effect on the association between the outcomes of interest and independent variables of interest by using Chi-square tests, we determined that the enlisted covariates above did not confound the associ- ation between the predictors and outcome variables (p-values > 0.05). Covariates were evenly distributed among the predictor variables. Therefore, the covariates listed above were not included in the model. All analyses were completed using SAS (version 9.4) [21]. The SAS code PROC LOGISTIC was used to conduct the multivariable logistic regression model. Results Sociodemographics and willingness to participate in future DPS research Details on the parent sample (N = 157) are reported elsewhere [11]. In this subsample analysis, only individuals who reported at least a slight willingness (1 = not at all, 2 = slightly, 3 = moder- ately, 4 = very, 5 = extremely willing) to participate in DPS-related research and self-identified as cisgender males were included (N = 131). There were no significant differences in sociode- mographic characteristics between participants who indicated they would not be willing to participate in DPS-related research (N = 20) and those who did. The mean age of the subsample (N = 131) was 36.6 (SD: 12). The majority were White (n = 100, 76.3%), non-Hispanic (n = 102, 77.9%), completed at least some college (n = 121, 92.4%), and reported an annual income of more than $60,000 (n = 77, 58.8%). More than half the sample reported being on PrEP for more than a year (n = 75, 57.3%), with the vast majority of participants self-reporting � 4 doses per week during a typical week (n = 124, 94.7%) (Table 1). Willingness to share biometric information via a wearable device paired to the DPS There was a statistically significant association between the willingness to use a wearable device to collect biometric information and both daily PrEP worry (p = 0.046) and medical mistrust (p = 0.005). Participants who reported being worried about daily PrEP adherence had 3.7 times the odds (95% CI: 1.026, 13.425) of being willing to share biometric data via a wearable device paired to the DPS, compared to those who were less worried, after adjusting for other predictors. Participants with higher medical mistrust were less likely to be willing to share bio- metric data. For every one unit increase in medical mistrust score, the odds of not being will- ing to share biometric data via a wearable device increased by 0.8 (95% CI: 0.739, 0.946), after adjusting for other predictors. No significant association was found between the degree of sub- stance use and willingness to share biometric data (p = 0.387; Table 2). Willingness to share smartphone data There was a statistically significant association between willingness to share smartphone data and both daily PrEP worry (p = 0.006) and medical mistrust (p <0.0001). Participants who reported worrying about daily PrEP adherence were more likely to be willing to share smart- phone data, compared to those who were less worried, with an odds ratio of 2.811 (95% CI: 1.163, 6.792), after adjusting for other predictors. In addition, participants with higher medical mistrust were less likely to be willing to share smartphone data. For every one unit increase in medical mistrust, the odds of being willing to share smartphone data decreased by 20% (OR: 0.818; 95% CI: 0.745, 0.898), after adjusting for other predictors. No statistically significant PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 7 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills Table 1. Sociodemographic characteristics (N = 131). Variable Age (years) Mean (SD) Race White African American Asian American Indian or Alaska Native More than one race Other Ethnicity Not Hispanic or Latinx Hispanic or Latinx Gender Identity Cisgender male Education High school degree or some high school College degree or some college Graduate/professional degree or some graduate work Annual Income Less than $24,000 $24,000 to $29,999 $30,000 to $59,999 $60,000 or more Geographic Region (in US) Midwest Northeast South West PrEP Adherence <4 doses per week � 4 doses per week PrEP Duration Less than 1 month 1 to 6 months 6 months to 1 year More than 1 year n (%) 36.6 (12) 100 (76.3) 5 (3.8) 5 (3.8) 2 (1.5) 15 (11.5) 4 (3.1) 102 (77.9) 29 (22.1) 131 (100.0) 10 (7.6) 78 (59.5) 43 (32.8) 22 (16.8) 12 (9.2) 20 (17.3) 77 (58.8) 20 (15.3) 40 (30.5) 47 (35.9) 24 (18.3) 7 (5.3) 124 (94.7) 7 (5.3) 31 (23.7) 18 (13.7) 75 (57.3%) https://doi.org/10.1371/journal.pdig.0000457.t001 association was found between the degree of substance use and willingness to share smart- phone data (p = 0.603; Table 2). Willingness to participate in self-collected blood work There was a statistically significant association between willingness to self-collect blood work and medical mistrust (p = 0.001). Participants with higher medical mistrust were less likely to be willing to self-collect blood work, with an odds ratio of 0.870 (95% CI: 0.800, 0.947), after adjusting for other predictors. No significant association was found between the degree of sub- stance use or daily PrEP worry and willingness to self-collect blood work (p = 0.483 and p = 0.225, respectively; Table 2). PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 8 / 14 PLOS DIGITAL HEALTHTable 2. Willingness to contribute digital phenotyping data and interact with ancillary systems in the context of DPS-based research (N = 131). Acceptance of digital phenotyping and biological data sharing in context of digital pills Outcome Variable Willingness to. . . Share biometric data Share smartphone data Self-collect blood work Receive text messages asking about substance use and sexual activity Receive text messages asking about general daily activities and location Statistically significant at the 0.05 level https://doi.org/10.1371/journal.pdig.0000457.t002 Exposure Variable P- value Beta Coefficient Estimates / Daily PrEP Worry Degree of Substance Use Medical Mistrust Daily PrEP Worry Degree of Substance Use 0.046 0.387 0.005* 0.006* 0.603 SE 1.311 / 0.656 0.710 / 0.822 -0.179 / 0.063 1.034 / 0.450 -0.243 / 0.468 Medical Mistrust <0.0001* -0.201 / 0.048 Daily PrEP Worry Degree of Substance Use 0.225 0.483 0.517 / 0.426 -0.317 / 0.451 Medical Mistrust 0.001* -0.139 / 0.043 Daily PrEP Worry Degree of Substance Use Medical Mistrust Daily PrEP Worry Degree of Substance Use 0.092 0.675 0.854 / 0.507 0.228 / 0.543 <0.0001* -0.273 / 0.058 0.003* 1.307 / 0.441 0.828 -0.050 / 0.230 Measure of Association (OR) and 95% CI 3.711 (1.026, 13.425) 0.784 (0.313, 1.962) 0.836 (0.739, 0.946) 2.811 (1.163, 6.792) 0.784 (0.313, 1.962) 0.818 (0.745, 0.898) 1.677 (0.727, 3.865) 0.729 (0.301, 1.765) 0.870 (0.800, 0.947) 2.349 (0.870, 6.343) 1.256 (0.433, 3.644) 0.757 (0.673, 0.851) 3.693 (1.557, 8.763) 0.905 (0.368, 2.229) Medical Mistrust 0.0002* -0.170 / 0.046 0.844 (0.770, 0.920) Willingness to receive text messages asking about substance use, sexual activity, general daily activities, and location Text messages asking about substance use and sexual activity. No statistically significant association was found between willingness to receive text messages asking about substance use and sexual activity, and daily PrEP worry (p = 0.092) or degree of substance use (p = 0.675). There was a statistically significant association between willingness to receive text messages asking about substance use and sexual activity, and medical mistrust (p <0.0001). For every one unit increase in medical mistrust score, the odds of being willing to receive text messages about substance use and sexual activity decreased by 0.76 (95% CI: 0.673, 0.851), after adjust- ing for other predictors (Table 2). Text messages asking about general daily activities and location. There was a statisti- cally significant association between willingness to receive text messages asking about daily activities and location, and both daily PrEP worry (p = 0.003) and medical mistrust (p = 0.0002). Participants who reported being worried or very worried about daily PrEP adherence had 3.7 times the odds of being willing to receive text messages asking about daily activities and location, compared to those who were not worried, after adjusting for other predictors (95% CI: 1.557, 8.763). Additionally, for every one unit increase in medical mistrust score, the odds of being willing to receive text messages asking about daily activi- ties and location decreased by 17% (OR: 0.844; 95% CI: 0.770, 0.920), after adjusting for other predictors. No significant association was found between degree of substance use and willingness to receive text messages asking about daily activities and location (p = 0.828; Table 2). PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 9 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills Discussion Digital pills are evolving as a system to directly measure adherence to medications, including PrEP. Using DPS technology to better understand the contextual basis of PrEP adherence and nonadherence may help provide support to individuals who struggle with PrEP adherence at key junctures of risk. The emerging use of wearable devices and collection of smartphone- based digital phenotyping data may provide insight into key events where nonadherence is likely and the delivery of proactive, personalized adherence support may mitigate nonadher- ence [22–25]. Contrary to perceptions that individuals with substance use may be less accept- ing of the collection of personal data via mobile devices and other systems, the degree of substance use in our subsample was not associated the willingness of MSM on PrEP to interact with ancillary devices or text message-based queries to contextualize DPS-detected adherence data. We also found that participants who worried about their daily PrEP adherence and were more trusting of the medical system reported more willingness to contribute S1 Data– including biometric or digital phenotypic data from wearable devices, as well as self-collected blood samples–and to engage with text messages that query contextual behaviors linked to their PrEP adherence as measured by the DPS. These findings importantly frame the potential expansion of DPS technology through the integration of other wearable devices, self-collected biological samples, and the development of context-aware behavioral interventions. They also suggest opportunities to engage with community partners to address potential concerns related to medical mistrust around DPS technology and other, related systems for PrEP adher- ence measurement. Overall, this subsample was also willing to contribute additional data to contextualize their adherence, despite their degree of self-reported substance use. This suggests that the addition of strategies like digital phenotyping or EMA surveys can add important context to observed PrEP adherence in MSM, and may present novel opportunities to teach and reinforce adher- ence skills in the setting of contextualized nonadherence behavior. Given the willingness of MSM to contribute smartphone-based data to further contextualize PrEP adherence behaviors, future work should focus on ethical, legal and social implications of smartphone data. Some potential strategies that address existing controversies in the ethics of digital phenotyping include responsible data collection strategies that only collect data that may be needed to understand contextual cues surrounding PrEP adherence, and design of security protocols that deidentify data, produce fuzziness in location data, and adequately explain the types of data collected to study participants. For example, clear explanation of the implications of loca- tion data and its relationship with PrEP adherence, substance use, and sexual activity should be disclosed to research participants, as well as, in the future, individuals who may leverage digital phenotyping in the context of their clinical care. Additionally, as PrEP initiation efforts continue to leverage telemedicine approaches to increase accessibility, the DPS may be an acceptable adherence measurement strategy that can be integrated into existing systems that already support self-collected biological samples for the assessment of sexually transmitted infections and regular HIV testing for PrEP users [26]. MSM in our subsample who reported more daily worry around PrEP adherence were sig- nificantly more likely to be willing to interact with text messages regarding their general daily activities and location that could be used to inform future adherence interventions. Partici- pants’ increased willingness to share additional contextual data via ancillary devices suggests that many MSM may also be accepting of more personalized adherence interventions grounded in digital phenotypic measurements. Additionally, in research that integrates DPS- based PrEP adherence data to ground analysis of digital phenotyping data, MSM with sub- stance use may be willing to respond to ecological momentary assessments that ask about PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 10 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills sensitive and potentially stigmatizing sexual health, location and substance use. This suggests that future work may integrate strategies like text message-based queries and self-collected bio- logical specimens into the DPS ecosystem to better understand PrEP adherence. As technolo- gies like DPS and digital phenotyping are adopted, this work should remind researchers and policymakers that individuals with stigmatized conditions and substance use also can benefit and uptake these systems. We also found that individuals who were more trusting of the medical system reported being significantly more willing to contribute additional digital phenotypic or contextual data in as part of DPS-based PrEP adherence research. A major challenge in light of these findings lies in decreasing the barriers to building participant/researcher, and ultimately patient/clini- cian, relationships that may improve overall trust in the medical system over time. Researchers may consider engaging with community partners or advocacy groups prior to the initiation of future studies in order to develop strategies for introducing the DPS to potential participants, and to adequately address concerns associated with trust in DPS technology and collection of phenotypic data from other systems. Such conversations should carefully consider the inter- section of race, ethnicity, and existing levels of medical mistrust on users’ perceptions of the DPS and ancillary systems [27]. Our previous work suggests that the use of these monitoring technologies may, in fact, increase one’s sense of personal accountability for their PrEP adher- ence, as well as improve relationships with medical providers by providing objective data around PrEP ingestions, and the context in which they occur, to long-term primary care and sexual health services [10]. This investigation should motivate the continued development of digital health tools including behavioral interventions responding to medication adherence measured through various strategies including ingestible sensors. Importantly, future research should continue to include individuals with substance use disorder given their risk for HIV and other comorbidi- ties. Research should also consider the role of care teams, including physicians, social workers, pharmacists, nurses and care coordinators in curating and responding to a suite of digital phe- notyping and ingestible sensor data. Implementation challenges will include identifying the members of care teams who should receive context aware data. Existing care models that inte- grate a clinical pharmacist in adherence counseling as well as maintenance of the DPS (overen- capsulation and technology teaching) may serve as a potential pathway to implementation of these systems. Integration of pharmacists into DPS infrastructure may also provide an imple- mentation pathway in clinical settings with patient centered medical homes. Digital phenotyping may also provide insights into how MSM with substance use experi- ence challenges to adherence. These insights may then be translated into other populations (e.g., transgender individuals) and disease states (e.g., heart failure or diabetes medication adherence). In individuals with stigmatized conditions like their sexual orientation or risk fac- tors including substance use, there may be a social desire to bias self-reported data in the con- text of research studies. Future development of digital phenotyping relying on native smartphone sensors may provide a more objective perspective to key behaviors that can be tar- gets for empiric interventions that mitigate risk, reinforce adherence (in the setting of PrEP), and improve linkage to care. Future work may include observational studies to further charac- terize digital phenotypes that may be associated with substance use or its comorbidities, inte- gration of digital phenotyping into adherence technologies like ingestible sensors, and research to develop predictive algorithms that present interventions at opportune moments to facilitation interaction with the user. This study had several limitations. First, our sample size was recruited online from a social network site popular among MSM. As this recruitment strategy was selected to identify indi- viduals with internet access, who were likely to have smartphones, and who engaged with PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 11 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills social media platforms, this approach may have therefore missed key populations with differ- ent perspectives on DPS technology and ancillary systems. Participants who have internet access and most likely smartphones, may have higher levels of digital literacy, which could impact their willingness to contribute phenotypic data and engage with ancillary devices and systems. Second, the majority of participants identified as White MSM. Perceptions of PrEP, trust in the medical system, and experiences with digital health technologies may vary across race and ethnicity; as such, the conclusions drawn in this investigation may not be generaliz- able to non-White MSM [27]. The generalizability of the findings are also limited to the MSM community who engage in substance use. Third, individuals who are more concerned about their PrEP adherence and are more willing to share phenotypic data may be more interested in participating in research that focuses on these concerns, which may introduce sampling bias. Finally, this study involved a one-time quantitative assessment among prospective DPS users; participants did not have direct experience using the DPS but instead viewed a video describ- ing its functionality and architecture prior to completing the quantitative assessment. Percep- tions of and attitudes towards ancillary devices that contribute additional data to DPS-based PrEP adherence measures may be different following lived experience with the DPS. Conclusion The DPS represents a unique opportunity for researchers, clinicians, and patients to better understand both PrEP adherence and nonadherence in the context in which it occurs. MSM with substance use may be accepting of DPS technology, willing to contribute digital pheno- typing data, and willing to interact with ancillary systems in order to contextualize PrEP adher- ence patterns in a research setting. While substance use did not impact the willingness of MSM to accept these systems in this subsample, increased trust in the medical system and increased worry about daily PrEP adherence increased the likelihood that participants reported a willingness to interact with digital phenotyping, wearable devices, self-collected bio- logical sampling, and text message queries to contextualize adherence. Supporting information S1 Data. Dataset with Codebook. (XLSX) Author Contributions Conceptualization: Conall O’Cleirigh, Kenneth H. Mayer, Celia B. Fisher, Peter R. Chai. Data curation: Hannah Albrechta, Georgia R. Goodman, Elizabeth Oginni, Yassir Mohamed, Jasper S. Lee, Peter R. Chai. Formal analysis: Elizabeth Oginni, Yassir Mohamed. Funding acquisition: Peter R. Chai. Investigation: Hannah Albrechta, Georgia R. Goodman, Peter R. Chai. Methodology: Hannah Albrechta, Georgia R. Goodman, Peter R. Chai. Project administration: Georgia R. Goodman, Peter R. Chai. Resources: Celia B. Fisher. Supervision: Peter R. Chai. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000457 February 22, 2024 12 / 14 PLOS DIGITAL HEALTHAcceptance of digital phenotyping and biological data sharing in context of digital pills Writing – original draft: Hannah Albrechta, Georgia R. Goodman, Elizabeth Oginni, Peter R. Chai. Writing – review & editing: Hannah Albrechta, Georgia R. Goodman, Elizabeth Oginni, Yas- sir Mohamed, Krishna Venkatasubramanian, Arlen Dumas, Stephanie Carreiro, Jasper S. Lee, Tiffany R. Glynn, Conall O’Cleirigh, Kenneth H. Mayer, Celia B. Fisher, Peter R. Chai. References 1. Gilmore HJ, Liu A, Koester KA, Amico KR, McMahan V, Goicochea P, et al. Participant experiences and facilitators and barriers to pill use among men who have sex with men in the iPrEx pre-exposure prophylaxis trial in San Francisco. AIDS Patient Care STDS. 2013 Oct; 27(10):560–6. https://doi.org/ 10.1089/apc.2013.0116 PMID: 24093809 2. Centers for Disease Control and Prevention. CDC Statement on FDA Approval of Drug for HIV Preven- tion [Internet]. 2012 July 16 [cited 2024 Jan 12]. Available from: https://www.cdc.gov/nchhstp/ newsroom/2012/fda-approvesdrugstatement.html 3. Burnham KE, Cruess DG, Kalichman MO, Grebler T, Cherry C, Kalichman SC. 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10.1371_journal.pclm.0000285	RESEARCH ARTICLE Stay or go? Geographic variation in risks due to climate change for fishing fleets that adapt in-place or adapt on-the-move Jameal F. SamhouriID Kate RichersonID 7, Lyall BellquistID H. BeaudreauID Abigail Harley11, Chris J. HarveyID Amanda Phillips1,5, Leif K. RasmusonID L. SeldenID 14 1, Blake E. Feist1, Michael Jacox2, Owen R. LiuID 3,4, 5, Erin Steiner5, John Wallace5, Kelly Andrews1, Lewis Barnett6, Anne 8,9, Mer Pozo BuilID 1, Isaac C. Kaplan1, Karma NormanID 2, Melissa A. HaltuchID 1, 5,10, 12,13, Eric J. Ward1, Curt WhitmireID 5, Rebecca 1 Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 2 Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Monterey, California, United States of America, 3 Under Contract to the Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Ocean Associates, Inc., Seattle, Washington, United States of America, 4 NRC Research Associateship Program, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 5 Fishery Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 6 Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 7 School of Marine and Environmental Affairs, University of Washington, Seattle, Washington, United States of America, 8 The Nature Conservancy, Sacramento, California, United States of America, 9 Scripps Institution of Oceanography, La Jolla, California, United States of America, 10 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 11 Sustainable Fisheries Division, West Coast Region, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America, 12 Marine Fisheries Research Project, Marine Resources Program, Oregon Department of Fish and Wildlife, Newport, Oregon, United States of America, 13 Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, United States of America, 14 Department of Biological Sciences, Wellesley College, Wellesley, Massachusetts, United States of America jameal.samhouri@noaa.gov Abstract From fishers to farmers, people across the planet who rely directly upon natural resources for their livelihoods and well-being face extensive impacts from climate change. However, local- and regional-scale impacts and associated risks can vary geographically, and the implications for development of adaptation pathways that will be most effective for specific communities are underexplored. To improve this understanding at relevant local scales, we developed a coupled social-ecological approach to assess the risk posed to fishing fleets by climate change, applying it to a case study of groundfish fleets that are a cornerstone of fish- eries along the U.S. West Coast. Based on the mean of three high-resolution climate projec- tions, we found that more poleward fleets may experience twice as much local temperature change as equatorward fleets, and 3–4 times as much depth displacement of historical a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Samhouri JF, Feist BE, Jacox M, Liu OR, Richerson K, Steiner E, et al. (2024) Stay or go? Geographic variation in risks due to climate change for fishing fleets that adapt in-place or adapt on- the-move. PLOS Clim 3(2): e0000285. https://doi. org/10.1371/journal.pclm.0000285 Editor: Athanassios C. Tsikliras, Aristotle University of Thessaloniki, GREECE Received: August 11, 2023 Accepted: December 28, 2023 Published: February 9, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pclm.0000285 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: R code and aggregated data used in climate risk calculations are available at https://github.com/groundfish- climatechange/fish-footprints. Confidential vessel- PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 1 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move level logbook, landings, and registration data may be acquired by direct request from the California, Oregon, and Washington Departments of Fish and Wildlife, subject to a non-disclosure agreement. Funding: JFS received funding for this work from the the David and Lucille Packard Foundation 2019-69817. The work of ORL and RLS was supported by that funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. environmental conditions in their fishing grounds. Not only are they more highly exposed to climate change, but some poleward fleets are >10x more economically-dependent on groundfish. While we show clear regional differences in fleets’ flexibility to shift to new fisher- ies via fisheries diversification (‘adapt in-place’) or shift their fishing grounds in response to future change through greater mobility (‘adapt on-the-move’), these differences do not completely mitigate the greater exposure and economic dependence of more poleward fleets. Therefore, on the U.S. West Coast more poleward fishing fleets may be at greater overall risk due to climate change, in contrast to expectations for greater equatorward risk in other parts of the world. Through integration of climatic, ecological, and socio-economic data, this case study illustrates the potential for widespread implementation of risk assess- ment at scales relevant to fishers, communities, and decision makers. Such applications will help identify the greatest opportunities to mitigate climate risks through pathways that enhance flexibility and other dimensions of adaptive capacity. Introduction Climate change is shaping the availability of nature’s benefits to people and will continue to do so for generations [1,2]. While global-scale projections provide coarse, qualitative expectations for how climate impacts will manifest in different regions and sectors, there is much more lim- ited understanding of risks due to climate change at local scales. Yet regionally-specific infor- mation about the effects of biophysical changes on natural resource-dependent industries and communities is critical for adaptation planning and strategic responses from resource manage- ment agencies [3–5]. For communities that rely upon harvest of natural resources for their lives and livelihoods, the scale and intensity of expected environmental change in customary use areas for agriculture, fisheries, forestry, and other industries is especially important [6,7]. A clear challenge lies in determining how adaptation within or outside of these areas can enhance climate resilience, using tractable, resonant, and scalable approaches. Environmental change is spatially heterogeneous and will intersect with dynamic social fac- tors to determine risk due to climate change [3,8,9]. For instance, it is already apparent that rates of warming at the poles exceed those toward the equator [10], patterns of historical vari- ability in local physical forcing will interact with anthropogenic climate change to determine future conditions [11–14], and short-term extreme events fueled by climate change, as well as long-term gradual change, can create localized hotspots of impact [15,16]. In the ocean, warm- ing waters can cause shifts in species’ ranges or alterations in target species productivity that lead to changes in local abundance that vary over space [17–19]. This heterogeneity will fuel divergent ecological responses of species to create spatial variability in the exposure of human communities to these impacts [20]. Social vulnerability of human communities, based on their sensitivity and adaptive capacity to respond to biophysical changes, also varies geographically. For fisheries and fishing commu- nities, the potential to adapt to change–whether driven by climate, markets, regulations, or other factors–differs enormously based on a variety of historical contingencies as well as con- temporary circumstances [21–26]. For example, the diversity of species a fishing community has access to or other potential sources of non-fishing revenue can act as buffers during times of ecological or financial volatility [27]. The ability to cope, adapt, and transform fishing prac- tices in response to climate change [28] is influenced strongly by variation across domains of PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 2 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move adaptive capacity, which include assets, flexibility, organization, learning, and agency [20,29– 31]. A recurrent challenge lies in determining how to measure and manage these different domains of adaptive capacity in tangible ways. Coupled social-ecological analyses of a fishing community’s risk due to climate change integrate the magnitude of environmental change it will experience, the sensitivity to such change, and adaptive capacity. The flexibility domain of adaptive capacity (e.g., occupational multiplicity, technological diversity; [30]) is especially pertinent to fishing communities. The potential for spatial redistri- bution of target species due to changing ocean conditions encourages particular focus on two of the more tangible, and non mutually-exclusive, attributes of flexibility: fisher or fleet mobil- ity and species diversification. More mobile fishers and fleets can ‘adapt on-the-move’, responding to changes in the availability of target species by changing where they fish [32], while more diversified fishers may ‘adapt in-place’, continuing to operate in historical fishing grounds while switching species [33]. Scientific advice that captures variability in mobility and diversification provides effective support for decision makers managing fisheries in the face of climate change [29]. In much of Europe and North America, groundfish fishing fleets that use bottom trawl gear to target demersal species have formed the backbone of fishing communities for decades to centuries. Many of the most well-developed future projections of the impacts of climate change for fisheries are rooted in predictions of declining abundance of groundfish species (e.g., [17,34–36]), which tend to be characterized by high-quality, fishery-independent data, strongly influenced by environmental forcing, and prone to overfishing due to their life-history charac- teristics. Surprisingly, however, there are relatively few studies that explicitly connect climate change to coupled social-ecological risk for groundfish fishing fleets. On the U.S. West Coast, this gap in understanding is a crucial one, as the groundfish fishery in this region is a corner- stone of the commercial fishing industry and economies of entire fishing communities [37– 39]. Groundfish are caught by bottom trawl off of the coasts of California, Oregon, and Wash- ington, including catch by some vessels participating in state-managed bottom trawl fisheries that capture federally-managed groundfish incidentally. Most catch is managed under the Pacific Coast Groundfish Fishery Management Plan by the Pacific Fishery Management Coun- cil (PFMC). This federally-managed fishery consists of nearly 100 species that include rock- fishes (Sebastes spp.), roundfishes (e.g., sablefish), and flatfishes (e.g., Dover sole). The bottom trawl groundfish fishery once generated >$100M USD (2021 USD) and engaged >400 vessels across all three US West Coast states (Fig 1A and 1B). As of 2019, these values have fallen by a factor of five or more, with annual revenues at just over $20M USD and fewer than 75 vessels remaining in the fleet despite consistency in the number of port groups buying bottom trawl groundfish over the same time period (Fig 1C and 1D). While several West Coast groundfish stocks were rebuilt during the last two decades [40] and total allowable catches have been increasing [41], utilization of many species remains low [42], and much of the revenue generated from this fishery is now concentrated within fewer ports, primarily in Oregon (Fig 1E). These patterns coincide with declines in the number of fish buyers, reduced processing capacity, and increased spatial consolidation of processing, which in turn may impact the magnitude and distribution of fishing effort [37,43,44]. Together, these trends suggest that port-level bottom trawl groundfish fishing fleets (hereafter, groundfish fleets) are a useful set of fleets on which to focus because each is subject to the same regulations and market forces, operates within a similar geographic area, experiences environ- mentally-driven change in species’ availability, and therefore shares common opportunities and challenges. The confluence of long-term declines in revenue and participation along with increased geographic consolidation (Fig 1E) suggests that the risk due to climate change for U.S. West PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 3 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 1. Historical changes in the groundfish fishery. (a) Ex-vessel revenue coastwide, (b) mean (±SD) annual ex-vessel revenue by state for 2011–2019, (c) number of port groups, (d) number of vessels, and (e) revenue consolidation (estimated with the absolute Theil Index, calculated for each port group; [45]). A port group represents a collection of individual ports; these groups were developed by the Pacific Fisheries Management Council (S1 Table). All revenue data were adjusted for inflation to 2021 USD. See S1 Text for methodological details. https://doi.org/10.1371/journal.pclm.0000285.g001 Coast groundfish fleets may be high and heterogeneous, yet neither these risks nor regional variability in the potential for these fleets to mitigate risk has been rigorously explored. To close this knowledge gap, we assessed the coupled social-ecological risk of groundfish fleets along the U.S. West Coast to climate change. We focused this assessment on projected envi- ronmental change within present-day fishing grounds, in combination with quantitative anal- yses surrounding the economic dependence of the fleets on groundfish and the fleets’ relative mobility and capacity to diversify into other fisheries, based on past fishing behaviors. We hypothesize that regional variation in the magnitude of future ocean change will create geo- graphically variable exposure. In addition, we predict that consolidation of the groundfish fleet over time has concentrated economic dependence on bottom trawl-caught groundfish in fewer places, altering sensitivity to future changes in groundfish fisheries. Finally, we expect that fleet composition and fisheries portfolios vary from place to place, causing inconsistency in the capacity for fleets to cope with risk posed by climate change across the coast. Methods Overview We approached the question of what climate change portends for groundfish fleets on the U.S. West Coast using a coupled social-ecological approach. We define coupled social-ecological risk due to climate change as the combination of exposure to projected environmental or PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 4 / 28 204060801001990200020102020Revenue (millions)ACaliforniaOregonWashington05101520Revenue(mean ± 1SD, millions)2011−2019B141618201990200020102020Number of Port GroupsC1002003004001990200020102020Number of VesselsD0.40.60.81.01990200020102020Absolute Theil IndexEPLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 2. Conceptual framework to consider coupled social-ecological risk due to climate change. (a) Assuming fleets change target species while remaining in current fishing grounds (adapt in-place); (b) assuming fleets shift fishing grounds while targeting current species (adapt on-the-move). We define coupled social-ecological risk due to climate change as the combination of exposure to projected environmental or ecological change and the sensitivity and adaptive capacity (i.e., social vulnerability) of the affected community, or more formally, Risk = (Exposure2+Vulnerability2)1/2 (Eq 7) where Vulnerability = (Sensitivity2+(Lack of Adaptive Capacity) 2)1/2 (Eq 6). This approach is adapted from frameworks in [3,20]. In both panels, redder colors indicate higher exposure due to warming. https://doi.org/10.1371/journal.pclm.0000285.g002 ecological change and the social vulnerability of the affected community. Social vulnerability is defined in terms of sensitivity and adaptive capacity. We assessed fleet-specific risk in two ways (Fig 2). First, we evaluated risk if fleets change target species while continuing to fish in current fishing grounds (the adapt in-place assessment). Second, we assessed risk if fleets shift fishing grounds while targeting current species (the adapt on-the-move assessment). This eval- uation builds on the general framework of the Intergovernmental Panel on Climate Change (IPCC) [3], and more recent reviews and developments introduced by [9,26,46–48]. We define each groundfish fleet as the collection of vessels landing groundfish caught using bottom trawl gear and delivered to buyers in the same port group (S1 Table). We note that this definition of a groundfish fleet is inclusive of vessels with federal permits for the fishery and vessels partici- pating in state-managed bottom trawl fisheries that capture federally-managed groundfish incidentally. For the adapt in-place assessment, we estimated exposure as the amount of thermal change expected between the periods 1990–2020 and 2065–2095 within the present-day fishing grounds used by each fleet. We estimated the flexibility dimension of adaptive capacity based on an index of diversification, defined as realized opportunities to participate in multiple fish- eries in each port group from 2011–2019, and encompassing a recent period of consistent management regulations [37]. For the adapt on-the-move assessment, we estimated exposure as the projected extent of horizontal (change in latitude and/or longitude) and vertical (change in depth) displacement PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 5 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move of near-bottom isotherms representative of present-day fishing grounds for each fleet between the periods 1990–2020 and 2065–2095 (S1 and S2 Figs; [49]). We estimated the flexibility dimension of adaptive capacity based on an index of mobility, defined based on documented distances of fishing grounds from landing ports during 2011–2019. For both the adapt in-place and adapt on-the-move assessments, we defined sensitivity as the economic dependence of each groundfish fleet on bottom trawl groundfish relative to total commercial fishing revenue, including pink shrimp, Dungeness crab, and Pacific whiting, gen- erated by those fleets within the U.S. Exclusive Economic Zone and state waters during the period of 2011–2019. This approach assumes that more economically-dependent fleets are more susceptible to harm if climate change negatively affects bottom trawl groundfish. To esti- mate overall risk due to climate change for groundfish fleets, we calculated a social vulnerabil- ity index based on the sensitivity and adaptive capacity estimates, and combined it with estimates of exposure. We describe these calculations in detail below. Defining fishing footprints The foundation of this risk assessment is the location of fishing grounds for each groundfish fleet. We defined the spatial footprints of each of 14 fleets based on fishery-dependent catch data available from logbooks from 2011–2019 in Washington, Oregon, and California. We retrieved these data from the Pacific Fisheries Information Network (PacFIN; http://pacfin. psmfc.org). To connect these data with specific fishing communities, we associated footprints with port groups of landing for each bottom trawl tow in the database (following [50,51]; S1 Table). There are nearly 300 ports where groundfish are landed and the distinction between ports can often be as small as two different sides of a small bay. The port groupings were devel- oped by the PFMC for biennial groundfish harvest specifications. In addition, aggregating individual ports into port groups is necessary to provide a feasible set of geographic areas for a coastwide climate risk analysis. Finally, analysis at the individual port-level would violate con- fidentiality requirements, because there are often fewer than three buyers in any one port. We pre-processed the logbook data to remove problematic hauls prior to development of footprints (https://zenodo.org/record/7916821). Specifically, we included hauls lasting at least 0.2 hours but not more than 24 hours, and removed hauls with coordinates outside of the U.S. EEZ, and those on land or outside of a customary catch depth (>2,000 m) or area (defined based on locations of bottom trawl tows during the period 2010–2015). We evaluated the depth reported for each haul using the Imap R package (https://github.com/John-R-Wallace- NOAA/Imap), which overlays hauls with the National Geophysical Data Center (NGDC) bathymetry (at a resolution of 3 arc-seconds, or ~90m at the Equator) [52–54]. We retained hauls reporting a depth within 250 m of the NGDC depth, assuming accurate reported haul locations. However, we assumed that if reported depths were inaccurate by >250 m, the haul locations were likely to be similarly erroneous. Finally, we assumed that failure to report depth was not indicative of positional error, but a simple misstep on the skipper’s part, so we acquired the missing depth from NGDC based on the geocoordinates of the set (start) point for each haul. Combined, these filters reduced the size of the logbook dataset by ~4% across all years (S2 Table). For each fleet, we extracted all tows from the period 2011–2019 from the logbook data, excluding fleets with fewer than 3 vessels reporting logbook data during that time period. We used the summed weight of landed catch of all groundfish species actively managed or listed as ecosystem component species in the groundfish fishery management plan used by the PFMC (Tables 3–1, 3–2 in https://www.pcouncil.org/documents/2016/08/pacific-coast-groundfish- fishery-management-plan.pdf/), along with the geocoordinates of trawl set points, to create a PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 6 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move kernel density surface [33]. We calculated kernel density with a 10 km bandwidth, using the density.ppp function in the sp package in R [55]. The kernel density allowed us to define the footprint of each fleet, using a percent volume contour that represents the boundary of the area that contains 75% of the volume of the kernel density distribution. The percent volume contour was determined using the getvolumeUD function in the adehabitat package in R [56]. The position of each fleet’s footprint on the coast was relatively unchanged by the choice of the 50, 75, 90, or 95 percent volume contour (S4 Fig), and would not influence the rank order exposure of fleets, or the relationships between exposure and latitude, described below given the large-scale patterns of projected bottom temperature change, horizontal displacement, and vertical displacement (S1 and S2 Figs). Exposure Poor ocean bottom conditions are the most relevant hazard for the life stages of groundfish species caught with bottom trawl gear, and temperature is an established predictor of ground- fish species’ range shifts [57]. We obtained projected bottom temperatures–the basis for a regional assessment of hazard–from an ensemble of regional downscaled ocean projections [11] produced using the Regional Ocean Modeling System (ROMS; S1 Fig). The ROMS domain spans the California Current ecosystem from 30˚-48˚N latitude and from the coast to 134˚W longitude at 0.1˚ degree (~7–11 km) horizontal resolution with 42 terrain-following vertical layers. The regional projections were forced with output from three Earth System Models (ESMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5): Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M, Hadley Center Had- GEM2-ES (HADL), and Institut Pierre Simon Laplace (IPSL) CM5A-MR. While we only used the high-emissions Representative Concentration Pathway (RCP) 8.5 scenario, which is the highest-emission scenario and one which appears to be increasingly unlikely [58], the ESMs were chosen to bracket the spread of potential future change. Specifically, GFDL and HADL represent low and high ends of the spectrum, respectively, for the projected magnitude of warming in the CMIP5 ensemble [11,59]. The relatively weak warming in GFDL under RCP8.5 is comparable to the CMIP5 ensemble mean warming under RCP4.5. We focused on 30-year historic (1990–2020) and future (2065–2095) periods to best capture interdecadal vari- ability [59] in ocean conditions characteristic of the California Current ecosystem. We estimated exposure based on analysis of projected bottom temperatures within each fleet’s fishing footprint. For the adapt in-place assessment, we calculated exposure eadaptin−place,p for each fleet operating out of port group p as the thermal state change normalized by historic thermal variability within each fishing footprint, addressing the question: if the footprint of fish- ing effort for a fleet remains stationary, how much will the environment change within it rela- tive to the scale of variability it normally experiences? To obtain estimates of eadaptin−place, ESM,p for each ESM we spatially joined bottom tempera- ture projections to the fleet footprints (using the sf library in R; [60]), and calculated the mean and standard deviation in bottom temperature during the historic period, thistoric,ESM,p,c and σhistoric,ESM,p,c, respectively, and the mean bottom temperature during the future period, tfuture, ESM,p,c, for each ROMS cell c within each footprint. We estimated exposure as the difference in the average future and historic temperatures across all cells within each footprint, t future;ESM;p and t historic;ESM;p, divided by the average standard deviation in historic bottom temperature across all cells within each footprint, shistoric;ESM;p, or eadaptin(cid:0) place;ESM;p ¼ t future;ESM;p (cid:0) t historic;ESM;p shistoric;ESM;p : PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 ð1Þ 7 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Therefore, the units for this exposure metric are essentially standard deviations of tempera- ture change relative to the historic baseline. For the adapt on-the-move assessment, we calculated exposure for each fleet based on hori- zontal (change in latitude and/or longitude) and vertical (change in depth) displacement of isotherms representative of present-day fishing grounds (S2 Fig). Displacement is a metric that characterizes environmental change in terms of the minimum distance that must be traveled to track constant temperature contours [49], addressing the question: if the footprint of fishing effort for a fleet moves to find a future environment that matches the historical one, how far will it have to go? In the case of bottom temperature, we calculated both horizontal and vertical displacement for each ROMS cell. We excluded ROMS cells in which >10% of their area was inaccessible to the trawl fishery due to presence of untrawlable habitat or the most recent spa- tial fishery regulations (2020-present; S2 Text, S3 Fig). Sensitivity analysis revealed that the choice of the 10% threshold for inaccessible habitat did not qualitatively change conclusions. To capture movement on finer spatial scales than the 0.1˚ degree resolution of the ROMS out- put, displacements were interpolated to capture the minimum distance required (i.e., it is not necessary to move a full 0.1˚ degree to the next grid cell if a partial movement would account for the temperature change). As with eadaptin−place,ESM,p, we joined the summaries of displace- ment to the fleet footprints, and calculated the average value of horizontal and vertical dis- placement for each fleet and ESM, or eadapton−the−move,ESM,Hd,p and eadapton−the−move,ESM,VD,p, respectively. The units for the horizontal and vertical displacement metrics are in kilometers that would have to be shifted to maintain an isotherm. Sensitivity We calculated sensitivity in the same way for both the adapt in-place and adapt on-the-move assessments, focusing on the economic dependence of fleets on bottom trawl groundfish. To obtain information on fisheries landings by port group, on 3 October 2022 we downloaded data for all bottom trawl groundfish vessels for the period 2011–2019 from PacFIN’s compre- hensive fish tickets table. We calculated sensitivity s of vessel v in year y to changes in revenue r (adjusted for inflation to 2021 USD) from the bottom trawl-caught groundfish gbt in port group p in relation to all fisheries f and port groups in which it participates as sf ¼gbt;p;y;v ¼ PP rf ¼gbt;p;y;v PF p¼1 f ¼1 rf ;p;y;v : ð2Þ We calculated annual sensitivity of each fleet Sf = gbt,p,y based on the median value of sf = gbt, p,y across vessels for each year and port group as Sf ¼gbt;p;y ¼ medianðsf ¼gbt;p;y;vÞ: ð3Þ Adaptive capacity Adaptive capacity is a complex and multifaceted concept, defined by the Intergovernmental Panel on Climate Change as “[t]he ability of a system to adjust to climate change (including cli- mate variability and extremes), to moderate potential damages, to take advantage of opportu- nities, or to cope with the consequences” ([61], p. 9). Evaluating adaptive capacity comprehensively requires assessment of multiple domains, including assets, flexibility, organi- zation, learning, and agency [29–31]. Here we focused on the flexibility domain as it pertains to coping capacity, the “ability to react to and reduce the adverse effects of experienced haz- ards” ([62], p. 72). Specifically, we quantified diversification and mobility within the PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 8 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move groundfish fleets, equating reduced diversification and mobility with reduced capacity to cope and adapt. Adapt in-place: Diversification. For the adapt in-place assessment, we quantified pres- ent-day fisheries diversification within each of the port groups associated with each groundfish fleet in terms of opportunities to participate in other fisheries from 2011–2019. For this analy- sis, we selected a measure that invites consideration of the full cross-section of a port group (e.g., processors, deckhands, owners, captains, etc.) that may offer resilience to a groundfish fleet should it experience negative impacts of climate change. We did not subset to only those vessels that participated in the bottom trawl groundfish fishery, as we wanted to reflect the potential for future adaptation within a port group given current fishing opportunities defined as broadly as possible. Specifically, we generated an annual fisheries participation network [25,38] for each port group to derive an edge density metric. In these networks, different fisheries are depicted as nodes, while pairs of nodes are connected by lines, called edges, that integrate information about vessels participating in both fisheries (S5 Fig; further methodological details provided in [63]). Edge density of a network is defined as the ratio of the number of edges present to the total possible edges in the network [64]. Higher edge density implies that fishers in these ports have, on average, access to a greater range of alternative fishing opportunities if one node (fish- ery) is compromised because of poor stock availability, a fishery closure, or other regulatory actions [25,38]. Edge density scales with network size (it is easier to achieve a high density in a low complexity network), so comparisons across networks of different sizes should be made with the knowledge that port groups with fewer fisheries will necessarily have more diversifica- tion potential than those with more fisheries. We created annual fisheries participation networks using species landings data retrieved from PacFIN’s comprehensive fish tickets table on 29 December 2021. These networks repre- sent the most recent available data for the period 2011–2019 [63], and are summarized annu- ally from week 46 in one year through week 45 in the following year (e.g., November 2018 to November 2019) to capture the beginning of the Dungeness crab (Metacarcinus magister) fish- ing season, a fishery in which many bottom trawl groundfish vessels also participate. We classi- fied nodes based on the species groupings described by [65]. We report diversification as the annual edge density value of each port group’s fisheries participation network. Adapt on-the-move: Mobility. For the adapt on-the-move assessment, we characterized each fleet’s mobility based on documented changes in the distance of fishing grounds to port from 2011–2019. This approach assumed that fleets from port groups fishing farther from port were more mobile, while acknowledging that many factors influence this metric (e.g., bathym- etry, stock availability, vessel size and gear, spatial closures, substrate, etc.). We calculated mobility mp,y,v of vessel v in year y based on its landings-weighted distance from port. For each vessel v in year y, we calculated the straight-line distance d from the set location l of each haul to the port of landing p, then weighted each distance calculation by the groundfish landings associated with that haul before selecting the median value for each vessel in each year: mp;y;v ¼ medianðdp;y;v;lÞ We calculated annual mobility of each fleet Mp;y based on the median value of mp,y,v for each year and port Mp;y ¼ medianðmp;y;vÞ; ð4Þ ð5Þ and report the 95th percentile of Mp;y as our annual index of mobility. This approach assumes PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 9 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move each vessel contributes equally to fleet mobility, rather than weighting mobility by each vessel’s landings, and captures the upper limit of mobility for each fleet. Assessment of risk due to climate change We integrated measures of exposure, sensitivity, and adaptive capacity of the groundfish fleets on the U.S. West Coast to evaluate coupled social-ecological risk to climate change. Our defini- tions follow those of the IPCC [62], such that high exposure to climate change, given the haz- ard of projected warming bottom temperatures [11], and high vulnerability, together imply high risk. Vulnerability is defined broadly as “the propensity or predisposition to be adversely affected” ([3], p. 5), and here we calculate it by integrating our measure of sensitivity (eco- nomic dependence) with our measures of adaptive capacity (diversification or mobility). p, thermal change, E* Specifically, we calculated median exposure values based on thermal change relative to his- toric variability, horizontal displacement, and vertical displacement across the 3 ESMs for each fleet, and rescaled the median exposure values to index values of E* p, horizontal displacement, and E* p, vertical displacement such that their minimum values were 0 and their maxima were 1 (the maximum thermal change relative to historic variability, horizontal displacement, and vertical displacement expected across all fleets). We calculated the average value of Sf ¼gbt;p;y across 2011–2019 and rescaled it to create a sensitivity index S* and a maximum value of 1, with 1 reflecting the maximum observed across all fleets. For each of the measures of adaptive capacity, we calculated their average annual values across 2011– 2019, and rescaled the resultant quantities such that their minimum values were 0 and their maxima were 1, with 1 reflecting the minimum diversification or mobility observed across all fleets. This reversal of scale converted these indices into measures of a relative lack of capacity p and relative lack of mobility M* to cope and adapt, due to a relative lack of diversification D* p. We calculated vulnerability of each fleet under the adapt in-place assessment Vp, adapt n-place and under the adapt on-the-move assessment Vp, adapt on-the-move, as the Euclidean distance to the origin of the location represented by sensitivity S* p values, such that p with a minimum value of 0 p and either D* p or M* and Vp;adapt in(cid:0) place ¼ ðS∗ p 2 þ D∗ p 2Þ1=2 Vp;adapt on(cid:0) the(cid:0) move ¼ ðS∗ p 2 þ M∗ p 2Þ1=2: ð6AÞ ð6BÞ With this calculation, we assume vulnerability to be equally affected by sensitivity and adap- tive capacity. Following [46] (their Fig 2, right), we represented this vulnerability to climate change visually, and used it to distinguish between fleets of greater or lesser concern and those that are potential adapters or have high latent risk. Our ultimate interest was in the combined risk due to climate change of each fleet under the adapt in-place assessment Rp, adapt in-place and under the adapt on-the-move assessment Rp, adapt on-the-move. Specifically, we defined this integrated measure of exposure and vulnerability as the Euclidean distance to the origin of the location associated with each value of E*p,i and vulnerability Vp,j, Rp;adapt in(cid:0) place ¼ ðE∗ p;thermal change 2 þ Vp;adapt in(cid:0) place 2Þ1=2: Rp;adapt on(cid:0) the(cid:0) move ¼ ðE∗ p;vertical displacement 2 þ Vp;adapt on(cid:0) the(cid:0) move 2Þ1=2: PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 ð7AÞ ð7BÞ 10 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move With these calculations, we assume risk to be equally affected by exposure and vulnerability, and interpret fleet risk relative to other fleets in this analysis, rather than capturing an absolute measure of risk. Geographical patterns To evaluate whether there were geographical patterns in the exposure, sensitivity, adaptive capacity, and risk metrics, we conducted regressions of these variables against latitude. Specifi- cally, we used the glmmTMB package to evaluate (i) the fixed effects of latitude on thermal change relative to historic variability, horizontal displacement, or vertical displacement for each ESM separately; (ii) the fixed effect of latitude and the random effect of year on sensitivity, diversification, and mobility; and, (iii) the fixed effect of latitude on each of the risk metrics. In all of the models, we weighted the regressions by the number of vessels composing each fleet. For the sensitivity and diversification models, we used a logit link and the ordered beta family because the data represent proportions. For the mobility model, we used a log link and the Gaussian family to adequately capture the long tail in the distribution of landings-weighted distance from port across fleets, and included splines (number of knots = 3). All other models used an identity link and the Gaussian family. While the convention when plotting regressions is to have the explanatory variable on the x-axis, we decided to plot latitude on the y-axis because it provides a more intuitive representation of poleward and equatorward shifts in fish- ing fleets operating off the U.S. West Coast. To evaluate the leverage of individual fleets in these analyses, we re-ran the regressions described above using leave one out cross validation (LOOCV; see S3 Text for details). Results We found that the sensitivity of groundfish fleets along the U.S. West Coast, based on their share of earnings from the groundfish fishery, varied substantially from close to zero to near complete dependence (Fig 3). The more equatorward San Francisco, Santa Barbara, and Los Angeles fleets derived <10% of their revenue from the bottom trawl groundfish fishery during 2011–2019, while the more poleward fleets landing in Puget Sound, Astoria, and Fort Bragg captured �80% of their revenue from the bottom trawl groundfish fishery (Fig 3). Overall, though there was a fair amount of interannual variability in the relationship, sensitivity increased significantly with latitude (p <0.001; Fig 3, S3D Table). The Santa Barbara fleet had high leverage, but did not modify the positive relationship observed in the full data set (S15 Fig). These estimates of sensitivity based on economic dependence of groundfish fleets on bot- tom trawl groundfish were used in both the adapt in-place and adapt on-the-move risk assessments. We centered our analysis of exposure to climate change within present-day fishing foot- prints (Fig 4A) of U.S. West Coast groundfish fishing fleets. These footprints indicate extensive fishing along the coast, particularly off Washington and Oregon (Fig 4B) where fishing grounds overlapped considerably more and generally occupied larger areas, compared with the fishing footprints of fleets landing catch in California-based port groups (Fig 4C and 4D). The landings-weighted depth of the catch, while highly variable for some port groups, was gen- erally shallower for fleets landing catch in ports south of Point Conception, California, than those farther north (S6 Fig). In addition, these equatorward fleets tended to be composed of smaller-size vessels (S7 Fig). On average across the three ESMs, we estimated that between the historic (1990–2020) and projected (2065–2095) periods, there would be one standard deviation or more of near-bottom ocean warming within present-day fishing footprints, ~5km of horizontal displacement of PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 11 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 3. Economic dependence, as a measure of sensitivity, of U.S. West Coast groundfish fleets to changes in the fishery, in relation to latitude. The black line represents the relationship between mean economic dependence (2011– 2019; proportion of groundfish revenue relative to revenue from all commercial fisheries) and latitude, while grey shading indicates the SE of this relationship, which was statistically significant (p < 0.001, S3 Table). Colors correspond to the state in which each port occurs (blue: California, yellow: Oregon, red: Washington). https://doi.org/10.1371/journal.pclm.0000285.g003 bottom isotherms, and 10s to 100s of meters displacement of bottom isotherms into deeper waters (vertical displacement). We also found that exposure under adapt in-place and adapt on-the-move strategies increased significantly with latitude (S3A–S3C Table). Compared to more equatorward fleets, we found that poleward fleets will experience twice as much local temperature change within present-day fishing footprints (Fig 4E), relative to historic variabil- ity, and 3–4 times as much vertical thermal displacement if they move to follow thermal pro- files of present-day fishing footprints (Fig 4F). The Puget Sound, Astoria, Santa Barbara, and Los Angeles fleets had high leverage in the regressions with both measures of exposure (S9– S14 Figs), but did not modify the positive relationship observed in the full data set (except for the IPSL-based regression of local temperature change within present-day fishing footprints, which was highly uncertain; S11 Fig). Horizontal displacement of bottom isotherms in pres- ent-day fishing footprints is more uncertain across the ESMs and its association with latitude varied in sign depending on the ESM (S8 Fig). Because the sign of the association between hor- izontal displacement and latitude varied between ESMs, we did not calculate an average hori- zontal displacement across ESMs to include in the overall risk estimates reported below. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 12 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 4. Fishing footprints and geographic exposure to climate change within fishing footprints. (a) Fishing footprint from 2011–2019 (dark gray regions) for U.S. West Coast groundfish fleets. Alternating light/dark green regions on land delineate the 14 port groups, which are numbered with corresponding names listed in inset legend. Three enlargement maps to the right show the 14 port groups landing bottom trawl-caught groundfish on land (numbered), but with distinct, individually delineated fishing footprints (corresponding circled numbers) associated with fleets fishing off Oregon and Washington (b) and California (c, d). Estimates of exposure of these fleets to climate change based on comparison of 30-year historic (1990–2020) and future (2065–2095) periods for (e) bottom temperature change relative to historic variability, and (f) vertical displacement of bottom isotherms. In (e) and (f), point size scales with the number of vessels in each fleet and these relationships were statistically significant (p < 0.001, S3 Table). GFDL, HADL and IPSL correspond to the three Earth system models used to develop dynamically downscaled projections of bottom temperature. GEBCO 2023 (NOAA NCEI Visualization) base map (https://noaa. maps.arcgis.com/home/item.html?id=8050bfc4eb4444758f194db95f817184). Credit: General Bathymetric Chart of the Oceans (GEBCO); NOAA National Centers for Environmental Information (NCEI). https://doi.org/10.1371/journal.pclm.0000285.g004 Our two measures of the adaptive capacity of the groundfish fishing fleets showed contrast- ing changes with latitude (Fig 5). Diversification, which we used as a proxy for the potential to adapt if fleets continue to fish where they are now (adapt in-place), declined significantly with increasing latitude (Fig 5A, S3E Table; p<0.001). While statistically significant, the differences in diversification between poleward and equatorward fleets due strictly to latitudinal position were small and uncertain in absolute magnitude (S16 Fig) and unlikely to be especially impact- ful to fleet-specific vulnerability (65–75% of potential edges were realized in most networks). In addition, the Puget Sound and Santa Barbara fleets had high leverage (S16 Fig). In contrast, fleets in poleward ports generally caught groundfish farther from ports of landing (~80km- 250km) compared to ports in more equatorward California (in most cases <50km). Therefore fleet mobility (interquartile range of mobility: 40–90 km), which we use as a proxy for the potential for fleets to adapt by moving to new fishing grounds (adapt on-the-move), increased significantly with increasing latitude (Fig 5B, S3F Table; p<0.001). The Puget Sound fleet had high leverage in the regression of mobility against latitude, but did not modify the positive relationship observed in the full data set (S17 Fig). PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 13 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 5. Geographic variation in fleet fisheries diversification and fleet mobility. Relationships between the latitude of ports of landings for U.S. West Coast groundfish fleets and two elements of the flexibility dimension of adaptive capacity: (a) diversification based on edge density of fisheries participation networks; and (b) mobility based on landings-weighted distance from port to fishing grounds. Points indicate averages across 2011–2019, point size scales with the number of vessels in each fleet, and these relationships were statistically significant (diversification: p = 0.015, mobility: p < 0.001, S3 Table). Colors correspond to the state in which each port occurs (blue: California, yellow: Oregon, red: Washington). https://doi.org/10.1371/journal.pclm.0000285.g005 Collectively, we found that the coupled social-ecological risk of poleward groundfish fishing fleets was elevated compared to more equatorward fleets (Fig 6, S19 Fig). Sensitivity created the greatest variation in vulnerability (y-axes in S18 Fig), which tended to be highest for fleets landing at ports in northern California, Oregon, and Washington. Under an adapt in-place strategy, risk was greatest for more poleward fleets because of their greater exposure and higher sensitivity (Fig 6A). Under an adapt on-the-move strategy, the greater exposure and sensitivity of more poleward fleets to climate change was dampened by their greater mobility, and fleets had similar risk scores from either being more vulnerable or more exposed, but not necessarily both more vulnerable and more exposed simultaneously (S19 Fig). Overall, latitude had a greater effect on risk of groundfish fleets to climate change under an adapt in-place strat- egy (compare slopes in S3G and S3H Table, risk scores in S19 Fig). PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 14 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move Fig 6. Coupled social-ecological risk due to climate change for groundfish fleets on the U.S. West Coast. (a) Assuming fleets change target species while remaining in current fishing grounds (adapt in-place); (b) assuming fleets shift fishing grounds while targeting current species (adapt on-the-move). Larger points and font sizes indicate fleets composed of a greater number of vessels, and these relationships were statistically significant (p < 0.001, S3 Table). Colors correspond to the state in which each port occurs (blue: California, yellow: Oregon, red: Washington). https://doi.org/10.1371/journal.pclm.0000285.g006 Discussion The translation of global-to-local projected impacts of climate change can facilitate strategic planning that helps resource-dependent communities and industries take a proactive role in their futures. One form this translation can take is climate risk assessments that are performed at scales relevant to individuals, communities, and decision makers [4]. Such steps increase the reliability and relevance of information by representing important social and biophysical pro- cesses more accurately and providing user-specific context. Focusing on the bottom trawl groundfish fishery along the U.S. West Coast, we found that more poleward fleets face greater risk due to climate change because of higher exposure and greater sensitivity in the form of economic dependence on groundfish. Specifically, we showed that poleward risk was greater if fleets rely on existing groundfish fishing grounds, which necessitates diversifying to other spe- cies and can come at a cost (e.g., investment in additional permit and gear types), rather than shifting fishing grounds and maintaining current catch composition. This result suggests that PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 15 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move an adapt on-the-move strategy will better mitigate risk than an adapt in-place strategy for high-latitude fleets, assuming that the variable costs of fishing (e.g., due to changes in fuel prices and labor wages) relative to ex-vessel revenues remain similar to the present. These gen- eral inferences emerge from application of one indicator for each dimension of risk, which is an oversimplification, but also offers transparency and the potential for replicability for other fleets and regions. Our findings contrast with similar work in other parts of the world, such as Europe, where lower-latitude fleets and fisheries are expected to face greater climate risk [35,36,66]. While existing within-fishery flexibility on the U.S. West Coast provides some promise for coping with, reacting to, and adapting to projected impacts of climate change [67], our analysis highlights how further development of this and other dimensions of adaptive capacity could enhance resilience of these fishing fleets. Building climate resilience for fishing fleets Parsing risk into its constituents (exposure, sensitivity, and adaptive capacity, under two con- trasting adaptation strategies) suggests different types of interventions that can be imple- mented to reduce risk. Communities may have similar risk scores, but contrasting sources of risk, and therefore may respond favorably to customized interventions. Mitigating risk may require more proactive efforts to improve adaptive capacity, such as fisheries portfolio diversi- fication or enhancing fleet mobility, or to reduce sensitivity through expansion of revenue streams, among other solutions [29,46,68]. For example, in California, there are existing prece- dents for enhancing adaptive capacity for fleets with latent risk (low sensitivity and low adaptive capacity). For instance, following the implementation of individual fishing quotas in 2011, members of the Fort Bragg, Morro Bay, Monterey, and Santa Barbara fleets organized quota risk pools with the support of local government and non-government organizations to navigate bycatch constraints, thereby enhancing resilience within the new regulatory environment [69]. In contrast, the suite of interventions for fleets that are potential adapters (because they have higher adaptive capacity and sensitivity, e.g., Fort Bragg or Astoria) are more likely to focus on a reduction in sensitivity. Livelihood diversification (e.g., through mariculture or tourism activities) can dampen sensitivity while also improving adaptive capacity in-place, whereas improving access to fish for other target species and in new (or previously closed) fish- ing grounds are more exclusively directed at reducing sensitivity [46,68]. Finally, there are interventions that could rescale the risk landscape across all fleets, such as recent efforts to cre- ate increased market share for groundfish [70]. Increased consumer demand for a diversity of groundfish could increase profit margins, augment financial safety nets for fishers, and provide an opportunity to take advantage of currently underutilized and abundant stocks. However, creation of market demand in specific areas requires resolution of mismatches between loca- tions of fishery landings, seafood processing, and seafood markets (e.g., through accurate map- ping of seafood supply chains and rescuing of stranded capital; [71]). In addition, market demand interventions may exacerbate ecological risk if they incentivize localized depletion of stocks to meet growing local demand [68,72]. Historical contingencies in management, market, and ecological forces provide important context for evaluating the most useful interventions, regardless of whether risk due to climate change is higher or lower for these fleets. These forces create a geography of pre-existing vul- nerability, akin to that documented in other regions where shrinkage and disappearance of fishing communities has occurred [73] or where implementation of new management mea- sures has set the stage for responses to subsequent shocks [25,74]. For the bottom trawl groundfish fishery on the U.S. West Coast, revenue has become more concentrated within fewer fleets over the last several decades, a trend that continued throughout the 2011–2019 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 16 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move period we focused on in this study. Furthermore, the narrower continental shelf available to California fleets has led to smaller fishing footprints (areal extent) and a lower projected expo- sure to expected ocean warming for equatorward groundfish fleets (Fig 4), which also tend to be composed of smaller, less mobile vessels (Fig 5 and S7 Fig, [73]). These trends are a result of the biogeographic context in which each fleet operates, a changed regulatory environment, his- torical impacts to more equatorward groundfish stocks [75], and various other factors (e.g., geographic locations of buyers, processors, and associated infrastructure; [37,45]). As in other fisheries (e.g., Dungeness crab; [76]), practices that level the playing field for the many smaller vessels composing equatorward groundfish fleets may help to reduce their climate risk. In con- trast, for more poleward groundfish fleets that have high sensitivity, it may be more effective to employ approaches that bolster other dimensions of adaptive capacity such as organization, e.g., via social capital building to create cooperatives [46]. Each fleet’s history complicates the many possible paths forward, but potential futures are made less opaque with the information we have provided here on climate risk. Future directions for assessing climate risk in fisheries Our approach to understanding spatial heterogeneity in climate risk for fishing fleets in gen- eral, and on the U.S. West Coast in particular, highlights opportunities for future research. The data and methods we used to estimate exposure, sensitivity, and adaptive capacity, and to com- bine them into a risk index, deserve further examination. For instance, we found that estimates of exposure based on horizontal displacement of bottom isotherms are highly uncertain (S8 Fig). This result underscores the challenge of generating expectations about future ocean con- ditions and use, and brings into question how other environmental factors that affect species distributions, such as dissolved oxygen [77,78] may change and interact with the behavior of fishing fleets [79–82]. Another avenue of future research is integrating expectations for other fisheries in the participation networks (S5 Fig, [38,63]) that are likely to experience climate effects, which will add complexity to estimates of adaptive capacity. For example, Dungeness crab fisheries at higher latitudes may be negatively impacted by ocean acidification effects by the late 21st Century [51], and numerous Pacific salmon (Oncorhynchus spp.) populations along the U.S. West Coast are highly vulnerable to climate impacts at multiple life history stages [83]. An extension of this work could connect species distributions projected using dynamically downscaled ESM outputs (e.g., [84–86]) to fishing footprints directly, using expected changes in the resources themselves within customary use areas to derive estimates of exposure. Such an approach could capture the potential for more equatorward species mov- ing into footprints while others move out ([87–89]; but see [90]), and would also need to address the potential for fleets to capitalize on these changes under existing regulations. There is also the question of how best to identify fishing areas, or footprints, for estimating exposure. Here we identified the primary fishing grounds where the majority of harvested biomass is extracted based on vessel landings by port. Alternative approaches could use metrics such as revenue [91], fisher days [33], or could define fishing areas specific to vessel home ports [23]. There are also alternative approaches for describing sensitivity and adaptive capacity. For example, rather than focus solely on economic dependence on a target species relative to all other commercial fisheries, it would be informative to quantify the economic dependence of fleets on target species relative to all other income streams including those outside of commer- cial fisheries. Such data are not necessarily widely available, though household survey research in small-scale fisheries provides a template for pursuing this line of inquiry [92–94]. Addition- ally, the sensitivity and adaptive capacity of crew on fishing vessels may be quite different than for captains or owners. Strong social identity related to participation in particular fisheries PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 17 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move could affect fishers’ willingness or ability to adapt by shifting to new fisheries or livelihood activities [95,96]. Ideally, future work to understand risk of fishing communities will embrace a participatory approach in which notions of community, vulnerability, and adaptive capacity are co-developed [97] and considered alongside perceptions of other risks beyond climate change [98]. Approaches such as fisheries learning exchanges may have the added benefit of building trust amongst stakeholders to allow for increases in flexibility in response to climate change, without jeopardizing ecological sustainability [99]. While we chose to analyze fleets defined by common fishing grounds and ports of landing as one type of community, there are other units of community analysis that are equally or more compelling (e.g., communities-of-place defined shoreside, [100,101]; and fisher net- works emergent as communities-of-practice [102,103]). Different rubrics for describing com- munities may lead to greater or lesser emphasis on mobility and diversification as primary metrics to index adaptive capacity. Being able to fish a larger portfolio of species can buffer fishers’ revenues against change and high variability [65]–but doing so often requires owning multiple permits, which may be cost prohibitive for many participants or difficult to manage given current jurisdictional boundaries [104]. This insight could lead to deeper exploration of geographic gradients in the assets dimension of adaptive capacity. We do not know whether current levels of diversification and mobility are at an upper bound or if there is room for further adjustment given current costs (fuel consumption, insur- ance, etc.; [105]). Fishing new species may be constrained by fisheries regulations that are slow to adapt to shifting species distributions [21]. Specifically, for the bottom trawl groundfish fish- ery, some quota categories are restricted to certain geographic regions, which would be prob- lematic if stocks move out of the designated areas [104]. Similarly, mobility may be limited for smaller-vessel fleets and larger-vessel fleets with more diversified catch, as has been demon- strated on the U.S. East Coast [73]. Diversification and mobility aspects of flexibility are under- pinned by enabling conditions that intersect with other domains of adaptive capacity such as assets (e.g., financial resources), learning (e.g., access to knowledge, adaptable skill sets), and organization (e.g., community cohesion), all of which may vary across different community typologies [29,30,46]. Future work to explore these issues, for example through retrospective evaluation of community changes associated with adaptive capacity measures existing prior to a disruptive event [25,74], would be illuminating. Assessments of risk due to climate change can be used to communicate potential impacts to people, regions, or sectors at local scales [5], and in so doing can provide rationale for medium- to long-term policy decisions intended to improve resilience. This case study pro- vides a practical implementation of the widely-used IPCC risk assessment framework at a geo- graphic scale that is relevant to fishers, communities, and U.S. federal fisheries managers. It achieves this appropriately-scaled outcome by integrating climatic, ecological, and socio-eco- nomic data from a regionally large-volume, relatively profitable, lynchpin fishery. These kinds of data are commonly available from many of the largest-volume, greatest-value fisheries glob- ally. However, given that these data were also available for the relatively small fleets we assessed here, this framework may be viable for smaller-scale fisheries as well, especially with creative approaches to generating information streams (e.g., improving understanding of fishing grounds, economic dependence on target species, and mobility via structured surveys and par- ticipatory workshops; [97]). Similar analyses for fleets in other regions, coupled with scenario planning efforts [106,107], can provide more comprehensive insight into the risks of climate change for fisheries. This insight can be used to identify regions with the greatest potential to improve resilience to climate change through government-based regional action plans, self- determined actions, and via new legislation for fishery disaster responses (e.g., in the U.S. via the Fishery Resource Disasters Improvement Act) [26,29]. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 18 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move The contrasts observed here among U.S. West Coast groundfish fleets have explanations ranging from physics to market forces, and contingencies fueled by historical and present-day regulations. They add to evidence from the U.S. that more poleward fishing fleets may be at greater risk due to climate change [51,86], in contrast to expectations for greater equatorward risk in other parts of the world, such as Europe [35,36,66]. While the potential for the adapt on-the-move strategy to mitigate greater poleward risk exceeded that for the adapt in-place strategy, our results imply that neither of these within-fisheries flexibility measures are suffi- cient to disrupt fundamental geographic patterning of risk. Rather, alternative adaptation approaches that build out other attributes of flexibility, including those external to commercial fisheries, and alternative dimensions of adaptive capacity not addressed here, may prove most fruitful for ameliorating latitudinal patterns of climate risk. For example, increased agency for fishers to access new target species entering their fishing grounds, introduction of greater flexi- bility to shift fishing permits quickly, and organizational support to develop new markets are all aspects of adaptive capacity that can reduce climate risk. Evaluations of climate risk and adaptation approaches that capture these other types of issues need not be more complex, but instead can strive for transparency, replicability, and comparability with this one. While the insights presented here are specific to the U.S. West Coast, they suggest that coupled social- ecological risk assessments like this one offer a promising path forward for evaluating climate adaptation options in other regions around the world. Supporting information S1 Fig. Bottom temperature change, horizontal displacement of bottom temperature, and vertical displacement of bottom temperature projected by three dynamically downscaled Earth System Models (GFDL, HAD, IPSL) for the period 2025–2055 and 2065–2095. (TIFF) S2 Fig. Schematic of thermal displacement calculation. (a) Historical (1990–2020) bottom temperature, (b) bottom temperature change between historical and future (2065–2095) bot- tom temperatures, and (c) future bottom temperature and thermal displacement. The thermal displacement calculation is illustrated for an example location at 124.2˚W, 43.9˚N. At that location the historical mean temperature was 10.1˚C and the projected bottom temperature increase is 2.2˚C. In the future period, moving from the future temperature (12.3˚C) to the his- torical temperature (10.1˚C) requires an offshore horizontal displacement of 25 km, with an associated 98 m increase in bottom depth (vertical displacement). This example uses projec- tions forced by the IPSL Earth Systems Model, assuming Amendment 28 bottom trawl fishery closures. (TIFF) S3 Fig. Contextual map, indicating the landing ports and port groups for groundfish fleets on the U.S. West Coast, as well as fishery closure areas and untrawlable habitat. Landing ports are represented by white squares, while hatched regions show areas closed to bottom trawl fishing and red regions show untrawlable habitat. Green shading reflects 20km inland buffer for each of the 14 IO-PAC port groups. Left map shows fishery closures under Amend- ment 19, from ~2003–2019, and right map shows fishery closures from 2020 to present under Amendment 28 which were used for thermal displacement calculations. GEBCO 2023 (NOAA NCEI Visualization) base map (https://noaa.maps.arcgis.com/home/item.html?id= 8050bfc4eb4444758f194db95f817184). Credit: General Bathymetric Chart of the Oceans (GEBCO); NOAA National Centers for Environmental Information (NCEI). (TIFF) PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 19 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move S4 Fig. Fishing footprints from 2011–2019 for U.S. West Coast groundfish fleets, using the 50, 75, 90, and 95 percent volume contour. (TIFF) S5 Fig. Example fisheries participation networks for 3 port groups on the U.S. West Coast. Example fisheries participation networks for the Puget Sound (left), Coos Bay (middle), and Morro Bay (right) port groups on the U.S. West Coast (2019). Each fishery is depicted as a node, while pairs of nodes are connected by lines, called edges, that integrate information about vessels participating in both fisheries. In these examples, Coos Bay and Morro Bay have higher edge densities than Puget Sound, implying that fishers in these port groups have access to a greater range of alternative fishing opportunities if one node (fishery) is compromised because of poor stock availability, a fishery closure, or other regulatory actions. (EPS) S6 Fig. Groundfish fleet depths. Landings-weighted depth of fishing grounds for U.S. West Coast groundfish fleets from 2011–2019 (median with 95% confidence interval). (TIFF) S7 Fig. Groundfish fleet vessel lengths. Vessel lengths for U.S. West Coast groundfish fleets from 2011–2019 (median with 95% confidence interval). (TIFF) S8 Fig. Horizontal displacement of fishing footprints. Estimates of exposure of U.S. West Coast groundfish fleets to climate change based on comparison of 30-year historic (1990– 2020) and future (2065–2095) periods for horizontal displacement of bottom isotherms. Note that the direction of the association between horizontal displacement and latitude varied between the three Earth System Models (GFDL, HADL, IPSL). (TIFF) S9 Fig. Leave one out cross validation for regression of exposure based on bottom tempera- ture change relative to historical variability using the GFDL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coefficient with all fleets included in the analysis. Changes in sign of the coefficient indicate a difference in the qualitative directional relationship between exposure based on bottom tem- perature change relative to historical variability and latitude. (TIFF) S10 Fig. Leave one out cross validation for regression of exposure based on bottom temper- ature change relative to historical variability using the HADL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coefficient with all fleets included in the analysis. Changes in sign of the coefficient indicate a difference in the qualitative directional relationship between exposure based on bottom tem- perature change relative to historical variability and latitude. (TIFF) S11 Fig. Leave one out cross validation for regression of exposure based on bottom temper- ature change relative to historical variability using the IPSL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coefficient with all fleets included in the analysis. Changes in sign of the coefficient indicate PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 20 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move a difference in the qualitative directional relationship between exposure based on bottom tem- perature change relative to historical variability and latitude. (TIFF) S12 Fig. Leave one out cross validation for regression of exposure based on vertical dis- placement of bottom temperature using the GFDL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coeffi- cient with all fleets included in the analysis. Changes in sign of the coefficient indicate a differ- ence in the qualitative directional relationship between exposure based on vertical displacement of bottom temperature and latitude. (TIFF) S13 Fig. Leave one out cross validation for regression of exposure based on vertical dis- placement of bottom temperature using the HADL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coeffi- cient with all fleets included in the analysis. Changes in sign of the coefficient indicate a differ- ence in the qualitative directional relationship between exposure based on vertical displacement of bottom temperature and latitude. (TIFF) S14 Fig. Leave one out cross validation for regression of exposure based on vertical dis- placement of bottom temperature using the IPSL Earth System Model against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coeffi- cient with all fleets included in the analysis. Changes in sign of the coefficient indicate a differ- ence in the qualitative directional relationship between exposure based on vertical displacement of bottom temperature and latitude. (TIFF) S15 Fig. Leave one out cross validation for regression of economic dependence, as a mea- sure of sensitivity, against latitude. Points and error bars represent estimates of the coeffi- cient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coefficient with all fleets included in the analysis. Changes in sign of the coefficient indicate a difference in the qualitative directional relationship between economic dependence and latitude. (TIFF) S16 Fig. Leave one out cross validation for regression of diversification against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corresponding fleet removed from the data, red line indicates the mean estimate of the coeffi- cient with all fleets included in the analysis. Changes in sign of the coefficient indicate a differ- ence in the qualitative directional relationship between diversification and latitude. (TIFF) S17 Fig. Leave one out cross validation for regression of mobility against latitude. Points and error bars represent estimates of the coefficient of this regression (±2 SE) with the corre- sponding fleet removed from the data, red line indicates the mean estimate of the coefficient with all fleets included in the analysis. Changes in sign of the coefficient indicate a difference in the qualitative directional relationship between mobility and latitude. (TIFF) PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 21 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move S18 Fig. Social vulnerability of groundfish fleets on the U.S. West Coast. We assume that fleets either (a) adapt in-place by changing target species while remaining in current fishing grounds, or (b) adapt on-the-move by shifting fishing grounds while targeting current species. Font size and color scales with projected exposure to climate change. Vertical and horizontal lines represent median values across fleets. (EPS) S19 Fig. Social vulnerability of groundfish fleets on the U.S. West Coast relative to expo- sure to climate change. Social vulnerability, defined as sensitivity relative to adaptive capacity, in relation to exposure to climate change for U.S. West Coast groundfish fleets, under the assumption that fleets (a) adapt in-place by changing target species while remaining in current fishing grounds, or (b) adapt on-the-move by shifting fishing grounds while targeting current species. Font size, point size, and Euclidean distance from the origin scales with risk, while color corresponds to latitude. (EPS) S1 Table. Linkage between individual ports and IO-PAC port groups. The port groupings were developed by the PFMC for biennial groundfish harvest specifications. Aggregating indi- vidual ports into port groups is necessary to provide a feasible set of geographic areas for a coastwide climate risk analysis. Analysis at the individual port-level would violate confidential- ity requirements, because there are often fewer than three buyers in any one port. (DOCX) S2 Table. Percent reduction in hauls to achieve a clean dataset. Percent reduction in hauls to achieve a clean dataset by reason for years 2011–2019, based on processing steps detailed here: https://zenodo.org/record/7916821. (DOCX) S3 Table. Statistical results. Summary of statistical results of regressions of (a-c) exposure, (d) sensitivity, (e-f) adaptive capacity, and (g-h) risk indices relative to latitude of each fleet. (DOCX) S1 Text. Methods related to Fig 1. Methods Related to Fig 1. (DOCX) S2 Text. Exposure: Spatial considerations for thermal displacement. Description of fishery closure areas and untrawlable habitat that influenced calculations of horizontal and vertical thermal displacement. (DOCX) S3 Text. Leave one out cross validation analyses for regressions. (DOCX) Acknowledgments This study was supported by the David and Lucille Packard Foundation 2019–69817 and the NOAA Integrated Ecosystem Assessment (IEA) and Climate and Fisheries Adaptation (CAFA) Programs. The authors appreciate the data sharing and discussions with the Califor- nia, Oregon, and Washington Departments of Fish and Wildlife and the Pacific States Marine Fisheries Commission. This manuscript benefited from reviews by Mary Hunsicker, Kristin Marshall, and Kayleigh Somers, as well as from inspiring discussions and presentations at the Effects of Climate Change on the World’s Oceans Conference held in Bergen, Norway in April PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000285 February 9, 2024 22 / 28 PLOS CLIMATEClimate risk for fishing fleets that adapt in-place or on-the-move 2023. We thank Su Kim and Vicky Krikelas for designing Fig 1, all of the groundfish that hopped into trawl nets to make this work possible, and The Clash for their entire catalog. Author Contributions Conceptualization: Jameal F. Samhouri, Michael Jacox, Owen R. Liu, Lyall Bellquist, Melissa A. Haltuch, Abigail Harley, Chris J. Harvey, Isaac C. Kaplan, Karma Norman, Leif K. Ras- muson, Rebecca L. Selden. Data curation: Jameal F. Samhouri, Michael Jacox, Owen R. Liu, Kate Richerson, Erin Steiner, John Wallace, Mer Pozo Buil, Amanda Phillips, Curt Whitmire, Rebecca L. Selden. Formal analysis: Jameal F. Samhouri, Blake E. Feist, Michael Jacox, Owen R. Liu, Kate Richer- son, Erin Steiner, John Wallace, Mer Pozo Buil, Amanda Phillips, Eric J. Ward, Curt Whit- mire, Rebecca L. Selden. Funding acquisition: Jameal F. Samhouri. Investigation: Jameal F. Samhouri, Owen R. Liu, Kate Richerson, Erin Steiner, Kelly Andrews, Lewis Barnett, Anne H. Beaudreau, Lyall Bellquist, Melissa A. Haltuch, Abigail Harley, Chris J. Harvey, Isaac C. Kaplan, Karma Norman, Leif K. Rasmuson, Rebecca L. Selden. Methodology: Jameal F. Samhouri, Blake E. Feist, Michael Jacox, Owen R. Liu, John Wallace, Melissa A. Haltuch, Abigail Harley, Chris J. Harvey, Curt Whitmire, Rebecca L. Selden. Project administration: Jameal F. Samhouri. Resources: Jameal F. Samhouri. Software: Jameal F. Samhouri, Owen R. Liu, Kate Richerson, Erin Steiner, John Wallace, Amanda Phillips, Eric J. Ward, Rebecca L. Selden. Supervision: Jameal F. Samhouri. Validation: Jameal F. Samhouri. Visualization: Jameal F. Samhouri, Blake E. Feist, Michael Jacox, Rebecca L. Selden. Writing – original draft: Jameal F. Samhouri, Erin Steiner, Rebecca L. Selden. Writing – review & editing: Jameal F. Samhouri, Blake E. Feist, Michael Jacox, Owen R. Liu, Kate Richerson, Erin Steiner, John Wallace, Kelly Andrews, Lewis Barnett, Anne H. Beau- dreau, Lyall Bellquist, Mer Pozo Buil, Melissa A. Haltuch, Abigail Harley, Chris J. Harvey, Isaac C. Kaplan, Karma Norman, Amanda Phillips, Leif K. Rasmuson, Eric J. Ward, Curt Whitmire, Rebecca L. Selden. References 1. 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10.1371_journal.pgen.1011144	RESEARCH ARTICLE A quantitative genetic model of background selection in humans Vince BuffaloID 2 1,2, Andrew D. KernID 1 Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America, 2 Institute of Ecology and Evolution and Department of Biology, University of Oregon, Eugene, Oregon, United States of America vsbuffalo@gmail.com Abstract Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This “linked selection signal” reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selec- tion, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geo- graphically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitu- tion rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in con- served regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans. Author summary Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This “linked selection signal” reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Buffalo V, Kern AD (2024) A quantitative genetic model of background selection in humans. PLoS Genet 20(3): e1011144. https://doi.org/ 10.1371/journal.pgen.1011144 Editor: Bret Payseur, University of Wisconsin– Madison, UNITED STATES Received: September 17, 2023 Accepted: January 19, 2024 Published: March 20, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgen.1011144 Copyright: © 2024 Buffalo, Kern. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All code from bgspy and our Jupyter Lab (Kluyver et al. n.d.) notebooks for analysis are available on GitHub (https://github. com/vsbuffalo/bprime). The main model fits are available as Python Pickle objects on Data Dryad PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 1 / 32 PLOS GENETICSrepository (https://doi.org/10.5061/dryad. qnk98sfnv). Funding: This research was supported by National Institute of Health awards R35GM148253 and R01HG010774 to ADK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. A quantitative genetic model of background selection in humans parameters from linked selection models have relied on classic Background Selection the- ory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly pre- dicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference bet- ter predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans. Introduction The continual influx of new mutations into populations is the ultimate source of all adapta- tions, but the vast majority of mutations either do not affect fitness or are deleterious. Natural selection works to eliminate these deleterious mutations from the population, thus we expect them to appear at low frequencies within populations [1], and be less likely to fix between line- ages. Conserved genomic regions reflect the product of hundreds of millions of years of evolu- tionary optimization; thus the overwhelming majority of segregating variation in these regions will have deleterious fitness effects. Consequently, a good predictor of whether a new mutation will reduce fitness is if it occurs in a region of the genome that has been conserved over phylo- genetic timescales [2, 3]. Moreover, segregating rare variation in these regions is responsible for a significant proportion of the genetic contribution to phenotypic variation and disease in humans [4–7]. Selection on both beneficial and deleterious variants perturbs the allele frequencies of neighboring linked sites, a phenomenon known as linked selection [8–12]. Since deleterious variation is clustered in functional portions of the genome, we expect linked selection to reduce levels of diversity around evolutionarily constrained segments (e.g. coding sequences, splice sites, regulatory elements, etc.). The genomic arrangement of these conserved regions coupled with heterogeneous recombination rates create a large-scale spatial signal of linked selection of genetic diversity along chromosomes. Since genome-wide recombination maps and functional annotations are available for many species, there has been consistent effort to fit models of linked selection to patterns of diversity. This general approach provides estimates of population genetic parameters such as the strength of selection and the deleterious mutation rate [13, 14], and potentially distinguishes the roles of positive and negative selection and esti- mate the rate of beneficial mutations [15, 16]. In humans, previous work has shown that nega- tive selection plays the dominant role in shaping megabase-scale patterns of diversity, with positive selection having a nearly negligible impact [16]. Prior work to model the reduction in linked diversity due to deleterious mutations has largely relied on the classic Background Selection (BGS) model [8, 11, 13, 17]. While the BGS model has been successful in fitting many patterns of diversity, some of its simplifying assump- tions may distort inferences about the selective process. First, since fixation probabilities ulti- mately depend on the product of the deleterious selection coefficient (s) and population size PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 2 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans (N), the efficacy of selection depends on past population sizes. Unfortunately, accommodating such demography into analytic models of purifying selection remains an open, difficult prob- lem [18, 19] though simulation-based inference may be a route forward [20]. Second as the BGS model builds off classic models of mutation-selection balance [21, 22], it assumes that new mutations are sufficiently deleterious that they are invariably driven to loss. Under this assumption, the effect of selection is well-approximated by simply rescaling the neutral coales- cent by a reduction factor known as B ¼ Ne =N [23]. However, this simple rescaling approach is not appropriate across parts of parameter space that are relevant to natural populations [24, 25]. In particular, the BGS model cannot accommodate the possibility of weakly deleterious mutations (those with fitness effects 2Ns � 1) reaching fixation, which leads to incorrect pre- dictions of diversity levels as the strength of selection diminishes. Finally, the classic BGS model assumes that the selective dynamics at one site are not impacted by selection at other positions, i.e. no selective or “Hill–Robertson” interference [24, 26, 27]. In this work, we use another class of linked selection models that derive from quantitative genetics to address limitations of the classic BGS model [28–31]. These models consider how polygenic fitness variance spread along the genome increases the variance of stochastic allele frequency change, as alleles become randomly linked to fitness backgrounds over time and their frequency trajectories are perturbed by selection at other sites. While these models can theoretically accommodate additive fitness variance from any source as long as its rate of change is not too rapid, we focus specifically on a deleterious-mutations-only model of fitness variance from [29]. This model is identical to BGS when selection against deleterious muta- tions is strong, but it also correctly predicts the reduction in diversity when selection is weak by jointly predicting the deleterious substitution rate. We extend the Santiago and Caballero (hereafter the SC16) model of the negative selection process so that it can be fit using a com- posite likelihood approach to patterns of genome-wide diversity, according to the spatial dis- tribution of genomic features that could harbor deleterious fitness variation. Using forward simulations, we show this model leads to more accurate estimates of the distribution of fitness effects (DFE) under weak selection. We apply our composite-likelihood method to human population genomic data and provide new parameter estimates of the genome-wide impact of purifying selection in humans. We show that our new method is better able to predict the pat- terns of diversity along human chromosomes than previous models. However, our model leads to predictions of the deleterious substitution rate that disagree with observed levels of divergence. We discuss the potential causes and implications of such discrepancies and what it might mean for future efforts to fit linked selection models to genomic patterns of variation. Theory Our work extends quantitative genetic models of linked selection ([28–31]; see also the Appen- dix of [8]), which approximate the reduction in genetic diversity due to linked selection in terms of polygenic additive fitness variance (VA). These models are approximations to fairly complicated selection dynamics; in reality, background selection cannot be fully summarized by simply rescaling effective population size across all of parameter space [25, 32, 33]. Conse- quently, we use extensive, realistic forward simulations (in the next section) to demonstrate the validity of these models in the region of parameter space that the human genome occupies. Here we review the relevant theory before introducing our genome-wide extension. These linked selection models stem from Robertson [31], which in essence describes how polygenic additive fitness variation increases the pace of stochastic allele frequency change, thus reducing effective population size. At the individual level Robertson considered, selection generates an autocorrelation in fecundity as offspring from large families tend to beget many descendants PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 3 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans themselves (and likewise with small families) when fitness is heritable. This same across-gener- ation autocorrelation occurs at the genomic level due to linkage [28, 34], as the perturbations to a neutral allele’s trajectory from its particular fitness background tend to occur in the same direction across generations until the background recombines off. Quantitative genetic models such as Santiago and Caballero’s [28] quantify the total impact of the autocorrelation generated by selection in terms of what we think of as a fitness-effective population size Nf (to differentiate it from the drift-effective population size, which is the size of the ideal population when there is no fitness variation). The key insight is that in the long run, the steady presence of additive genetic fitness vari- ance (VA > 0) contributes an extra source of variance in offspring number beyond the variance expected under pure drift [35]. However, because heritable fitness variation generates across- generation autocorrelation, the cumulative effect of this fitness variance on the variance in allele frequency change is inflated by a factor of Q2. Intuitively, the product VAQ2 represents the expected total variance in reproductive success a neutral mutation experiences over its life- time in a system with weak selection at linked sites. Following Robertson and Santiago and Caballero [30, 31], we define the fitness-effective population size Nf by including the total additional variance created by heritable fitness into Wright’s equation [35] for effective population size, Nf ¼ N Q2VA þ 1 ð1Þ (c.f. [30, 31]; see S1 Text Section 1 for a proof). The benefit of modeling linked selection with Robertson’s forward-time model is that the inflation factor under weak selection is invariant with respect to the particular fitness background (i.e. high or low fitness backgrounds are exchangeable in this model) the neutral allele becomes stochastically associated with. By con- trast, modeling diversity levels under linked selection backwards in time requires tracking the particular associated fitness backgrounds, as coalescence rates experienced by a lineage are not invariant with respect to their fitness background, i.e. high and low fitness backgrounds are not exchangeable. Eq (1) is general, since different modes of selection and linkage can be accommodated by different expressions for VA and the inflation factor Q2 [28, 30]. When fitness variation has a multiplicative polygenic basis, as is often assumed for genome-wide selection processes, the fit- ness-effective population size experienced by an arbitrary neutral site under the influence of all S linked regions is, Nf � N exp (cid:0) ! XS VA;i i¼1 Q2 i 2 ð2Þ where the factor of one-half comes from ignoring the short-lived associations with the homol- ogous chromosome, which have a weak effect on the focal allele (see S1 Text Section 1.3). In our genome-wide model, we consider the summation in Eq (2) over non-overlapping contigu- ous, putatively conserved segments i 2 {1, 2, . . ., S} (e.g. exons, splice sites, etc.) each undergo- ing selection such that segment i contributes additive fitness variance VA,i to the total additive genetic fitness variance. The impact this segment’s fitness variance VA,i has on the fitness-effec- tive population size is mediated by the autocorrelation term Qi, which is a decaying function of the recombination rate between the segment and focal neutral allele. Specifically, the autocor- relation function for a neutral allele associated with segment i is C(t) = [(1 − ri)(1 − κi)]t, where ri is the recombination fraction to the segment and κi is the rate that the associated fitness vari- ance decays due to selective dynamics. Then, the cumulative autocorrelation over the lifespan PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 4 / 32 PLOS GENETICS A quantitative genetic model of background selection in humans of the allele is, Qi ¼ 1 þ X1 ½ð1 (cid:0) riÞð1 (cid:0) kiÞ�t t¼1 1 ki þ rið1 (cid:0) kiÞ : ¼ ð3Þ (see S1 Text Equation 20). This general equation can accommodate models of polygenic selec- tion as long as the equilibrium additive fitness variation VA,i can be specified and the change in variance due to selection can be approximated as a geometric decay, i.e. ΔVA,i = −κVA,i [28, 36–38]. This is usually a reasonable assumption since within-generation selection removes a fraction of phenotypic variation from the population, and some fraction of that is additive genetic variation [36, 37, 39]. The remaining required expressions are for the equilibrium additive fitness variance VA and the decay rate in associated fitness 1 − κ. Fitness variance could arise from beneficial or deleterious alleles, but given prior work has found selection against new deleterious mutations in conserved regions plays a dominant role in shaping genome-wide patterns of diversity and divergence [14, 16], we focus specifically on purifying selection. We imagine a mutation-selec- tion process that creates fitness variation as deleterious mutations enter a population at rate μ per basepair per generation in a conserved region of L basepairs, such that the region-wide per generation diploid mutation rate is U = 2μL. Each mutation imposes a selective cost of s in het- erozygotes and 2s in homozygotes, and fitness effects are multiplicative across sites. Þ � s2� Under this selection model, the additive genic fitness variance created by a new mutation (at frequency x ¼ 1 . For the entire population of 2N chromosomes, the mutational variance input each generation in segment i is VM,i � Uis2 where Ui = 2μLi is the diploid mutation rate per generation within the segment. Under the mutation-selection balance assumed by classic BGS theory, an Li-basepair segment has equilibrium additive genetic variance V BGS A;i � Uis (see S1 Text Equation 40) and thus kBGS and kBGS in Eq (2) and simplifying, we have i ¼ s. Substituting V BGS A;i 2N= ) is 2s2x 1 (cid:0) x ð N i Nf ¼ N exp (cid:0) ! XS i mLi sð1 þ rið1 (cid:0) sÞ=sÞ2 ð4Þ which is identical to the genome-wide model of background selection used in previous studies [14–16]. Thus, the classic background selection model is a special case of the more general the- ory of Santiago and Caballero [29], which they had shown previously [28]. However, when new mutations are only weakly deleterious, they can drift up in frequency before their eventual loss or fixation. At this point, the number of deleterious mutations per haplotype is no longer well-approximated by the deterministic mutation-selection-balance theory, and their dynamics are strongly influenced by stochastic perturbations due to both drift and linked selection. In this weak selection regime, classic BGS theory no longer accu- rately predicts levels of linked diversity [11, 24, 25, 40]. Moreover, selection against weakly del- eterious mutations alters the topology of genealogies, such that they are no longer well- approximated by a rescaled neutral coalescent as assumed under the classic BGS model [41– 43]. To further complicate matters, the distribution of the number of deleterious mutations (and its corresponding fitness distribution) is no longer a stationary Poisson distribution, instead becoming a traveling wave [33, 44, 45] towards increased numbers of deleterious alleles per chromosome and reduced mean population fitness. In asexual populations, each click of “Muller’s ratchet”, which is the stochastic loss of the least-loaded class [46, 47], on average PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 5 / 32 PLOS GENETICS A quantitative genetic model of background selection in humans leads to one deleterious substitution [43]. Unfortunately, determining the rate of deleterious substitutions is another difficult problem [40, 43, 45, 48, 49] related to Hill–Robertson interfer- ence [27, 50]. Quantitative genetic models of linked selection approximate an equilibrium VA under both strong and weakly deleterious mutations by concurrently modeling the rate of fixation of dele- terious alleles in the region, R (for clarity, we consider just a single segment and omit the index i). Santiago and Caballero [29] suggest that equilibrium fitness variance is lower than predicted by V BGS A once weakly deleterious mutations begin to have an appreciable rate of fixation R > 0 per generation in the region. The substitution rate R decreases fitness variance since each sub- stitution removes a segregating site and thus its contribution to fitness variance. Thus the steady-state additive genetic variance of fitness under mutation and negative selection is, VA ¼ ðU (cid:0) 2RÞs: ð5Þ 2N= , as is true for all deleterious mutations. This equation where the condition VA � 0 is met when the probability of fixation is less than or equal to the neutral fixation probability of 1 describes the equilibrium additive genetic fitness variance as the balance of the flux of new var- iation in to the population from deleterious mutations, and the removal of variation due to their substitution (and the decline in mean population fitness). When R = 0, selection is so strong deleterious alleles cannot fix, and the equilibrium fitness variation is due entirely to young rare mutations before their extinction VA ¼ V BGS A � Us. Santiago and Caballero derive Eq (5) through Fisher’s Fundamental Theorem of Natural Selection, but we find an alternative proof ([43]; see S1 Text Section 1.9). We also find that the steady-state additive genic variance in Eq (5) results from diffusion models with a flux of mutations into discrete sites ([51]). While using Eq (5) in Eq (1) leads to a prediction for the fitness-effective population size Nf, closed-form expressions for the deleterious substitution rate R have generally been hard to find [29, 43, 45, 48]. A key insight of Santiago and Caballero [29] is that the deleterious substi- tution rate with linked selection can be approximate by using the probability of fixation pF(Nf, s) [52, 53] using the rescaled fitness-effective population size, i.e. R = NUpF(Nf, s). Given this equation for the substitution rate and Eq (2) for Nf under linked selection, we have a sys- tem of two non-linear equations that can be solved numerically for Nf and R for each segment (again, omitting the segment index i for clarity), � Nf ¼ N exp (cid:0) ðU (cid:0) RÞs � Q2 2 R ¼ 4Nf Us expð4Nf sÞ (cid:0) 1 fitness-effective population size equation ð6Þ substitution rate equation ð7Þ We denote the solutions to these equations, which represent equilibria under mutation- selection-drift process, as eN f and eR. These equilibria also imply an equilibrium level of additive fitness variation eV A in the segment, which are used to calculate the reduction factor B xð Þ ¼ Nf =N at any other genomic position x (see Methods Section Calculating the reduction maps). In our inference method, described in Methods Section Composite likelihood and optimiza- tion), we extend these equations to handle a distribution of selection coefficients, and multiple feature classes. During inference, we also consider an alternate “local rescaling” model that sets the N in the fitness-effective population size equation to B(x)N and re-solves these equations. This alternative model approximates the impact of other segments on each focal segment’s selection dynamics by using the local effective population size implied by the estimated B map. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 6 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans We note that solutions to these equations do not accurately model the substitution rate under all regions of parameter space [25, 49, 54]; in the next section we show through extensive simu- lations that this approximation works quite well under human parameters. Results We provide two main classes of results. First, we show simulation results which demonstrate the accuracy of the SC16 model over the BGS approximation across the parameter space, as well as validations of the composite likelihood strategy we use to fit the SC16 model. Second, we provide fits of our method to human genome data, where we show comparison of models fit using different annotations, the estimated DFEs, and predictions of the deleterious substitu- tion rate. Simulation validation of theory and methods Given that modeling the interplay of mutation, drift, and linked selection under both weak and strong selection has proven to be a difficult problem, we first sought to verify the SC16 the- ory and our genome-wide extension with three levels of simulations: forward simulations of purifying selection in a region, chromosome-scale forward simulations of purifying selection, and simulations of a “synthetic genome” (i.e. by combining independently simulated chromo- somes) to test our composite-likelihood method based on this theory. Simulations of a segment under purifying selection. Our first set of forward simulations was to ensure that the SC16 model adequately captures selective dynamics in a single 100 Mbp basepair region under selection, across a variety of mutation rates and selection coefficients (see Methods Forward simulations). We find a close correspondence between the observed and predicted reductions in effective population size B ¼ Nf =N over all selection and mutation parameters including weak selection (Fig 1A), in contrast to classic BGS theory. Furthermore, to investigate whether this accuracy was caused by the model correctly predicting the equilib- rium fitness variance and substitution rate, we also measured these throughout the simulation. Again, we find diploid SC16 theory accurately predicts both the deleterious substitution rate (Fig 1B) and the genic fitness variance (Fig 1C). Moreover, these simulations provide intuition about the underlying selection process. When mutations are strongly deleterious, there is no chance they can fix, and the substitution rate is zero (Fig 1B for 2Ns > 1). In this strong selection regime, the additive genic fitness vari- ation closely matches the theoretic deterministic equilibrium of VA = Us (dashed gray line, Fig 1C) However, around 2Nes � 1, the substitutions begin to occur as pF moves away from zero. When this occurs, each fixation eliminates variation, and the equilibrium variation diverges from the deterministic mutation-selection equilibrium (Fig 1C). Chromosome-wide simulations and models of negative selection. Given the accuracy of the SC16 model in predicting the reduction factor B and the deleterious substitution rate for a single segment under general mutation-selection processes, we next extended their model so that it could be fit to patterns of windowed genome-wide diversity through a composite likeli- hood approach. Our software method bgspy numerically solves Eq (6) to compute the equi- librium additive genic fitness variance ( eV a) and the deleterious substitution rate (eR) across grids of mutation rates and selection coefficients. This is done for each pre-specified segment in the genome (potentially tens of millions of small regions, which depend on the particular annotation of putatively conserved regions used) that may be under purifying selection (e.g. coding sequences or UTRs). We call the set of theoretic predicted reductions across these grids the B’ maps (to distinguish them from McVicker’s B maps [14]; these can be used to find the equilibrium reduction factor B(x) for any genomic position x. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 7 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Fig 1. Santiago and Caballero (2016) theory models the weak selection regime better than classic BGS theory. (A) The predicted reduction factor under classic B theory (dark gray line) and the diploid SC16 model (colored lines corresponding to mutation rate) compared to average reduction across 10,000 simulation replicates (points). The inset figure is zoomed out to show extent of disagreement under classic BGS. (B) The predicted deleterious substitution rate under the SC16 model, scaled by mutation rate (colored lines) compared to the substitution rate estimated from simulation (points). When 2Ns > 1, the substitution rate is near zero. (C) The genic variance from simulations (points) against the predicted variance under the SC16 model (colored lines). As substitutions begin to occur, the genic variance is decreased from the level expected under strong BGS (dashed line). (D, bottom) The mean squared error (MSE) between whole-chromosome simulations and predicted classic B (dots), new B’ (solid), and locally rescaled B’ (dashed) for different mutation rates (colors). Locally rescaled B’ (yellow lines) are omitted for clarity in the top and bottom rows, since they are identical to B’; Local rescaling only impacts B’ in the 2Ns � = 1 domain. The dashed horizontal line is the approximate theoretic minimum MSE. (D, top) The build-up of negative linkage disequilibria around 2Ns = 1 in whole-chromosome simulations shown in the bottom panel. (E) The average B map from 100 chromosome 10 simulation replicates (gray) against different predictions, for parameters that correspond to 2Ns < 1, 2Ns = 1, and 2Ns > 1. The chromosome shows the density of conserved sites and recombination map used in simulations. https://doi.org/10.1371/journal.pgen.1011144.g001 We validated our predicted B’ reduction maps with realistic chromosome-scale forward simulations of purifying selection using putatively conserved regions and recombination maps for the human genome. We find that our B’ maps and the classic BGS theory B maps closely match simulations when selection is strong (top row of Fig 1E), apart from slight discrepancies in low recombination regions (Fig 1E). Second, we find our theory is vastly more accurate than the classic BGS when selection is very weak (2Nes � 1; bottom row of Fig 1E). In essence, these findings represent the facts that classic BGS theory (which as shown in (4) is a special case of the SC16 model) is accurate when selection is relatively strong and Eq (6) are accurate as s ! 0. Across all mutation and selection parameters simulated, the relative error of the clas- sic B maps is 14.6% whereas the relative error in the new B’ maps is 5%. Nearly all of this error is in the nearly neutral domain (2Nes � 1 domain); for strong and weak selection, the mean squared error between simulations and B’ maps is close to the theoretic lower bound of the mean squared error, � 2 9n= set by the coalescence variance for a 10 kb region [55]. We hypothesized this error in the nearly neutral domain may be due to selective interfer- ence between segments that is not taken into account when we numerically solve Eq (6) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 8 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans independently for each segment. In particular, when we numerically solve these equations, we use a fixed drift-effective population size, N = 1, 000, corresponding to the number of diploids in the simulations. However, in reality, selection throughout the genome would lead a segment at position x to experience a locally reduced effective population size of approximately B(x)N, which is a consequence of selective interference [26, 27, 50]. To test this, we implemented a “locally rescaled” version of the B’ maps, which uses B(x)N as the population size when numer- ically solving these equations. We use this approach because (1) iteratively solving Eq (6) for the entire genome in an inference framework is computationally infeasible, and (2) comparing the initial fits and local rescaling fits allows us to observe how incorporating the local fitness- effective population size impacts parameter estimates, if at all. We find the locally rescaled B’ maps reduce the relative error from 5% to 0.4% and mean squared error (Fig 1D, dashed col- ored lines), but does not entirely eliminate the error in the 2Ns � 1 domain (where the linkage disequilibrium build up is the highest, Fig 1D, top row). Validation of composite-likelihood method using forward Simulations. Our compos- ite-likelihood method estimates the distribution of selection coefficients for each feature type, the mutation rate, and the diversity in the absence of linked selection (π0) by fitting the theo- retic reduction map to windowed genome-wide diversity (see Methods Composite likelihood and optimization). We validated that our method can accurately estimate the selective parame- ters by simulation a “synthetic genome” of the first five human chromosomes (see Methods Forward simulations). We note three findings from these simulations. First, both our implementation of classic BGS theory and our B’ method accurately infer the average selection coefficient under strong selection (Fig 2, middle row). However, when selection was weak, the classic BGS model erroneously estimated strong selection and a very low mutation rate. By contrast, our B’ method estimated selection coefficients much more accurately. A minor discrepancy occurs around 2Ns = 1, likely due to the sensitivity of muta- tions in this region to selective interference (these results do not use local rescaling). To ease computational costs, we only simulated fixed selection coefficients and five chromosomes, and we only assessed the accuracy of average selection coefficients rather than the full estimated DFE. Second, we find slight biases in mutation rate estimates from both our B’ and the classic BGS methods (Fig 2, bottom row). However, mutation rate estimates based on our B’ method are more accurate than classic BGS theory across a range of selection coefficients. Overall, this bias in estimated mutation rates suggests that benchmarking genome-wide negative selection models based on their agreement with pedigree-based rate estimates may not be appropriate. When BGS is not occurring, either due to weak selection or a low rate of deleterious mutations (Fig 2, right column), all estimates deteriorate. This is understandable, as the overall signal from linked selection weakens relative to drift-based noise. We should note, though, that this is an unlikely region of parameter space and this issue can be readily diagnosed from the low R2 values. Finally, we find in additional tests that our estimates are robust to demographic expansions but inaccurate when mutations have recessive effects, since our model assumes additive effects (see S1 Text Section 5.3). We did not test the influence of population bottle- necks, since parameter estimates of out-of-Africa bottlenecked populations (CHB and CEU) did not differ much from YRI estimates (see below). Third, we find the coefficient of determination, R2, between predicted and simulated mega- base-scale diversity serves as a measure of the strength of the linked selection signal in genome-wide data. R2 increases with the intensity of selection against new deleterious muta- tions and mutation rate (Fig 2, top row). Under just drift or weak purifying selection, the vari- ance in diversity is driven by unstructured coalescence noise along the genome and the predicted reduction map, B(x), and does not fit the data well. Under very strong selection PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 9 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Fig 2. Comparison of parameter estimates using classic BGS theory (green lines) with our new B’ method (blue lines) across both full and sparse track types (dark versus light hue), and different mutation rates (columns). Both classic BGS and B’ methods correctly estimate strong selection coefficients when annotation tracks are sparse, but only B’ can accurately estimate selection coefficients when selection is weak or full annotation tracks are used (first row). Mutation rate estimates (second row) are more accurately estimated by the B’ method than classic BGS across selection parameters, but overall show slight biases. Additionally, R2 between predictions and observations increases with selection intensity (third row). Overall, classic BGS methods break down as expected when full-coverage tracks are used, since it cannot accommodate weak selection and neutrality in putatively conserved regions. See Methods for details on sparse versus full tracks. https://doi.org/10.1371/journal.pgen.1011144.g002 (s = 0.05), R2 is reduced; this is likely due to very strong selection having less localized effects and impacting overall genome-wide diversity [30, 31]. Application to human genomic data Annotation model comparison. Our composite likelihood method takes tracks of anno- tated features (an “annotation model”) that are a priori expected to have a similar distribution of fitness effects, and estimates the overall mutation rate and distribution of fitness effects for each feature type. These annotation models specify the putatively conserved “segments” used in Eqs (2) and (4). We consider two classes of annotation models: (1) CADD-based models, which consider the top x% most pathogenic basepairs according to the CADD score, and (2) and more interpretable, feature-based models that includes protein coding regions, introns and UTRs, and PhastCons regions. We include PhastCons regions because they include PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 10 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans highly-conserved, non-coding regions known to harbor important functions [2, 56–58], that would be missed by gene feature only annotation. These two classes of annotation models have a trade-off between fine-scaled specificity to which basepairs are likely to be under negative selection, and interpretability of the DFE estimates for each feature. Finally, for each annota- tion model, we fit a “sparse” track version (conserved regions only) and a “full track” version (which includes another feature class called “other” that includes the rest of the genome). Our method estimates a distribution of fitness effects (DFE) for each feature class. While CADD-based models only have a single conserved feature class (e.g. CADD 6%), feature-based models can have multiple feature classes under varying levels of selective constrain. However, overlapping features (e.g. a basepair that is annotated as both PhastCons and coding sequence) must be assigned to one category or the other. Since this assignment impacts DFE estimates, we fit both of the two alternative models. First, a PhastCons Priority model, where genic fea- tures that overlap PhastCons regions are classified as PhastCons, and all remaining coding basepairs are labeled as CDS. Second, a Feature Priority model, where all coding basepairs are assigned to CDS, and the PhastCons class catches the remaining highly-conserved non-genic regions. In total, we fit four annotation models (CADD 6%, CADD 8%, PhastCons Priority, and Feature Priority) to high-coverage 1000 Genome data for three reference samples: Yoruba (YRI), Han Chinese (CHB), and European (CEU). We assess and compare our models accord- ing to how well they predict patterns of diversity on whole chromosomes left-out during the model fitting process (e.g. leave-one-chromosome-out, LOCO). We use the metric R2 which is the proportion of the observed variance in genomic diversity at the megabase scale predicted by our model on held out data. We experimented with a few smaller spatial scales (e.g. 100 kbp), but our results were consistent with previous results suggesting the human linked selection signal due to purifying selection fits best at the megabase scale [16]. Intuitively, the poorer model fits at smaller spatial scales can be understood as a result of fewer mutations and marginal coalescent genealogies being averaged over, increasing these sources of noise rel- ative to the linked selection signal. LOCO, Overall, we find the PhastCons Priority and CADD 6% models fit equally well (Fig 2A), consistent with recent work using classic BGS theory [16]. However, we find that our models predict out-of-sample diversity levels slightly better than previous methods. For these two models, we find that our B’ method predicts R2 sample variance in Yoruba pairwise diversity at the megabase-scale, respectively. By contrast, the best-fitting CADD 6% model from Murphy et al. [16] explained 60% of diversity in left-out 2 Mbp windows across YRI samples. We note that this difference could be explained by other differences in data processing, optimization, etc. For lineages impacted by the out-of-Africa bottleneck, the goodness-of-fit was lower across all models (e.g. 61.0% and 58.8% for CEU and CHB respectively in the PhastCons Priority model). LOCO ¼ 66:7% of the out-of- LOCO ¼ 67:3% and R2 Since our method is built upon theory that fixes the weak selection problem of classic BGS theory, it should in principle fit equally well when an annotation model includes regions that are under no or little selective constraint and thus (nearly) neutrally evolving. Consequently, our B’ method should fit equally well when applied to “sparse” and “full” track models, since our method in principle can accommodate weak selection and neutrality. Indeed, we find that both in-sample R2 and out-of-sample R2 LOCO values are nearly identical across full and sparse- track models (Fig 2A and 2B, round points), which demonstrates that our method is able to deal with weak selection and that there is little more predictive power to gain from including sites considered “other” as annotations. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 11 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans By contrast, full annotation models fit poorly under classic BGS theory, and lead to unrea- sonable parameter estimates. Additionally, when sparse annotation models contain genomic features that are likely under weak constraint (such as introns and UTRs), models fit worse under classic BGS theory than our B’ method (Fig 2A). However, among the CADD annota- tion models, the goodness-of-fit is nearly identical between B’ and classic BGS methods. This behavior is what we would expect given that the CADD models contain only the most patho- genic sites, which are a priori very likely under the strong selection domain under which B’ and classic BGS theory agree. Finally, we note that the predicted classic B and B’ maps are nearly identical under the CADD 6% model (R2 = 99.99%, see S1 Text Section 5.6 for a com- parison). This reflects the fact that the top 6% most pathogenic CADD sites are under strong selection, and both models are identical in this domain. Overall, our R2 LOCO estimates suggest our model explains up to 67% of out-sample variance in diversity of the megabase scale, even though our method assumes constant demography and homogeneous mutation rates along the genome. A worthwhile question is: how much var- iation could we expect to fit at this scale? Given that selection alters genealogies in ways beyond just decreasing mean pairwise coalescence time and populations have non-constant demogra- phy, an exact analytic answer is intractable. However, we can get an approximate idea if we bðpb (cid:0) bpbÞ2 is determined entirely by the expected neutral assume that the residual variance coalescence noise around the expected coalescence time 2B(b)N. This can be be found analyti- cally, plugging-in our predictions for B(b). This allows us to calculate R2 coal to ballpark the theo- retic variance that is capable of being explained, assuming this coalescent noise process alone (see S1 Text Section 3.6). We note that selection is expected to decrease the variance in coales- cence times beyond a rescaled effective population size implies, thus our R2 underestimate under models with selection. coal would be an P We find that our out-sample R2 LOCO for the Yoruba samples (R2 LOCO � 67%) is slightly above coal � 63% (cid:0) 64% for both samples, compared to the observed R2 LOCO � 59% for CHB under the PhastCons Priority model. Given that bottlenecks the theoretic R2 coal � 66:6%. This suggests our model is in the vicinity of fitting all the signal possible, under the coalescence-only noise assumption. By contrast, for bottlenecked out-of- Africa samples, we find a larger discrepancy between R2 theoretic R2 CEU and R2 would act to increase the residual variance in coalescence times beyond the level implied by the effective population size, this gap would likely shrink under more realistic models or simu- lation-based approximations for R2 coal. Overall, this suggests that purifying selection models fit the vast majority coalescence time variation at the megabase-scale that is capable of being explained (i.e. that is not coalescent noise). coal and observed out-sample R2 LOCO � 61% for LOCO. The Estimated distribution of fitness effects. Our composite likelihood method has three sets of parameters: the expected diversity in the absence of linked selection diversity π0, the mutation rate μ, and the matrix of distribution of fitness effects W across the selection grid for each of the K feature types. Given that the relationship between π and the strength selection is U-shaped (i.e., see Fig 1A), we wondered whether our new B’ model accommodating weak selection would fit the linked selection signal under a different combination of weak and strong selection parameters than observed previously. However, across all of our annotation models, new deleterious mutations in conserved feature classes (e.g. CADD tracks and Phast- Cons regions) were consistently estimated to have strongly deleterious effects (Fig 3A), consis- tent with previous work [14, 16]. The DFE estimates for CADD and PhastCons regions consistently places � 75% of mass on the largest selection coefficient we used, s = 10−2. The CADD 6% DFE estimates imply an average selection coefficient of �s ¼ 0:0065 for CEU, �s ¼ 0:0057 for CHB, and �s ¼ 0:0079 for YRI. Similarly, the PhastCons Priority model implies PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 12 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans average selection coefficient estimates of �s ¼ 0:0063, �s ¼ 0:0059, and �s ¼ 0:0077 for CEU, CHB, and YRI respectively for PhastCons regions. Our DFE estimates for CDS under the Fea- ture Priority model are weaker than those for non-coding PhastCons regions; this reflects the fact that around 30% of mutations to coding sequences result in a synonymous change [59] and are thus likely effectively neutral. Our results are qualitatively consistent with the U- shaped DFEs found for amino acids through Poisson Random Field method [60], but differ from other estimates based on the depletion of rare variants in functional regions [61]. Given the large differences in sample size between the present study and that of [61] as well as the dif- ferences in methodology, it is perhaps unsurprising that our results are in closer alignment with SFS approaches. However, we note that our BGS parameterization of Eq (2), excludes a role for weak positive selection; this form of model misspecification may bias our DFE esti- mates and our results should be interpreted in light of this. Following previous work, our method used a grid of selection coefficients up to s = 10−2. However, we also experimented with a strong selection grid that includes s = 10−1. We find that models fit with the strong selection grid have predictive accuracy, as measured with R2 that were about one percentage point higher. This is suggestive of stronger selection than has previously been estimated using constrained grids (see S1 Text Table 2). For this strong selec- tion grid, we estimate average selection coefficients for the CADD 6% model of �s ¼ 0:042 for CEU, 0.032 for CHB, and 0.044 for YRI. Thus, the predefined selection coefficient grid affects ultimate estimates of the DFE and average selection coefficient. LOCO, However, we find indications of model non-identifiability across the strong selection grid runs. First, estimates of the DFE with the strong grid are bimodal (S1 Fig). For example, under the CADD 6% strong selection grid model, new mutations are estimated to have a selection coefficients of s = 10−1 with 43% chance, s = 10−2 with 7.8% chance, and s = 10−3 with 41% chance in the YRI samples. We propose that one mechanism for this non-identifiability is that very deleterious mutations lead to larger whole-genome reductions in diversity, which are dif- ficult to distinguish from a smaller drift effective population size (i.e. the π0 parameter). One Fig 3. The distribution of fitness effects of new mutations estimates for YRI reference samples. (A) The DFEs using sparse (left column) and full-coverage (right column) tracks, across different annotation models (row). Color indicates the feature type. (B) The DFE of the full-coverage Feature Priority model comparing the estimates across reference population samples. Although this model fit the data less well than alternatives, its results are more interpretable. https://doi.org/10.1371/journal.pgen.1011144.g003 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 13 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans way to test this hypothesis is to look to see if there is a systematic positive relationship across models between average selection coefficient and π0, which is includes the drift-effective popu- lation size Ne. We find this is the case for all of our CADD 6% models. Across all reference samples, average selection was about 7.1 times larger using the strong selection grid, and π0 was 5.6% higher (see S2 Fig). There was no similar consistent change in mutation rate esti- mates among reference samples. In the CADD 6% model, genome-wide average reduction fac- tor �B was �6.1% lower in the default versus constrained grid. Overall, this suggests that the linked selection signal alone cannot differentiate very strong selection from a slightly smaller drift-effective population size. Given that it is debated how strongly demography impacts the deleterious mutation load [62–66], we were curious how consistent our DFE estimates are across samples from different reference populations. Overall, we find DFE estimates are relatively stable across samples from different reference populations and annotation models (S1 Text Section 6). Only in our Fea- ture Priority model (Fig 3B, top row) do we see a slightly different DFE estimate for coding sequences between YRI and CEU/CHB samples, but this could be due to the poorer fit this model has to data. Although the Feature Priority model fits the data less well than alternative models, its DFE estimates are more interpretable. We find that our B’ method estimates a bimodal DFE for coding sequences for the Feature Priority model, with a large mass placed on 10−3 � s � 10−2 and another on the neutral class s = 10−8. This is expected, given that the synonymous and non-synonymous sites that constitute coding sequences are under vastly different levels of constraint and are lumped together in our annotation class. Moreover, features expected to be only weakly constrained such as introns and UTRs have the bulk of DFE mass on the neutral class, with a small but significant amount of mass (� 3%) placed on s = 10−2. As expected, the DFE for PhastCons regions (which in this model correspond to highly-conserved non-coding elements) suggests it is under strong selective constraint; however, we note that block jack- knife-based uncertainty estimates suggest the model is uncertain whether there is some mass on the neutral class. Finally, we highlight one result from our PhastCons Priority annotation model (Fig 4A bottom row): the DFE estimate for coding sequences excluding PhastCons regions is estimated as neutral. This too is expected; the selection signal in coding regions is absorbed by the PhastCons feature, leaving only conditionally neutral sites. Estimates of the deleterious mutation rate are sensitive to model choice. Prior work on genome-wide inference using the classic BGS model fit the patterns of diversity well, but led to unusually high estimates of the mutation rate [14]. This led to the hypothesis that these models could be absorbing the signal of positive selection [67], though other work has found a limited role for hitchhiking at amino acid substitutions [16, 68, 69]. While our simulation results sug- gest estimates of the mutation rate from linked selection models are biased, we still check for rough agreement with pedigree-based estimates [70, 71]. We find across all populations, our mutation rate estimates from CADD-based models are roughly consistent with pedigree-based estimates (Fig 5A), consistent with recent work [16]. Our full-track CADD 6% model estimates the mutation rate as bm ¼ 1:56 � 10(cid:0) 8 for YRI, 1.64 × 10−8 for CEU, and 1.60 × 10−8 for CHB reference samples (S1 Text Section 3.6). As expected, the sparse-track CADD model mutation rate estimates are nearly identical between the B’ and classic BGS methods (Fig 5A top row). However, mutation rate estimates for feature-based annotation models do not agree with pedigree-based estimates. First, mutation rate estimates under from classic BGS theory are an order of magnitude below the expected range (Fig 5A top row). We observe similar behavior when we use the classic BGS model to fit full-coverage annotation models (Fig 5A bottom row). This behavior is consistent with classic BGS theory being unable to fit the DFE to PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 14 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Fig 4. The R2 estimates for sparse (A) and full (B) models, for all samples (colors) fit at the megabase-scale. Round points are our B’ method and diamonds are the classic BGS (we exclude classic BGS in the full track subfigure, since these all fit very poorly). Lighter color round points are the out- sample R2 calculated for classic B values due to computational costs). The horizontal dashed lines are the R2 theoretic variance in coalescence times due to drift alone. LOCO estimates for our B’ method, and arrows show the decline in goodness-of-fit due to in-sample overfitting (out-sample R2 drift expected when the residual variance is given by the LOCO were not https://doi.org/10.1371/journal.pgen.1011144.g004 features under weak constraint (e.g. introns, UTRs, and the “other” feature), and thus must compensate by estimating too low a mutation rate. Second, we noticed that across all populations and sparse and full tracks, the CADD 6% model consistently led to slightly higher mutation rates than the CADD 8% model (Fig 5A bot- tom row; S1 Text Section 3.6). This same pattern was observed in Murphy et al. [16] (Appen- dix 1, Fig 16). This behavior suggests a non-identifiability issue between higher per-basepair mutation rates and annotation tracks that contain more conserved sequence. This is expected from theory, since both classic BGS and SC16 models only depend on mutation rate through the compound parameter μL, where L is the length of the conserved segment. Even though our method is much more robust to the inclusion of non-conserved regions like introns, we still observe this non-identifiability issue. Finally, we note that mutation rate estimates from the Feature Priority model are them- selves too high (bm � 3 � 10(cid:0) 8), reminiscent of the high mutation rate estimates found under McVicker et al.’s model. While both our and Murphy et al.’s CADD and PhastCons-based models alleviate this issue, it is worth considering why this could occur. We can potentially gain some insight from comparing the estimated mutation rates from our Feature and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 15 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Fig 5. Mutation rate estimates across the sparse (top row) and full-coverage tracks (bottom row) models, for the new B’ (circles) and classic BGS (diamonds) methods. Estimates of the mutation rate are consistent between classic BGS and B’ methods for sparse tracks CADD models (overlapping diamonds and circles, top row). Overall, mutation rate estimates are sensitive to the underlying annotation model. https://doi.org/10.1371/journal.pgen.1011144.g005 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 16 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans PhastCons Priority annotation models, which each contain the exact same number of feature basepairs, but whose composition varies based on the priority of overlapping features. That one of these models is our best-fitting model and the other our worst indicates that model fits are sensitive to feature classes which themselves have heterogeneous DFEs. CADD-based mod- els fit better in part due to their fine-scale resolution of selective effects across the genome. While ideally we would fit a CADD model with different features corresponding to the differ- ent percentiles of pathogenicity, these features are on the basepair scale and thus too memory- intensive for our method to currently accommodate. LOCO (Fig 6A). Once scaled by the genome-wide average, predicted and Despite close fit, residual purifying selection signal remains. Comparing predicted against observed diversity along chromosomes, we find a close correspondence consistent with the high R2 observed diversity levels across the genome differ little across samples from reference popula- tions. Given that the CEU and CHB samples are from bottlenecked out-of-Africa populations and their mutation rate and DFE estimates are similar, this is an empirical demonstration that our model is fairly robust to violations of the constant population size assumption of the Fig 6. (A) Observed and predicted diversity of the B’ model fit with the CADD 6% full-track annotation. Once scaled by average diversity, predicted diversity for populations (colored lines) differs little across populations, and closely matches observed diversity within each population (light gray lines). Additionally, we show summaries of CADD density and recombination rate along the chromosome below. (B) PredictedB and observed π for each window. The red dashed line indicates the observed 2 standard deviation ellipsoid, which has nearly the same width as the expected by R2 residual variance is close to theoretic expectations. The yellow points are binned means, and the yellow line is the lowess curve through predicted and observed values. (C) CADD 6% residuals (YRI shown) plotted against the average LoF selection coefficient across genes in megabase windows (estimated by [72]). coal, indicating the https://doi.org/10.1371/journal.pgen.1011144.g006 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 17 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans theory (see S1 Text Section 5.7 for a comparison of the predicted B’ maps across different populations). However, we note a few large (tens of megabases) regions with systematically poorer fit (S1 Text Section 5.6). In Fig 6A we see one such region on the short arm of chromosome 2, from 30 Mbp to 60 Mbp. Interestingly, predicted diversity closely follows the peaks and troughs of this region, however, predicted diversity is lower than observed. We note that a small region within this stretch had been found by a genome-wide scan for associative overdominance [73]. We further investigate this by inspecting whether observations are systematically different from predictions. We confirm a finding of Murphy et al. [16] that regions predicted to experi- ence little reduction in diversity due to background selection (i.e. B � 1) have higher diversity than predicted (Fig 6B, orange line). Murphy et al. [16] suggested that this could reflect ancient introgression between archaic humans and ancestors of contemporary humans. Despite the prediction error in this region, the variance around observed and predicted diversity levels falls very close to what we would expect under the theoretic coalescent-noise-only expectation (R2 coal). As DFE heterogeneity within a class of sites may be poorly fit by our model, we looked for unaccounted selection in our model residuals. First, we inspected whether there was a relation- ship between the fraction of CADD 2% and 6% basepairs and the residual across megabase windows (S3 Fig), finding a negative significant relationship in both cases. CADD 2% was used in this case to search for a residual signal from highly-constrained regions. Moreover, our model over-predicted diversity in windows containing more CADD 2% basepairs than CADD 6%, consistent with heterogeneity in site pathogenicity being poorly fit by our model. How- ever, the total residual variance explained is R2 = 0.3% and R2 = 0.9% for the CADD 2% and 6% tracks respectively, suggesting only a modest amount of selection signal remains within the CADD annotations. There was no relationship between residuals and recombination rate (S4 Fig); we note predicted B values per megabase window are strongly correlated with CADD 6% and recombination rate as expected by theory (S5 and S6 Figs). Since our method does not include the possible effects of linked positive selection, we might expect windows containing hard or soft sweeps would have systematically lower diver- sity levels than predicted. Using the locations of soft and hard sweeps detected using a machine learning approach [74, 75], we tested whether the residuals of the CADD 6% model containing sweeps were systematically different than those not containing sweeps. We find no significant difference between the magnitude of residuals of windows containing sweeps versus those that do not (S7 Fig; Kolmogorov–Smirnov p-value = 0.71). The same was true if we looked at hard or soft sweeps individually as a class. We further tested for remaining selection signal in our CADD 6% model residuals by using gene-specific estimates of the fitness cost of loss-of-function (LoF) mutations from Agarwal et al. [72]. These estimates are based on an Approximate Bayesian Computation approach that estimates the posterior distribution over LoF fitness costs from the observed dearth of LoF mutations per gene, and thus is an independent approach to assess the strength of purifying selection. We averaged the estimated LoF fitness costs across genes for each of our megabase windows, and plotted our residuals against these average LoF fitness costs. Contrary to the weak CADD residual signal described above, we find evidence of a fairly strong relationship between our residuals and average LoF fitness cost (Fig 6C; R2 = 2.1%, p-value 1.27 × 10−10). In other words, roughly 2% of the variance in these residuals is explained by the average fitness costs of LoF mutations in the window. Consequently, our model over-predicts diversity by about s 2= or more in windows harboring the top 1.7% most LoF-intolerant genes. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 18 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Predicted substitution rates indicate potential model misspecification. Since our B’ method also predicts deleterious substitution rates (bR) for each feature class, it allows for another check of model sufficiency by comparing the predicted substitution rates to observed levels of divergence. We estimated sequence divergence on the human lineage using a multiple alignment of five primates for each feature in our feature-based models (Methods Section Sub- stitution rate prediction and divergence estimates). We compared these to the predicted sub- stitution rates per feature, averaging over all segments in the genome. Since our simulations show that mutation rate estimates can be biased, we predicted substitution rates under a fixed mutation rate of μ = 1.5 × 10−8. Fixing the mutation rate also allows us to more easily compare the predictions across our feature-based models. Unfortunately, a careful comparison between our predictions and observed divergence rates is hindered by considerable uncertainty in gen- eration times, heterogeneity in the functional constraint across genes, and the human-chim- panzee divergence time. We assume a generation time of 28 years [76], and calculate the sequence divergence implied by our predicted substitution rates over a range of divergence times, from 6 Mya to 12 Mya [77–80]. We find that predicted substitution rates are qualitatively consistent with the observed divergence along the human lineage for all features except the PhastCons regions (Fig 7). As expected, the predicted substitution rates in features under reduced selective constraint (introns and UTRs, and the “other” feature) are very close to the mutation rate. Throughout, we report our substitution rates as a percent relative to the total mutation rate, μ (here fixed to 1.5×10−8). In our Feature Priority model, coding sequences are predicted to have a substitution rate of 41.20% of the mutation rate, introns and UTRs 94.71%, PhastCons regions 0%, and the “other” feature 99.98%. For comparison, the substitution rates along the human lineage (as a proportion to the substitution rate in putatively neutral regions) are 74.15% in UTRs, 92.44% in introns, 50.96% in coding sequences, and 49.56% in PhastCons regions. The large discrep- ancy between predicted and observed PhastCons substitution rates is driven by our DFE esti- mates suggesting that the bulk of mass is on selection coefficients greater than 10−3, which have no chance of fixation in a population of Ne � 10, 000. We note that our DFE estimates are qualitatively similar to those inferred using the classic BGS model, so the disagreement between observed divergence and predicted substitution rates could indicate a potential model misspecification problem. Possible signal of selective interference. Given the prediction error for substitution rates in highly-conserved regions and that simulation indicates that B(x) is more accurately pre- dicted when we use local rescaling, we modified our composite-likelihood method so that it can be run a second time, on B’ maps locally rescaled by the predicted bBðxÞ from the initial fit. Intuitively, this is based on the notion that if a neutral allele experiences a fitness-effective pop- ulation size of B(x)N, so too should a selected allele, and this should be considered in how the SC16 equations are solved. This is an approximation to selective interference, since interfer- ence acts to lowers the effective population size in other regions [26, 27, 50]. There are five important but tentative results to draw from this analysis. First, estimated mutation rates are in general higher. Under the CADD 6% model, they are bm ¼ 6 (cid:0) 7:3 � 10(cid:0) 8 across populations; for the PhastCons Priority model, they reach the upper limit of our optimization boundary of μ = 8 × 10−8 (S1 Text Section 5.1). Second, all of our leave-one-chro- mosome-out R2 the DFE estimates for both CADD 6% and PhastCons regions in the PhastCons Priority model is now U-shaped (S8 Fig), with 70–77% of mass being placed on a weakly deleterious class, s = 10−5. Interestingly, this is the first of all of our models where such an appreciable mass has been placed on a midpoint in our selection coefficient grid; in all other cases, non-strongly LOCO are about one percentage point higher than the unrescaled model. Third, PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 19 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans Fig 7. The divergence implied from predicted substitution rates under the B’ model versus observed divergence along the human lineage. Black points are the PhyloFit divergence rate estimates per feature (on x-axis). Line ranges are the implied divergences across a range of human-chimpanzee divergence times of 6–12 Mya (using a generation time of 28 years). We show the predicted divergences for our Feature (turquoise) and PhastCons priority (green) annotation models. Additionally, we show the predicted PhastCons region divergences when local rescaling is applied (blue; we omit other locally rescaled predictions since these to not differ substantially). https://doi.org/10.1371/journal.pgen.1011144.g007 deleterious estimates were neutral (s = 10−8). Fourth, predicted pairwise diversity is nearly identical to our original, non-rescaled fit (see S9 Fig). Finally, since local rescaling increases the DFE mass over s < 10−3, mutations in PhastCons regions now have the possibility of fixa- tion. We find that local rescaling the PhastCons Priority, leads the predicted substitution rates in PhastCons regions to be much closer to observed levels (blue line, Fig 6). Finally, we note an important caveat about this analysis. Since local rescaling is done using the first round of maximum likelihood estimates, there is some possibility of statistical “double dipping”, since the B(x) at this position includes the contribution of the focal segment that is being rescaled, and it has already been included in the initial fit that lead produced the pre- dicted B(x) map. Ideally, one would exclude this segment’s contribution to B(x); however, this is computationally unfeasible. However, two observations indicate our findings here are rela- tively robust despite this limitation. First, the results do not change based on whether the B(x) is averaged at the 1 kbp level or at the megabase scale; for the latter, a single segment makes lit- tle contribution to the average. Second, we investigated the extent to which local rescaling modified the B’ maps across selection parameters. We find minor differences between the locally rescaled and standard B’ maps for fixed selection coefficients (i.e. before model fitting) in the nearly-neutral domain (0.2 � 2Nes � 2). Additionally, the locally rescaled and standard maps are identical under strong selection (2Nes = 20) as expected (S10 Fig). Moreover, the cor- relations between the standard and locally rescaled B’ maps across the genome are high (100% PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 20 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans for 2Nes = 20, 96.5% for 2Nes = 2, and 60.21% for 2Nes = 0.2). The overall realized effect of local rescaling is to just alter how deep the “U” is in the relationship between the reduction fac- tor B and the selection coefficient (S11 Fig). Overall, this suggests that models of the signal of linked selection are worryingly sensitive to the theoretic B’ values in the 2Ns � 1 domain. The fact that predicted diversity differs little between standard and locally rescaled B’ methods indicates there may not be enough informa- tion in pairwise diversity alone to differentiate when interference is occurring or the causes of fitness variance along the genome. Moreover, local rescaling turns out to only slightly alter the B’ maps, yet significantly modifies the DFE estimates. This brings the deleterious substitution rate in agreement with observations (since this is predicted with a fixed μ = 1.5 × 10−8; how- ever, the maximum likelihood estimate of mutation is implausibly high. This suggests either the local rescaling approximation to interference is not suitable (though our chromosome- wide simulations show locally rescaled B’ maps are close to the reductions observed from sim- ulations), or that the deleterious mutations-only model does not adequately describe the pro- cesses generating fitness variance. Discussion New mutations at functionally important regions of the genome are a major source of fitness variation in natural populations, as the vast majority of such mutations are deleterious. Purify- ing selection, working to remove these deleterious variants, perturbs genealogies at linked sites, creating large-scale patterns in genomic diversity. While this has been recognized for decades [8, 17], the availability of genomic data allows for methods to estimate the degree to which purifying selection shapes genomic variation and at what scale. Accordingly, there have been a number of recent efforts to fit parametric models of linked selection to polymorphism and divergence data in Drosophila [15] and humans [14, 16]. These efforts have yielded reasonable estimates of the strength of selection on new mutations as well as provided mutation rate estimates that largely agree with pedigree-based estimates. However, previous methods have relied on the canonical background selection model, which assumes that mutations are sufficiently deleterious such that they cannot fix. Consequently, statistical methods using the BGS model should only be expected to fit well when some regions are a priori under strong selective constraint. In reality, the relationship between neutral diver- sity levels and the strength of selection from purifying selection in linked regions is U-shaped, which implies there could be more uncertainty than previously appreciated in the distribution of weak and strongly deleterious mutations. In this work, we developed and fit a different class of linked selection models based on the equilibrium fitness variance [28, 29]. Fundamentally, we model the reduction in diversity as a function of how additive fitness variance is distributed along the recombination map of the genome [28]. We fit a specific model for this fitness variance that supposes all variation is the result of selection against new additive deleterious mutations [29]. Unlike classic background selection theory [8, 11, 13, 17], the SC16 model explains equilibrium fitness variance across all selection coefficients by jointly predicting another central quantity in evolutionary genomics: the substitution rate of deleterious alleles. Our method has at least four improvements over previous whole-genome linked selection methods based on the BGS model. First, our model leads to better fits to data than those based on classic BGS, as measured by predicted out-of-sample diversity. Second, unlike BGS-based methods, our model is capable of fitting weak selection. When regions under weak or little selective constraint are included in methods using classic BGS, parameter estimates can become severely biased. By contrast, we have demonstrated via simulation that our method PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 21 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans can estimate the strength of selection even for weakly constrained features (e.g. introns and UTRs), as well as remaining unannotated regions of the genome. Third, fitting our model pro- duces a simultaneous prediction of substitution rates, which can be compared to observed divergence rates. Finally, the effect of selective interference can be approximated by locally rescaling the B’ maps, which our forward simulations show reduce prediction error of genome-wide diversity levels. Even though our model is able to fit weak selection, our initial estimates of mutation rate and DFEs were consistent with prior work [16]. This, at first glance, suggests further confirma- tion that strong purifying selection is the dominant mode of linked selection in the genome. However, we find that predicted substitution rates for highly-conserved PhastCons features disagree with observed rates of divergence along the human lineage. This disparity between observed divergence and predicted substitution rates is likely a consequence of our DFE esti- mate for PhastCons regions containing little mass over weakly deleterious and neutral selec- tion coefficients that would have some possibility of fixation—a characteristic of DFE estimates from other work too [16]. Our simulation results reveal another possible source of disagreement: in the weak selection domain of 2Nes � 1 there is an appreciable level of disagreement between theory and simula- tion. We hypothesize that this could be because as 2Nes approaches 1, a segment under selec- tive constraint experiences a local fitness-effective population size of B(x)N, and not just N. This local fitness-effective population size is induced by selection at other segments that is not being taken into account by classic BGS theory or our standard SC16 model. When we experi- mented by fitting our model and then using the predicted reduction map to locally rescale Ne to the fitness-effective population size Nf ¼ bBðxÞ, we found the disagreement between pre- dicted and observed substitution rates disappears. This is expected since locally rescaled DFE estimates have an appreciable mass on weakly deleterious selection coefficients, contrary to the standard fits. This pattern is consistent with a scenario where the same selection processes that reduce diversity over long stretches along the chromosome also decrease the efficacy of purifying selection. This idea has been proposed before in an extension to the McDonald–Kreitman test that accounts for how background selection can bias estimates of the proportion of adaptive substitutions [81]. While our simulation results indicate local rescaling reduces error in the weak selection domain, it is worth noting some caveats about this approach. First, local rescal- ing is only an approximation to selective interference; as our simulations show, this approxi- mation reduces error in the predicted reduction B(x), but this may not fully account for how negative linkage disequilibrium builds up and reduces fitness variance. A benefit of the SC16 approach is that the equations can be solved with a locally rescaled effective population that approximates this process. Second, there is the possibility of circularity, since a preliminary fit must be made to estimate B(x), which is then used to re-solve the SC16 equations with a local fitness-effective population size. Despite these caveats, the local rescaling approach suggests selective interference could alter inferences about the DFE and bring predicted substitution rates into agreement with observed divergence rates. However, these results also demonstrate that parameter estimates are extremely sensitive to how accurate the theoretic B’ maps are in this domain. Moreover, we find that predicted diversity differs little across DFE and mutation rate estimates, suggesting there may be limited information in pairwise diversity to differentiate between models, thus inclusion of allele or haplotype frequency information might be informative in the future. Still, our mutation and DFE estimates are relatively stable across reference populations, suggesting these estimates are not too noisy, though they may be biased due to model misspecification. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 22 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans While local rescaling brings substitution rates into agreement, it also re-introduces a similar problem found by Murphy et al. [14]: the estimated mutation rate is too high. While our muta- tion rate includes point mutations as well as all other forms of deleterious variation (e.g. inser- tions/deletions, copy number variants, etc.), our estimations suggest exceedingly high rates of deleterious variation per generation. Examination of the local residuals of our predicted diversity levels demonstrated no system- atic effect of previously identified hard and soft selective sweeps [75]. This result echoes what has been observed in previous efforts to look at genome-wide patterns of linked selection [16, 69], and suggests that the scale of perturbation due to selection sweeps is more restricted (e.g. at the kilobase scale, [82]) than the scale at which we are modeling variation. Taken together this suggests that selective sweeps are not likely responsible for shaping the majority of vari- ance in large-scale patterns of chromosomal variation in humans. Given that our model is essentially parameterized by levels of fitness variance along the genome, the high estimates of mutation rate could suggest that purifying selection is not the only source of fitness variance generating the genome-wide linked selection signal in humans. Selection on polygenic traits could be another source of fitness variance, since the underlying theory suggests levels of pairwise diversity are determined by total additive fitness variance (i.e. Eq (2)). Alternatively, purifying selection could be the main source of fitness variance, but the complexity of selective interference may be poorly approximated by the local rescaling approach. Another possibility is that our assumption throughout of additive effects may lead to biases given the vast majority of deleterious mutations are partially recessive. Additionally, our approach has ignored the possibility of back mutations at sites at which there had been a prior deleterious substitution, which also can bias the predicted reduction in diversity. Future theoretic work testing the robustness to these forms of model misspecification, perhaps with realistic forward simulations of multiple modes of linked selection (e.g. [83]) are needed to fully disentangle these processes. Additionally, our DFE estimates are done using a discretized logarithmic-spaced grid following previous work [15, 16]; future work could explore continu- ous and parametric forms for the DFE. As we find evidence of strong selection against loss-of- function in our model residuals, it is also possible that the bulk of fitness variance is due to purifying selection, but our model is unable to account for strong heterogeneity in the DFE per annotation class. Moving forward it remains a central goal to understand how the sources of fitness variation shape the striking patterns of diversity along the human genome. Our work embeds this ques- tion in the quantitative genetic framework that is more accurate and flexible than proceeding models, but there is much work yet to do to incorporate important population genetic features such as dominance effects and selective interference. Overall, the complex interplay of muta- tion, selection, drift, and interference may confound our understanding of selection in the human genome for some time. Methods Solving the B’ equations for each segment Our software bgspy [84] first calculates the equilibrium additive genetic fitness variation eV A and deleterious substitution rate eR for each user-specified segments in the genome. These equilibria are calculated across grids of mutation rate weighted by the DFE mi and selection PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 23 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans coefficient sj, by numerically solving the following system of equations, � Nf ¼ N exp (cid:0) VA � Q2ðmi; sjÞ 2 R ¼ 4Nf Usj expð4Nf sjÞ (cid:0) 1 where, VA ¼ ðU (cid:0) 2RÞsj Q2ðmi; sjÞ ¼ 2 ð1 (cid:0) ZÞð2 (cid:0) ð2 (cid:0) MÞZÞ Z ¼ 1 (cid:0) Usj U (cid:0) M effective population equation ð8Þ substitution equation ð9Þ fitness variance equation ð10Þ linkage inflation factor ð11Þ variance decay rate ð12Þ and U = miL and M = rBPL are the total mutation and recombination rates in the segment. A detailed derivation of these equations can be found in S1 Text Section 1. The recombination rate in a segment is determined by a user-supplied recombination map. Calculating the reduction maps Our method uses the pre-computed equilibria eV A;g for each segment g (specified by the partic- ular annotation model) to calculate the reduction map B(x;mi, sj) at positions x across the parameter grids described above. Since we assume multiplicative fitness, the reduction is the product of each segment’s contribution accounting for the recombination is, Bðx; mi; sjÞ ¼ exp (cid:0) 1 2 X g2G ! eV A;gðmi; sjÞQ2 gðmi; sj; rx;gÞ ð13Þ where rx,g is the recombination fraction between the focal site and segment g. Here, Q2(mi, sj, rx,g) is given by Eq (3) squared. A separate reduction map is calculated for all features G within a specific feature type. We calculate B’ calculate for log10-spaced grids over 10−1 � s � 10−8 and 10−11 � m � 10−7, in 10kb increments across the genome. Composite likelihood and optimization Following previous approaches [14–16], we use a composite likelihood approach to fit our neg- ative selection model. Per-basepair allele count data (described below) is summarized into the number of same and different pairwise differences per window. All of our primary models were fit with megabase windows, since previous work has found the strongest selection signal at this scale (we confirm this with one CADD 6% fit at the 100 kbp scale). Our binomial likelihood models the number of different pairwise comparisons observed per window given the total number of pairwise comparisons. The binomial probability for �Bðb; m; WÞ, where bars indicate averages over some bin width. The window b is �pðb; CÞ ¼ p0 free parameters C = {π0, μ, W} are the expected diversity in the absence of selection (π0), the mutation rate (μ), and the distribution of fitness effects for the discretized selection grid and K PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 24 / 32 PLOS GENETICS A quantitative genetic model of background selection in humans features (W). The reduction at position x is then, log B x; m; Wð ð Þ Þ ¼ (cid:0) 1 2 X Xns g2G j¼1 VgðmWj;kðgÞ; sjÞQ2 gðmi; sj; rx;gÞ: ð14Þ See S1 Text Sections 2.5 and 3.11 for more details. Our method uses two tricks to improve optimization over the mutation and DFE parame- ters. First, we find our pre-computed B(x;μ, W) reduction maps (described in the previous sec- tion) are exponential over columns of μW, which allows for optimization over this smooth function rather than the grid. Second, we use softmax to convert constrained optimization over the DFE columns (which must sum to one) to unconstrained. We tested multiple differ- ent optimization routines, finding that BOBYQA outperformed alternatives [85, 86]. We inspected and confirmed convergence with diagnostic plots finding stable optima across 10,000 random starts (see S1 Text Section 3.10). We assessed model fit using out-sample pre- dictive error, calculated by leaving out a whole chromosome during fitting and predicting its diversity. To calculate uncertainty, we used a block jackknife approach in 10 Mbp windows (S1 Text Section 3.13). All model fits, analyses, and produced data are available on Dryad [87]. Human population genomic data Our analyses was conducted on the Yoruba (YRI), European (CEU), and Han Chinese (CHB) reference sample individuals from the high-coverage 1000 Human Genomes data aligned to GRCh38/hg38 [88]. Since nucleotide diversity is a ratio estimator, it can be biased when subtly different filtering criteria are applied to variant and invariant sites. To prevent this, we con- ducted our analyses on Genomic VCF (gVCF) files that contain genotype calls for both variant and invariant sites [89]. Then, we apply the same genotype filtering criteria to all called sites (S1 Text Section 3.2). We also applied sequence accessibility masks that containing only non- repeat, non-centromeric sequence that passed the 1000 Genomes strict filter ([90]; S1 Section 3.3). Since our theory only considers the indirect effects of linked selection on a site, we addi- tionally masked sites that are likely under direct selective constraint (see S1 Text Section 3.4). Finally, for every basepair passing these filtering and masking criteria, we counted the number of reference and alternative allele counts (excluding all multiallelic, indel, and CNV variants). For all of our main analyses, we used the recombination map from Halldorsson et al. [91] estimated from a trio-based design to avoid circularity that could occur by using LD-based maps. We use Ensembl gene annotation [92], a special CADD Score dataset with McVicker B scores removed (to avoid circularity; [93, 94]), and PhastCons regions [2]. We did not account for mutation rate heterogeneity along the genome, since this would require using divergence- based estimates of local mutation rates that would introduce circularity when we predict diver- gence rates under our model. Forward simulations We conducted forward simulations of negative selection on whole human chromosomes to validate our method at two stages. First, we simulated negative selection on chromosome 10 using a realistic recombination map and putatively conserved features to confirm that our clas- sic B and new B’ maps matched the average simulation reduction map across mutation and selection parameters. Second, we evaluated our composite likelihood method by simulating negative selection on the first five human chromosomes, across grids of fixed mutation and selection parameters. We then combined these into a synthetic genome, and overlaid muta- tions on the ARG. Then, we ran our likelihood methods on the resulting allele count data to PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 25 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans assess model accuracy. We ran additional synthetic genome simulations like these to evaluate the impact of two model violations: recessivity of deleterious mutations and expanding popu- lations. For the latter, after 9.3 generations, we grew the population by factor of 1.004 each gen- eration to mimic the human expansion out-of-Africa [95]. We did not simulate population bottlenecks since our analyses showed little difference between bottlenecked out-of-Africa samples (CEU and CHB) and YRI samples. More details about these and the segment simula- tions shown in Fig 1A–1C can be found S1 Text Section 4. Substitution rate prediction and divergence estimates Substitution rates were predicted by resolving Eq (6) for the given estimated product between mutation rate and DFE weight, wi,j = μWi,j. We estimated the divergence along the human branch using PhyloFit [96] run on a subset of the UCSC 17-way Multiz alignments [97] con- sisting of humans and four other primates (Pongo abelii, Pan troglodytes, Pan paniscus, Gorilla gorilla). PhyloFit was run using the HKY85 substitution model per-feature; estimates from alternate substitution models yielded equivalent results. Further details about this process can be be found in the GitHub repository (https://github.com/vsbuffalo/bprime). Supporting information S1 Text. Supporting information. (PDF) S1 Fig. The DFE estimates for the strong selection grid (up to s = 10−1). (TIF) S2 Fig. The maximum likelihood estimates of π0 and average selection coefficient implied by the estimated DFE for the CADD 6% models. Diamonds indicate estimates under the strong selection grid (up to s = 10−1) and circles indicate estimates under the default grid (up to s = 10−2). (TIF) S3 Fig. Residuals from the CADD 6% sparse track plotted against the fraction of basepairs in a window annotated by a CADD region. (TIF) S4 Fig. Residual plotted against the average recombination rate in megabase window (for CADD6 model). (TIF) S5 Fig. Predicted B per Mb window plotted against fraction of window overlapping a CADD 6% element, colored by recombination rate. (TIF) S6 Fig. Predicted B per Mb window plotted against average rec. rate per Mb window. (TIF) S7 Fig. The distributions of residuals in windows containing hard or soft sweeps (blue) and not containing sweeps (orange) found by [75]. (TIF) S8 Fig. The DFE for YRI samples with local rescaling. The maximum likelihood mutation rate estimate for this is bm ¼ 8 � 10(cid:0) 8, which is the upper boundary of the range used during PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011144 March 20, 2024 26 / 32 PLOS GENETICSA quantitative genetic model of background selection in humans optimization. (TIF) S9 Fig. Chromosome 2 predictions on the YRI samples, with locally rescaled model fits. The observed data is the dark gray line, and the normal MLE for the PhastCons Priority model is the blue line. The locally rescaled predictions are the green line. The dashed red line are the prediction using the standard B’ map (without local rescaling) and the maximum likelihood estimates from the locally rescaled fits. The large discrepancy in this shows that estimates are highly dependent on the B’ map. (TIF) S10 Fig. The standard (dark gray) and locally rescaled (blue) B’ maps for different μ = 1.6 × 10−8 and three different selection coefficients. For s = 10−5 (2Nes = 0.2), locally rescal- ing alters the predicted reduction so that it is essentially insignificant (B � 1). For mid-strength selection s = 10−4 (2Nes = 2), there is only a very slight difference between standard and locally rescaled B’ maps. Finally, for strong selection s = 10−3 (2Nes = 20), local rescaling does not change the B’ maps, as expected. (TIF) S11 Fig. Genome-wide average B(x) values across selection coefficients for μ = 1.58 × 10−8, for both the standard (blue) and locally rescaled B’ (orange) maps. This indicates that locally rescaling the B’ maps only in practice changes how deep the “U” is. (TIF) Acknowledgments We would like to thank Doc Edge, Ben Good, Taylor Kessinger, Graham McVicker, Priya Moorjani, David Murphy, Rasmus Nielsen, Guy Sella, Joshua Schraiber, and Peter Sudmant for helpful discussions, and Martin Kircher for providing modified CADD tracks. We thank Brian Charlesworth, Graham Coop, Matt Hahn, Nate Pope, Enrique Santiago for comments on the manuscript. Author Contributions Conceptualization: Vince Buffalo, Andrew D. Kern. Data curation: Vince Buffalo. Formal analysis: Vince Buffalo. Funding acquisition: Andrew D. Kern. Investigation: Vince Buffalo, Andrew D. Kern. 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10.1371_journal.pdig.0000349	RESEARCH ARTICLE Microsoft Bing outperforms five other generative artificial intelligence chatbots in the Antwerp University multiple choice medical license exam Stefan MorreelID 1, Veronique VerhoevenID 1, Danny Mathysen1,2 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Morreel S, Verhoeven V, Mathysen D (2024) Microsoft Bing outperforms five other generative artificial intelligence chatbots in the Antwerp University multiple choice medical license exam. PLOS Digit Health 3(2): e0000349. https:// doi.org/10.1371/journal.pdig.0000349 Editor: Imon Banerjee, Mayo Clinic, Arizona, UNITED STATES Received: August 19, 2023 Accepted: January 10, 2024 Published: February 14, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pdig.0000349 Copyright: © 2024 Morreel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The questions of this exam cannot be made publicly because they will be used again in future exams. Consequently, the authors cannot share all the AI responses. Access 1 Department of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium, 2 Dean’s Department, University of Antwerp, Antwerp, Belgium stefan.morreel@uantwerpen.be Abstract Recently developed chatbots based on large language models (further called bots) have promising features which could facilitate medical education. Several bots are freely avail- able, but their proficiency has been insufficiently evaluated. In this study the authors have tested the current performance on the multiple-choice medical licensing exam of University of Antwerp (Belgium) of six widely used bots: ChatGPT (OpenAI), Bard (Google), New Bing (Microsoft), Claude instant (Anthropic), Claude+ (Anthropic) and GPT-4 (OpenAI). The pri- mary outcome was the performance on the exam expressed as a proportion of correct answers. Secondary analyses were done for a variety of features in the exam questions: easy versus difficult questions, grammatically positive versus negative questions, and clini- cal vignettes versus theoretical questions. Reasoning errors and untruthful statements (hal- lucinations) in the bots’ answers were examined. All bots passed the exam; Bing and GPT-4 (both 76% correct answers) outperformed the other bots (62–67%, p = 0.03) and students (61%). Bots performed worse on difficult questions (62%, p = 0.06), but outperformed stu- dents (32%) on those questions even more (p<0.01). Hallucinations were found in 7% of Bing’s and GPT4’s answers, significantly lower than Bard (22%, p<0.01) and Claude Instant (19%, p = 0.02). Although the creators of all bots try to some extent to avoid their bots being used as a medical doctor, none of the tested bots succeeded as none refused to answer all clinical case questions.Bing was able to detect weak or ambiguous exam questions. Bots could be used as a time efficient tool to improve the quality of a multiple-choice exam. Author summary Artificial chatbots such as ChatGPT have recently gained a lot of attention. They can pass exams for medical doctors, sometimes they even perform better than regular students. In this study, we have tested ChatGPT and five other (newer) chatbots in the multiple-choice exam that students in Antwerp (Belgium) must pass to obtain the degree of medical doc- tor. All bots passed the exam with results similar or better than the students. Microsoft PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 1 / 11 PLOS DIGITAL HEALTHto the study data can be requested by contacting fampop@uantwerpen.be and will be granted as long as the requestor can guarantee that they will not be made publicly and no students will have access to them. As supplementary material, we do provide a datasheet with our raw data excluding the answers and the questions (S1 Data and S2 Data). Individual student results, even anonymised will never be shared as it is impossible to ask permission to all students. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Performance of AI bots in a medical license exam Bing Chat (name at the time of writing, at the time of publication called Microsoft Copi- lot) scored the best of all tested bots but still produces hallucinations (untruthful state- ments or reasoning errors) in seven percent of the answers. Bots performed worse on difficult questions but they outperformed students on those questions even more. Maybe they are most useful when humans don’t know the answer themselves? The creators of the bots try to some extent to avoid their bots being used as a medical doctor, none of the tested bots succeeded as none refused to answer all clinical case questions. Microsoft Bing also turned out to be useful to find weak questions and as such improved the studied exam. Introduction The development of AI applications announces a new era in many fields of society including medicine and medical education. Especially artificial intelligence (AI) chatbots based on large language models (further called bots) have promising features which could facilitate education by offering simulation training, by personalizing learning experiences with individualised feedback, or by acting as a decision support in clinical training situations. However, before adopting this technology in the medical curriculum, its capabilities have yet to be thoroughly tested [1,2]. Soon after the first bots became publicly available, higher medical education institutes started to report on their performance in medical exam simulations [3]. A scoping review listed its potential use in medical teaching: automated scoring, teaching assistance, personal- ized learning, research assistance, quick access to information, generating case scenarios and exam questions, content creation for learning facilitation, and language translation [4]. Whereas bots seem to be informative and logical in many of their responses, in others they answer with obvious, sometimes dangerous, hallucinations (confident responses which how- ever contain reasoning errors or are unjustified by the current state of the art) [5]. They will reproduce flaws in the datasets they are trained by; they may reflect or even amplify societal inequality or biases or generate inaccurate or fake information [6]. Mostly, bots perform near the passing mark [6–9], although they outperform students in some reports [10–12]. Performance is in general better on more easy questions and when the exam is written in English [13,14]. Notably their score is generally worse as exams at more advanced stages in the medical curriculum are offered. However, bots seem to learn rapidly, and new versions do considerably better than their prototypes [15–17]. As bots evolve, their proficiency needs continuous monitoring and updating. Whereas media articles state that higher education institutes already anticipate the dangers of bots in terms of possible exam fraud, they also offer opportunities to assist in developing exams, for example by identifying ambiguous or badly formulated exam questions. Very few comparisons between different bots have been made, and those that do exist only compare two or three bots and do not report hallucination rates [18,19]. In this study, we use the final theory exam that all medical students need to pass to obtain the degree of Medical Doctor. It is followed by an oral exam which is not part of this study. The current exam was used in 2021 at the University of Antwerp, Belgium. It is similar to countrywide exams used in other countries, such as the United States Medical Licensing exam step 1 and step 2CK [20]. In this study we have tested the current performance of six publicly available bots on the University of Antwerp medical licensing exam. The primary outcomes concern the PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 2 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam performance of each bot on the exam. Secondary outcomes include performance on subsets of questions, interrater variability, proportion of hallucinations and the detection of possible weak exam questions. Material and methods Ethics This experiment has been approved by the Ethics Committee of the University of Antwerp and the Antwerp University Hospital (reference number MDF 21/03/037, amendment num- ber 5462). Materials At the end of the undergraduate medical training at the University of Antwerp, medical stu- dents must pass a general medical knowledge examination before being licensed as medical doctor. Besides an oral viva examination, this general medical knowledge examination con- tains 102 multiple choice questions covering the entire range of curricular courses. In this study, the exam as it was presented to the students in their second master year (before their final year of clinical training) was used. The scoring system was adapted afterwards, so the stu- dent’s scores in this paper do not reflect the actual grades given to the students. The questions were not available online, so they were not used for the training of the studied bots. Bot selection Six bots that are publicly available and can currently be used by teachers and students were tested. The most widely used free bots were selected: ChatGPT (OpenAI), Bard (Google), and New Bing (Microsoft, called Bing Chat at the time of writing and Microsoft Copilot at the time of publication). Claude instant (Anthropic), Claude+(Anthropic) and GPT-4(OpenAI) were added to the list because they allow for an evaluation of the difference between a free and a paying version. Even though Bing is based on the GPT-4 large language model, it also uses other sources such as Bing Search so it is a customized version of the pure GPT-4 bot [21]. Data extraction The exam was translated using Deepl (DeepL SE), a neural machine translation service. Clear translation errors were corrected by author SM, but the writing style and grammar were not improved in order to mimic an everyday testing situation. Questions containing images/tables (N = 2) and local questions were excluded (N = 5). Local questions were excluded because they concern theories, frameworks or models that have only been described in Dutch and are only applicable to Belgium and the Netherlands. Literal translation of these questions leads to non- sense questions in English. Details on how and when the bots were used can be found in Table 1. By coincidence, the authors found out that when Bard refuses to answer a medical question, prompting it with “please regenerate draft” may force it to answer the question anyhow. This was not the case for the other bots. In all cases where Bard refused to answer, this additional prompt was used. Outcomes The primary outcome was the performance on the exam expressed as a proportion of correct answers (score). This outcome was also measured in the same way as the students were rated on this exam (adapted score): eleven questions contained a second best answer (an acceptable alternative to the best answer), a score of 0.33 was awarded when this option was chosen; PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 3 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam Table 1. Overview of the tested generative chat bots. Bot Bing Bard Large Language Model GPT-4 PALM 2 ChatGPT GPT-3.5 Properties Avoiding memory retention Log in? Conversation style = More precise Accessed using a virtual private network to emulate US location Accessed through Poe* “New topic” function is used after each question Microsoft account “Reset Chat” function is used after each question A new chat is started using the broom button Google account Poe* log in Claude+ Claude version 1 Accessed through Poe* Broom button Claude Instant GPT-4 Lighter version of Claude version 1 Accessed through Poe* Broom button GPT-4 Accessed through Poe* Broom button Poe* log in Poe* log in Poe* log in Access dates Price 7-9/6/2023 Free 12-14/06/ 2023 12-26/06/ 2023 12-26/06/ 2023 12-26/06/ 2023 12-26/06/ 2023 Free Free, A paying version exists based on GPT-4. Free trial on Poe paying afterwards Free trial on Poe paying afterwards Free trial on Poe paying afterwards GPT: generative pre-trained transformer PaLM: Pathways Language Model : Poe (Platform for Open Exploration, Quora) was used because it allows fluent testing of multiple bots at the same time. A trial subscription of one week was used. https://doi.org/10.1371/journal.pdig.0000349.t001 twenty questions contained a fatal answer (this option is dangerous for the patient) leading to a score of -1. For calculation of the student’s scores, the image, table, and local questions were excluded as well. The primary outcomes were assessed in four subsets of answers. Firstly, the difficulty of the questions: thirteen questions were difficult (recorded P-value in question bank below 0.30 meaning that less than 30% of the students answered the question correct [22]), 36 easy (recorded P-value in question bank above 0.80) and 46 moderate (recorded P-value in ques- tion bank between 0.30 and 0.80). Secondly, the grammar of the questions: negative formu- lated questions (e.g., “which statement is not correct?”) vs positive statements. Five questions were negatively formulated. Thirdly, the type of question: theory (50 questions) or describing a patient (clinical vignette, 45 questions). Finally, questions with vs without fatal answers. In those cases where a bot answered a question incorrectly with a fatal answer, the propor- tion of selected fatal answers among all wrong answers was calculated. The primary outcome was also assessed for a virtual bot (called Ensemble Bot), the answer of this bot was the most common value (mode) of the answers of all six bots [23]. The reason- ing behind an ensemble bot is that it enables possible improvements to a decision system’s robustness and accuracy by combing several bots and thus reducing variance [24]. Three additional outcomes were assessed. Firstly, the proportion of hallucinations as rated by the authors among the incorrect answers of the best scoring bot. Authors VV and DM read all incorrect answers and judged them as containing a hallucination or not. In case of discor- dance, author SM made a final decision. A hallucination was previously defined as content that is nonsensical or untruthful in relation to certain sources [25]. This definition is not usable for the current research so the authors defined a hallucination as content that either contains clear reasoning or is untruthful in relation to current evidence based medical literature. To detect reasoning errors, no medical knowledge is required. For example: “the risk is about 1 in 100 (3%)”. To detect untruthful answers, the authors had to use their own background knowl- edge combined with common online resources to verify the AI answers. One clear example of an untruthful answer given by several bots: “This is a commonly used mnemonic to remember the order: "NAVEL"—Nerve, Artery, Vein, Empty space (from medial to lateral).” The bots PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 4 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam suggested this is the order of the inguinal structures from lateral to medial. This mnemonic does exist, but it should be used from lateral to medial. Because a multiple-choice exam was studied, the hallucinations could not be found in the answer itself but in the arguments sup- porting the selected answer. Bots never answer with a simple letter, they all produce written out answers of varying length. The authors wanted to report reasoning errors and untruthful answers separately but found out that often, these two were both present in a bot’s answer so this outcome was suspended. Secondly, the proportion of possible weak questions among the incorrect answers of the best scoring bot was assessed. For this outcome, all authors discussed all incorrect answers of the best scoring bot and reached unanimous consensus. Thirdly, the interrater variability was examined. Originally, the authors planned to test whether user interpretation of the answers would be different from strict interpretation of the bot’s answer as this difference was significant in a previous study [9]. This outcome was sus- pended because such cases occurred only in ChatGPT and Bard. Analysis The differences in performance among the bots/students, differences in performance among categories of questions, and differences in the proportion of hallucinations were tested with a one-way ANOVA test and pairwise unpaired two-sample T-tests. P-values were 2-tailed where applicable, and a p-value of less than 0.05 was considered statistically significant. A p-value between 0.05 and 0.10 was considered a trend. For the wrong answers on questions with a fatal answer, a chi2 test was used to assess the difference between the bot’s proportion of fatal answers and the random proportion of fatal answers (which equals 0.33). Fleiss’ Kappa was used to assess the overall agreement among the bots. Cohen’s kappa was used to assess pairwise interrater agreement between the different bots. Raw data was collected using Excel 2023 (Microsoft). JMP Pro version 17 (JMP Statistical Discovery LLC) was used for all analyses except Fleiss’ kappa which was calculated in R version 4.31 (DescTools package). Results Overall exam performance See Table 2 for an overview of the scores of the tested bots. Bing and GPT-4 scored the best with 76% correct answers and an adapted score (the way students were rated) of 76% as well. Table 2. Performance of generative chat bots on the University of Antwerp Medical License Exam (95 questions). Correct Answers (N) Score (%) 95% Lower CI 95% Upper CI No answer (N) Refusal to answer (N) Several answers without clear choice (N) Unclear answer (N) Wrong answer (N) Adapted score (%) 72 64 58 72 64 60 76 67 61 76 67 63 66 57 51 66 57 53 83 76 70 83 76 72 3 1 0 1 1 2 1 0 4 0 2 2 1 3 2 3 5 3 5 2 0 1 0 0 13 25 31 18 23 28 76 67 62 76 67 62 Bing ChatGPT Bard GPT-4 Claude+ Claude Instant *This is the score that was used to assess students. A second-best answer was rated as +0.33 and a fatal answer as -1. CI: confidence interval for the score (%) To illustrate this performance S1 Table contains a question and the responses from all selected bots. https://doi.org/10.1371/journal.pdig.0000349.t002 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 5 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam The mean score of all bots was 68%, the scores of the individual bots were not significantly dif- ferent from this mean (p = 0.12). However, Bing and GPT-4 scored significantly better than Bard (p = 0.03) and Claude Instant (P = 0.03). GPT-4 had the same score as Bing but had more wrong answers (25 versus 13). Claude+ did not significantly score better than Claude Instant. All Bots gave one fatal answer (on different questions) except Bard which did not give any fatal answers. Bing gave four second best answers, ChatGPT/Bard/GPT three, Claud two and Claud Instant only one. For thirteen questions, Bard refused to answer. After prompting Bard up to five times with “regenerate draft”, it still refused to answer four questions, seven were answered correctly and two were wrongly. The performance of the bots using the adapted score was very similar because the added points of second-best answers were smoothed out by the lost points due to fatal answers. The mean score of the 95 students was 61% (standard deviation 9), the mean adapted score for students was 60% (standard deviation 21). The Ensemble Bot (answers with the most common answer among the six bots) scored the same as Bing (72 correct answers, 76%). Performance for subsets of questions The bots scored on average 73% for easy questions and 62% for difficult questions (P = 0.06). The students scored on average 75% for easy questions and 32% for difficult questions (p<0.01). Assessing difficult questions only, ChatGPT performed best with a score of 77%, Bing/GPT4 scored 69%. The students scored 32% on difficult questions which is significantly lower as compared to ChatGPT, Bing, and GPT-4 (p<0.01). A similar but smaller effect was found for moderate questions (Bing versus students, 72% versus 59%, p = 0.07) but not for easy questions (69 vs 74%, p = 0.30) No significant difference in performance on negative versus positive questions (p = 0.16) and on clinical vignettes versus theory questions (p = 0.16) was found. Such a difference was not found for the students either (p = 0.54 and 0.38 respectively). When examining individual questions, errors on clinical vignette questions were often caused because Bing missed an important clue in the context or the history of the patient. For example, in a question concern- ing the timing of a flu vaccine for a pregnant patient consulting in august, Bing answers that the flu vaccine was necessary now. Bing missed the clue about august: flu vaccines should be given later and are generally not available yet in August (in Europe) [26]. The bots scored on average 72% on questions with a fatal answer which is not significantly different from questions without a fatal answer (68%, p = 0.39). Among the 34 wrong answers, the fatal answer was chosen five times (15%) which is lower than can be expected by chance only (11 wrong answers or 33%, p = 0.09). The students did perform worse on these questions (mean 64% versus 52%, p = 0.03). Among the 843 wrong student answers, the fatal option was chosen in 111 answers (13%). Detection of hallucinations Hallucinations were found in 7% of Bing’s and GPT4’s answers. This was significantly lower than Bard (22%, p <0.01) and Claude Instant (19%, p = 0.02). ChatGPT had 15% hallucina- tions and Claude+ 12%, this was not significantly different from Bing and GPT4 (P>0.10 for all these comparisons). See Table 3 for a question on which five bots hallucinated (reasoning errors). Detection of weak questions using Bing Among the 23 incorrect answers of Bing, three questions were unclearly written and two were not in line with current literature. An example of a detected weak questions is one concerning PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 6 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam Table 3. Example of all generative chatbot hallucinations on one question. Reasoning errors are indicated in bold. Question ChatGPT Bard Claude Instant Claude+ What is the approximate risk of having a child with Down syndrome for a 40-year-old woman? a) 1% (correct answer) b) 3% c) 5% d) 10% The approximate risk of having a child with Down syndrome for a 40-year-old woman is d) 10%. As women age, the risk of having a child with Down syndrome increases. At age 40, the risk is approximately 1 in 100 or 10%. This is compared to a risk of approximately 1 in 1,250 or 0.08% at age 25. The correct answer is b) 3%. The risk of having a child with Down syndrome increases with the mother’s age. For a 40-year-old woman, the risk is about 1 in 100 (3%). This means that about 3 out of every 100 babies born to a 40-year-old woman will have Down syndrome. For a 40-year-old woman, the approximate risk of having a child with Down syndrome is about 1 in 50 or 2%. So the closest answer is b) 3%. The risk of Down syndrome increases with the mother’s age. At age 25, the risk is about 1 in 1,250. At age 35, the risk is about 1 in 400. By age 40, the risk increases to about 1 in 100. And by age 45, the risk is about 1 in 30. The risk of having a child with Down syndrome increases with maternal age. For a 40-year-old woman, the risk is approximately: c) 5% The risk of Down syndrome roughly doubles every 5 to 6 years after age 30: Under 30 years old: 0.1% At age 35: 1% At age 40: About 2% to 5% At age 45: About 5% to 10% So for a 40-year old woman, the approximate risk is in the ballpark of 5%, making C the closest option. https://doi.org/10.1371/journal.pdig.0000349.t003 renal replacement therapy: “Complete. Renal function replacement therapy is indicated. . . a) in any symptomatic patient with an eGFR <15 ml/min/1.73m2. b) only in patients under 65 years of age. c) in anyone with an eGFR < 6 l/min/1.73m2 d) only when urea is elevated”. Bing answered “a)”. After review of current literature, the authors judge that an eGFR below 15 is indeed a commonly used cut of value for starting renal replacement therapy but it is not the only reason so start dialysis. Because statement a contains “any”, Bing’s answer is wrong, but the authors do understand why Bing gave this answer and why a student might give this answer as well. The same argument applies to answer c which is supposed to be the correct answer. Even more, the eGFR cut-off of six is odd. This question needs improvement. Interrater variability among the studied bots For 34 questions (36%), all bots agreed. Fleiss’ Kappa for all raters was 0.54 (moderate agree- ment). The agreement between ChatGPT and GPT-4 was the highest (Cohen’s Kappa = 0.66, substantial agreement). The agreement between Bing and Bard was the lowest (Cohen’s Kappa = 0.48, moderate agreement). Discussion In this study, significant differences in the performance of publicly available AI chatbots on the Antwerp Medical License Exam were found. Both GPT-4 and Bing scored the best, but Bing turns out more reliable as it produces fewer wrong answers. This performance is in line with previous research [15–17]. An ensemble bot which combines all tested bots scored equally so we cannot recommend its use based on the current study. The proportion of hallucinations was much lower for Bing than for Bard and Claude+/Claude Instant. The improvement of these new bots both in scores as in proportion of hallucinations sounds impressing, it might however increase the risk as users will have more confidence in wrong or even dangerous answers as the bots (in general) answer more correctly. The risk of replicating biases in the data on which these models are trained remains. Other authors already pointed out the meaning of these results: bots can pass exams, but this does not make them medical doctors as this requires far more capacities than reproduction of knowledge alone. The current study raises the questions whether a multiple choice exam is a useful way to assess the competencies modern doctors need (mostly concerning human interactions) [27]. Bing performed equally as GPT-4 but with less wrong answers, so currently it is not worth paying for a bot in order to test a medical exam, neither is it useful to create an ensemble bot based on PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 7 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam the mode of all bot’s answers. Ensemble bots based on more complex rules than just the mode of all answers should be studied further. We can recommend the use of Bing to detect weak questions among the wrong answers. This is a time-efficient way to improve the quality of a multiple-choice exam. In this study, the labour-intensive work of discussing and revising questions was narrowed down from all 95 included questions to the 23 questions on which Bing answered incorrectly. The argumenta- tion of Bing was used to check these questions. Machine translating, inputting in Bing and recording the answers for the entire exam took about two working hours. Three questions were improved for future examens. Further research on the efficiency of this method is necessary. The trend we found towards better bot performance on easy questions is in line with previ- ous research [13]. However, the difference in performance between students and bots was large for difficult questions and absent for easy questions. This compelling new finding demands further research. Maybe bots are most useful in those situations that are difficult for humans? The lack of a significant difference in performance between positive and negative questions, and between clinical vignettes and theory questions needs confirmation on larger datasets and on other exams. The finding on clinical vignettes has been found before [12]. Next to the field of medical education, bots might also be useful in clinical practice [28,29]. Numerous authors in various fields have tried to pose clinical questions. The results are vari- able but all authors conclude that thus far AI can’t compete with a real doctor [30–34]. In a study on paediatric emergencies for example, ChatGPT/GPT-4 reliably advised to call emer- gency services only in 54% of the cases, gave correct first aid instructions in 45% and incor- rectly advised advanced life support techniques to parents 13.6% [35]. However, some companies are developing new AI tools that might assist clinicians. Google’s medicine-specific large language model called Med-PaLM delivered highly accurate answers to multiple-choice and long-form medical questions but it fell short of clinicians’ responses to those queries [36,37]. The aim of this study was not to assess this aspect but by coincidence we noticed that in some cases, bots refuse to answer because they are not medical doctors. The creators of all studied bots try, to a certain extent, to avoid their bots being used as a medical doctor. None of the tested bots succeeded as none refused to answer all clinical case questions. Only Claude + and Claude instant refused (at times) to answer the question and closed the conversation. For all other bots users can try to pursue them to answer the question anyhow. This finding was most compelling for Bard where after entering the same questions repeatedly, Bard did answer it in nine out of thirteen cases. The rise of generative AI also raises many ethical and legal issues: their enormous energy consumption, use of data sources without permission, use of sources protected by copyright, lack of reporting guidelines and many more. Before widely implementing AI in medical exams, more legislation and knowledge is necessary on these topics [38,39]. The strengths of this study mainly concern its novelty: the comparison of six different bots had not been published yet. The bots tested are available to the public so our methodology can easily be re-used. This study, however, has got several limitations as well. It only concerned one exam with a moderate size set of questions. There was no usable definition of hallucina- tions, neither a validated approach to detect them available at the time of writing. The defini- tion we have used (chatbot generated content that either contains clear reasoning or is untruthful in relation to current evidence based medical literature) might inspire other authors although we found out that a distinction between reasoning errors and untruthful statements was not feasible. The exclusion of tables, local questions and images reduces the use of the comparison to real students. Future bots will most likely be able to process such questions as PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000349 February 14, 2024 8 / 11 PLOS DIGITAL HEALTHPerformance of AI bots in a medical license exam well. Finally, the exam was translated in English to make the current paper understandable for a broad audience. Further research on other languages is necessary. Conclusion Six generative AI chatbots passed the Antwerp multiple choice exam necessary for obtaining a license as a medical doctor. Bing (and to a lesser extent GPT-4) outperformed all other bots and students. Bots performed worse on difficult questions but outperformed students on those questions even more. Bing can be used to detect weak multiple-choice questions. Creators should improve their bot’s algorithm if they do not want to them to be used as tool for medical advice. Supporting information S1 Table. Responses from all selected bots on an example question. (DOCX) S1 Data. Selected Study Data. Study data excluding selected columns. See Data Availability Statement for more information. (XLSX) S2 Data. Study Data Variables Overview. An overview of the properties of all variables used in file S1 Data. (DOCX) Acknowledgments The authors would like to thank Professor David Martens for proofreading this manuscript. Author Contributions Conceptualization: Stefan Morreel, Veronique Verhoeven, Danny Mathysen. Data curation: Stefan Morreel. Formal analysis: Stefan Morreel. 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10.1371_journal.pntd.0011975	RESEARCH ARTICLE Involving patients in drug development for Neglected Tropical Diseases (NTDs): A qualitative study exploring and incorporating preferences of patients with cutaneous leishmaniasis into Target Product Profile development Marı´a del Mar CastroID M. Denkinger3, Nicole Harrison8, Julia Kutyi8, Liliana Lo´ pez-Carvajal9, Emma Plugge10, Julia Walochnik11, Piero Olliaro12 4,5☯, Byron Arana6, Gla´ ucia Cota7, Claudia 1,2,3☯, Astrid C. ErberID 1 Centro Internacional de Entrenamiento de Investigaciones Me´dicas (CIDEIM), Cali, Colombia, 2 Universidad Icesi, Cali, Colombia, 3 Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany; German Center of Infection Research, partner site Heidelberg, 4 Department of Epidemiology, Center for Public Health, Medical University of Vienna, Vienna, Austria, 5 Infectious Diseases Data Observatory (IDDO), Oxford, United Kingdom, 6 Drugs for Neglected Diseases Initiative (DNDi), Geneva, Switzerland, 7 Instituto Rene´ Rachou (IRR), Fundac¸ão Oswaldo Cruz (FIOCRUZ), Minas Gerais, Brazil, 8 Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Austria, 9 Programa de Estudio y Control de Enfermedades Tropicales (PECET), Universidad de Antioquia, Medellı´n, Colombia, 10 Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, United Kingdom, 11 Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria, 12 International Severe Acute Respiratory and Emerging Infection Consortium, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom ☯ These authors contributed equally to this work. astrid.erber@meduniwien.ac.at Abstract Background Target Product Profiles (TPPs) are instrumental to help optimise the design and develop- ment of therapeutics, vaccines, and diagnostics – these products, in order to achieve the intended impact, should be aligned with users’ preferences and needs. However, patients are rarely involved as key stakeholders in building a TPP. Methodology Thirty-three cutaneous leishmaniasis (CL) patients from Brazil, Colombia, and Austria, infected with New-World Leishmania species, were recruited using a maximum variation approach along geographic, sociodemographic and clinical criteria. Semi-structured inter- views were conducted in the respective patient’s mother tongue. Transcripts, translated into English, were analysed using a framework approach. We matched disease experiences, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Castro MdM, Erber AC, Arana B, Cota G, Denkinger CM, Harrison N, et al. (2024) Involving patients in drug development for Neglected Tropical Diseases (NTDs): A qualitative study exploring and incorporating preferences of patients with cutaneous leishmaniasis into Target Product Profile development. PLoS Negl Trop Dis 18(2): e0011975. https://doi.org/10.1371/journal. pntd.0011975 Editor: Gregory Deye, Uniformed Services University: Uniformed Services University of the Health Sciences, UNITED STATES Received: September 21, 2023 Accepted: February 7, 2024 Published: February 21, 2024 Copyright: © 2024 Castro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. The study protocol is available at: http://dx. doi.org/10.1136/bmjopen-2017-021372. Funding: This work was supported by grants from the Special Programme for Research and Training in Tropical Diseases (TDR) (www.who.int/tdr/en/) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 1 / 21 PLOS NEGLECTED TROPICAL DISEASESto MC and GC, a grant from the Drugs for Neglected Diseases initiative (DNDi) (www.dndi. org/) to LLC, and a grant from CNPq (project number 302069-2022-4) to GC. ACE is the recipient of a DOC-fFORTE fellowship of the Austrian Academy of Sciences (www.oeaw.ac.at/ en/) at the Nuffield Department of Medicine, University of Oxford. TDR had the dual role of being the funder and having an active role in the planning, execution and interpretation of the research as well as in strengthening the researchers’ capacity in qualitative research, in line with its mission to support research and capacity for research. Competing interests: The authors have declared that no competing interests exist. Incorporating CL patient preferences into TPPs for drug development preferences, and expectations of CL patients to a TPP developed by DNDi (Drug for Neglected Diseases initiative) for CL treatment. Principal findings Patients’ preferences regarding treatments ranged from specific efficacy and safety end- points to direct and significant indirect costs. Respondents expressed views about trade- offs between efficacy and experienced discomfort/adverse events caused by treatment. Reasons for non-compliance, such as adverse events or geographical and availability barri- ers, were discussed. Considerations related to accessibility and affordability were relevant from the patients’ perspective. Conclusions/Significance NTDs affect disadvantaged populations, often with little access to health systems. Engaging patients in designing adapted therapies could significantly contribute to the suitability of an intervention to a specific context and to compliance, by tailoring the product to the end- users’ needs. This exploratory study identified preferences in a broad international patient spectrum. It provides methodological guidance on how patients can be meaningfully involved as stakeholders in the construction of a TPP of therapeutics for NTDs. CL is used as an exemplar, but the approach can be adapted for other NTDs. Author summary Our study addresses the challenge of involving patients in defining which medical product would work for their condition. When designing a new medical product, it is customary to identify a “target product profile” (TPP), which identifies the characteristics the product should have to meet in order to address the medical need it is intended for. For the prod- uct to be used as intended and achieve the desired effects, it should be adapted to the con- ditions and the people who will use it, so, patients’ views are important, but rarely heard. Here, we use as an example cutaneous leishmaniasis, a skin and mucosal disease caused by a protozoan parasite, which disproportionally affects poor people across tropical and subtropical areas of the world. We collected patients’ views about product safety, efficacy, costs, treatment administration, and perceived barriers, that contribute to specifying product characteristics in the TPP. Overall, our study contributes to the limited body of knowledge with an example and an adaptable methodology to give patients a voice in designing adapted medical products. Patients input may also contribute to redefining aspects of a TPP, such as affordability, instead of just the cost per unit. The methodology used here can be adapted and used for other neglected diseases to give patients a voice in designing medical products. Background Neglected tropical diseases (NTDs) continue to burden low- and middle-income countries (LMICs) [1]. Designing therapeutics, vaccines and diagnostics for NTDs requires a deep understanding of the specific conditions in which they will be used, for them to be aligned PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 2 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development with needs of fragile health systems and users [2,3]. Target Product Profiles (TPPs) are a valu- able instrument to include these requirements and optimise the design and development of medicinal products. The Health Product Profile Directory, a dedicated directory of product profiles for health interventions containing a large number of TPPs for NTDs, was published by TDR, the Special Programme for Research and Training in Tropical Diseases [4,5]. The concept of TPPs was developed in 1997 by a Clinical Development Working Group composed of representatives from the Food and Drug Administration (FDA) and the pharma- ceutical industry. It recommended using a template summing up drug labelling concepts to focus discussions and help understanding between the FDA and product developers, in addi- tion to product design specifications [6]. There is no common format for TPPs and their use. They are mostly used either as a document signifying what a commercial sponsor would like to claim in labelling and product development [6,7], or what attributes are being sought for a public health intervention to achieve the intended health impact [8–10], in which case they are often shared with collaborators and/or made publicly available. TPPs have been indicated as useful tools for drug and diagnostic development for NTDs [11,12]. There is little methodological guidance on how to construct TPPs; actual methodologies vary. For example, PATH has constructed TPPs for diagnostics for three NTDs using compre- hensive approaches involving literature reviews, surveys and interviews with experts and stake- holders, process maps and review of available diagnostic tools [13]. A TPP for a point-of-care diagnostic test for CL was developed by FIND and DNDi, based on a draft and discussions with experts at a meeting, followed by an online survey with a larger audience of stakeholders and experts [14]. Few studies describe patients’ involvement as stakeholders in the construc- tion of a TPP [15]. A high-level guideline [16] and a roadmap [17] have been published recently. Adepoyibi et al. [18] conducted a survey among laboratory personnel, national tuber- culosis control program managers, donors, technical experts, patients and researchers, and asked them to rank the items in a TPP for tuberculosis diagnostic tools by their perceived importance. Denkinger et al. [19] included patients among stakeholders in the prioritization of TPP items for tuberculosis diagnostic tools. Studies using exploratory approaches (e.g., using qualitative semi-structured interviews or focus group discussions) of patient involve- ment are scarce [20]. We present a methodological and analytical approach as to involve patients in the identifi- cation of preferred characteristics of a new drug, and how this could inform TPPs. We focussed on NTDs, taking cutaneous leishmaniasis (CL) as an exemplar. CL, a parasitic vector- borne disease, disproportionally affects poor populations across tropical, subtropical and tem- perate regions [21]. It is caused by different Leishmania species, with a range of clinical mani- festations. The disease results in visible lesions on exposed parts of the body, which can be distressing and discomforting, and typically leave lifelong scars. At present, there is no treat- ment which is effective, safe and easy to administer, supported by a robust evidence-base [22,23]. Currently, in the main endemic regions, treatment largely relies on antimonials administered intramuscularly or intravenously during 20 to 28 days, causing frequent adverse events [23]. Drug and diagnostic development efforts are ongoing mostly within public private partnerships (PDPs), such as DNDi or FIND. American cutaneous leishmaniasis, or New- World CL (NWCL), is a form of CL caused by distinct Leishmania species endemic to the Americas, which has a low propensity for self-healing [24] and a risk of progression to a muco- sal or mucocutaneous form (where destructive sores develop in the mucous membranes of the mouth, nose and throat) [25]. We report the methods and findings of a qualitative study assessing preferences for treat- ment of CL patients from Brazil, Colombia, and Austria, who were infected with New-World Leishmania species and received different types of treatment. This study is following an PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 3 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development exploratory approach, that is, not restricted to pre-defined categories, or patients’ roles as end users. It complements a previous analysis where we reported patient-preferred outcomes for CL treatments, and suggested ways of considering them in the conduct of clinical trials and in clinical practice [26,27]. Methods Ethics statement Ethical clearance of the protocol was obtained from the following institutional review boards (IRBs) and ethics committees (ECs). World Health Organization Research Ethics Review Committee (WHO ERC), Geneva, Switzerland. Comite´ Institucional de E´tica de Inves- tigacio´n en Humanos (CIEIH), Ethics committee of the Centro Internacional de Entrena- miento e Investigaciones Me´dicas (CIDEIM), Cali, Colombia. Instituto Rene´ Rachou, Fundac¸ão Oswaldo Cruz (FIOCRUZ), Minas Gerais, Brazil and Comissão Nacional de E´tica em Pesquisa—CONEP, Brası´lia, Brazil. Comite´ de Bioe´tica Sede de Investigacio´n Universitaria (CBE-SIU), Universidad de Antioquia, Medellı´n, Colombia. Oxford Tropical Research Com- mittee (OxTREC), University of Oxford, Oxford, UK. Ethics Committee of the Medical Uni- versity of Vienna, Vienna, Austria. Only patients above the age of consent were interviewed. Consent was obtained by signature, or an appropriate alternative, as specified by the relevant IRBs. We obtained consent from participants unable to sign by including at least one literate witness, chosen, if ever possible, by the participants themselves. Ethical procedures at each site observed the respective IRB guidance, and are further detailed in the study protocol [26]. Study sites and populations Using a comprehensive interview topic guide [26], individual semi-structured interviews of about one-hour duration, related to their disease experiences, preferences and expectations, were conducted with 33 CL patients at four sites in Austria, Brazil and Colombia. Under the assumption that this would cover the range of patients’ profiles and experiences, we purpo- sively sought maximum variation along characteristics such as patients’ gender, age, treatment status (before, during, after treatment), clinical lesion presentation and causative New-World Leishmania species. We took the socioeconomic context into consideration by including par- ticipants from a high-income country (Austria) and low- and middle-income countries (Brazil and Colombia). Austria: Three interviews were conducted at the Vienna General Hospital, Austria, which is the largest hospital in Austria and manages most CL cases imported into Austria. Austria is considered a non-endemic country for leishmaniasis, although two presumably autochtho- nous cases have been reported [28,29]. Brazil: Ten interviews were conducted at the Centro de Referência em Leishmaniose do Instituto Rene´ Rachou (CRL-IRR), Belo Horizonte, Brazil. CRL-IRR works as a reference cen- ter for management of CL for the state of Minas Gerais, in southeastern Brazil, the third Brazil- ian state in CL cases. Most patients were residents in small towns or rural areas within a radius of 500 kilometers from the center. Colombia: Ten interviews were conducted at the CIDEIM facilities in Cali and Tumaco. The Cali facility works as a reference center for management of CL in south-west Colombia. Tumaco is a municipality located in southern Colombia, where there is endemic transmission of CL, and it is one of the areas reporting most cases of CL in Colombia. CIDEIM operates as a primary health and research facility. Patients of these two facilities are mostly civilians. Fur- thermore, researchers from the PECET (Program for the Study and Control of Tropical Dis- eases) of the University of Antioquia conducted ten interviews with soldiers who were in the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 4 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Leishmaniasis Recovery Center of the national army in Boyaca´. The program for management of CL for the Colombian military includes a special facility, Directly Observed Therapy (DOT) and follow-up lab tests and medical support until the end of treatment. In Colombia, the mili- tary population due to their professional duties (fight against armed groups and drug traffick- ers) is one of the groups most affected by CL. Interviews occurred prior to signing the peace agreement with the FARC guerrilla. Data collection Interviews were conducted in the patients’ mother tongues by four researchers (MC, GC, JK, LLC) and audio recorded; interviewers took notes. Interviews from Colombia and Brazil were conducted as part of the study published by Erber et al. [26], and the Austrian interviews were conducted in 2019 following the same protocol and interview guide. Each recording was tran- scribed into the language in which the interview was conducted, and subsequently translated into English for analysis. Quality of the original transcripts and translations were verified by the researchers conducting the interviews. Data analysis Transcripts were analysed using a framework approach [30,31], with pre-defined categories imported into Nvivo 12 (QSR International) as nodes for coding of transcripts. As a frame- work, we used the TPP developed by DNDi specifically for CL treatment [32]. Two additional categories (Perceived barriers and Other development needs) were added as they emerged from the interviews and were deemed realistic to address in a broader drug development context. Two categories (Target species and Stability) were omitted as they were not addressed in the interviews. Coding of transcripts was conducted independently by two researchers (MC and ACE), and results discussed. During analysis, the coding framework was subject to continuous updating. The final coding framework is shown in Table 1. Themes, concepts or propositions that describe, help to interpret and explain aspects of the data [31] were articulated and developed by comparison between and within interviews by two researchers (MC and ACE). Themes were then assessed for potential incorporation into a TPP, and for being considered during drug development in general. In line with the explor- atory nature of the study, we did not want to prioritize; therefore, we did not count the number Table 1. Coding framework. The coding framework is based on the TPP developed by DNDi [32]. TPP categories are in bold; existing ones were amended by two added during the coding process (denoted with an asterisk ). 1. Safety and tolerability 5. Treatment regimen 1.1 Safety monitoring requirements 5.1. Administration outside of the health care facility 1.2 Tolerability 2. Contraindications 3. Efficacy 3.1 Absence of sequelae 3.2 Complete clinical cure 3.3 Improved scar formation 3.4 Parasitological endpoint requirement 5.2. Compliance 6. Duration of treatment 7. Target population 8. Cost 8.1 Costs of products or procedures per treatment 8.2 Indirect costs 9. Perceived barriers 3.5 Prevention of relapse and recurrence 9.1 Availability 4. Formulation 4.1 Oral 4.2 Parenteral 4.3. Topical https://doi.org/10.1371/journal.pntd.0011975.t001 9.2 Geographical barriers 9.3 Time to correct diagnosis and start of treatment 10. Other development needs* PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 5 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development of times a specific aspect was mentioned (frequency analysis) and did not present themes within a TPP category in a particular order. Results Thirty-three cutaneous leishmaniasis (CL) patients from Brazil, Colombia, and Austria were interviewed. Most participants were male (n = 25, 76%) and the median age was 32 years (range 18–71 years). Leishmania species were determined either by DNA sequencing (Austria) or based on epidemiological criteria (predominant species in the region of infection; Brazil, Colombia). On average, patients had 1.7 lesions (median = 1, range 1–5). Four (12%) patients have had CL diagnosed but not started treatment, 11 (33%) were under treatment, 18 (55%) had completed their treatment; 8 (24%) had received more than one treatment (due to treat- ment failures or reinfection). The overview of the characteristics of the study sites and the characteristics of patients enrolled in the study are presented in S1 Table. We present patient preferences along the top-level domains of the DNDi’s TPP, 1 Safety and tolerability, 2 Contraindications, 3 Efficacy, 4 Formulation, 5 Treatment regimen, 6 Target population, 7 Cost, and two emerging categories 8 Perceived barriers and 9 Other development needs. An overview of themes by TPP categories can be found in S1 File. Differences related to participants’ country, sociodemographic (e.g., income, occupation) or clinical characteristics are described when relevant. Representative quotes by TPP categories and themes can be found in Table 2. Trade-offs across attributes in the TPP (domains), described by the partici- pants, are summarized in Table 3. 1 Safety/Tolerability Patients described their experience with the types, timing and sequence of tests required to monitor the treatment safety. These descriptions were most detailed for the Colombian soldier population, the majority of them treated with systemic meglumine antimoniate. Information on preferences of monitoring was limited. Among these, two patients mentioned how monitor- ing needs determine the treatment location: One Colombian patient decided to stay in a city during the treatment to have better monitoring of the treatment, while one Austrian patient preferred to be treated in a hospital instead of on an outpatient basis, due to the severity of the experienced side effects. When speaking about systemic antimonials, some patients preferred a lower volume of drug to be administered due to the pain and adverse events (AEs) experienced. Patients’ descriptions of multiple adverse events in response to pentavalent antimony, pent- amidine and miltefosine are in line with the literature. Many discussed tolerability of treatment and reported adverse events (AEs), at times in an emotional manner, describing the often- complex considerations they face. Patients often saw AEs as an inevitable part of the treatment, and accepted them. Fears of receiving treatment due to the injections and the side effects were illustrated by two Colombian soldiers. They met others requiring more than one course of antileishmanial treatment and were afraid of receiving a second treatment and its potential consequences (“it seems like a common pimple, but what they inject you for that pimple it’s what hits you hard" -CP02-). In contrast, three Brazilian patients described their positive experiences with intralesional administration of antimonials. Trade-offs made are reflected in how patients chose to ‘ignore’ the risks associated to the treatment when weighted against the need of being cured. 2 Contraindications Patients described the reasons for which the systemic antimonial treatment was contraindi- cated, including hypertension and arrhythmia, and alternative therapies offered to them. Some PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 6 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 2. Representative quotes reflecting patients’ preferences across the attributes in a TPP for leishmaniasis treatment. TPP Attributes and themes [] Authors’ remarks 1 Safety/tolerability Safety monitoring requirements Tolerability Description of AEs Fears about (second) treatment due to AEs Preference for lower volume of drug Trade-off: Cure vs. tolerability 2 Contraindications Reasons for contraindication Self-care Representative quotes* Back then I didn’t want to [be hospitalized] because I had the feeling it was more relaxed when you can go home every evening. But thinking back, I am now of the opinion it might have been better just to stay there. [. . .] Often, I didn’t know in the evening at home how I should lie down, because my kidneys hurt, and I was very nauseous. I was given Zydis to take home, but sometimes it wasn’t enough. Then I had taken too much already and didn’t want to take any more. Then my ankle hurt again so much, then I looked at the clock, counting hours until I had to go to the hospital. 3 weeks long, that was a bit exhausting. (AT03) Well, at times I feel a bit scared [of the treatment], but I would not back down, I am going to continue, because I got in, and I am going to finish it. Besides it is the only way to get cured. [I fear] too many shots. So many injections, and the reaction is really strong. [. . .] Well, right now I would say [as a message to people with leishmaniasis] it is best to use the medication even if it’s uncomfortable and painful. (CC09) If we could choose to not have side effects, it would be ideal, right? The problem is that it is part of the treatment . . . (BR09) [When they told me I had leishmaniasis] I thought "once again that drug that fucks you up". It appears that drug is toxic and leaves you sequels. (CP08) The treatment for me [intralesional Glucantime] was super cool. I had imagined that I could have side effects, but it was very good. I didn’t feel anything. Only the days of the infiltrations I had some swelling, which sometimes bothered me a little. (BR03) That thing it’s so strong. . . Just look at the drug they are injecting you. It’s like a poison. It seems to be a common pimple, but what they inject you for that pimple it’s what hits you hard. That’s what screws you up. The cure itself fucks you up from the inside. (CP02) If there isn’t another [treatment], I would take this one. I think the dosage is too much for one person though. Maybe if during the day the amount of drug is less, I think the body would assimilate it better. (CP06) To tell you the truth, I really don’t know [anything I am afraid of in terms of treatment], as long as I get cured [. . .]. No, I’m not scared about any risk. (CC08) I had had few months before a cardiac diagnosis (arrhythmia) and I was submitted to a heart ablation. Despite having been cured of the arrhythmia by ablation, the leishmaniasis treatment was considered more complicated and I was hospitalized. [. . .] Yes [I received amphotericin B]. . . they were afraid to give me another treatment, the first line treatment, because of my previous heart condition. But today I know it should have been different. (BR01) I think not everyone has the same capacity of process the medicine. There are weaker people and so. But [leishmaniasis] can also appear again because the person doesn’t take care of himself/herself, so the treatment doesn’t work as it should. [. . .] My nephew was one of those who smoked during treatment, so the disease got worse. I think if you drink, smoke, stay up late and do the things you should not, then that’s bad. [. . .] That’s why I’m taking care of myself a lot. (CP10) (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 7 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 2. (Continued) Shared decision making 3 Efficacy Absence of sequelae Trade-off: Cure vs risks related to treatment Trade-off: Cure vs. scars/aesthetic results 4 Formulation Oral Parenteral Perceived efficacy of parenteral administration [. . .] As I lost contact with the health promoter from there [where I live], my sister and my son told me that it was better to have the treatment here, in case there were some kind of adverse reactions to the medicine. So, the doctor told me what we could do and we agreed on a health center in near La Nave [a location in Cali, Colombia]. (CC09) [I think in the future I will have] maybe heart problems. Sometimes you hear that you can have problems because of the crazy amount of medicine you received. [I am afraid of] not having the same strength again. [. . .] You make physical effort and the body doesn’t respond the same way. (CP06) Scars do not bother me much. But [. . .] the doctor told me that the drug was strong [. . .], what if that leaves me with the pain, what if it will not stop? That’s why I do not want that the treatment be extended further. (CC09) Yes, I do [still want to be treated, even thinking that the lesion does not threaten your life and the remedy has risks]. [. . .] [I could not keep this lesion, but] I would look for a treatment every way. (BR08) Look, the first thing I asked the doctor: " Will I have a scar?" He said "probably you will get a mark, a colour change in the skin" "But will it heal?" "Yes, it will". I’m going to travel on vacation in few days, and I was worried, because I’m planning to go to the beach and to use a bikini. Then my husband asked me: "Will you find a wider bikini or something like that?” and I never had to think about that . . .. [. . .] The most important thing is that I am healed. [. . .] If you can live with another scar caused by another injury, why not? (BR01) The one I’m taking now that is oral, it’s less traumatic than the first one I had. [. . .] This one suits me better, I mean, I feel much more comfortable. [. . .] Because you don’t get sick leave, I mean, you have to continue working somehow, and every day you have to go to get an injection, that is traumatizing (laughs). (CC01) In my case, when I came here for the treatment, I did not sleep thinking about the injections. [. . .] I was up all-night thinking about the injections. It was horrible. [. . .] It is the only thing I could think about. (CC08) I had a little trauma in my childhood–I have fear of injection. I complain because I had a surgery years ago when I saw the entire procedure, I wasn’t anesthetized properly. [. . .] For me, on a scale of one to ten, it’s ten. [. . .] But I’m not afraid of the medicine, the drug itself. (BR05) I’ve always preferred injection. [. . .] I like it. [. . .] I think injection produces a faster effect. [. . .] It’s painful, but it’s better (BR06) Pills for me don’t work. [. . .] Every time I feel sick I go and say what I have so they can give me injections. I think that’s the best thing you can have, because it goes directly through the bloodstream and starts killing the viruses. [. . .] For leishmaniasis I think the injection is the best. [. . .] [I think an injection in the wound] would be good. I guess you would receive fewer doses. Also, stronger! So it works faster. (CP10) (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 8 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 2. (Continued) Local vs. systemic administration of parenteral treatment Trade-off: Pain due to injections vs. perceived disease severity With the second, it was worse [regarding symptoms]. [. . .] I was very feverish and my heartbeat was fast. [. . .] [I think this is] maybe because of the crazy amount of medication they inject you. [. . .] For me intravenous would be good [as the ideal treatment]. You suffer less. (CP03) For me [the ideal treatment] wouldn’t be applied in the buttocks. For me it would be appliable as a serum (the patient used the term ‘suero’, a generic term for intravenous infusions–author). [. . .] 20 syringes are a torture. [. . .] I was constantly affected psychologically because I always thought I was going to get injected in the same place, and the scar was going to remain where I had the wound. [. . .] If by any chance I get leishmaniasis again, I’m quitting the job. [. . .] All those injections. [. . .] It leaves you marked for your whole life. (CP04). [The ideal treatment would be] pills, creams or something not based on syringes. [. . .] The injections are very strong. [. . .] You have to go through all the needles for such an insignificant thing. (CP09) Lack of alternatives: Injections as only treatment option The treatment is already okay, because there is no other way but the injections. (CP02) Topical Preference for creams Thermotherapy 5 Treatment regimen Optimal treatment duration Trade-off: Treatment duration vs. cure Administration of therapy outside of treatment facility due to long treatment duration Recovery time after treatment [Before release to combat area (soldiers)] Compliance [Fear of disease progression] Well, [I think the best treatment to cure Leishmaniasis would be] something you just put there. [. . .] That easy. Something you smear it on and no more. [. . .] I was told that if one does not act upon right away, it may go through the bloodstream and reach the liver. (CC08) [. . .] Hopefully they develop a cream that one could put on. But I have a question, why so? Why is the drug for the body [administered via injection,] knowing that one has the lesion on the skin, and the parasite is supposed to be there? (CC08) [As I have access to scientific publications, I did some research and noted] that heat treatment is a standard low-cost treatment. [. . .] Actually, heat treatment helped me best, but it was never mentioned that it could be tried. [. . .] I would advise everyone to try a heat therapy themselves if it’s not being offered to them. (AT04) The shorter [the treatment duration], the better. [. . .] You can recover faster and you can be completely healthy. [. . .] (CP07) It is a long treatment. . . but the important thing is to be cured. [. . .] Absolutely. (BR06) I had to stay about fifteen days here, I got about 20 or 30 [injections here in Tumaco]. And the others I took home with me. Because in the countryside is where we have our farm, and we just couldn’t abandon it. [. . .] [The doctor] told me, that once I got the shots I (couldn’t) keep working, but I just can’t stop working. [. . .] A cousin gave me the injections, [. . .] in Gualao. [. . .] Here in Tumaco, a sister-in-law gave me the shots. She is a nurse. (CC03) Because the soldier finishes the treatment and is sent back to the Battalion right away. [. . .] Or they leave you here if you’re not good enough. [. . .] So, for me, some recovery days would be okay. Like that, the wound can recover well. (CP02) The treatment is the only thing that can cure leishmaniasis. [. . .] [If I decide not to have treatment] the wound keeps growing and growing and it will be my problem because I decided not to get the treatment. So, when the wound gets bigger, I will have to repeat the treatment like other partners that finish the first treatment with Glucantime and it didn’t cure them because the wound is too big. (CP08) (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 9 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 2. (Continued) Trade-off: Compliance vs. side effects Low compliance due to fear of injections Treatment interruptions due to AEs Treatment frequency Trade-off: Place of administration vs. treatment frequency [I received the injections] into the vein [. . .] every day, I was tired, it was painful. . . I had to come here every week, I had to have my blood collected, the other morning I had to collect blood again and receive the medication in my town. . . it was painful, yes, but I did everything correctly. (BR06) Yes, really, I have [been compliant] with the pills, I have been taking them, she prescribed three daily; but with the injections, I was not. (CC07) [They sent me 60 injections and I received 48] because] I couldn’t tolerate it anymore, I couldn’t even sit down, nothing. [. . .] So I rested for three days and then started again; so that’s why I think the treatment didn’t do me good because later, [. . .] it came back. (CC07) I went in and then I received the first Pentacarinat. Then I got problems with my blood glucose, extremely low blood glucose levels, there I wasn’t well. [. . .] And then I think [I took] 2 days of break and one more infusion, 3 days break, one more infusion, about that. . .perhaps about one week or a little more in the hospital. (AT03) If we had an oral medication to treat, a faster treatment, it would be great, because there is a certain disorder in going daily to receive medication. If there was something to take home it would be better, it would be easier too. (BR01) [When doctors were discussing treatment options with me,] they asked what would be my preference: come here to receive the infiltration once a week or receive the medicine every day in my town. I decided to receive the remedy here, [. . .] because once a week is much easier. [. . .] It was a blessing. Since the day I got here, my lesion is just improving! (BR08) 6 Target population–Quotes and detailed description are available in S2 File 7 Cost Administration of injections Consultation fees Transport costs I had to pay to my cousin [to get the injection]; my sister-in-law did not charge me anything. But my cousin, I had to pay her, 20,000 pesos. [. . .] She told me to give her whatever I felt like. So, I gave her 20,000 pesos. (CC03) In my town there is no infectious disease specialist. [. . .] I mean, there are several physicians on the private network, but considering my financial condition I couldn’t afford it. I would have to wait until it was scheduled by SUS [Sistema Único de Saúde, the Brazilian public health system], in Belo Horizonte. It usually takes too long. (BR05) Ah, it takes 1 hour [. . .] by canoe [to reach] Tumaco. By boat [with motor] 20 or 30 minutes, but sometimes it’s hard, because sometimes you do not get a boat or a canoe to come. I have my own [aquatic] vehicle to come if there is an emergency, but [sometimes is not possible to cover the costs by transporting passengers or goods] and one uses a lot of gasoline [. . .], so it is better to pay the 15,000 pesos as a passenger because if not, you buy 100,000 pesos of gasoline for a round trip. Things are so expensive. (CC03) It was tough arriving here. I had to pawn my cell phone, I had to arrive to my sister’s and then she lent me money. [. . .] No help at all from the army, despite the fact that I was working there. They took me out, they gave me their permission and that’s it. [. . .] Only my brother-in-law lent me the money for these tickets to come here. (CP02) (Continued ) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 10 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 2. (Continued) Profession-related costs (inability to work, or change of occupation) Having to stay away from home for treatment My payment is based on days of work. [. . .] I want to do my treatment. [. . .]. No work, no money. I’m pleased to get here and do the treatment. [. . .] [It is] impairing my income, [. . .] [but] No, it doesn’t bother me. [. . .] I’m [coming on my own] [. . .], by bus. [. . .] I paid the ticket. [. . .] (BR08) I was unemployed [when symptoms started]. I’ve been looking for a job the last months. [. . .] Several employers told me that [they couldn’t hire me because of the wound, an ulcer in a visible area on the arm]. They told me directly [that this was the reason]. [. . .] [This happened] twice. [. . .] After that, I gave up, I said "there is no way". [. . .] My concern is [not about the disease or the possibility of future complications, but] about work. (BR02) I live with a family member, I live there and thank god, they have helped me. [. . .] Right now I’m not doing anything [work-wise], because I work with leather, I make belts and that kind of things for farm animals. But not right now. (CC09) 8 Perceived barriers–Quotes and detailed description are available in S2 File 9 Other development needs Investments in research Information and dissemination activities I think we must invest more in research, to improve things. Because I know, in Brazil, at least based on the information that I had, we have just one medicine and that this treatment has many risks. (BR05) You should keep on researching about the cream. Hopefully soon it becomes more effective than the injections or maybe it can help heal quicker, that would be ideal. (CP01) I would like that for leishmaniasis there was something like a vaccine. [. . .] For example in my case, I go through this treatment now, I spend a lot of time and all that stuff, effort; and sometimes one has to be in those areas where the disease remains [. . .], and what if one gets infected again, then he would have to go through the same treatment again. (CC09) [I would provide] information, more dissemination of information about the disease. You need to explain to people how the disease is transmitted, that there is treatment and people need to be seen by a doctor, [. . .] they shouldn’t think it is a normal wound. (BR05) * Patients’ unique identifiers contain a two-letter code corresponding to the study site (AT-Austria, BR-Brazil, CC-Colombia/CIDEIM, CP-Colombia/PECET). https://doi.org/10.1371/journal.pntd.0011975.t002 participants, mainly soldiers, highlighted the need for self-care or ‘taking care’, such as avoid- ing certain food or activities, to contribute to the healing process. Notably, one Brazilian patient with contraindications described moments of shared decision making with the treating medical team, which was not observed in other groups. One Colombian patient described dis- cussing the location of treatment, based on the need of need of monitoring, with his family. 3 Efficacy Treatment efficacy was discussed extensively by patients. Efficacy was understood as clinical cure in relation to the lesion, the absence of sequelae, scar formation and the prevention of dis- ease relapse/reinfection. As part of a larger study on outcomes, a number of aspects were already reported on in a separate publication [27]. In this category, we identified efficacy as an important driver in the trade-offs made about treatment (Table 3). Patients discussed the pref- erence for an effective treatment in healing the wound, regardless of scar appearance, potential side effects or treatment duration. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 11 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Table 3. Identified trade-offs. Six trade-off pairs could be identified. Among these, three were risks traded off with cure. Aspect 1 Cure Pain due to injection Compliance Place of administration https://doi.org/10.1371/journal.pntd.0011975.t003 Aspect 2 Risks related to treatment Scars/aesthetic results Long treatment duration Perceived disease severity Side effects Treatment frequency As described in Tolerability, part of the fear of disease relapse and reinfection was having to repeat the treatment. Some patients were aware and afraid of the risk of reinfection when living in an endemic area. Thus, long-term sequelae of the treatment, particularly antimonials, and fears of sequelae related to the disease itself, such as disease progression, could be avoided by successful treatment. 4 Formulation In general, patients preferred oral and topical treatments due to the easier administration and being perceived as ‘fitting’ the disease. This conceptually interesting discussion was mentioned by different patients, who expressed that, since the infection is local, they would prefer the treatment to be also applied locally: a topical treatment for a local infection. This in addition to the perceived contrast between the treatment and the perceived disease severity, i.e., a long parenteral treatment for a ‘pimple’. By some patients, oral formulations are seen as easier, and an ideal treatment when compared to systemic antimonials. Most Colombian patients, both from the military and the civilian population, had a prefer- ence for ointments over injections. One mentioned the potential timeliness of applying a cream to the wound, assuming than an early treatment may limit the infection. Physical ther- apy in the form of thermotherapy was only mentioned by Austrian patients. When discussing parenteral formulations, patients often reported ‘fear of needles’—an aversion, or even a trauma, against injections. In contrast, some patients perceived injections as working faster and more effective than alternative modes of administration. While others, such as two Colombian patients, reported injections as the only treatment option. Mainly Colombian military patients discussed localized (directly on the lesion) versus systemic admin- istration of antimonials. In this context, some patients prefer intravenous infusion (against intramuscular) due to the volume of medication, and less pain. However, intralesional injec- tions were seen as preferable by being ‘stronger’, faster, and requiring fewer doses. This high- lights the trade-offs between efficacy and other product characteristics. 5 Treatment regimen Treatment regimen was found to be a TPP category rich in themes and trade-offs. Patients per- ceived long treatment durations as burdensome, particularly against the need to balance work obligations, and in connection with indirect costs (days of work lost). These were more marked for those living in remote areas. Twenty days to one month were seen as acceptable by some patients, corresponding to their experience, although they prefer the faster option to heal the skin lesion. In contrast, two patients at the military treatment center wanted additional recovery time before being sent back to operations. Patients described alternatives to mitigate the impact of the long treatment, including seeking help from others, often relatives, to PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 12 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development administer the drug at or near home. Long treatments were accepted as a trade-off to achieving clinical cure. The theme of compliance was central. In general, being treated, and taking the full treat- ment course, was seen as important to avoid relapse and sequelae. Many respondents contin- ued treatment despite side effects. Reasons for this were the fear of disease progression and its consequences. Other patients reported treatment being paused, or suspended, due to adverse events, thus prolonging the overall treatment duration. Regarding treatment frequency, daily administration of parenteral drugs was considered as too frequent, and patients preferred weekly injections. This was partly related to the logistics of daily parenteral drugs, for example, having a different health provider during the weekends at the hospital, which may not be familiar with the case or the drug. In contrast, daily administra- tion of oral treatment or a drug that can be used at home, was accepted. The availability of the drug or trained healthcare providers also influenced the decisions of where to receive treatment. When facing this situation, a Brazilian patient opted for the weekly injections in another city instead of daily ones near their residence. 6 Target population Not all respondents addressed this category; often interviewers were being asked to clarify. Most agreed that everyone should be treated, for reasons such as an intrinsic right to treat- ment, patients’ quality of life, or to avoid disease progression. A few patients noted that special populations, such as children, should not be treated due to the pain and frequent adverse events of the medication. Findings are available in more detail in S2 File, as they were difficult to interpret and incorporate in a TPP. 7 Cost Costs were frequently mentioned by patients, especially those related to procedures such as administration of injections, consultations, diagnostic tests, and transport. Inadequate or over- the-counter medications before CL diagnosis were also discussed, but rarely costs related to antileishmanial drugs, in line with the treatment being provided for free in Austria, Brazil and Colombia. One Colombian patient mentioned paying for administration of the injections out- side of the health care system. Costs of diagnostic and pre-treatment tests required to define eligibility to systemic antileishmanial drugs (e.g., blood cell count) were sometimes paid by patients to expedite the initiation of treatment. In a high-income setting, an Austrian patient describes paying a 140 EUR (154 USD) fee for specialized care to shorten waiting times, while a Brazilian patient described the challenges try- ing to access specialized care in their town, both due to the costs of the appointment and limited of availability of specialists. Some patients, particularly among those living in remote areas, reported significant transport costs to treatment facilities using a variety of means of transport, as well as time lost due to the journey. One patient describes pawning and borrowing money to afford transportation costs. This aspect is closely related to Geographical barriers (S2 File). Indirect costs were mainly related to days of work lost and were mentioned as significant. Taking time off during treatment without payment was considered difficult for patients who worked as day laborers, or self-employed, some were not able to work at all during treatment, or not able to find work. Having to stay away from home for treatment was another reason for indirect costs and often affected patients’ income prospects. This contrasted with patients with formal employment, such as the Colombian soldiers or the Austrian patients. One Austrian received a sick leave of 4–5 weeks, noting that she was lucky and was not afraid of losing her job, due to her employer being treated for CL at the same time. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 13 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development 8 Perceived barriers Patients reported barriers including geographical location, availability of treatment, migratory status or occupation (for example, civilian patients from Colombia reported difficult or no access to CL treatment for patients employed in illegal professions, or migrant workers) S2 File. Often there was a combination of different barriers that resulted in indirect costs and delayed initiation of treatment. 9 Other development needs Patients emphasized the need for investments in research, to develop alternative drugs with a better safety profile. In addition, some patients emphasized the need to invest in vaccine devel- opment, knowing the risk of reinfection for people living in endemic areas. In addition, patients emphasized the need for general information and dissemination activ- ities, including information about the disease, such as the mode of transmission and treatment options, as well as campaigns to de-stigmatize the disease. Discussion By collecting the perceptions, values, and preferences of a range of patients regarding product characteristics for cutaneous leishmaniasis treatment, this study provides a pathway to inte- grating patients’ perspectives in the design and development of TPPs for novel treatments. Overall, our findings show that the treatment preferences of a broad spectrum of CL patients fitted well to a framework comprising the categories described in DNDi’s TPP [32]. This was particularly evident for Safety and tolerability, Efficacy, Formulation, Treatment regimen, and Costs, which yielded rich findings, and less for Contraindications, where patients mostly stated experiences. Preferences for Target population were difficult to identify, thus, along with Tar- get species and Stability, might be less suitable or would require a modified approach due to their rather technical nature. Two additional categories, Perceived barriers and Other develop- ment needs, were identified. Often, preferences connected several categories, e.g., via trade- offs. Efficacy was a central topic, discussed extensively by patients [27] and often assessed in rela- tion to experienced discomfort, or as part of trade-offs influencing treatment decisions. Impact of scars was mentioned, but social stigmatization, including gender-specific, was described to a lesser extent than patients suffering from old-world CL forms, likely indicating a cultural component [27,33–38]. Findings about safety and tolerability were closely related to experi- ences following treatment, with adverse events similar to those reported in clinical trials and observational studies for NWCL treatments [22,23]. Fears of sequelae and relapses motivated patients to complete the full treatment regimens, despite adverse events and burden associated with the long duration of treatment. Duration of treatment was related to the category of formulation. Parenteral treatment once-a-day was considered as too frequent, while daily administration of oral treatment or a drug that can be used at home was accepted. In general, patients preferred oral and topical for- mulations, in line with WHO’s road map for neglected tropical diseases 2021–2030, which proposes development and scale-up of an easy to administer oral or topical treatment that could be used in health centres as a critical action for CL [39]. At the same time, parenteral administration (infusion and injection) was perceived as more efficacious by some patients. This relates to reported perceptions of injections as more effective [40] and act faster, relieve symptoms quicker and involve less risks than oral drugs [41]. Notably, parenteral meglumine antimoniate was the most widely used treatment in this study population. As with PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 14 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development contraindications, the lack of shared decisions in case management was apparent, with inter- viewees seeing injections as the only option to get cured. Costs were frequently mentioned and widely discussed by patients. Loss of work and income contrasted between day laborers and those with formal employment (e.g., military) and between Latin America and Austrian patients. Indirect costs have been identified as an important part of the economic burden of leishmaniasis in Asia [42]. They compounded with perceived and encountered barriers (S2 File), reflecting the often-complex life realities of patients. This was most pronounced in the interviews with patients living in very remote areas, migrant agricultural workers and those working with illicit crops, which is aligned with experi- ences in rural areas of Colombia [43] and Latin America [44]. NTDs are diseases of poverty [45]. These findings are in line with studies addressing the considerable socioeconomic impact of CL [35,46], and reflect the need to expand the attribute of costs of a product to its affordabil- ity. Whereas a target product price is often included in a TPP, affordability to end-users is not [5] and accessibility is only included in a limited number of TPPs [5]. Findings related to the target population were difficult to interpret, probably because the question ‘Who should be treated?’ was poorly understood. This could be improved e.g., by pro- viding explanations, or moderated focus group discussions (FGDs) to allow for clarifications in a group context. As we only interviewed patients who wanted to be treated, this is likely reflected in their opinions. Incorporation of patients’ preferences in the TPP process Central among the barriers to involving patients in drug development is the lack of methodo- logical guidance, as reported by drug developers, patients and patient advocates, regulators and funders [47,48]. Recent initiatives have addressed this at a general level [16,17], and our study provides methodological guidance using an open, exploratory approach taking an NTD as an example. We designed and published an interview topic guide that allows an in-depth exploration of experiences and preferences [26], and reflections upon these, instead of focusing on TPP categories a priori. This interview topic guide could be adapted for similar diseases, in particular skin NTDs. Similarly, as done in this study with DNDi’s TPP, a coding framework for analysis could be adapted from existing TPPs for any particular disease or condition of interest, or guidance documents [6]. We thus advocate for incorporating patient preferences during technical discussions with experts and stakeholders when a TPP is being initially constructed, and then during revisions (often happening at regular intervals, or as required) [5]. This could inform the definition of ideal, acceptable or minimal requirements for each category, which very often feature in TPPs [6], in addition to parameters such as safety or efficacy endpoints which could, in turn, be con- sidered in clinical studies. We posit that exploring patients’ perspectives would add value by bringing up aspects not otherwise considered by stakeholders and may also guide the R&D agenda. For example, the findings reported under other development needs highlight the importance of research into better treatments and a vaccine for CL [49]. Findings could be consolidated by quantitative instruments focusing on selected aspects of interest; identified themes could directly inform questionnaire design. A survey on preferred for- mulations pre- and post-treatment among patients has been shown to complement clinical trials of CL treatments [50]. The trade-offs identified in our study, and more general, identified risks and benefits could be further investigated using a quantitative discrete-choice-experiment (DCE) or triadic comparisons [51]. DCEs, used in healthcare research [52,53], have been used previ- ously to inform drug development [54–56]. Previous studies have successfully used an initial qualitative phase in order to inform a subsequent discrete-choice experiment [57]. This could be PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 15 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development complemented by consultations with drug developers to gain insights into the construction and updating of a TPP, as well as to identify stages where patient input could be useful. As suggested by others [5,17], considerations related to accessibility in combination with the closely related concept of affordability (as represented by the categories Cost and Perceived barri- ers) should be embedded within a TPP, in particular in any prospective profile describing pre- ferred product characteristics. Both ideal and acceptable targets for direct and, if possible, indirect costs should be included. Compliance is seen as a central issue across themes, and could be addressed meaningfully; first, by incorporating patients’ preferences in drug development in gen- eral and second, by addressing the actual and potential reasons for non-compliance that were brought up, such as experienced adverse events or geographical and availability barriers. Generalizability and transferability of findings As this is a qualitative study using a non-probability sample [58], it is therefore not likely to be representative of the entire spectrum of patients across regions. In our study, we paid attention to the transferability of methods, and, to a certain extent, of findings by clearly laying out set- tings (see Study sites and populations in the Methods section) and limitations (see Strengths and limitations of the study). Furthermore, we found the different settings reflected in the pref- erences, such as the civilian vs. the military population in Colombia, and a limited number of patients from a high-income, non-endemic area. We did not include patients suffering from Old-World CL (OWCL) forms, and concen- trated our efforts on NWCL forms, by a distinctive Leishmania species spectrum endemic to the Americas with very limited self-healing, and often progression to a mucosal or mucocuta- neous form [24,25]. Strengths and limitations of the study Interviews were conducted in patients’ mother tongues, including German, Portuguese, and Spanish. The analysis of pooled interviews transcripts, translated into English, was performed independently by two researchers, and informed by notes taken during the interviews as well as discussions with the interviewers; this process ensured consistency while taking the cultural context into consideration. Instead of designing a topic guide focussing on TPP categories a priori and asking for pref- erences, we chose a broad topic guide to allow for an in-depth exploration of experiences, and reflections upon these. Using such a comprehensive interview guide, other aspects of the lives of patients, such as the impact of disease and treatment on everyday life, or disease-specific topics such as stigmatization due to disfigurement and scars, could be explored as well. We only interviewed patients with NWCL who had sought, started, or completed their treatments and whom we were able to follow up. Hence, by design we are only able to report on patients’ experiences with therapies, which may influence their perceived ideal and accept- able treatments. In addition, each participating country has marginalized populations dispro- portionally affected by CL, e.g., refugees or migrant workers, which were found to be difficult to be followed up and reluctant to be interviewed (JK, MCN, personal communication). Finally, we used medical terminology instead of patients’ own words, in line with the aims and existing literature. We are aware that this might compromise presenting the richness of patients’ experiences. Further research Future studies could try to include CL patients who were lost to follow-up in trials or routine treatments, or marginalized populations (e.g., immigrants, migrant workers). Methods such as PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 16 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development a discrete-choice-experiment (DCE) or triadic comparisons, as outlined above, could provide a quantitative assessment of identified trade-offs, and contribute to the generalizability of results. The methodology is designed so that it could be adapted for other NTDs, in particular other skin NTDs [59]. Modifications to the sampling strategy, the interview topic guide and, if necessary, the analysis framework (which could be based on an existing treatment TPP) would allow for consideration of the specific context, including patients’ social and cultural circumstances. Conclusions Addressing NTDs will require innovative approaches [60] and this study contributes to the diverse efforts required to tackle these diseases effectively. We were able to show that patients can be meaningfully engaged in the construction of TPPs, and demonstrate the feasibility of a methodology involving an international patient population of an NTD. We recommend that Access and Affordability, directly informed by patients’ experiences and preferences, be included as separate categories in any TPP. Patient involvement is critical in the development of any TPP, and funding should be set aside to facilitate meaningful involvement. Contributors ACE, BA, EP, GC, LLC, MC and PO conceived of and designed the study, and developed study instruments. MC, JK, GC, MC and LLC collected data. EP, JW, NH and PO supervised data collection and analysis. ACE and MC coded the data, conducted the analysis, and wrote the first draft of the manuscript. All authors contributed to the manuscript and had final responsibility for the decision to submit for publication. Data sharing All relevant data are within the paper and its Supporting Information files. The study protocol is published [26]. Supporting information S1 Table. Study sites and patients enrolled in the study. (PDF) S1 File. Overview of themes by TPP categories. (PDF) S2 File. Additional findings. (PDF) Acknowledgments We are particularly grateful to all the CL patients who have contributed to the study. Author Contributions Conceptualization: Marı´a del Mar Castro, Astrid C. Erber, Byron Arana, Gla´ucia Cota, Clau- dia M. Denkinger, Piero Olliaro. Data curation: Marı´a del Mar Castro, Astrid C. Erber, Gla´ucia Cota, Julia Kutyi, Liliana Lo´pez-Carvajal. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0011975 February 21, 2024 17 / 21 PLOS NEGLECTED TROPICAL DISEASESIncorporating CL patient preferences into TPPs for drug development Formal analysis: Marı´a del Mar Castro, Astrid C. Erber, Julia Kutyi. Funding acquisition: Byron Arana, Piero Olliaro. Investigation: Marı´a del Mar Castro, Astrid C. Erber, Byron Arana, Gla´ucia Cota, Claudia M. Denkinger, Nicole Harrison, Julia Kutyi, Liliana Lo´pez-Carvajal, Emma Plugge, Piero Olliaro. Methodology: Marı´a del Mar Castro, Astrid C. Erber, Piero Olliaro. Project administration: Astrid C. Erber. Resources: Julia Walochnik, Piero Olliaro. Supervision: Marı´a del Mar Castro, Astrid C. Erber, Nicole Harrison, Emma Plugge, Julia Walochnik. Validation: Marı´a del Mar Castro, Astrid C. Erber, Astrid C. Erber, Byron Arana, Gla´ucia Cota, Claudia M. Denkinger, Claudia M. Denkinger, Nicole Harrison, Julia Kutyi, Liliana Lo´pez-Carvajal, Emma Plugge, Julia Walochnik, Piero Olliaro. Visualization: Marı´a del Mar Castro, Astrid C. Erber. Writing – original draft: Marı´a del Mar Castro, Astrid C. Erber. Writing – review & editing: Marı´a del Mar Castro, Astrid C. Erber, Byron Arana, Gla´ucia Cota, Claudia M. Denkinger, Nicole Harrison, Julia Kutyi, Liliana Lo´pez-Carvajal, Emma Plugge, Julia Walochnik, Piero Olliaro. References 1. Fitzpatrick C, Nwankwo U, Lenk E, de Vlas SJ, Bundy DAP. 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10.1371_journal.pmed.1004327	RESEARCH ARTICLE 1-year weight change after diabetes diagnosis and long-term incidence and sustainability of remission of type 2 diabetes in real-world settings in Hong Kong: An observational cohort study 1,2,3, Alice P. S. KongID 1, Aimin Yang1,2, Eric S. H. LauID 1,2,3, Elaine ChowID 1, Xinge Zhang1, Baoqi FanID 1, Wing-Yee So1,4, Juliana C. 1, Ronald C. Hongjiang WuID W. MaID N. ChanID 1,2,3, Andrea O. Y. LukID 1,2,3* 1 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China, 2 Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China, 3 Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China, 4 Hong Kong Hospital Authority, Hong Kong Special Administrative Region, China * andrealuk@cuhk.edu.hk Abstract Background AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: Clinical trials have demonstrated that remission of type 2 diabetes can be achieved following sustained weight loss. However, the feasibility of achieving diabetes remission through weight management in real-world settings remains unclear. In this study, we aimed to exam- ine the association of weight change at 1 year after diabetes diagnosis with long-term inci- dence and sustainability of type 2 diabetes remission in real-world settings in Hong Kong. Methods and findings This was a population-based observational cohort study. The territory-wide Risk Assess- ment and Management Programme for Diabetes Mellitus (RAMP-DM) provides regular comprehensive assessments of metabolic control and complication screening for people with diabetes in Hong Kong. We included 37,326 people with newly diagnosed type 2 diabe- tes who were enrolled in the RAMP-DM between 2000 and 2017, followed until 2019. Diabe- tes remission was defined as 2 consecutive HbA1c <6.5% measurements at least 6 months apart in the absence of glucose-lowering drugs (GLDs) and with no record of GLDs at least 3 months before these measurements. During a median follow-up of 7.9 years, 6.1% (2,279) of people achieved diabetes remission, with an incidence rate of 7.8 (95% CI: 7.5, 8.1) per 1,000 person-years. After adjusting for age at diabetes diagnosis, sex, assessment year, body mass index, other metabolic indices, smoking, alcohol drinking, and medication use, the hazard ratio (HR) for diabetes remission was 3.28 (95% CI: 2.75, 3.92; p < 0.001) for people with �10% weight loss within 1 year of diagnosis, 2.29 (95% CI: 2.03, 2.59; p < 0.001) for those with 5% to 9.9% weight loss, and 1.34 (95% CI: 1.22, 1.47; p < 0.001) for a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Wu H, Yang A, Lau ESH, Zhang X, Fan B, Ma RCW, et al. (2024) 1-year weight change after diabetes diagnosis and long-term incidence and sustainability of remission of type 2 diabetes in real-world settings in Hong Kong: An observational cohort study. PLoS Med 21(1): e1004327. https:// doi.org/10.1371/journal.pmed.1004327 Received: August 24, 2023 Accepted: December 5, 2023 Published: January 23, 2024 Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the results presented in the study are hosted by the Hong Kong Hospital Authority. Due to local regulation, the data are not available to the public. Request for data can be made via Hong Kong Hospital Authority: https://www3.ha.org.hk/data. Funding: The author(s) received no specific funding for this work. Competing interests: AOYL has received research grants or contracts from Amgen, AstraZeneca, Bayer, Biogen, Boehringer Ingelheim, Eli Lilly, Junshi, Lee Pharmaceutical, MSD, Novo Nordisk, PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 1 / 19 PLOS MEDICINERoche, Sanofi, Shanghai Junshi Biosciences, Sugardown, Takeda, received travel grants and honoraria for speaking from AstraZeneca, Boehringer Ingelheim, Eli Lilly, MSD. JCNC has received research grants through institutions and/ or honoraria for consultancy and/or giving lectures from Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Hua Medicine, Lee Powder, Merck Serono, Merck Sharp & Dohme, Pfizer, Sanofi and Viatris. APSK has received research grants and/or speaker honoraria from Abbott, Astra Zeneca, Bayer, Boehringer Ingelheim, Eli-Lilly, Kyowa Kirin, Merck Serono, Nestle, Novo- Nordisk, Pfizer and Sanofi. RCWM has received research funding from AstraZeneca, Bayer, Merck Sharp & Dohme, Novo Nordisk, Pfizer, Roche Diagnostics and Tricida Inc. for carrying out clinical trials or studies, and from AstraZeneca, Bayer, Boehringer Ingelheim, Daiichi Sankyo and Merck for speaker honoraria or consultancy in advisory boards. All proceeds have been donated to the Chinese University of Hong Kong to support diabetes research. RCMW is member of the editorial board of PLOS Medicine. Other authors have no competing interests to declare. Weight change and diabetes remission those with 0% to 4.9% weight loss compared to people with weight gain. During a median follow-up of 3.1 years, 67.2% (1,531) of people who had achieved diabetes remission returned to hyperglycaemia, with an incidence rate of 184.8 (95% CI: 175.5, 194.0) per 1,000 person-years. The adjusted HR for returning to hyperglycaemia was 0.52 (95% CI: 0.41, 0.65; p < 0.001) for people with �10% weight loss, 0.78 (95% CI: 0.68, 0.92; p = 0.002) for those with 5% to 9.9% weight loss, and 0.90 (95% CI: 0.80, 1.01; p = 0.073) for those with 0% to 4.9% weight loss compared to people with weight gain. Diabetes remission was associated with a 31% (HR: 0.69, 95% CI: 0.52, 0.93; p = 0.014) decreased risk of all- cause mortality. The main limitation of the study is that the reliability of HbA1c used to define diabetes remission can be affected by other medical conditions. Furthermore, we did not have data on bariatric surgery. Conclusions In this study, greater weight loss within the first year of diabetes diagnosis was associated with an increased likelihood of achieving diabetes remission and a decreased risk of return- ing to hyperglycaemia among those who had achieved diabetes remission. However, both the incidence of diabetes remission and the probability of its long-term sustainability were low with conventional management in real-world settings, in an era when the importance of weight loss was not fully appreciated. Our study provides evidence for policymakers to design and implement early weight management interventions and diabetes remission initiatives. CI, confidence interval; DiRECT, Abbreviations: AU : Anabbreviationlisthasbeencompiledforthoseusedthroughoutthetext:Pleaseverifythatallentriesarecorrectlyabbreviated: Diabetes Remission Clinical Trial; EMR, Electronic Medical Record; GLD, glucose-lowering drug; HA, Hospital Authority; HbA1c, haemoglobin A1C; HR, hazard ratio; MICE, multiple imputation by chained equations; PH, proportional hazard; RAMP-DM, Risk Assessment and Management Programme for Diabetes Mellitus; SD, standard deviation. Author summary Why was this study done? • Remission of type 2 diabetes is defined as a return to normal blood glucose levels with- out the need for pharmacotherapy. • Recent clinical trials have demonstrated that remission of type 2 diabetes can be achieved following sustained weight loss through bariatric surgery or lifestyle interventions. • However, the feasibility of achieving diabetes remission through weight management and its long-term sustainability in real-world settings remain unclear. What did the researchers do and find? • Using data from 37,326 people with newly diagnosed type 2 diabetes from a diabetes complication screening program in Hong Kong, we examined the associations of 1-year weight change (%) after diabetes diagnosis with long-term incidence and sustainability of diabetes remission. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 2 / 19 PLOS MEDICINEWeight change and diabetes remission • We found only 6% of people achieved diabetes remission during a median follow-up of 8 years. Among people who achieved diabetes remission, 67% of them returned to hyperglycaemia during a median follow-up of 3 years. • Compared to people who had weight gain, those who had a greater 1-year weight loss (%) after diabetes diagnosis were more likely to achieve diabetes remission and had a decreased risk of returning to hyperglycaemia. What do these findings mean? • Remission of type 2 diabetes is achievable in real-world settings, but both the incidence of diabetes remission and the probability of its long-term sustainability are low with conventional management. • Implementation of early weight management interventions should be considered as dia- betes remission initiatives. • A limitation of the study is that the reliability of HbA1c as a measure for diabetes remis- sion can be influenced by other medical conditions. Introduction Type 2 diabetes is characterised by persistent hyperglycaemia and has traditionally been con- sidered an irreversible condition that requires lifelong drug treatment for glucose control. However, recent clinical trials have demonstrated that remission of type 2 diabetes, defined as a return to normal blood glucose levels without the need for pharmacotherapy, can be achieved following sustained weight loss through bariatric surgery [1,2] or lifestyle interven- tions [3,4] in those who were overweight or obese. The STAMPEDE trial showed that around one-third of obese people with type 2 diabetes achieved a haemoglobin A1C (HbA1c) level of less than 6% without glucose-lowering drugs (GLDs) 1 year after bariatric surgery [1]. In the Diabetes Remission Clinical Trial (DiRECT), approximately half of the participants with type 2 diabetes achieved a HbA1c level of less than 6.5% without GLDs after losing an average of 10 kg body weight through a primary care–led weight management intervention over a 12-month period [3]. However, our understanding of diabetes remission remains limited, largely because the majority of existing evidence comes from clinical trials. The strictly controlled environments and selective participants in clinical trials have inherently limited the generalisability of their findings to broader populations. Evidence from more diverse populations is needed to better understand the feasibility of achieving diabetes remission through weight management in real- world settings to inform clinical practice. More importantly, given the short follow-up time of clinical trials, the long-term incidence, sustainability, and benefits of diabetes remission are largely unknown. Weight control is a key component in the management of type 2 diabetes. Effective man- agement of body weight during the early stage of diabetes disease trajectory can prevent diabe- tes-related complications and improve long-term outcomes [5]. The first year after diabetes diagnosis represents a critical period for early intervention and reflects the initial response to PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 3 / 19 PLOS MEDICINEWeight change and diabetes remission lifestyle changes and treatment [6,7]. In this study, we aimed to use real-world data from Hong Kong Chinese to examine (1) the associations between 1-year weight change with conventional management after diabetes diagnosis and the long-term incidence and sustainability of remis- sion of type 2 diabetes; and (2) the association between diabetes remission and all-cause and cause-specific mortality. Methods Data source and study setting Details on the Hong Kong Hospital Authority (HA) Electronic Medical Record (EMR) system and the Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM) have been reported [8–12]. Briefly, the Hong Kong HA is a statutory body established in 1990 that governs all public hospitals and the majority of specialist and general outpatient clinics. Due to the highly subsidised public healthcare system, the HA provides about 90% of total health services in Hong Kong. In 2000, the HA implemented a territory-wide RAMP-DM in 18 hospital-based diabetes centres to provide regular comprehensive risk assessment and com- plication screening to people with diabetes referred from outpatient clinics. In 2009, the HA expanded the RAMP-DM from hospital-based diabetes centres to all general outpatient clinics in primary care settings. All people with diabetes were eligible to participate in the RAMP-DM with no specific criteria, and approximately 60% of the Hong Kong population with diabetes have been enrolled in this program. We planned the study in December 2022, conducted the analyses between January and July 2023, and performed additional analyses in October 2023 in response to suggestions from journal reviewers. This study is reported as per the Strength- ening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). Study population We included people with newly diagnosed type 2 diabetes (self-reported diabetes duration �1 year) who underwent their first RAMP-DM assessment between 1 January 2000 and 31 December 2017. To reduce the potential influence of age-related weight changes, we excluded people who were younger than 18 years (weight would increase as part of pubertal develop- ment) or older than 75 years (weight could decrease due to age-related loss of muscle mass) at diabetes diagnosis. Furthermore, we excluded people who had an extreme BMI (<15 or >50 kg/m2), had potentially unreliable values on 1-year weight change (%) falling outside the 0.1% to 99.9% range of the data distribution, or had missing data on 1-year weight change. To reduce the probability that weight changes were caused by severe illness, we excluded people with preexisting cardiovascular disease, cancer, or end-stage renal disease. Additionally, we excluded people who did not use any GLDs but with no record of an HbA1c �6.5% (48 mmol/ mol) within 1 year after diabetes diagnosis or those with prevalent diabetes remission. We also excluded people who used insulin at baseline as these individuals might have type 1 diabetes. Finally, a total number of 37,326 people were included in the analysis (Fig 1). Assessment of 1-year weight change The exposure variable was the percentage of 1-year weight change (%) after diabetes diagnosis. We used the body weight measured at the first RAMP-DM assessment at diabetes diagnosis as the baseline weight and the body weight at the follow-up RAMP-DM assessment conducted closest (±6 months) to 1 year after the baseline as the 1-year weight. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 4 / 19 PLOS MEDICINEWeight change and diabetes remission Fig 1. Flowchart of the selection of participants in the Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM). https://doi.org/10.1371/journal.pmed.1004327.g001 Outcome measures The primary outcome was incident remission of type 2 diabetes. We defined diabetes remis- sion based on the 2021 International Consensus report as 2 or more consecutive HbA1c <6.5% (48 mmol/mol) measurements at a time interval of at least 6 months in the absence of GLDs between these measurements and with no record of GLDs at least 3 months before the first HbA1c <6.5% [13,14]. The date of the first HbA1c <6.5% was used as the date of incident diabetes remission. The secondary outcomes included the following: (1) incident return to hyperglycaemia, defined as an HbA1c �6.5% or use of GLDs among people who had achieved diabetes remission; and (2) all-cause and cause-specific mortality among people with and with- out diabetes remission. Cause-specific mortality included mortality due to cancer (ICD-10: PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 5 / 19 PLOS MEDICINEWeight change and diabetes remission C00-C97), cardiovascular disease (ICD-10: I00-I99, F01 and G45), and pneumonia (ICD-10: J12-J18), which were the 3 most common causes of death in type 2 diabetes in Hong Kong [11]. Routinely collected data on HbA1c, GLD prescriptions, and mortality were obtained from the HA EMR database. Definition of follow-up period In the analysis of diabetes remission, people were followed from their baseline RAMP-DM assessment to the date of incident diabetes remission, death, or 31 December 2019, whichever came first. In the analysis of return to hyperglycaemia, people were followed from the date of diabetes remission to the date of incident return to hyperglycaemia, death, or 31 December 2019, whichever came first. In the analysis of mortality, to reduce immortal time bias [15], peo- ple with diabetes remission were followed up from the date of diabetes remission, while those without diabetes remission were followed up from their baseline RAMP-DM assessment to death or 31 December 2019, whichever came first. Statistical analysis We categorised the study population based on their 1-year weight change (%) into 4 groups: (1) weight loss of �10%; (2) weight loss of 5% to 9.9%; (3) weight loss of 0% to 4.9%; and (4) weight gain of >0%. The decision to categorise 1-year weight change (%) and the selection of cutoffs were made to align with previous publications for comparison purposes [3,4,14,16]. We fitted multivariable Cox proportional-hazards regression models to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of 1-year weight change (%) categories after diabetes diagnosis with incident diabetes remission and incident return to hyperglycaemia, as well as for the association of diabetes remission with all-cause and cause- specific mortality. Subgroup analyses were performed according to the key characteristics of the study population. We checked the Cox proportional hazards (PHs) assumption using Schoenfeld residuals. We found that the assumption was violated for the association between 1-year weight change (%) categories and incident return to hyperglycaemia but not for other models. Thus, the HRs were interpreted as an average effect during the entire follow-up period [17]. To explore the possible nonlinear relationship between 1-year weight change (%) as a continuous variable and diabetes remission, a restricted cubic spline term with 5 knots placed at 2.5%, 25%, 50%, 75%, and 97.5% through the data distribution was included in the model. The number of knots in the spline was selected based on the Bayesian information criterion. We managed missing data using the multiple imputation by chained equations (MICE) method and assuming that data were missing at random (S1 Table). We generated 15 imputed datasets based on the greatest percentage of incomplete cases in the dataset, and we pooled the results using the Rubin’s rule [18]. Additional and sensitivity analyses For comparison purposes, we examined the associations of 1-year change (%) in waist circum- ference with incident diabetes remission and incident return to hyperglycaemia. Waist cir- cumference is a measure of central obesity and is considered an important risk factor for metabolic disease in Asians. In addition, the definition of diabetes remission based on 2 HbA1c measurements taken at least 6 months apart could potentially introduce immortal time bias as people who did not survive to receive the second HbA1c measurement were inherently classified as not having achieved diabetes remission. Therefore, we performed a sensitivity analysis excluding people who died within 6 months after achieving their first HbA1c mea- surement <6.5% to examine the association between diabetes remission and all-cause PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 6 / 19 PLOS MEDICINEWeight change and diabetes remission mortality. Moreover, given the high prevalence of cardiovascular disease in people with diabe- tes, we further examined the association between 1-year weight change (%) and incident diabe- tes remission by including those (n = 2,956) with preexisting cardiovascular disease to enhance the generalisability of the results. We also compared the characteristics of people included in the analysis with those excluded due to missing data on weight change but who still met other inclusion criteria. All analyses were performed using R software, version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria). This study was approved by the Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (CREC Ref. No. 2020.032). Results Among the 37,326 people included in the analysis, 50.5% (n = 18,832) were men. At baseline, the mean age was 56.6 (standard deviation [SD]: 9.9) years, the mean BMI was 26.4 (SD: 4.2) kg/m2, the mean HbA1c was 7.7% (SD: 1.8%), and 65.0% were using GLDs (Table 1). On aver- age, people experienced a 0.2% (SD: 5.2%) reduction in body weight 1 year after diabetes diag- nosis (Table 1). Overall, 2.8% of people had a 1-year weight loss of �10%, 10.4% had a weight loss of 5% to 9.9%, 40.2% had a weight loss of 0% to 4.9%, and 46.6% had weight gain. People who had a greater 1-year weight loss were more likely to be women, had higher blood pressure and lipid levels, were less likely to be current smokers and alcohol users, and were less likely to use GLDs at baseline. They had higher levels of BMI, waist circumference, and HbA1c at base- line, but lower levels for these measurements 1 year after baseline. There was no clear trend in age at diabetes diagnosis and proportion of people using blood pressure–lowering drugs or lipid-lowering drugs at baseline across the weight change groups. Weight change and incident diabetes remission During a median follow-up of 7.9 years (IQR: 4.8, 10.5), 6.1% (n = 2,279) of people achieved diabetes remission. The overall crude incidence rate of diabetes remission was 7.8 (95% CI: 7.5, 8.1) per 1,000 person-years and 88% of the remission events occurred within the first 5 years of the follow-up (Fig 2). Characteristics of people with and without diabetes remission are shown in the S2 Table. The proportion of people achieving diabetes remission was higher among those with greater weight loss. Specifically, 14.4% (n = 152) of people who lost �10% of their body weight achieved remission, compared to 9.9% (n = 384) in those with a 5% to 9.9% weight loss, 6.5% (n = 969) in those with a 0% to 4.9% weight loss, and 4.5% (n = 774) in those who experienced weight gain. The restricted cubic spline model showed a nonlinear associa- tion between 1-year weight change (%) and diabetes remission (p < 0.001 for nonlinearity) (Fig 3). The HR for diabetes remission increased with increasing weight loss but was constant as the gain in weight increased. Compared to people with weight gain, the adjusted HR for dia- betes remission was 3.28 (95% CI: 2.75, 3.92; p < 0.001) for those with �10% weight loss, 2.29 (95% CI: 2.03, 2.59; p < 0.001) for those with 5% to 9.9% weight loss, and 1.34 (95% CI: 1.22, 1.47; p < 0.001) for those with 0% to 4.9% weight loss (Fig 4 and S3 Table). In subgroup analy- sis, the strength of the association between 1-year weight change (%) and diabetes remission was stronger in people who had a higher HbA1c level (p < 0.001 for interaction) and those with central obesity (p = 0.003 for interaction) at baseline but did not appear to be modified by other characteristics of the study population (S1 Fig). The association between 1-year waist cir- cumference change (%) and diabetes remission was similar in direction but was weaker in magnitude (for the same % change) compared to the 1-year weight change (%) (Figs 4 and S2 and S3 Table). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 7 / 19 PLOS MEDICINETable 1. Baseline characteristics and selected 1-year measures of the study population according to 1-year weight change (%) after diabetes diagnosis. Weight change and diabetes remission �10% weight loss 5% to 9.9% weight loss 0% to 4.9% weight loss >0% weight gain 1-year weight change (%) group Overall Characteristics Number (%) Age at diabetes diagnosis (years) Male sex Assessment year 2000–2009 2010–2013 2014–2017 BMI category <24 kg/m2 24–27.9 kg/m2 �28 kg/m2 BMI (kg/m2) at baseline BMI (kg/m2) at 1 year Weight (kg) at baseline Weight (kg) at 1 year 1-year absolute weight change (kg) 1-year weight change (%) Central obesity (%) Waist circumference (cm) at baseline Men Women Waist circumference (cm) at 1 year Men Women 1-year waist circumference change (%) Men Women HbA1c at baseline % mmol/mol HbA1c at 1 year % mmol/mol 1-year HbA1c change % mmol/mol Blood pressure (mm Hg) SBP DBP Total cholesterol (mmol/L) LDL-C (mmol/L) HDL-C (mmol/L) Triglycerides (mmol/L) eGFR (mL/min/1.73 m2) Smoking status Current Former Never Alcohol drinking status 1,059 (2.8) 56.4 (10.6) 440 (41.5) 351 (33.1) 438 (41.4) 270 (25.5) 253 (23.9) 407 (38.4) 399 (37.7) 27.4 (4.7) 23.6 (3.9) 69.0 (14.6) 59.5 (12.3) −9.6 (3.8) −13.7 (3.7) 721 (75.7) 94.1 (11.4) 88.6 (10.0) 86.0 (10.6) 81.6 (9.6) −8.0 (6.7) −7.4 (6.8) 7.7 (1.7) 61.3 (18.7) 6.4 (1.1) 46.1 (12.4) −1.4 (1.8) −15.0 (20.1) 135.1 (17.9) 77.7 (10.4) 5.1 (1.1) 3.1 (0.9) 1.3 (0.3) 1.4 (0.9, 1.9) 89.4 (17.3) 120 (12.1) 108 (10.9) 763 (77.0) 3,864 (10.4) 57.3 (9.8) 1,717 (44.4) 1,349 (34.9) 1,465 (37.9) 1,050 (27.2) 1,021 (26.4) 1,572 (40.7) 1,271 (32.9) 26.7 (4.1) 24.8 (3.9) 67.6 (12.7) 63.0 (11.9) −4.6 (1.3) −6.9 (1.3) 2,456 (71.8) 92.4 (10.0) 88.0 (10.1) 88.1 (9.7) 84.4 (9.7) −4.6 (5.0) −4.0 (6.5) 7.6 (1.5) 59.8 (16.6) 6.6 (1.0) 48.6 (11.4) −1.0 (1.6) −11.3 (17.4) 135.3 (18.0) 78.0 (10.3) 5.1 (1.1) 3.1 (0.9) 1.3 (0.3) 1.4 (1.0, 2.0) 89.6 (16.2) 441 (12.5) 446 (12.6) 2,652 (74.9) 15,016 (40.2) 57.2 (9.7) 7,326 (48.8) 17,387 (46.6) 37,326 (100) 56.0 (10.0) 56.6 (9.9) 9,349 (53.8) 18,832 (50.5) 5,159 (34.4) 5,561 (37.0) 4,296 (28.6) 3,938 (26.2) 6,142 (40.9) 4,936 (32.9) 26.7 (4.1) 26.2 (4.0) 68.3 (12.7) 66.9 (12.5) −1.4 (1.0) −2.1 (1.4) 9,532 (71.8) 92.3 (9.8) 88.7 (10.2) 91.1 (9.6) 87.6 (10.0) −1.2 (4.8) −1.0 (6.0) 7.5 (1.5) 58.2 (16.3) 6.8 (0.9) 51.4 (10.3) −0.6 (1.5) −6.8 (15.9) 134.6 (17.0) 78.2 (10.0) 5.0 (1.0) 3.0 (0.9) 1.2 (0.3) 1.5 (1.0, 2.1) 89.5 (15.9) 1,924 (13.9) 1,936 (14.0) 9,994 (72.1) 5,898 (33.9) 6,285 (36.1) 5,204 (29.9) 5,906 (34.0) 6,902 (39.7) 4,579 (26.3) 25.9 (4.2) 26.9 (4.3) 67.0 (13.0) 69.5 (13.4) 2.5 (2.4) 3.8 (3.7) 9,817 (63.9) 12,757 (34.2) 13,749 (36.8) 10,820 (29.0) 11,118 (29.8) 15,023 (40.2) 11,185 (30.0) 26.4 (4.2) 26.3 (4.2) 67.6 (12.9) 67.5 (13.1) −0.2 (3.5) −0.2 (5.2) 22,526 (68.3) 90.6 (10.4) 87.2 (10.5) 91.5 (10.2) 87.9 (10.3) 92.8 (10.1) 89.1 (10.4) 91.6 (10.0) 87.7 (10.3) 2.6 (5.5) 2.4 (6.5) 0.2 (5.9) −0.1 (6.8) 7.8 (2.1) 62.1 (23.0) 7.7 (1.8) 60.3 (19.9) 7.0 (1.0) 53.2 (11.2) 6.9 (1.0) 51.8 (11.0) −0.8 (2.0) −9.0 (22.1) −0.8 (1.8) −8.6 (19.4) 132.8 (17.7) 133.8 (17.5) 77.8 (10.3) 78.0 (10.2) 4.9 (1.0) 2.9 (0.9) 1.2 (0.3) 5.0 (1.0) 3.0 (0.9) 1.3 (0.3) 1.4 (1.0, 2.0) 1.4 (1.0, 2.0) 91.2 (16.4) 90.3 (16.2) 2,579 (16.0) 2,458 (15.3) 5,064 (14.7) 4,948 (14.3) 11,073 (68.7) 24,482 (71.0) (Continued ) 8 / 19 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 PLOS MEDICINEWeight change and diabetes remission Table 1. (Continued) Characteristics Current Former Never Oral glucose-lowering drugs (yes) Any Metformin Sulfonylureas Others Blood pressure–lowering drugs (yes) Lipid-lowering drugs (yes) Number (%) of people achieved diabetes remission �10% weight loss 5% to 9.9% weight loss 0% to 4.9% weight loss >0% weight gain 1-year weight change (%) group Overall 182 (18.5) 69 (7.0) 733 (74.5) 609 (57.5) 548 (51.7) 161 (15.2) 3 (0.3) 564 (53.3) 167 (15.8) 152 (14.4) 739 (21.0) 244 (6.9) 2,529 (72.0) 2,287 (59.2) 2,049 (53.0) 584 (15.1) 4 (0.1) 2,122 (54.9) 693 (17.9) 384 (9.9) 3,171 (23.2) 992 (7.2) 9,529 (69.6) 9,360 (62.3) 8,151 (54.3) 3,071 (20.5) 20 (0.1) 8,639 (57.5) 3,026 (20.2) 969 (6.5) 3,741 (23.6) 7,833 (23.0) 1,380 (8.7) 2,685 (7.9) 10,731 (67.7) 23,522 (69.1) 11,998 (69.0) 24,254 (65.0) 10,151 (58.4) 20,899 (56.0) 5,074 (29.2) 8,890 (23.8) 62 (0.4) 8,724 (50.2) 3,339 (19.2) 774 (4.5) 89 (0.2) 20,049 (53.7) 7,225 (19.4) 2,279 (6.1) Data are mean (standard deviation), median (interquartile range), or n (%) as appropriate. Summary statistics are reported based on the complete data for each variable. Proportion of missing data for each variable is shown in the S1 Table. All p-values for the comparisons across weight change groups are less than 0.05. Central obesity is defined as waist circumference �90 cm in men and waist circumference �80 cm in women. Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate, HbA1c, haemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein; SBP, systolic blood pressure. https://doi.org/10.1371/journal.pmed.1004327.t001 Fig 2. Kaplan–Meier plot of cumulative incidence of remission of type 2 diabetes (A) and cumulative incidence of return to hyperglycaemia among people with diabetes remission (B) according to 1-year weight change (%) after diabetes diagnosis. https://doi.org/10.1371/journal.pmed.1004327.g002 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 9 / 19 PLOS MEDICINEWeight change and diabetes remission Fig 3. Density plots (A) and restricted cubic splines (B) of the associations of 1-year change (%) in weight and waist circumference after diabetes diagnosis with incident remission of type 2 diabetes. Density plots show the distribution of 1-year change (%) in weight and waist circumference. A negative value indicates weight and waist circumference loss. In the restricted cubic splines, fully adjusted Cox regression models were fitted with 5 knots at 2.5%, 25%, 50%, 75%, and 97.5% through the data distribution. The reference value for each hazard ratio (HR) of 1.0 was set as 0% change. Solid/dashed lines are HR estimates, with shaded areas showing 95% confidence intervals. The y-axis is natural log-transformed. https://doi.org/10.1371/journal.pmed.1004327.g003 Weight change and incident return to hyperglycaemia During a median follow-up of 3.1 years (IQR: 2.1, 4.7) from the date of diabetes remission, 67.2% (n = 1,531) of people who had achieved diabetes remission returned to hyperglycaemia, with a crude incidence rate of 184.8 (95% CI: 175.5, 194.0) per 1,000 person-years. Among those who achieved diabetes remission, 99.9% (n = 2,277) had at least one more valid HbA1c measurement following the HbA1c measurements that confirmed their remission status dur- ing the follow-up. Around 39% of people retuned to hyperglycaemia within 3 years and 58% within 5 years after achieving remission (Fig 2). During the follow-up, mean body weight increased by 0.82% (SD: 5.8%) compared to the weight measured at 1 year after diabetes diag- nosis in people who returned to hyperglycaemia, whereas it decreased by 0.88% (SD: 7.7%) in those who maintained remission (S3 Fig). The median time to return to hyperglycaemia was 3.6 (95% CI: 3.4, 3.8) years. Characteristics of people who returned and those did not return to hyperglycaemia are shown in the S4 Table. Greater 1-year weight loss after diabetes diagnosis was associated with a decreased risk of returning to hyperglycaemia (Fig 4 and S5 Table). Compared to people with weight gain, the adjusted HR for returning to hyperglycaemia was 0.52 (95% CI: 0.41, 0.65; p < 0.001) for those with �10% weight loss, 0.78 (95% CI: 0.68, 0.92; p = 0.002) for those with 5% to 9.9% weight loss, and 0.90 (95% CI: 0.80, 1.01; p = 0.073) for PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 10 / 19 PLOS MEDICINEWeight change and diabetes remission Fig 4. Hazard ratios (HRs) for the associations of 1-year change (%) in weight and waist circumference after diabetes diagnosis with incident remission of type 2 diabetes (A) and incident return to hyperglycaemia among people with diabetes remission (B). For the analyses of diabetes remission, the HRs were adjusted for age at diabetes diagnosis, sex, assessment year, BMI, waist circumference, HbA1c, SBP, LDL-C, HDL-C, triglycerides, eGFR, smoking, alcohol drinking, oral glucose-lowering drugs, blood pressure–lowering drugs, and lipid-lowering drugs. For the analyses of return to hyperglycaemia, the HRs were additionally adjusted for diabetes duration. Squares represent HRs and lines represent 95% CIs. The area of each square is inversely proportional to the variance of log HR, which also determines the 95% CI. The axis for HR is natural log-transformed. Abbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HR, hazard ratio; HbA1c, haemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein; SBP, systolic blood pressure. https://doi.org/10.1371/journal.pmed.1004327.g004 those with 0% to 4.9% weight loss. Similarly, the HR for returning to hyperglycaemia decreased with larger reduction in waist circumference. Diabetes remission and risk of mortality During a median follow-up of 7.9 years (IQR: 5.0, 10.4) for mortality, 2,163 deaths were recorded, of which 785 were due to cancer, 341 to cardiovascular disease, and 383 to pneumo- nia. People who experienced diabetes remission had a significantly decreased risk of all-cause mortality (HR: 0.69, 95% CI: 0.52, 0.93; p = 0.014) compared to those not achieving diabetes remission (Fig 5). On sub-analysis, the decreased risks for mortality from cancer (HR: 0.72, 95% CI: 0.43, 1.21; p = 0.21), cardiovascular disease (HR: 0.53, 95% CI: 0.22, 1.27; p = 0.15) or pneumonia (HR: 0.93, 95% CI: 0.50, 1.73; p = 0.82) were not statistically significant at a level of PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 11 / 19 PLOS MEDICINEWeight change and diabetes remission Fig 5. Crude mortality rates and adjusted hazard ratios (HRs) for the association of incident diabetes remission with all-cause and cause-specific mortality. The HRs were adjusted for 1-year change (%) in weight, age at diabetes diagnosis, sex, assessment year, BMI, waist circumference, HbA1c, SBP, LDL-C, HDL-C, triglycerides, eGFR, smoking, alcohol drinking, oral glucose-lowering drugs, blood pressure–lowering drugs, lipid-lowering drugs, and diabetes duration. Squares represent HRs and lines represent 95% CIs. The area of each square is inversely proportional to the variance of log HR, which also determines the 95% CI. The axis for HR is natural log-transformed. Abbreviations: BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HR, hazard ratio; HbA1c, haemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low- density lipoprotein; SBP, systolic blood pressure. https://doi.org/10.1371/journal.pmed.1004327.g005 0.05 but remained in the same direction. Among those with diabetes remission who returned to hyperglycaemia, there was a trend that the HR for all-cause mortality decreased as the dura- tion of remission increased (p = 0.032 for trend) (S4 Fig). Additional and sensitivity analyses The association between diabetes remission and all-cause mortality remained significant (HR: 0.71, 95% CI: 0.53, 0.95; p = 0.023) after excluding people (n = 34) who died within 6 months after achieving their first HbA1c measurement <6.5%. The HRs for the association between 1-year weight change (%) and diabetes remission changed little after further including people with preexisting cardiovascular disease (S6 Table). There were statistically significant differ- ences in baseline characteristics between people included in the analysis and those excluded due to missing data on weight change (S7 Table). However, the absolute differences were small. Discussion In this large long-term cohort study of Hong Kong Chinese with newly diagnosed type 2 dia- betes, greater weight loss 1 year after diabetes diagnosis was associated with an increased likeli- hood of incident diabetes remission as well as a decreased risk of returning to hyperglycaemia among those who had achieved diabetes remission. People who experienced diabetes remis- sion had a lower risk of all-cause mortality compared to those without diabetes remission. However, the overall incidence of diabetes remission was considerably low, with only 6% of people achieving remission over a median follow-up of 8 years. Maintaining long-term remis- sion was challenging as approximately half of those with remission returned to hyperglycaemia within 3 years after achieving remission. The incidence of diabetes remission in our study was comparable to that in other epidemio- logical studies but significantly lower than in clinical trials. A cohort study of 122,781 US adults with type 2 diabetes, in which remission was defined as 2 HbA1c measurements without use of GLD in the range of 5.7% to 6.4% over at least 12 months, reported an incidence of 2.8 per 1,000 person-years in the overall study population and 8.8 per 1,000 person-years among those with new-onset diabetes [19]. A study of 2 million people with type 2 diabetes in primary care settings in England, using the same remission definition as our study, reported an incidence of 9.7 per 1,000 person-years in the overall population and 44.9 per 1,000 person-years among people with newly diagnosed diabetes [14]. However, in the DiRECT clinical trial, 73% of par- ticipants who lost 10 kg or more body weight achieved diabetes remission (defined as an PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 12 / 19 PLOS MEDICINEWeight change and diabetes remission HbA1c <6.5% with no GLD for at least 2 months) at 12 months, following interventions that included total diet replacement with a low-energy formula diet, stepped food reintroduction, and structured support for long-term weight loss maintenance [3]. In the DIADEM-I clinical trial, 61% of participants in the intervention group achieved diabetes remission (defined as an HbA1c <6.5% with no GLD for at least 3 months) at 12 months following a similar regimen used in the DiRECT trial, which resulted in an average weight loss of 12 kg [4]. By contrast, our study found that only 14% of people who experienced a weight loss of 10% or more and 9.9% of those with a weight loss between 5% and 10% achieved remission over 8 years, respec- tively. The incidence of diabetes remission in our study was also significantly lower than that in the control groups of the DiRECT [3] and DIADEM-I [4] clinical trials, where 4% and 12% of participants achieved remission at 12 months, accompanied by average weight losses of 1 kg and 4 kg, respectively. A number of factors might contribute to the observed discrepancy between clinical trials and studies in real-world settings. Participants in clinical trials underwent intensive lifestyle interventions that comprised complex dietary intervention, physical exercise, and cognitive- behavioural support. These structured programmes included regular monitoring, feedback, and reinforcement to provide a holistic approach to managing diabetes. However, it is unclear whether people in real-world settings engaged in any dietary modifications or other targeted interventions. Moreover, the majority of clinical trials adopted a less stringent remission crite- rion, generally utilising a single HbA1c measurement [3,4], in comparison to epidemiological studies that usually required 2 HbA1c measurements over time [14,16,19]. Prescription prac- tices also influence the incidence of remission in real-world settings. Doctors may be less inclined to discontinue GLDs for their patients, even when their HbA1c level approaches nor- mal. This practice could inadvertently limit the opportunities for patients to demonstrate potential remission. However, in both DiRECT and DIADEM-I clinical trials [3,4], all partici- pants discontinued GLDs at the start of the intervention. More importantly, during our study period, the concept of achieving diabetes remission was not as widely recognised or incorpo- rated into clinical practice. Innovative weight management strategies, which have been proven effective for achieving diabetes remission in recent clinical trials [3,4,20], were neither imple- mented nor available in real-world settings in our locality. This partially explains the low inci- dence of diabetes remission observed in our study, reflecting the conventional management approaches of the era. Our study focused on weight changes during the first year following a diabetes diagnosis, a period that serves as a prime opportunity for individuals to initiate lifestyle modifications in response to their diagnosis. We found that any degree of weight loss was associated with increased likelihood of diabetes remission. People who lost �10% of their body weight within 1 year after diabetes diagnosis were 3 times more likely to achieve diabetes remission, and those who lost 5% to 9.9% were twice as likely, compared to those who experienced weight gain. This highlights the potential of early weight management as an important intervention to improve glycaemic control and achieve diabetes remission in real-world settings. Although our study observed a dose–response relationship between weight loss and incidence of diabetes remission, it is important to emphasise that the decision to undertake weight loss as part of diabetes management should consider priorities beyond diabetes remission and be tailored to each individual’s clinical profile. It is also noteworthy that over 80% of remission cases occurred within the first 5 years following diabetes diagnosis, and remission rarely developed in people with a long disease duration, irrespective of baseline weight changes. This is consis- tent with other studies suggesting that a longer duration of diabetes was associated with a reduced likelihood of remission [14,16,19], related to progressive deterioration of beta cell function under conventional management [21]. This is also supported by the Counterbalance PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 13 / 19 PLOS MEDICINEWeight change and diabetes remission study, which found that following adequate weight loss on a very low–calorie diet and weight maintenance intervention with concomitant cessation of GLD in people with type 2 diabetes, those who did not achieve fasting blood glucose levels <7 mmol/L (nonresponders) had a con- siderably greater beta cell defect and a longer diabetes duration at baseline compared to responders [22]. Our study observed a significant association between weight loss and diabetes remission even among people with a BMI <24 kg/m2. This finding is supported by the ReTUNE clinical trial in the United Kingdom, which showed that 70% of participants with normal or near-nor- mal BMI (<27 kg/m2) went into diabetes remission through diet-induced weight loss [23]. The accumulation of intrahepatic and intrapancreatic fat is one of the key mechanisms under- lying pathogenesis of type 2 diabetes, which increases hepatic insulin resistance and beta-cell function, respectively [23–25]. Weight loss could lead to reduction in these specific fat deposits irrespective of BMI, potentially contributing to diabetes remission [23]. Although type 2 diabetes is potentially reversible, remission was attained in only 6% of peo- ple over an 8-year follow-up period in our study. Notably, maintaining long-term remission proved to be a challenge. Approximately 20% of people who achieved remission returned to hyperglycaemia annually. This aligns with results from clinical trials. In the Look AHEAD study, 30% of those in the intervention group who achieved remission (defined as an HbA1c <6.5% with no GLD) returned to a clinical diabetes status every single year [26]. The DiRECT study reported that among those who achieved remission at the 12 months of intervention, 22% returned to hyperglycaemia by 24 months [3,20]. The low remission rate and the difficulty in maintaining remission reflect the complex nature of diabetes. Sustaining long-term diabetes remission may require a comprehensive and multifaceted approach that includes ongoing commitment to a healthy lifestyle and weight management with maintenance of near normo- glycaemia. New gut hormone-based compounds promise large reduction in body weight in addition to blood glucose lowering [27,28]. However, clinical trials have shown rapid weight regain following withdrawal of glucagon-like peptide-1 receptor agonists [29]. The impact of diabetes remission on long-term health outcomes has not been well studied. Our study found a 30% reduction in all-cause mortality among people who experienced diabetes remission compared to those who did not. Diabetes remission, which results in a reduction of hypergly- caemia, could lower the cumulative glycaemic burden, thereby preventingAU : Pleasecheckandconfirmthattheeditsto}Diabetesremission; whichresultsinareductionofhyperglycaemia; could:::}didnotaltertheintendedmeaningofthesentence: complications and mortality [30]. diabetes-related This study has a number of strengths, including a large sample size, high-quality data, and a long-term follow-up period up to 20 years, which enabled us to comprehensively capture long- term patterns of diabetes remission in real-world settings. This study also has several limita- tions. First, HbA1c alone may not always be an appropriate test to define diabetes remission, because its reliability can be affected by other medical conditions such as anaemia and haemo- globinopathy [31]. The consensus report [13] recommends HbA1c as the usual criterion for diabetes remission due to its clinical utility, but other glucose criteria should be used if HbA1c is unavailable or deemed unreliable. Although we lacked data on anaemia and haemoglobino- pathy, we used at least 2 HbA1c values to define diabetes remission. This approach could ade- quately identify people with sustained changes in glycaemic control and provide reliable assessment of diabetes remission. Second, we did not have data on bariatric surgery, which can lead to diabetes remission predominantly through its effects on weight loss and alterations in nutrition [24,32–34]. However, bariatric surgery is performed infrequently in Hong Kong, even among those with obesity-related complications, including diabetes [35]. Third, our study only examined weight change 1 year after diabetes diagnosis and did not capture the full spectrum of weight fluctuations over the study period. Fourth, the HA EMR system did not capture GLD prescriptions and HbA1c measurements in the private sector, which would affect PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 14 / 19 PLOS MEDICINEWeight change and diabetes remission the incidence of both diabetes remission and return to hyperglycaemia. However, this bias should be minimal and is likely to be nondifferential across different weight change groups. Fifth, a large proportion of people were excluded from the analysis due to missing data on weight change. This resulted in a sample that was not representative of the RAMP-DM cohort and potentially limited the generalisability of our findings. However, the absolute differences in baseline characteristics between the people included and those excluded were small. The findings of this study demonstrate the important role of early weight management in achieving and maintaining diabetes remission in real-world settings. The low incidence of dia- betes remission with conventional management and the difficulty in maintaining it underscore the importance of setting realistic expectations and prioritising the global effort of using both public and personalised measures to prevent the onset of diabetes in high-risk individuals. Innovative weight management programmes, including low-calorie diets and total meal replacement for the treatment of type 2 diabetes and obesity, have demonstrated efficacy in achieving diabetes remission in clinical trials. However, further studies are needed to examine the feasibility, sustainability, and cost-effectiveness of these innovative weight management interventions at the population level to ensure they can be applied broadly and safely to benefit a wider population in real-world settings. Moreover, the association between diabetes remis- sion and other clinical outcomes warrants examination. In conclusion, our findings suggest that remission of type 2 diabetes is achievable in real- world settings. Greater weight loss within the first year of diabetes diagnosis was associated with an increased likelihood of achieving diabetes remission and a decreased risk of returning to hyperglycaemia among those who had achieved diabetes remission. However, both the inci- dence of diabetes remission and the probability of its long-term sustainability were low with conventional management in real-world settings. Our study provides evidence for policy- makers to design and implement early weight management interventions and diabetes remis- sion initiatives. Supporting information S1 STROBE Checklist. STROBE Statement Checklist. (DOC) S1 Table. Proportion (%) of missing data and methods for imputation. (DOCX) S2 Table. Baseline characteristics and selected 1-year measures of the study population according to incident remission of type 2 diabetes. (DOCX) S3 Table. Hazard ratios (HRs) for the associations of 1-year change (%) in weight and waist circumference after diabetes diagnosis with incident remission of type 2 diabetes. (DOCX) S4 Table. Baseline characteristics and selected 1-year measures of people with remission of type 2 diabetes stratified by subsequent return to hyperglycaemia. (DOCX) S5 Table. Hazard ratios (HRs) for the associations of 1-year change (%) in weight and waist circumference after diabetes diagnosis with subsequent return to hyperglycaemia among people with remission of type 2 diabetes. (DOCX) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004327 January 23, 2024 15 / 19 PLOS MEDICINEWeight change and diabetes remission S6 Table. Hazard ratios (HRs) for the associations of 1-year change (%) in weight and waist circumference after diabetes diagnosis with incident remission of type 2 diabetes in the study population (n = 40,282) that further included people with preexisting cardiovas- cular disease. (DOCX) S7 Table. Baseline characteristics of people included in the analysis compared to those excluded due to missing data on 1-year weight change but met other inclusion criteria. (DOCX) S1 Fig. Hazard ratios (HRs) for the association of 1-year change (%) in weight after diabe- tes diagnosis with incident remission of type 2 diabetes according to baseline characteris- tics. (DOCX) S2 Fig. Hazard ratios (HRs) for the association of 1-year change (%) in waist circumference after diabetes diagnosis with incident remission of type 2 diabetes according to baseline characteristics. (DOCX) S3 Fig. Proportion of people who had weight gain compared to their weight measured at 1 year after diabetes diagnosis during the follow-up for return to hyperglycaemia. (DOCX) S4 Fig. Hazard ratios for the association of remission duration with all-cause mortality in people with diabetes remission who returned to hyperglycaemia. (DOCX) Acknowledgments We acknowledge the Hong Kong Hospital Authority for providing the data. Author Contributions Conceptualization: Hongjiang Wu, Juliana C. N. Chan, Andrea O. Y. Luk. Data curation: Andrea O. Y. Luk. Formal analysis: Hongjiang Wu. Funding acquisition: Andrea O. Y. Luk. Investigation: Hongjiang Wu, Aimin Yang, Eric S. H. Lau, Xinge Zhang, Baoqi Fan, Ronald C. W. Ma, Alice P. S. Kong, Elaine Chow, Wing-Yee So, Juliana C. N. Chan, Andrea O. Y. Luk. Methodology: Hongjiang Wu, Aimin Yang, Eric S. H. Lau, Baoqi Fan, Ronald C. W. Ma, Alice P. S. Kong, Elaine Chow, Wing-Yee So, Juliana C. N. Chan, Andrea O. Y. Luk. Project administration: Hongjiang Wu, Andrea O. Y. Luk. Resources: Juliana C. N. Chan, Andrea O. Y. Luk. Software: Andrea O. Y. Luk. Supervision: Juliana C. N. Chan, Andrea O. Y. Luk. Validation: Hongjiang Wu, Aimin Yang, Eric S. H. Lau. 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10.1371_journal.pone.0227230	RESEARCH ARTICLE Optogenetically induced cellular habituation in non-neuronal cells Mattia BonzanniID 1, Nicolas Rouleau1, Michael Levin2, David L. KaplanID 1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Bonzanni M, Rouleau N, Levin M, Kaplan DL (2020) Optogenetically induced cellular habituation in non-neuronal cells. PLoS ONE 15(1): e0227230. https://doi.org/10.1371/journal. pone.0227230 Editor: Mark S. Shapiro, University of Texas Health Science Center, UNITED STATES Received: September 5, 2019 Accepted: December 13, 2019 Published: January 17, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0227230 Copyright: © 2020 Bonzanni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: Funded by 1) ML and DK, No. 12171, Paul G. Allen Frontiers Group, https://alleninstitute. 1 Department of Biomedical Engineering, Allen Discovery Center, Tufts University, Medford, United States of America, 2 Department of Biology, Allen Discovery Center, Tufts University, Medford, United States of America * david.kaplan@tufts.edu Abstract Habituation, defined as the reversible decrement of a response during repetitive stimulation, is widely established as a form of non-associative learning. Though more commonly ascribed to neural cells and systems, habituation has also been observed in single aneural cells, although evidence is limited. Considering the generalizability of the habituation pro- cess, we tested the degree to which organism-level behavioral and single cell manifesta- tions were similar. Human embryonic kidney (HEK) cells that overexpressed an optogenetic actuator were photostimulated to test the effect of different stimulation protocols on cell responses. Depolarization induced by the photocurrent decreased successively over the stimulation protocol and the effect was reversible upon withdrawal of the stimulus. In addi- tion to frequency- and intensity-dependent effects, the history of stimulations on the cells impacted subsequent depolarization in response to further stimulation. We identified tetra- ethylammonium (TEA)-sensitive native K+ channels as one of the mediators of this habitua- tion phenotype. Finally, we used a theoretical model of habituation to elucidate some mechanistic aspects of the habituation response. In conclusion, we affirm that habituation is a time- and state-dependent biological strategy that can be adopted also by individual non- neuronal cells in response to repetitive stimuli. Introduction The behavioral manifestation of habituation is intuitive and can be simplified as a reversible asymptotic response decrement after repeated stimulations [1]. The seminal work of Thomp- son and Spencer [2] delineated the original characteristics of habituation which remain largely unchanged today [1]. The principal features, which are now succinctly summarized in ten points by Rankin and colleagues [1], represent the gold standard for the definition of behav- ioral habituation in organisms. Briefly, the habituation profile is, in most cases, an exponen- tial-like curve and, most importantly, the decremental response is reversible–a condition that distinguishes habituation from fatigue. The dependence of the habituation profile upon the parameters of the stimulus cannot be overstated. Indeed, they are affected by both the intensity and frequency of stimulation as well as by the stimulation history (i.e., series of stimulation]. A PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 1 / 14 org/what-we-do/frontiers-group/. 2) M.L, No. TWCF0089/AB55, Templeton World Charity Foundation, https://www.templetonworldcharity. org/. 3) D.K., P41EB002520, National Institutes of Health, https://www.nih.gov/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Habituation in single non-excitable cells generalizable mechanism for this phenomenon, however, is still lacking. So far, the dual pro- cess theory, proposed by Groves and Thompson [3], the stimulus-model comparator by Soko- lov [4] and the “negative-image model” by Ramaswami [5] are the most prominent theories which offer explanatory value. The formulation of a general hypothesis that explains the pro- cess is challenging, mainly due to the multivariate cellular mechanisms that underlie the pro- cesses. In order to overcome this difficulty, we recently proposed a model of habituation that does not require a priori knowledge of the system’s biological components [6]. Interestingly, some features of habituation can also be detected in non-neuronal system, [7] [8] [9] [10]. The evolutionary and cell-biological origins of learning are nowadays the focus of an emerging field—basal cognition; recent and classic work has sought to identify and mechanistically char- acterize primitive forms of learning in non-neural biological systems[11, 12]. So far, a clear understanding of the potential general nature of the habituation process has not been achieved. We took advantage of the overexpression of channelrodopsin2 (ChR2) to optogenetically stim- ulate human embryonic kidney (HEK) cells to highlight, if present, the fundamental similari- ties between behavioral and cellular manifestations of the habituation response and to potentially reveal new findings that can lead to a mechanistic understanding of the process itself. Here, we explored the first five of the ten points listed in the paper by Rankin and col- leagues (as the last five points refer to special cases or instances with more than one stimulus) in the in vitro aneural system. We found that the system responded to the repetitive stimula- tion with a reversible asymptotical, exponential-like profile; moreover, the cell system response was stimulation-dependent. This indicates that responses associated with single non-neuronal cells share a high degree of similarity with behavioral manifestations of habituation. Material and methods Cell culture and transfection For electrophysiological recordings, human embryonic kidney (HEK) cells were maintained in DMEM high glucose (Thermofisher) supplemented with 10% of fetal bovine serum (FBS; Gibco) and 2 mM of L-Glutamine (Sigma) at 37 C in a 5% C02 incubator. HEK were plated in 35 mm dishes and transfected with 1.5 μg of the pcDNA3.1/hChR2(H134R)-mCherry plasmid (Addgene #20938) using LipofectamineTM 3000 (Thermofisher) accordingly manufacturer instructions. After 24–36 hours, mCherry-expressing cells were selected for patch clamp analysis. Electrophysiology and optogenetic stimulation Patch clamp experiments in the whole-cell configuration were carried out 24–36 hours post- transfection on mCherry-expressing cells at room temperature. HEK cells were superfused with an extracellular-like solution containing (mM): NaCl 140, KCl 5.4, CaCl2 1.8, MgCl2 1, Hepes-NaOH 10, Glucose 5.5, pH = 7.4. The pipette (7–9 MO) were filled with an intracellu- lar-like solution containing (mM): K-Asp 130, NaCl 10, EGTA-KOH 5, MgCl2 2, CaCl2 2, ATP (Na2-salt) 2, creatine phosphate 5, GTP 0.1, Hepes-KOH 10; pH 7.2. Optogenetic stimula- tion was delivered by the OptoPatcher system using LSD-1 light stimulation device (ALA Sci- entific Instruments) as previously described [13]. Data acquisition and light triggering were controlled with pCLAMP software via DigiData 1440A interfaces (Molecular Devices). The channelrodopsin (ChR2) photocurrent was measured under voltage-clamp conditions from a holding potential of 0 mV applying concomitantly hyperpolarizing test steps in the range 0/-90 mV and high-intensity illumination for 2600 ms. Peak and stationary currents were normal- ized by cell capacitance. Patch-clamp currents were acquired with a sampling rate of 4 kHz without lowpass filter. Neither series resistance compensation nor leak or liquid junctional PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 2 / 14 Habituation in single non-excitable cells potential corrections were applied. The light stimulation was delivered for 20 s as pulse train (or cosine wave) in I/0 configuration at three different frequencies (in Hz: 0.5; 1; 2) and three intensities (Low: 1V; Middle: 2V; High: 5V. Voltage values referrers to the LSD-1 light stimula- tion device). The mono-exponential decay fitting was used to calculate the percentage of depo- larization at the steady state and the tau of habituation (τH), defined as the number of events/ time necessary to reach the 37% of the percentage of depolarization at the steady state. The probability of habituation (p(H)) was defined as 1 if the cell response fitted or 0 if the cell response did not fit with a mono-exponential fitting. Statistical analysis Data were analyzed with Clampfit10 (Axon) and Origin Pro 9. To test the impact of the stimu- lation features on the habituation profile, we compared the mean percentage of depolarization at the steady state and the mean tau of habituation (τH) at different conditions. These two parameters are sufficient to uniquely describe a mono-exponential profile. Data were com- pared using either One-Way ANOVA followed by Fisher’s LSD post-hoc test or Student’s T- test; significance level was set to p = 0.05. Data outliers were excluded using Tukey’s method. Data were collected from different transfection experiments ranging from a minimum of four to a maximum of twelve. Results Optogenetically-induced depolarizations are reduced by repetitive stimulation To explore the habituation process in single aneural cells, human embryonic kidney (HEK) cells were transfected with a Channelrodopsin2 (ChR2)-expressing plasmid and the functional expression of the photocurrent was assessed in mCherry-positive cells (S1 Fig). Subsequently, ChR2-expressing cells were photostimulated (pulse train) and the membrane potential (Vmem) was simultaneously recorded using a patch clamp approach in the whole cell configuration. A representative stretch of the Vmem profile during 1Hz/5V light stimulation is shown in Fig 1A, in which the depolarization induced by the photocurrent (hν, blue lines) is visibly reduced over time. A similar reduction is also observed when the stimulation was given as cosine waves rather the pulse train (S2 Fig) suggesting the independence of the cell’s response from the shape of the delivered stimulation. In the absence of the ChR2 channel expression, the light stimulation did not induce any change in the Vmem (S3 Fig). The decremental reduction of the depolarization is summarized in Fig 1B. All data points were normalized by the magnitude of depolarization of the first event, obtaining the percentage of depolarization (y-axis, Fig 1B); data were plotted against either the number of events or time. For each profile, the percentage of depolarization at the steady state and tau of habituation (% of dep. at s.s. and τH, respec- tively) are computed using a monoexponential decay fitting and used to define the magnitude (% of dep. at s.s.) and kinetic (τH) characteristics of habituation. By definition, τH is the num- ber of events or time necessary to reach 37% of the amplitude value (Fig 1B). The observed asymptotical response reduction during repetitive stimulation is a key feature necessary to define any habituation profile. The frequency and intensity of stimulation affects both magnitude and kinetic of habituation The frequency and intensity characteristics of the stimulation are well-known modulators of the habituation. First, we explored the impact of the frequency of stimulation on the PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 3 / 14 Habituation in single non-excitable cells Fig 1. Definition of the habituation profile. A) Representative trace of voltage recorded in the I/0 configuration during a light stimulation at 465 nm (blue lines). B) Normalized values of depolarization during 20 s of 1Hz/5V stimulation protocol. Monoexponential fitting curve of the plotted data (circle) is shown in red. Percentage of depolarization at the steady state (% of dep. at s.s.) and tau of habituation (τH) are indicated. https://doi.org/10.1371/journal.pone.0227230.g001 habituation profile. In Fig 2 (top panel), HEK cells were stimulated at 5V for 20 s at three different frequencies as indicated (top panel, in Hz: 0.5, 1, 2; black square, purple circle and green triangle, respectively). The resulting mean traces are shown either superimposed (Fig 2A) or divided (Fig 2B) plotting the number of events on the x-axis. Mean τH and % of dep. at s.s. values are summarized in Fig 2C and 2D in a frequency-dependent fashion. When we considered the number of events, other things being equal, higher stimulation frequencies were associated with a slower kinetic (Fig 2C; p<0.05 among groups) and more pronounced amplitude (Fig 2D and 2H; p<0.05). On the other hand, when we considered time rather than events as displayed on the x-axis (S4 Fig), higher stimulation frequency was associated with a faster kinetic (S4 Fig). From these results, the frequency of stimulation clearly affects both the kinetic and magnitude of the habituation profile indicating a frequency-dependent response. We also explored the impact of different intensities of stimulation on the habituation profile (bottom panel). HEK cells were stimulated at 1 Hz for 20 s at three different intensities: Low: 1V; Medium:2V; High:5V (bottom panel: black square, purple circle and green triangle, respectively). The resulting mean traces were shown superimposed (Fig 2E) or separated (Fig 2F) plotting the number of events on the x-axis; mean τH and % of dep. at s.s. values are sum- marized in Fig 2G and 2H in an intensity-dependent fashion. Other factors being equal, at 1V the kinetic is significantly slower when compared to both 2V and 5V stimulations (Fig 2G). Moreover, at 1V the magnitude of habituation is less pronounced (p<0.05) than both 2V and 5V conditions (Fig 2H). Taken together, these results highlight both frequency- and intensity- dependent behavior of the cellular system. The recovery profile is frequency-dependent A hallmark of habituation is the reversibility of the decremental response. We thus explored the recovery profile from the steady state condition (filled symbols, Fig 3) increasing the recov- ery time between consecutive series of stimulations. We evaluated the recovery profile in a PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 4 / 14 Habituation in single non-excitable cells Fig 2. The impact of the stimulation features on the habituation profile. HEK cells were stimulated at 5V at three different frequencies as indicated (in Hz: 0.5, black square; 1, purple circle; 2 green triangle). A) Superimposed (solid line is the mean and colored area the S.E.M.) and B) separated mean profiles are shown plotting the number of events. C) Mean τH (in events: 0.5Hz: 2.66±1.00, n = 21; 1Hz: 3.43±0.12, n = 43; 2Hz: 4.70±0.16, n = 43) and D) mean % of dep. at s.s. (0.5Hz: 19.87±1.00, n = 21; 1Hz: 23.00±0.23, n = 43; 2Hz: 29.84±0.19, n = 43) are shown. HEK cells were also stimulated at 1Hz at three different intensities as indicated (Low: 1V black square; Medium: 2V purple circle; High: 5V green triangle). E) Superimposed and F) separated mean profiles are shown, plotting the number of events. G) Mean τH (in events: Low: 10.53±3.69, n = 12; Medium: 3.74±0.18, n = 9; High: 3.43±0.12, n = 43) and H) mean % of dep. at s.s. (Low: 16.00±2.96, n = 12; Medium: 21.11±0.32, n = 9; High: 23.00±0.23, n = 43) are shown in the event-domain. One-way Anova, �p<0.05 vs 0.5Hz or 1V; #p<0.05 vs 1Hz. https://doi.org/10.1371/journal.pone.0227230.g002 frequency-dependent manner. After reaching the steady state of the habituation profile, we normalized the following stimulation profile based on the first event of the first stimulation (filled symbol) and reported on the graph the mean % of depolarization after increasing recov- ery times (unfilled symbols). In Fig 3A, the mean recovery profiles are shown for 0.5, 1 and 2Hz (square, circle and triangle, respectively). It is clear that the time necessary to reach again the 100% of the response is frequency-independent (26.7 s). On the other hand, the recovery trajectory appeared to be conserved at 1Hz and 2Hz and different at 0.5 Hz, suggesting poten- tial different frequency-dependent mechanisms. We then analyzed both τH (Fig 3B) and the % of dep. at s.s. (Fig 3C) of the profiles during the consecutive series of stimulation; the x-axis indicates the resting period between consecutive stimulations and the dotted line represent the value of the descriptor during the first stimulation (filled symbols). Both descriptors displayed a frequency signature; it is also interesting to notice that at 3.5 s and 4.2 s (1Hz stimulation) the kinetic is slower. We also reported the probability to generate a habituation profile (p(H)) (Fig 3D); we found that in all conditions, when examining cases where recovery time is below 2.3s, the probability to generate the habituation profile is null. Taken together, the results indi- cate that the decremental response was reversible and that, based on the recovery time, the kinetic and magnitude of the profiles have complex frequency-dependent behavior. Moreover, the probability to generate a habituation profile during consecutive stimulations is not an assumption that can be made a priori. PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 5 / 14 Habituation in single non-excitable cells Fig 3. Frequency-dependent recovery profile. HEK cells were stimulated at 5V at three different frequencies (in Hz: 0.5, square, top; 1, circle, middle; 2 triangle, bottom) for 20s and, after a recovery time, the same frequency protocol was applied. A) Mean profiles and normalized values of the first event (filled symbols) after different resting periods (unfilled symbols). B) Mean τH (in events) and C) % of dep. at s.s. of the profiles at different recovery times (dot lines indicate the values of the initial profile). D) Mean bar graphs indicating the probability of habituation profile (p(H)) at different recovery times. Mean values are reported in S1 Table. https://doi.org/10.1371/journal.pone.0227230.g003 Frequency transitions influence the kinetics of the habituation profile We then explored what would happen to the cell’s output if the photostimulation suddenly changed frequency without an intervening rest period. Our aim was to simulate the rhythmic transition changes that could occur in quasi-periodic biological systems. The mean profile dur- ing the 1Hz-2Hz-1Hz transition is shown in Fig 4A (1Hz purple; 2Hz green). The mean τH and % of dep. at s.s. values are summarized in Fig 4B and 4C, respectively. Both the kinetic profile and magnitude at 2Hz are not affected by the previous 1Hz stimulation; indeed, the val- ues are not different from the 2Hz stimulation alone (Fig 2). However, after the 2Hz stimula- tion, the 1Hz profile is faster whereas the magnitude is invariant with respect to the 1Hz condition alone (Fig 2). Moreover, after the first stimulation, the change of frequency reduces the probability of generating a habituation profile to 50% (Fig 4D). The mean profile during the 2Hz-1Hz-2Hz transition is shown in Fig 4E. The first 2Hz stimulation influences the 1Hz kinetic profile during the 2Hz-1Hz transition as shown in Fig 4F; particularly, the τH is signifi- cantly slower compared to the 1Hz stimulation alone but, again, reached the same magnitude with a p(H) of about 60% (Fig 4H). The following 1Hz-2Hz transition did not produce any habituation profile (Fig 4H). Collectively, these results indicated that the frequency transitions without resting periods in between affect the kinetic profile but did not affect the magnitude of the habituation. Native channels participate in the habituation response Since habituation and desensitization share the same decremental response over time, we ana- lyzed the ChR2 photocurrent profile upon stimulation to address any channel-related PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 6 / 14 Habituation in single non-excitable cells Fig 4. Intra-protocol frequency transitions influence the habituation profile. HEK cells were stimulated at 5V at either 1Hz (purple) or 2Hz (green) without a resting period in between. A) Mean profiles at 1Hz-2Hz-1Hz transition (solid line is the mean and colored area the S.E.M.). B) Mean τH (in events: Alone: 1Hz: 3.43±0.12, n = 43; 2Hz: 4.70±0.16, n = 43. Transitions: First 1Hz: 3.52±0.71; 2Hz: 6.17±1.02; Second 1Hz: 1.55±0.62, n = 12), C) mean % of dep. at s.s. (Alone: 1Hz: 23.00±0.23, n = 43; 2Hz: 29.84±0.19, n = 43. Transitions: First 1Hz: 26.16±1.33; 2Hz: 33.56±3.66; Second 1Hz: 20.28±2.61, n = 12) and D) mean bar graphs indicating the probability of habituation profile (p(H): First 1Hz: 100±0; 2Hz: 55.56±17.57; Second 1Hz: 57.14±20.20, n = 8) are shown. E) Mean profiles at 2Hz-1Hz-2Hz transition (solid line is the mean and colored area the S.E.M.). F) Mean τH (in events: Alone: 1Hz: 3.43±0.12, n = 43; 2Hz: 4.70±0.16, n = 43. Transitions: First 2Hz: 5.24±0.60; 1Hz: 17.59±7.0, n = 12) and G) mean % of dep. at s.s. (Alone: 1Hz: 23.00±0.23, n = 43; 2Hz: 29.84±0.19, n = 43. Transitions: First 2Hz: 29.73±1.79; 1Hz: 23.30±1.45, n = 12) and H) mean bar graphs indicating the probability of habituation profile (p(H): First 1Hz: 100±0; 2Hz: 66.67±21.08; Second 1Hz: 0, n = 12) are shown. Student’s T-test �p<0.05 vs Alone condition. https://doi.org/10.1371/journal.pone.0227230.g004 desensitization effect. In Fig 5A, representative traces of the photocurrent at -30, -40 and -50 mV (square, circle and triangle, respectively) are shown during the application of the 1Hz,5V stimulation protocol for 10 seconds (blue lines); we chose three voltage values near the mean value of the resting potential of HEK cells (-40.75±1.38 mV; n = 58). The steady current was then analyzed in an event- and voltage-dependent manner. The graph in Fig 5B shows the mean density current values of the photocurrent during the applied stimulations. No signifi- cant decrement of the density current appeared during repetitive stimulation. In order to address any active cell-autonomous processes, we explored the impact of native potassium channels in the habituation process; we thus blocked them using 10 μM of TEA, as previously reported[14]. After confirming that TEA does not influence the photocurrent (Fig 5B), we ana- lyzed the effect of the drug on the habituation profile at 1Hz, 5V. The mean profile is shown in Fig 5D and mean τH and % of dep. at s.s. values are summarized in Fig 5E and 5F indicating a significantly slower and more pronounced profile in the presence of TEA. This result high- lights that the TEA-sensitive native potassium channels actively participate in defining the photocurrent-induced habituation process. PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 7 / 14 Habituation in single non-excitable cells Fig 5. ChR2-independent and ion-dependent habituation profile. A) Representative traces of the photocurrent at -30, -40 and -50 mV (square, circle and triangle, respectively) during a 1Hz,5V repetitive stimulation. B) Mean current density/event plot of the photocurrent. C) Mean photocurrent density currents with or without TEA (filled circle, empty square, respectively; n = 8 each). D) Mean habituation profiles with TEA 10 μM in the extracellular solution. E) Mean τH (in events: CTRL: 3.43 ±0.27, n = 43; TEA: 4.27±0.30, n = 18) and F) amplitude (CTRL: 23.38±0.89, n = 43; TEA: 28.03±1.63, n = 18). Student’s T-test �p<0.05 vs CTRL. https://doi.org/10.1371/journal.pone.0227230.g005 Mathematical modeling of habituation in HEK cells We recently proposed a generalization of the habituation process which could be applied inde- pendently of the biological details of the given system [6]. As outlined in the paper, the habitu- ation process was described as the dynamic interplay between different elements, namely the stimulation, transducer, habituation, receiver and background elements. Each element is described by a variable and, overall, the process is described by the following equation: Rn ¼ T0 n þ H0 ðnsÞ0 � s i¼0 Di þ B Pn(cid:0) 1 ð1Þ where Rn is the output of the receiver element (the element that we monitor during the stimu- lation), T0 n is the output of the transducer elements (influenced by the frequency (t(s)) and the intensity of stimulation and the nature of the modules composing the element itself), H0 (ns)0 is an index of the initial state of the habituation element and thus the output of the habituation element before the stimulation, sigma (σ) is the stimulation factor, delta (Δ) is the spontaneous decay factor during the recovery phase from the stimulation, B is the output of the background elements (stimulation invariant elements) and n is the number of events delivered to the sys- tem. Through a mathematical manipulation of the Eq 1 (S1 File), we computed from the raw data Δ, σ and A (where A = T0 (ns)0+B) associated with some conditions tested throughout the paper. Each parameter, as detailed in the S1 File, is influenced either by the stimulation fea- tures (t(ns), t(s) and intensity) or by the nature/composition of the habituation system (T’, B n+H0 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 8 / 14 Habituation in single non-excitable cells Table 1. Relationship between the different combinations of parameters and the variables. In the table are indi- cated the variables when more than one parameter is different among conditions. B is the output of the background element, H(ns)0 is the output of the habituation element before the stimulation, T’ is the output of the transducer ele- ments, int is the intensity of the stimulation, t(s) is the time of stimulation, t(ns) is the time of non-stimulation between two events and H’ is the output of the habituation element. AND σ AND A Δ AND A Δ AND σ Δ All σ All A All https://doi.org/10.1371/journal.pone.0227230.t001 �Δ B, H(ns)0, T’, int, t(s) �σ B, H(ns)0, t(ns) �A H’, t(ns) and H(ns)0). The detailed relationships between the parameters (Δ, σ and A), the variables (B, H(ns)0, T’, H’, t(ns)) are reported in the S1 File. In Table 1 we summarize the variables that can influence all the possible combinations of the significant (i.e. Δ) and not significant (i.e. �D) parameters. In Table 2 we report the signifi- cant parameters among the indicated conditions, and the variables that can be neglected are also listed (S1 File); with this eliminative procedure, we then obtained the significant variables that can explain the observed parameter combinations (for numerical details, see S1 Table). It emerges that the differences among 1Hz and 2Hz stimulations (Fig 2A) arose just from the dif- ferent stimulation protocol (t(ns)), whereas during the 0.5 Hz condition the differences must also be related to a different nature/composition of the habituation system (T’, H’). When we compare 2V vs 5V (Fig 2B), we can see that a different response of T’ is the explanation of the different output (in particular, reflecting the different intensities of stimulation). Upon TEA application at 1Hz 5V stimulation (Fig 5D), we can conclude that native K+-channels partici- pate either in the composition of the translator (T’) or habituation element (H’ and/or H(ns)0). Finally, during the frequency transitions, the first 1Hz stimulation and the second 1Hz stimu- lation after the 2Hz stimulation (Fig 4A) differs because of either a difference in the pre-stimu- lation habituation elements (H(ns)0) or a difference in the nature of the translator element (T’). In conclusion, the previously proposed model could be instrumental in narrowing the biologi- cal processes involved in the different responses through an experimentally-driven eliminative procedure. Limitations In the present work, two main limitations are present: the non-physiological source of stimula- tion (the photostimulation of the ChR2) and the use of just one cellular type. Indeed, the over- expression of the ChR2 channels is an implausible physiological situation driven by the experimental need to fine-tune the stimulation features, which practically limited the use of Table 2. Experimental-driven eliminative procedure. After the computation of Δ, σ and A in each group, we identified the statistically significant parameters and using Table 1 we highlight the significant variables. Moreover, based on each specific group comparison, we could also identify the variables which are invariant based on the applied stimulation. Experimental feature Figure Group Comparison Statistically significant parameters Neglectable Variables Significant Variables Frequency Intensity Native Channels Frequency transitions Fig 2A Fig 2A Fig 2A Fig 2E Fig 5D Fig 4A 0.5 vs 1 Hz 0.5 vs 2 Hz 1 vs 2 Hz 2 vs 5 V (-)TEA vs (+)TEA First vs Second 1Hz Δ AND A AND σ Δ AND A AND σ Δ AND A A AND σ Δ AND A AND σ A AND σ https://doi.org/10.1371/journal.pone.0227230.t002 B, mag, H(ns)0, t(s) B, mag, H(ns)0 B, mag, H(ns)0 B, H(ns)0, t(s), t(ns) B, mag, t(s), t(ns) B, mag, t(s), t(ns) H’, T’, t(ns) H’, T’, t(ns), t(s) t(ns) T’, mag T’, H’, H(ns)0 T’, H(ns)0 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 9 / 14 Habituation in single non-excitable cells more biologically relevant stimulation sources. On the other hand, the ionic currents gener- ated by the opening of the channels (from which the depolarization arose) is a universal lan- guage for cells. Nonetheless, it is important to mention that the use of a single type of channelrodopsin prevents us to conclude which ionic current-dependent phenomena (namely the depolarization of the membrane or any other ion-dependent mechanisms) is responsible for the habituation. Moreover, we explored the process only in HEK cells (since it is a well- established heterologous system in electrophysiology); this limits any robust claim of generali- zation of the presented results to other non-neuronal system. Finally, we only explored the non-associative aspect of the habituation, namely using one and only one form of stimulation and, because of the intrinsic instability of the whole cell configuration over long recording periods (more than an hour), we did not explore any potential long-term effects of the stimula- tion. In light of these limitations, the present work should be seen as a proof of concept of the ability of non-neuronal cells to habituate rather than an indication for habituation as a biologi- cally universal process with defined features and rules; more data must be collected to prove this claim. Discussion Whether they are self-generated by the body (i.e. heartbeat, brain waves, circadian rhythms, hormone release, etc.) or delivered from environmental sources (new drug treatment, training, routine behaviors, etc.), repetitive stimulations are ubiquitous and essential to the adaptive behavior and physiology of living organisms. A common behavioral strategy to deal with repetitive stimulations is to reversibly reduce the output of the system; a process which is termed habituation [2]. Over the last 50 years, an extensive characterization of the behavioral manifestation of habituation has been performed [1] mostly confirming the characteristics previously identified [2]. So far, the list of features reported by Rankin and colleagues [1] rep- resents the most up-to-date guideline to correctly classify behavioral habituation. Habituation is considered within an exclusively neural-based framework even though some experiments demonstrate the process clearly emerges within aneural systems [7] [8] [9] [10]. While data continue to accumulate to broaden our view of the gradual evolution of learning capacities from basal taxa, it is essential to develop platforms that facilitate the study of universal cellular mechanisms for computation and optimization of behavior. While single-cell habituation is apparently robust, a deeper characterization has not yet been achieved. A proper comparison between the cellular and behavioral manifestations of habituation could reveal a more general process that is not restricted to neuronal substrates. In the present work, we explored the habituation process in ChR2-expressing HEK cells. The main advantage of using the ChR2 is to uniquely stimulate a singular element of the cell (indeed, the blue light stimulation did not affect the resting membrane potential of the cells, the output that we monitored throughout the study). The impact of ChR2-mediated depolari- zation on the voltage profile of the cells was studied, defining three descriptors: percentage of depolarization at the steady state (% of dep. at s.s.) and τH to describe the magnitude and kinetic of habituation, respectively, and p(H), the probability to generate an exponential-like profile. From Fig 1A, the repetitive series of stimulations decreased the amplitude of the photo- current-induced depolarization within the protocol with an asymptotic profile (Fig 1B). It is also important to notice that the photocurrent amplitude was invariant throughout the stimu- lation (Fig 5B), demonstrating that the decrement was ChR2 independent. In support of this hypothesis, the blockage of native potassium channels with TEA changed the profile’s features (Fig 5D) indicating that the cell was actively responding to the repetitive stimulation; it is also relevant to mention that TEA dosage did not influence the photocurrent characteristics (Fig PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 10 / 14 Habituation in single non-excitable cells 5C). Finally, the recovery of the output after a resting period (Fig 3) led us to exclude any dele- terious effects of the stimulation on the cell output. Taken together, these results point toward a robust indication of habituation in the analyzed cell system. As previously described from a behavioral standpoint [1], the stimulation characteristics must affect the response. We thus tested the impact of different frequencies of stimulation (Fig 2) finding that increasing the stimulation frequency produced a more pronounced profile (Fig 2D). Plotting the time on the x-axis, higher stimulation frequencies were associated with faster profiles (S4 Fig), which is in line with the behavioral data; we observed the opposite effect when plotting the number of events (Fig 2C). This apparent contradiction highlights the neces- sity to always clarify if the analysis of the kinetic is made with respect to either time or events. We then manipulated the intensity of the stimulation and found a less pronounced and slower profile at 1V (p<0.05) and no differences between 2V and 5V. Taking into consideration the limited range of intensities that we explored, our results are clearly in opposition with the behavioral data. So far, we discussed the response of the cell system to a novel stimulation; in Fig 3, however, we explored the profile after consecutive stimulations. We found that the kinetic profile was not necessarily faster after consecutive stimulations, as it was framed for the behavioral habituation; a similar contradiction between behavioral and cellular data can also be highlighted when considering the magnitude. Most importantly, it emerged that habitua- tion cannot be considered granted without satisfying certain temporal criteria; indeed, below a recovery period of 2.3 s, it seems that the cells cannot generate any habituation profile (Fig 3D). An absolute habituation refractory period emerged below which the habituation itself could not occur; in other words, the habituation elements in the system are not responsive during the absolute habituation refractory period. Moreover, in Fig 4 we explored systematic changes in rhythmicity without deliberate recov- ery. This protocol was designed to mimic physiological changes in the frequency of biological periodic stimulation: actually, considering stimulations that arise inside the body, it is more common that the system experiences a modification in the rhythmic event rather than a new type of stimulation. It appears that the kinetic, but not the magnitude, was affected by the sequence of the frequency transitions. It follows that the magnitude of habituation can be con- sidered the only invariant frequency-dependent signature during the frequency transitions. Most importantly perhaps, it highlights that the same stimulation (1Hz) can lead to either a habituation or sensitization profile based on the pre-1Hz stimulation state (Novel vs 2Hz vs 1Hz-2Hz). The evidence that habituation and sensitization arise from the same protocol of stimulation suggests that the state of the system before the stimulation is a crucial factor, more so than the features of the stimulation itself in defining the ultimate phenotype. In particular, we can speculate that a habituation profile emerges if the % of dep. at s.s. of the previous state is smaller than the one associated with the frequency of the second stimulation; on the other hand, if it is greater, a sensitization profile emerges. It also leads to the speculation that habitu- ation and sensitization are two facets of the same process. In other words, the system seems to achieve a defined frequency-dependent steady state using either habituation or sensitization phenomena accordingly to the previous state of the system. The determinant of whether one emerges over the other would be the pre-stimulation state of the system; however, any robust conclusion cannot be irrefutable considering the limited data presented here. Most impor- tantly, perhaps, this establishes the experimental basis to explore the effect, if any, of patho- physiological changes of rhythmic processes generated by excitable cells (i.e. cardiomyocytes, neurons) on non-excitable cells (i.e. endothelial cells, fibroblasts, macrophages, microglia). Even if we confirmed the habituation process in HEK cells, those results reveal little about any mechanistic explanation. PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 11 / 14 Habituation in single non-excitable cells Using a mathematical generalization of the habituation process [6], we narrowed some potential mechanisms of the habituation in the present system. In particular, we can see that any difference between 1Hz and 2Hz is due to just the different frequency but not because of recruitment/dismissal of elements in the HEK system: in other words, the system that reacts to the stimulation is, in both activity and composition, identical. A similar picture arises when we compared 2V vs 5V. On the contrary, when we analyzed the 0.5Hz vs 1Hz (or 2Hz) stimula- tion, we realized that the differences were not only because of the different stimulation proto- cols, but also because of a different activity/composition of the HEK system reacting at those frequencies. In other words, different frequencies are processed differently by the system because of a change in its state. This hypothesis also seems to be reflected in the different pro- file of the recovery in Fig 3. Taken together, our data show that both the behavioral and our cellular model share a dec- remental decrease during repetitive stimulation that, after a resting period, is reversible. More- over, they both showed a frequency and intensity dependence of the habituation profile; however, it is critical to report that the similar changes in the stimulation features do not nec- essarily lead to the same habituation profile changes in the behavioral vs cellular comparison. The authors suggest that this is due to the fact that the specific response to stimulation changes are not amenable to generalization. Namely, the responses lie on the peculiar composition of the system that we are monitoring and must be tested de novo for any new system. To summa- rize, the behavioral and cellular habituation processes shares 1) an asymptotical decrement of the output during repetitive stimulation, 2) the reversibility of the profile after a resting period and 3) a dependence on both frequency and intensity of stimulation. Based on these findings, we propose to consider and define habituation as a time- and state-dependent process which could occur if and only if 1) the time between two consecutive stimulations is smaller than the time necessary to the system to achieve a pre-stimulation state and larger than the absolute habituation refractory period, 2) satisfy the three points above-mentioned. Future experiments using many more cell substrates will test the solidity of our definition and clarify any claim as to the universality of the habituation process. Conclusions Bearing in mind the aforementioned limitations, the present work: 1) demonstrates that non- neuronal cells can habituate in a stimulation-dependent manner, 2) highlights similarity and discrepancies between the behavioral rules and our model responses, 3) gives defined descrip- tors to analyze the process (% of effect at s.s., τH and probability of habituation), 4) shows that systems respond differently in case of preceding history of stimulation and 5) guides the explo- ration of mechanistic information using an experimental-driven shortcut approach based on a mathematical generalization of the habituation process. Supporting information S1 File. Description of the mathematical model. (DOCX) S1 Fig. Photocurrent current density-voltage plot. A) Representative photocurrent density traces (holding potential: 0 mV) recorded in the range 0/-90 mV (ΔV = 10 mV). B) Current density-voltage plot analyzed at the peak (square) or steady state (circle). n = 14. (TIF) PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 12 / 14 Habituation in single non-excitable cells S2 Fig. Cosine wave-induced habituation profile. A) Representative voltage trace upon the application of B) a 1Hz,5V cosine wave light stimulation. (TIF) S3 Fig. Non-transfected HEK cell does not respond to light. Representative voltage profile of non-transfected HEK cells (in black) in response to the light stimulation protocol (in blue). (TIF) S4 Fig. The stimulation’s features impact the habituation profile. HEK cells were stimulated at 5V at three different frequencies as indicated (in Hz: 0.5, black square; 1, purple circle; 2 green triangle). A) Superimposed and B) separated mean profiles are shown plotting the time pf stimulation. C) Mean τH (in s: 0.5Hz: 6.11±0.81, n = 21; 1Hz: 3.43±0.12, n = 43; 2Hz: 2.32 ±0.11, n = 43) and D) mean amplitude (in % of depolarization: 0.5Hz: 19.78±1.00, n = 21; 1Hz: 23.00±0.23, n = 43; 2Hz: 29.84±0.19, n = 43) are shown. One-way Anova �p<0.05 vs 0.5Hz; #p<0.05 vs 1Hz. (TIF) S1 Table. Fig 3 parameters details. (TIF) Author Contributions Conceptualization: Mattia Bonzanni, Nicolas Rouleau. Data curation: Mattia Bonzanni. Formal analysis: Mattia Bonzanni. Funding acquisition: Michael Levin, David L. Kaplan. Investigation: Mattia Bonzanni. Methodology: Mattia Bonzanni. Project administration: Mattia Bonzanni. Resources: Michael Levin, David L. Kaplan. Supervision: Mattia Bonzanni. Validation: Mattia Bonzanni. Visualization: Mattia Bonzanni. Writing – original draft: Mattia Bonzanni, Nicolas Rouleau. Writing – review & editing: Mattia Bonzanni, Nicolas Rouleau, Michael Levin, David L. Kaplan. References 1. Rankin CH, Abrams T, Barry RJ, Bhatnagar S, Clayton D, Colombo J et al. (2009) Habituation revisited: an updated and revised description of the behavioral characteristics of habituation. Neurobiol Learn Mem 92(2):135–138. https://doi.org/10.1016/j.nlm.2008.09.012 PMID: 18854219 2. Thompson RF & Spencer WA (1966) Habituation: a model phenomenon for the study of neuronal sub- strates of behavior. Psychol Rev 73(1):16–43. https://doi.org/10.1037/h0022681 PMID: 5324565 3. Groves PM & Thompson RF (1970) Habituation: a dual-process theory. Psychol Rev 77(5):419–450. https://doi.org/10.1037/h0029810 PMID: 4319167 4. Sokolov EN (1963) Higher nervous functions; the orienting reflex. Annu Rev Physiol 25:545–580. https://doi.org/10.1146/annurev.ph.25.030163.002553 PMID: 13977960 PLOS ONE \| https://doi.org/10.1371/journal.pone.0227230 January 17, 2020 13 / 14 Habituation in single non-excitable cells 5. Ramaswami M (2014) Network plasticity in adaptive filtering and behavioral habituation. 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Pischke MS, Huttlin EL, Hegeman AD, & Sussman MR (2006) A transcriptome-based characterization of habituation in plant tissue culture. Plant Physiology 140(4):1255–1278. https://doi.org/10.1104/pp. 105.076059 PMID: 16489130 11. Lyon P (2006) The biogenic approach to cognition. Cogn Process 7(1):11–29. https://doi.org/10.1007/ s10339-005-0016-8 PMID: 16628463 12. Baluska F & Levin M (2016) On Having No Head: Cognition throughout Biological Systems. Front Psy- chol 7:902. https://doi.org/10.3389/fpsyg.2016.00902 PMID: 27445884 13. Katz Y, Yizhar O, Staiger J, & Lampl I (2013) Optopatcher—an electrode holder for simultaneous intra- cellular patch-clamp recording and optical manipulation. J Neurosci Methods 214(1):113–117. https:// doi.org/10.1016/j.jneumeth.2013.01.017 PMID: 23370312 14. Ponce A, Castillo A, Hinojosa L, Martinez-Rendon J, & Cereijido M (2018) The expression of endoge- nous voltage-gated potassium channels in HEK293 cells is affected by culture conditions. 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10.1371_journal.pgph.0003036	RESEARCH ARTICLE The effectiveness of community health worker training, equipping, and deployment in reducing COVID-19 infections and deaths in rural Western Kenya: A comparison of two counties Neema KasejeID Marcel Tanner5, Andy Haines2 1,2, Kennedy Oruenjo3, Dan Kaseje4, Meghna Ranganathan2, 1 Surgical Systems Research Group, Kisumu, Kenya, 2 London School of Hygiene & Tropical Medicine, London, United Kingdom, 3 Siaya Ministry of Health, Siaya, Kenya, 4 Tropical Institute of Community Health, Kisumu, Kenya, 5 Swiss Tropical & Public Health Institute, Basel, Switzerland nkaseje@gmail.com Abstract COVID-19 and other pandemics remain significant threats to population health, particularly in rural settings where health systems are disproportionately weak. There is a lack of evi- dence on whether trained, equipped, and deployed community health workers (CHWs) can lead to significant reductions in COVID-19 infections and deaths. Our objective was to mea- sure the effectiveness of deploying trained and equipped CHWs in reducing COVID-19 infections and deaths by comparing outcomes in two counties in rural Western Kenya, a set- ting with limited critical care capacity and limited access to COVID-19 vaccines and oral COVID-19 antivirals. In Siaya, trained CHWs equipped with thermometers, pulse oximeters, and KN95 masks, visited households to convey health information about COVID-19 preven- tion. They screened, isolated, and referred COVID-19 cases to facilities with oxygen capac- ity. They measured and digitally recorded vital signs at the household level. In Kisii county, the standard Kenya national COVID-19 protocol was implemented. We performed a com- parative analysis of differences in CHW skills, activity, and COVID-19 infections and deaths using district health information system (DHIS2) data. Trained Siaya CHWs were more skilled in using pulse oximeters and digitally reporting vital signs at the household level. The mean number of oxygen saturation measurements conducted in Siaya was 24.19 per COVID-19 infection; and the mean number of temperature measurements per COVID-19 infection was 17.08. Siaya CHWs conducted significantly more household visits than Kisii CHWs (the mean monthly CHW household visits in Siaya was 146,648.5, standard devia- tion 11,066.5 versus 42,644.5 in Kisii, standard deviation 899.5, p value = 0.01). Deploying trained and equipped CHWs in rural Western Kenya was associated with lower risk ratios for COVID-19 infections and deaths: 0.54, 95% CI [0.48–0.61] and 0.29, CI [0.13–0.65], respectively, consistent with a beneficial effect. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kaseje N, Oruenjo K, Kaseje D, Ranganathan M, Tanner M, Haines A (2024) The effectiveness of community health worker training, equipping, and deployment in reducing COVID-19 infections and deaths in rural Western Kenya: A comparison of two counties. PLOS Glob Public Health 4(3): e0003036. https://doi.org/10.1371/ journal.pgph.0003036 Editor: Julia Robinson, PLOS: Public Library of Science, UNITED STATES Received: June 6, 2023 Accepted: February 26, 2024 Published: March 25, 2024 Copyright: © 2024 Kaseje et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are publicly available using the following Harvard Dataverse repository links: https://doi.org/10.7910/DVN/ NTXO8M; https://doi.org/10.7910/DVN/RNLU3J; https://doi.org/10.7910/DVN/ETQZO2; https://doi. org/10.7910/DVN/ZF6SJE. Funding: The intervention in Siaya was funded by Wellcome Trust (grant number 221407/Z/20/Z) (NK). The funder had no role in study design, data PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 1 / 18 PLOS GLOBAL PUBLIC HEALTHcollection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Community health workers and COVID-19 infections and deaths in rural Western Kenya Introduction The COVID-19 pandemic has had a significant impact on the health of populations across the globe. As of January 2024, globally, there have been more than 774,291,287 million infections and up to 18 million deaths directly and indirectly linked to COVID-19 [1]. In sub-Saharan Africa (SSA), as of January 2024, 9,571,930 million COVID-19 infections were confirmed and 175,486 deaths were recorded [2]. The pandemic response was a challenge in sub-Saharan Africa (SSA) because of under-resourced health systems with limited access to critical care capacity, COVID-19 vaccines and COVID-19 therapeutics [3]. Moreover, over 50% of the population in SSA lives in rural areas, with under-resourced rural health systems that are more vulnerable to disruptions during pandemics and poorer access to health services compared with urban health systems [4, 5]. In the current literature, there is evidence to support the key role CHWs can play during pandemics including the COVID-19 pandemic [6]. Qualitative data from India, Bangladesh, Pakistan, Sierra Leone, Kenya and Ethiopia, document the important role CHWs played in surveillance, community education and support of community members with COVID-19 [7]. An assessment of CHW preparedness in Kenya, Senegal, and Uganda by Chengo et al. found, however, that CHWs faced significant challenges because of the lack of training and personal protective equipment (PPE) [8]. Other studies demonstrated that training, especially of rural CHWs, led to improved COVID-19 surveillance efforts and reduced COVID-19 infections. In rural Thailand, trained rural CHWs were effective in screening and referring suspected COVID-19 cases which limited the transmission of COVID-19 to rural parts of Thailand [9]. In rural Niger, trained CHWs reported valid COVID-19 alerts in 84% of cases [10]. In line with global trends, SSA and Kenya experienced progressive increases in the number of COVID-19 cases during the early phase of the COVID-19 pandemic. The timeline of COVID-19 infections in SSA and Kenya, is shown in Fig 1 [11]. Fig 1. An epicurve of confirmed cases of COVID-19 in the WHO African Region 25 February—8 September 2020 n = 1 091 012 [6]. https://doi.org/10.1371/journal.pgph.0003036.g001 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 2 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Additionally, there were differences in COVID-19 measures implemented across countries and in Kenya lockdown measures were implemented early during the COVID-19 pandemic. Below is a summary of the COVID-19 timeline and key measures that were taken to mitigate the impact of the COVID-19 pandemic in Kenya. COVID-19 timeline Kenya Kenya reported its first case of COVID-19 on March 13th 2020 and immediately instituted mandatory quarantine for positive cases [12]. There were movement restrictions and curfews from April 2020 and lockdown measures in high-risk counties including Nairobi, Mombasa, Kilifi, and Kwale [12]. In addition, schools and universities were closed, and there were restric- tions on social gatherings [12]. However, in July 2020, movement restrictions and lockdowns were lifted [12]. Kenya received its first batch of COVID-19 vaccines in March 2021 [12, 13]. Rural health systems in SSA face unique challenges [5, 14]. They are under-resourced because of a lack of adequate equipment, infrastructure, and health personnel [5, 14]. We con- firmed these challenges in a baseline assessment of the Siaya rural health system conducted early during the COVID-19 response [15]. The baseline assessment demonstrated a limited workforce consisting mostly of nurses at the health facility level [15]. A cohort of community health workers (CHWs) active since the 1970s provided preventive and promotive health information to households [16]. They conformed to the 2018 WHO definition of CHWs: CHWs provide health education and referrals for a wide range of services, and provide support and assistance to communities, families and individuals with preventive health measures and gaining access to appropriate curative health and social services [17]. They create a bridge between providers of health, social and community services and communities that may have difficulty accessing these services [17]. In the Kenyan context, CHWs function within commu- nity units (CUs) each serving a population of 5000 people [18]. They are trained to increase demand for health services at the community level [18]. In Kenya, there are an estimated 6359 CUs and 63590 CHWs [18]. Early during the COVID-19 pandemic, we recognized the critical role CHWs could poten- tially play in the COVID-19 response, because they have the most frequent contact with house- holds of all health workers, and they are trusted members of their communities [19–21]. As a result, they are well-positioned to address sensitive and stigmatizing issues including detection of active COVID-19 infections and the need to isolate active COVID-19 cases without instill- ing fear among community members or policing them. We therefore assessed CHW knowl- edge of COVID-19 and its case management in rural Western Kenya [15]. This baseline assessment showed that CHWs had no prior knowledge of COVID-19 and its case manage- ment; nor did they have prior experience of using thermometers and pulse oximeters or of dig- itally reporting vital signs at the household level [15]. Although the current evidence of the effectiveness of rural CHWs in improving health out- comes during the COVID-19 pandemic is suggestive, there are limitations in the research methodologies used to date and the outcome measures reported [9, 10, 22–26]. First, studies measuring the effectiveness of rural CHWs during the COVID-19 pandemic lacked compara- tive components in their research designs limiting conclusions about the strength of the causal link between building the capacity of CHWs and improved health outcomes during the COVID-19 pandemic [9, 10, 22–26]. Second, outcome measures included CHW knowledge, CHW household visits, CHW referrals of symptomatic cases, the incidence of COVID-19 infections, and the rate of valid COVID-19 alerts; however, none of the studies provided mor- tality data [9, 10, 22–26]. Lastly, none of the studies addressed rural CHWs and rural popula- tions in Western Kenya [9, 10, 22–26]. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 3 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Therefore, the objective of this study was to measure the effectiveness of deploying trained and equipped rural CHWs in reducing COVID-19 infections and deaths in rural Western Kenya using a comparative analysis of an intervention county (Siaya) and a non-intervention county (Kisii). Methods Ethics statement We received ethical review approvals from the Jaramogi Oginga Odinga Teaching and Referral Hospital Ethics Review Committee, and the London School of Hygiene and Tropical Medicine Ethics Review Committee (approval numbers IERC/JOOTR/219/20 and 27252). Study design This study is a comparative analysis of Siaya where CHWs were trained, equipped and deployed and Kisii where the CHW intervention did not take place. For our analyses, we used the DHIS2, an anonymized aggregated health information database following approval from the Siaya Ministry of Health. As we had no access to information that could identify individual participants in the DHIS2, informed consent was not required. Settings Siaya is a rural county in Western Kenya with a population of 993,183. Siaya has 240 commu- nity health units (CHUs); with approximately 10 CHWs covering each CHU. Kisii is also a rural county in Western Kenya with a population of 1,266,860. It has 291 CHUs, with approximately 10 CHWs covering each unit. The standard Kenya national COVID-19 protocol did not include CHW training, equip- ping with pulse oximeters, and digital monitoring of vital signs at the household level. Participants CHWs and households in Siaya and Kisii Western Kenya. The intervention in Siaya sought to reach 2000 CHWs covering 200,000 households. Research question What is the effectiveness of training, equipping, and deploying CHWs in addition to the stan- dard Kenya national COVID-19 protocol in reducing COVID-19 infections and deaths in rural Western Kenya? Intervention in Siaya county Under the leadership of Siaya Ministry of Health (MOH), the intervention focused on training, equipping, and deploying CHWs to reduce COVID-19 infections and deaths in Siaya. In line with the human and financial resources that were available, the intervention took place from August 2020 to November 2020. A: Training and equipping CHWs in Siaya county. The training was conducted using the following sequence: a) Raising awareness about COVID-19, and preventing and detecting COVID-19 cases: CHWs were trained on: raising awareness about COVID-19; COVID-19 prevention (with universal mask-wearing, frequent handwashing, physical distancing, and principles of PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 4 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya infection prevention and control (IPC)); screening of potential cases; and contact tracing of community members potentially exposed to suspected and confirmed COVID-19 cases. b) Isolating COVID-19 cases: CHWs were also trained on how to isolate suspected and confirmed COVID-19 cases to minimize further community transmission. c) Managing COVID-19 cases: CHWs were trained to manage minor and moderate COVID-19 cases at home. In addition, they were trained to recognize severe COVID-19 cases and refer severe COVID-19 cases to health facilities with oxygen capacity. To optimize their diagnostic, clinical monitoring, and referral capacities, CHWs were also trained on the use of contactless thermometers and pulse oximeters. This component of the CHW training covered i) the pathophysiology of COVID-19 infection and how it affects the lung leading to hypoxemia ii) how pulse oxime- ters measure the oxygen saturation of hemoglobin molecules iii) possible causes of spurious readings iv) normal and abnormal oxygen saturation rates v) the action that needs to be taken when oxygen saturation rates were below 90%. d) Measuring and digitally reporting vital signs at the household level: CHWs were trained on how to measure body temperatures and oxygen saturation levels and digitally report vital signs at the household level using Commcare, and the Whatsapp platform when additional support from a clinician was needed. Furthermore, they were updated on facilities with oxygen capacity. Commcare is an open source mobile platform that supports frontline workers in low-resource communities (https://www.dimagi.com/ commcare/). e) Use of essential health services, providing mental health support, and leadership during the COVID-19 pandemic: CHWs were trained to ensure households continued to use essential health services includ- ing maternal and child health services, during the COVID-19 pandemic. In addition, CHWs received training in leadership skills and providing psychological first aid. In addition to training Siaya CHWs, we equipped them with KN95 masks, thermometers, and pulse oximeters. They received internet data for the reporting of household level vital signs. B: CHW deployment: CHW household visits. Following training and receipt of equip- ment, CHWs visited households to raise awareness about COVID-19 and educate households about COVID-19 prevention with physical distancing, mask wearing, and handwashing. CHWs ensured households had functional handwashing points. They identified and isolated any symptomatic household members and educated them about minimizing transmission within households. They measured body temperatures and oxygen saturation levels of sus- pected and confirmed COVID-19 cases and digitally reported household level vital signs using Commcare and Whatsapp platforms. They referred severe COVID-19 cases to health facilities with oxygen capacity. They counselled households on the importance of the continued use of maternal and child health services during the COVID-19 pandemic. In addition, they con- ducted psychological support as needed. Fig 2 summarizes the intervention in Siaya. For this study, we defined training as a process that provides conditions in which individu- als gain knowledge, skills or ability [27]. In addition, equipping CHWs in this study meant the process by which CHWs had access to medical equipment including pulse oximeters and ther- mometers and digital tools for reporting vital signs. Additionally, CHWs were equipped with PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 5 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Fig 2. Training, equipping and deploying CHWs to reduce COVID-19 infections and deaths. https://doi.org/10.1371/journal.pgph.0003036.g002 KN95 masks during a time when COVID-19 vaccines were not available. Furthermore, we defined reductions in COVID-19 infections and deaths as reductions in absolute numbers of COVID-19 infections and deaths and also reductions in the risk ratios of COVID-19 infections and deaths. COVID-19 vaccines were not available in Kenya when the intervention took place. We therefore ensured all participants were masked and training activities and household visits in Siaya were conducted outside with universal mask wearing, and appropriate physical distancing. We used the following algorithm to determine household members that were at a high risk for COVID-19 exposure and developing severe disease [28] (Table 1). If household members presented with symptoms of COVID-19 with a recent history of pos- sible exposure to COVID-19, isolation and testing was recommended to them. In addition, we used the WHO SARS-CoV-2 contact definition as follows: A SARS-CoV-2 contact is a person who has had any one of the following exposures to a probable or a confirmed case of SARS-CoV-2 infection: 1. face-to-face contact with a probable or confirmed case within 1 meter and for at least 15 minutes, or 2. direct physical contact with a probable or confirmed case, or 3. direct care for a patient with probable or confirmed COVID-19 disease without the use of recommended personal protective equipment (PPE) [29]. Comparison non-intervention county—Kisii county Comparison county. We compared Siaya county to Kisii county because both are rural; they are geographically distant (reducing the chance of contamination during the intervention period); and they have similar baseline health indicators in terms of age standardized mortal- ity, life expectancy, and HIV deaths. In addition, both counties had similar critical care capac- ity: Kisii had 9 ICU beds and Siaya had 8 ICU beds. Table 2 summarizes baseline health and sociodemographic indicators in Siaya and Kisii counties. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 6 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Table 1. Algorithm for determining household members that were at a high risk for COVID-19 exposure and developing severe COVID-19 disease. Risk factors for COVID-19 exposure and developing severe COVID-19 Yes/No Are you over 60 years old: Do you have hypertension: Do you have heart disease: Do you have diabetes: Do you have chronic respiratory disease: Do you have cancer: Have you been to any weddings, funerals, social gatherings or religious gatherings in the last 7 days: Have you had fever: Have you had a cough: Have you felt more tired than usual: Have you had any shortness of breath: Have you had any changes in taste or smell: Have you traveled to Asia, Europe, or the United states in the last 2 weeks? Have you traveled to Nairobi or Mombasa in the last 2 weeks? https://doi.org/10.1371/journal.pgph.0003036.t001 Siaya and Kisii counties have similar baseline health indicators in terms of age standard- ized mortality, life expectancy, and HIV deaths. In Kisii county, the standard national COVID-19 protocol was implemented. The stan- dard national COVID-19 protocol emphasized the identification and preparation of isola- tion and treatment facilities at referral hospitals. It emphasized the capacity building of facility-based health workers. It required enhanced surveillance at all ports of entry and sub- nationally at county borders. It also emphasized the procurement of supplies, pharmaceuti- cals, and PPE for health facilities. The national government COVID-19 protocol did not include CHW training, equipping, and deployment. Table 2. Baseline health and sociodemographic indicators in Siaya versus Kisii counties [30, 31]. Health and sociodemographic indicator Population Population density Life expectancy Baseline standardized mortality/ 100,000 HIV deaths/100,000 Fertility rate Skilled birth attendance Nutritional status of children, percent below—3 SD HIV testing coverage Education attainment women: % no education Education attainment: men % no education Literacy: women Literacy: men Teenage pregnancy ANC percentage receiving skilled antenatal care Delivery in health facilities % delivered by a skilled provider All basic vaccinations https://doi.org/10.1371/journal.pgph.0003036.t002 Siaya 993,183 Kisii 1,266,860 393 64.7 1190 179 4.2 97.8 7.1 98.8 1.9 0.7 33.7 42.3 13.6 97.8 69.6 70.4 79.3 957 67 1140 186 3.7 97.7 9.3 99.1 0.9 0.4 48.3 58.7 15.9 97.7 69.3 72.8 84.6 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 7 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Data collection and analysis i) We collected data on CHW household visits, COVID-19 infections, and COVID-19 deaths in Siaya and Kisii counties using the Kenya DHIS2 database. DHIS2 data collection protocols are the same across counties. ii) We compared the number of CHW household visits in Siaya and Kisii counties. We measured risk ratios for COVID-19 infections and deaths in Siaya and Kisii counties. We orga- nized data into contingency tables and divided the cumulative incidence of COVID-19 infec- tions and COVID-19 deaths in Siaya by the cumulative incidence of COVID-19 infections and COVID-19 deaths in Kisii. In addition, we compared CHW skills in Siaya and Kisii counties based on digital records of vital signs at the household level. Statistical analysis. Our goals for the statistical analysis were to determine effect sizes linked to the CHW intervention and to evaluate their significance. As previously described, the effect size is a quantitative summary measure obtained by comparing outcome measures between two or more groups [32]. Both dichotomous outcome measures (COVID-19 infec- tions and deaths) and continuous outcome measures (number of households visited and num- ber of vital signs measured at the household level) were recorded. To determine the effect sizes of our dichotomous data, we organized our data into 2x2 contingency tables and calculated risk ratios of COVID-19 infections and COVID-19 deaths in Siaya County compared to Kisii County. To further interpret risk ratios, we converted them to percentage change taking into consideration percentage differences between the 2 counties in COVID-19 infections and COVID-19 deaths for each month of the intervention as described by Ogallo et al. using the formula below. In this formula “i” represents a given month of the intervention, “yi” is the out- come at month i in the site where the intervention took place; and “y^i” is the outcome at the control site [33]. Percent change ¼ XN i¼1 1 N ðyi (cid:0) ^yiÞ ^yi We calculated confidence intervals to determine the significance of the risk ratios. We con- sidered confidence intervals that excluded 1 and p-values less 0.05 to be statistically significant. For continuous data (CHW household visits), we calculated the effect size by determining whether there were differences in the mean number of CHW household visits conducted in Siaya and Kisii counties. We determined if the differences in means were statistically signifi- cant using the Student T test. Table 3 summarizes our statistical analyses. Our null hypothesis was that training, equipping and deploying CHWs is not associated with improved CHW skills and performance, and downstream reduced COVID-19 infections and deaths. We made the assumption that observations of COVID-19 infections and deaths in each county were independent. We used SPSS version 20.0 to conduct the statistical analyses. Table 3. Summary of statistical analyses performed. Type of outcome measure Dichotomous Dichotomous Continuous Continuous Outcome measure Summary measure Effect size measure Significance COVID-19 infections COVID-19 deaths Proportion Proportion Risk ratio Risk ratio 95% confidence interval 95% confidence interval CHW household visits Mean and standard deviation Difference between 2 means p-value using the Student T test Vital signs measurements at household level Mean number of vital signs measurements per COVID-19 infection Difference in the average number of vital signs No household vital signs measurements completed in Kisii county https://doi.org/10.1371/journal.pgph.0003036.t003 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 8 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Table 4. CHW skills: Digital reports of vital signs at the household level. Vital signs Oxygen saturation measurements Body temperature measurements Total number of Siaya CHW digitally reported vital signs for a total of 383 COVID-19 infections Mean number of vital signs measurements per COVID-19 infection Total number of digitally reported vital signs in Kisii for a total of 897 infections Mean number of vital signs measurements in Kisii per COVID-19 infection 9266 6541 24.2 17.1 0 0 0 0 https://doi.org/10.1371/journal.pgph.0003036.t004 Results i) Training and equipping CHWs: In Siaya, 1359 CHWs were trained and equipped. No specific COVID-19 CHW training and equipping took place in Kisii County. ii) CHW skills following the intervention in Siaya: Following the CHW intervention in Siaya, Siaya CHWs conducted household visits and digitally reported vital signs. Table 4 summarizes the number of vital signs digitally reported by CHWs in Siaya (Table 4). Siaya CHW digital reports of vital signs at the household level The mean number of oxygen saturation measurements conducted in Siaya and the mean num- ber of temperature measurements per COVID-19 infection are summarised above. In Kisii County, CHWs did not digitally report vital signs at the household level. iii) CHW activity: Monthly CHW household visits in Siaya versus Kisii counties: We found significant differences in the mean number of CHW household visits conducted in Siaya and Kisii Counties. The mean monthly number of CHW household visits was signifi- cantly higher in Siaya versus Kisii (the mean monthly CHW household visits in Siaya was 146,648.5, standard deviation 11,066.5 versus 42,644.5 in Kisii, standard deviation 899.5, p value = 0.01). The differences in the mean number of household visits conducted was statisti- cally significant. Fig 3 summarizes differences in CHW household visits in Siaya and Kisii Counties (Fig 3). Differences in CHW household visits in Siaya and Kisii Counties iv) COVID-19 infections: We observed that increased CHW skills and increased CHW household visits were associ- ated with fewer COVID-19 infections. Siaya County had a total of 383 infections from August to November 2020 and Kisii County had a total of 847 infections during the same period as shown in Table 5. We found that the risk ratio for COVID-19 infections in Siaya was 0.54, 95% CI [0.48–0.61] implying a 46% reduction in the risk of COVID-19 infection in Siaya. Because the confidence interval excludes 1, we rejected the null hypothesis that there is no association between build- ing the capacity of CHWs and a lower risk of COVID-19 infections in rural Western Kenya. v) COVID-19 deaths: We observed that increased CHW skills and increased CHW household visits with digital reporting of vital signs at the household level was associated with fewer COVID-19 deaths. Siaya County had a total of 7 deaths from August 2020 to November 2020; and Kisii County had 31 deaths during the same period as shown in Table 6. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 9 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Fig 3. Monthly household visits in Siaya and Kisii counties. https://doi.org/10.1371/journal.pgph.0003036.g003 The risk ratio for COVID-19 deaths in Siaya was 0.29, 95% CI [0.13–0.65] a 71% reduction in risk, consistent with an effect of building the capacity of CHWs and lower risk of COVID- 19 deaths in rural Western Kenya. Discussion In this study, we found that training, equipping, and deploying CHWs in Siaya County was associated with increased CHW skills, increased CHW activity, and significantly lower COVID-19 infections and COVID-19 deaths compared with Kisii County that followed the Kenya national COVID-19 protocol but where no such training occurred. Following the addi- tion of the CHW intervention to the standard national COVID-19 protocol, Siaya CHWs were skilled in capturing and digitally reporting vital signs at the household level; they conducted significantly more household visits than Kisii CHWs. The increased CHW skill and number of household visits were associated with reduced risk ratios of COVID-19 infections and COVID-19 deaths in Siaya. The likely mechanism explaining the effectiveness of rural CHWs in improving COVID-19 outcomes is that training and equipping CHWs led to increased CHW skills and CHW activity that in turn led to better COVID-19 case management at the household level with reduced COVID-19 infections and deaths. i) Training and equipping CHWs during the COVID-19 pandemic: A key component of the CHW intervention in Siaya was the training and the equipping of CHWs with pulse oximeters, thermometers, and KN95 masks. The ability to measure vital signs at the household level was particularly important in rural Western Kenya during this time, Table 5. 2x2 contingency table for COVID-19 infections in Siaya and Kisii counties. COVID-19 infection Siaya (CHW intervention) Kisii (no CHW intervention) Total Yes 383 897 1280 No 992,800 1,265,963 2,258,763 Total 993,183 1,266,860 2,260,043 https://doi.org/10.1371/journal.pgph.0003036.t005 PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 10 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya Table 6. 2x2 contingency table for COVID-19 deaths in Siaya and Kisii counties. COVID-19 deaths Siaya (CHW intervention included) Kisii (CHW intervention not included) Total Yes 7 31 38 https://doi.org/10.1371/journal.pgph.0003036.t006 No 993,176 1,266,829 2,260,005 Total 993,183 1,266,860 2,260,043 because COVID-19 testing capacity was limited [34]. Measuring vital signs at the household level likely contributed to the early identification of potential COVID-19 cases and the isolation of these cases by CHWs leading to the break in the community transmission of COVID-19 and fewer COVID-19 infections. Furthermore, the identification of COVID-19 cases at the household level likely increased the timely referral of severe cases to facilities with potentially life-saving oxy- gen capacity, probably contributing to significant reductions in COVID-19 deaths [35]. Other studies report interventions to train rural CHWs during the COVID-19 pandemic. Singh SS et al. trained 15 ’000 Ashas in the rural state of Bihar during the second COVID-19 wave in India [22]. Following this training, they found that > 80% of trained CHWs were satis- fied with the training [22]. Kharel et al. trained 300 CHWs on COVID-19 in rural Nepal to increase their knowledge of COVID-19 [23]. Their preliminary results showed an increase in CHW knowledge of COVID-19 following their intervention. Another study from Uganda trained rural CHWs to help them identify, refer, and care for potential COVID-19 cases using a call center [25]. Similar to our study, these aforementioned studies show that it was feasible to train rural CHWs during the COVID-19 pandemic, and that the training of rural CHWs can lead to increased CHW capacity regarding a new disease. Furthermore, training rural CHWs during the COVID-19 pandemic was not only feasible in Asia, but also in SSA [22, 23, 25]. Our study reported additional outcome measures including CHW household visits and CHW measurement and digitally reporting vital signs following training. ii) CHW activity: Other studies have reported CHW visits during the COVID-19 pandemic. In a mixed methods study of rural indigenous communities in the Peruvian amazon, Reinders et al. docu- ment the resumption of household visits by two thirds of CHWs during the COVID-19 pan- demic [36]. In this context, CHWs had little access to external support and training [36]. Similarly, in a mixed methods study Chengo et al. document CHW household visits in Kenya, Uganda, and Senegal during the COVID-19 pandemic [8]. Chengo et al. also highlight key challenges experienced by CHWs including the lack of protective gear, training, and reporting tools that were addressed by the intervention in Siaya County [8]. Addressing these challenges experienced by CHWs likely led to significant increases in CHW motivation and performance as shown by the significantly higher number of household visits conducted in Siaya; which subsequently led to the increased use of health services. According to Penchansky and Thomas’ theory of access, access to health services is optimized when the demand for services and the supply of services are maximized [37]. During a pandemic CHW activity will continue even in the absence of training and equip- ping CHWs. In our study, Kisii County reported CHW visits in the absence of additional CHW training and equipping. Salve et al. posit that CHWs tend to cope in the absence of adequate support and will continue to visit households during a pandemic; however, to maximize the performance and effectiveness of CHWs during a pandemic, our study shows that additional training and equipping is critical [7]. In rural Thailand, CHWs visited 14 million households from March to April 2020 following training according to Kaweenuttayanon 2021 et al, but our study establishes a stronger causal link between CHW training and CHW activity during the COVID-19 pandemic because of the comparative component in our research design [9]. PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 11 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya iii) CHW skills: In our study, we found that training and equipping CHWs was linked to increased CHW ability to measure and digitally report vital signs at the household level. Other studies have also reported CHW use of digital tools during the COVID-19 pandemic in Uganda, Ethiopia, and Mozambique [38]. To our knowledge, none of these studies used digital tools to report vital signs including oxygen saturation levels at the household level. There is evidence to support the importance of oxygen level measurements for the early identification and management of hypoxemia during COVID-19 infections [35]. To our knowledge, no other study rolled out an intervention with CHWs measuring oxygen levels at the household level during the COVID-19 pandemic in rural SSA. There is critical care evi- dence that links the early identification and management of hypoxemia during COVID-19 infections with a reduction in COVID-19 deaths [35]. Sun et al provide useful algorithms for the identification and management of severe cases which includes the measurement of oxygen levels [35]. However, these oxygen saturation measurements were hospital based; in contrast to our experience in Siaya [35]. Our study shows that it is feasible for CHWs to measure and report vital signs including oxygen levels at the household level. CHW oxygen level measure- ments likely led to earlier referrals and management of severe COVID-19 in Siaya; and refer- rals to the right facility with oxygen capacity likely led to fewer COVID-19 deaths. Additionally, triaging of severe cases at the household level with CHW oxygen level measure- ments probably prevented hospitals from being overwhelmed with mild or moderate COVID- 19 cases. In the rural context, this was a critically important consideration when human and material resources were even more scarce during an ongoing pandemic. iv) Effect of CHWs on COVID-19 infections: We also found that the increase in CHW skills and household visits in Siaya was linked to reduced risk ratios for COVID-19 infections in Siaya compared to Kisii. It is likely that because of these CHW efforts, COVID-19 cases were identified earlier and subsequently isolated earlier, thus reducing community transmission. The added ability to identify probable cases at the household level using vital signs was important because there were insufficient COVID-19 testing kits [30]. More CHW household visits in Siaya also meant increased likelihood of receiving health information which may have led to increased adherence to COVID-19 preventive measures. CHWs could have also influenced dimensions of the health belief model at the household level; specifically households’ perception of their susceptibility to COVID-19 infection, their perception of the severity of COVID-19 as a disease, their self-efficacy and perceived benefits and barriers to adopting certain health behaviors to prevent COVID-19 infection [39]. Kawee- nuttayanon et al demonstrated reduced community COVID-19 transmission following the deployment of trained rural CHWs in March 2020 [9]. Within a 1 month of deploying trained rural CHWs, the daily numbers of new COVID-19 cases in Thailand dropped dramatically [9]. This study however was limited by the lack of mortality data. v) Effect of CHWs on COVID-19 deaths: In contrast to the Kaweenuttayanon et al experience, our study also showed an association between increased CHW skill and increased household visits and subsequent lower COVID- 19 death rates in Siaya compared to Kisii. The most likely mechanism explaining this differ- ence in mortality is that trained and equipped CHWs in Siaya were able to detect and manage COVID-19 cases earlier using their equipment. They likely were able to identify severe cases by measuring low oxygen levels and referring COVID-19 cases with low oxygen levels to facili- ties with oxygen capacity. In rural contexts, the capacity to identify severe cases and to refer them to a facility with oxygen capacity is important because distances to facilities are often far, means of transport are often unavailable, and road networks can be poor especially during the rainy season resulting in greater delays in reaching facility-based care compared to urban PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 12 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya settings [5, 40]. Moreover, in rural health systems, health facilities are fewer in number with a limited health workforce, equipment, and supplies which can contribute to a delay in accessing care [5, 40]. Consequently, strengthening the diagnostic, monitoring, and case management capacity of CHWs in Siaya was particularly effective in mitigating the usual delays in access to care by bringing critical components of care to the household level which likely led to earlier referrals of severe COVID-19 cases, and referrals to health facilities with oxygen capacity avert- ing a potential additional delay if patients are referred to a facility without oxygen capacity. In summary, household contact with trained and equipped CHWs probably increased timely decision making and timely action to seek health services in facilities with oxygen capacity reducing delays in accessing lifesaving health services which led to fewer COVID-19 deaths in Siaya. Moreover, as previously mentioned training, equipping and deploying CHWs led to increased access to care and reduced transmission at the household level which also con- tributed to the fewer deaths observed in Siaya. In Kenya, barriers to accessing care were heightened during the COVID-19 pandemic as a result of curfews and movement restrictions. Trained and equipped CHWs can reduce delays in making the decision to seek care, minimizing delays in reaching the hospital, and minimiz- ing delays in receiving care [40]. To our knowledge, no other study links a rural CHW inter- vention with vital signs measurements at the household level to improved COVID-19 mortality outcomes. Recently published Kenya DHS 2022 COVID-19 outcomes data validate our findings show- ing significantly lower COVID-19 mortality rates in Siaya compared to bordering counties in addition to Kisii County: Kakamega County was reported to have 55 COVID-19 deaths per 100’000 population; Kisumu County was reported to have 34 COVID-19 deaths per 100’000 population; Homa Bay County was reported to have 37 COVID-19 deaths per 100’000 popula- tion; and Busia County was reported to have 29 COVID-19 deaths per 100,000 population [41]. In the same survey, Kisii County was reported to have 34 COVID-19 deaths per 100,000 population and Siaya county 8 COVID-19 deaths per 100,000 population [41]. Strengths of our study Our study has several strengths. First, it has a comparative component in its research design which strengthens the causal link between trained, equipped, and deployed rural CHWs and significantly reduced COVID-19 infections and deaths. Second, this study demonstrates the feasibility of training, equipping, and deploying rural CHWs during the COVID-19 pandemic. This contributes to the implementation research evidence addressing the role of CHWs during pandemics. Our study showed its feasibility and contributed evidence towards its effectiveness. Third, our study has mortality data which prior studies addressing the role of rural CHWs in pandemics do not have. Fourth, our study addresses the rural population in SSA. To date, most studies addressing the role of CHWs during the COVID-19 pandemic are from Asia. Few COVID-19 studies emerged from rural SSA and there is a need for more evidence in this population because SSA is > 50% rural and Kenya is > 70% rural [4]. Lastly, we used routinely collected data which reduced potential biases including recall bias, reporting bias, and observer bias linked to the Siaya intervention. There was consistency in the variables collected across counties using the same methodology across counties. Limitations of our study There were several limitations to our study. First, we compared Siaya to a county with similar characteristics; however there could be persistent confounding factors. As shown in Table 2, health indicators were similar in both counties. However, there were sociodemographic PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 13 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya differences: the population density was higher in Kisii compared to Siaya which could lead to a higher risk of COVID-19 infection in Kisii. However, the evidence supporting the link between increased population density and increased respiratory disease transmission remains uncertain. In a systematic review of 21 studies by Zhang X et al, there was no consistent associ- ation between increased population density and increased respiratory disease transmission including COVID-19 [42]. Hamidi et al found that the connectivity of a location was a more important predictor of COVID-19 spread rather than population density [43]. Siaya being in close proximity with Busia, a county bordering Uganda, and Kisumu, an urban county with an airport, is more connected than Kisii, and would therefore be expected to have more COVID- 19 infections. We observed the opposite in our study and adjusting for this confounding factor could potentially have demonstrated a greater effect measure associated with building the capacity of CHWs and reducing the risk of COVID-19 infections and deaths. Moreover, edu- cation attainment was higher in Kisii compared to Siaya. Increased education level of a popula- tion is often linked to better health literacy and therefore reduced disease transmission and death. Yoshikawa et al found that educational attainment was associated with a lower risk of severe COVID-19 disease [44]. Similarly, Gomes da Silva et al found that increased educational attainment was associated with increased health literacy about COVID-19 in Portugal [45]. Adjusting for a lower education attainment in Siaya could potentially have resulted in a greater effect measure being observed in our study. To address confounding factors, we considered conducting regression analyses including the Poisson regression as described by Ogallo W et al.; however, our data was limited by the fact that we did not have individual level data [33]. Additional limitations to our study could be delays in capturing and reporting COVID-19 infections; however, reporting mandated by the National Emergency Response Committee on Coronavirus ensured daily reporting of new COVID-19 cases and deaths by all counties. In addition, there could have been differences in oxygen use in both counties and not measuring differences in oxygen use is a limitation of our study. However, in the Kenyan context, the major rate limiting factor across all counties in Kenya was not the amount of oxygen available but the ability to deliver oxygen using ventilators; the number of ventilators was in the single digits in both counties (8 in Siaya and 9 in Kisii). Furthermore, transport delays may have played a role in the differences in mortality; however, we observed better COVID-19 outcomes in Siaya where distances travelled to reach health facilities are greater. This finding further sup- ports the effectiveness of CHWs in reducing barriers to care including distance to facilities. Lastly, we used DHIS2 data to conduct our comparative analysis. DHIS2 data includes data from public, non-governmental, and faith-based health facilities. It does not include data from private health facilities. Therefore, our findings may not apply to populations with significant use of private facilities or to urban populations. Large scale cluster randomised trials could provide more robust evidence but are resource intensive and difficult to launch rapidly in pan- demics. They would need to be planned in advance with an agreed protocol that could be adapted to the prevailing circumstances. Policy implications Disease outbreaks and pandemics remain a significant threat to rural populations that are par- ticularly vulnerable because they are under-resourced in terms of infrastructure, equipment, and a health workforce. Our study showed that training, equipping, and deploying CHWs can strengthen pandemic preparedness and response and lead to fewer COVID-19 infections and deaths compared to standard measures. In a global context of continued disparities in access to COVID-19 therapeutics and vaccines, a pattern repeated at all stages of the COVID-19 pan- demic, defining interventions that would leverage assets already present in rural health PLOS Global Public Health \| https://doi.org/10.1371/journal.pgph.0003036 March 25, 2024 14 / 18 PLOS GLOBAL PUBLIC HEALTHCommunity health workers and COVID-19 infections and deaths in rural Western Kenya systems, including CHWs is crucial for robust current and future pandemic preparedness and response efforts [46]. The policy implications of our results from rural SSA are that building a community workforce is a critical component of pandemic preparedness and response espe- cially in under-resourced health systems including rural health systems. Policymakers with sig- nificant rural populations particularly in SSA should consider investing in CHW capacity building as part of current and future pandemic preparedness and response strategies to save lives. Conclusion In conclusion, our study showed that training, equipping, and deploying rural CHWs was associated with significantly lower risks of COVID-19 infections and deaths in rural Western Kenya. Regions with significant rural populations should strongly consider training, equip- ping, and deploying rural CHWs to strengthen their pandemic preparedness and response efforts to save lives. Supporting information S1 Checklist. Completed Inclusivity-in-global-research-questionnaire August 14 2023. (PDF) Acknowledgments We would like to acknowledge and thank all team members of the Siaya County Ministry of Health and the Rapid Response Team and all CHWs in Western Kenya who worked extremely hard to reach households under their care. We would also like to acknowledge and thank all clinicians, especially nurses and clinical officers who worked tirelessly to provide health ser- vices in Western Kenya during the COVID-19 Response. In addition, we would like to sin- cerely thank Professor Melissa Neuman and Professor Monica Magadi for their guidance on appropriate statistical approaches and limitations to address. Author Contributions Conceptualization: Neema Kaseje, Kennedy Oruenjo, Dan Kaseje, Marcel Tanner, Andy Haines. Funding acquisition: Neema Kaseje. Methodology: Neema Kaseje, Kennedy Oruenjo, Dan Kaseje, Meghna Ranganathan, Marcel Tanner, Andy Haines. Project administration: Neema Kaseje. Writing – original draft: Neema Kaseje. Writing – review & editing: Kennedy Oruenjo, Dan Kaseje, Meghna Ranganathan, Marcel Tanner, Andy Haines. 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10.1371_journal.pgen.1010931	RESEARCH ARTICLE Modeling of African population history using f- statistics is biased when applying all previously proposed SNP ascertainment schemes Pavel FlegontovID Piya Changmai2, David ReichID 1,2,3☯, Ulaş IşıldakID 1,4,5,6 2☯¤, Robert Maier1☯, Eren Yu¨ ncu¨ 2¤, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Flegontov P, Işıldak U, Maier R, Yu¨ncu¨ E, Changmai P, Reich D (2023) Modeling of African population history using f-statistics is biased when applying all previously proposed SNP ascertainment schemes. PLoS Genet 19(9): e1010931. https://doi.org/10.1371/journal. pgen.1010931 Editor: Charleston Wen-Kai Chiang, University of Southern California, UNITED STATES Received: January 22, 2023 Accepted: August 21, 2023 Published: September 7, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgen.1010931 Copyright: © 2023 Flegontov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All the genetic data analyzed in the manuscripts were either simulated or published. The software used in this manuscript 1 Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America, 2 Department of Biology and Ecology, Faculty of Science, University of Ostrava, Ostrava, Czechia, 3 Kalmyk Research Center of the Russian Academy of Sciences, Elista, Russia, 4 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 5 Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, United States of America, 6 Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America ☯ These authors contributed equally to this work. ¤ Current address: E.Y., Department of Biological Sciences, Middle East Technical U., Ankara, Turkey; U.I., Leibniz Institute on Aging—Fritz Lipmann Institute (FLI), Jena, Germany. * pflegontov@gmail.com (PF); reich@genetics.med.harvard.edu (DR) Abstract f-statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. Not only are they guaranteed to allow robust tests of the fits of proposed models of population history to data when analyzing full genome sequencing data—that is, all single nucleotide polymorphisms (SNPs) in the individuals being analyzed —but they are also guaranteed to allow robust tests of models for SNPs ascertained as poly- morphic in a population that is an outgroup in a phylogenetic sense to all groups being ana- lyzed. True “outgroup ascertainment” is in practice impossible in humans because our species has arisen from a substructured ancestral population that does not descend from a homogeneous ancestral population going back many hundreds of thousands of years into the past. However, initial studies suggested that non-outgroup-ascertainment schemes might produce robust enough results using f-statistics, and that motivated widespread fitting of models to data using non-outgroup-ascertained SNP panels such as the “Affymetrix Human Origins array” which has been genotyped on thousands of modern individuals from hundreds of populations, or the “1240k” in-solution enrichment reagent which has been the source of about 70% of published genome-wide data for ancient humans. In this study, we show that while analyses of population history using such panels work well for studies of relationships among non-African populations and one African outgroup, when co-modeling more than one sub-Saharan African and/or archaic human groups (Neanderthals and Deni- sovans), fitting of f-statistics to such SNP sets is expected to frequently lead to false rejec- tion of true demographic histories, and failure to reject incorrect models. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, has limited statistical power and retains important biases. However, PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 1 / 44 PLOS GENETICSis publicly available at: https://uqrmaie1.github.io/ admixtools/. Funding: P.F., U.I., and P.C. were supported by the Czech Ministry of Education, Youth and Sports (program ERC CZ, project no. LL2103). P.F. and P. C. were supported by the Czech Science Foundation (project no. 21-27624S). P.F. was also supported by a subsidy from the Russian federal budget (project No. 075-15-2019-1879 “From paleogenetics to cultural anthropology: a comprehensive interdisciplinary study of the traditions of the peoples of transboundary regions: migration, intercultural interaction and worldview”). This research was funded by NIH grant HG012287, by the Allen Discovery Center program, a Paul G. Allen Frontiers Group advised program of the Paul G. Allen Family Foundation, by John Templeton Foundation grant 61220, by private gifts from Jean-Francois Clin to D.R. and P.F., and by the Howard Hughes Medical Institute. Computational resources for this work were supplied by the projects "e-Infrastruktura CZ" (e-INFRA CZ LM2018140) and “IT4Innovations National Supercomputing Center – LM2015070” supported by the Ministry of Education, Youth and Sports of the Czech Republic. The funders played no role in planning of this work, in interpretation of the results, and in preparation of the manuscript. Competing interests: The authors declare no competing interests. Modeling of African population history can be highly biased by common SNP ascertainment schemes by carrying out simulations of diverse demographic histories, we show that bias in infer- ences based on f-statistics can be minimized by ascertaining on variants common in a union of diverse African groups; such ascertainment retains high statistical power while allowing co-analysis of archaic and modern groups. Author summary Archaeogenetic research on humans remains heavily biased towards Europe, Central and East Asia due to poor preservation of ancient DNA in hot climate. However, the number of studies focused on the history of African human populations is growing. Due to the DNA preservation problems, using targeted enrichment for selected variable loci is almost unavoidable in archaeogenetic research focused on Africans. Moreover, poor quality of archaeogenetic data makes the analytical toolkit rather limited: it is often restricted to methods based on f-statistics, PCA, and ADMIXTURE. It is known that f-statistics may be biased when they are calculated not on whole-genome data, but on sets of SNPs selected in a non-random way. Although this is common knowledge, biases affecting f-statistics on such SNP sets (“ascertainment biases”) remain poorly explored in practice, and our study is designed to fill this gap. We investigate biases affecting individual f4-statistics and fits of admixture graph models on simulated and real data, explore dozens of ascertainment schemes, and provide a set of guidelines for minimizing bias. We show that ascertainment bias is particularly strong in situations when several African populations are co-analyzed with non-African and archaic (Neanderthal or Denisovan) human groups. Introduction Archaeogenetics has achieved remarkable progress in the last decade [1,2], with genome-wide data for thousands of ancient humans now being published each year. No region of the world is now inaccessible to archaeogenetic research, although isolation of enough authentic DNA from skeletons excavated in tropical and sub-tropical areas [3] or from Pleistocene individuals [4,5] remains a challenge. For generating usable archaeogenetic data from Africa, targeted enrichment of human DNA on dedicated single nucleotide polymorphism (SNP) capture pan- els is almost always necessary. A majority of ancient DNA studies on African populations [6– 13] relied on a SNP capture panel usually termed "1240K" [14,15], and some studies on Upper Paleolithic humans relied on a supplementary panel ("1000K", comprising transversion poly- morphisms found in two Yoruba individuals and transversion polymorphisms in the Altai Neanderthal genome) or on its union with 1240K [4,14], or on standalone 1240K [5]. The 1240K panel was constructed of the following elements: all SNPs on the Human Origins array (itself composed of 13 sub-panels, each ascertained as heterozygous in a single high-coverage human genome [16]), all SNPs on the Illumina 650Y array, all SNPs on the Affymetrix 50k XBA array, and smaller numbers of SNPs chosen for other purposes [14]. The 1240K capture panel is now used routinely for analyzing thousands of ancient humans across the world [1,17], and successor panels including the full set of 1240K sites are now available [18]. f-statistics [16,19–22] are one of the most widely used tools for analyzing allele frequency data in population genetics, and especially in archaeogenetics where high-quality data required for many analytical approaches (based on, e.g., site frequency spectra or autosomal haplotypes) are typically unavailable. f-statistics are of three types (see Patterson et al. [16] and a recent PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 2 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes review by Maier et al. [23]): f4, f3, and f2 (the latter two statistics are special cases of the former). The f4-statistic f4(A, B; C, D) measures correlation in allele frequency differences between pop- ulations A and B and populations C and D ((pA−pB) × (pC−pD) for allele frequencies p), typi- cally averaged over thousands of biallelic single-nucleotide polymorphisms [16,20,22]. The f4- statistic is identical to the ABBA/BABA statistic, also known as the D-statistic [24,25], up to a normalization factor, and is a test for treeness. Statistically significant deviations of this statistic from zero (with standard deviation calculated on jackknife replicates of a SNP dataset split into blocks) suggest that the unrooted tree ((A, B), (C, D)) does not fit the data, and the sign of the statistic points to pairs of populations potentially connected by gene flow [26]. The f2-sta- tistic (A, B) is identical to f4(A, B; A, B) and can be interpreted as the genetic distance between groups A and B. The f3-statistic (outgroup; A, B) is identical to f4(outgroup, A; outgroup, B) and measures genetic drift shared between groups A and B so it is often used for finding groups/ individuals that are genetically closest to the group of interest. A significantly negative value of the f3-statistic (target; A, B) provides a formal test for the target group being a mixture of ancestry sources related to groups A and B [16,20,22], although a signal from this “admixture- f3” test can be masked by genetic drift in the target group since admixture. Non-zero f4-statis- tics can sometimes detect admixture even in a population that has experienced substantial genetic drift. All f-statistics can be expressed as linear sums of other f-statistics [16,20,22] and have straightforward interpretations in terms of admixture graph edges [16,20,22] and arrangement of individuals in principal component spaces [22]. f4-statistics form the foundation of qpAdm [27,28], a method that is used widely in archaeogenetics [29] for fitting simple admixture mod- els (target group A = proxy source B + proxy source C, etc.) that are not phylogenetically explicit. Admixture graphs are also often fitted to f-statistics, namely to all possible f2-, f3-, and f4-statistics for a given set of populations [16,23,26]; see more on admixture graphs below. Considering that f-statistics and methods relying on them are very useful and popular in popu- lation genetics, and in archaeogenetics in particular, it is important to explore carefully biases in f-statistics that may arise due to non-random selection (“ascertainment”) of SNP loci for analysis. Bergstro¨m et al. [30], relying on high-quality genomic data for present-day humans, showed that f4-statistics including three sub-Saharan African groups and one non-African group, or four sub-Saharan African (hereafter “African”) groups can be biased when computed on common SNP panels such as Illumina MEGA, the panel used by Li et al. [31], and the Affy- metrix Human Origins array [16]. Influence of ascertainment on common population genetic analyses (ADMIXTURE, FST) was also demonstrated. However, the bias in f4-statistics includ- ing archaic humans and apes was not explored. Bergstro¨m et al. [30] found that selecting approximately 1.3M SNPs polymorphic in the group composed of high-coverage archaic human genomes (the Altai and Vindija Neander- thals, the “Denisova 3” Denisovan) effectively eliminated the biases affecting f4-statistics calcu- lated on anatomically modern humans (AMH) and including 3 or 4 sub-Saharan African groups. A similar approach (selecting ca. 814K transversion sites variable between the Altai Neanderthal and Denisovan) was proposed by Skoglund et al. [6]. A SNP capture reagent rely- ing on this principle, the myBaits Expert Human Affinities Kit “Ancestral 850K” module, became available in 2021 from Daicel Arbor Biosciences (https://arborbiosci.com/genomics/ targeted-sequencing/mybaits/mybaits-expert/mybaits-expert-human-affinities/). This module targets approximately 850K biallelic transversion SNPs (autosomal and X-chromosomal) ascertained as polymorphic in the group composed of high-coverage archaic human genomes: the Altai [32], Vindija [33], and Chagyrskaya Neanderthals [34], as well as the “Denisova 3” Denisovan genome [35]. This set of variable sites was shown to yield nearly unbiased FST PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 3 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes values for pairs composed of an African and a non-African group within the Simons Genome Diversity Panel (SGDP) dataset [36], in contrast to the 1240K panel (see a technical note on manufacturer’s website: https://arborbiosci.com/wp-content/uploads/2021/03/Skoglund_ Ancestral_850K_Panel_Design.pdf). These recommendations are motivated by a theoretical property of f-statistics: if a SNP is the result of a single historical mutation and there has not been natural selection, the statistics are expected to be unbiased if SNPs are either unascertained or ascertained as polymorphic in a population that is an outgroup for all populations being analyzed [16,37], and the results in Bergstro¨m et al. [30] and in the technical note published on the Daicel Arbor Biosciences product page are consistent with this theoretical property of outgroup ascertainment since in these studies SNPs were ascertained on archaic humans, but only AMH populations were ana- lyzed. The problematic case is non-outgroup ascertainment, that is ascertainment on a popula- tion that is co-analyzed with others. A series of papers explored non-outgroup ascertainment affecting measures of population divergence on simulated data and real data for humans and domestic animals [37–47]. However, D- and f-statistics which have more robustness than other allele frequency-based statistics in many cases [16], were not considered in those studies. Limited exploration of non-outgroup ascertainment schemes was performed on simulated data in publications introducing the D- and f-statistics, with the conclusion that biases are not noticeable in practice [16,25]. A limitation of those initial studies was that they focused on the robustness of formal tests of admixture such as f4-symmetry tests, and did not consider the effects of ascertainment on statistics expected to have non-zero values, which are heavily used in methods that fit proposed topologies of population relationships to data, whether these are full topologies such as the admixture graphs fitted with the qpGraph software [16,23], or par- tially specified topologies as fitted with the qpAdm software [27,28]. Existing recommendations for a bias-free SNP enrichment panel also rely on the assump- tion that archaic humans are nearly perfect outgroups with respect to all AMH, and the expec- tation that the low-level archaic admixture in non-Africans [24,48] subsequently carried back into Africa to a small extent [33,49] does not contribute substantial bias. But evidence is accu- mulating in favor of long-lasting population structure in Africa or introgression from an unsampled deeply-diverging archaic group to a common ancestor of AMH [50–55], and it remains unclear how this complex demographic history affects the performance of archaic ascertainment. Moreover, for outgroup ascertainment to be unbiased from the theoretical per- spective, the outgroup (or a closely related population) should not be then co-analyzed with other populations [16,37], and the individuals used for ascertainment should not be used as sole representatives of the respective groups. However, given the paucity of high-coverage archaic genomes [32–35] and the usefulness of archaic or African outgroups for calculation of f4-and D-statistics and for constraining search spaces of admixture graph topologies [23], these recommendations are often ignored in published f-statistic, qpAdm, qpGraph, and TreeMix analyses (e.g., [4,6,9,12, 56–58]). For instance, archaic individuals are co-analyzed with anatomically modern humans on archaic-ascertained SNPs [4,6] or a Yoruba group is co-ana- lyzed with non-Africans on Yoruba-ascertained SNPs [56,57]. Since outgroup ascertainment that is “clean” from the theoretical point of view is rarely used in practice, and since the statistical power of outgroup ascertainment to reject incorrect models of population history was not investigated, it is reasonable to examine the performance of archaic ascertainment and common SNP panels such as 1240K in situations that are often encountered in practice. A technical development important for the work reported here is the ADMIXTOOLS 2 package [23], which extends the functionality of the original ADMIXTOOLS package [16], enabling bootstrap resampling for most tools and a rapid algorithm for finding optima in complex admixture graph topology spaces. The ADMIXTOOLS 2 package also PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 4 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes makes calculating millions of f4-statistics and fitting tens of thousands of admixture graphs to data a routine task. These developments, taken together, allow us to explore biased f-statistics more systematically and provide more informed guidelines for future studies. Admixture graphs (fitted to all f-statistics for a selected group of populations) are simplified demographic models that represent population history as a bifurcating tree with few pulse-like two-way admixture events “mapped” onto it, and with no parameters specifying effective pop- ulation sizes or explicit dates. The framework was introduced by Patterson et al. [16] for check- ing if a complex historical model inferred from individual f-statistics and other methods fits the totality of f-statistic data (see also mathematical definitions in [21]). Another series of methods based on f-statistics or very similar data (TreeMix [59,60], MixMapper [61], miqo- Graph [62], AdmixtureBayes [63], and findGraphs [23]) aims at finding a best-fitting admix- ture graph (or several graphs) by automated exploration of the topology space, with resulting models often considered as approximations of the true population history (see a review in [23]). As demonstrated by Maier et al. [23], the latter approach is deeply problematic: f-statis- tics do not constrain even moderately complex topology spaces (e.g., graphs including 8–9 groups and 4–5 admixture events) well enough, and topologically diverse graphs often fit the data significantly better than true simulated histories. However, the former approach, i.e., admixture graph fitting as an easy sanity check of complex historical scenarios, remains a valid use of the method (e.g., [64–66]). Since absolute quality of model fit and relative fits of alterna- tive topologies are crucial for this method, it is worth exploring if ascertainment bias affects fits of topologically diverse admixture graphs to f-statistic data. Results 1. Empirical analyses: exploration of the effect of ascertainment bias on real data We assembled a set of diploid autosomal genotype calls for 352 individuals (S1 Table) sequenced at high coverage [36,67], including mostly present-day individuals from the Simons Genome Diversity Project (SGDP), several high-coverage ancient genomes with diploid geno- type calls [68,69], and three archaic human genomes: the “Denisova 3” Denisovan [35], Vin- dija [33] and Altai Neanderthals [32]. Relying on this "SGDP+archaic" dataset, we explored a wide array of ascertainment schemes: 1) A/T and G/C SNPs (henceforth “AT/GC”) that are, unlike the other mutation classes, unaffected by biased gene conversion [70], and are also unaf- fected by deamination ancient DNA damage; 2) random thinning of the unascertained or "AT/GC" sets down to the size approximately equal to that of the 1240K SNP panel if missing data are not allowed on a given population set; 3) the 1240K panel [14]; 4) the 1000K panel composed of 997,780 SNPs comprising all transversion polymorphisms found in two African (Yoruba) individuals sequenced to high coverage and transversion polymorphisms found in the Altai Neanderthal genome [14]; 5) the union of the 1000K and 1240K panels termed 2200K [4]; 6) various components of the 1240K panel (the sites included in the Illumina 650Y and/or Human Origins SNP arrays, sites included exclusively in one of them, and the remain- ing sites); 7) the largest Human Origins sub-panels–panel 4 ascertained as sites heterozygous in a single San individual, panel 5 ascertained as sites heterozygous in a single Yoruba individ- ual, their union (panels 4+5), and panel 13 including sites where a randomly chosen San allele is derived relative to the Denisovan [16] (abbreviated as, e.g., “HO panel 4”); 8) all sites poly- morphic in a group uniting three high-coverage archaic genomes: the”Denisova 3” Denisovan, the Altai and Vindija Neanderthals (this ascertainment scheme is abbreviated in this study as “archaic asc.” and is similar to those proposed by Bergstro¨m et al. [30] and in the technical note published on the Daicel Arbor Biosciences product page); 9) transversion sites variable in PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 5 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes the group comprising these three high-coverage archaic genomes (abbreviated as “archaic asc., transv.” or “archaic asc., tv.”); 10) restricting to SNPs that have high minor allele frequency (MAF >5%) in the whole "SGDP+archaic" dataset, i.e. high global MAF (abbreviated as “global MAF”); 11) restricting to SNPs having high global MAF combined with taking A/T and G/C SNPs only (abbreviated as “AT/GC, global MAF”); 12) restricting to SNPs that have >5% MAF in a selected African or non-African continental meta-population (abbreviated as, e.g., “AFR MAF”), irrespective of their frequency in the other meta-populations (there are nine such meta-populations in our dataset, and thus nine different ascertainments, see S1 Table); 13) restricting to SNPs that have >5% MAF in a selected continental meta-population, A/T and G/C SNPs only (abbreviated as, e.g., “AT/GC, AFR MAF”). For a list of SGDP-derived SNP sets explored in this study and their sizes in terms of groups, individuals, and SNPs see S2 Table. Although this list is surely not exhaustive, it includes all ascertainment schemes most popular in archaeogenetic publications. To investigate the influence of SNP ascertainment on the ranking of admixture graph models according to their fits to data, we analyzed real data, considering sets of five populations, and as a way of evaluating the effect of SNP ascertainment on the ability to discriminate among differ- ent topologies, tested their fit to all possible admixture graph topologies with two admixture events (32,745 distinct topologies with no fixed outgroup; we considered graphs of this com- plexity as it was unfeasible to work with exhaustive collections of more complex graphs). First, we explored such exhaustive collections of admixture graphs for three combinations of groups (Fig 1). Residuals of admixture graph model fits on all sites, on a random subset of them approximately equal to the size of the 1240K set, and on AT/GC sites, are tightly corre- lated (Pearson’s r approaches 1). Residuals of admixture graph models restricted to non-Afri- cans only are also highly correlated on all sites and 1240K sites (r = 0.95–0.99, Fig 1). In contrast, the worst f4-statistic residuals (WR) for graph models including one archaic human, three African groups, and one African group with ca. 60% of non-African ancestry [67] are poorly correlated on all sites and 1240K sites (r = 0.31–0.35). WR, also referred to as “admix- ture graph Z-score”, is one of two key metrics of admixture graph fit used in this study: it is the Z-score measuring deviation between observed and expected values of an f4-statistic that is predicted most poorly by the admixture graph being tested [23,26]. WR is measured in stan- dard error (SE) intervals, and, by convention, admixture graphs with WR below 3 SE are con- sidered to fit the data well (see, e.g., [68,71–74]). Thus, WR is typically used in the literature to assess absolute fit of admixture graph models to data (e.g., [68,71–75]), and it is used for model ranking in some cases (strictly speaking, WR is just an approximation of absolute model fit, which is hard to calculate since many f-statistics for a given population set are correlated [23]). Log-likelihood score (LL score or simply LL) is another metric that is dependent on deviations of all f-statistics (for populations included in the model) from their predicted values and on their covariance, and thus more accurately reflects model fit to data [23,71]. However, unlike WR measured in SE units, LL is not easily comparable across admixture graph complexity clas- ses, population sets, and SNP sets (but comparable across topologies of the same complexity on the same set of SNPs and populations), and thus WR is used as the primary admixture graph fit metric in this study. These results show that admixture graph fit rankings are severely affected by the 1240K ascertainment if certain population combinations are involved. We considered the possibility that this case of poor correlation was driven by admixture graph topologies that were obviously inconsistent with the data–that is, topologies that could be shown to be inconsistent with the data based on gold standard SNP sets without ascertainment bias. However, the lack of strong correlation for some combinations of populations is not just driven by graphs with poor fits to the data. For example, WRs of admixture graphs fitting the data well (WR <2.5 SE) on a PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 6 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Fig 1. Scatterplots illustrating the effects of the 1240K ascertainment on worst f4-statistic residuals of admixture graphs (WR), explored on exhaustive collections of simple graphs. Five thousand best-fitting graphs (according to log-likelihood scores on all sites) of 32,745 possible graphs were selected for each combination of five populations, and correlation of WRs was explored for graphs fitted on all sites and on ascertained datasets. WR, also known as admixture graph Z-score, is the residual of an f4-statistic that is fitted the worst by the admixture graph model. Log-likelihood score of an admixture graph model (LL [23]) reflects deviation of all relevant f-statistics from their values predicted under the model and their covariance. Results are shown for three population combinations indicated in plot titles. On x-axes results for all sites or for a randomly thinned site set are shown. Results for the 1240K ascertainment are shown in the upper row on the y-axes, and results for AT/GC sites are shown in the lower row on the y-axes. Linear trends fitted to the plotted points are shown in blue, along with Pearson correlation coefficient. https://doi.org/10.1371/journal.pgen.1010931.g001 random subsample of 840,000 sites range from nearly 0 SE to about 10 SE on ca. 845,000 sites included in the 1240K panel (Fig 1). Rejecting a model that fits on unascertained data runs the risk of rejecting the true model, as we show on simulated data in the next section. The converse problem also applies: some admixture graphs are well-fitting (WR <2.5 SE) on the 1240K sites but fit a random sample of sites poorly (WR >5 SE, Fig 1). Next, we explored the same exhaustive set of admixture graph topologies including five groups and two admixture events on the wider collection of ascertainment schemes listed above and on a larger collection of populations. Twelve combinations of five groups including up to two archaic humans, up to five African groups, and up to five non-African groups were tested. In S1 Fig we compare various ways of looking at the effects of ascertainment on admix- ture graph fits, using the population quintuplet "Denisovan, Khomani San, Mbuti, Dinka, Mursi" as an example. In Table 1 we focus on the fraction of all graph topologies tested that are considered poorly-fitting under ascertainment (WR >3 SE) but well-fitting on all sites (WR <3 SE) as a metric appropriate for approximately quantifying the most serious effects of ascer- tainment bias, namely the probability of rejecting the true model. In the supplementary mate- rials, we also show alternative ways of quantifying ascertainment bias: a metric reflecting the statistical power of ascertainment, namely the fraction of all graph topologies tested that are PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 7 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes h t o b s c i r t e m r e b m u n r e b m u n d e s a i b f o d e s a i b f o , s t e s . p o p s t e s . p o p 0 4 3 4 6 3 2 4 6 4 8 1 2 1 0 3 2 3 2 1 1 1 1 0 2 0 1 1 ) d e u n i t n o C ( % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 3 5 . 5 % 7 9 . 7 % 0 0 0 . % 2 0 0 . 4 1 8 7 8 , 7 9 5 2 7 , 5 r f a n ; 1 r f a n n o i t a l u p o p 5 s t e l p u t n i u q o n g n i w o h s d e s a i b * s t l u s e r , o u L , B n i u o d e B , n a i n a d r o J , n a i s a h k b A n a i n i d r a S 2 , i t u b M , a k a i B , K B L n a i n a r I , 4 r f a ; 5 r f a 4 r f a n , 1 r f a r f a n , 3 r f a 1 r f a n , 4 r f a , 2 r f a , 1 h c r a 2 r f a n , 1 h c r a , 3 r f a 1 r f a n 4 r f a , 1 h c r a 3 r f a , 2 h c r a n o d e t p e c c a t u b a t a d . c s a n o d e t c e j e r s l e d o m f o e g a t n e c r e p s e t i s l l a % 0 0 0 . % 0 0 0 . % 1 3 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 2 0 0 . % 6 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 3 5 . 5 % 3 5 . 5 % 3 5 . 5 w a g A % 0 0 0 . % 7 9 . 7 % 9 9 . 7 % 7 9 . 7 % 1 0 0 . % 0 0 0 . % 2 0 . 1 % 2 0 0 . % 1 0 0 . % 2 0 0 . % 1 0 0 . % 0 0 0 . , 0 4 8 7 5 7 1 , , 2 4 0 5 0 8 C > < G d n a T > < A C G / T A 8 7 4 6 1 2 , 0 8 6 3 8 1 , 9 3 2 3 6 6 , , 9 2 4 1 0 5 l e n a p K 0 4 2 1 s n o i t a t u m 2 9 2 4 0 3 , , 7 7 2 6 5 2 s e t i s Y 0 5 6 a n m u i l l I s t n e n o p m o c K 0 4 2 1 K 0 4 2 1 , a b m u g N , n a s E , A M S , a y h u L n o o r e m a C , h t r o N n a o h , e t i b a z o M , n a i n i t s e l a P i a s a M h s i n a p S , a b u r o Y n a o h , h t r o N , a k a i B i n a m o h K , i t u b M , n a S , a k n i D i s r u M , i a t l A , a b u r o Y , a k n i D a l a l u B e h t f o P N S l e n a p e h t f o P N S l e n a p t n e m n i a t r e c s a e h t e p y t , n a z d e B u J , i a t l A u J , i a t l A , n a v o s i n e D , n a v o s i n e D e z i s . x a m e z i s . n i m n o s l i a t e d r e h t r u f t n e m n i a t r e c s a Y 0 5 6 a n m u i l l I h t o b s n i g i r O n a m u H d n a n i d e d u l c n i s e t i s o t e v i s u l c x e s e t i s Y 0 5 6 a n m u i l l I % 0 0 0 . % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . 3 9 4 4 9 , 2 6 8 2 5 , a n o d e s a b 4 l e n a p % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 2 . 5 % 0 0 0 . % 2 7 . 0 0 8 1 3 7 , 4 7 6 4 4 , a n o d e s a b 5 l e n a p l a u d i v i d n i a b u r o Y l a u d i v i d n i n a S % 0 0 0 . % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 0 0 0 . 0 6 4 4 5 3 , 2 2 9 4 4 2 , s n i g i r O n a m u H % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 8 8 . 6 % 0 0 0 . % 0 0 0 . 6 4 6 6 6 2 , 9 4 2 1 7 1 , % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 9 9 . 6 % 0 0 0 . % 7 1 0 . % 0 0 0 . % 0 0 0 . 1 0 3 2 9 , 5 5 6 9 8 , 6 9 0 7 6 , 7 5 5 7 6 , o t e v i s u l c x e s e t i s s n i g i r O n a m u H s e t i s r e h t o , K 0 4 2 1 s e t i s a n o d e s a b 3 1 l e n a p n a m u H d n a l a u d i v i d n i n a S ) O H ( s n i g i r O n a v o s i n e D s l e n a p % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 1 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 9 8 . 6 % 0 0 0 . % 0 0 0 . % 2 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 2 0 0 . 6 2 1 7 5 1 , 1 0 7 6 4 , 5 d n a 4 s l e n a p 5 7 7 0 9 5 , , 9 7 0 4 6 3 , 8 5 7 0 9 1 1 , 5 1 9 4 1 8 , n i d n a . d n i a b u r o Y 2 n i s n o i s r e v s n a r t . d n a e N i a t l A : K 0 0 0 1 K 0 0 0 1 = l e n a p K 0 4 2 1 + l e n a p K 0 0 2 2 l e n a p & K 0 0 0 1 K 0 0 2 2 - r e c s a r e d n u d e t c e j e r e r a t a h t s e i g o l o p o t h p a r g e r u t x i m d a e l b i s s o p l l a f o n o i t c a r f e h t s a d e s s e s s a d n a s t e l p u t n i u q n o i t a l u p o p 2 1 s s o r c a d e r o l p x e s e m e h c s t n e m n i a t r e c s a f o e c n a m r o f r e P . 1 e l b a T d e s a i b r o d e s a i b n u s e c u d o r p e m e h c s t n e m n i a t r e c s a n a f i e n m r e t e d o t i r e i f i s s a l c y r a n i b e h t d e i l p p a o s l a e W . ) E S 3 < R W h t i w l l e w t i f ( s e t i s l l a n o d e t p e c c a t u b ) E S 3 > R W h t i w y l r o o p t i f ( t n e m n i a t e r a , r e w o p l a c i t s i t a t s w o l d n a s a i b h t o b y b r o , s a i b y b d e t c e f f a s e m e h c s t n e m n i a t r e c s a r o s t e l p u t n i u q n o i t a l u p o p f o s r e b m u n e h T . ) t x e t d e n i l r e d n u d n a d o b n l i d e t h g i l h g i h e r a s e s a c r e t t a l e h t ( s t l u s e r , s e t i s l l a n o d e t p e c c a t u b t n e m n i a t r e c s a r e d n u d e t c e j e r e r a t a h t s e i g o o p o t l f o n o i t c a r f e h t y b d e t a m i x o r p p a s i s a i b f o l e v e l e h T . y l e v i t c e p s e r , s w o r m o t t o b o w t n l i d n a s n m u o c t s o m t h g i r o w t e h t n i n w o h s e h t e v o b a n w o h s s i s t e s n o i t a l u p o p e h t f o n o i t i s o p m o c e h T . s e t i s l l a n o d e t c e j e r t u b t n e m n i a t r e c s a r e d n u d e t p e c c a , a s r e v e c i v , e r a t a h t s e i g o o p o t l f o n o i t c a r f e h t y b d e t a m i x o r p p a s i r e w o p l a c i t s i t a t s d n a - i r f A - n o n , r f a n ; s p u o r g n a c i r f A f o r e b m u n e h t y b d e w o l l o f , s n a c i r f A , r f a ; d e t s e t s l e d o m h p a r g e r u t x i m d a n i s p u o r g c i a h c r a f o r e b m u n e h t y b d e w o l l o f , s n a m u h c i a h c r a , h c r a : y a w d e t a i v e r b b a n a n i e l b a t - n i a t r e c s a o n g n i t a r t s n o m e d ) e t o n t o o f e h t n i d e t s i l ( s t e l p u t n i u q n o i t a l u p o p e v i f r o f s t l u s e r e h T . s p u o r g h c u s f o r e b m u n e h t y b d e w o l l o f , ] 7 6 [ e r u t x i m d a n a c i r f A - n o n l a i t n a t s b u s h t i w s n a c i r f A r o s n a c . e l b a T 2 S e e s , n e k a t e r e w s t e l p u t n i u q n o i t a l u p o p d e z y l a n a e h t h c i h w m o r f s p u o r g f o s n o i t c e l l o c r e g r a l n i c i h p r o m y l o p s e t i s o t d n o p s e r r o c s t n u o c P N S e h T . l n m u o c e n o o t n i d e s p a l l o c e r a s a i b t n e m l . s n m u o c e t a r a p e s n i n w o h s i e r a s e u l a v l a m i x a m d n a l a m n m d n a , s t e s n o i t a l u p o p e h t i s s o r c a y r a v s t n u o c P N S PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 8 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes h t o b s c i r t e m r e b m u n r e b m u n d e s a i b f o d e s a i b f o , s t e s . p o p s t e s . p o p 8 8 2 2 1 3 2 3 2 1 4 2 2 2 1 2 1 2 1 2 1 1 3 1 ) d e u n i t n o C ( 5 r f a n ; 1 r f a n n o i t a l u p o p 5 s t e l p u t n i u q o n g n i w o h s d e s a i b * s t l u s e r , o u L , B n i u o d e B , n a i n a d r o J , n a i s a h k b A n a i n i d r a S 2 , i t u b M , a k a i B , K B L n a i n a r I , 4 r f a ; 5 r f a 4 r f a n , 1 r f a r f a n , 3 r f a 1 r f a n , 4 r f a , 2 r f a , 1 h c r a , n a z d e B u J , i a t l A u J , i a t l A , n a v o s i n e D , n a v o s i n e D e z i s . x a m e z i s . n i m n o s l i a t e d r e h t r u f t n e m n i a t r e c s a 2 r f a n , 1 h c r a , 3 r f a 1 r f a n 4 r f a , 1 h c r a 3 r f a , 2 h c r a n o d e t p e c c a t u b a t a d . c s a n o d e t c e j e r s l e d o m f o e g a t n e c r e p s e t i s l l a ) d e u n i t n o C ( . 1 e l b a T , a b m u g N , n a s E , A M S , a y h u L n o o r e m a C , h t r o N n a o h , e t i b a z o M , n a i n i t s e l a P i a s a M h s i n a p S , a b u r o Y n a o h , h t r o N , a k a i B i n a m o h K , i t u b M , n a S , a k n i D i s r u M , i a t l A , a b u r o Y , a k n i D a l a l u B % 0 0 0 . % 8 4 . 0 1 % 0 0 0 . % 0 0 0 . % 2 0 0 . % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . w a g A % 0 9 . 6 % 8 8 . 6 % 5 9 . 7 % 0 0 0 . % 0 0 0 . % 8 4 . 0 1 5 7 6 4 8 4 , , 9 4 2 5 6 1 % 0 0 0 . , 5 3 3 1 1 5 2 , , 1 0 2 9 2 1 2 , , 1 8 7 5 5 5 1 , , 4 1 0 5 2 5 e h t f o P N S l e n a p e h t f o P N S l e n a p t n e m n i a t r e c s a e h t e p y t % 0 0 0 . % 0 0 0 . % 1 0 0 . % 2 0 0 . % 0 0 0 . % 3 9 . 6 % 0 0 0 . % 3 0 . 1 % 0 0 0 . % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 3 9 . 6 % 0 0 0 . % 4 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 9 1 . 5 % 9 9 . 7 % 0 0 0 . % 2 0 0 . % 0 0 0 . % 0 0 0 . % 1 0 0 . % 4 2 0 . % 0 0 0 . % 7 9 . 7 % 0 0 0 . % 2 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 3 5 . 5 % 9 9 . 7 % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 1 0 0 . % 0 0 0 . % 0 0 0 . % 1 3 0 . % 4 2 0 . % 3 5 . 5 % 0 0 . 8 % 0 0 0 . % 3 0 . 1 % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 7 9 . 7 % 0 0 0 . % 0 0 0 . , 5 7 8 1 3 2 3 , , 6 2 3 0 2 1 3 , , 5 1 7 4 6 7 1 , , 5 7 6 0 5 1 2 , , 0 6 8 0 2 0 2 , , 1 7 5 2 9 1 2 , , 9 1 3 6 0 3 2 , , 0 9 3 1 9 7 1 , , 4 2 0 5 3 2 2 , , 9 6 7 5 4 0 2 , , 8 0 8 9 0 1 2 , , 7 0 2 3 1 5 1 , , 2 6 2 3 4 8 1 , , 1 3 8 3 2 7 1 , , 6 3 3 5 8 8 1 , , 4 8 8 8 1 0 2 , , 2 2 0 5 1 5 1 , , 9 5 4 8 0 9 1 , h t i w s e t i s g n n i a t e r i F A M l a b o l g % 5 > : r o d e x i m d a n u s n a c i r f A s n a c i r f A - n o n h t i w n i F A M % 5 > s n a c i r e m A e v i t a N n i F A M % 5 > d n a s n a i s A l a r t n e C n i F A M % 5 > s n a i r e b i S t s a E n i F A M % 5 > s n a i s A l l a n i F A M % 5 > s n a c i r f A d n a s n o i t i s n a r t s n o i s r e v s n a r t s n o i s r e v s n a r t n i F A M % 5 > s n a e p o r u E n r e t s a E e l d d i M n i F A M % 5 > s p u o r g n i F A M % 5 > d n a s n a u p a P l a n i g i r o b A s n a i l a r t s u A h t u o S n i F A M % 5 > s n a i s A . c s a c i a h c r a F A M PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 9 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes h t o b s c i r t e m r e b m u n r e b m u n d e s a i b f o d e s a i b f o , s t e s . p o p s t e s . p o p 2 1 1 3 3 2 3 3 4 3 1 1 1 2 2 1 1 1 3 1 5 r f a n ; 1 r f a n n o i t a l u p o p 5 s t e l p u t n i u q o n g n i w o h s d e s a i b * s t l u s e r , o u L , B n i u o d e B , n a i n a d r o J , n a i s a h k b A n a i n i d r a S 2 , i t u b M , a k a i B , K B L n a i n a r I , 4 r f a ; 5 r f a 4 r f a n , 1 r f a r f a n , 3 r f a 1 r f a n , 4 r f a , 2 r f a , 1 h c r a 2 r f a n , 1 h c r a , 3 r f a 1 r f a n 4 r f a , 1 h c r a 3 r f a , 2 h c r a n o d e t p e c c a t u b a t a d . c s a n o d e t c e j e r s l e d o m f o e g a t n e c r e p s e t i s l l a ) d e u n i t n o C ( . 1 e l b a T % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 3 9 . 6 % 1 0 0 . % 0 0 0 . 0 7 0 0 7 4 , , 3 5 0 9 1 3 F A M % 5 > , C G / T A s n a c i r f A l l a n i % 0 0 0 . % 0 0 0 . % 2 3 0 . % 5 0 0 . % 1 1 . 6 % 7 9 . 7 % 0 0 0 . % 0 0 0 . 3 1 1 6 6 2 , , 9 3 9 9 2 2 F A M % 5 > , C G / T A s n a c i r e m A e v i t a N n i % 0 0 0 . % 0 0 0 . % 5 0 0 . % 5 2 . 0 % 0 0 0 . % 7 9 . 7 % 0 0 0 . % 0 0 0 . 5 4 2 4 2 3 , , 3 0 1 0 8 2 F A M % 5 > , C G / T A s n a i s A l a r t n e C n i s n a i r e b i S d n a % 0 0 0 . % 0 0 0 . % 2 3 0 . % 0 0 0 . % 0 0 0 . % 9 9 . 7 % 0 0 0 . % 0 0 0 . 7 6 5 4 0 3 , 7 5 8 1 6 2 , F A M % 5 > , C G / T A s n a i s A t s a E n i % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 0 0 0 . 4 4 2 0 3 3 , 3 2 7 5 8 2 , F A M % 5 > , C G / T A s n a e p o r u E n i % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 7 % 0 0 0 . % 0 0 0 . 6 3 5 7 4 3 , , 0 5 4 6 0 3 F A M % 5 > , C G / T A n r e t s a E e l d d i M n i s p u o r g % 0 0 0 . % 0 0 0 . % 2 3 0 . % 4 2 0 . % 3 5 . 5 % 0 0 . 8 % 0 0 0 . % 3 0 . 1 3 9 0 2 7 2 , , 4 2 1 0 3 2 F A M % 5 > , C G / T A d n a s n a u p a P n i l a n i g i r o b A s n a i l a r t s u A % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 5 9 . 6 % 0 0 0 . % 0 0 0 . 6 9 9 6 3 3 , , 9 3 7 9 8 2 F A M % 5 > , C G / T A h t i w d e x i m d a n u s n a c i r f A - n o n s n a c i r f A n i % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 3 9 . 6 % 0 0 0 . % 0 0 0 . 6 0 9 6 8 4 , 2 7 1 9 0 3 , F A M % 5 > , C G / T A % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . % 0 0 0 . w a g A % 5 9 . 7 % 0 0 0 . % 0 0 0 . 7 8 2 8 7 3 , , 6 9 2 3 2 3 C G / T A g n n i a t e r i F A M C G / T A % 5 > h t i w s e t i s : r o F A M l a b o l g 0 * * 4 1 5 0 0 1 0 1 9 9 3 3 1 6 1 2 6 6 t e l p u t n i u q n o i t a l u p o p r e p s t n e m n i a t r e c s a d e s a i b f o r e b m u n e g a r e v a * * d e s a i b f o r e b m u n u t n a B , i n a l u F , a k a L , a k a B , i t u b M ) 1 * s n a i s A h t u o S n i > = . c s a d e s a i b f o r e b m u n = > s c i r t e m h t o b , . c s a , i s r u M , o b g I , a l o k a B , n a S i n a m o h K ) 2 , i k i n e r i S o m i k s E , e e r C , a n a i t i r a K ) 5 o m i k s E , n a y w e p i h C , n a u p a P ) 4 n a i n i d r a S , h s i n n i F , n a k u a N c i d n a l e c I , n a i r a g n u H , n a y a M , a u h c e u Q , n a i l a r t s u A ) 3 h c n e r F , n i g z e L i r a A a n a w s T 1 0 0 t . 1 3 9 0 1 0 1 . n e g p . l a n r u o j / 1 7 3 1 . 0 1 / g r o . i o d / / : s p t t h , a b m u g N , n a s E , A M S , a y h u L n o o r e m a C , h t r o N n a o h , e t i b a z o M , n a i n i t s e l a P i a s a M h s i n a p S , a b u r o Y n a o h , h t r o N , a k a i B i n a m o h K , i t u b M , n a S , a k n i D i s r u M , i a t l A , a b u r o Y , a k n i D a l a l u B e h t f o P N S l e n a p e h t f o P N S l e n a p t n e m n i a t r e c s a e h t e p y t , n a z d e B u J , i a t l A u J , i a t l A , n a v o s i n e D , n a v o s i n e D e z i s . x a m e z i s . n i m n o s l i a t e d r e h t r u f t n e m n i a t r e c s a PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 10 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes well-fitting under a given ascertainment (WR <3 SE) but poorly-fitting on all sites (WR >3 SE) (S3 Table), and squared Pearson correlation coefficient for fits (WR or LL) of admixture graphs on unascertained vs. ascertained data (S2 and S3 Figs, S4 and S5 Tables). Although we recognize that there can be no strict rule for classifying ascertainments into biased and unbiased ones since they form a continuum, for a high-throughput analysis a classi- fier is useful. Moreover, fits of a given admixture graph model vary even in the absence of ascertainment bias, due to random site sampling effects (Figs 2 and S1), as was shown in previ- ous work [23]. In this study, we considered a SNP set biased if a bias metric (such as the frac- tion of topologies fitting poorly under ascertainment but well on all sites) was above (or below, as appropriate) the 2.5th percentile of this metric’s distribution across 200 sets of randomly sampled SNPs equal to the size of the 1240K set for a given population combination (with no missing data allowed at the group level). Inspecting the key metric of ascertainment performance (the fraction of topologies that fit poorly under ascertainment but well on all sites), we found only three site sampling schemes that, following the above-mentioned rule, were classified as unbiased for all the population quintuplets tested: AT/GC, HO panel 4, and the union of HO panels 4 and 5 (Table 1). How- ever, due to the small number of sites in the latter two panels, the union of HO panels 4 and 5, and especially panel 4, lack power to reject admixture graph models as compared to the 1240K panel and to AT/GC, as we show in S3 Table. Thus, the only ascertainment scheme that is problem-free according to both metrics is a random one: taking the A/T and G/C mutation classes. Among the population quintuplets tested, "Altai Neanderthal, Ju\|’hoan North, Biaka, Yor- uba, Agaw" (Figs 2 and S2A) and "Altai Neanderthal, Ju\|’hoan North, Luhya, Palestinian, Span- ish" (S2B Fig) are most susceptible to ascertainment bias (Table 1). A very similar quintuplet "Altai Neanderthal, ancient South African hunter-gatherers, Biaka, Yoruba, Agaw" is encoun- tered within more complex admixture graph models that occupy a central place in Lipson’s et al. [9,12] analyses based on 1240K data (see an investigation of bias affecting the admixture graphs from these studies in S1 Text and S4 and S5 Figs). As explored below on real and simu- lated data, a class of f4-statistics that are strongly affected by non-outgroup SNP ascertainment underlies admixture graphs for both problematic population quintuplets: f4(Africanx, archaic; Africany, non-African). On the other hand, population sets including no archaic human were virtually unbiased (Table 1), but some ascertainment schemes showed limited power to reject admixture graph models in these cases (S3 Table). Archaic ascertainment has been suggested in the literature [6,30] as a way to reduce ascer- tainment bias. However, this approach is guaranteed to work only if the outgroup or a related group is not included itself in admixture graphs or f-statistics, and if individuals used for ascer- tainment are not sole representatives of the respective groups in an analysis. Indeed, we show that archaic ascertainment is biased in the case of the most problematic population quintuplet "Altai Neanderthal, Ju\|’hoan North, Biaka, Yoruba, Agaw" (Table 1); in fact, archaic ascertain- ment is by far the most biased ascertainment approach for population sets including both Neanderthal and Denisovan individuals (Table 1, see also results on simulated data below), and in our analysis it also emerged as the scheme with the lowest statistical power to reject admixture graph models (S3 Table). If we combine both key metrics of ascertainment performance (the fraction of topologies fitting poorly under ascertainment but fitting well on all sites, and the fraction of topologies fit- ting well under ascertainment but poorly on all sites), the 1240K and archaic ascertainments are out-performed by many ascertainment schemes, and most notably by the following: 1) HO panels 4+5; 2) the 2200K panel, which combines various kinds of ascertainment such as the 1240K panel, ascertainment on two Yoruba individuals, and on the Altai Neanderthal [14]; PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 11 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Fig 2. The effect of ascertainment bias on admixture graph fits illustrated on a population combination "Altai Neanderthal, Ju\|’hoan North, Biaka, Yoruba, Agaw". Five thousand best-fitting graphs (according to log-likelihood scores on all sites) of 32,745 possible graphs were selected, and correlation of worst f4-statistic residuals (WR) of admixture graphs was explored for models fitted on all sites and on ascertained sites. Results for ascertainment on variants common in Africans (either those having no detectable West Eurasian ancestry or all Africans in the SGDP dataset) are circled in red. Thirty eight site subsampling schemes were analyzed (see panel a): 1) AT/GC; 2) random thinning of the AT/GC dataset to the 1240K SNP count for a given combination of groups (no missing data allowed at the group level), results for 100 thinned replicates are shown; 3) random thinning of all sites to the 1240K SNP count, results for 100 thinned replicates are shown; 4) the 1240K enrichment panel; 5) major components of the 1240K panel: sites included in the Illumina 650Y and/or Human Origins (HO) SNP arrays, sites included exclusively in one of them, and remaining sites; 6) the 1000K and 2200K SNP panels; 7) archaic ascertainment (either all such sites or transversions only); 8) the largest HO panels (4, 5, 13) or their union (4+5); 9) MAF ascertainment in one of nine continental-scale groups; 10) the same procedure repeated on AT/GC sites. The size of the resulting SNP panels is coded by point size, and the ten broad ascertainment types are coded by color according to the legend. Squared Pearson correlation coefficients (R2) for admixture graph WR on unascertained vs. ascertained data are plotted. The 2.5th WR percentile of all the thinned replicates combined, including those on all sites and AT/GC sites, is marked with the brown line. The area of the plot where ascertainments are considered biased according to this classifier is highlighted in red on the left. Scatterplots illustrating effects of selected ascertainment schemes on WR are shown in panels b–i. Dots on these scatterplots correspond to distinct admixture graph topologies. The corresponding ascertainment schemes are marked with letters b–i in panel a. https://doi.org/10.1371/journal.pgen.1010931.g002 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 12 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes and 3) restricting to variants that are common in the African meta-population in SGDP (abbreviated as “AFR MAF”, S1 Table), optionally followed by removal of all mutation classes except for A/T and G/C (Table 1). Squared Pearson correlation coefficient (R2) for admixture graph WR on unascertained vs. ascertained data is in some cases informative in a way that the fractions of poorly/well-fitting topologies are not. As illustrated in Fig 2, R2 may differ substan- tially across ascertainment schemes while the fractions of topologies fitting poorly under ascer- tainment but well on all sites or vice versa stay nearly constant across most ascertainment schemes (Tables 1 and S3). Considering R2 for admixture graph WR, the AFR MAF scheme emerges as the least biased form of ascertainment (S4 Table). We note that conclusions of this sort are not quantitative since our collection of 12 population quintuplets, although diverse, is just a small sample from the vast set of all possible population combinations. However, explor- ing all possible combinations is infeasible, and we consider our approach to be useful as a prac- tical guide for assessing the performance of SNP ascertainment when admixture graphs including archaic humans, Africans, and non-Africans are fitted to genetic data. 2. Simulation studies confirm the qualitative patterns from exploration of empirical data A major limitation of our empirical analyses of ascertainment bias is that fitting a model with two admixture events is almost certainly inadequate for the histories relating various sets of five popu- lations being analyzed. Thus, it is almost certain that all fitted models will be wrong. When we fit wrong models, we have no guarantee that the (incorrect) admixture graph fit to the data will give the same signal of deviation for different SNP ascertainments. Different SNP ascertainments including random ascertainments will simply be sensitive to different aspects of the deviations between the wrong model and the true history. Thus, while the poor correlation between model fits on all sites and under different SNP ascertainment schemes for combinations of archaic humans, sub-Saharan Africans, and non-Africans is a potential signal of bias in analyses, it is valuable to analyze data where the truth is known, as is the case for simulations, to provide clear evidence that typical ascertainment schemes can cause false-positive inferences about history. Using msprime v.1.1.1 [76], we simulated genetic data (a diploid genome composed of three 100 Mb chromosomes with recombination) that reproduce the FST values (S6A Fig) observed when comparing AMH groups, AMH and archaic humans, and AMH and chimpanzee [77]. First, ten independent simulations were performed under one admixture graph topology (Fig 3) serving as a case study, and then the analysis was expanded to dozens of random topol- ogies (Fig 4). The former admixture graph (Fig 3A) reproduces some known features of the genetic history of anatomically modern and archaic humans, but differs in other respects from the widely accepted model [32,53]. The Neanderthal gene flow to the ancestors of non-Afri- cans (via an unsampled proxy group) was either simulated or omitted. We tested several non-outgroup ascertainment schemes (Fig 3): 1) “HO, 1 panel”, ascertain- ment on heterozygous sites in a randomly selected individual from the “African 2” group (this ascertainment follows the scheme used for generating some of the 12 panels of sites compris- ing the Human Origins SNP array [16]); 2) “HO, 4 panels”, ascertainment on heterozygous sites in four randomly selected individuals, one per each “AMH” group (we consider the resulting SNP set to be qualitatively similar to the whole Human Origins SNP set); 3) archaic ascertainment (sites polymorphic in a group composed of one “Denisovan” individual and one individual per each “Neanderthal” group; the same individuals were subsequently used for calculating f-statistics); 4) “AFR MAF”, that is restricting to sites with MAF >5% in the union of two “African” groups; 5) similar MAF-based ascertainment on two “non-African” groups (“non-AFR MAF”) or 6) on all four “AMH” groups (“global MAF”). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 13 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Fig 3. Exploring the influence of non-outgroup ascertainment on fits of admixture graphs in the case of a single simulated history reproducing some known features of the genetic history of anatomically modern and archaic humans (but differing in other respects from the widely accepted model [53]). Results are presented for two topologies (with or without the Neanderthal to non-African gene flow simulated) and for eight types of SNP sets: 1) 10 sets of randomly selected variable sites matching the average size of the “HO one-panel” set, 500K sites (abbreviated as “subsampled non-asc.”); 2) unascertained sites (on average 5.55M polymorphic sites without missing data at the group level); 3) HO one-panel ascertainment based on the “African 2” group (500K sites on average across simulation iterations); 4) HO four-panel ascertainment, based on randomly selected individuals from four groups (“African 1”, “African 2”, “non-African 1”, and “non-African 2”, 1.34M sites on average); 5) archaic ascertainment (1.05M sites on average); 6) “AFR MAF”, that is restricting to sites with MAF >5% in the union of the “African 1” and “African 2” groups (1.85M sites on average); 7) global MAF ascertainment on the union of the “African 1”, “African 2”, “non-African 1”, and “non-African 2” groups (1.62M sites on average); 8) non-African MAF ascertainment on the union of the “non-African 1” and “non-African 2” groups (1.48M sites on average). (a) The simulated topology, with dates (in generations) shown on the y-axis (for the sake of visual clarity, the axis is not to scale). The Neanderthal to non-African gene flow was simulated either at 0% or at ~2% as shown in the figure. Effective population sizes and population split times are omitted for clarity (see S13 Table). The out-of-Africa bottleneck is marked with a star. (b) Boxplots illustrating the effects of various ascertainment schemes on fits (worst f4-statistic residuals, WR) of the correct admixture graphs. The dashed line on the logarithmic scale marks a WR threshold often used in the literature for classifying models into fitting and non-fitting ones, 3 standard errors. The observation that common PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 14 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes ascertainment schemes consistently produce much higher Z-scores than this threshold provides unambiguous evidence that ascertainment bias can profoundly compromise admixture graph fitting. The topologies fitted to the data are shown beside the boxplots. In the panels on the right, simple graphs including only one archaic lineage are fitted (with “Neanderthal 1” used as an example, but very similar results were obtained for the “Neanderthal 2” and “Denisovan” groups). In the panels on the left, results for the full simulated model fitted to the data are shown. https://doi.org/10.1371/journal.pgen.1010931.g003 Fig 4. Effects of non-outgroup and true outgroup ascertainment on fits of admixture graphs explored on a collection of 80 random simulated histories. The worst f4-statistic residual (WR) of the correct admixture graph was used as a measure of bias. (a) WRs for unascertained data and four ascertainment schemes are summarized with boxplots: 1) HO one-panel; 2) HO four-panel; 3) MAF ascertainment on random sets of four populations (abbreviated as “MAF asc.”); 4) ascertainment on sites polymorphic in random sets of three individuals (one individual sampled per population; abbreviated as “3 groups poly asc.”). HO one-panel ascertainment was performed on various types of simulated populations: on non-outgroup populations (“non-OG groups”) or on more or less drifted phylogenetic outgroups (having effective population sizes of 1,000 or 100,000 diploid individuals, respectively) co-modelled with the other populations (abbreviated as “drifted OG” and “OG”, respectively). The individual that was used for HO one-panel ascertainment either acted as the only representative of its group for model fitting, or the whole group of 10 individuals was included in the fitted graph. Alternatively, the group used for HO one-panel ascertainment was not included in the fitted graph: it was either the more or less drifted outgroup, the true root, or the last common ancestor of non-outgroup populations (“non-OG root”). These details are reflected in the plot labels on the right and on the y-axis. The dashed vertical line corresponds to WR = 3 SE. (b) Correct admixture graphs under HO one-panel ascertainment are guaranteed to be well-fitting (WR < ca. 4 SE) if FST between the whole population sample used for ascertainment vs. the sample at the root of the simulation is below 0.12. (c) DAF spectra (derived allele count in a sample of 20 chromosomes vs. proportion of sites in square root scale) are summarized across simulated non-outgroup populations binned by the level of bias in admixture graph fits (approximated by WR of the true admixture graph) observed when HO ascertainment is performed on the respective population. DAF spectra in populations that are the last common ancestors of non-outgroup populations (abbreviated as “non-OG root”) are shown for comparison. The spectra shown here are based only on sites polymorphic in a sample of 20 chromosomes drawn at the root of the simulation (data for sites that are fixed derived or fixed ancestral are not shown, see complete results in S8 Fig). The boxplots summarize DAF across all the simulated admixture graph topologies, and are binned by derived allele counts from 1 to 19. https://doi.org/10.1371/journal.pgen.1010931.g004 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 15 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes First, we fitted the correct admixture graph as often practiced in the literature (e.g., [9,12,78]): including an “ape” outgroup, only one “archaic” individual, and all “AMH” groups. HO one-panel ascertainment always leads to rejection of the correct model in this case, both in the absence and in the presence of the Neanderthal gene flow to non-Africans, with WR rang- ing from 3.4 to 8.8 SE (Fig 3B). HO four-panel ascertainment is less problematic but led to rejection of the correct model (WR >3 SE) in 9 of 30 cases (in the presence of the Neanderthal gene flow to non-Africans), with WR up to 4.6 SE. Only the archaic and AFR MAF non-out- group ascertainments (in the presence of the Neanderthal gene flow to non-Africans) did not lead to rejection of these simplified graph topologies, known to be correct since we simulated them. However, when the full simulated model (with the Neanderthal gene flow to non-Afri- cans) including the outgroup and three “archaic” lineages is fitted to the data, all non-outgroup ascertainment schemes become problematic, except for the AFR MAF ascertainment (Fig 3B). Next, we moved beyond fitting only one true admixture graph to ascertained data and used the exhaustive approach for exploring the stability of model ranking under ascertainment that was applied to the real data above. All possible graph topologies with two admixture events (32,745) were fitted to population quintuplets of the following composition: “Denisovan or Neanderthal 1 or Neanderthal 2”, “African 1”, “African 2”, “non-African 1”, “non-African 2”. The fractions of all topologies tested that were rejected/accepted under ascertainment but accepted/rejected on all sites (and the bias classifier) were then used to reveal simulation itera- tions and ascertainment schemes that demonstrated biased model fits (S6 Table). When no Neanderthal/non-African gene flow was simulated, only the non-AFR MAF ascertainment emerged as problematic (at least half of simulation iterations for at least one population quin- tuplet were classified as affected by bias) according to the fraction of topologies rejected under ascertainment but accepted on all sites (S6 Table). When the Neanderthal to non-African gene flow was simulated, all ascertainment schemes, except for the HO four-panel ascertainment, emerged as problematic according to the same metric (S6 Table). Summarizing these results on model ranking and on fits of the true model, we note that the HO four-panel ascertainment is relatively problem-free in this case study on one simulated topology, unlike archaic, MAF, and HO one-panel ascertainment, but it still led to rejection of the true model more often than on all sites or on random site subsamples (Fig 3B). Finally, we explored non-outgroup ascertainment schemes that are similar to those pre- sented in Fig 3B but are based on randomly chosen groups (see Methods for details) and were applied to SNP sets resulting from simulated genetic histories in the form of random admix- ture graphs. Graphs of four complexity classes including 9 or 10 populations and 4 or 5 admix- ture events were simulated using msprime v.1.1.1. Only simulations where pairwise FST for groups were in the range characteristic for anatomically modern and archaic humans were selected for further analysis, resulting in 20 random topologies per graph complexity class, each including an outgroup (see examples of the simulated histories and FST distributions in S7 Fig). Fits of the true admixture graph (WR) including an outgroup were compared on all sites and on ascertained SNP sets for each topology and ascertainment iteration (Fig 4A). We note that our simulation setup generated groups sampled at different dates in the past (from 0 to ca. 40,000 generations), and thus widely different levels of genetic drift with respect to the root (Fig 4B). As illustrated by distributions of true admixture graph WRs in Fig 4A, ‘blindly’ ascertaining on individuals or sets of groups randomly sampled across the graph almost guarantees reject- ing the true historical model by a wide margin. Ascertainment on sites polymorphic in ran- domly composed sets of three individuals (one individual per group) and restricting to variants common (MAF >5%) in randomly composed sets of four populations are two forms of ascertainment that are especially problematic (Fig 4A). One-panel and four-panel HO PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 16 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes ascertainments often yield acceptable results (WR <3 SE), although median WR of the true graphs equals 4.6 SE for these ascertainment schemes across all graph topologies and all (non- root and non-outgroup) populations used for ascertainment (Fig 4A). An illuminating result is that FST between the population used for Human Origins-like ascertainment and the root of the simulation influences WR of the true graph: all ascertain- ments with FST < 0.12 produce relatively unbiased fits of true graphs (WR <4 SE, see Fig 4B). In other words, ascertainment on heterozygous sites in a single individual taken from a popu- lation that is not an outgroup and is co-analyzed with other populations, but is relatively undrifted genetically compared to the root of the simulation, is unbiased, unlike ascertainment on a single individual from a more drifted population. We directly illustrate this effect by com- paring results on outgroups co-analyzed with other populations that are more or less drifted with respect to the root (with effective population sizes differing by two orders of magnitude) (Fig 4A and 4B). We also show that co-analyzing the individual used for ascertainment with other groups does not exacerbate bias if that individual is a part of a wider population of 10 individuals. However, if that individual is the only representative of its group for model fitting, WRs are inflated drastically (Fig 4A). We finally illustrate the difference between ascertain- ment on an outgroup that is not co-modelled with the other groups (true outgroup ascertain- ment) and ascertainment on a phylogenetic outgroup that is included in the fitted model (Fig 4A). The former ascertainment is indeed unbiased even for highly drifted outgroups (Fig 4A), while the latter is not [16,37]. Another way of looking at this phenomenon is through derived allele frequency (DAF) spectra. Ascertainment schemes resulting in relatively unbiased fits of true models (WR <4 SE, Fig 4C) are most often based on populations where the DAF spectrum of sites that were polymorphic at the root is preserved relatively well (see a full version of this plot in S8 Fig). We note that some ascertainments may be unbiased with respect to the true graph but may have low power to reject incorrect admixture graph models due to the paucity of sites with high MAF in “present-day” populations. Indeed, ascertainments on the root itself or on groups genetically close to the root (such as outgroups with a large effective size) are unbiased (Fig 4A and 4B), but on average demonstrated lower power to reject incorrect models as compared to HO one-panel ascertainments on more drifted groups (S9 Fig). Summarizing these observa- tions on a range of random simulated histories, we expect that it is difficult to find an ascer- tainment scheme that is optimal (at least for the purpose of admixture graph fitting), that is, demonstrates both low bias when testing the true model and high power to reject incorrect models. Our observations on the real data in the preceding section agree with this expectation. Our results on randomized ascertainment schemes (not to be confused with random site sampling) and simulated histories in the form of random admixture graphs show that ascer- tainment on groups that are highly drifted with respect to the root of the groups being co-ana- lyzed is problematic. Thus, if proper outgroup ascertainment is impractical (if an outgroup shares few polymorphisms with the other populations analyzed, or if an outgroup is needed for constraining the analysis), unascertained or randomly sampled sets of sites should be treated as a gold standard for admixture graph inference. The 1240K ascertainment is much more complex [14,15] than the ascertainment schemes we explored on simulated data, but its effects are possibly intermediate between the effects of a MAF-based ascertainment (since all common SNP panels are more or less depleted for rare variants) and ascertainment on hetero- zygous sites in single individuals from several groups (since approximately half of the 1240K sites are derived from the Human Origins SNP array ascertained this way [14]). Thus, we expect an accurate admixture graph including at least one archaic human, at least two African groups, and at least one non-African group (Fig 3A) to fit the data poorly under the 1240K ascertainment. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 17 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes We also checked if non-outgroup ascertainment could bias the simplest cladality tests in the absence of gene flow. f4-statistics are tests for treeness that are essentially the same [16] as the ABBA-BABA test (D-statistic [24,25]) which was used to detect Neanderthal admixture in non-Africans [24]. A tree of four groups conforming to the f4-statistic (A, B; C, O) was simu- lated using msprime v.1.1.1, with a tree depth of 4,000 generations (S10A Fig). All the groups had a uniform effective population size of 100,000 diploid individuals, except for a 10x to 10,000x size reduction immediately after the A-B divergence (1,999 generations in the past). While the dramatic drop in the effective population size of group A yields a complex shape of the derived allele frequency spectrum in {A, B} [79], two of three ascertainment schemes explored here (HO one-panel and MAF ascertainment, but not removal of the derived end of the allele frequency spectrum; see Methods) increase the noise in the f4-statistic (A, B; C, O), but do not shift the statistic away from its expectation at 0 (S10 Fig). These results confirm an observation by Patterson et al. [16] that in the case of perfect trees non-outgroup SNP ascer- tainment does not lead to false rejection of cladality. However, as demonstrated in Figs 3 and 4, non-outgroup ascertainment is generally problematic in the case of complex demographic histories with multiple admixture events. This is due to biases on f-statistics that have non- zero expected values, which are not relevant to the f4-symmetry test but are very important in admixture graph fitting. 3. An overview of f4-statistic biases caused by non-outgroup ascertainment We explored various classes of f4-statistics exhaustively to obtain a "bird’s-eye view" of ascer- tainment biases that was previously difficult to obtain due to technical challenges in calculating millions of f-statistics [30]. Another motivation for this analysis was the fact that it is infeasible to explore fits of large collections of admixture graphs on thousands of population sets, ascer- tainment schemes and random site subsamples. However, if an exhaustively sampled class of f- statistics is demonstrated to be unbiased, all admixture graph fits based on those statistics are expected to be unbiased too. For this analysis we used residual standard deviation ("residual SE") of a linear trend as a way of measuring correlation between f4-statistic Z-scores on all sites and under ascertain- ment. We found this metric more convenient than the squared Pearson correlation coefficient (R2) since it is expressed in the same units as Z-scores and thus is an intuitive way of represent- ing deviation of f4-statistic sets on ascertained data from those on unascertained data. We note that it reflects both bias introduced by ascertainment and variance generated by random site sampling. In S7 Table and S11 and S12 Figs we show residual SE values for a collection of 27 exhaustively sampled f4-statistic classes and for the large collection of ascertainment schemes introduced in section 1. The f4-statistic classes explored here can be described concisely as African(all SGDP populations)x;archaicy;chimpanzee1, African(unadmixed with West Eura- sians)x;archaicy;Mediterranean/Middle Eastern (abbreviated as Med/ME or ME)z, African (unadmixed with West Eurasians)x;East Asiany(y>0), American/Siberianx;Europeany;Papuanz. Here, x, y, and z stand for the number of groups in the population quadruplet; thus, “African3; East Asian1” would mean three Africans and one East Asian. All possible distinct f4-statistics composed of those "ingredients” were considered. Although this analysis is by no means exhaustive, the chosen classes of f4-statistics underly a very wide array of admixture graph and qpAdm models from the archaeogenetic literature. The effects of SNP ascertainment vary dramatically across the classes of f4-statistics, but ascertainment schemes based on one or two African individuals (HO panels 4, 5, 13, 4 and 5 combined), on the three archaic individuals (either all sites or transversions only), and compo- nents of the 1240K panel such as Illumina 650Y emerged as the worst-performing when results PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 18 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes across all the f4-statistic classes were considered (S7 Table, S11 Fig). Ascertainment schemes based on a global MAF threshold or on a MAF threshold in a single non-African continental meta-population, and the 1000K and 2200K panels are similar in their effects to the 1240K ascertainment (S7 Table, S11 Fig). We recognize that there is a continuum between unbiased and biased ascertainment schemes, and that for nearly all schemes and f4-statistic classes a majority of statistics remain unaffected by ascertainment, but for describing our results in a concise way and for partially factoring out effects of SNP panel size, we applied the criterion similar to that employed above for admixture graph fits: residual SE for an f4-statistic class is higher than the 97.5th percentile across 200 randomly thinned datasets matching the 1240K panel in size. According to this criterion, the 1240K ascertainment is problematic in the case of the following nine f4-statistic classes (S7 Table): 1) f4(Africanw, Africanx; Africany, Africanz), 2) f4(Africanx, Africany; Africanz, Med/ME), 3) f4(Africanx, Africany; Africanz, East Asian), 4) f4(Africanx, Africany; Africanz, archaic), 5) f4(Africanx, Africany; Africanz, chimpanzee), 6) f4(Africanx, archaic; Africany, non-African), 7) f4(Africanx, archaic; Africany, chimpanzee) and f4(Africanx, Africany; archaic, chimpanzee), 8) f4(archaicx, archaicy; archaicz, Med/ME), 9) f4(archaicx, archaicy; archaicz, African), and unproblematic for the remaining 18 classes exhaustively explored in this analysis. Unlike all the other classes explored here (S7 Table, S11 Fig), statistics of the form f4(Africanx, archaic; Africany, non-African) are substantially biased under all types of SNP ascertainment (Fig 5A). The classes f4(Africanx, Africany; Africanz, X) are problematic under all ascertainment schemes except for AFR MAF (S7 Table, see an exam- ple in Fig 5B), and the class f4(Africanw, Africanx; Africany, Africanz) is problematic under all ascertainment schemes except for the 1000K, 2200K, and AFR MAF (S7 Table). Scatterplots underlying these residual SE estimates are also shown in Fig 6 (for some of the most problem- atic classes highlighted above) and in S13–S15 Figs (for all classes). Importantly, the classes of statistics most affected by ascertainment (f4(Africanx, archaic; Africany, non-African), f4(Afri- canx, archaic; Africany, chimpanzee), f4(Africanx, Africany; Africanz, X), and f4(Africanw, Afri- canx; Africany, Africanz)) are often relevant for fitting admixture graph models of African population history (see S1 Text). However, for most classes of f4-statistic Z-scores that were classified as problematic, i.e., if residual SE of a linear trend under ascertainment exceeds resid- ual SE under random site sub-sampling to the size of the 1240K panel, absolute residual SE val- ues are below 1 SE (S7 Table and S11 Fig), and thus these statistics are probably not problematic in practice. If we consider f4-statistics instead of their Z-scores and R2 instead of residual SE, results remain virtually the same (S8 Table). For additional details on f4-statistic classes see S2 Text (and S12–S15 Figs), and for a dissection of effects of ascertainment on few selected f4-statistics see S3 Text (and S9–S12 Tables). In contrast to the f4-statistic classes relevant for modelling African population history, f4-statistics including non-Africans only, or one or two African groups and non-Africans (see an example in Figs 5C and 6), are unproblematic under the 1240K, 2200K, AFR MAF and other MAF-based ascertainments (but demonstrate increased variance due to paucity of sites with high MAF under some other ascertainment types such as HO panels 4 & 5 and archaic ascertainment, S7 Table). The AFR MAF ascertainment (restricting to variants common across 68 African individuals with little or no Holocene-era ancestry derived from West Eurasians, or across 94 African indi- viduals, S1 Table), emerged as the best-performing non-outgroup ascertainment scheme. Unlike the other ascertainment schemes explored in this study, this type of ascertainment demonstrates a bias only in the case of the (Africanx, archaic; Africany, non-African) class of f4-statistics (when only statistics with \|Z\| <15 SE on all sites were considered, S7 Table, S11 Fig). Another class of f4-statistics is biased under this ascertainment scheme when all statistics are considered: f4(non-Africanx, archaic; African, non-Africany) (S11 Fig), and AFR MAF PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 19 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Fig 5. Variance in f4-statistic Z-scores resulting from ascertainment and random site subsampling expressed as residual standard deviation of a linear trend fitted to a scatterplot of Z-scores on unascertained vs. ascertained data (abbreviated as “residual SE” and expressed in the same units as f4-statistic Z-scores). Results are shown for three classes of f4-statistics: f4(Africanx, archaic; Africany, Mediterranean/Middle Eastern), f4(Africanx, Africany; Africanz, archaic), and f4(African, Med/MEx; Med/MEy, Med/MEz). The following abbreviations are used for naming the f4-statistics classes: AFR, African populations; ARCH, archaic human individuals (Neanderthals and Denisovans); Med/ME or ME, Mediterranean and Middle Eastern populations. Results for ascertainment on variants common in Africans (either those having no detectable West Eurasian ancestry according to Fan et al. [67] or on all Africans in the SGDP dataset) are circled in red. Residual SE values for f4-statistic Z-scores lying not far from 0 (absolute Z-scores on all sites < 15) are plotted. The 97.5% percentiles of all the thinned replicates combined, including those on all sites and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 20 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes AT/GC sites, are marked by the brown lines. Size of the SNP panels is coded by point size, and the broad ascertainment types are coded by color according to the legend. Thirty eight ascertainment schemes were explored, identical to those in Fig 2. https://doi.org/10.1371/journal.pgen.1010931.g005 ascertainment is unbiased in the case of the other 25 classes of f4-statistics explored in this study (S7 Table, S11 Fig), which also translates into downstream analyses such as fits of admix- ture graph models (Figs 2 and S2, Tables 1 and S3–S5). Discussion f-statistics [16] form a foundation for a range of methods (qpWave, qpAdm, qpGraph) that are used widely for studying population genetic history of humans and other species (see, for instance, [75,80–82]). Here, we focused on f4-statistics, which are used as standalone tests for cladality [16,19] and underlie the qpAdm method for fitting admixture models [27,28]. The other f-statistics (f2 and f3) can be defined as special cases of f4-statistics [f2(A, B) = f4(A, B; A, B) and f3(A; B, C) = f4(A, B; A, C)], and are subject to the same kinds of biases. The existence of bias in the case of non-outgroup ascertainment was recognized in a publication introducing a suite of methods relying on f-statistics [16], but its effects on large collections of f4-statistics or on fits of diverse admixture graph models were not explored in that study and in subsequent studies. Since usage of ape, archaic or African genomes as outgroups is often unavoidable for Fig 6. Scatterplots illustrating the effects of two ascertainment schemes on Z-scores of f4-statistics of four classes including African (abbreviated as AFR) and/or archaic (ARHC) and/or Mediterranean/Middle Eastern (ME) groups. The f4-statistic classes were selected to represent severe ascertainment bias (leftmost panels), moderate level of bias (two middle panels) and no bias (rightmost panels). The ascertainments selected are 1240K (a SNP enrichment panel most widely used in the archaeogenetic literature) and the new “African MAF” ascertainment scheme proposed in this study to mitigate bias for nearly all f4- statistic classes. For results on other f4-statistic classes see S13 Fig, and results for a wider range of ascertainment schemes are summarized in S11 and S12 Figs. Labels of f4-statistic classes and numbers of statistics plotted are shown above the panels. Instead of individual points, heatmaps illustrating point density are shown. Z-scores on all sites (10 million sites, as indicated on the x-axes) are compared to Z-scores on ascertained datasets on the y-axes. Ascertainment schemes and site counts are shown on the y-axes. All plots include only statistics with absolute Z-scores below 15 on all sites. A linear model fitted to the data and lines representing ± 2 SE are shown in red. Residual standard deviations (residual SE) for those linear trends are shown in each plot in red. https://doi.org/10.1371/journal.pgen.1010931.g006 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 21 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes calculation of f4-and D-statistics and for construction of admixture graph or qpAdm models (e.g., [4,6,9,12,56–58]), unbiased ascertainment on an outgroup that is not co-analyzed with other populations (as illustrated on simulated data in Fig 4A) is uncommon in practice. And frequently used SNP panels such as 1240K were built using very complex forms of non-out- group ascertainment. Therefore, in this study we focused on practical rather than theoretical aspects of the SNP ascertainment bias problem and considered forms of non-outgroup ascer- tainment that are common in the literature on archaeogenetics of humans, including ascer- tainment on a phylogenetic outgroup co-analyzed with other populations. The present analysis showed that f4-statistics of specific types are affected by ascertainment bias. The most striking example we found is a class of statistics f4(Africanx, archaic; Africany, non-African). All statistics in this class are strongly biased in the same direction under the 1240K ascertainment (S14 Fig) and under all other non-random ascertainment schemes explored on real (S7 Table, S11D Fig) and simulated data (S6B Fig). In contrast, all f4-statistic classes we explored including one or two African groups and non-Africans, or non-Africans only, turned out to be unbiased under the 1240K ascertainment (S7 Table, S11 Fig). Thus, numerous studies relying on fitting qpAdm and/or admixture graph models including one African group and various non-Africans are probably minimally affected by ascertainment bias, as we also demonstrated on exhaustive collections of simple admixture graphs for few population sets (Figs 1 and 2, Tables 1 and S3–S5). When these classes of methods are applied to African population history, the situation is different, however. As we demonstrated, the 1240K panel emerges as biased when fits of simple admixture graphs including five African groups or one to two archaic and three to four African groups are considered (Figs 1 and 2, Tables 1 and S3–S5). We also demonstrated that the 1240K ascertainment affects fits of more complex admixture graphs including in all cases chimpanzee and Altai Neanderthal, and also four or six African groups and one or two groups with substantial non-African ancestry (S1 Text, S4 and S5 Figs). We expect fits of many other admixture graphs for Africans beyond those tested in this study to be affected by the 1240K ascertainment since the f4-statistic classes f4(Africanx, archaic; Africany, non-African), f4(Africanx, archaic; Africany, chimpanzee), and f4(Africanx, Africany; Africanz, X) are substantially biased under this ascertainment (S7 Table). These effects were reproduced on simulated data when true simulated graphs including “chim- panzee”, one “archaic” lineage, and several “African” and “non-African” lineages were fitted to the data ascertained in various ways (Fig 3B). In line with theoretical expectations, f4-statistics including AMH groups only are largely unbiased under archaic ascertainment ([6,30], technical note published on the Daicel Arbor Biosciences product page). However, as compared to other SNP panels of similar size, archaic ascertainment increases variance in nearly all f4-statistic classes of the types f4(non-Africanx, non-Africany; non-Africanz, X) and f4(non-Africanw, non-Africanx; non-Africany, non-Afri- canz) (S7 Table, S11–S13 Figs). Increased variance in these cases can be explained by the low information content of an archaically ascertained panel: unlike the other non-random ascer- tainment schemes we tested, archaic ascertainment preserves most sites with nearly fixed ancestral variants and leads to just a moderate enrichment for common variants (DAF between 5% and 95%), especially if DAF is based on non-Africans (S3 Text, S6D Fig, S10 Table). Thus, the archaically ascertained panel includes a relatively small number of variants that are common in AMH and especially in non-Africans (S10 Table), and that increases the noise level. This elevated noise level in f-statistics under archaic ascertainment translates to reduced power to reject admixture graph models based on these f-statistics (Figs 1 and 2, Tables 1 and S3–S5). This effect was also reproduced on simulated data (S9 Fig). If archaic humans are included in an f-statistic or an admixture graph, archaic ascertainment is no lon- ger guaranteed to be unbiased (see Fig 4A and 4B), and indeed due to the existence of the PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 22 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Neanderthal to non-African gene flow it fails to fix the bias affecting the most problematic class of statistics f4(Africanx, archaic; Africany, non-African), as demonstrated on simulated data in S6B and S16 Figs. Many ascertainment schemes such as the 1240K, 2000K, Illumina 650Y panels and MAF- based ascertainment on non-Africans skew average DAF across four populations in an f4-sta- tistic since these panels are enriched for derived variants common in non-Africans vs. Africans and in AMH vs. archaic humans (S3 Text, S10 Table). Overrepresentation of derived variants in certain groups of the quadruplet skews f4-statistics. We conclude that two ascertainment schemes most often used for studies of African population history (1240K and archaic ascer- tainment) are not optimal for various reasons: overrepresentation of derived variants common in non-Africans in the former case and a small number of variants common in AMH in the lat- ter case. We found that there exists a non-outgroup ascertainment scheme that demonstrates a nearly optimal balance of bias and statistical power: restricting to variants that are common in a diverse collection of African groups. This scheme demonstrated a bias only in the case of the f4(Africanx, archaic; Africany, non-African) and f4(non-Africanx, archaic; African, non-Afri- cany) classes of f4-statistics among the 27 classes investigated (S7 Table, S11, S13, and S16 Figs). This scheme does not favor derived variants common in non-Africans and supplies many variants common in both Africans and non-Africans (S10 Table). While for many f4-sta- titic classes and admixture graphs, the difference in performance of the AFR MAF and archaic ascertainment schemes is small (Table 1, S2, S3 and S11 Figs, S3–S5 and S7 Tables), the AFR MAF scheme is applicable when Neanderthals and Denisovans are co-analyzed (S2 and S3 Figs), while archaic ascertainment generates extreme shifts in f4-statistics in this case (Figs 3B and S17; see also S4 Text). The AFR MAF scheme is also effective for analyses focused on non- Africans, demonstrating no elevated noise level typical for archaic ascertainment (S7 and S3 Tables). Thus, the AFR MAF ascertainment is the most widely applicable scheme among those explored in this study. According to our results on collections of admixture graphs (Table 1) and on f4-statistic classes (S7 Table), a similar form of ascertainment, namely combining sites heterozygous in a single San and a single Yoruba individual (HO panels 4 & 5) is also largely unbiased, with the exception of statistics of the form f4(Africanx, archaic; Africany, non-Afri- can). However, this ascertainment is also noisier due to the low number of sites available. We tested several of the panels comprising the Affymetrix Human Origins SNP array (the largest of them), each ascertained as sites heterozygous in a high-coverage human genome from a selected population, since they were proposed to be “clean” forms of ascertainment in the pub- lication where they were introduced [16]. HO panels are rarely used in practice individually because of their small size, which is especially problematic for ancient individuals with high rates of missing data, and our results confirm that this practice is justified. As we demonstrated on simulated data, for a non-outgroup ascertainment to be unbiased it should be based on a population that is genetically close to the root (Fig 4B and 4C) (however, such an ascertainment usually has relatively low statistical power for rejection of incorrect admixture graph models, see S9 Fig). We note that in our analysis on simulated data the group where ascertainment was performed was co-modelled with the other groups, as is often done in practice (especially considering not only identical but also closely related groups). In the light of these results, archaic ascertainment’s sub-optimal performance as a non-outgroup ascertainment is because Denisovans and Neanderthals have probably had a low long-term effective population size [34], and thus are highly drifted with respect to the root. Moreover, it is often unavoidable that individuals used for archaic ascertainment are used as sole represen- tatives of the respective groups analyzed, and that is also problematic (Fig 4A). Africans, in contrast, have had much higher effective population sizes for a long period [34], and we PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 23 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes propose that restricting to variants common in a diverse set of African genomes is much more reliable (than archaic ascertainment or ascertainment on a single African population or indi- vidual) for preserving the spectrum of variants that existed at the root of archaic and anatomi- cally modern humans (see Fig 4C). At the same time, AFR MAF ascertainment supplies enough variants that are common in non-Africans, making it also relatively powerful statisti- cally for analyses focused on non-Africans. An enrichment approach is powerful for large-scale ancient DNA research in Africa due to DNA preservation issues in the hot climate [6]. We did not test a range of allele frequency cut- offs or many alternative sets of individuals for AFR MAF ascertainment, and we do not pro- pose a specific list of sites for a new DNA enrichment panel. However, our results imply that an effective approach for designing such a panel, which would also be useful for human archaeogenetic studies worldwide, would be to combine selection of the A/T and G/C muta- tion types with restricting to variants common in Africa. Frequencies of alleles at A/T and G/C loci are not affected by biased gene conversion (its rate depends on population heterozygosity [70]), these loci are not hypermutable, and are not affected by deamination damage in ancient DNA. As we demonstrated, restricting to A/T and G/C sites does not bias f4-statistics (S7 and S10 Tables, S11 and S15 Figs) or admixture graph fits (Tables 1 and S3–S5). Another reason for taking A/T and G/C sites only is simply reducing the number of sites since enrichment reagents have limited capacity, and this ascertainment scheme with a 5% MAF threshold yields about 1.6 million variable sites on the “SGDP+archaic” dataset. Methods 1. Simulating genetic data 1.1 Simulating the relationships of AMH and archaic humans with msprime v.0.7.4. Twenty-two chromosomes matching the size of the human chromosomes in the hg19 assembly were simulated with a flat recombination rate (2 x 10−8 per nt per generation) and a flat muta- tion rate, 1.25 x 10−8 per nt per generation [83]. The standard coalescent simulation algorithm was used [84], and diploid genomes were assembled from these independently simulated 22 haploid chromosomes. Although this approach does not recapitulate the linkage disequilib- rium pattern in real human genomes, it does not make a difference for simulating allele fre- quencies in deeply divergent groups since chromosome histories become quickly independent in the past [85]. The following groups were simulated: chimpanzee (“Chimp”, one individual sampled at “present” in the simulation time), the Vindija Neanderthal (“Neanderthal”, one individual sampled 2,000 generations or 50,000 years in the past, considering a generation time of 25 years), the high-coverage Denisovan “Denisova 3” (“Denisovan”, one individual sampled 2,000 generations in the past), five African groups (10 individuals per group sampled at “pres- ent”) and three non-African groups (10 individuals per group sampled at “present”). Five clas- ses of simulated topologies are shown in S16B Fig; for a full list of simulation parameters and their values see S13 Table. Only one simulation iteration was performed for each combination of parameters. We applied archaic ascertainment to the simulated data: restricting to sites polymorphic in the group composed of two “archaic” individuals, “Denisovan” and “Neanderthal” (this scheme reproduces the archaic ascertainment applied to real data, the “SGDP+archaic” data- set, in S16 and S17 Figs). For calculating f-statistics on unascertained and ascertained SNP sets, the software package ADMIXTOOLS 2 [23] was used. Since there was no missing data at the group level and all individuals were diploid, we first calculated all possible f2-statistics for 4 Mbp-sized genome blocks (with the “maxmiss = 0”, “adjust_pseudohaploid = FALSE”, and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 24 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes “minac2 = FALSE” settings), and then used them for calculating f4-statistics as linear combina- tions of f2-statistics. This protocol was used for generating the results shown in S4 Text and S16C–S17C Figs. 1.2 Simulating the relationships of AMH and archaic humans with msprime v.1.1.1. More realistic simulations were performed with msprime v.1.1.1 which allows accurate simula- tion of recombination and of multi-chromosome diploid genomes relying on the Wright- Fisher model [76,85]. We simulated three chromosomes (each 100 Mb long) in a diploid genome by specifying a flat recombination rate (2 x 10−8 per nt per generation) along the chro- mosome and a much higher rate at the chromosome boundaries (loge2 or ~0.693 per nt per generation, see https://tskit.dev/msprime/docs/stable/ancestry.html#multiple-chromosomes). A flat mutation rate, 1.25 x 10−8 per nt per generation [83], and the binary mutation model were used. To maintain the correct correlation between chromosomes, the discrete time Wright-Fischer model was used for 25 generations into the past, and deeper in the past the standard coalescent simulation algorithm was used (as recommended by Nelson et al. [85]). The following groups were simulated: chimpanzee (“chimp”, one individual sampled at “present” in the simulation time), the Altai Neanderthal (“Neanderthal 1”, one individual sam- pled 3,790 generations in the past), the Vindija Neanderthal (“Neanderthal 2”, one individual sampled 1,700 generations in the past), the high-coverage Denisovan “Denisova 3” (“Deniso- van”, one individual sampled 1,700 generations in the past), two African groups (“African 1” and “African 2”, 10 individuals per group sampled at “present”) and two non-African groups (“non-African 1” and “non-African 2”, 10 individuals per group sampled at “present”). The topology is shown in Fig 3A; for a full list of simulation parameters see S13 Table. Ten simula- tion iterations were performed for each combination of parameters, and two combinations were tested: with or without the Neanderthal to non-African gene flow. Upon assessing genetic distances across the simulated groups using FST, the following ascer- tainment schemes were applied: 1. “HO, 1 panel”, that is restricting to sites that are heterozygous in a randomly selected indi- vidual from the “African 2” group (this scheme simulates the generation of one Human Origins SNP panel [16]); 2. “HO, 4 panels”, taking heterozygous sites from one randomly selected individual per “AMH” population (“African 1”, “African 2”, “non-African 1”, “non-African 2”) and merg- ing these SNP sets (this scheme simulates the generation of the whole Human Origins SNP array [16]); 3. “AFR MAF”, restricting to sites having high minor allele frequency (>5%) in the union of “African” groups “African 1” and “African 2”; 4. “non-AFR MAF”, restricting to sites having high minor allele frequency (>5%) in the union of “non-African” groups “non-African 1” and “non-African 2” (this scheme simu- lates MAF ascertainment on a non-African continental meta-population); 5. “global MAF”, restricting to sites having high minor allele frequency (>5%) in the union of all “AMH” groups “African 1”, “African 2”, “non-African 1”, and “non-African 2”; 6. archaic ascertainment, restricting to sites polymorphic in the group composed of three “archaic” individuals, “Denisovan”, “Neanderthal 1”, and “Neanderthal 2”. 1.3 Simulating random admixture graphs and simple trees with msprime v.1.1.1. Genetic histories in the form of random admixture graphs were simulated using the msprime PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 25 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes v.1.1.1 settings described above. We simulated histories of four complexity classes: including 8 or 9 sampled non-outgroup populations, one outgroup, and 4 or 5 pulse-like admixture events. Demographic events were separated by date intervals ranging randomly between 1,500 and 8,000 generations, with an upper bound on the tree depth at 40,000 generations. To be more precise, demographic events were not placed in time entirely randomly, but were tied to one or few other events of the same “topological depth” within the graph, as illustrated by examples of the simulated topologies in S7 Fig. The same principle was applied to the sampling dates, which were tied to other demographic events such as divergence and admixture of other popu- lations. The random graph topologies and parameter sets were generated using the random_- sim function from the ADMIXTOOLS 2 package: https://uqrmaie1.github.io/admixtools/ reference/random_sim.html Outgroups facilitate automated exploration of graph topology space. The outgroup branches diverged from the other populations at 40,000 generations in the past and had a large constant effective population size of 100,000 diploid individuals. Other effective population sizes were constant along each edge and were picked randomly from the range of 2,000–40,000 diploid individuals. Admixture proportions for all admixture events varied randomly between 10% and 40%. The root of the simulation and the root of all non-outgroup populations were sampled, and the other branches were sampled at tips exclusively. This setup generates groups sampled at widely different dates in the past (from 0 to ca. 40,000 generations) or, in other words, located at various genetic distances from the root (Fig 4B). The outgroup population was sampled at the “present” of the simulation. Sample sizes for all populations were identical: 10 diploid individuals with no missing data. For subsequent analyses we selected only simulations where pairwise FST for groups were in the range characteristic for anatomically modern and archaic humans (in each simulation there was at least one FST value below 0.15; see S7 Fig). In this way, 20 random topologies were simulated per complexity class. Each topology was simulated only once, and 80 simulations were generated in total (see examples of the topologies and respective FST distributions in S7 Fig). Another set of simulations was prepared with the same topologies and parameters, except for the effective population size on the outgroup branch which was set at 1,000 diploid individ- uals instead of 100,000. The following ascertainment schemes were applied to the outcomes of these randomized simulations: 1) HO one-panel ascertainment (repeated for all simulated groups including the outgroup and root groups, generating 920 ascertained datasets); 2) HO four-panel ascertain- ment (10 random sets of four groups excluding the outgroup and root groups were explored per topology, generating 800 ascertained datasets); 3) ascertainment on sites polymorphic in a group composed of three randomly selected individuals, with only one individual per group considered (10 random sets of three groups excluding the outgroup and root groups were explored per topology, generating 800 ascertained datasets); and 4) MAF ascertainment, that is restricting to sites having MAF >5% in random meta-groups (10 random sets of four groups excluding the outgroup and root groups were explored per topology, generating 800 ascer- tained datasets). Group sets used for each ascertainment were recorded. Genetic distances (FST) were calculated for all populations (including the outgroup and the last common ances- tor of all non-outgroup populations) vs. the root sample (Fig 4B). Alternatively, simple trees were simulated using the msprime v.1.1.1 settings described above. A tree of four groups conforming to the f4-statistic (A, B; C, O) was simulated using msprime v.1.1.1, with a tree depth of 4,000 generations (S10 Fig). All the groups had a uniform effective population size of 100,000 diploid individuals, except for a bottleneck happening immediately after the A-B divergence (at 1,999 generations in the past) and lasting until “pres- ent” in the simulation time. The following bottleneck classes were simulated: no bottleneck PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 26 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes (control), 10x, 100x, 1,000x, and 10,000x reduction in effective population size. For each bot- tleneck class, 20 independent simulations were performed. All the samples were drawn at “present”: sample sizes were 25, 25, 25 and 10 for populations A, B, C and O, respectively (except for the 10,000x bottleneck class since group A included 10 individuals only in that case). Three ascertainment schemes were tested for the simulated trees: 1) HO one-panel ascertainment (repeated for all simulated groups, including group O); 2) restricting to sites having MAF >5% (or 10%, or 2.5%) in the union of groups A and B composed of 50 diploid individuals; and 3) removal of sites with derived allele frequency >95% (or 90%, or 97.5%) in the union of groups A and B. The latter ascertainment scheme was added since the ascertain- ments we tested on real data deplete the derived end of the allele frequency spectrum more than the ancestral end (S10–S12 Tables). For calculating f-statistics and fitting admixture graphs to unascertained and ascertained SNP sets, the ADMIXTOOLS 2 [23] software package was used. Since there was no missing data and all individuals were diploid, we first calculated all possible f2-statistics for 4 Mbp- sized genome blocks (with the “maxmiss = 0”, “adjust_pseudohaploid = FALSE”, and “minac2 = FALSE” settings) and then used them for calculating f4-statistics as linear combina- tions of f2-statistics or for fitting admixture graphs (with the “numstart = 100” and “diag = 0.0001” settings). This calculation protocol was used for generating the results shown in Figs 3 and S6–S10. When true admixture graphs were fitted to ascertained data, full popula- tion samples of 10 individuals were used by default, and in some cases, as indicated in the fig- ure legends, the individual used for ascertainment was used as the only representative of the respective population. Unless stated otherwise, phylogenetic outgroups were included in fitted graphs; in other words, they were co-analyzed with the other groups. For assessing the power of ascertained simulated datasets to reject incorrect admixture graph models, we first generated a set of such incorrect graphs per each simulated topology. For that purpose, an algorithm for finding well-fitting topologies (findGraphs from the ADMIXTOOLS 2 package) was started on unascertained data 300 times, seeded by random graphs containing either the simulated number of admixture events (n, 100 runs), or n-1 events (100 runs), or n+1 events (100 runs). For a list of settings for the findGraphs algorithm see Maier et al. [23]. Thousands of diverse graphs explored by findGraphs in the process of topology optimization were generated in this way for each simulated graph, and 100 poorly-fit- ting graphs were randomly picked from a subset of these graphs having LL scores between 70 and 300. This subset of graphs was then fitted to all ascertained datasets derived from the same simulated admixture graph, and the analysis was repeated for all simulated topologies. 2. Constructing the set of real data We used the cteam-lite dataset described Mallick et al. [36], composed of the full SGDP set (300 high-coverage genomes from present-day populations), the chimpanzee genome (pseudo-haploid genotype calls, see http://hgdownload.cse.ucsc.edu/goldenPath/panTro2/ bigZips/), and the Altai Neanderthal, “Denisova 3” Denisovan, Ust’-Ishim, WHG Loschbour, and LBK Stuttgart ancient genomes (see SI section 3 in Mallick et al. [36]). We supplemented cteam-lite by 44 present-day African genomes sequenced using the SGDP protocols by Fan et al. [67], the Vindija Neanderthal’s genome [33], and the genome of an ancient African for- ager individual I10871 sequenced by Lipson et al. [9] (S1 Table). Sites polymorphic in this set of 352 individuals were extracted from the cteam-lite files of the “hetfa” format using the cpoly tool [36]: alleles were grouped into derived and ancestral (polarized) according to the chim- panzee genome; missing data and heterozygous sites were allowed. For each genome, we used individual base quality masks included in cteam-lite or constructed using the same protocol PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 27 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes for other genomes (Vindija Neanderthal and those sequenced by Fan et al. [67]): minimum base quality was set by default at 1, as recommended by Mallick et al. ([36], see SI section 3 in that study), which discarded lowest-quality regions marked as “0”, “?”, or “N”. The individual I10871 was not included in most analyses in this study (except for the complex admixture graphs in S1 Text, and S4 and S5 Figs) due to its relatively high rate of deamination errors. The resulting dataset prior to missing data removal and ascertainment includes 94,691,841 biallelic autosomal SNPs (S2 Table). To keep the polarity of alleles, all data manipulations and ascertainments were performed using PLINK v.2.0 alpha [86]. For calculating f4-statistics, sets of continental-level meta-populations were selected (e.g., Africans and East Asians or Africans and archaic humans) and then f4-statistics were calculated for all possible combinations of populations in the resulting subset of the “SGDP+archaic” dataset, with no missing data (at the population level) allowed within the selected subset. This was done to avoid potential biases associated with data missing non-randomly across groups. Alternatively, f4-statistics were drawn randomly from a certain class of statistics, and no missing data (at the population level) were allowed in the resulting population quadruplets. 3. Influence of ascertainment on fits of admixture graphs to real data First, we fitted all possible graphs including two admixture events (32,745 distinct topologies with no fixed outgroup) for three combinations of groups: 1) one archaic individual, three African groups, and one African group with substantial West Eurasian-related ancestry (Altai Neanderthal, Ju\|’hoan North, Biaka, Yoruba, and Agaw, respectively); 2) five deeply-divergent ancient and present-day non-African groups (Ust’-Ishim, Papuan, Onge, LBK Stuttgart, Even); and 3) five deeply-divergent present-day non-African groups (Papuan, Onge, Palestin- ian, Even, Mala). These three sets of simple graphs were fitted to all sites, AT/GC sites, and 1240K sites (no missing data were allowed at the group level within these sets of five popula- tions); 5,000 best-fitting models were selected according to LL scores on all sites and WRs of those models were compared across SNP sets (Fig 1). Next, we explored the same exhaustive set of admixture graph topologies including five groups and two admixture events on the wider collection of ascertainment schemes. Twelve combinations of five groups including up to two archaic humans, up to five African groups, and up to five non-African groups were tested. To ensure fair comparison across at least a sub- set of population combinations, as a starting point for generating ascertained site sets we used either 11,706,773 sites (with no missing data at the group level) polymorphic in a set of 48 archaic and African groups composed of 97 individuals in total; or 10,051,585 such sites in a set of 59 archaic, African, European, and Middle Eastern groups composed of 120 individuals in total; or 5,296,653 such sites in a set of 51 Papuan, Native American/Siberian, European, Anatolian, and Caucasian groups composed of 112 individuals in total (S1 and S2 Tables). We examined fits of these collections of admixture graphs to the real data from different perspectives. (1) We considered just 5,000 topologies per each population quintuplet that are best-fitting on the unascertained site set (Figs 1, S1 and S2) or all 32,745 topologies tested (S3 Fig). (2) We also considered alternative admixture graph fit metrics, LL or WR. LL as a fit metric (see left-hand panels in S2 and S3 Figs) is more accurate than WR, but difficult to com- pare across different population sets. Finally, in addition to (squared) Pearson correlation coef- ficient of admixture graph fits on ascertained vs. unascertained data (Figs 1, 2 and S1–S3) as a way of measuring ascertainment bias, we considered the fraction of all possible admixture graph models of a certain complexity that are fitting the data poorly (“rejected”) under ascer- tainment (WR >3 SE) but fitting well (“accepted”) on all sites (WR <3 SE). The fraction of all possible models that are fitting the data well under ascertainment (WR <3 SE) but fitting PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 28 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes poorly on all sites (WR >3 SE) was used to measure the power to reject incorrect admixture graph topologies. 4. Automated inference of fitting admixture graphs on real data The 12-population admixture graph published by Lipson et al. [9] (and later used as a skeleton graph in Lipson et al. [12]) and simpler 7- and 10-population intermediate graphs presented in the former study were revisited by Maier et al. [23], and thousands of alternative well-fitting graphs of the same complexity were found using the find_graphs function from the ADMIX- TOOLS 2 package (https://uqrmaie1.github.io/admixtools/articles/graphs.html). Maier et al. [23] used the 1240K dataset only, and in the current study we re-fitted the admixture graphs found by the algorithm on the 1240K SNP panel to the AT/GC and unascertained datasets derived from the “SGDP+archaic” dataset, and also repeated automated admixture graph inference on these two additional SNP sets. Advantages and pitfalls of automated admixture graph inference are described in detail in Maier et al. [23], along with justifications for the spe- cific protocol used in that study, and here we used the protocols identical to those employed by Maier et al. [23]. We first calculated all possible f2-statistics for 4 Mbp-sized genome blocks (with the “maxmiss = 0”, “adjust_pseudohaploid = FALSE”, and “minac2 = 2” settings, see Maier et al. [23] for details on the settings) and then used them for fitting admixture graphs (with the “numstart = 100” and “diag = 0.0001” settings) and for automated admixture graph inference with the find_graphs function (see the Methods section in Maier et al. [23] for a com- plete list of arguments for this function). Only one topology constraint was used at the graph space exploration step: chimpanzee was assigned as an outgroup. Supporting information S1 Fig. Two alternative approaches for measuring the effect of ascertainment bias on admixture graph fits are illustrated using one population combination, “Denisovan, Kho- mani San, Mbuti, Dinka, Mursi”. 1) residual standard deviation (residual SE) of linear trends and 2) squared Pearson correlation coefficient (R2) for two admixture graph fit metrics (worst f4-statistic residuals, WR, or log-likelihood scores, LL) calculated on unascertained vs. ascer- tained data. Five thousand best-fitting graphs (according to LL on all sites) of 32,745 possible graphs were selected, and correlation of LL (left-hand panels) or WR (right-hand panels) was explored for graphs fitted on all sites and on ascertained datasets. Results for ascertainment on variants common in Africans (either those having no detectable West Eurasian ancestry or all Africans in the SGDP dataset) are circled in red. As a starting point for generating different ascertained datasets, we used 11,706,773 sites (with no missing data at the group level) poly- morphic in a set of 48 archaic and African groups composed of 97 individuals (S1 Table). Thirty-eight site subsampling schemes were explored (see a list in the legend for Fig 2). The size of the resulting SNP panels is coded by point size, and ten broad ascertainment types are coded by color according to the legend in the upper right corner. The 97.5th (in the case of residual SE) or 2.5th (in the case of R2) LL or WR percentiles of all the thinned replicates com- bined, including those on all sites and AT/GC sites, are marked by brown lines. Areas of the plots where ascertainments are considered biased according to these thresholds are highlighted in red on the left-hand side of the plots. Scatterplots illustrating effects of selected ascertain- ment schemes (marked with numbers 1 to 7) on LL or WR are shown in the middle of the fig- ure. Each dot on these scatterplots corresponds to a distinct admixture graph topology. (PDF) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 29 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes S2 Fig. Variance in fits of a collection of simple admixture graphs (five groups and two admixture events) resulting from ascertainment or random site subsampling expressed as squared Pearson correlation coefficient (R2). Five thousand best-fitting graphs (according to log-likelihood scores on all sites) of 32,745 graphs were selected for each combination of popu- lations, and correlation of admixture graph log-likelihood scores (LL) or worst f4-statistic residuals (WR) was explored for graphs fitted to unascertained vs. ascertained datasets. Results are shown for twelve population combinations indicated in plot titles (panels a, b). Results for ascertainment on variants common in Africans (either those having no detectable West Eur- asian ancestry or on all Africans in the SGDP dataset) are circled in red. As a starting point for generating different ascertainments, we used either 11,706,773 sites (with no missing data at the group level) polymorphic in a set of 48 archaic and African groups composed of 97 individ- uals in total, or 10,051,585 such sites in 59 archaic, African, European, and Middle Eastern groups composed of 120 individuals, or 5,296,653 such sites in 51 Papuan, Native American/ Siberian, European, Anatolian, and Caucasian groups composed of 112 individuals (S1 Table). Thirty eight site subsampling schemes were explored (see a list in the legend for Fig 2). The size of the resulting SNP panels is coded by point size, and ten broad ascertainment types are coded by color according to the legends. R2 values for LL are plotted in the left-hand panels, and R2 values for WR are plotted in the right-hand panels. The 2.5th LL or WR percentiles of all the thinned replicates combined, including those on all sites and AT/GC sites, are marked by brown lines. (PDF) S3 Fig. Variance in fits of an exhaustive collection of 32,745 simple admixture graphs (five groups and two admixture events) resulting from ascertainment or random site subsam- pling expressed as squared Pearson correlation coefficient (R2). Correlation of admixture graph log-likelihood scores (LL) or worst f4-statistic residuals (WR) was explored for graphs fitted to unascertained vs. ascertained datasets. Results are shown for twelve population combi- nations indicated in plot titles (panels a, b). Results for ascertainment on variants common in Africans (either those having no detectable West Eurasian ancestry or on all Africans in the SGDP dataset) are circled in red. As a starting point for generating different ascertainments, we used either 11,706,773 sites (with no missing data at the group level) polymorphic in a set of 48 archaic and African groups composed of 97 individuals, or 10,051,585 such sites in 59 archaic, African, European, and Middle Eastern groups composed of 120 individuals, or 5,296,653 such sites in 51 Papuan, Native American/Siberian, European, Anatolian, and Cau- casian groups composed of 112 individuals (S1 Table). Thirty eight site subsampling schemes were explored (see a list in the legend for Fig 2). The size of the resulting SNP panels is coded by point size, and ten broad ascertainment types are coded by color according to the legends. R2 values for LL are plotted in the left-hand panels, and R2 values for WR are plotted in the right-hand panels. The 2.5th LL or WR percentiles of all the thinned replicates combined, including those on all sites and AT/GC sites, are marked by brown lines. (PDF) S4 Fig. Results of a search for optimal admixture graph models based on population sets and admixture graph complexity classes from Lipson et al. [9]. The search for optimal topol- ogies was performed on three datasets: 1240K, AT/GC mutation types, and all sites. (a) The published 10-population model [9] with 8 admixture events is plotted, with parameter esti- mates obtained on the three datasets, and with corresponding worst f4-statistic residuals (WR) and log-likelihood scores (LL) shown above the graphs. Distinct populations are colored along with their ancestral lineages; for instance, the cluster of West African populations is colored in brown. (b) Density plots illustrating fits of ca. 10,000 distinct topologies per dataset (found PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 30 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes with findGraphs [23]) in the LL vs. WR coordinates. Green vertical lines mark median LL of the highest-ranking newly found model fitted to bootstrap replicates of the dataset, and red lines mark 95th percentile of that distribution. Position of the published 10-population model in these coordinates is marked by the cyan dot. (c) Highest-ranking models (according to LL) with 8 admixture events found with findGraphs on each dataset. Populations and ancestral lin- eages are color-coded in the same way as in panel a. WR and LL of these models are shown above the plots. We also compared the fit (i.e., LL) of the highest-ranking newly found model with that of the published model on each dataset relying on a bootstrap resampling approach: comparison of two LL distributions on 500 resampled sets of SNP blocks [23]. For all three SNP sets, the difference in LL between the highest-ranking model found by the automated search and the published model was statistically significant, with empirical two-tailed p-values ranging from <0.002 to 0.032. In other words, it was shown for the three datasets explored that the published model fits significantly worse than the newly found highest-ranking models (however, this does not prove that the newly found models approximate the true population history better, see an analysis in Fig 1 from Maier et al. [23]). (PDF) S5 Fig. Density scatterplots illustrating the effects of the 1240K and AT/GC ascertain- ments on log-likelihood scores, LL (a, c), or worst f4-statistic residuals, WR (b, d), of admixture graphs found using findGraphs on the set of groups from Lipson et al. [9]. (a, b) Between 9,927 and 9,990 unique topologies including 10 groups and 8 admixture events were outcomes of 10,000 independent runs of the findGraphs algorithm on three datasets (shown in three columns). (c, d) Between 779 and 971 unique topologies including 7 groups and 4 admixture events were outcomes of 2,000 independent runs of the findGraphs algorithm on the three datasets. On x-axes LL or WR of admixture graphs fitted to all sites are shown. LL or WR of admixture graphs fitted to the 1240K SNP set are shown in the upper row on the y- axes, and LL or WR of admixture graphs fitted to AT/GC sites are shown in the lower row. Pearson correlation coefficients for these two sets of admixture graph fit metrics are displayed beside each plot in red. The WR threshold used often for fitting models (3 SE) is marked with green vertical and horizontal lines. (PDF) S6 Fig. Effects of ascertainment on f4-statistics explored on the simulated demographic history chosen as a case study. (a) Influence of ascertainment on FST across all population pairs on data simulated with or without the “Neanderthal” admixture in “non-Africans”. Median FST values are shown across 10 simulation iterations. Results for seven types of SNP sets are presented: 1) unascertained sites (on average 5.55M polymorphic sites without missing data); 2) HO one-panel ascertainment, based on the “African 2” group (500K sites on average across simulation iterations); 3) HO four-panel ascertainment (on the “African 1”, “African 2”, “non-African 1”, and “non-African 2” groups, 1.34M sites on average); 4) archaic ascertain- ment (1.05M sites on average); 5) AFR MAF ascertainment, that is restricting to sites with MAF >5% in the union of “African 1” and “African 2” groups (1.66M sites on average); 6) global MAF ascertainment on the union of “African 1”, “African 2”, “non-African 1”, “non- African 2” (2.75M sites on average); 7) non-African MAF ascertainment (1.04M sites on aver- age). (b) Boxplots summarizing various f4-statistics of the form f4(“African 1”, “archaic”; “Afri- can 2”, “non-African”) for the two simulated topologies, on unascertained and ascertained data across 10 simulation runs. f4-statistics are shown on the left and their Z-scores are shown on the right. (c) Boxplots summarizing various f4-statistics of the form f4(“non-African 1 or 2”, “archaic”; “African 1”, “non-African 2 or 1”) for the two simulated topologies, on unascer- tained and ascertained data across 10 simulation runs. f4-statistics are shown on the left and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 31 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes their Z-scores are shown on the right. (d) Simplified derived allele frequency (DAF) spectra for the unascertained and ascertained datasets. DAF was defined on the union of groups “non- African 1” and “non-African 2”, and three allele frequency bins were defined: <5%, > = 5% & < = 95%, >95%. Median site counts across 10 simulation iterations are presented. Similar results on real data are shown in S10 Table. (e and f) Boxplots summarizing DAF-stratified f4- statistics of the form f4(“African 1”, “archaic”; “African 2”, “non-African 1 or 2”) (e) and their Z-scores (f) for the two simulated topologies, on unascertained and ascertained data. In all f- statistic notations, the simulated populations are abbreviated as follows: a1 and a2, “Africans” 1 and 2; c, “chimpanzee”; d, “Denisovan”; n1 and n2, “Neandertals” 1 and 2; na1 and na2, “non-Africans” 1 and 2. (PDF) S7 Fig. Examples of simulated genetic histories in the form of random admixture graphs including 8 (a, b) or 9 (c, d) non-outgroup populations and 4 (a, c) or 5 (b, d) admixture events. One topology per graph complexity class is shown as an example, along with FST values for all population pairs. Time in generations is shown on the y-axis (for visual clarity, points on this axis are not spaced proportionately). Effective population sizes (in diploid individuals) are shown beside each edge. Sampled populations are labelled by letters in squares. Outgroups are not visualized; their divergence was placed at 40,000 generations ago for all the simulated histories, and their effective population size was 100,000 or alternatively 1,000 diploid individ- uals (both series of simulations were prepared). The color gradients have no special meaning and are used for visual clarity only. (PDF) S8 Fig. Derived allele frequency (DAF) spectra (derived allele count in a sample of 20 chro- mosomes vs. proportion of sites) in simulated root, outgroup, and non-outgroup popula- tions grouped according to the level of ascertainment bias. The spectra shown here are based only on sites polymorphic in a sample of 20 chromosomes drawn at the root of the simu- lation. We note that outgroups had an effective population size of 100,000 diploid individuals and were included in the fitted admixture graphs; in other words, they were co-analyzed with the other populations. Populations sampled at branch tips ("non-OG groups”) are binned by worst f4-statistic residual (WR) of the true graph under HO one-panel ascertainment based on that population: 0 to 4, 4 to 10, and 10 to 100 SE. The boxplots summarize DAF across all the simulated admixture graph topologies. DAF bins are shown in three separate panels with dif- ferent y-axis ranges: 0 derived alleles; 1 to 9 and 20 derived alleles; 10 to 19 derived alleles. (PDF) S9 Fig. Assessing the power of ascertained SNP datasets to reject incorrect admixture graphs. A set of 100 topologically diverse poorly-fitting graphs was generated for each simu- lated random topology (see Methods) and fitted to both non-ascertained data and to all SNP sets ascertained using the HO one-panel scheme. (a) Boxplots summarizing distributions of worst f4-statistic residuals (WR) of incorrect graphs fitted to datasets of several types: non- ascertained, ascertained on the outgroup with an effective population size of 100,000 that was co-modelled with the other populations (“OG”), on the root population sample that was not co-modelled with the other populations (“true root”), on the root of all non-outgroup popula- tions that was not co-modelled with the other populations (“non-OG root”), and on non-out- group populations co-modelled with the other populations (“non-OG leaves”). Results for all the simulated topologies were pooled. (b) SNP sets generated by HO one-panel ascertainment on simulated data in the “bias” vs. “power” coordinates. WR of the true simulated graph serves as a measure of ascertainment bias (on the x-axis), and median difference in WRs of 100 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 32 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes poorly-fitting graphs fitted to ascertained and non-ascertained data (on the y-axis) serves as a measure of power to reject incorrect models. Results are shown separately for SNP sets ascer- tained on OG, true root, non-OG root, and non-OG groups. Each dot represents an ascer- tained SNP set. (c) This series of plots is similar to that in panel b, but another measure of statistical power is used: the proportion of graphs which fit non-ascertained data poorly (WR >3 SE) but fit ascertained data well (WR <3 SE). (PDF) S10 Fig. The influence of ascertainment on f4-statistic cladality tests in the case of a simple tree (O, (C, (A, B))) shown in (a). Effective population size was constant across the tree, except for a drop in group A’s size at 1,999 generations in the past: from 100,000 to 10,000, 1,000, 100, or 10 diploid individuals. There was also a set of control simulations without any reduction in effective population size. In panel (b), f4-statistics f4(A, B; C, O) and their Z-scores on 20 non- ascertained simulated datasets per bottleneck class and on datasets ascertained in various ways are summarized with boxplots. The ascertainment schemes are as follows: 1) keeping sites with MAF >5% in the union of groups A and B (abbreviated as “A+B MAF”); 2) keeping sites with derived allele frequency <95% in the union of groups A and B (abbreviated as “A+B DAF”); 3) HO one-panel ascertainment on either group A, B, C, or O (results pooled for all ascertainment groups are shown). Very similar results were obtained for other MAF (2.5%, 10%) and DAF thresholds (90%, 97.5%) used for ascertainment; these results are not shown for brevity. In the case of HO ascertainment, no results are shown for simulations with the smallest effective size of group A since too few SNPs were available due to rapid fixation of variants in the population with the extremely low effective size. (PDF) S11 Fig. Variance in f4-statistic Z-scores resulting from ascertainment and random site subsampling expressed as residual standard deviation of a linear trend fitted to a scatter- plot of Z-scores on unascertained vs. ascertained data (abbreviated as “residual SE” and expressed in the same units as f4-statistic Z-scores). Results for ascertainment on variants common in Africans (either those having no detectable West Eurasian ancestry according to Fan et al. [67] or on all Africans in the SGDP dataset) are circled in red. Results are shown for 27 classes of f4-statistics indicated in plot titles in panels a-e. The following abbreviations are used for naming the f4-statistics classes: AFR, African populations; AMRSIB, Native American and Siberian populations; ARCH, archaic human individuals (Neanderthals and Denisovans); chimp, chimpanzee; EAS, East Asian populations; EUR, European populations; ME, Mediter- ranean and Middle Eastern populations; PAP, Papuan and Australian populations. Residual SE values for f4-statistic Z-scores lying not far from 0 (absolute Z-scores on all sites < 15) are plotted in the right-hand panels, and residual SE values for all Z-scores are plotted in the left- hand panels. The 97.5% percentiles of all the thinned replicates combined, including those on all sites and AT/GC sites, are marked by the brown lines. Most y-axis scales are the same (from 0 to 2.5 SE), except for few cases in panels a, b, c, and d. Size of the resulting SNP panels is coded by point size, and ten broad ascertainment types are coded by color according to the leg- ends. Thirty eight site subsampling schemes were explored (see a list in the legend for Fig 2). (PDF) S12 Fig. Violin plots illustrating the effects of 10 ascertainment schemes on Z-scores of 14 f4-statistic classes that were explored exhaustively: statistics including x African, y archaic, and z Mediterranean/Middle Eastern groups (abbreviated as ME). For brevity, f4-statistic classes are labelled on y-axes as x,y,z. For instance, “2.1.1” stands for all possible f4-statistics including two African, one archaic, and one Mediterranean/Middle Eastern groups. Z-score PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 33 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes difference was calculated as the Z-score on all sites (ca. 10 million sites) minus the Z-score on an ascertained dataset, and the distributions of absolute difference values are visualized. Ascer- tainment types and site counts are shown in plot titles. Only statistics with absolute Z-scores below 15 on all sites were considered for this analysis. (PDF) S13 Fig. Scatterplots illustrating the effects of four ascertainment schemes on Z-scores of f4-statistics of 14 classes including African (abbreviated as AFR) and/or archaic (ARCH) and/or Mediterranean/Middle Eastern groups (ME). The class labels and numbers of statis- tics plotted are shown in the top row of each panel (a, b, and c). Instead of individual points, heatmaps illustrating point density are shown. Z-scores on all sites (ca. 10 million sites, as indi- cated on the x-axes) are compared to Z-scores on ascertained datasets on the y-axes. Ascertain- ment types and site counts are shown on the y-axes. All plots are based only on statistics with absolute Z-scores below 15 on all sites. A linear trend fitted to the data and lines represent- ing ± 2 SE are shown in red. Residual SE values of those linear trends are shown in each plot in red. (PDF) S14 Fig. A scatterplot illustrating the effect of the 1240K ascertainment on Z-scores of f4- statistics f4(Africanx, archaic; Africany, non-African), where any non-African groups except for the Mediterranean and Middle Eastern groups were considered (results for the latter groups are shown in S13 Fig). The following abbreviations are used: AFR, African pop- ulations; ARCH, archaic human individuals (Neanderthals and Denisovans); non-AFR, non- African populations. Instead of individual points, a heatmap illustrating point density is shown. Z-scores on all sites are compared to Z-scores on the 1240K dataset on the y-axis (site counts varied across population quadruplets since no missing data at the group level was allowed in each quadruplet). The plot includes only statistics with absolute Z-scores below 15 on all sites. A linear trend fitted to the data and lines representing ± 2 SE are shown in red. The intercept of the linear trend was set at 0. (PDF) S15 Fig. Scatterplots illustrating the effects of 10 ascertainment schemes on Z-scores (a) or f4-statistics (b) of two classes: 854,358 distinct statistics including up to three African groups and/or up to four East Asian groups, pooled with 749,700 distinct statistics including American/Siberian and/or European and/or Papuan groups. Instead of individual points, heat- maps illustrating point density are shown. Z-scores on all sites (from ca. 5.3 to 15 million sites, as indicated on the x-axes) are compared to Z-scores on ascertained datasets on the y-axes. Ascertainment schemes and site counts are shown in plot titles. The 2nd and 4th rows of plots in each panel represent close-up views on the origin of the plots: they are based on statistics with absolute Z-scores below 15 on all sites. Linear trends fitted to the data and lines represent- ing ± 2 SE are shown in red. Squared Pearson correlation coefficients (R2) for these data are shown on the y-axes. First, linear trends were fitted to the sets of Z-scores or f4-statistics lying near the origin (having absolute Z-score below 15 on all sites), and then the same linear equa- tions were re-fitted to complete sets of Z-scores or f4-statistics. Since we show both f4-statistics and Z-scores side by side here, R2 was used instead of residual SE as a measure of correlation. (PDF) S16 Fig. The effects of ascertaining SNPs polymorphic in archaic humans on real (a) and simulated data (b, c). We focused on two f4-statistic classes that are most strongly affected by any type of ascertainment on real data: f4(Africanx, Neanderthal or Denisovan; Africany, non- African) and f4(non-Africanx, Neanderthal or Denisovan; African, non-Africany). On real PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 34 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes data, statistics from these classes were sampled randomly and were calculated on AT/GC sites and on archaic-ascertained sites (transitions and transversions), using all sites without missing data at the level of each quadruplet (i.e., using the “allsnps = TRUE” or “useallsnps: YES” set- ting). Papuans and Australians were excluded from the pool of AMH groups due to their Deni- sovan ancestry component, which was not simulated; and Africans with substantial non- African ancestry (S1 Table) were also removed to make the distinction between various classes of statistics clearer. Archaic ascertainment was performed either on a group composed of the Altai Neanderthal and Denisovan (panel a, left), or Vindija Neanderthal and Denisovan (panel a, right). A slightly different protocol was used for archaic ascertainment in other parts of this paper since it was performed on a group composed of both Neanderthal individuals and the Denisovan. Graphs illustrating five classes of simulated demographic histories are shown in panel b and scatterplots illustrating the effects of ascertaining SNPs polymorphic in the group composed of one “Neanderthal” and one “Denisovan” individual on genetic data simulated according to those histories are shown in panel c. The same “Neanderthal” and “Denisovan” individuals were used for ascertainment and for calculating f4-statistics, which is a non-opti- mal (Fig 4A) but inevitable approach in practice. On the graphs (b), the following abbrevia- tions are used: Afr., Africans; nAfr., non-Africans; Den., Denisovan; Neand., Neanderthal. Alternative positions of the out-of-Africa bottleneck simulated at 65, 70, or 75 kya (generation time = 25 years) are marked with red dots. Gene flows from ghost unsampled lineages are shown in green, and those from sampled lineages are shown in blue. Divergence or split times are shown in kya, and effective population sizes are not shown for clarity (see S13 Table). In the first sub-panel (b, Model 1) parameters that are the same across all five simulations are shown, and they are omitted from the other sub-panels (b, Models 2-5). Results for 68 simu- lated histories are presented in panel c, and by “history” we assume a combination of simulated admixture graph topology, admixture proportions, effective population sizes, population divergence and bottleneck times. The following key simulation parameters are shown in plot titles for Models 1 through 4: model name, proportion of “super-archaic” or Neanderthal- related admixture in ancestral AMH (“Parc”), proportion of Neanderthal or ghost AMH admixture in non-Africans (“PnAfr”), out-of-Africa bottleneck date in kya (“BN”), diploid effective population size during the out-of-Africa bottleneck (“Ne”). The following additional simulation parameters are shown in plot titles for Model 5: proportion of AMH admixture in the Neanderthal lineage (“Pneand”), proportion of Neanderthal admixture in non-Africans (“P1nAfr”), proportion of “ghost AMH” admixture in non-Africans (“P2nAfr”). (PDF) S17 Fig. The effects of ascertaining SNPs polymorphic in archaic humans on real (a) and simulated data (b, c). We focused on 15 f4-statistic classes that are most strongly affected by archaic ascertainment on simulated data (see a list of classes in the legend for panel a). On real data, statistics from these classes were sampled exhaustively and were calculated on AT/GC sites and on archaic-ascertained sites (transitions and transversions), using all sites without missing data at the level of each quadruplet (i.e., using the “allsnps = TRUE” or “useallsnps: YES” setting). Papuans and Australians were excluded from the pool of AMH groups due to their Denisovan ancestry component, which was not simulated; and Africans with substantial non-African ancestry (S1 Table) were also removed to make the distinction between various classes of statistics clearer. Archaic ascertainment was performed either on a group composed of the Altai Neanderthal and Denisovan (panel a, left), or Vindija Neanderthal and Denisovan (panel a, right). A slightly different protocol was used for archaic ascertainment in other parts of this paper since it was performed on a group composed of both Neanderthals and the Deni- sovan. Graphs illustrating five classes of simulated demographic histories are shown in panel b PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 35 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes and scatterplots illustrating the effects of ascertaining SNPs polymorphic in the group com- posed of one “Neanderthal” and one “Denisovan” individual on genetic data simulated accord- ing to those histories are shown in panel c. The same “Neanderthal” and “Denisovan” individuals were used for ascertainment and for calculating f4-statistics, which is a non-opti- mal (Fig 4A) but inevitable approach in practice. On the graphs (b), the following abbrevia- tions are used: Afr., Africans; nAfr., non-Africans; Den., Denisovan; Neand., Neanderthal. Alternative positions of the out-of-Africa bottleneck simulated at 65, 70, or 75 kya (generation time = 25 years) are marked with red dots. Gene flows from ghost unsampled lineages are shown in green, and those from sampled lineages are shown in blue. Divergence or split times are shown in kya, and effective population sizes are not shown for clarity (see S13 Table). In the first sub-panel (b, Model 1) parameters that are the same across all five simulations are shown, and they are omitted from the other sub-panels (b, Models 2-5). We focused on 15 f4- statistic classes that are most affected by archaic ascertainment on simulated data (see a list of classes in the legends for each panel). Results for 68 simulated histories are presented in panel c, and by “history” we assume a combination of simulated admixture graph topology, admix- ture proportions, effective population sizes, population divergence and bottleneck times. The following key simulation parameters are shown in plot titles for Models 1 through 4: model name, proportion of “super-archaic” or Neanderthal-related admixture in ancestral AMH (“Parc”), proportion of Neanderthal or ghost AMH admixture in non-Africans (“PnAfr”), out- of-Africa bottleneck date in kya (“BN”), diploid effective population size during the out-of- Africa bottleneck (“Ne”). The following additional simulation parameters are shown in plot titles for Model 5: proportion of AMH admixture in the Neanderthal lineage (“Pneand”), pro- portion of Neanderthal admixture in non-Africans (“P1nAfr”), proportion of “ghost AMH” admixture in non-Africans (“P2nAfr”). (PDF) S1 Table. A list of present-day and ancient human genomes analyzed in this study. (XLSX) S2 Table. A list of non-simulated SNP sets (ascertainment schemes) analyzed in this study and their sizes. (XLSX) S3 Table. Performance of ascertainment schemes explored across 12 population quintu- plets and assessed as the fraction of all admixture graph topologies that are accepted under ascertainment (fit well with WR <3 SE) but rejected on all sites (fit poorly with WR >3 SE). We also applied the binary classifier to determine if an ascertainment scheme pro- duces unbiased or biased results (the latter cases are highlighted in bold and underlined text). The numbers of population quintuplets or ascertainment schemes affected by bias (according to this classifier) are shown in the rightmost column and in the bottom row, respectively. The composition of the population sets is shown above the table in an abbreviated way: arch, archaic humans, followed by the number of archaic groups; afr, Africans, followed by the num- ber of African groups; nafr, non-Africans or Africans with substantial non-African admixture [67], followed by the number of such groups. The SNP counts correspond to sites polymorphic in larger collections of groups from which the analyzed population quintuplets were taken, see S2 Table. SNP counts vary across the population sets, and minimal and maximal values are shown in separate columns. (XLSX) S4 Table. Performance of ascertainment schemes explored across 12 population quintu- plets and assessed as squared Pearson correlation coefficient (R2) for worst f4-statistic PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 36 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes residuals (WR) of admixture graphs fitted to unascertained vs. ascertained data, based on 5,000 graphs that are best-fitting according to log-likelihood (LL) scores on all sites, or based on all graphs. We also applied the binary classifier to determine if an ascertainment produces unbiased or biased results (the latter cases are highlighted in bold and underlined text). Median R2 values across all population quintuplets or ascertainment schemes are shown in the second rightmost column and in the bottom row, respectively, and the numbers of pop- ulation quintuplets affected by bias (according to this classifier) are shown in the rightmost column. The composition of the population sets is shown above the table in an abbreviated way: arch, archaic humans, followed by the number of archaic groups; afr, Africans, followed by the number of African groups; nafr, non-Africans or Africans with substantial non-African admixture [67], followed by the number of such groups. The SNP counts correspond to sites polymorphic in larger collections of groups from which the analyzed population quintuplets were taken, see S2 Table. The same results based on LL scores are shown in S5 Table. SNP counts vary across the population sets, and minimal and maximal values are shown in separate columns. (XLSX) S5 Table. Performance of ascertainment schemes explored across 12 population quintu- plets assessed as squared Pearson correlation coefficient (R2) for log-likelihood scores (LL) of admixture graphs fitted to unascertained vs. ascertained data, based on 5,000 graphs that are best-fitting according to log-likelihood (LL) scores on all sites, or based on all graphs. We also applied the binary classifier to determine if an ascertainment produces unbi- ased or biased results (the latter cases are highlighted in bold and underlined text). Median R2 values across all population quintuplets or ascertainment schemes are shown in the second rightmost column and in the bottom row, respectively, and the numbers of population quintu- plets affected by bias (according to this classifier) are shown in the rightmost column. The composition of the population sets is shown above the table in an abbreviated way: arch, archaic humans, followed by the number of archaic groups; afr, Africans, followed by the num- ber of African groups; nafr, non-Africans or Africans with substantial non-African admixture [67], followed by the number of such groups. The SNP counts correspond to sites polymorphic in larger collections of groups from which the analyzed population quintuplets were taken, see S2 Table. The same results based on WR of admixture graphs are shown S4 Table. SNP counts vary across the population sets, and minimal and maximal values are shown in separate col- umns. (XLSX) S6 Table. Performance of ascertainment schemes on simulated data (those shown in Fig 3) explored across three population quintuplets (including either “Denisovan”, or “Neanderthal 1”, or “Neanderthal 2” “archaic” individuals, in addition to the “African 1”, “African 2”, “non-African 1”, and “non-African 2” groups) and assessed as the fraction of all topologies that are rejected under ascertainment (fit poorly with WR >3 SE) but accepted on all sites (fit well with WR <3 SE), or as the fraction of all topologies that are accepted under ascertainment (WR <3 SE) but rejected on all sites (WR >3 SE). We also applied the binary classifier (based on a 10th percentile threshold and 10 random site subsamples matching the average size of the HO one-panel set, 500K sites) to determine if the ascertainment produces unbiased or biased results (the latter cases are highlighted in bold and underlined text). Ten independent simulations with the same parameters were performed, and the following ascer- tainment schemes were explored on each of them: 1) archaic ascertainment (1.05M sites on average across simulation iterations); 2) HO one-panel ascertainment, based on the “African 2” group (500K sites on average); 3) HO four-panel ascertainment (based on the “African 1”, PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 37 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes “African 2”, “non-African 1”, and “non-African 2” groups, 1.34M sites on average); 4) AFR MAF ascertainment, that is restricting to sites with MAF >5% in the union of the “African 1” and “African 2” groups (1.85M sites on average); 5) global MAF ascertainment on the union of the “African 1”, “African 2”, “non-African 1”, and “non-African 2” groups (1.62M sites on average, abbreviated as “AMH MAF”); 6) non-African MAF ascertainment (1.48M sites on average). (XLSX) S7 Table. Performance of ascertainment schemes explored across 27 exhaustively sampled f4-statistic classes and assessed as residual standard deviation of a linear trend fitted to a scatterplot of Z-scores on unascertained vs. ascertained data (abbreviated as “residual SE” and expressed in the same units as f4-statistic Z-scores). Only f4-statistics having absolute Z- scores <15 SE (on all sites) were considered. We also applied the binary classifier to determine if an ascertainment produces unbiased or biased results (the latter cases are highlighted in bold and underlined text). The numbers of f4-statistic classes or ascertainment schemes affected by bias are shown in the rightmost column and in the bottom row, respectively. For each f4-statis- tic class, total number of distinct statistics and the number of statistics having absolute Z- scores <15 SE (on all sites) are shown. The composition of the f4-statistic classes is shown above the table in an abbreviated way: AFR, African populations; AMRSIB, Native American and Siberian populations; ARCH, archaic human individuals (Neanderthals and Denisovans); chimp, chimpanzee; EAS, East Asian populations; EUR, European populations; ME, Mediter- ranean and Middle Eastern populations; PAP, Papuan and Australian populations. The SNP counts correspond to sites polymorphic in larger collections of groups from which the ana- lyzed population quintuplets were taken, see S2 Table. SNP counts vary across the population sets, and minimal and maximal values are shown in separate columns. (XLSX) S8 Table. Performance of ascertainment schemes explored across 27 exhaustively sampled f4-statistic classes and assessed as squared Pearson correlation coefficient (R2) for f4-statis- tics calculated on unascertained vs. ascertained data. Only f4-statistics having absolute Z- scores <15 SE (on all sites) were considered. We also applied the binary classifier to determine if an ascertainment produces unbiased or biased results (the latter cases are highlighted in bold and underlined text). The numbers of f4-statistic classes or ascertainment schemes affected by bias are shown in the rightmost column and in the bottom row, respectively. For each f4-statis- tic class, total number of distinct statistics and the number of statistics having absolute Z- scores <15 SE (on all sites) are shown. The composition of the f4-statistic classes is shown above the table in an abbreviated way: AFR, African populations; AMRSIB, Native American and Siberian populations; ARCH, archaic human individuals (Neanderthals and Denisovans); chimp, chimpanzee; EAS, East Asian populations; EUR, European populations; ME, Mediter- ranean and Middle Eastern populations; PAP, Papuan and Australian populations. The SNP counts correspond to sites polymorphic in larger collections of groups from which the ana- lyzed population quintuplets were taken, see S2 Table. SNP counts vary across the population sets, and minimal and maximal values are shown in separate columns. (XLSX) S9 Table. Values of three selected f4-statistics and corresponding Z-scores across eight ascertainment schemes and on unascertained data (all sites). Five ascertainment schemes featured in S10–S12 Tables are highlighted in bold. (XLSX) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 38 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes S10 Table. Dissecting the statistic f4(Altai Neanderthal, Biaka; Mbuti, Saharawi) belonging to the (archaic, Africanx; Africany, non-African) class. Sites were stratified by DAF in Afri- cans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column. The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z-scores are shown for these frequency bins. (XLSX) S11 Table. Dissecting the statistic f4(Fulani, Juǀʼhoan North; Igbo, Ogiek) composed of four African groups. Sites were stratified by DAF in Africans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z- scores are shown for these frequency bins. (XLSX) S12 Table. Dissecting the statistic f4(Burmese, Dinka; Juǀʼhoan North, Sengwer) composed of three African groups and one East Asian group. Sites were stratified by DAF in Africans or DAF in Europeans into three bins: nearly fixed ancestral (DAF < = 5%), non-fixed (DAF 5–95%), and nearly fixed derived (DAF > = 95%). This was done for unascertained data (all sites), for random ascertainment (AT/GC sites), and for four non-random ascertainment schemes as indicated in the leftmost column. The number and proportion of f4-informative sites falling into each DAF bin are shown. Mean DAF in four populations, mean differences in DAF between populations 1 and 2, populations 3 and 4, mean products of the DAF differences (i.e., f4-statistics) and their Z-scores are shown for these frequency bins. (XLSX) S13 Table. Parameters of the simulated demographic histories that are not shown on the respective graphs in Figs 3A, S16B and S17B: effective population sizes and sampling or divergence dates. (PDF) S1 Text. Effects of ascertainment on real data: fits of complex admixture graphs. (PDF) S2 Text. An overview of f4-statistic biases caused by ascertainment. (PDF) S3 Text. Mechanisms of bias in selected f4-statistics. (PDF) S4 Text. Archaic ascertainment explored on simulated genetic data. (PDF) Acknowledgments The authors are grateful to Mark Lipson and Nick Patterson for discussions. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010931 September 7, 2023 39 / 44 PLOS GENETICSModeling of African population history can be highly biased by common SNP ascertainment schemes Author Contributions Conceptualization: Pavel Flegontov, Robert Maier, David Reich. Data curation: Pavel Flegontov. Formal analysis: Pavel Flegontov, Ulaş Işıldak, Eren Yu¨ncu¨, Piya Changmai. Funding acquisition: Pavel Flegontov, Piya Changmai, David Reich. Investigation: Pavel Flegontov, Ulaş Işıldak. Methodology: Pavel Flegontov, Robert Maier. Resources: David Reich. Software: Ulaş Işıldak, Robert Maier. Supervision: Pavel Flegontov, David Reich. Visualization: Pavel Flegontov, Ulaş Işıldak, Eren Yu¨ncu¨. Writing – original draft: Pavel Flegontov, David Reich. Writing – review & editing: Pavel Flegontov, Ulaş Işıldak, Robert Maier, Eren Yu¨ncu¨, Piya Changmai, David Reich. References 1. Skoglund P, Mathieson I. Ancient genomics of modern humans: The first decade. 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10.1371_journal.pdig.0000438	RESEARCH ARTICLE Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis Jongyun JungID 1, Jingyuan DaiID 1, Bowen LiuID 2, Qing WuID 1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Biomedical Informatics (Dr. Qing Wu, Jongyun Jung, and Jingyuan Dai), College of Medicine, The Ohio State University, Columbus, Ohio, United States of America, 2 Department of Mathematics and Statistics, Division of Computing, Analytics, and Mathematics, School of Science and Engineering (Bowen Liu), University of Missouri-Kansas City, Kansas City, Missouri, United States of America * Qing.Wu@osumc.edu Abstract OPEN ACCESS Citation: Jung J, Dai J, Liu B, Wu Q (2024) Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digit Health 3(1): e0000438. https://doi.org/10.1371/ journal.pdig.0000438 Editor: Martin G. Frasch, University of Washington, UNITED STATES Received: May 3, 2023 Accepted: December 25, 2023 Published: January 30, 2024 Copyright: © 2024 Jung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data generated or analyzed during the study are included in the published paper. Funding: The research and analysis described in the current publication were supported by a grant (R21MD013681 to QW) from the National Institute on Minority Health and Health Disparities and a grant (R01AG080017 to QW) from the National Institute of Aging. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer- reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-anal- ysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible stud- ies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87– 96, p< 0.01) and specificity (90%; 95% CI: 85–93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90–94, p< 0.01; and 91%; 95% CI: 88–93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77–85, p< 0.01; and 83%; 95% CI: 76– 88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90–96, p < 0.01) and specificity (92%; 95% CI: 89–94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicat- ing potential utility in healthcare systems for fracture diagnosis. However, enhanced trans- parency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359). Author summary Artificial Intelligence (AI) is increasingly employed to detect fractures by using various imaging modalities and data types. Our search of Medline (via PubMed), Web of Science, PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 1 / 22 PLOS DIGITAL HEALTHCompeting interests: The authors have declared that no competing interests exist. A systematic review and meta-analysis of AI in fracture detection and IEEE revealed numerous primary studies demonstrating AI’s superior performance in fracture detection. This systematic review and meta-analysis is the first to assess and compare the diagnostic accuracy of AI models across different imaging modalities and data types for various fracture outcomes. We found that AI models achieve high accuracy in fracture detection, particularly with radiograph images. However, we identified signifi- cant flaws in study design and reporting, limiting real-world applicability. Few studies provided patient characteristics, and only half reported the hyperparameter selection pro- cess. Our findings underscore the benefits of using AI models with radiographs for frac- ture detection, as they outperform other imaging modalities. Despite similar results across modalities, inadequate methodology and reporting in AI model evaluations call for improvement. Considering AI’s high diagnostic performance, integrating it into existing fracture risk assessment tools could enhance patient identification and enable early intervention. Introduction Bone fractures represent a significant public health concern globally [1], particularly for indi- viduals with osteoporosis [2]. Fractures contribute to work absences, disability, reduced quality of life, health complications, and increased healthcare costs, affecting individuals, families, and societies [3,4]. A meta-analysis of 113 studies reported the pooled cost of hospital treatment for a hip fracture after 12 months as $10,075, with total health and social care costs amounting to $43,669 per hip fracture [5]. Artificial Intelligence (AI), encompassing Machine Learning (ML) and Deep Learning (DL), has been extensively employed for fracture outcome prediction due to technological advancements and accessibility. Various imaging modalities, including X-rays [6,7], computed tomography (CT) [8,9], and magnetic resonance imaging (MRI) [10,11], have been used in fracture diagnosis and detection. AI can also predict fractures using tabular data, such as elec- tronic medical records (structured patient-level data). However, few studies [12–14] have applied AI with tabular data in fracture prediction despite its growing importance over the past decade. Recent systematic reviews and meta-analyses have reported high accuracy for AI in fracture detection and classification. Kuo et al. [15] summarized 42 studies with 115 contin- gency tables, finding pooled sensitivity of 92% (95% CI: 88, 94) and specificity of 91% (95% CI: 88, 93). Yang et al. [16] reviewed 14 studies on orthopedic fractures, reporting pooled sensitiv- ity and specificity of DL models as 87% (95% CI: 78, 93) and 91% (95% CI: 85, 95), respectively. However, existing systematic review and meta-analysis studies focused solely on image- based analyses, neglecting comprehensive examination of various imaging modalities and data types (image, tabular, or both). Despite the superior performance of AI for medical image anal- ysis and using tabular data, a critical gap exists in the current literature concerning the optimal choice of image modalities and the choice between image, tabular, or combined data types. There is a lack of comprehensive guidance on the most effective selection of image modalities and data types for fracture diagnosis. This gap in knowledge underscores the need for system- atic investigation to determine which image modality, and by extension, which data type, yields the highest diagnostic accuracy and clinical relevance in AL algorithms. Addressing this gap will not only optimize the design of AI-based diagnostic tools but also enable healthcare practitioners to make informed decisions when selecting appropriate imaging modalities and data types for improved patient care. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 2 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Thus, this study primarily aims to evaluate the diagnostic accuracy of AI in fracture detec- tion using diverse imaging modalities and data types, reflecting AI’s growing role in health- care. Additionally, we seek to synthesize current evidence on AI-based fracture detection, offering a concise overview and discerning the strengths and limitations of various data types, whether image, tabular, or combined. Materials and methods Identification and selection of studies This systematic review, registered with PROSPERO (CRD42021240359), follows PRISMA guidelines (S1 PRISMA Checklist) [17]. We searched Medline (via PubMed), Web of Science, and IEEE. The last search was conducted on December 15, 2022, and we manually searched bibliographies, citations, and related articles of included studies. S1 Text lists each search term. Two independent reviewers (JJ and JD) assessed study eligibility, resolving disagree- ments through discussion or involving a third author (BL) if necessary. Eligible studies predicted fracture outcomes using structured patient-level health data (elec- tronic health records and cohort studies data) and image-related data (MRI, DXA, and X-ray). We excluded reviews, gray literature, non-human subject studies, studies without machine learning or deep learning models, fracture outcomes, AUC, accuracy, sensitivity, specificity, validation, and insufficient algorithm development details. We only considered studies pub- lished in English without time restrictions. Data extraction All three categories of data were considered: image-related, tabular, and both. Image-type studies used MRI, DXA, CT, or X-ray; tabular-type studies used structured electronic health records data; image and tabular studies used both data types. Two investigators (JJ and JD) independently evaluated study eligibility, extracting relevant data for articles meeting inclusion criteria. A structured data collection form was used to capture general study characteristics, population, data preprocessing, clinical outcomes, analytical methods, and results. A third author (BL) resolved discrepancies if necessary. We constructed the contingency table (true positive, true negative, false positive, and false negative) based on the provided information of sensitivity, specificity, positive predictive value, and negative predictive value for each study (S4 Table). If the study reported multiple sensitivity and specificity, we used the highest sensi- tivity and specificity. Statistical analysis Meta-analyses were performed using a random-effects model to calculate the pooled sensitivity and specificity based on logit transformation [18,19], using the Clopper-Pearson interval to calculate 95% confidence intervals for each study [20]. We used a unified hierarchical sum- mary receiver operating characteristic curve (HSROC) to investigate the relationship between logit-transformed sensitivity and specificity. We calculated the diagnostic odds ratio and used inverse variance weighting for pooling with random effect models [21]. Sensitivity analysis The logit transformation does not consider the correlation between sensitivity, specificity, and threshold effects; another model is desired to capture this missing part. Barendregt et al. [22] recommend using the Freeman-Tukey double arcsine transformation instead of the logit PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 3 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection transformation. Hence, we used the Freeman-Tukey double arcsine transformation as a sensi- tivity analysis [22] for a random-effects model. Subgroup analysis Two subgroup analyses were conducted: 1) three data types (images, tabular, or images and tabular) and 2) different image modalities among image data used in AI. Statistical analysis was performed using R [23], with ‘meta’ [24] and ‘mada’ [25] packages. A p-value of < 0.05 was considered statistically significant. Publication bias We utilized the contour-enhanced funnel plot [26] to illustrate the assessment of publication bias for each fracture outcome and data type used. Each data point in the contour-enhanced funnel plot represents an individual study, and the plot incorporates contour lines that delin- eate expected areas of symmetry in the absence of bias. The plot provides insights into poten- tial publication bias, with asymmetry suggesting a deviation from expected publication patterns. We employed the trim-and-fill method to address publication bias [22] further. This statistical approach helps adjust for the potential missing studies due to publication bias by imputing hypothetical “filled” studies and recalculating the effect size accordingly. Risk of bias and applicability Two reviewers (JJ and JD) independently evaluated the risk of bias in each study using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) [27], assessing four domains: patient selection, index test, reference standard, and flow and timing. The risk of applicability was evaluated with the first three domains. Results Study selection and characteristics Our search identified 1,128 studies, yielding 717 unique ones after removing duplicates (Fig 1). We screened titles and abstracts and selected 496 studies for full-text review based on our inclusion criteria. We then excluded 254 studies for lacking sensitivity and specificity informa- tion (149 studies), not having fracture-related outcomes (75 studies), not using ML models (28 studies), or being survey or review articles (2 studies). We further removed 176 studies because no contingency table could be calculated from the provided information. Ultimately, 66 stud- ies were included in our systematic review and meta-analysis. The selected studies were published between 2007 and 2022, with 73% (48 studies) pub- lished in the last three years (Table 1). The studies were conducted in various countries, including Asian countries (26 studies) [6,9,11,28–50], North American countries (19 stud- ies) [14,34,36,51–66], European countries (14 studies) [13,59,67–78], Australia (1 study) [79] and Brazil (2 studies) [10,80] (Table 1). Four studies did not provide the country infor- mation [81–84]. Fracture identification was performed using imaging-related data in 54 studies, tabular data in nine studies, and imaging and tabular data in three. Of the 57 studies using imaging-related and combined data, 33 analyzed radiograph images [6,7,28–31,35–38,40–42,45,47–49,52– 57,59,61,62,66–68,72–74,78], 12 analyzed computed tomography (CT) images [8,9,39,43,50,63,65,69,75,81–83], and the remaining studies analyzed other imaging modalities (S1 Table, and S2 Table). The most common fracture outcome was vertebral fracture (20 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 4 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Fig 1. Flow chart of the literature selection in PubMed, Web of Science, and Institute of Electrical and Electronics Engineers (search conducted on December 15, 2022). IEEE: Institute of Electrical and Electronics Engineers. https://doi.org/10.1371/journal.pdig.0000438.g001 studies) [8,10,11,28,31,34,35,38,44,46,50,51,58,59,65,72,77,80,83,84], followed by hip [6,13,29,32,33,37,39–43,48,53,62,64,66,68,79], and other fracture types (Table 1). AI algorithms summary Among the 54 studies that utilized imaging-related data, convolutional neural networks (CNN), a deep learning approach, emerged as the predominant choice, followed by instances where transfer learning was adopted. In some cases, the limited availability of labeled image data prompted the utilization of transfer learning [53,69], and certain studies incorporated pre-trained CNNs with non-fracture-related radiological images [6,28,85]. The prevailing pref- erence was for fully connected artificial neural networks within the subset of nine studies involving tabular data. Logistic regression and ensemble learning models were commonly employed, including Random Forest, Gradient Boosting, and XGBoost. Among the three stud- ies that harnessed both image and tabular data, a notable trend was the adoption of the support vector machine with various kernel models [57,68]. Handling imbalanced data and data augmentation Imbalanced fracture outcomes were reported in 48 studies (S3 Table). Only 12 studies addressed the handling of imbalance outcomes during model development, using Synthetic Minority Over-sampling Technique (SMOTE) [86] or undersampling [35]. Data PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 5 / 22 PLOS DIGITAL HEALTHTable 1. Fracture detection of 66 selected studies using machine learning and deep learning models and general characteristics of the study. A systematic review and meta-analysis of AI in fracture detection Country Data type Outcome Model First author (Year published) Almog et al. (2020) [12] Bae et al. (2021) [7] Beyaz et al. (2020) [67] Burns et al. (2017) [8] Chen et al. (2021) [28] Chen et al. (2022) [46] Cheng et al. (2019) [6] Cheng et al. (2020) [29] Cheng et al. (2021) [30] USA Canada Turkey USA Taiwan China Taiwan Taiwan Taiwan Choi et al. (2020) [47] South Korea Chou et al. (2022) [31] Taiwan Chung et al. (2018) [45] Derkatch et al. (2019) [51] Korea Canada Galassi et al. (2020) [68] Spain Guermazi et al. (2022) [52] Gupta et al. (2020) [53] Hayashi et al. (2022) [54] USA USA USA Inoue et al. (2022) [9] Kim et al. (2018) [69] Japan England Kitamura et al. (2020) [55] Korfiatis et al. (2018) [81] USA NA Kruse et al. (2017) [13] Denmark Del Lama et al. (2022) [80] Lemineur et al. (2007) [70] Lindsey et al. (2018) [56] Liu et al. (2015) [32] Liu et al. (2022) [48] Mawatari et al. (2020) [37] Minonzio et al. (2020) [71] Monchka et al. (2021) [58] Brazil France Taiwan China Japan France Mehta et al. (2020) [57] USA Image Image Image Image Tabular Tabular +Image Tabular Tabular Image Image Tabular +Image Image USA Image Canada Image Ho-Le et al. (2017) [79] Australia Tabular Tabular Image Osteoporotic Fracture Femoral Neck XGBoost, Ensemble CNN, Four Different Convolutional Block Attention Modules Image Image Image Image Image Image Image Image Image Image Image Tabular +Image Image Image Image Femoral Neck Vertebral Vertebral Vertebral Hip Hip Hip Supracondylar Vertebral Proximal humerus Vertebral CNN SVM CNN CNN, Other: Used ResNetSt-50 as the backbone network of the baseline model CNN CNN CNN CNN CNN, Transfer Learning, Ensemble model (ResNet34, DenseNet121, DenseNet201) CNN CNN Hip LR, SVM, Decision Trees, Random Forest Hip, Wrist, Pelvic, Thoracolumbar, Foot, Ankle, Arm, Shoulder, Rib Hip Detectron2 Transfer Learning: used VGG16 architecture with pre- trained weights using the ImageNet Hand, Elbow, Shoulder, Foot, Leg Detectron2 Hip Pelvic, Spine, Rib Wrist Hip Trabecular bone Hip Vertebral Osteoporotic Fracture Wrist Hip Hip Hip ANN, KNN, SVM CNN Inception v3 CNN model (transfer learning, trained in non-fracture images) CNN Multilayer Perceptron SVM Twenty-four statistical models were built CNN, Multilayer Perceptron ANN CNN ANN CNN CNN Lumbar Spine SVM with a different kernel Hip Vertebral SVM, LR CNN (Continued ) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 6 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Model CNN, Active Learning CNN CNN CNN CNN SVM CNN CNN CNN CNN CNN Table 1. (Continued) First author (Year published) Country Data type Monchka et al. (2022) [59] Switzerland, Canada Image Image Image Image Image Outcome Vertebral Femoral Neck Vertebral Femoral Neck Foot, Ankle, Knee, Leg, Hand, Wrist, Elbow, Arm, Shoulder, Clavicle China Japan USA USA Japan Image Finland Image USA, Australia Image Hip Vertebral Hip Ozkaya et al. (2022) [73] Turkey NA Image Image Scaphoid Thoracolumbar Finland Image Wrist Mu et al. (2021) [49] Murata et al. (2020) [38] Mutasa et al. (2020) [60] Nguyen et al. (2022) [61] Nishiyama et al. (2014) [39] Nissinen et al. (2021) [72] Oakden-Rayner et al. (2022) [62] Raghavendra et al. (2018) [82] Raisuddin et al. (2021) [74] Ramos et al. (2022) [10] Regnard et al. (2022) [75] Rosenberg et al. (2022) [76] Salehinejad et al. (2021) [83] Brazil France Italy NA Image Image Image Image Sato et al. (2021) [40] Japan Image Small et al. (2021) [63] Su et al. (2019) [64] Tomita et al. (2018) [65] USA USA USA Tseng et al. (2013) [33] Taiwan Ulivier et al. (2021) [77] Urakawa et al. (2019) [41] Italy Japan Ureten et al. (2022) [78] Turkey Wang et al. (2022) [84] Wu et al. (2020) [14] Yabu et al. (2021) [11] Yamada et al. (2020) [42] Yamamoto et al. (2020) [43] NA USA Japan Japan Japan Yeh et al. (2022) [34] Taiwan, USA Yi-Chu Li et al. (2021) [35] Taiwan Yoda et al. (2022) [44] Japan Yoon et al. (2021) [36] Taiwan, USA Yu et al. (2020) [66] USA Image Tabular Image Tabular Tabular Image Image Image Tabular Image Image Image Image Image Image Image Image Vertebral Pelvic, Limbs Thoracolumbar Vertebral Hip Cervical Spine Hip Vertebral Hip Vertebral Hip Hand Vertebral Major Osteoporotic Fractures Vertebral Hip Hip Vertebral Vertebral Vertebral Scaphoid Hip CNN, SVM, KNN, ExtraTrees, QDA CNN CNN CNN with ResNet-50+BLSTM layer CNN with EfficientNet-B4 model (a pre-trained ImageNet model) CNN Classification and regression tree CNN LR, Ensemble ANN ANN Transfer learning of CNN (VGG_16 network) Transfer Learning CNN LR, GB, RF, ANN CNN CNN CNN Transfer Learning Transfer learning, Ensemble model CNN, Transfer Learning CNN Transfer Learning (Continued ) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 7 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Table 1. (Continued) First author (Year published) Yuan Li et al. (2021) [50] Country Data type China Image Outcome Vertebral Model CNN (ResNet50) CNN, Convolution Neural Network; SVM, Support Vector Machine; LR, Logistic Regression; RF, Random Forest; ANN, Artificial Neural Network; MLP, Multi Layers Perceptron; KNN, K-Nearest Neighbors; GB, Gradient Boosting; NLP, Natural Language Processing; QDA, Quadratic Discriminant Analysis https://doi.org/10.1371/journal.pdig.0000438.t001 augmentation was frequently utilized in image studies, including horizontal and vertical rota- tion [45,50,58,67,69,72], adding Gaussian noise [67], random rescaling and flipping [30,53], mirroring, and lighting and contrast adjustments [56]. Hyperparameter optimization Thirty-six studies reported the detailed process for optimizing hyperparameters in the final selected models (S3 Table). Beyaz et al. utilized genetic algorithms to identify the optimal hyperparameters for their CNN architecture [67]. Liu et al. explored the impact of varying the number of hidden neurons in the output layer [32]. Nissinen et al. [72] employed two approaches for hyperparameter searches: random search [87] and hyperband [88]. Data split and validation in an external data set Fifty-one studies reported the split sample for model development (training) and validation (test- ing) (S3 Table). No universal rule of data separation was found. A different set of split samples was utilized, e.g., 80% training and 20% testing [10,28,47,57,71],90% training and 10% testing [32,33,56,81],and 80% training, 10% validation, and 10% testing [40,41,65,69].Twenty studies reported the cross-validation with 20-folds [66], 10-folds [8,14,33,34,39,45,50,53,57,64,72,76,80,81], 5-folds [13,28,32,38,44,46,48,67,74,78,79], and 7-folds [83]. Thirteen studies performed an out-of- sample external validation [6,7,29–31,35,47,49,56,59,62,72,74].Choi et al. [47] performed external tests using two types of distinct datasets: temporal data, which was obtained at a different period from the model development, and other geographically separated data, which was collected from a different center. Li et al. [35] utilized a dataset from another medical center that used a different plain radiographic technique. Meta-analysis We extracted 66 contingency tables for each selected study (S4 Table). The overall pooled sen- sitivity and specificity, calculated using logit transformation, were 91% (95% CI: 88, 93) and 90% (95% CI: 88, 92), respectively (Table 2). The pooled sensitivities for hip and vertebral frac- tures were found to be 92% (95% CI: 87–96) and 86% (95% CI: 82–89), respectively, while the pooled specificities for these fractures were 90% (95% CI: 85–93) and 86% (95% CI: 81–90), respectively (Table 2). The unified hierarchical summary receiver operating characteristic curve for different fracture types is shown in Fig 2. The area under the curve (AUC) was high- est for femoral neck fractures at 0.98, followed by other fractures (0.97), multiple fractures (0.93), hip fractures (0.91), wrist (0.86), and vertebral (0.84). Sensitivity analysis Arcsine transformation yielded similar results with the pooled sensitivity at 89% (95% CI: 87, 91) and specificity at 88% (95% CI: 86, 91). Among data types, studies using only image data PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 8 / 22 PLOS DIGITAL HEALTHTable 2. Pooled Sensitivities, Specificities, and Diagnostic Odds Ratio for 60 studies in different fractures outcome. Studies with only one selected fracture outcome (cervical spine, hand, lumber spine, proximal humerus, supracondylar, and trabecular bone) were omitted. A systematic review and meta-analysis of AI in fracture detection Sensitivity (%)1) 0.91 (0.88, 0.93) Specificity (%)1) 0.90 (0.88, 0.92) Sensitivity (%)2) 0.89 (0.87, 0.91) Specificity (%)2) 0.88 (0.86, 0.91) 81.14 (53.69, 122.63) Diagnostic Odds Ratio No. of Studies included Outcome Overall Vertebral Hip Multiple 0.86 (0.82, 0.89) 0.86 (0.81, 0.90) 0.86 (0.82, 0.89) 0.86 (0.81, 0.90) 38.26 (21.36, 68.51) 0.92 (0.87, 0.96) 0.90 (0.85, 0.93) 0.90 (0.85, 0.95) 0.89 (0.85, 0.93) 99.50 (39.37, 251.48) 0.90 (0.81, 0.96) 0.92 (0.87, 0.95) 0.88 (0.81, 0.94) 0.91 (0.85, 0.95) 88.71 (33.54, 234.64) Femoral Neck 0.94 (0.87, 0.97) 0.90 (0.64, 0.98) 0.93 (0.86, 0.98) 0.85 (0.68, 0.97) 125.82 (10.96, 1444.74) Wrist Scaphoid 0.90 (0.76, 0.96) 0.93 (0.85, 0.97) 0.89 (0.75, 0.97) 0.93 (0.85, 0.98) 105.68 (56.44, 197.89) 0.92 (0.68, 0.98) 0.81 (0.54, 0.94) 0.89 (0.61, 1.00) 0.80 (0.49, 0.98) 65.27 (44.16, 96.46) Thoracolumbar 0.97 (0.84, 0.99) 0.92 (0.90, 0.95) 0.95 (0.80, 1.00) 0.92 (0.90, 0.95) 278.30 (15.99, 4843.58) 66 20 18 11 4 3 2 2 Data in parentheses are 95% confidence intervals. 1): the logit transformation was used to calculate the pooled sensitivity and specificity. 2): the arcsine transformation was used to calculate the pooled sensitivity and specificity. * Multiple fractures outcome studies include hip and pelvic (2), hip and spine (1), major osteoporotic fractures (1), multiple (3), osteoporotic fractures (2), pelvic and limbs (1), pelvic, spine, and rib (1). https://doi.org/10.1371/journal.pdig.0000438.t002 Fig 2. The hierarchical summary receiver operating characteristic curve for different fracture types in the meta-analysis. A: Hip (18 studies), B: Vertebral (20 studies), C: Wrist (3 studies), D: Femoral Neck (4 studies), E: Multiple (11 studies), and F: Others (10 studies). https://doi.org/10.1371/journal.pdig.0000438.g002 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 9 / 22 PLOS DIGITAL HEALTHTable 3. Pooled Sensitivities, Specificities, and Diagnostic Odds Ratio for 66 studies in different data type used. A systematic review and meta-analysis of AI in fracture detection Sensitivity (%)1) 0.81 (0.77, 0.85) Specificity (%)1) 0.83 (0.76, 0.88) Sensitivity (%)2) 0.81 (0.76, 0.85) Specificity (%)2) 0.82 (0.76, 0.87) 20.06 (12.14, 33.16) Diagnostic Odds Ratio No. of Studies included Data Type Tabular Image 0.92 (0.90, 0.94) 0.91 (0.88, 0.93) 0.91 (0.88, 0.93) 0.89 (0.87, 0.91) 104.20 (65.12, 166.72) Tabular + Image 0.84 (0.76, 0.89) 0.95 (0.88, 0.98) 0.84 (0.77, 0.90) 0.96 (0.89, 1.00) 73.15 (27.23, 196.52) Data in parentheses are 95% confidence intervals. 1): the logit transformation was used to calculate the pooled sensitivity and specificity. 2): the arcsine transformation was used to calculate the pooled sensitivity and specificity. https://doi.org/10.1371/journal.pdig.0000438.t003 exhibited superior diagnostic performance with sensitivity and specificity at 91% (95% CI: 88, 93) and 89% (95% CI: 78, 91) using the arcsine transformation (Table 3). Studies employing radiographs displayed the highest sensitivity (92% [95% CI: 89, 95]) and specificity (90% [95% CI: 87, 93]) using the arcsine transformation (Table 4). Subgroup analysis Among data types, studies using only image data exhibited superior diagnostic performance with sensitivity and specificity at 92% (95% CI: 90, 94) and 91% (95% CI: 88, 93), respectively, when using logit transformation (Table 3). Studies employing radiographs displayed the high- est sensitivity (94% [95% CI: 90, 96]) and specificity (92% [95% CI: 89, 94]) using logit trans- formation (Table 4). The AUC for radiograph studies (0.94) was higher than studies using radiograph and CT together (0.89) or MRI alone (0.88). The diagnostic odds ratio (DOR) was highest for hip fractures at 99.50 (95% CI: 39.37, 251.48) compared to vertebral fractures (38.26 [95% CI: 21.36, 68.51]) (Table 2). The AUC for image data studies (0.96) was higher than that for those using tabular and images together (0.83) or tabular data alone (0.81) (Fig 3). Publication bias The assessment of publication bias encompassed each fracture outcome and the utilization of distinct data types (S5 and S6 Tables, S1–S3 Figs). The Contour-Enhanced Funnel Plot illus- trated the study distribution, and its enhanced contour facilitated the identification of poten- tial bias (S1—S3 Figs). Notably, asymmetrical distribution was evident in the context of hip and vertebral fracture outcomes, and the studies used image data only (S1 Fig and S3 Fig). Table 4. Pooled sensitivities, specifications, and diagnostic odds ratios for 54 studies (including three from the tabular and image data used) in different image modalities. Studies with only one selected image modality (Radiograph + CT + MRI, Radiograph + MRI, UGWSI) were omitted. Sensitivity (%)1) 0.89 (0.80, 0.94) Specificity (%)1) 0.90 (0.85, 0.93) Sensitivity (%)2) 0.86 (0.79, 0.92) Specificity (%)2) 0.89 (0.84, 0.93) 67.16 (28.34, 159.18) Diagnostic Odds Ratio No. of Studies included Image Modality CT MRI 0.91 (0.83, 0.95) 0.89 (0.84, 0.93) 0.91 (0.84, 0.96) 0.91 (0.84, 0.95) 89.46 (26.41, 302.99) Radiograph 0.94 (0.90, 0.96) 0.92 (0.89, 0.94) 0.92 (0.89, 0.95) 0.90 (0.87, 0.93) 150.92 (76.75, 296.78) Radiograph + CT 0.93 (0.79, 0.98) 0.84 (0.81, 0.87) 0.92 (0.75, 1.00) 0.84 (0.80, 0.88) 66.11 (16.48, 265.26) VFAI 0.87 (0.86, 0.89) 0.88 (0.87, 0.89) 0.87 (0.86, 0.89) 0.88 (0.87, 0.89) 50.64 (42.14, 60.86) Data in parentheses are 95% confidence intervals. 1): the logit transformation was used to calculate the pooled sensitivity and specificity. 2): the arcsine transformation was used to calculate the pooled sensitivity and specificity. UGWSI: Ultrasonic Guided Wave Spectrum Image, VFAI: Vertebral Fracture Assessment Image https://doi.org/10.1371/journal.pdig.0000438.t004 9 54 3 12 5 33 2 2 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 10 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Fig 3. Unified hierarchical summary receiver operating characteristic curve for different data types in the meta-analysis. A: image (54 studies), B: tabular (9 studies), and C: image and tabular (3 studies). https://doi.org/10.1371/journal.pdig.0000438.g003 This asymmetry implies the presence of possible publication bias, particularly pronounced in studies with smaller sample sizes. However, the trim-and-fill method corrected this asymme- try, rendering the distribution symmetrical (S2 Fig and S3 Fig). After using the trim-and-fill method to adjust for publication bias, the diagnostic odds ratio (DOR) has revealed that the effect size remains statistically significant (S5 and S6 Tables). Risk of bias and applicability The assessment of bias and applicability for 66 studies revealed moderate to low concerns (Table 5 and Fig 4). Patient selection and reference standards were the primary concerns for bias and applicability. Many studies lacked the reporting of sample characteristics such as gen- der and age, limiting generalizability. Some studies did not report patient selection or refer- ence standard computation methods [62,75,78]. Threshold adjustments in some studies might have led to overfitting, reducing the generalizability of the models [72]. Most studies exhibited applicability concerns and needed to be more easily generalizable to other populations. For example, one study [66] focused on patients visiting the emergency department for acute prox- imal femoral fracture, limiting generalizability to the general population. Another study included patients with existing vertebral fractures, reducing generalizability to the general pop- ulation. Data preprocessing often involves the removal of occult fractures, with some studies excluding radiographic occult fractures requiring additional modalities for confirmation [53]. Other studies excluded images with uncertain, traumatic, or pathological fractures or those with insufficient quality or resolution [58]. A few studies did not provide specific locations for fracture types or specify which ones were included [12,70]. Discussion Our systematic review and meta-analysis offer the most current and comprehensive evaluation of the diagnostic accuracy of Artificial Intelligence (AI) for predicting various osteoporotic fracture outcomes using various imaging modalities and data types. This study represents the first systematic review and quantitative meta-analysis of AI’s diagnostic accuracy and compari- son using different data types across multiple fracture outcomes. Our analysis reveals four major findings. First, AI provides high classification accuracy for fracture detection when uti- lizing imaging data, with a pooled sensitivity of 92% (95% CI: 90, 94). Convolutional neural networks with transfer learning exhibit significantly high accuracy when using image data in classifying fractures. Second, our study comprehensively reviews diagnostic accuracy among PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 11 / 22 PLOS DIGITAL HEALTHTable 5. The result of methodological quality for 66 included studies in the assessment of the risk of bias and applicability. A systematic review and meta-analysis of AI in fracture detection First author (Year published) Almog et al. (2020) [12] Bae et al. (2021) [7] Beyaz et al. (2020) [67] Burns et al. (2017) [8] Chen et al. (2021) [28] Chen et al. (2022) [46] Cheng et al. (2019) [6] Cheng et al. (2020) [29] Cheng et al. (2021) [30] Choi et al. (2020) [47] Chou et al. (2022) [31] Chung et al. (2018) [45] Derkatch et al. (2019) [51] Galassi et al. (2020) [68] Guermazi et al. (2022) [52] Gupta et al. (2020) [53] Hayashi et al. (2022) [54] Ho-Le et al. (2017) [79] Inoue et al. (2022) [9] Kim et al. (2018) [69] Kitamura et al. (2020) [55] Korfiatis et al. (2018) [81] Kruse et al. (2017) [13] Del Lama et al. (2022) [80] Lemineur et al. (2007) [70] Lindsey et al. (2018) [56] Liu et al. (2015) [32] Liu et al. (2022) [48] Mawatari et al. (2020) [37] Mehta et al. (2020) [57] Minonzio et al. (2020) [71] Monchka et al. (2021) [58] Monchka et al. (2022) [59] Mu et al. (2021) [49] Murata et al. (2020) [38] Mutasa et al. (2020) [60] Nguyen et al. (2022) [61] RISK OF BIAS APPLICABILITY CONCERNS PATIENT SELECTION INDEX TEST REFERENCE STANDARD FLOW AND TIMING PATIENT SELECTION INDEX TEST REFERENCE STANDARD + + + − + + + + + + + + + + + + + − + + + + + + + + + + + + + O + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − + + + + + + + + + + + + + + + + + − + + + + + + + + + + + + − + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + O + + + + − + + + + + − + − + + + + − + − + + − + + − + + + + − + − + + + + + + + + − + + + + + + + + + + + + + + + + + + + + + + + + + + − + + + + + + + + + + + + + + + + + − + + + + + + + + + + + + − + + + + + + + + + + O + (Continued ) PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 12 / 22 PLOS DIGITAL HEALTHTable 5. (Continued) First author (Year published) Nishiyama et al. (2014) [39] Nissinen et al. (2021) [72] Oakden-Rayner et al. (2022) [62] Ozkaya et al. (2022) [73] Raghavendra et al. (2018) [82] Raisuddin et al. (2021) [74] Ramos et al. (2022) [10] Regnard et al. (2022) [75] Rosenberg et al. (2022) [76] Salehinejad et al. (2021) [83] Sato et al. (2021) [40] Small et al. (2021) [63] Su et al. (2019) [64] Tomita et al. (2018) [65] Tseng et al. (2013) [33] Ulivier et al. (2021) [77] Urakawa et al. (2019) [41] Ureten et al. (2022) [78] Wang et al. (2022) [84] Wu et al. (2020) [14] Yabu et al. (2021) [11] Yamada et al. (2020) [42] Yamamoto et al. (2020) [43] Yeh et al. (2022) [34] Yi-Chu Li et al. (2021) [35] Yoda et al. (2022) [44] Yoon et al. (2021) [36] Yu et al. (2020) [66] Yuan Li et al. (2021) [50] A systematic review and meta-analysis of AI in fracture detection RISK OF BIAS APPLICABILITY CONCERNS PATIENT SELECTION INDEX TEST REFERENCE STANDARD FLOW AND TIMING PATIENT SELECTION INDEX TEST REFERENCE STANDARD O + + + O + + − + + + + + + + + + − + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + − + + + + + + O − + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + O O + O + + − + + + + + + + + + − + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + O − + + + + + + + + + + + + + + + + + + + + + + + + + + +: Low risk of bias/no concerns regarding applicability, −: High risk of bias/concerns regarding applicability, O: Unclear risk of bias/unclear whether there are concerns regarding applicability. https://doi.org/10.1371/journal.pdig.0000438.t005 different image modalities with AI. While all image modalities provide comparable results, AI with radiograph images yields the highest results with a pooled sensitivity of 94% (95% CI: 90, 96). Third, our sensitivity analysis, employing the arcsine transformation, which was comple- mented by the primary analysis utilizing the logit transformation, provides the robustness of our findings. Both methodologies yielded similar results regarding pooled sensitivity and PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 13 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Fig 4. Summary of the Quality Assessment of Diagnostic Accuracy Studies for the risk of bias and applicability in the included 66 studies. The risk of bias was measured in four domains: patient selection, index test, reference standard, and flow and timing. The risk of applicability was evaluated with three domains: patient selection, index test, and reference. https://doi.org/10.1371/journal.pdig.0000438.g004 specificity, which underscores the reliability and consistency of our findings. Fourth, signifi- cant flaws were observed in the study design and reporting of AI for real-world applicability. For example, only a few studies described the patient characteristics of data, and only half (n = 33) reported the hyperparameter selection process. Our findings align with other systematic reviews and meta-analyses [15,16], showing that AI demonstrates considerably higher pooled sensitivity and specificity. However, inconsistent results have been observed when comparing different image modalities in fracture detection. External validation enables a more robust demonstration of clinical utility versus simple inter- nal train/test cross-validation. Our study shows that only thirteen studies (20%) out of sixty- six performed external validation. The limitation of validating in an external dataset is the lack of availability of large, labeled datasets due to resistance to sharing data across institutions because of patient privacy issues and the necessity of experts for labeling the datasets. Although external validation enhances the robustness of AI systems, it could potentially attenuate their impact on the system. Consequently, it’s crucial to acknowledge that external validation might not always be advisable due to the potential impact of factors like sample size and the diversity of the training set. Two systematic reviews [89,90] provide valuable insights into the current limitations of AI studies. A broad discussion of possible solutions is necessary because meth- odological challenges, risk of bias, and applicability concerns can arise in AI during all stages of development, including data curation, model selection, implementation, and validation. Both reviews recommend that researchers follow standardized reporting guidelines to deter- mine the risk of bias and improve methodological quality assessment. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 14 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection Our study has limitations; the major one is that only a few studies that employed tabular data or combined tabular and image data are eligible. Second, we excluded non-English-lan- guage articles, which may have overlooked some studies published in a different language. Third, many of these included studies had study design flaws. They were classified as having great concern for bias and applicability, limiting the conclusions that could be drawn from the meta-analysis because studies with a high risk of bias and applicability overestimated algo- rithm performance. This systematic review and meta-analysis have important implications for clinical practice. Given the high diagnostic performance of AI, these techniques could be integrated into exist- ing fracture risk assessment tools to enhance the identification of patients at risk and facilitate early intervention. Healthcare professionals should be trained in interpreting and applying these methods in clinical practice. This study observed superior prediction performance with single radiograph input data over multimodal imaging, which can be attributed to the radiographs’ consistent and stan- dardized anatomical view, reducing noise and variability inherent in multimodal inputs [91]. Radiographs precisely capture fracture-relevant features, while added modalities like CT and MRI can diversify and possibly weaken these key features [92]. Multimodal inputs can also ele- vate overfitting risks, particularly with limited datasets [93]. Radiographs, being more accessi- ble and cost-effective than CT or MRI, allow for larger, representative datasets enhancing model performance. The decision between single radiographs and multimodal inputs should be rooted in the research context, data availability, and prediction objectives. Despite the evi- dent advantages of radiographs, specific scenarios may warrant multimodal integration for improved predictions. We also observed that solely relying on image data produced better AUC values than combining it with tabular data. Image data’s richness and direct relevance to fracture detection offer clear diagnostic advantages [94]. Convolutional neural networks (CNNs), identified in our study, are adept at processing this data, emphasizing subtle fracture- related visual nuances [95]. In contrast, tabular data could infuse noise and inconsistencies. Sole image data ensures focus on vital visual features and offers a more standardized data for- mat than diverse tabular inputs. Further research is needed to address the limitations identified in the included studies and to explore the performance of specific ML and DL algorithms. Researchers should provide more detailed information about their study populations and methods, including patient selec- tion, fracture type location, and the reference standard used. Future studies should also investi- gate the impact of factors such as training dataset size, model architecture, and the inclusion of clinical and demographic variables on the diagnostic performance of AI. Future research will help develop more accurate and generalizable models for predicting osteoporotic fractures and inform evidence-based clinical practice. Several novel diagnostic meta-analysis methodologies have recently been introduced [96–98]. Nevertheless, due to the limited sample sizes within selected studies focusing on fractures beyond vertebral and hip injuries and studies involving tabular and tabular and image data types, incorporating these methodologies into our present study was unfeasible. While we acknowledge their potential applicability, the current study’s unique characteristics led us to refrain from their implementation. We will implement these methodologies in our forthcoming investigations, particularly as more comprehensive studies become available. In aid of future researchers, we provide an array of crucial challenges and their potential resolutions pertinent to applying machine learning or deep learning for fracture diagnosis (S7 Table). In conclusion, our meta-analysis highlights the high diagnostic accuracy of AI in various fracture outcomes. As AI demonstrates reliable results in fracture detection, it holds the poten- tial to streamline fracture diagnosis in healthcare systems. However, transparent reporting of PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 15 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection study methods and designs for AI development and validation is essential to ensure their real- world applicability. By addressing the current research landscape’s limitations and promoting standardized guidelines, we can facilitate the integration of AI technologies into clinical prac- tice and enhance the prediction of osteoporotic fractures, ultimately leading to improved patient care. Supporting information S1 PRISMA Checklist. PRISMA DTA Checklist. (DOCX) S1 Text. The search term used for each engine: 1) PubMed, 2) Web of Science, and 3) IEEE. (DOCX) S1 Table. A characteristic of 57 selected studies for Image modality, Image Data Type, and Data Source. (DOCX) S2 Table. The data source of 9 selected studies used tabular data, and 3 studies (in bold) used both tabular and image data. (DOCX) S3 Table. A characteristic of 66 selected studies for the unbalanced outcome, a technique used for an unbalanced outcome, data preprocessing, hyperparameters optimization, and performance measurement used. (DOCX) S4 Table. A summary of the contingency table for 66 selected studies. (DOCX) S5 Table. Summary of Publication Bias Assessment across different fracture outcomes. TF: Trim and Fill method, DOR: Diagnostic Odds Ratio, CI: Confidence Interval. (DOCX) S6 Table. Summary of Publication Bias Assessment across different data types. TF: Trim and Fill method, DOR: Diagnostic Odds Ratio, CI: Confidence Interval. (DOCX) S7 Table. Overview of Key Challenges and Potential Resolutions in the Utilization of Machine Learning or Deep Learning for Fracture Diagnosis. (DOCX) S1 Fig. Contour-Enhanced Funnel Plot for Publication Bias Assessment across Different Fracture Outcomes. (DOCX) S2 Fig. Contour-Enhanced Funnel Plot for Publication Bias Assessment across Different Fracture Outcomes after Employing the Trim & Fill Method. The open circle represents the “filled” studies from the Trim & Fill Method in each fracture outcome plot. (DOCX) S3 Fig. Contour-Enhanced Funnel Plot: Evaluating Publication Bias Across Various Data Types. The top row illustrates the funnel plot encompassing all studies. The second row shows the Contour-Enhanced Funnel Plot for Publication Bias Assessment after employing the Trim & Fill Method. The open circle designates the studies “filled” through the Trim & Fill Method PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000438 January 30, 2024 16 / 22 PLOS DIGITAL HEALTHA systematic review and meta-analysis of AI in fracture detection within each contour-enhanced funnel plot in the second row. (DOCX) Acknowledgments This research was partially conducted under the affiliation of the Nevada Institute of Personal- ized Medicine, College of Sciences (QW, JJ, and JD), Department of Epidemiology and Biosta- tistics, School of Public Health (QW and JJ), Department of Mathematical Sciences, College of Sciences (BL), the University of Nevada, Las Vegas. Author Contributions Conceptualization: Jongyun Jung, Jingyuan Dai, Qing Wu. Data curation: Jongyun Jung, Jingyuan Dai. Formal analysis: Jongyun Jung. Funding acquisition: Qing Wu. Investigation: Qing Wu. Methodology: Jongyun Jung, Qing Wu. Resources: Qing Wu. Software: Jongyun Jung. Validation: Jongyun Jung. Visualization: Jongyun Jung. Writing – original draft: Jongyun Jung, Qing Wu. Writing – review & editing: Jongyun Jung, Jingyuan Dai, Bowen Liu, Qing Wu. References 1. Court-Brown CM, Caesar B. Epidemiology of adult fractures: A review. Injury. 2006; 37: 691–697. https://doi.org/10.1016/j.injury.2006.04.130 PMID: 16814787 2. 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10.1371_journal.pone.0297420	RESEARCH ARTICLE Cell contacts and pericellular matrix in the Xenopus gastrula chordamesoderm Olivia Luu, Debanjan Barua¤, Rudolf Winklbauer* Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada ¤ Current address: Department of Quantitative Biosciences, Merck Research Laboratories, South San Francisco, CA, United States of America * r.winklbauer@utoronto.ca Abstract Convergent extension of the chordamesoderm is the best-examined gastrulation movement in Xenopus. Here we study general features of cell-cell contacts in this tissue by combining depletion of adhesion factors C-cadherin, Syndecan-4, fibronectin, and hyaluronic acid, the analysis of respective contact width spectra and contact angles, and La3+ staining of the pericellular matrix. We provide evidence that like in other gastrula tissues, cell-cell adhesion in the chordamesoderm is largely mediated by different types of pericellular matrix. Specific glycocalyx structures previously identified in Xenopus gastrula tissues are absent in chorda- mesoderm but other contact types like 10–20 nm wide La3+ stained structures are present instead. Knockdown of any of the adhesion factors reduces the abundance of cell contacts but not the average relative adhesiveness of the remaining ones: a decrease of adhesive- ness at low contact widths is compensated by an increase of contact widths and an increase of adhesiveness proportional to width. From the adhesiveness-width relationship, we derive a model of chordamesoderm cell adhesion that involves the interdigitation of distinct pericel- lular matrix units. Quantitative description of pericellular matrix deployment suggests that reduced contact abundance upon adhesion factor depletion is correlated with excessive accumulation of matrix material in non-adhesive gaps and the loss of some contact types. Introduction The blastocoel wall of the Xenopus embryo consists of an outer epithelial sheet that coats layers of deep cells. For the epithelial layer, subapical adherens and tight junctions are characteristic [1–3] while deep cell adhesive contacts appear amorphous and are interspersed between non- adhesive interstitial gaps. We recently showed that such contacts can be characterized by their width spectra—the frequency distributions of membrane-membrane distances—and by the modifications of the spectra when adhesion factors are depleted [4, 5]. From the requirement for factors such as fibronectin (FN) or hyaluronic acid (HA), and from the large widths of con- tacts we concluded that gastrula cell-cell adhesion is largely mediated by the pericellular matrix (PCM) [4, 6]. In the Xenopus early embryo, a fraction of the PCM contains La3+ staining mate- rial (LSM) [7], which can be used to further characterize contact types. Thus, we found that some LSM patches resembled known endothelial glycocalyx variants which we termed a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Luu O, Barua D, Winklbauer R (2024) Cell contacts and pericellular matrix in the Xenopus gastrula chordamesoderm. PLoS ONE 19(2): e0297420. https://doi.org/10.1371/journal. pone.0297420 Editor: Michael Schubert, Laboratoire de Biologie du De´veloppement de Villefranche-sur-Mer, FRANCE Received: August 26, 2023 Accepted: January 4, 2024 Published: February 12, 2024 Copyright: © 2024 Luu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting information files. Funding: Canadian Institutes of Health Research: PJT-15614; Natural Sciences and Engineering Research Council of Canada: RGPIN-2017-06667. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors declare that they have no conflict of interest. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 1 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm glycocalyx I, II and III, and which mediated cell adhesion but occurred also on the free cell sur- faces at interstitial gaps [4]. Previously, we analyzed ectoderm and prechordal mesoderm, which represent different germ layers and morphogenetic behaviors. Whereas ectoderm is stretched during epiboly, prechordal mesoderm performs active, directional cell-on-cell migration [8]. Despite the differences, contact types are largely shared between these tissues [4, 5]. Here, we analyze cell contacts in the chordamesoderm (CM), which is continuous with the ectoderm at its posterior and the prechordal mesoderm at its anterior end. It elongates by convergent extension, a cell intercalation process driven by protrusive activity at the ends of mediolater- ally oriented bipolar cells, and by junction remodelling at antero-posterior cell-cell contacts [9–11]. Convergent extension depends on CM cell adhesion via the main cadherin of the Xenopus gastrula, C-cadherin (C-cad) [10, 12] and on the extracellular matrix protein FN [13], and we depleted these factors using morpholino antisense oligonucleotides. We also knocked down the small transmembrane/extracellular heparan sulfate proteoglycan Syndecan-4 (Syn- 4) [4, 14], and we impeded the synthesis of the large glycosaminoglycan, HA, by knocking down HA synthases Has1 or Has2 [15]. Combined with the analysis of contact width spectra and La3+ staining, the knockdown experiments identified new contact types and showed that the CM contact pattern differs from both ectoderm and prechordal mesoderm. In all mor- phants the abundance of cell-cell contacts was reduced, but this was not paralleled by reduced average adhesiveness of the remaining contacts: a decrease of adhesiveness at low contact widths was compensated by the massive addition of wide contacts and an increase of adhesiveness proportional to width. From the latter observation we derive a model of CM cell-cell adhesion which is based on the interdigitation of PCM units from opposite membranes. Materials and methods Embryo manipulations Adult Xenopus laevis were maintained in accordance with University of Toronto Animal Use Protocol (20011765). Eggs were fertilized in-vitro, de-jellied using 2% cysteine in 1/10 Modi- fied Barth’s Solution (MBS; 88 nM NaCl, 1 mM KCl, 2.4 mM NaHCO3, 0.82 mM MgSO4, 0.33 mM Ca(NO3)2, 0.41 mM CaCl2, 10 mM Hepes (+NaOH), 1% streptomycin, 1% penicillin (pH 7.4) and kept in 1/10 MBS until stage 11. Morpholino antisense oligonucleotides (Gene Tools) were injected at the two-cell stage in 4% ficoll solution and embryos were incubated in 1/10 MBS at 15˚C until stage 11. The following previously characterized morpholinos were used, at the efficiencies as percent reduction of protein levels indicated. Morpholino Sequence (5’–3’) ng injected per blastomere Efficiency (% reduction of protein) C-cadherin [16, 17] CCACCGTCCCGAACAGAAGCCTCAT Fibronectin (xFN1) [13, 18] CGCTCTGGAGACTATAAAAGCCAAT Fibronectin (xFN2) [13, 18] CGCATTTTTCAAACGCTCTGAAGAC xHas-1 [19, 20] xHas-2 [19, 20] xSyn-4.1 [14, 21–23] xSyn-4.2 [14, 21–23] GTTGCCGAATGAAGAGGCCCCAAGA TGCATATAAACCGTTCACAGTGCAT GCACAAACAGCAGGGTCGGACTCAT CTAAAAGCAGCAGGAGGCGATTCAT 20 20 20 27 30 20 20 65 63 63 Unknown Unknown 75 75 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 2 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Transmission electron microscopy (TEM) For the TEM pictures, 4% paraformaldehyde and 2.5% glutaraldehyde in 0.05M cacodylate buffer at pH 7.0 were used to fix stage 11 gastrulae. Bisected samples were rinsed in 0.1M caco- dylate and then fix in 0.1M cacodylate containing osmium tetroxide (1%). To visualize the gly- cocalyx, 1% lanthanum nitrate (Sigma-Aldrich Canada) was added to the fixatives. Samples were washed with 0.1M cacodylate and dehydrated in a series of graded ethanol solutions before embedding in Spurr’s resin. Semi-thin and ultrathin sections were obtained using a Leica EM UC6 microtome. Sections were stained with 3% uranyl acetate in methanol for 1 hour followed by 10 minutes in Reynold’s lead citrate. Images were taken with a Hitachi HT7700 microscope. Analysis of TEM images Cell contacts were analysed as previously described [4]. In short, differences in cell and yolk platelet size were used to identify morphant gastrula tissues in TEM images from 3 embryos from different egg batches for each condition [4]. Stretches of cell perimeter were treated as contacts when the contours of adjacent cells followed each other. Their abrupt divergence indi- cated the end of a contact at a non-adhesive gap. Contact angles between cells were measured at the transitions between contacts and interstitial gaps. Contact width was measured as sepa- ration distance between membranes, distances were binned in 50 nm wide steps. LSM height in gaps was similarly measured. All contacts or angles in a TEM image were measured; for number of TEM images per treatment (see Figure legends). The error due to random tilting of the sectioning plane relative to the plane of a contact, as estimated from the width variation of tight junctions, can amount to an up to 1.5-fold apparent increase in contact width [4]. The respective distortion of contact angles broadens the angle distribution by a factor k, by making the narrowest angles appear even narrower and widening the widest angles, depending on the inclination of the sectioning plane [24, 25]. The factor k depends on the shape and size of the objects sectioned at random and the angle distribution. It is difficult to derive theoretically, given that the gaps in the tissues examined here are a heterogeneous mixture of differently sized bubbles and 3-to-multisided gaps. However, we found that when setting k � 1.5 the cor- rected maximal and minimal boundary lines would intersect within the observed width range, whereas at a slightly lower k = 1.33 the lines would meet only beyond this range (S4 Fig). Vari- ances between treatments were statistically analyzed using one-way ANOVA. Data visualiza- tion and statistical analyses were performed using Graphpad Prism 7 2017 v7.0.3. Vector graphics and figures were assembled using Inkscape v0.92 and v1.0. Results Abundance of cell contacts but not their adhesiveness is reduced in adhesion factor morphants At the stage 11 middle gastrula, the blastopore has invaginated to form the archenteron, and the CM has involuted at the blastopore lip to elongate in the animal-vegetal direction between the endodermal archenteron lining and the neural ectoderm (Fig 1A). Depletion of any of four select adhesion factors—C-cad, FN, HA and Syn-4—by morpholino antisense oligonucleotides affects the CM [4]. For example, knockdown of C-cad arrests CM involution, elongation, and archenteron formation (Fig 1B), like that of FN [4], and the depletion of Syn-4 severely alters cell packing and shape but surprisingly, preserves CM movements including convergent extension (Fig 1C). PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 3 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 1. Abundance of cell contacts but not relative adhesiveness is reduced in adhesion factor morphants. (A–C) Dorsal side of normal (A) and C-cad depleted stage 11 gastrulae (B). In Syn-4 depleted embryos (C) convergent extension appears slightly accelerated in 6 out of 7 gastrulae. CM, chordamesoderm; NE, neural ectoderm; PCM, prechordal mesoderm; EN, suprablastoporal endodermal epithelium; red arrow, tip of archenteron; red arrowhead, position of blastopore. (D–H’) Cell packing in normal (wt) and morphant (MO) stage 11 chordamesoderm. Blue arrowheads, narrow contacts between cells; green arrows, two-sided gaps (“bubbles”) between two cells; light green arrowheads, wide contacts between cells; g, gaps at 3- or 4-cell junctions. (I) Abundances of narrow (< 50 nm) and wide (> 50 nm) contacts, and interstitial gaps. Bars, standard deviations. (wt) is from Barua et al. [4]. n, number of TEM images analyzed. (J) Tensions at 3-sided gap. For tension βf at free gap surface to balance tension per cell βc at contact interface it must act at a contact angle θ. (J’) Same contact angle θ and thus relative adhesiveness α can combine with large (left) or small (middle) contacts (small or large gaps, respectively). Same θ can be generated by smaller (b0 measured angle 2θ in normal and morphant CM; av., average. c ) tensions, provided that their ratio is retained. (K) Each dot represents a c) or larger (b00 f and b00 f and b0 https://doi.org/10.1371/journal.pone.0297420.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 4 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm CM cells are tightly packed, with occasional interstitial gaps at 3-cell junctions and intersti- tial “bubbles” between two cells (Fig 1D and 1D’). C-cad knockdown increases the number and size of interstitial gaps, and evenly spaced wide cell-cell contacts become prominent (Fig 1E and 1E’). FN depletion increases interstitial gaps moderately and widens cell-cell contacts or separates cells (Fig 1F and 1F’). Knockdown of Has-2 also increases gaps and contact widths (Fig 1G and 1G’), suggesting an adhesive role of HA. Contact loss is most severe in Syn-4 mor- phants, where cells remain connected only through thin processes. The cell surface is often concave, generating extensive interstitial space and a serrate cell outline (Fig 1H and 1H’). Overall, all four factors normally contribute to the dense packing of the CM cells. To quantify the abundance of adhesive cell contacts (Fig 1I), we measured the lengths of stretches where the membranes of adjacent cells ran parallel until they diverged abruptly at interstitial gaps [4]. We distinguished between narrow contacts with membrane distances < 50 nm, and the remaining, highly variable wide contacts. Non-attached free sur- faces are present at interstitial gaps [4]. In CM cells, 4/5 of the surface is normally engaged in adhesive contacts. Narrow contacts occupy almost 2/3 of the cell surface, and 80–90% of these depend on both Syn-4 and C-cad. Unexpectedly, half of the narrow contacts also require the large HA and FN molecules. As in ectoderm or prechordal mesoderm [4], Syn-4 depletion has the strongest effects, suggesting that virtually all narrow contacts and 1/3 of wide contacts require the presence of Syn-4 (Fig 1I). All treatments increase the abundance of non-adhesive gaps. The abundances of contact widths were collected in 50 nm bins to generate width spectra; changes due to experimental interference are best seen in difference spectra where values of untreated samples are subtracted from experimental ones [4]. In CM, width abundances decrease sharply after a maximum at < 50 nm and vanish beyond 700 nm [4] (S1 Fig). C-cad, FN, Has-2 and Syn-4 depletion strongly reduce abundances of narrower contacts, increase the frequency of wider ones, and generate contacts beyond the normal width range (S1 Fig). Nota- bly, Has-2 knockdown does not produce the signature glycocalyx I difference spectrum—a dis- tinct decrease at 50–100 nm and increase at < 50 nm that is induced by this treatment in prechordal mesoderm or ectoderm [4]. In fact, compared to the ectoderm [4], the 50–100 nm width abundance is lowered in the CM spectrum, reproducing the glycocalyx I depletion sig- nature in the CM-ectoderm difference spectrum (S1 Fig) and suggesting a lack of this structure in the CM. The adhesive strength of contacts depends on the difference in surface energy per area between free and contacting cell surfaces at gaps, βf − βc, i.e., on how much tension at contacts is reduced relative to the free surface, for example by the release of adhesion factor binding energy. The ratio of the tensions is βc/βf = cos θ with θ the contact angle between adjacent cells. The less the tension is reduced at contacts, the smaller would be the adhesion strength at these contacts and hence θ (Fig 1J) [16, 26]. On the other hand, smaller or larger contact area and correspondingly increased or decreased gap size is compatible with the same θ, the same ten- sion magnitudes, and hence the same adhesion strength in contacts (Fig 1J’). However, all ten- sions being altered proportionally would change adhesion strength, also without affecting θ (Fig 1J’). In the CM, angles remain the same upon FN- and Has-2 knockdown and are increased by C-cad and Syn-4 depletion (Fig 1K). Thus, while adhesive contact abundances are diminished in morphants, the relative reduction of tension at the residual contacts—their relative adhesiveness—is not decreased. This indicates that the abundance and the adhesive- ness of contacts can be decoupled experimentally, and both parameters must be determined independently. If absolute adhesion strength, measured as surface energy per area, were changed in the morphants while relative adhesiveness and hence contact angles were retained, this would still be compatible with large or small gaps (Fig 1J’). The case that both relative PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 5 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm adhesiveness—the ratio of tensions—and absolute adhesion strength—the difference between tensions—are altered at the same time is analyzed in detail in the Discussion section. Gener- ally, however, diminished cell packing density is not necessarily linked to reduced adhesion strength. La3+ staining and adhesion factor depletion identify contact types La3+ stains sections of the adhesive contacts, and together with the knockdown of adhesion factors, this can be used to identify contact types. In the ectoderm, isolated LSM plaques occur in contacts and on the surface of interstitial gaps (Fig 2A) [4]. Cells in the more densely packed CM are often outlined by delicate lines of La3+ staining, which are occasionally interrupted, meet at gap-less 3-cell junctions (Fig 2B), can be as narrow as 10 nm (Fig 2C). Some contacts and rare gaps at 3-cell junctions are unlabeled (Fig 2D). In triple-layered contacts, La3+ resolves into two parallel lines with or without small LSM dots between (Fig 2E). No bush-like glycocalyx II or brush-like glycocalyx III structures [4] were observed. Upon C-cad depletion, LSM becomes concentrated in plaques on the surfaces of interstitial gaps (Fig 2F). FN morphants show a similar pattern (Fig 3A and 3C), and moreover, parallel lines of strong staining can enclose a less stained central layer in contacts (Fig 3C and 3D). Depletion of HA generates long unlabeled contacts (Fig 3E) or breaks LSM lines up into short plaques or rows of droplets that sit with a broad base on one cell surface and touch with their apex the opposite cell (Fig 3F). In Syn-4 morphants, cell surfaces are mostly devoid of La3+ staining (Fig 3G), but 10–20 nm LSM contacts are preserved (Fig 3H), and some surfaces are coated with small, sparse LSM dots (Fig 3I). Three types of LSM shedding occur. In gaps of C- cad or FN morphants, complete plaques and long, faintly stained ribbons detach (Figs 2G and 3B); groups of small, dense LSM flakes are also shed (Figs 2H and 3B); and in Syn-4 mor- phants, hairballs of fine fibrils (Fig 3J) consist probably of HA [4]. Thus, as in ectoderm or pre- chordal mesoderm, Syn-4 is the main contributor to LSM formation. Syn-4 and HA seem to generate extended LSM plaques, which are prone to shedding though in the absence of C-cad or FN. LSM-filled contacts were further characterized by their width spectra. In ectoderm, width abundances decrease from a maximum at 20–30 nm (Fig 4A). In the CM (Fig 4B and 4B’), widths decrease first gradually from a maximum at 10–20 nm, then abruptly at 60 nm, and abundances remain low in the 60–120 nm range which in ectoderm harbors glycocalyx I, con- sistent with this glycocalyx type being diminished in the CM. A new CM peak at 10–20 nm is apparent in the CM-ectoderm difference spectrum (Fig 4B’), and C-cad-MO (Fig 4C and 4C’) and Syn-4-MO (see Fig 4F and 4F’) spectra confirm the subdivision of narrow LSM contacts into C-cad/Syn-4-independent 10–20 nm and -dependent 20–50 nm contacts. In FN and Has1 morphants, all LSM contacts < 50 nm are strongly diminished, and 50–100 nm contacts increased. HA and FN are particularly required in 10–20 nm contacts (Fig 4D–4E’). In C-cad and Syn-4 morphants, triple-layered contacts (see Fig 2E) are absent, and in Has-1 and FN morphants, their peak is shifted from 30 to 70 nm (S2 Fig). Thus, C-cad and Syn-4 are essential for these contacts while HA and FN control their width. A large part of the PCM is not stained by La3+ and respective contacts are less well defined (Fig 4G–4K). Almost half of narrow < 50 nm contacts are unlabeled in the CM. They are essentially removed by C-cad and Syn-4 knockdown while wider than normal contacts appear, consistent with a widening of the original contacts. In FN and Has1 morphants, unlabeled contacts are little affected. Thus, narrow C-cad- and Syn-4-dependent unlabeled PCM contacts form part of the CM contact complement. Of note, LSM is restricted to widths below 200 nm, leaving the wider contacts, predominant in morphants, unlabeled. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 6 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 2. LSM in normal and C-cad depleted CM. (A) LSM in ectoderm, for comparison. (B–E) LSM in normal CM. (F–H) C- cad morphant CM. g, interstitial gaps; y, yolk platelets; m, mitochondria; dark blue arrowheads, LSM plaques in contacts; light blue arrowheads, plaques on gap surfaces; red arrowheads, LSM-free contacts; black arrow, triple-layered contact; light green arrow, bubble; dark green arrow, shed plaque; orange arrows, lightly stained shed ribbons; purple arrows, darkly stained shed LSM. https://doi.org/10.1371/journal.pone.0297420.g002 PCM distribution in contacts and in non-adhesive gaps LSM is typically more prominent in non-adhesive gaps than in contacts (Fig 5A–5F). Thick layers of LSM can accumulate symmetrically on neighboring sides of a gap in C-cad, FN and Has morphants while the width of an adjoining LSM contact is much thinner than the height of the gap surface LSM (Fig 5A–5D). Examples from the more frequent gaps in prechordal PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 7 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 3. LSM in FN, Has1 and Syn-4 depleted CM. Dark red arrowheads, semi-drop-like LSM sitting on upper or lower membrane; dark blue arrowheads, LSM plaques in contacts; light blue arrowheads, plaques on gap surfaces; red arrowheads, LSM-free contacts; black arrow, triple-layered contact; light green arrow, bubble; dark green arrow, shed plaque; orange arrows, lightly stained shed ribbons; purple arrows, darkly stained shed LSM; magenta arrows, LSM dots. https://doi.org/10.1371/journal.pone.0297420.g003 mesoderm show that this is also true for normal tissue (Fig 5E and 5F). Notably, when the LSM forms distinguishable units, these seem to interdigitate in contacts (e.g. Fig 5B and 5E; see also [4]) to generate contacts only as wide as the units are high in each single layer. Com- pared quantitatively to LSM contact widths in normal CM (see Fig 4), even single LSM layers are higher in gaps in the morphants (Fig 5G–5J), and the respective frequency distributions PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 8 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 4. LSM widths in contacts. (A–F) Width frequency distributions in ectoderm (A) for comparison, in CM (B) and in various CM morphants (C–F). n, number of width measurements from 18, 4, 6, 8, 3 TEM images, respectively; av., average. (B’–F’) Corresponding difference (ΔAbundance) spectra comparing width distributions of CM to ectoderm (B’), and of morphants to normal CM (C’–F’). (G–K) Comparison of widths of LSM-containing (black parts of bars) and LSM-free (grey parts of bars) contacts, using the data from (B–F) and S1 Fig. https://doi.org/10.1371/journal.pone.0297420.g004 indicate 2.2-fold, 2.8-fold, 4.0-fold and 1.4-fold increases in gaps of C-cad, FN, Has1 and Syn-4 morphants, respectively. Gap-contact difference spectra confirm reduced abundances of nar- row LSM layers and an increase of thicker layers, consistent with the width of contacts not being derived from adding up the heights of LSM layers in gaps. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 9 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 5. LSM in gaps. (A–F) Examples of LSM at transitions from gaps (g) to cell-cell contacts. PM, prechordal mesoderm. (G–J) Frequency distributions of LSM height in gaps (corresponding to LSM width in contacts). n, number of measurements from 5, 6, 3, 7 TEM images, respectively; av., average. (G’–J’) Difference (ΔAbundance) spectra corresponding to (G–J), comparing LSM height in gaps to LSM width in contacts. https://doi.org/10.1371/journal.pone.0297420.g005 In CM contacts, the lengths of continuous LSM, non-stained, or triple-layered stretches are similar (Fig 6A), and similar to those of LSM plaques in prechordal mesoderm [4]. In mor- phants, LSM stretches are lengthened or shortened moderately while the lengths of non- stained stretches are always increased, in C-cad-MO embryos by 10-fold (Fig 6B–6E). Random removal of some LSM plaques interspersed in non-labeled contact stretches would cause this effect. LSM plaque length in gaps is comparable to that in contacts in C-cad and Syn-4 mor- phants and increased in FN and Has1 morphants (Fig 6B’–6E’). The lengths of unlabeled PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 10 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 6. Lengths of PCM stretches. (A–E) Lengths of continuous stretches of LSM-containing (LSM(+)) contacts (plaques), and of LSM-free (LSM(-)) and triple-layered (LSM partial) contacts. (B’–E’) Lengths of continuous stretches of LSM (plaques) (LSM(+)) on gap surfaces or LSM-free surfaces (LSM(-)). Data from 4, 5, 9, 8, 6 TEM images, respectively; av., averages. (F–H) Lengths of shortest LSM units discernible in normal CM contacts (F), and in contacts and gaps of morphants (G,H). n, number of unit LSM structures measured, from 4, 7, 5 TEM images, respectively. (I) Summary diagram integrating LSM (black areas) height and length data for contacts (left, rectangular) and gaps (right, wedged), and non-labeled contact height and length data for contacts (grey, rectangular). Area size relative to LSM in normal CM contacts (1.00) is indicated. Scale on top, relative average lengths of contact types. Height of rectangles, relative average widths of respective structures. Areas are proportional to respective PCM volumes. https://doi.org/10.1371/journal.pone.0297420.g006 PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 11 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm stretches in gaps (Fig 6B’–6E’) suggest also random removal or addition of LSM stretches in non-labeled regions. To see whether large plaques could be composed of smaller units, we measured the lengths of the smallest discrete LSM structures in normal CM contacts (e.g. Fig 2E), in contacts and gaps of FN morphants (e.g. Fig 5B), and the LSM droplets in HA depleted CM (e.g. Fig 3F). We consistently found a peak at 100 nm with a slow decrease towards higher values (Fig 6F– 6H). In the plaque size distribution of CM (Fig 6A), these unit structures would occupy the lower margin. This is consistent with larger plaques being assembled from small units, and combinations of different LSM and non-LSM units generating patterns of alternating contact types. The effects of adhesion factor depletion on PCM distribution are summarized in Fig 6I. In CM, more than half of adhesive contact length is La3+-stained. In morphants, overall contact length is reduced and LSM contacts shrink disproportionally, from half of the total cell surface in normal CM to 1/4th or 1/5th in FN, C-cad and Has2 morphants, and less than 1/10th upon Syn-4 depletion. Non-labeled contacts remain at normal lengths except in Syn-4 morphants. In randomly sliced samples, volumes of object are proportional to their averaged cross-section areas, and for the volume of LSM or unlabeled PCM in contacts, relative contact length was multiplied by relative width (Fig 6I). C-cad or Syn-4 depletion reduces the contact volume of LSM by an order of magnitude. HA and FN depletion combine shortening of LSM contacts with their widening. Non-labeled contacts, although shorter, are wider in normal CM and their volume equals that of the LSM contacts (Fig 6I). In C-cad morphants, width is dramati- cally increased, and the volume of non-labeled contact augmented by almost 8-fold. Width increases lead also to 2–5.5-fold higher contact volumes in the other morphants (Fig 6I). The volumes of LSM and non-labeled contacts combined increase between 1.4- and 3.8-fold in the morphants, except for Syn-4 knockdown where the total is unchanged. In gaps (Fig 6I), more than half of the cell surface is coated with LSM in FN and C-cad mor- phants, and almost a quarter in HA or Syn-4 depleted CM. LSM layer thickness is increased compared to contacts, and together with the increase in overall gap size in all morphants, this amounts to a large LSM volume in gaps which dominates the total volume of LSM. In FN mor- phants, total LSM volume exceeds that of untreated CM by 2.5-fold, with the excess being accumulated in the gaps. In C-cad and Has1 morphants, a reduced LSM volume in contacts combines with increased LSM in gaps, as if material were redistributed with only a modest increase in total volume. In Syn-4 morphants total LSM volume is reduced by half, suggesting that Syn-4 promotes LSM deposition or is itself a main part of the LSM [4]. The amount of non-labeled PCM in gaps remains unknown, but even if it were completely absent, adhesion factor depletion causes a considerable increase of total PCM volume. This non-intuitive effect of adhesion factor loss could be due to the disregulated, excessive production of PCM material, or to the swelling of existing PCM for example in response to reduced PCM cross-linking, or to both. Relative adhesiveness is a function of contact width Contact angles and contact width are both spread over a wide range in normal and morphant CM, and we asked whether the two parameters are correlated. Importantly, the contact angle θ between cells is related to the dimensionless relative adhesion strength α, i.e. at gaps to the adhesion strength σi = βf − βc normalized to the tension at free surfaces, α = σi/βf = 1 − βc/βf = 1 − cos θ (Fig 1J; see also Fig 9) with 0 (no adhesion) �α � 1 (maximal adhesion) [16, 26]. Over- all, α increases with width w in normal and in morphant CM (Fig 7A–7E). In respective scatter plots, α values are concentrated above a lower boundary but become more dispersed farther PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 12 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 7. Relationship between relative adhesiveness and contact width. (A–E) Relative adhesiveness α was determined from contact angles and plotted as a function of contact width w for each gap-contact transition. The average value of α, αav, is indicated for w smaller or larger than 250 nm (see S1 Table). n, number of transitions measured in 26, 86, 59, 41, 80 TEM images, respectively. (A’–E’) A linear regression line (dotted blue line) was fit to the lowest values in each plot (blue dots). The slope Δα/Δw and the α-axis intercept α0 of the regression line α = (Δα/Δw)w + α0 are indicated. r, correlation coefficient for regression line. Green dots, values for α frequency peaks (see Fig 8 and S3 Table). (B’’–E’’) Higher magnification of (B’–E’) focusing on small w. α frequency peak data in (B’–E’) were pooled (red dots and lines) (see S3 Table) and compared to lower-boundary regression lines (blue). https://doi.org/10.1371/journal.pone.0297420.g007 above and with increasing w. In normal CM, most α values reside at w < 250 nm, and their average in this range is significantly lower in C-cad, FN, and Has morphants compared to nor- mal CM (S1 Table), as perhaps expected from a removal of adhesion factors. However, for w > 250 nm, α is strongly increased, and the expanded width ranges in the morphants allow to PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 13 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm compensate their reduced adhesiveness at w < 250 nm such that overall relative adhesion strength remains at a normal, high level. Syn-4 morphants differ, showing increased average α at both width ranges. The α-w relationship is conveniently analyzed for the lower boundary of α values. We con- structed it by selecting the data points on the enveloping curve, proceeding from one such point to the next point which is higher but does not require moving lower again subsequently. This was repeated up to where points became sparse (Fig 7A’–7E’). A linear regression line was then fitted through the enveloping points as α = (Δα/Δw)w + α0 with Δα/Δw the slope of the line and α0 its extrapolated value at w = 0 (Fig 7A–7E’). These parameters were similarly decreased in C-cad, FN and Has morphants compared to normal CM. Syn-4 morphants had increased values (see S2 Table). To examine the α-w relationship above the lower boundary, we determined the frequency distributions of α values for consecutive width brackets. Overall, the distributions are skewed with a first peak near the lower boundary and a long tail tapering off irregularly at higher val- ues (Fig 8A–8E and S3 Fig). Except for Syn-4 morphants, this first peak is usually the main peak of the distribution. It is not shifting noticeably to higher values over the first two width brackets, centered around 25 and 75 nm (S3 Fig), which were thus combined in Fig 8A–8E. As distributions shift to higher α values with widths above 200 nm, minor peaks or shoulders appear or become more prominent, and differences between treatments more obvious (Fig 8A–8E). Particularly, in Syn-4 morphants, frequencies are spread out almost evenly over a large α range (Fig 8E). The frequency of α values declines rapidly with increasing w (Fig 7A–7E), and with the heavily skewed α frequency distributions (Fig 8), large α values disappear preferentially at higher w, artificially lowering the nominal average of α. Instead, the first peak of the α fre- quency distributions can be followed to analyze the α-w relationship above the lower boundary (Fig 7A’–7E’’). Only few points, at low resolution, are obtained (Fig 7A’–7E’), but the values for all morphants are similar (S3 Table) and are thus pooled. The line connecting the averages increases linearly for w > 100 nm, but at a steeper slope than the lower-boundary line. For smaller widths, it seems to remain constant, in contrast to the lower boundary (Fig 7B’’–7E’’). Contact-gap transitions are sectioned at randomly oriented planes and distortion of contact angles broadens their distribution, letting narrow angles appear even narrower and wide angles even wider [25]. The variation of α values is broadened accordingly by the factor k, and we estimated k � 4/3 (see Materials and methods). Correction by this factor tilts lower bound- ary and peak lines upward (S4 Fig). The effect should be the same for all treatment conditions, and the span of the different α frequency distributions is also of the same order of magnitude. It is the range of contact widths w that differs several-fold between treatments, and together with α * w, this determines the overall relative adhesiveness of normal and morphant CM. Discussion Cell-cell contact types in the chordamesoderm In terms of cell packing density, in vitro cell motility, and contact spectra, the CM resembles the ectoderm more than the prechordal mesoderm [4, 27]. However, ectoderm and prechordal mesoderm share contact types while the CM, located between and linking these two regions, is different. In prechordal mesoderm and ectoderm, a glycocalyx I is identified via its difference spectrum characteristics, and glycocalyx II and III are recognized by their morphology [4]. None of these structures are seen in the CM. Instead, a 30 nm wide, triple-layered contact type with a non-labeled layer between two LSM sheets is present in the CM. Another, 10–20 nm wide LSM contact in the CM is narrower than adherens junctions and insensitive to C-cad PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 14 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 8. Frequency distributions of α values at different widths. (A-E’’’) Different treatments are arranged vertically and width brackets horizontally, as indicated on top of each diagram. n, number of α-w data points. Additional width brackets are shown in S3 Fig (F) Model of cell-cell adhesion by PCM interdigitation. Adhesion between two thin or thick PCM layers (light red) on cell membranes (blue) occurs in principle through a narrow interaction zone (deep red), yielding in each case the basic adhesiveness α0 (left). Interdigitation of the two apposed PCMs corresponds to a folding of the interaction surface which increases linearly with PCM height w at constant interdigitation distance d (right). https://doi.org/10.1371/journal.pone.0297420.g008 depletion but depends on the large HA and FN molecules. Despite their size, these factors can reside in narrow spaces. CNS synapses 20 nm wide harbor HA and its CD44 receptor [28, 29], and the string-like FN protein likewise occurs in synaptic clefts [30]. HA chains thousands of nm long occupy large volumes when randomly coiled, but when attached to a surface can form 0.3 nm thin layers [31]. Thus, HA and FN could directly build the 10–20 nm contacts. Non-LSM contacts are prominent in the gastrula but due to the lack of respective staining are less well characterized. In the CM, some narrow < 50 nm non-LSM contacts depend on C- cad, consistent with the presence of adherens junctions [1]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 15 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Pleiotropic and polygenic control of contacts by adhesion factors The CM results confirm our previous findings that adhesion factors are pleiotropic, each affecting various contact types, while contact types in turn are each controlled by several adhe- sion factors [4, 5]. As an example for pleiotropic functions, HA acts together with Syn-4 and other factors in ectoderm and prechordal mesoderm to build a 50–130 nm wide glycocalyx I, and in prechordal mesoderm a micron-wide brush-like glycocalyx III [4]. In the CM, HA sup- ports instead extremely narrow 10–20 nm LSM contacts. On the other hand, glycocalyx I is the prime example for a structure that depends on multiple adhesion factors, in the prechordal mesoderm at least on HA, Syn-4, Glypican-4, PAPC, ephrinB3 and EphB4 [4, 5]. The CM pro- vides additional examples. Among only 4 factors tested, the 10–20 nm contacts require HA and FN, some 30–50 nm contacts C-cad and Syn-4, and triple-layered contacts all four factors. The picture emerges that different adhesion factors, in variable combinations, build a mosaic of adhesive contact complexes whose molecular and mechanical details are yet to be studied. Depletion of a factor often modifies a contact. For example, upon depletion of HA, 10–20 nm LSM contacts disappear and wider, beaded LSM contacts appear, suggesting a change in width and structure, but not in adhesive role. Likewise, triple layered LSM contacts become wider in FN morphants, and non-LSM contacts apparently in all morphants. Modifi- cation, not dismantling was also observed in endothelial glycocalyx when HA or heparan sul- fate were enzymatically removed [32]. Modification of a contact type could include becoming non-adhesive. Thus, the apparent redistribution of LSM from contacts to gaps could be due to the accumulation in gaps of LSM rendered non-adhesive. The overall reduction of LSM upon Syn-4 depletion and the absence of triple layered contacts in C-cad and Syn-4 morphants sug- gest that contact types can also be lost. Quantitatively similar α-w curves in different mor- phants and across contact widths ranges suggest similar adhesiveness of the different residual adhesion types. Predominantly non-specific adhesion between PCMs by van der Waals forces, hydrogen bonds, Ca2+ bridges, or chain entanglement [33–37] would contribute to this, while specific interactions of adhesion factors could play mostly structural roles in the PCM. Adhesion strength in the chordamesoderm The average relative adhesiveness α at gaps in normal and in different adhesion factor-depleted CM is not correlated with contact abundance. To understand this unexpected result, we ask how relative adhesiveness α is related to a measure of absolute adhesion strength, the tissue surface tension σ, which has been determined for Xenopus gastrula tissues including the CM [16, 17, 38, 39]. It corresponds to the difference between the tensions at the free tissue surface, β, and at cell contacts, βc. β is essentially the tension generated by the contractile cell cortex, and βc is determined by a cadherin-dependent downregulation of this cortical tension to a within-tissue level βf and an oppositely oriented adhesion tension Γ/2, the binding energy released per unit area and per cell upon adhesion (Fig 9A) [26, 40]. Thus, σ = β − (βf − Γ/2), with β − βf being large compared to Γ/2 in gastrula tissues and cortex downregulation domi- nating adhesion strength [16, 25]. The contact angle θs at the tissue surface is given by cos θs = (βf − Γ/2)/β (Fig 9A). Importantly, at the free surface of interstitial gaps, tension does not return to the tissue surface level but remains at βf [25]. Tension at contacts around gaps is again βc = βf − Γ/2, and adhesion strength at gaps, relevant for the attachment and detachment of cells within the tissue, is σi = βf − (βf − Γ/2) = Γ/2, which in the gastrula is small compared to cortical tensions. Accordingly, tension equilibrium requires a contact angle θ much smaller than that at the tissue surface: cos θ = (βf − Γ/2)/βf (Fig 9A). For the strength of C-cad mediated adhesion, quantitative data are available. Depleting C- cad reduces absolute adhesion strength σ in gastrula tissues by about half: the tension β at the PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 16 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Fig 9. Diagrams schematically depicting the relationships between tensions (shown per cell) and contact angles at tissue surfaces (top) and at interstitial gaps (triangles). (A) In normal CM, cortical tension β at the tissue surface (black) is strongly reduced upon cell adhesion to tension βf (red). Release of binding energy due to adhesion factor interactions at the narrow CM contacts generates an adhesion tension Γ/2 (green). Tensions βf and Γ/2 balance the resultant tension βc (grey); surface contact angle, θs. The same tensions βf and Γ/2 act at the transition to interstitial gaps, but at the gap surface not β but the much smaller βf balance these tensions (orange), requiring a much smaller contact angle θ. (B) In C-cad morphants, tension β at the free surface remains; at contacts tension it is much less diminished. In the width range of normal CM, Γ/2 may remain the same, contact angle θ becomes smaller, and the relative adhesiveness α appears reduced. (C) When the average Γ/2 is increased with contact width much beyond the normal CM range, the contact angle θ at gaps can remain the same or even increase while a lowered angle θs at the surface still indicates the reduced overall adhesion strength (the difference σ = β − βc) due to C-cad depletion. https://doi.org/10.1371/journal.pone.0297420.g009 tissue surface is less reduced at contacts within the tissue, i.e. βc remains relatively high and thus the difference σ between a higher βc and an unchanged β is smaller [16]. At an unaltered adhesion tension Γ/2, this would decrease contact angles θ at gaps and thus α (Fig 9B), which represents the situation of diminished α in the < 250 nm width range in C-cad morphants (see Fig 7B). However, most contacts are much wider in these morphants, and as α increases in proportion to w, the average α for w > 250 nm is much higher, implying that Γ/2 is also increasing linearly with w. The resulting overall increase of Γ/2 upon C-cad depletion tends to compensate for the diminished reduction of β at contacts and the higher βf (Fig 9C). Quantita- tively, from βc = 0.06 mJ/m2 in normal CM [16], we calculate that βf = 0.07 mJ/m2 and Γ/ 2 = 0.01 mJ/m2, values which are almost identical for ectoderm [25]. Assuming for C-cad mor- phants a similarly increased βc = 0.15 mJ/m2 in CM as in the ectoderm, βf would be 0.19 mJ/ m2 and Γ/2, 0.04 mJ/m2. Thus, βf is 2.5-fold higher but Γ/2 is increased 4-fold in morphants, suggesting overcompensation, as is in fact apparent from the contact angles (see Fig 1K). A similarly increased Γ/2 after C-cad depletion had also been found for the ectoderm [25] but had been left unexplained at the time as the width dependence of α had not yet been uncovered. In summary, at gaps the effect of C-cad knockdown on cortical tensions is compensated by an increase in average adhesion tension Γ/2 due to the widening of contacts in morphants and a proportional increase of adhesiveness. Adhesion strength changes in FN and Has morphants have yet to be examined. The observed reduction of adhesiveness at low widths could in prin- ciple occur as in C-cad morphants (e.g. [41]). It could also be due to reduced adhesion tension PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 17 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm Γ/2: if the observed increase in contact PCM volume was due to swelling and accompanied by a decrease in PCM density, less interaction between binding sites would take place per PCM-PCM interface area and less binding energy would be released. Adhesion in Syn-4 depleted CM appears very different and may require different concepts for its analysis. A mechanism of pericellular matrix-based cell-cell adhesion To mediate cell-cell adhesion, PCM surfaces have been proposed to interact molecularly in a narrow contact zone by the limited interdiffusion of chain ends and the exchange of non-cova- lent binding interactions [6]. With only a thin slice at the surface of PCMs involved, adhesive- ness would be independent of total PCM thickness and hence of contact width (Fig 8F). For w < 100 nm, this is indeed seen for the line tracing α frequency peaks, but for this line at w > 100 nm and for the whole low-boundary line, the linear increase of α with w implies that adhesive interaction increases with the thickness of the PCM, most simply achieved by the interpenetration of the PCMs of two cells. Complete interpenetration of the glycocalyx layers has been observed at erythrocyte-macrophage contacts [42]. In the gastrula, the observed change in LSM layer thickness at transitions between contacts and gaps is consistent with such a process. Interpenetration of coherent PCM meshworks, molecule by molecule, over hundreds of nanometers, seems unlikely. To estimate the diameters of putative interpenetrating units in the contact plane, we assume that the units adhere on all their sides, and that the slope (Δα/ Δw) of the α-w curve is thus proportional to the density of unit-unit contacts, i.e. to twice the inverse of the diameter d of a unit, Δα/Δw * 2/d. Further, the slope should be proportional to the basic PCM-PCM adhesiveness α0, as seen in the absence of interpenetration, and thus Δα/ Δw = 2α0/d. For calculations the lower-boundary regression line provides the best data. Here α ! α0 for w ! 0 (Fig 8F) and with α0 and Δα/Δw read off from the α-w curves (Fig 7), d can be calculated as between 130–235 nm, except for Syn-4MO (S2 Table). The main peaks from the α distributions provide only few, low resolution data points for α-w curves but between 100 nm and 600 nm the combined data are consistent with a linear increase, in proportion to the lower-boundary line. Lines corrected for contact angle distortion are constructed by propor- tionally increasing the original lines by a factor k and will thus also give the same d values. Minor peaks in the α frequency distributions may indicate other contact types in a certain width range, with higher basic adhesiveness. The estimated d values are an order of magnitude larger than the deduced endothelial gly- cocalyx molecule spacing of *20 nm but agree with a 100 nm periodicity that reflects the size of whole glycocalyx bushes [43, 44], and with the sizes of minimal LSM units identified here. Interdigitation of such units is apparent in prechordal mesoderm where glycocalyx II bushes from opposite membranes intercalated in antiparallel fashion at the transition between inter- stitial gap and adhesive contact [4]. Contacts wider than 200 nm are not labeled with La3+ in the CM, but the linear α-w relationship is maintained, predicting that non-labeled PCM also consists of similarly sized structural building blocks, or PCM stubs, which can interdigitate for adhesion. In summary, the stub interdigitation model assumes a thin PCM-PCM interaction zone, determined by short-range molecular interactions, whose surface is increased by inter- digitation equivalent to an effective large-scale folding, such that adhesion tension per cell sur- face area, Γ/2, increases with PCM width (Fig 8F). Intercalation unit diameter d of Syn-4 morphants differs strongly from that of other mor- phants, suggesting a unique contact structure. Overall contact and LSM contact abundances, average relative adhesiveness α, and the α value distribution are also different. At the same time, cell shape differs fundamentally. In a single step, by the depletion of Syn-4, a spindly, PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 18 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm serrate mesenchymal cell outline is attained which is common in migratory embryonic tissues of other vertebrates [45–48] but not in amphibian gastrulae. Remarkably, early gastrulation movements including the well-studied convergent extension of the CM are not affected by this transformation whose cellular basis remains to be elucidated. However, mild axis defects caused by Syn-4 depletion can be detected later in development, hours after gastrulation, and these are to a large extent characterized by defective neural tube extension and closure [14]. Shedding of whole plaques but also of isolated stubs suggests that stubs are capable of reversible lateral adhesion. This raises the question of how the interdigitation of stubs from opposite membranes is favored over their lateral association as plaques on the same mem- brane. Possibly, their antiparallel lateral attachment during interdigitation releases more bind- ing energy than parallel contact. Being linked to the cortical cytoskeleton [44], stubs within plaques could also be actively separated to facilitate interdigitation, thus controlling the initia- tion of adhesion. Conversely, active lateral compression in existing contacts could promote de-adhesion and cell separation. Generally, controlled deployment of PCM materials at contacts and in gaps and regulation of PCM height and density may determine contact abundance and adhesion, and be essential for proper cell movements. Overproduction of PCM in C-cad, FN and Has morphants could clog the extracellular space, render cell surfaces non-adhesive and impede cell migration and rearrangement in multiple gastrula regions. By contrast, the sparse PCM in Syn-4 morphants could permit accelerated movements which may depend on cadherin functions as gastrulation is inhibited in Syn-4/C-cad double morphants. Such general mechanical effects of adhesion factor depletion may be considered as alternative or complementing explanations for gastrula- tion defects which so far are often attributed to aberrant cell signaling alone. On the other hand, while upon adhesion factor depletion some contact types seem to be modified but remain adhesive, others become non-adhesive and still others may disappear. Such lowering of contact abundance without affecting the average adhesion strength of remaining contacts may also contribute to gastrulation defects. Supporting information S1 Fig. Frequency distributions of contact widths. (A–E) Contact width spectra for normal CM (A), from Barua et al. [4], and for CM morphants (B–E). Contact widths abundances were collected in 50 nm width bins. (A’) The difference spectrum comparing normal CM and ecto- derm shows the signature of glycocalyx I (encircled), suggesting absence of this contact type in the CM. (B’–E’) Difference spectra comparing CM morphants to normal CM (the spectrum for normal CM subtracted bin by bin from respective morphant spectra). (TIF) S2 Fig. Width frequency distributions of triple-layered (LSM-unlabeled-LSM) contacts. Wt, 101 measurements from 4 TEM images; FN knockdown (FNMO) 107 measurements from 8 TEM images; Has1 knockdown (Has1MO) 138 measurements from 8 TEM images. No triple-layered contacts were seen in C-cad or Syn-4 morphants. (TIF) S3 Fig. Frequency distributions of α values at different widths. (A–E, A’–E’) Each 0–100 nm width bracket in Fig 8 is broken up into a 0–50 nm and a 50–100 nm bracket, respectively. (A’’–E’’, B’’’–E’’’) Distributions for larger widths brackets not shown in Fig 8. Treatments and width brackets indicated on top of each diagram. n, number of α-w data points. (TIF) PLOS ONE \| https://doi.org/10.1371/journal.pone.0297420 February 12, 2024 19 / 23 PLOS ONECell contacts in Xenopus gastrula chordamesoderm S4 Fig. Relative adhesiveness α plotted as a function of contact width w for combined data, to estimate a correction factor k for contact angle values. Dotted lines, regression lines for lowest (red) and highest values (green). Dashed lines, corrections for contact angle distortions due to random orientation of sectioning planes, as described in the Methods section. k = 4/3 is compatible with the intersection of the corrected lines just beyond the α-w distribution. Equa- tions for lines are indicated as in Fig 7. (TIF) S1 Table. Relative adhesiveness α at low (w < 250 nm) and high (w � 250 nm) contact widths. Av, average of α values; SD, standard deviation; n, number of α-w data points, i.e. gap- contact transitions; p (MO/wt), p-values for significance morphants vs. untreated CM (wt); p (low/high), p-values for significance low vs. high contact widths. (PDF) S2 Table. Regression line parameters. Regression lines delineating the lower boundaries of α- w distributions were determined as described in the main text. α0, intersection of regression line with α axis; Δα/Δw, slope of regression line; r, regression coefficient; d, interdigitation dis- tance d = 2α0/(Δα/Δw). The calculated d is comparable to the measured average lengths of the shortest LSM units (“stubs”) in normal contacts (156 nm), FN morphants (128 nm) and Has1 morphants (111 nm) shown in Fig 6F–6H. (PDF) S3 Table. Positions of first peak in α frequency distributions in consecutive width brackets. Frequencies of α were binned in 0.025 intervals of α and the position of the first peak is indi- cated as the mid-point of a bin, as read off from Fig 8 and S3 Fig, for width brackets 0–50 nm, 50–100 nm, 100–200 nm, 200–400 nm, 400–800 nm, and 800–1200 nm. The mid-points of these brackets are indicated. (PDF) S1 Dataset. (ZIP) Acknowledgments We thank A. Chong of the imaging facility at the University of Toronto’s Department of Cell and Systems Biology for help with TEM imaging, and S. E. Parent for comments and for converting the manuscript from a Microsoft Word document to a formatted LATEX document. Author Contributions Conceptualization: Debanjan Barua, Rudolf Winklbauer. Data curation: Olivia Luu, Debanjan Barua. Formal analysis: Olivia Luu, Rudolf Winklbauer. Funding acquisition: Rudolf Winklbauer. Investigation: Olivia Luu, Debanjan Barua. Project administration: Rudolf Winklbauer. Supervision: Rudolf Winklbauer. Visualization: Olivia Luu. 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10.1371_journal.pgen.1011192	RESEARCH ARTICLE Canadian COVID-19 host genetics cohort replicates known severity associations 1,2, Paola Arguello-Pascualli3,4, Olga Vishnyakova1,5, Anat R. HalevyID 2, 2,6, Jennifer D. BrooksID Elika GargID Samantha YooID T. Greenwood9,10, Rayjean J. HungID Jessica K. DennisID Bhooma Thiruvahindrapuram2, Steven J. M. JonesID J. Strug2,7,14, Andrew D. Paterson2,7, Lei Sun7,14, Lloyd T. ElliottID 7, Shelley B. BullID 7,8, Jerald F. LawlessID 3,4, Rohan J. S. Abraham5, Jean-Michel GarantID 1* 7,8, France Gagnon7, Celia M. 11, Jordan Lerner-EllisID 8,12,13, 5, 5,4, CGEn HostSeq Initiative¶, Lisa a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Garg E, Arguello-Pascualli P, Vishnyakova O, Halevy AR, Yoo S, Brooks JD, et al. (2024) Canadian COVID-19 host genetics cohort replicates known severity associations. PLoS Genet 20(3): e1011192. https://doi.org/10.1371/journal. pgen.1011192 1 Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada, 2 Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada, 3 BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada, 4 Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada, 5 Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada, 6 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada, 7 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada, 8 Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada, 9 Gerald Bronfman Department of Oncology, Department of Epidemiology, Biostatistics and Occupational Health, Department of Human Genetics, McGill University, Montreal, Quebec, Canada, 10 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada, 11 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, 12 Mount Sinai Hospital, Toronto, Ontario, Canada, 13 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada, 14 Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada ¶ https://www.cgen.ca/hostseq-contributing-studies-implementation-committee * lloyd_elliott@sfu.ca Editor: Giorgio Sirugo, University of Pennsylvania, UNITED STATES Abstract The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic con- tributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospital- ized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and con- structed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation. Received: June 30, 2023 Accepted: February 22, 2024 Published: March 22, 2024 Copyright: © 2024 Garg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: HostSeq sequencing and clinical data are available through a Data Access Agreement and Data Access Compliance Office (DACO) approval (https://www.cgen.ca/ daco-main). The subset of HostSeq data (N = 8,474) that was analysed here can be made available upon DACO approval. The code used for conducting GWAS, G x Sex interaction, SKAT-O, and PRS can be found in a publicly accessible repository (https://github.com/eg-r/HostSeq). HostSeq and HGI7no GWASes are available via myLocusZoom (HostSeq: https://my.locuszoom. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 1 / 25 PLOS GENETICSorg/gwas/570140/?token=18b0349bf40545cda 7a92ce665219a89, HGI7no: https://my.locuszoom. org/gwas/477715/?token=c297add610b040b58e 732228855cfb7f). For HostSeq, summary statistics from the primary regenie GWAS are provided for all variants passing the MAF > 0.05, excluding the GIAB difficult-to-sequence regions. For HGI7no, summary statistics for all HGI7 variants are provided for the B1 contrast after meta-subtract (leaving out BQC19, CGEN and 23andMe). Funding: ADP and LS were supported by Canadian Institutes of Health Research Project Grant 470360 (https://cihr-irsc.gc.ca). LJS was supported by Canadian Institutes of Health Research Foundation Grant 167282 (https://cihr-irsc.gc.ca) and Canada Research Chairs (https://www.chairs-chaires.gc.ca/ ). JL-E was supported by Canadian Institutes of Health Research Foundation Grant VR4-172753 (https://cihr-irsc.gc.ca). LTE was supported by Michael Smith Health Research BC Scholar Award SCH-2022-2784 (https://healthresearchbc.ca). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Canadian COVID-19 host genetics cohort Author summary Host genetics determine how human genetics contribute to the response to infectious dis- ease. HostSeq is a Canada-wide effort to contribute to our understanding of host genetics for COVID-19. The HostSeq study involves genetic and clinical data from individuals who tested positive for SARS-CoV-2 across Canada. This work examines locations in the human genome that have been reported to be involved in severe COVID-19 worldwide and determines if we also see the involvement of those genomic locations in Canadians. This work also explores several ‘genetic quantities’ such as a determination of how much of COVID-19 severity is due to genetics in Canada. Introduction The HostSeq project was initiated as a Canadian response to the global COVID-19 pandemic in April 2020 by CGEn (Canada’s national platform for genome sequencing and analysis). In brief, HostSeq assembled a databank based on 15 clinical and epidemiological studies with DNA samples and clinical information from ~10,000 Canadians infected by the SARS-CoV-2 virus. We have described the HostSeq resource in detail in previous work [1]. In this paper, we present genetic analyses of N = 8,474 of 10,059 joint-called HostSeq genomes that passed our extended quality control measures. Our primary outcome variable is hospitalization due to COVID-19. There has been a long history of human genetic studies of susceptibility and severity of infectious diseases [2,3]. In the last 15 years, genome-wide association studies (GWAS) have identified numerous variants associated with complex human diseases or traits [4]. Variants associated with susceptibility to or severity of infectious diseases have provided insight into the genes and mechanisms involved. Loci identified from GWAS can be combined into a single score that reflects some of the genetic contributions to a complex trait, often called a polygenic risk score (PRS) [5]. Here we aim to conduct a GWAS and a PRS analysis for severe COVID- 19, defined as being hospitalized after infection with SARS-CoV-2, using the CGEn HostSeq resource. Our hospitalization outcome maps to the ‘B1’ variable of the Host Genetics Initiative (HGI) [6], a global working group dedicated to COVID-19 host genetics [7]. Given our relatively small sample size (N = 8,474) compared to the much larger HGI meta- analyses (over 85,000 individuals included in the B1 contrast), we did not expect to have novel GWAS findings. Therefore, besides conducting a whole-genome scan in these Canadian data, we sought to replicate top associations reported by HGI and evaluate consistency between the two studies. As some HostSeq samples were part of the HGI meta-analysis (‘CGEN’ and ‘BQC19’; 8), we created a version of HGI results (referred to as ‘HGI7no’) by removing the effect of overlapping samples; see Methods for details. In HGI7no, there were three genome- wide significant loci (chr3:45805277, chr6:41515629, chr21:33249643), of which two were rep- licated in the HostSeq GWAS. The FOXP4 locus on chromosome 6 passed genome-wide sig- nificance in our primary analysis. Additionally, a PRS constructed from these three loci was significantly associated with hospitalization status. We also conducted post-GWAS analyses, including an ancestry stratified meta-analysis (SAIGE and MR-MEGA) [9,10]. We examined genotype-by-sex interactions (G x Sex) since there is strong epidemiological evidence that males are at increased risk for severe COVID-19 [11]. We performed functional mapping analysis on our primary GWAS results using FUMA GWAS [12], finding a significant association with MRAS. We analyzed gene-based coding vari- ants using the optimal sequence kernel association test (SKAT-O) [13] to include rare variants PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 2 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort in our analysis, finding a significant association with WDR89. Finally, we performed a SNP- based heritability analysis, using the linkage-disequilibrium score regression approach (LDSC) [14], and compared the HostSeq heritability estimate to that of HGI. Related work While COVID-19 is caused by infection with the SARS-CoV-2 virus, work since the beginning of the pandemic has shown that human genetic variation modulates both susceptibility to infection and the severity of COVID-19. The current version of HGI, released in April 2022, found 51 loci across all phenotypic contrasts [8]. Of the 51 loci, 38 have been reported by ear- lier studies [15–20]. Variants identified in these COVID-19 GWAS are linked to viral entry into host cells (SLC6A20, SFTPD, and TMPRSS2), the type I interferon pathway (IFNAR2, TYK2, JAK1, IRF1, IFNA10, TLR7, and DOCK2), the inflammatory pathway (OAS gene cluster, DPP9 and TYK2), as well as lung function and respiratory diseases (such as MUC5B, DPP9, and FOXP4). The strongest and most consistent finding for COVID-19 severity is the 3p21.31 region containing multiple protein-coding genes, including LZTFL1, SLC6A20, FYCO1, and chemokine receptor genes (CCR9, CXCR6 and XCR1). Among these genes, LZTFL1 is broadly expressed in pulmonary epithelial cells, FYCO1 is involved in transporting of autop- hagic vesicles, and SLC6A20 encodes a sodium transporter interacting with ACE2, the recep- tor that SARS-CoV-2 binds to [18]. Two other robust genetic associations to disease severity point to inflammasome regulator DPP9 (19p13.3) and high-affinity interferon α/β receptor IFNAR2 (21q22.11), originally reported by the Genetics of Mortality in Critical Care (GenO- MICC) GWAS [20]. One of the first risk loci for COVID-19 severity identified by GWAS was the OAS gene clus- ter (12q24.13 including OAS1, OAS2 and OAS3), carrying a Neanderthal-derived haplotype [20]. These OAS genes encode proteins involved in viral clearance. A plausible causal variant in OAS1 (rs10774671) was independently identified by two groups to predict an isoform of OAS1 using a two-sample Mendelian randomization method [21] and a trans-ancestry fine- mapping approach [22]. On the X chromosome, a non-coding upstream variant of ACE2 (Xp22.2) was associated with disease susceptibility [23]. Analysis of RNA sequencing data from liver tissue showed that the protective rare rs190509934-C allele downregulates ACE2 expression and subsequently impacts disease risk [23]. The T allele of rs2285666, an ACE2 intronic variant, was associated with critical outcomes among male COVID-19 patients [24]. Carrying the rs2285666-T allele was associated with increased risk for critical pneumonia in males with COVID-19 and was linked to impaired type I interferon responses [25]. Many lead variants reported by GWAS of COVID-19 severity are located in non-coding regions and within large haplotype blocks in high linkage-disequilibrium (LD), such as the 3p21.31 locus. CRISPR (clustered regularly interspaced short palindromic repeat) genome editing technology identified CCR9 and SLC6A20 as plausible causal genes associated with COVID-19 severity [26], while joint genome-scale CRISPR loss-of-function screens and expression quantitative trait locus analysis pointed to SLC6A20 and CXCR6 as target genes [27]. A recent study used a CRISPR technology to link the risk allele of rs11385942 (an intronic variant in LZTFL1) with reduced expression of LZTFL1 in lung epithelial cells [28]. Among these previously reported GWAS findings, associations at FOXP4 and LZTFL1/SLC6A20 are present in both the ‘HGI7no’ summary statistics and in our HostSeq results. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 3 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Materials & methods Ethics statement HostSeq was approved by the Research Ethics Board of The Hospital for Sick Children (#1000070720 from 2020-present). Written informed consent was obtained from all partici- pants or parents/guardians/substitute decision makers prior to inclusion in the study. HostSeq genotype and phenotype data We analyzed genetic data from version 9 (v9) of the HostSeq project [1], which was released in March 2023, and included 10,059 genomes from 15 studies across Canada. Recruitment, sequencing and joint-calling details are provided in the HostSeq resource paper [1]. We extracted the following phenotypic variables from the Case Report Forms (CRFs) for our analysis: hospitalization status, sex and age. When an individual’s age was not directly available in CRF, their age was inferred using the dates of birth and sample collection. If the latter was unavailable, June 2020 was used as a proxy endpoint. For this analysis, we removed 1,585 participants during the quality control and preprocess- ing steps (described in Data processing below and shown in S1 Fig), yielding a sample size of N = 8,474. We note that 1,312 of 1,585 participants were excluded because their phenotype information was either unavailable or insufficient in the harmonized clinical database. Table 1 shows the sample size for each of the contributing studies, as well as sample overlap if multiple studies recruited the same participant. We categorized the 8,474 HostSeq individuals into five major ancestry groups (S2–S4 Figs) using ancestry-inference from GRAF-pop [29]: 455 ‘AFR’ African (5.4%), 537 ‘AMR’ Admixed Table 1. Summary of study sizes for N = 8,474 HostSeq samples analyzed. Details about the design of these contributing studies and the institutions and investigators involved are provided in the HostSeq resource paper [1]. Stars * indicate that the study has overlapping samples with another study (due to recruitment in multiple studies). The size of the overlap between CANCOV and Concor-Donor is 11 samples. Between CANCOV and GENCOV: 10 samples. Between GENCOV and Concor-Donor: 2 samples. Between BQC19 and IPCO: 9 samples. Between genMARK and Concor-Donor: 2 samples. Between GenOMICC and GENCOV: 1 sample. Between SCB and gen- MARK: 3 samples. Removing duplicated samples yields N = 8,474. Study Hospitalized Non- Hospitalized Total Alberta Childhood COVID-19 Cohort Study (AB3C) Convalescent Plasma for COVID-19 Research (Concor-Donor) Genetic Markers of Susceptibility to COVID-19 (genMARK) Genomic Determinants of COVID-19: Integration of Host and Viral Genomic Data to Understand the COVID-19 Epidemiologic Triangle (GD-COVID) Host Genetic Susceptibility to Severe Disease from COVID-19 Infection (AB-HGS) HostSeq—Canadian COVID-19 Human Host Genome Sequencing Ottawa (LEFT-GEN) Host Genetic Factors Underlying Severe COVID-19 Implementation of Serological and Molecular Tools to Inform COVID-19 Patient Management (GENCOV) The IRCM POST-COVID-19 Research Clinic: a multidisciplinary approach to evaluate short and long-term complications of COVID-19 (IPCO) Screening Protocol for Detection of Infections and Immunodeficiencies and Characterization of Susceptibility to Infectious Diseases The Canadian COVID-19 Prospective Cohort Study (CANCOV) The Genetics of Mortality in Critical Care (GenOMICC) The Hospital for Sick Children’s COVID-19 Biobank (SCB) The Quebec COVID-19 Biobank (BQC19) Understanding Immunity to Coronaviruses to Develop New Vaccines and Therapies against 2019-nCoV Total with 38 duplicates https://doi.org/10.1371/journal.pgen.1011192.t001 16 27 34 91 9 43 10 61 5 30 430 320 92 2334 3 3505 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 151 748 702 391 0 10 34 874 52 7 577 7 158 1289 7 5007 167 775* 736* 482 9 53 44 935* 57* 37 1007* 327* 250* 3623* 10 8512 4 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort American (6.3%), 519 ‘SAS’ South Asian (6.1%), 654 ‘EAS’ East Asian (7.7%), and 6107 ‘EUR’ European (72.1%); the AFR set combines African-American (1.6%) and African-only (3.7%) groups. In addition, 202 samples remained uncategorized (2.4%). Data processing We implemented a comprehensive quality control (QC) procedure on the multi-ancestry joint-called data of the HostSeq genomes available on the human genome build GRCh38 (S1 Fig). We used bcftools (v1.11) [30] to determine our variant exclusion list, VerifyBAMID2 (v2.0.1) [31] to estimate DNA contamination, average read depth and number of reads, and PLINK (v2.0.0) [32] to calculate heterozygosity, test Hardy-Weinberg equilibrium (HWE), perform linkage-disequilibrium (LD) pruning, and conduct principal component analysis (PCA). We used the R platform (v3.6.3) [33] to conduct descriptive and statistical analyses as well as to create figures. We performed multiple rounds of alternating variant and sample QC. First, we applied the GATK hard-filtering protocol (Resources) on the joint-called data to exclude variants with low quality measures (details are in S1 Fig). Then, we used the retained variants and information from the CRF to assess the quality of the samples. Here we checked their genome quality (removing 102 samples with genotyping call rate < 99%, or number of reads < 2E6, or contamination > 3%), sample identity (removing 160 samples because of mismatch between reported and predicted sex, or because they were identified to be duplicates), information in clinical database (removing 695 samples because their phenotypic information was not yet harmonized, or because their consent for research was withdrawn), phenotype availability (removing 617 samples due to missing age, sex or hospitalization status), and heterozygosity (removing 11 outliers). After the sample QC we used the retained samples to assess the quality of the variants and removed variants with genotyping call rate < 98%. We then performed principal component analysis on a total of 8,474 samples passing the above QC checks, after LD pruning the variants (details are in S1 Fig). We did not find any extreme sample outliers to remove based on PCA. Finally, we checked for variants with deviations from Hardy-Weinberg Equilibrium (HWE) using the non-hospitalized (control) sample of European ancestry, and removed deviating variants with P < 1E-50 from all samples used in the analyses. S1 Fig pro- vides details for the variant and sample QC criteria. In our summaries of GWAS results (described below), we further excluded variants in difficult-to-sequence regions as annotated by the Genome-In-A-Bottle consortium (GIAB v3.3) [34]. Our comprehensive QC resulted in a final set of ~153M variants and N = 8,474 individuals. S5 Fig shows the quality of the retained samples with regards to missingness, contamination and coverage. The samples include multiple ancestries, as well as some related individuals. Genetic analysis variables Our phenotype of interest is COVID-19 severity as defined by hospitalization status (yes/no), where both cases and controls were SARS-CoV-2 positive. Covariates of interest include age, sex, age x sex, age2, age2 x sex, and seven genetic PCs; this list of covariates is often used in GWAS [35]. Due to the extensive QC conducted earlier, there were no missing phenotypes or covariates for any of our N = 8,474 samples. We identified seven important genetic PCs from the scree plot of the final round of PCA (scree plot, pairwise PCA plots, and distribution of PCs are shown in S6–S8 Figs). We created a standardized age variable, defined as (age-50)/10 [36]. We included age2 as a covariate because the incidence of hospitalization for COVID-19 may increase non-linearly with age. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 5 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Sex is also an important risk factor for hospitalization, so in addition to its main effect, we also included age x sex and age2 x sex interaction terms as covariates. Genome-wide association analyses: Single-variant, G x Sex and gene-based We used regenie (v3.2.9;37) for single-variant association study, interaction testing and gene- based analysis (see S9 Fig for details). Single-variant GWAS is our primary analysis, but accounting for G x Sex (genotype by sex) interaction is also of interest, as the risk for severe COVID-19 differs between males and females [11]. Furthermore, we performed a rare-variant gene-based test because individual coding variants are typically rare, and power to detect asso- ciation with each single rare variant is low. Such a joint analysis of multiple coding variants in a gene is a commonly employed approach to improve power. Analyses were performed on bi-allelic variants across chromosomes 1–22 and X. The X chromosome was analyzed separately for the pseudo-autosomal regions (PAR) and non- pseudo-autosomal regions (NPR). In total, 147M autosomal, 0.14M PAR and 6M NPR variants were analyzed. All autosomal and PAR variants, in both males and females, were coded addi- tively as 0, 1 and 2. NPR variants in females were also coded as 0, 1 and 2, but in males, they were coded as 0 and 2, which assumes X-inactivation, as specified by regenie. We used the rec- ommended block-size of 1,000 and default parameter values in all regenie steps. The regenie implementation involves two steps. Step one uses a subset of variants that “cap- tures a good fraction of the phenotype variance attributable to genetic effects” [37] and forms phenotype predictions [37], and step two performs the association analysis conditional on the predictions from step one. For step one, we prepared the required subset by restricting variants to Illumina’s Global Screening Array (GSA v3 b151 GRCh38; Resources) with minor allele fre- quency (MAF) > 10% and minor allele count (MAC) > 100. Using the predictions from step one, we executed step two to obtain our primary GWAS results, as well as G x Sex interaction and gene-based testing results. In the second step of regenie, we opted for the Firth-approxi- mation option for more accurate association p-value calculation. For the G x Sex interaction analysis, we report results from jointly testing for G main and G x Sex interaction effects. This two degrees-of-freedom (2 d.f.) joint test is better powered to detect variants with sex-specific genetic effects, and in the absence of effect heterogeneity, it is comparable to the standard GWAS approach of testing for main effect only [38]. For the gene-based analysis inclusive of rare variants, we first annotated coding regions out- side difficult-to-sequence regions using Ensembl (v110.1; 39) and selected variants with high/ moderate impact. We then performed the SKAT-O test [13] as implemented in regenie with the default weighting factors of a1 = 1 and a2 = 25. Coding variants in 17,886 genes were cate- gorized into two regenie masks: (i) high impact and (ii) high/moderate impact. We performed the test twice with maximum alternate allele frequency (AAF) set to 0.01 or 0.05, as estimated from HostSeq. Genome-wide association analyses: Single-variant meta-analysis Although our primary single-variant HostSeq-wide analysis via regenie accounted for popula- tion stratification, as a complementary alternative approach, we additionally conducted a meta-analysis with stratification across ancestry groups. To this end, we first used SAIGE [9] to obtain single-variant summary statistics for each ancestry group. We then meta-analyzed these ancestry-specific GWASes using MR-MEGA [10]. More specifically, within each of the five ancestry groups (N: AFR = 455, AMR = 537, SAS = 519, EAS = 654, and EUR = 6,107), we conducted GWAS using the SAIGE mixed model. This involved incorporating a kinship matrix as a random effect and covariates as fixed PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 6 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort effects. SAIGE uses Firth’s Bias-Reduced Logistic Regression to estimate effect sizes and the saddlepoint approximation to calibrate unbalanced case-control ratios, which is essential for some ancestry groups (S1 Table). Similar to the primary mega-analysis, the ancestry-stratified GWAS here was restricted to bi-allelic variants across the whole genome including the X chro- mosome, and accounting for the same set of covariates. But considering the smaller ancestry- specific sample size and SAIGE recommendations, we further removed variants with MAF < 1%, MAC < 20, and genotyping call rate < 99%. To aggregate the above ancestry-spe- cific GWAS summary statistics (total N = 8,272), MR-MEGA considers the potential heteroge- neity in effect sizes across ancestry groups. To be conservative, MR-MEGA first applies a genomic control correction to each ancestry-specific GWAS to account for residual population structure before meta-analysis. It then includes the first two PCs, derived from a matrix of alle- lic frequency similarities between GWASes, as covariates. We note that, unlike the regenie analysis (N = 8,474), we did not include the 202 individuals without clear ancestry categoriza- tion from the meta-analysis, because MR-MEGA may not be effective when analyzing admixed individuals [10]. Comparison to HGI and functional analysis Using our HostSeq association results for COVID-19 severity we aimed to replicate HGI find- ings for the B1 contrast. The available HGI GWAS summary statistics are from a meta-analysis of several studies including two studies from HostSeq (BQC19 and CGEN). Therefore, we sought to subtract out the effect due to sample overlap using the R package MetaSubtract (v1.60;40). To achieve this, we used the available ‘leave-one-out BQC19’ HGI GWAS results (which also does not include 23andMe) and further removed the effect of CGEN using Meta- Subtract. We refer to this HGI v7 non-overlapping version as HGI7no (cases = 15,591, con- trols = 70,608). We used HGI7no findings for our replication study. We identified three genome-wide significant loci in HGI7no and examined their colocalization in HostSeq through myLocusZoom (v0.14.0) [41,42] and LocusFocus (v1.4.9 alpha) [43]. The LocusFocus colocalization tool considered a local area around each lead variant spanning 0.1Mb on either side (including 300–600 variants for each lead variant). Additionally, we performed functional analysis using MAGMA (v1.08) [44] as implemented in the FUMA GWAS (Functional Map- ping and Annotation of Genome-Wide Association Studies v1.6.1; 12) software package. Polygenic risk score We used PRSice-2 (v2.3.5) [45] to calculate polygenic risk scores (PRS) using HGI7no as our base data (see S9 Fig for details). We calculated standardized PRS (scaling so that mean = 0 and SD = 1) using the three variants in HGI7no that passed the genome-wide significance level of p-value < 5E-8 [46] after LD-clumping (window-size = 750kb, r2 = 0.1). To determine the extent of polygenicity in our study, we also calculated PRS using HGI7no at additional p- value thresholds of 1E-5, 1E-4, 1E-3, 1E-2, 5E-2, 1E-1, 5E-1, and 1; the X chromosome is not included in the PRSice computation. PRSice is a clumping and thresholding method which works by selecting a single variant with the highest p-value from LD blocks constructed with the target population. This process is prone to discarding potentially relevant information (especially when considering a large number of SNPs) and imposes constraints on the genetic architectures that can be modeled [45]. To complement this clumping and thresholding approach employed by PRSice, we also applied an alternative method, PRS-CS (v1.1.0) [47]. In this approach, the weights assigned to SNPs in the PRS are updated based on their association strength in the GWAS using a Bayes- ian framework. The advantages of using this approach are: (i) flexibility in accommodating PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 7 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort diverse genetic architectures, especially considering the unknown architecture of COVID-19 severity, and (ii) integration of information from an external reference panel for LD patterns, as previous studies [48] have demonstrated its efficacy in enhancing predictive performance. Specifically, we used PRS-CS to adjust the effect sizes of autosomal SNPs present in both HGI7no and HostSeq based on the LD reference panel [47] pre-computed by PRS-CS from the European super-population of the 1000 Genomes Project phase 3 (Resources). We allowed for the global shrinkage prior (the ϕ parameter) to be estimated from the data using a fully Bayesian approach. Finally, we used these modified effect sizes to calculate PRS using PLINK (v1.9) [49]. Heritability We used LDSC (v1.0.1) [50] to estimate SNP-based heritability in HostSeq and HGI. Summary statistics were quality-controlled using the mungeStats pipeline recommended by LDSC (using the LD scores from the 1000 Genomes Project phase 3); the X chromosome is not included in the LDSC computation. Results Table 2 shows the basic demographics of the N = 8,474 HostSeq v9 participants analyzed. As expected, age was significantly associated with COVID-19 hospitalization status, with older individuals having a higher risk (Welch two-sample t-test: P < 2.2E-16; T = -43.93; S10 Fig). Sex at birth was also associated with being hospitalized with females having a lower risk (Fish- er’s exact test: P < 2.2E-16; OR = 0.41; 95% CI = [0.37, 0.45]). S1 and S2 Tables provide counts by sex and hospitalization status, stratified by ancestries and studies, respectively. S11 Fig com- pares the allele frequency distribution between HostSeq and gnomAD (v3.1.2) [51], where HostSeq samples are the 100% European ancestry (as predicted by GRAF-pop) subset and gno- mAD samples are the non-Finnish European subset. Genome-wide association analyses: Single-variant Fig 1 shows the primary HostSeq GWAS results from regenie for variants that are not in diffi- cult-to-sequence regions and with MAF > 5% (genomic control inflation statistic, λ = 1.048). MAF-stratified QQ-plots and p-value histograms are provided in S12 Fig and show that the study has a well-controlled type I error rate by focusing on MAF > 5%. S13 Fig presents the results prior to the removal of difficult-to-sequence regions, showing the importance of excluding variants in difficult-to-sequence regions as part of QC since sporadic signals appear in some of these regions. Here we report the top five HostSeq loci, including one genome-wide significant hit on chromosome 6 (Table 3). Of the five variants, rs4714474 (chr6:41535823 on 6p21.1) and rs35731912 (chr3:45848457 on 3p21.31), are respectively in LD (Resources) with lead variants reported by HGI at the FOXP4 (rs12660421) and LZTFL1 loci (rs17713054). S14 Fig shows the Table 2. Summary statistics for the N = 8,474 samples in the examined HostSeq study cohort. All samples were COVID-19 positive. While females were slightly more represented in the recruited population, more than half of the hospitalized participants were males. Sample size Age (years) Sex Mean (SD) Male Female https://doi.org/10.1371/journal.pgen.1011192.t002 Hospitalized 3,499 59.31 (20.65) 1,954 (55.8%) 1,545 (44.2%) Non-Hospitalized 4,975 40.84 (16.52) 1,692 (34.0%) 3,283 (66.0%) Total 8,474 48.46 (20.47) 3,646 (43.0%) 4,828 (57.0%) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 8 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Fig 1. Genome-wide association study of hospitalization status in 8,474 HostSeq samples with COVID-19 from the March 2023 release (V9). In the Manhattan plot, Y-axis indicates -Log10 p-values of regenie analysis for variants with MAF > 5%, X-axis indicates chromosomes. Variants falling in the GIAB difficult-to-sequence regions have been excluded. Grey horizontal line indicates genome-wide significance level of P < 5E-8. Chromosome 6 and chromosome 3 loci have been previously identified in HGI. In the corresponding QQ-plot, the X and Y axes indicate expected and observed -Log10 p-values, respectively (genomic control λ = 1.048). https://doi.org/10.1371/journal.pgen.1011192.g001 surrounding regions in myLocusZoom for the other three variants. These are rs78173596 (chr15:54131608 on 15q21.3), an intronic variant of UNC13C, rs17122332 (chr10:107238146 on 10q25.1) an intergenic variant upstream of SORCS1, and rs1199346 (chr3:138353967 on 3q22.3) an intronic variant of MRAS. eQTLGen Phase I [52] also identifies the chromosome 3 hits to be significant cis-eQTLs (rs35731912 of FLT1P1, CCR3, CXCR6, CCR1, SACM1L, CCR5, CCR9, CCR2 and RP11-24F11.2; rs1199346 of MRAS, CEP20 and FAIM). Additionally, we performed functional analysis using MAGMA as implemented in FUMA GWAS [12]. The gene-based test as computed by MAGMA found MRAS with P = 3.52E-7 to be genome-wide significant at ɑ = 0.05/18,329 = 2.73E-6 (S15 Fig). Furthermore, the MAGMA gene-set analysis found a curated gene set ‘HASEGAWA_TUMORIGENESIS_BY_R- ET_C634R’ comprising 7 genes to be significant with P = 4.02E-4 after Bonferroni-correction (S3 Table provides results for each of the 7 genes). The multi-ancestry meta-analysis did not reveal any new loci (genomic control inflation statistic, λ = 0.991; S16 Fig). MR-MEGA meta-analysis of the five categorized ancestries included an ancestry-specific genomic control correction on summary statistics from SAIGE PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 9 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Table 3. Association details of lead variants from HostSeq. Top loci in HostSeq after applying a MAF > 5% filter and removing variants in the GIAB difficult-to- sequence regions. Chromosome 6 hit passes the genome-wide significance threshold of P < 5E-8, and is in LD with a HGI7no lead variant (rs2496646: D’ = 0.87; r2 = 0.42). Chromosome 3 hit is also in LD with a HGI7no lead variant (rs17763742: D’ = 0.95; r2 = 0.82). Nearest-gene annotation is from myLocusZoom. Multi-ancestry meta-analysis p-values of HostSeq are added in paranthesis after primary HostSeq results (MR-MEGA); ‘m’ indicates number of ancestries MR-MEGA used for the result. Marker rs4714474 Chromosome Position Nearest-Gene Effect Allele Reference Allele Effect Allele Freq. Beta SE 6 41,535,823 FOXP4-AS1 A G 0.07 0.47 0.09 rs35731912 3 45,848,457 LZTFL1 T C 0.10 0.37 0.07 rs78173596 15 54,131,608 UNC13C C T 0.10 0.36 0.07 HostSeq rs17122332 10 107,238,146 SORCS1 G A 0.15 -0.29 0.06 rs1199346 3 138,353,967 MRAS A G 0.79 -0.26 0.05 P-value 4.1E-08 (8.3E-7, m = 4) 1.1E-07 (1.1E-7, m = 5) 3.0E-07 (1.4E-6, m = 3) 5.4E-07 (1.5E-4, m = 5) 5.5E-07 (5.1E-5, m = 5) Effect Allele Freq. Beta SE 0.07 0.30 0.05 P-value 3.5E-11 https://doi.org/10.1371/journal.pgen.1011192.t003 0.16 0.36 0.03 1.3E-29 HGI7no 0.12 0.05 0.03 1.4E-1 0.12 0.02 0.03 5.0E-1 0.78 0.01 0.03 7.4E-1 (λgc: EAS = 1.05; AMR = 1.06; EUR = 1.00; AFR = 1.05; SAS = 1.04). Result for the LZTFL1 locus (rs35731912: P = 1.13E-7) is similar to the primary GWAS but results for the SORCS1 (rs17122332: P = 1.54E-4) and MRAS loci (rs1199346: P = 5.09E-5) are less significant (Table 3). We note that MR-MEGA meta-analysis only reports results for variants that have a SAIGE result in each of the five ancestries. We then compared the three HGI7no lead variants that were genome-wide significant to the primary HostSeq GWAS results using myLocusZoom. Fig 2 shows that out of the three loci (chr3:45805277, chr6:41515629 and chr21:33249643 on 3p21.31, 6p21.1 and 21q22.11, respec- tively), the patterns at two loci (on chromosomes 3 and 6) colocalize between the two studies. A formal analysis using LocusFocus revealed that the colocalization is statistically significant with p-values of 6.46E-7, 2.09E-6 and 0.007 for chr3:45805277, chr6:41515629 and chr21:33249643, respectively. For each locus, the colocalization is further supported by the consistent variant effect sizes and directions between HGI and HostSeq (Table 3 and Fig 3). A power calculation (Resources) shows that in HostSeq, the power to replicate the three loci at ɑ = 0.05/3 = 0.0167 are 100%, 100% and 84.2% for chr3:45805277, chr6:41515629, and chr21:33249643, respectively. S4 Table provides a comparison between HGI7no and HostSeq for 47 of the 51 hits reported by HGI (Table 2 of [8]) that were present in HGI7no. This Table includes rs190509934 on ACE2 which is reported in HGI; this variant is not significant in Hos- tSeq but its effect size is directionally consistent with HGI7no. To examine within-HostSeq consistency for these three variants, we performed additional association analyses using various subsets: (i) stratified by ancestry, (ii) stratified by sex, (iii) stratified by study and leave-one-out study, (iv) unrelated samples up to 2 degrees away. For the leave-one-out study subsets we chose the BQC19 and GenOMICC studies to be sequen- tially excluded, because BQC19 is the largest study within HostSeq and accounts for more than half of the cases in HostSeq [53], and GenOMICC is the most restricted study in terms of recruitment and predominantly consists of hospitalized cases. Furthermore, we tested these three variants in a different model, by adding study as a categorical covariate. Fig 3 shows that PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 10 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Fig 2. Region plots for the top three loci from HGI7no compared with HostSeq. Querying the three regions: a) chr3:45805277, b) chr6:41515629, c) chr21:33249643 in HGI7no (top row in each pane) with HostSeq (bottom row in each pane) shows similar patterns for two out of three loci (chr3:45805277, chr6:41515629). Plots were generated using myLocusZoom. https://doi.org/10.1371/journal.pgen.1011192.g002 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 11 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Fig 3. Within-HostSeq comparison of the three lead variants from HGI7no. Examination of the three lead variants from HGI7no, depicting beta and SE for all N = 8,474 HostSeq samples and various stratifications of the HostSeq samples. The top panel shows results for all HostSeq samples passing QC. The last panel shows results from HGI7no. The panels in between show results for various stratifications of HostSeq including ancestry, sex, study, model and kinship. In the ‘model’ panel, ‘study-covariate’ indicates that HostSeq study centre was added as a categorical covariate to the main model (top panel). In the ‘kinship’ panel, ‘not-related’ indicates the subset that excludes samples within 2 degrees of relatedness as determined by KING. At the chromosome 3 locus all subsets, except for AFR, are consistent in sign of beta (i.e. effect direction). At the chromosome 6 locus, in addition to AFR, BQC19 differs from all other HostSeq subsets in sign of beta. The chromosome 21 locus is the most variable within HostSeq. Note that the X-axis scale varies among the three variants. https://doi.org/10.1371/journal.pgen.1011192.g003 the effect directions for all three variants are consistent with a few notable exceptions: African- ancestry subset stands out for all three variants; the BQC19-study subset is inconsistent with the rest of the cohort at the chromosome 6 locus; and the chromosome 21 locus has the small- est effect size and is the most variable across ancestry-subsets. The ancestry composition of PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 12 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort BQC19 is diverse but has a higher proportion of European and African samples than HostSeq overall, which may be affecting results: 231 African (6.4%), 240 Admixed American (6.6%), 118 South Asian (3.3%), 166 East Asian (4.6%), 2813 European (77.6%), and 55 uncategorized (1.5%). Note that the African-ancestry subset is a heterogeneous group consisting of admixed African-American groups as well as diverse African groups. S5 and S6 Tables provide allele fre- quencies of the top three HGI7no variants, stratified by ancestries and studies, respectively. Genome-wide association analyses: G x Sex and gene-based The G x Sex interaction analysis did not yield any genome-wide significant results (genomic control inflation statistic, λ = 1.194; S17 Fig). We note that the interaction analysis conducted was based on the 2 d.f. joint testing of G main and G x Sex interaction effects, which is more robust to model misspecification than the interaction analysis alone and, in the absence of interaction effects, provides comparable results with the main effect GWAS [38]. Indeed, the Manhattan plot in this analysis (S17 Fig) is similar to the Manhattan plot of the primary GWAS (Fig 1). Specifically, two of the top hits in this analysis (Prs4714474 = 2.08E-7, Prs35731912 = 2.42E-7) are the same SNPs as in the primary GWAS at the FOXP4 and LZTFL1/SLC6A20 loci. Additionally, this analysis identified a locus with suggestive sex-specific effects, although the association evidence at the lead SNP rs79973703 (chr7:107127037 on 7q22.3; P = 9.33E-8) did not reach genome-wide significance. This variant is an intronic variant of PRKAR2B, and a significant cis-eQTL of COG5, AC002467.7, HBP1 and PIK3CG [52]. S18 Fig compares this locus with sex-stratified results through region plots on myLocusZoom and shows that the effect is driven by an association in the male-subset of HostSeq which has β = -0.50 and P = 3.21E-7 (S7 Table). The genome-wide gene-based SKAT-O investigation produced results for two regenie masks: (i) 3,351 genes in high impact, (ii) 17,342 genes in high/moderate impact (S19 Fig). A protein-coding gene on chromosome 14 (14q23.2), WDR89, passed genome-wide significance (P = 1.89E-10) in the high/moderate impact tests with both no alternate allele frequency (AAF) filter and a maximum AAF 5% filter. WDR89 encompassed 23 missense variants of moderate impact. S8 Table shows the results of 7 of these variants which had a non-NA (not available) p-value in the unfiltered (without the MAF restrictions and difficult-to-sequence screening) primary GWAS. Three of these 7 variants, which are within an 8 bp region and in LD with each other, are significant in the unfiltered primary GWAS (chr14:63599677, chr14:63599680, chr14:63599684) with MAF around 2% and the lowest p-value being 9.56E- 11. These variants are in the third and final exon of WDR89. Removing all three variants (chr14:63599677–63599684) resulted in a non-significant SKAT-O result for WDR89 (P = 0.855), and adding chr14:63599684 back made it significant again (P = 1.93E-10), illus- trating the effect of this sub-region. Notably, in gnomAD (v4.0; 51) all three of these variants have similar MAF at 2–3% but failed gnomAD quality control measures (specifically AS_VQSR, which is an allele-specific quality control protocol) in both exome and genome sequence data, raising concerns about their quality. The HostSeq quality measures for these three variants and all of the other variants discussed in Tables 3 and 4 are provided in S9 Table. No other gene-based analysis produced genome-wide significant results, including ACE2 (SKAT-O result for 44 moderate impact variants: P = 0.25). Polygenic risk scores We constructed a PRS using the three variants that passed the stringent threshold of genome- wide significance (P < 5E-8) and LD-clumping in HGI7no. These are the same three lead vari- ants of HGI7no that are described in Table 4. As expected, our PRS was significantly associated PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 13 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Table 4. Association details of the three lead variants from the HGI7no GWAS (N = 86,199) compared with the HostSeq study (N = 8,474). Effect direction and mag- nitudes are consistent between HGI7no and HostSeq at the three loci (chr3:45805277, chr6:41515629, chr21:33249643). Nearest-gene annotation is from myLocusZoom. Multi-ancestry meta-analysis p-values of HostSeq (MR-MEGA) are added in paranthesis after primary HostSeq results; ‘m’ indicates number of ancestries MR-MEGA used for that result. Marker Chromosome Position Nearest-Gene Effect Allele Reference Allele rs17763742 3 45,805,277 SLC6A20 G A rs2496646 6 41,515,629 FOXP4-AS1 C T rs2834164 21 33,249,643 IFNAR2 C A Study HGI7no HostSeq HGI7no HostSeq HGI7no HostSeq Effect Allele Freq. Beta SE 0.16 0.38 0.03 0.10 0.33 0.07 0.85 -0.29 0.04 0.91 -0.29 0.08 0.43 -0.10 0.02 0.48 -0.09 0.04 P-value 2.4E-32 2.5E-6 (2.4E-7, m = 3) 2.2E-11 1.8E-4 (6.1E-5, m = 5) 1.7E-8 2.9E-2 (2.2E-2, m = 5) https://doi.org/10.1371/journal.pgen.1011192.t004 with the hospitalization status (P = 5.25E-13), and explains 1.01% proportion of variance after accounting for all the covariates as shown in Table 5. S10 Table shows the results of the PRS with PCs excluded from the list of covariates. Both the model with PCs and without PCs main- tain the signal, providing evidence that population structure does not confound our analysis. PRS calculated at additional p-value thresholds yielded significant R2 at the ɑ = 0.05 level for P < 1E-5 (53 SNPs were included in the PRS at this threshold), and was significant under Bonferroni correction. However, as more SNPs were included in the PRS, the significance and R2 lowered S11 Table. In the alternative PRS-CS approach a total of 1,033,441 SNPs were analyzed, but there was no improvement in association result with hospitalization status (P = 4.63E-5) over the PRSice analysis of top three loci (S11 Table). Table 5. Association of PRS with hospitalization status accounting for covariates and PC effects. PRS was con- structed using the three variants that passed the genome-wide significant P < 5E-8 threshold. The PRS association is significant (P = 5.25E-13) after controlling for genetic PCs and covariates (where sex is coded as males = 1 and females = 2, and age is standardized as (age-50)/10). Proportion of variance explained by PRS is 1.01% (calculated as 1- (1-R2 null represent R2 of models with and without the PRS, respectively). null), where R2 full and R2 full)/(1-R2 Term Intercept PRS Sex Age Age2 Age x Sex Age2 x Sex PC1 PC2 PC3 PC4 PC5 PC6 PC7 Beta 0.457 0.215 -0.840 0.629 0.074 0.034 0.044 52.668 19.465 21.261 13.487 -22.048 35.459 7.459 SE 0.113 0.030 0.070 0.058 0.021 0.036 0.013 2.693 2.497 2.473 2.410 2.569 2.585 2.639 T-statistic 4.06 7.22 -12.00 10.93 3.55 0.96 3.26 19.56 7.79 8.60 5.60 -8.58 13.72 2.83 P-value 4.81E-05 5.25E-13 3.43E-33 8.75E-28 3.89E-04 3.37E-01 1.11E-03 3.66E-85 6.49E-15 8.22E-18 2.18E-08 9.29E-18 7.83E-43 4.71E-03 https://doi.org/10.1371/journal.pgen.1011192.t005 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 14 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Table 6. Software resources. Version numbers, links, and references for the software packages used in this study. Process GWAS Variant annotation Querying loci of interest Colocalization Functional analysis PRS Power calculation Software regenie ensembl-vep myLocusZoom LocusFocus FUMA GWAS PRSice Genetic Association Study (GAS) Power Calculator Remove a GWAS from meta- analysis MetaSubtract GWAS GWAS Meta-analysis PRS Heritability estimation SAIGE MR-MEGA PRS-CS LDSC https://doi.org/10.1371/journal.pgen.1011192.t006 Version URL Reference 3.2.9 110.1 0.14.0 1.5.0 alpha 1.6.1 2.3.5 2017 1.60 1.3.0 0.2 1.1.0 1.0.1 https://rgcgithub.github.io/regenie/ https://useast.ensembl.org/info/docs/tools/vep/index.html https://my.locuszoom.org/ https://locusfocus.research.sickkids.ca/ https://fuma.ctglab.nl https://choishingwan.github.io/PRSice/ https://csg.sph.umich.edu/abecasis/gas_power_calculator/ https://cran.r-project.org/web/packages/MetaSubtract/ index.html https://saigegit.github.io/SAIGE-doc/ https://genomics.ut.ee/en https://github.com/getian107/PRScs https://github.com/bulik/ldsc [37] [39] [41] [43] [12] [45] [59] [40] [9] [10] [47] [60] Heritability SNP-based heritability estimates were calculated using LDSC to determine the extent to which genetics impact COVID-19 severity in the HostSeq dataset. The SNP-heritability was esti- mated to be h2 = 0.0159 (se = 0.0484) in the HostSeq dataset, similar to the HGI counterpart (h2 = 0.016, se = 0.0045). Version numbers, links, and references for all software used in this study are provided in Table 6. Discussion Genetic variants found to be associated with COVID-19 severity or susceptibility may impli- cate genes in biological pathways relevant to the SARS-CoV-2 virus. Genetic associations for other infectious diseases have often led to drug targets and drug discovery [3,54]. Therefore, host genetics can inform therapeutics and treatment by suggesting targets for drug development. In this work, we present a GWAS of COVID-19 severity in HostSeq, a Canadian WGS cohort. Our HostSeq GWAS replicated two main loci from the HGI meta-analysis. However, there are some limitations to our analysis. First, the HostSeq participating studies recruited individuals in different ways, and have variable proportions of hospitalized cases (Table 1). Thus, unweighted logistic regression (as implemented in regenie for example) does not pro- duce unbiased estimates (and standard errors) of regression coefficients. Although, studies in other areas [55] suggest the bias may not be large for the estimation of genetic effects when genotypes are unrelated to the probability of recruitment, this assumption is not straightforward to verify. Second, the participating studies are heterogeneous in the relative proportions of cases and controls (see Table 1). The effect of combining them into a single study is not fully understood, and was discussed previously in our resource paper [1]. In this paper we examined the issue of study heterogeneity through various sensitivity analyses (Fig 3) which suggest that our study is not confounded. However, further exploration may improve study power. The third challenge is the overlap of samples between HostSeq and HGI. Since HostSeq consists of several independent studies, two studies had independently submitted their B1 GWAS results to HGI (BQC19 and CGEN) and were included in the HGI PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 15 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort v7 meta-analyses. Therefore, the publicly-available HGI meta-analysis results are not completely independent of HostSeq. We were able to utilize HGI’s summary statistics from their leave-one-out analyses to exclude BQC19 and CGEN and obtain the HGI7no summary statistics independent of our HostSeq study. However, a limitation of using the leave-one- out HGI meta-analyses is that the publicly available versions additionally excluded one of their largest studies (23andMe), reducing the HGI7no cohort size to 86,199 and dampening the association results. The omission of 23andMe results from the HGI meta-analysis results could have also limited the development of PRS using PRS-CS, which becomes increasingly powerful as the number of participants in the base GWAS increases beyond 100,000 [47]. Finally, there is a limitation for our ancestry- and sex-specific analyses due to unavailability of parallel results from the HGI B1 contrast. The lack of ancestry-specific GWAS results also precluded the use of PRS software specifically designed for cross-ancestry analyses, such as PRS-CSx [56]. Since we could not create ancestry-specific PRS, we provided PRS association results stratified by ancestry post-computation. Our analysis aimed to include all individuals available including related individuals and individuals from diverse ancestries. To ensure the validity of our analysis, we performed rigor- ous quality control where we checked samples for their heterogeneity as well as principal com- ponent scores so that we would be able to include all the samples that passed these filters. Due to our comprehensive QC and use of regenie (which employs a genetic relatedness matrix), we did not exclude samples due to their ancestry or relatedness, or apply genomic inflation adjust- ments in our primary analysis (as confirmed by QQ-plots and λgc genomic control estimate). Nevertheless, we performed additional analyses to show the effect of ancestry-stratification and kinship-restriction on the three variants that we sought to replicate from HGI7no. These additional analyses show the robustness of our primary results, and yield further evidence of replication. Our GWAS analysis also included the often overlooked X chromosome [57] and considered G x Sex interaction. Although neither analyses led to genome-wide significant results, there was one suggestive finding from the G x Sex interaction analysis. We found a locus on chro- mosome 7 which has an effect driven by males. Inclusion of the X chromosome allowed us to investigate ACE2, but unsurprisingly as the variant reported by HGI is a rare variant and HGI suggested that the association is with infection susceptibility, we did not find any association with disease severity in our study of N = 8,474. For future studies of larger cohorts, analyzing the X chromosome and testing G x Sex interactions are worthwhile considerations. Conclusion In this work, we investigated 10,059 participants from the multi-ancestry and Canada-wide HostSeq. Of these, N = 8,474 participants passing quality control were analyzed. Our GWAS replicated two (LZTFL1/SLC6A20 and FOXP4) out of three loci that were reported in Version 7 of HGI for the B1 contrast. The third locus (IFNAR2) has a relatively smaller effect size and is directionally inconsistent among the HostSeq ancestries, which contributes to its diminished overall effect. The standard errors of effect estimates for all three variants are larger in the Afri- can-ancestry subset relative to the other ancestry groups. This is likely due in part to the smaller sample size (N = 455), lower MAC, and heterogeneity within this group. This may also be due to differences in risk factors across studies and ancestry groups. More importantly, it may be caused by the within-group diversity of the African-ancestry subset, which includes both recently admixed populations (African-American) and African-only groups (see S3 Fig). Our GWAS led to a genome-wide significant locus in LD with the known FOXP4 locus. Gene-based analyses identified two significant genes, MRAS (from FUMA GWAS), and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 16 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort WDR89 (from SKAT-O). Examination of genotype-by-sex effects for host genetics of COVID- 19 severity did not lead to genome-wide significant novel loci, but we did find a locus with sex- specific effects. We also examined heritability and constructed a polygenic risk score (PRS) using summary statistics. Heritability estimates were found to be almost identical between the HostSeq and the HGI7no dataset (h2 = 0.0159, h2 = 0.016). Our polygenic risk score defined on the three genome-wide significant loci (P<5E-8) from HGI7no provided a statistically significant R2 = 1.01%. Including additional variants did not improve the PRS fit, regardless of the construc- tion strategy (S11 Table). PRS performance is impacted by the heritability, polygenicity and heterogeneity of the phenotype of interest [58]. In our study, the lower heritability of COVID- 19 severity may account for the small portion of variability in hospitalization status explained by our PRS. The degree of polygenicity in COVID-19 severity remains unclear; however, we attempted to address this uncertainty by using an additional PRS method that allows for flexi- ble genetic architectures. The heritability estimate, PRS, and the colocalization analysis further indicate concordance between HostSeq and HGI, suggesting that the COVID-19 severity loci chr3:4580527 and chr6:41515629 are robust. Supporting information S1 Fig. Quality Control (QC) in HostSeq. Flowchart describing the multi-step process of sample and variant QC of joint-called HostSeq data. N = 8,474 / 10,059 samples were retained for genetic analysis. PCA was performed on a subset of variants; these PCs are used as covari- ates in genetic analysis. HWE was performed on the subset of controls with European ancestry [N = 3,876], and variants with P < 1E-50 were removed from all samples. (PDF) S2 Fig. PCA projection of HostSeq genomes against reference population. HostSeq genomes were merged with the 1000 Genomes reference set (see Methods of the HostSeq resource paper [1]). First two principal components of this merged data are shown here with HostSeq genomes in black and 1000 Genomes samples colored by their ancestry classification: AFR = African, AMR = Admixed American, EAS = East Asian, SAS = South Asian, EUR = European. (PDF) S3 Fig. Predicted population admixture and ancestry classification in HostSeq. Each bar represents a genome. Proportion of African, East Asian and European ancestries is determined and genomes classified into 8 groups using GRAF-pop (see Methods). They are further com- bined into 5 ancestry groups: (i) AFR—African and African-American, (ii) AMR—Latin American Asian and Latin American African, (iii) EAS—Asian-Pacific Islander and East Asian, (iv) SAS—South Asian, and (v) EUR—European. 2% of genomes remain uncategor- ized. (PDF) S4 Fig. Genetic distances score of HostSeq genomes. The four genetic distances (GD1-4) scores from GRAF-pop (see Methods) represent distance of each genome from several refer- ence populations, and are used to predict ancestry. Barycentric coordinates of GD1 and GD2 are used to predict admixture proportion of African, East Asian and European ancestries. (PDF) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 17 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort S5 Fig. Quality of HostSeq genomes. (A) Missing rate < 5% (B) Contamination rate < 3% (C) Mean coverage > 10. (PDF) S6 Fig. Scree plot of PCA. This plot indicates that the eigenvalues start to plateau around PC7. We used the top seven PCs (PC7 is highlighted in red) as covariates in genetic analysis. (PDF) S7 Fig. Scatter plots of PCs. Pairwise heatmaps of PC1-PC2, PC3-PC4, and PC5-PC6. No out- liers are seen on these pairwise plots. (PDF) S8 Fig. Distribution of PCs. Stacked histograms for the top seven PCs colored by hospitaliza- tion status. (PDF) S9 Fig. Genetic analysis of HostSeq. Flowchart describing the methods for genetic analysis of HostSeq data [N = 8,474] using regenie and PRSice. Primary GWAS was performed on all samples. Additional stratified GWAS results were obtained to check for heterogeneity within HostSeq. A 2 degrees-of-freedom (d.f.) GxSex test was performed to check the effect of geno- type-sex interaction. SKAT-O tests analyzed gene-based effects. HGI7no was constructed by removing overlapping HostSeq samples from HGI7. GWAS results were filtered to remove the GIAB difficult-to-sequence regions and MAF < 5% variants. PRS was constructed using the HGI7no summary statistics. (PDF) S10 Fig. Distribution of Age. Stacked histogram of age (bin width 10), colored by hospitaliza- tion status. This shows association between age and hospitalization. (PDF) S11 Fig. Comparison of HostSeq allele frequencies with gnomAD. gnomAD allele frequen- cies of non-Finnish European samples for variants passing quality filters are compared with HostSeq allele frequencies of 100% predicted European samples [N = 1,153]. The heatmap of 27 million variants largely shows concordance between the two sets. (PDF) S12 Fig. Paired QQ-plots and p-value histograms, stratified by MAF. Left) QQ-plots show the expected and observed -Log10 transformed p-values on the X and Y axes. Right) Paired histograms show p-values binned at width 0.05. Genomic control for each MAF-stratification is: λ = 1.073 for 0 > MAF > 0.05 (first panel), λ = 1.046 for 0.05 > MAF > 0.1 (second panel), λ = 1.048 for 0.1 > MAF > 0.25 (third panel), and λ = 1.048 for 0.25 > MAF > 0.5 (fourth panel). (PDF) S13 Fig. GWAS results including difficult-to-sequence regions. GWAS of all HostSeq sam- ples passing QC. In the Manhattan plot, Y-axis indicates -Log10 p-values of regenie analysis for variants with MAF > 5%, X-axis indicates chromosomes. Grey horizontal line indicates genome-wide significance level of P < 5E-8. In the corresponding QQ-plot, the X and Y axes indicate expected and observed -Log10 p-values, respectively (genomic control λ = 1.05). (PDF) S14 Fig. Region plots for the top three novel loci from HostSeq compared with HGI7no. Querying the three regions: a) chr15:54131608, b) chr10:107238146, c) chr3:138353967 in PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 18 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort HostSeq (top row in each figure) with HGI7no (bottom row in each figure) shows that these variants are in LD with nearby variants. Plots were generated using myLocusZoom. (PDF) S15 Fig. Gene-based test results of primary GWAS. Post-GWAS functional analysis of the primary HostSeq GWAS included a gene-based test computed by MAGMA. In the Manhat- tan plot, Y-axis indicates -Log10 p-values of MAGMA analysis for genes, X-axis indicates chromosomes. Grey horizontal line indicates Bonferroni significance level of P < 2.7E-6. In the corresponding QQ-plot, the X and Y axes indicate expected and observed -Log10 p-values, respectively (genomic control λ = 1.1). The significant hit on chromosome 3 is the MRAS gene with 91 SNPs and P = 3.52E-7. (PDF) S16 Fig. Meta-analysis results of ancestry-stratified GWAS. SAIGE GWAS of five HostSeq ancestries (EAS, SAS, AFR, AMR and EUR) were meta-analyzed using MR-MEGA [N = 8,272]. In the Manhattan plot, Y-axis indicates -Log10 p-values of MR-MEGA analysis for variants with MAF > 5%, X-axis indicates chromosomes. Variants falling in the GIAB diffi- cult-to-sequence regions have been excluded. Variants missing in any of the ancestry sets did not have a meta-analysis result. Grey horizontal line indicates genome-wide significance level of P < 5E-8. In the corresponding QQ-plot, the X and Y axes indicate expected and observed -Log10 p-values, respectively (genomic control λ = 0.991). (PDF) S17 Fig. GWAS testing the G x Sex interaction effect. The p-values are derived from a 2 degrees-of-freedom test that considers both genotype, and interaction between genotype and sex jointly. In the Manhattan plot, Y-axis indicates -Log10 p-values of regenie analysis for vari- ants with MAF > 5%, X-axis indicates chromosomes. Variants falling in the GIAB difficult-to- sequence regions have been excluded. Grey horizontal line indicates genome-wide significance level of P < 5E-8. In the corresponding QQ-plot, the X and Y axes indicate expected and observed -Log10 p-values, respectively (genomic control λ = 1.194). (PDF) S18 Fig. Region plot for the top novel locus identified through the G x Sex interaction test compared with other results. Querying the chr7:107127037 region in G x Sex GWAS (top row) shows that this variant is in LD with nearby variants. Comparing it with the following in order: primary GWAS, sex-stratified GWAS for males, sex-stratified GWAS for females, and HGI7no shows that there is a sex-effect for this locus in HostSeq, where males [N = 3,646] have an association with hospitalization. Plots were generated using myLocusZoom. (PDF) S19 Fig. SKAT-O results for gene-based testing including rare variants. Top) Manhattan plot for the high impact set (3,350 genes). Middle) Manhattan plot for the high/moderate impact set (17,341 genes). Bottom) QQ-plots for the high impact (bottom left), and high/mod- erate impact (bottom right) sets. SKAT-O analysis was performed on variants outside the GIAB difficult-to-sequence regions. (PDF) S1 Table. Summary statistics per ancestry. Samples were assigned ancestry based on predic- tion by GRAF-pop (see Methods), and then categorized into 5 superpopulations: AFR = African, AMR = Admixed American, EAS = East Asian, SAS = South Asian, EUR = European. EUR is the largest ancestry in HostSeq. (XLSX) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 19 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort S2 Table. Summary statistics per study HostSeq constitutes several studies of varying sizes and hostpitalization proportions. Some studies share samples, however, in this table overlap- ping samples have been only been counted once. BQC19 is the largest study in HostSeq. Gen- OMICC has the highest proportion of hospitalized samples. (XLSX) S3 Table. Per-gene association details from the MAGMA gene-set analysis. The significant gene-set ‘HASEGAWA_TUMORIGENESIS_BY_RET_C634R’ has an effect size of 1.72 +/-0.31 and a raw p-value of 2.36E-8. (XLSX) S4 Table. Association details of 47 variants from the HGI GWAS comparing HGI7no results with HostSeq. Direction and magnitude of effect size is consistent between HGI7no and HostSeq for most of the loci. Nearest-Gene and Suggested-Phenotype annotations are as provided by HGI (table S2 of HGI 2023 Nature paper) [8]. Suggested-phenotype indicates the result of HGI’s phenotypic impact assessment to determine if ‘disease severity’ or ‘infection susceptibility’ is the main impact. (XLSX) S5 Table. Ancestry-stratified allele frequencies for HGI7no hits in HostSeq. Columns labeled by ancestry codes indicate the effect allele frequency for each variant in HostSeq ances- tries. (XLSX) S6 Table. Study-stratified allele frequencies for HGI7no hits in HostSeq. Columns labeled by study acronyms indicate the effect allele frequency for each variant in HostSeq studies. (XLSX) S7 Table. Association details of lead variant from HostSeq G x Sex interaction analysis. Variant rs79973703 (at the GRCh38 genomic location of chr7:107127037) was identified from a joint 2 d.f. test after applying a MAF > 5% filter and removing variants in the difficult-to- sequence regions. Top row indicates results from the primary single-variant analysis. Follow- ing two rows indicate sex-stratified single-variant results. Subsequent rows indicate results from the G x Sex interaction GWAS. (XLSX) S8 Table. Association details of variants in WDR89. WDR89 (at the GRCh38 genomic loca- tion of chr14:63597039–63641871) was identified from a SKAT-O analysis of high/moderate impact variants. 7 missense variants in WDR89 have a result in the primary unfiltered GWAS. Amino Acid change is depicted in the HGVS (Human Genome Variation Society) notation. (XLSX) S9 Table. Quality metrics for selected HostSeq variants. Variants were selected to include the top three HGI7no loci (first three rows), the top five hits from the primary HostSeq GWAS (next five rows), and the three WDR89 variants driving the SKAT-O results (last three rows). Values have been extracted from the joint-called VCF of N = 10,059 samples before QC removals (these variants passed all filters described in S1 Fig). WDR89 variants have the lowest ‘MAF’ and higher ‘ExcessHet’ but they pass HostSeq thresholds. However, they failed in gno- mAD (v4.0) which used a different method, AS_VQSR, for QC protocol. Description of col- umn headers is given below the table. (XLSX) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 20 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort S10 Table. Association of PRS with hospitalization status. PRS is constructed with PRSice using the top three loci which pass the P < 5E-8 threshold in HGI7no. Association results in this table exclude genetic PCs as covariates from the model. While an examination of PRS without genetic PCs is significant, inclusion of genetic PCs reduces significance from P = 1.96E-48 (Table 5) to 5.25E-13. Retained signal indicates that population structure does not confound our analysis. (XLSX) S11 Table. Association between PRS and hospitalization status by different methods. P- value thresholds are indicated for PRSice results, and results are compared to PRS-CS method (last row). PRSice thresholds at P < 5E-8 and 1E-5 are significant at 0.05 level and include a small number of variants indicating low polygenicity (at most 53, after LD clumping with a window-size of 750kb). PRS-CS result is also significant but uses a large number of SNPs. All significant thresholds have positive effect size, indicating that polygenic risk for hospitalization calculated with summary statistics from HGI7no is positively associated with hospitalization in HostSeq. R-squared represents difference in pseudo-R-squared of full model with PRS and null model without PRS. (XLSX) S12 Table. Per-ancestry association of PRS with hospitalization status. PRS is constructed with (a) PRSice using the top three loci which pass the P < 5E-8 threshold in HGI7no, and (b) PRS-CS using 1,033,441 SNPs. Association is tested per-ancestry using the same model as in S9 Table, i.e., with all covariates except genetic PCs. Beta, SE, t-statistic and p-value is reported for the PRS term in the table for all ancestries. R-squared represents difference in pseudo-R- squared of full model with PRS and null model without PRS. (XLSX) Acknowledgments We wish to express gratitude to all HostSeq project participant studies and the individual par- ticipants within these studies for their contribution. We would also like to thank Natalie Sun, Samantha Roper and Charlene Bradbury for help with administration. Resources GATK Hard-filtering germline short variants: https://gatk.broadinstitute.org/hc/en-us/ articles/360035890471-Hard-filtering-germline-short-variants Long-range LD regions: https://genome.sph.umich.edu/wiki/Regions_of_high_linkage_ disequilibrium_(LD) GSA v3 b151 GRCh38: https://support.illumina.com/content/dam/illumina-support/ documents/downloads/productfiles/global-screening-array-24/v3-0/GSA-24v3-0-A2- manifest-file-csv.zip Summary statistics for ‘leave-one-out BQC19’ HGI GWAS: https://storage.googleapis.com/ covid19-hg-public/freeze_7/results/20220403/leave_one_out/sumstats/COVID19_HGI_B1_ ALL_leave_23andme_and_BQC19_20220403.tsv.gz GIAB difficult-to-sequence regions: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/ v3.3/GRCh38@all/Union/GRCh38_alldifficultregions.bed.gz LDpair: https://ldlink.nci.nih.gov PRS-CS LD panel: https://github.com/getian107/PRScs eQTL catalogue: https://www.eqtlgen.org/cis-eqtls.html PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011192 March 22, 2024 21 / 25 PLOS GENETICSCanadian COVID-19 host genetics cohort Author Contributions Conceptualization: Jennifer D. Brooks, Shelley B. Bull, France Gagnon, Celia M. T. Green- wood, Rayjean J. Hung, Jerald F. Lawless, Jordan Lerner-Ellis, Jessica K. Dennis, Lisa J. Strug. Data curation: Anat R. Halevy, Samantha Yoo, Rohan J. S. Abraham, Jean-Michel Garant, Bhooma Thiruvahindrapuram. Formal analysis: Elika Garg, Paola Arguello-Pascualli, Olga Vishnyakova. Investigation: Elika Garg, Paola Arguello-Pascualli, Olga Vishnyakova. Methodology: Jennifer D. Brooks, Shelley B. Bull, France Gagnon, Celia M. T. Greenwood, Rayjean J. Hung, Jerald F. Lawless, Jordan Lerner-Ellis, Jessica K. Dennis, Andrew D. Pater- son, Lei Sun, Lloyd T. Elliott. Project administration: Lloyd T. Elliott. Resources: Steven J. M. Jones. Supervision: Lisa J. Strug, Andrew D. Paterson, Lei Sun, Lloyd T. Elliott. Validation: Rohan J. S. Abraham, Jean-Michel Garant, Bhooma Thiruvahindrapuram. Visualization: Elika Garg, Paola Arguello-Pascualli, Olga Vishnyakova. Writing – original draft: Elika Garg, Andrew D. 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10.1371_journal.pclm.0000345	RESEARCH ARTICLE Exposure of African ape sites to climate change impacts 1, Paul Tehoda2, Onyekachi Chukwu3, Godfred Bempah4, Hjalmar Razak KiribouID S. Ku¨ hl5,6,7,8, Julie Ferreira9, Tenekwetche SopID Lars Kulik5, Jean Pierre Samedi Mucyo12, Yntze van der Hoek12, Stefanie HeinickeID 5, Joana Carvalho10, Matthias Mengel11, 11 1 African Centre of Excellence for Climate Smart Agriculture and Biodiversity Conservation, Haramaya University, Haramaya, Ethiopia, 2 Faculty of Built and Natural Environment, Department of Environmental Management and Technology, Koforidua Technical University, Koforidua, Ghana, 3 Department of Forestry and Wildlife, Nnamdi Azikiwe University, Awka, Nigeria, 4 College of Forestry, Nanjing Forestry University, Nanjing, China, 5 Senckenberg Museum Fu¨ r Naturkunde Go¨ rlitz, Go¨ rlitz, Germany, 6 International Institute Zittau, Technische Universita¨t Dresden, Zittau, Germany, 7 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany, 8 Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, 9 Faculte´ des Sciences de la Vie, University of Strasbourg, Strasbourg, France, 10 School of Built and Natural Environment, University of Derby, Derby, United Kingdom, 11 Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany, 12 Dian Fossey Gorilla Fund, Musanze, Rwanda * krazakou200248@gmail.com (RK); heinicke@pik-potsdam.de (SH) Abstract Large gaps remain in our understanding of the vulnerability of specific animal taxa and regions to climate change, especially regarding extreme climate impact events. Here, we assess African apes, flagship and highly important umbrella species for sympatric biodiver- sity. We estimated past (1981–2010) and future exposure to climate change impacts across 363 sites in Africa for RCP2.6 and RCP6.0 for near term (2021–2050) and long term (2071– 2099). We used fully harmonized climate data and data on extreme climate impact events from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Historic data show that 171 sites had positive temperature anomalies for at least nine of the past ten years with the strongest anomalies (up to 0.56˚C) estimated for eastern chimpanzees. Climate projec- tions suggest that temperatures will increase across all sites, while precipitation changes are more heterogeneous. We estimated a future increase in heavy precipitation events for 288 sites, and an increase in the number of consecutive dry days by up to 20 days per year (maxi- mum increase estimated for eastern gorillas). All sites will be frequently exposed to wildfires and crop failures in the future, and the latter could impact apes indirectly through increased deforestation. 84% of sites are projected to be exposed to heatwaves and 78% of sites to river floods. Tropical cyclones and droughts were only projected for individual sites in western and central Africa. We further compiled available evidence on how climate change impacts could affect apes, for example, through heat stress and dehydration, a reduction in water sources and fruit trees, and reduced physiological performance, body condition, fertility, and survival. To support necessary research on the sensitivity and adaptability of African apes to climate change impacts, and the planning and implementation of conservation measures, we provide detailed results for each ape site on the open-access platform A.P.E.S. Wiki. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kiribou R, Tehoda P, Chukwu O, Bempah G, Ku¨hl HS, Ferreira J, et al. (2024) Exposure of African ape sites to climate change impacts. PLOS Clim 3(2): e0000345. https://doi.org/10.1371/ journal.pclm.0000345 Editor: Lalit Kumar Sharma, Zoological Survey of India, INDIA Received: June 2, 2023 Accepted: December 27, 2023 Published: February 28, 2024 Copyright: © 2024 Kiribou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data that support the findings of this study are openly available via the ISIMIP data repository (https://data.isimip.org/ ). Summary results are included in the Supporting Information and detailed results for each site are available on the A.P.E.S. Wiki (wiki.iucnapesportal. org). Funding: SH was supported by the German Federal Ministry of Education and Research (BMBF) under the research project QUIDIC (01LP1907A). Substantial part of this work emerged from the workshop “Training of young African academics in PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 1 / 19 PLOS CLIMATEusing R to process, analyze and interpret wildlife survey data” that was funded by the Volkswagen Foundation in Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Exposure of African ape sites to climate change impacts Introduction Around one million species are threatened with extinction [1]. Even though climate change is not yet the main driver of biodiversity decline [2], it is projected to increasingly threaten biodi- versity [3]. Species have already responded to climate change, for example, by changes in phe- nology [4], and latitudinal and elevational range shifts [5,6]. Though large uncertainties remain, it has been estimated that between 10 and 30% of terrestrial species could become locally extinct due to climate change [3]. Large taxonomic and geographic gaps remain in our understanding of the impact of climate change on species, and one of these understudied taxa are primates [7,8]. In addition, there are large gaps for Sub-Saharan Africa, even though the region has a high diversity in species and ecosystems, and large remaining forests essential for the global climate system [9]. Primates play an important role within their ecosystems; they contribute to forest community structure by aiding seed dispersal and plant pollination, ecosystem services that could be threatened by climate change impacts [10]. They are also one of the most prominent conservation flagship species [11], and African apes are a major focus of research and conservation activities, and an umbrella species for sympatric biodiversity. For example, the protection of African apes moti- vates the creation of new conservation areas, benefitting co-occurring species [12]. African apes occur in 21 countries across tropical Africa. There are four species and nine taxa (Table 1). Most African apes have experienced population decline (except mountain goril- las) and all are either listed as Endangered or Critically endangered by the IUCN Red List of Threatened Species [13]. Climate projections show that across Africa, 61% of primate habitat is likely to be exposed to increases in maximum temperatures of more than 3˚C by 2050 and to changes in precipita- tion patterns [14]. Carvalho et al. [15] estimated that the combination of climate, land-use, and population changes could lead to decreases of up to 85% of African ape ranges. As studies often investigate species exposure to average changes in climate, the impact of extreme events remains understudied [16]. Zhang et al. [17] conducted the first global assessment of primate vulnerability to droughts and tropical cyclones, and found that 16% of primate taxa are vulner- able to cyclones and 22% to droughts. Extreme events can affect apes, for example, by reducing food resources and sources of drinking water, or by the destruction of ape habitat (Table 2). African apes’ behavioral adaptability could allow them to adapt to a certain extent. For example, chimpanzees seem to cope with high temperatures by sitting in water pools or resting in caves [18], and being more active during the night [19]. Mountain gorillas drink more Table 1. Overview of the nine African ape taxa. Species Subspecies Bonobo (Pan paniscus) Chimpanzee (Pan troglodytes) Eastern gorilla (Gorilla beringei) Western gorilla (Gorilla gorilla) Central chimpanzee (P. t. troglodytes) Eastern chimpanzee (P. t. schweinfurthii) Nigeria-Cameroon chimpanzee (P. t. ellioti) Western chimpanzee (P. t. verus) Grauer’s gorilla (G. b. graueri) Mountain gorilla (G. b. beringei) Cross River gorilla (G. g. diehli) Western lowland gorilla (G. g. gorilla) Population size 1 > 15,000–20,000 114,000–317,000 170,000–250,000 < 9,000 17,600–96,700 ~3,800 > 1,000 250–300 ~ 360,000 1 Sop et al., 2021 2 IUCN, 2022; CR–critically endangered, EN–endangered. https://doi.org/10.1371/journal.pclm.0000345.t001 IUCN Status 2 EN Number of sites EN EN EN CR CR EN CR CR 39 114 59 29 77 15 4 8 113 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 2 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Table 2. Evidence of impact of changes in climatic variables and extreme events on apes. Climatic variable / extreme event Temperature Evidence of impact on apes and other taxa Expected impact on apes chimpanzees at a site with high temperatures experience heat stress and sit in caves and pools, likely a thermoregulatory behaviour [18]; they also show increased nocturnal behaviour [19], gorillas drink more frequently [20] and capuchins rest more and travel less during hottest hours of the day [21]; more energy allocated to thermoregulation can lead to physiological trade-offs (e.g., reduced function of immune system observed for birds [22]); high temperatures can lead to reduced performance (e.g., reduced cognitive performance found for humans [23]) high temperatures lead to reduced physiological performance; energy and time allocated to thermoregulation lead to physiological and behavioural trade-offs which can put constraints on the time budget [24] and reduce survival and fertility; extremely high temperatures can lead to direct mortality Precipitation Consecutive dry days chimpanzees at a site with low annual precipitation experience dehydration stress [18] lower precipitation leads to lower availability of standing water sources and as a consequence dehydration higher mortality in capuchin infants and reduced offspring production in spider monkeys during long periods of water shortage [25]; long periods without rainfall lead to unavailability of water sources and high levels of stress hormones in vervet monkeys [26] direct: longer periods without rainfall lead to reduced offspring production and survival; indirect: reduced food availability [27] and increased uncertainty in food availability, changes in phenology of fruiting trees [28], contamination of water sources [29] Heavy precipitation heavy rainfall can lead to the destruction of ape nests [30] causing stress and accidents, heavy rainfall leads to higher prevalence of infectious disease [31] which can lead to higher mortality [32] Crop failure increased deforestation in years with crop failure [33] Drought (based on soil moisture) Heatwave (hot and humid) River flood Tropical cyclone reduction in food availability with a negative impact on body condition, fertility and survival [8,17,27], 22% of primate taxa are vulnerable to droughts [17] heatwave can lead to direct mortality (e.g., in humans [35] or flying foxes [36]) and a cross-taxa review found evidence for decline in body condition and fecundity [16] flooding can increase prevalence of water-borne diseases [31]; restrict movement leading to longer travel times; for humans floods are the most prevalent climatic hazard leading to high mortality and economic damages [37] destruction of food resources for lemurs [38], lower fertility among rhesus macaques [39], 16% of primate taxa are vulnerable to tropical cyclones [17] direct: higher number of incidences of nest destruction leading to higher stress levels and injuries, higher mortality caused by increased prevalence of infectious diseases; indirect: sudden rise in water level of rivers leading to flooding and causing temporary splitting of social groups or inaccessibility of areas indirect: increased destruction of ape habitat, increased resource use in ape habitat (wildlife hunting, wood collection) to compensate for loss of harvest leading to disturbance of apes reduction of food resources leading to increased competition between neighbouring social groups [34] and lower fertility and survival direct: mortality, decline in body condition and fecundity, indirect: increased risk of forest fires increased disease prevalence, restriction of ape movement, displacement of people resulting in increased pressure on ape habitat (e.g., extraction of wood as building material or charcoal production) direct mortality, reduction in food resources Wildfire restricting movement leading to longer travel times, destruction of feeding and nesting trees [40] restricting ape movement, causing longer travel times to avoid fire, and destruction of nesting and feeding trees https://doi.org/10.1371/journal.pclm.0000345.t002 frequently with increasing temperatures [20]. However, African apes are likely to be vulnerable to climate change impacts due to their slow reproduction [41], limited dispersal ability [42], and the restricted range of some ape taxa (e.g., Cross River gorilla and Nigeria-Cameroon chimpanzee [43]). Climate change has rarely been considered in conservation planning, and adaptation measures have not been included in recent conservation action plans for African apes (e.g., western chimpanzees [44], or western lowland gorillas and eastern chimpanzees [45]). Heinicke et al. [46] reported that climate change was listed as a threat only in 3 out of 59 western chimpanzee sites, and only one site (Moyen Bafing NP, Guinea) has implemented cli- mate change-focused measures. In Senegal additional water holes were created recently for farmers to water their livestock in Senegal, so that natural water holes would be available for chimpanzees in this arid region (Ku¨hl pers. com.). One reason why climate change adaptation measures are not yet planned for or being implemented, is the prevalence of other threats, such as land-use change [14], while climate PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 3 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts change is perceived as having a more long-term impact. However, this underestimates the more immediate impact from extreme events [16]. We used state-of-the-art climate data to calculate climatic variables for 363 ape sites across Africa for the past and future, including average temperature and precipitation, consecutive dry days, and heavy precipitation days. We also used a comprehensive data set on projected extreme climate impact events to estimate future exposure to six types of extreme events: crop failure, drought, heatwave, river flood, tropical cyclone, and wildfire. We estimated exposure at the scale of sites, because this is where decisions on funding allocation and the implementa- tion of specific conservation measures are made. Importantly, there is a need to make this type of information publicly available to conservation decision-makers, which is why results on all sites are made available via the A.P.E.S. Wiki (wiki.iucnapesportal.org). Materials and methods Ape data We included all sites across Africa with known current or historical presence of great apes according to the IUCN SSC A.P.E.S. database [47]. In total, there were 363 sites covering 21 countries (Fig 1) including 333 sites with apes’ presence and 30 sites where apes are likely extir- pated. Spatial outlines of these sites were compiled from the IUCN SSC A.P.E.S. database, the World Database on Protected Areas [48], and Carvalho et al. [15]. For eight sites, spatial out- lines were not available and we used the midpoint of the sites. Analyses were implemented for each of the 363 sites and made available on the open-access A.P.E.S. Wiki. Since apes have been extirpated at some of these sites for several decades, results described below are restricted Fig 1. Geographic range for the nine ape taxa for bonobo (Pan paniscus), chimpanzee (Pan troglodytes), eastern gorilla (Gorilla beringei) and western gorilla (Gorilla gorilla). Country outline data was obtained from the R package ‘mapdata’ (cran.r-project.org/package=mapdata). https://doi.org/10.1371/journal.pclm.0000345.g001 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 4 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts to the 333 sites with apes’ presence, covering around 42% of the current distribution of African apes. Ape abundance estimates were compiled from the A.P.E.S. database and A.P.E.S. Wiki. Climate and extreme event data We used climate and extreme event data provided by the Inter-Sectoral Impact Model Inter- comparison Project (ISIMIP, www.isimip.org), the largest platform of the global climate impact modelling community. ISIMIP provides bias-adjusted and downscaled forcing data for the historical and future period, and modelling protocols fully harmonized across climate impact sectors. ISIMIP data has been extensively evaluated and used in cross-sectoral analyses (e.g., [49]). For the historical period, we used temperature (mean and maximum daily temperature) and precipitation from the bias-corrected daily observational EWEMBI dataset from ISIMIP2a [50] described in [51]. For the future period, we used climate projections for four global cli- mate models (GCMs; IPSL-CM5A-LR, HadGEM2-ES, MIROC5, GFDL-ESM2M) and two Representative Concentration Pathways (RCP2.6 and RCP6.0) from ISIMIP2b [52] described in [53], to be in line with the extreme event data (see below). RCP2.6 is a scenario with strong mitigation measures in which global temperatures would likely rise below 2˚C by 2100, and RCP6.0 is a scenario with medium emissions where the lack of additional mitigation efforts would lead global temperatures to likely rise up to 3˚C by 2100 [54]. The spatial resolution of the climate data is 0.5 degrees (approximately 50 km at the equator). We used a previously published dataset of extreme climate impact events provided by Lange et al. [55]. This dataset includes six types of extreme events: crop failure, drought, heat- wave, river flood, tropical cyclone, and wildfire. Data is based on climate impact simulations from ISIMIP2b [53] and provides extreme event data for the future period for the same four GCMs and two RCPs described above (spatial resolution 0.5 degrees). For each year and grid cell, the proportion of area exposed to an extreme event is provided. Lange et al. [55] based exposure to crop failure on three crop models, drought and river flood on eight hydrological models, wildfire on five vegetation models, and tropical cyclones on one model. Exposure to heatwaves was derived from temperature directly. Details are described in the original publica- tion [55]. For each ape site, we first determined which grid cell midpoints from the climate and extreme event datasets fell within the spatial outline of the site. For the eight sites where we did not have a spatial outline, we identified the grid cell midpoint closest to the site. We then extracted data for each grid cell, and in cases where several grid cell midpoints were within one site, we calculated the average per site. Climatic variables To comprehensively describe climatic conditions at each site, we derived four climatic vari- ables based on published evidence of how temperature and precipitation can influence great apes (Table 2). For each year from 1979 to 2016, we calculated: • temperature (annual mean of mean daily and maximum daily temperature in ˚C) • precipitation (total annual precipitation in mm/day) • consecutive dry days (maximum number of consecutive dry days per year, with a dry day defined as precipitation <1mm/day) • heavy precipitation (number of days with heavy precipitation per year, for the reference period 1979–2013 we calculated the 98th percentile of all precipitation days (>1mm/day) as PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 5 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts a site-specific threshold for a heavy precipitation event, and then derived for each year the number of days above that threshold) To quantify changes in temperature and precipitation, we calculated temperature and pre- cipitation anomalies. For this, we first calculated the mean temperature for the reference period 1979–2013 (as also used in ISIMIP2b) and then for each year the difference between temperature and the reference value. Thus, a positive anomaly implies that the temperature in that year was higher than the reference period, and a negative anomaly that temperatures were lower. We implemented this approach for mean and maximum temperature and for precipitation. To be able to compare future climate with past climate, we also calculated the average for each climatic variable across three 30-year periods. We calculated the past average from 1981 to 2010, and future averages from 2021 to 2050 (referred to as ‘near term’) and from 2071 to 2099 (‘long term’). We derived calculations separately for each GCM and then calculated the median across all four GCMs [49,55]. Extreme climate impact events We analysed the exposure of African apes to six types of extreme events for which there is evi- dence that they can negatively impact African apes (Table 2) and that were available from the dataset by Lange et al. [55]. For each year of the ‘near term’ and ‘long term’ period described above, we extracted the proportion of area affected within each site. For crop failure, we extracted data for the site with a buffer of 50 km to account for the effect that crop failure in areas surrounding an ape site can lead to increased destruction of ape habitat (Table 2). Lange at al. [55] defined droughts based on soil moisture and thus differ from the climatic variables described above which are based on precipitation. Heatwaves were defined by Lange et al. [55] as hot and humid conditions. Thus, climatic variables and extreme events describe different aspects of climate change impacts on apes. For each time period and site, we calculated the number of years with an extreme event and the average proportion of area exposed to events. As above, we first implemented analyses separately for each GCM and then calculated the median across all four GCMs. Maps of projected exposure for the scenarios RCP2.6 near term and RCP6.0 long term typically reflect the range of projected exposure and are shown in the main text (maps for all four scenarios are shown in Fig E-J in S1 Text). Data processing and analysis was implemented in QGIS version 3.20 [56] and R version 3.6 [57] with the following R packages: ‘geosphere’ [58], ‘maps’ [59], ‘mapdata’ [60], ‘ncdf4’ [61], ‘raster’ [62], ‘shapefiles’ [63] and ‘splancs’ [64]. Results Climatic variables Across the 333 sites analysed, the average annual temperature for the past period (1981–2010) was 24.70˚C. Temperatures were lowest for sites where mountain gorillas occur and highest for western chimpanzees (Fig 2). Sites with eastern chimpanzees covered the widest range of temperatures (9.29˚C, Table A in S1 Text, Fig 2) while sites with bonobos covered the narrow- est temperature range (1.29˚C). At the majority of sites temperatures have increased since 1979 (Fig 3). 36 sites with 13,986 apes had positive temperature anomalies for each of the past ten years (2007–2016), and for an additional 135 sites with 106,623 apes, nine of the past ten years had positive temperature anomalies. Average temperature anomalies across the past ten years ranged from 0.01 to 0.56˚C (relative to the reference period 1979–2013) across all sites, with a mean of 0.23˚C (Fig 3). Of the 30 sites with the highest average temperature anomalies, PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 6 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Fig 2. Mean temperature and annual precipitation for the past period (1981–2010) across 333 ape sites. Sites where chimpanzees and gorillas occur are drawn as squares with two colours. https://doi.org/10.1371/journal.pclm.0000345.g002 all but one were within the range of eastern chimpanzees exposing 13,469 apes. For RCP2.6, an increase in annual temperatures of around 1˚C (relative to the past period, 1981–2010) was projected for the near and the long term across all ape taxa. For RCP6.0, the projected increase in the near term was also around 1˚C, and an increase of more than 2˚C was projected for the long term, with a cross-site average of 2.43˚C increase (Supporting information). The maximum daily temperature averaged across all sites, was 30.53˚C for the past period (Table B in S1 Text), and was highest for sites with western chimpanzees reaching an annual average of 35.42˚C for Niokolo Koba National Park in Senegal. General patterns regarding temperature anomaly and magnitude of projected increases were very similar compared to the patterns described above for daily mean temperature (Table B in S1 Text, Fig A in S1 Text). PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 7 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Fig 3. Temperature anomaly across all ape sites. Temperature anomaly is the difference to the average annual temperature of the reference period 1979–2013. Thick lines in the boxplots show the median, bottom end of the box the first quartile and top end of the box the third quartile. Dark blue: Third quartile below zero, light blue: Median below zero and third quartile above zero, light red: Median above zero and first quartile below zero, dark red: Median and first quartile above zero. https://doi.org/10.1371/journal.pclm.0000345.g003 Maximum daily temperatures have increased at the majority of sites (Fig A in S1 Text) and temperature anomalies were highest for sites with western and eastern chimpanzees (e.g., aver- age temperature anomaly of 0.58˚C for Semuliki National Park in Uganda). For RCP2.6 near and long-term and RCP6.0 near term, an increase in average maximum daily temperatures by around 1˚C was projected, and for RCP6.0 long term an increase by 2.41˚C was estimated (Table B in S1 Text). Annual precipitation differed strongly across ape sites and ranged from 978.14 to 4962.42 mm, with an average of 1940.02 mm across all sites for the past period (Fig 2, Table C in S1 Text). Western chimpanzees occur at sites with the lowest annual precipitation, and western gorillas and central chimpanzees at sites with the highest annual precipitation. For 79 sites with 145,203 apes, seven of the past ten years (2007–2016) were wetter than the reference period (mean precipitation anomaly: 135.40 mm, max: 1056.11 mm). For 54 sites with 51,987 apes, seven of the past ten years were drier than the reference period (mean: -168.48 mm, min: -453.63 mm). Increased precipitation occurred at ape sites in coastal areas of central Africa, and at some savanna sites in western Africa, with drier conditions found in coastal areas at the border between Coˆte d’Ivoire and Ghana, and around the tri-border area of the Central Afri- can Republic, the Democratic Republic of the Congo (DRC) and Gabon (Fig B in S1 Text). Regarding future projections of annual precipitation, there was no clear trend with drying and wetting projected across both RCPs and time periods (Fig B in S1 Text). Decreases in precipi- tation were consistently projected for chimpanzee sites in western Guinea, Guinea-Bissau, and Senegal. Increases in precipitation were consistently projected for chimpanzee sites at the tri- border area of Guinea, Liberia and Coˆte d’Ivoire, and north-eastern Coˆte d’Ivoire and Guinea. Similarly, increases in precipitation were also consistently projected for most sites in central Africa, and the northern range of eastern chimpanzees. For the number of consecutive dry days, the average across all sites for the past period was 35 days per year (Table D in S1 Text), with lowest values for eastern gorillas (mean: 12 days) PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 8 / 19 −1.0−0.50.00.51.01.5Temperature anomaly [°C]19801985199019952000200520102015YearPLOS CLIMATEExposure of African ape sites to climate change impacts and bonobos (mean: 15 days), and longest dry period for western chimpanzees (mean: 47 days). An increase in the number of dry days was consistently projected for all eastern gorilla sites (exposing 4,161 apes) with an increase by more than 20 days in, for example, Kahuzi- Biega and Luama-Kivu in eastern DRC. A strong decrease by more than 30 days was projected for sites in coastal Gabon which was in line with the projected increase in precipitation. For the number of days with heavy precipitation events, the average for the past period was six days (average across all sites) and similar across all taxa (Table E in S1 Text). For future periods, an increase in heavy precipitation events was consistently projected across 288 sites with 429,924 apes, while only for two sites a decrease in heavy precipitation was consistently projected. Extreme events In terms of number of sites affected, wildfires (Fig 4) and crop failures (Fig 5) were the most prevalent extreme events, as across all scenarios 100% of sites were exposed (exception: two sites not exposed to wildfires for RCP6.0 near term). For wildfires, the frequency of events was very high, with almost every year experiencing an event (Table K in S1 Text). For crop failures, frequency was the second highest across event types, with around 15 years (out of a 30-year period) exposed to crop failures for RCP2.6 near and long term (Table F in S1 Text). However, for both crop failure and wildfires, the proportion of area affected was low, with less than 5% exposed under all scenarios. River floods (Fig 6) and heatwaves (Fig 7) were also very prevalent in terms of number of sites affected. Most sites were exposed to floods under RCP2.6 near term (78%) and long term (92%, Table I in S1 Text). But the frequency was low with around one year with an event for RCP2.6 long term and three years with an event for RCP6.0 long term. River floods had low spatial extent with an average of 1–2% of area affected across all scenarios. However, the range Fig 4. Projected exposure of African ape sites (n = 333) to wildfires for two scenarios. (a) RCP2.6 near term (2021–2050) and (b) RCP6.0 long term (2071– 2099). Top row: Number of years with an event within the time period. Bottom row: Number of sites and number of apes projected to be exposed to the respective number of years with an event. Maps for all four scenarios in Fig J in S1 Text. Country outline data was obtained from the R package ‘mapdata’ (cran. r-project.org/package=mapdata). https://doi.org/10.1371/journal.pclm.0000345.g004 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 9 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Fig 5. Projected exposure of African ape sites (n = 333) to crop failure for two scenarios. (a) RCP2.6 near term (2021–2050) and (b) RCP6.0 long term (2071–2099). Top row: Number of years with an event within the time period. Bottom row: Number of sites and number of apes projected to be exposed to the respective number of years with an event. Maps for all four scenarios in Fig E in S1 Text. Country outline data was obtained from the R package ‘mapdata’ (cran.r-project.org/package=mapdata). https://doi.org/10.1371/journal.pclm.0000345.g005 Fig 6. Projected exposure of African ape sites (n = 333) to river floods for two scenarios. (a) RCP2.6 near term (2021–2050) and (b) RCP6.0 long term (2071–2099). Top row: Number of years with an event within the time period. Bottom row: Number of sites and number of apes projected to be exposed to the respective number of years with an event. Maps for all four scenarios in Fig H in S1 Text. Country outline data was obtained from the R package ‘mapdata’ (cran.r-project.org/package=mapdata). https://doi.org/10.1371/journal.pclm.0000345.g006 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 10 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Fig 7. Projected exposure of African ape sites (n = 333) to heatwaves for two scenarios. (a) RCP2.6 near term (2021–2050) and (b) RCP6.0 long term (2071– 2099). Top row: Number of years with an event within the time period. Bottom row: Number of sites and number of apes projected to be exposed to the respective number of years with an event. Maps for all four scenarios in Fig G in S1 Text. Country outline data was obtained from the R package ‘mapdata’ (cran.r-project.org/package=mapdata). https://doi.org/10.1371/journal.pclm.0000345.g007 was relatively large (up to 14%) and for Budongo, Bugoma and Mahale (eastern chimpanzee) more than 10% of area were affected in three out of four scenarios. Most sites were exposed to heatwaves for RCP2.6 near term (84%) and long term (85%, Table H in S1 Text). The fre- quency of events was on average around five years with events for RCP2.6 near and long term. Frequency was higher for RCP6.0 long term with an average of nine years with events. Sites with high frequency in heatwave events were located in southern Coˆte d’Ivoire and neighbour- ing areas, and in central Africa (Fig 7). The extent of spatial exposure was high with more than 80% of the area affected for RCP2.6 near and long term. For droughts, 8% of sites were exposed under RCP2.6 near and long term (Table G in S1 Text, Fig C in S1 Text, S6). They were located in the tri-border area of Guinea, Guinea-Bissau and Senegal, in Coˆte d’Ivoire and Ghana, and two sites in central Africa (Canbinda and Tshuapa-Lomami-Lualaba). The event frequency was low (mean across sites for RC2.6 near term 0.14 years), and highest for western chimpanzees with an average of 4.5 years for RCP2.6 near term. However, similar to heatwaves, the spatial extent of exposure was projected to be high with an average of 53.78% for RCP2.6 near term, and lower exposure of 35.75% for RCP2.6 long term. For tropical cyclones, only few sites were projected to be exposed (Table J in S1 Text, Fig D, I in S1 Text). For RCP2.6 near term only sites within the range of western chimpanzees were exposed, for example, Cantanhez Forest in Guinea-Bissau and Nialama in Guinea. For RCP2.6 long term, sites in Sierra Leone were also projected to be exposed (e.g., Loma Mountains and Western Area Peninsula). Only for RCP6.0 long term, tropical cyclones were also projected for coastal sites in central Africa (e.g., Cabinda and Conkouati-Douli) which could expose western lowland gorillas and central chimpanzees. In summary, crop failure and wildfires are projected to affect all sites at a high frequency and low spatial extent. A large majority of sites are projected to be affected by heatwaves with a PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 11 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts medium frequency but high spatial exposure, and river floods with a low frequency and typi- cally low spatial extent. Droughts and tropical cyclones were projected to only affect specific sites. Numbers were typically higher for RCP6.0 in comparison to RCP2.6, and only for droughts a decrease in average spatial exposure was projected. Discussion For the first time, we showed that African ape sites have already experienced changes in cli- matic conditions and are likely to be exposed to extreme events in the future. We found that temperatures have increased over the past decades at the majority of ape sites, and in line with a previous study [14], we found a consistent increase in future temperatures. Bonobo sites cov- ered the narrowest temperature range, which indicates a potentially lower physiological toler- ance that might make bonobos more sensitive to climate change impacts [65]. We also showed that the majority of ape sites will be exposed to a high frequency of heatwaves. It has been shown that chimpanzees occurring in an area with high temperatures experience heat stress [18]. The impact of heatwaves on primates has not yet been studied, but high mortality of humans during heatwaves [35] and mass die-offs for some taxa (e.g., flying foxes [36]) have been observed. Thus, given the projected prevalence of heatwaves across ape sites, there is a need to understand sensitivity of apes to this extreme event. Thermoregulation behaviours have already been observed in apes (e.g., higher drinking frequency, nocturnal behaviour, sit- ting in caves and pools [18–20]). Though the behavioural flexibility of apes allows them to adapt to higher temperatures to some degree, these behaviours have only been observed for a few study sites, and it is not known how effective these adaptation strategies are, given, for example, that apes compete for access to standing water sources with humans and their live- stock in dry habitats. In addition, these adaptive behaviours entail trade-offs, such as less time for feeding, or increased predation pressure at night. When more energy is used for thermo- regulation this can reduce other physiological processes such as reduced functionality of the immune system, as observed for birds [22]. Behavioural and physiological trade-offs can result in a decline of body condition, as well as lower survival and fertility (Table 2). For precipitation, the results were heterogenous for the historical as well as future period. For sites that have been and are projected to be exposed to less precipitation or an extended period without any precipitation (e.g., projected for eastern gorillas), this can result in a reduced availability of standing water sources. As chimpanzees at a site with low annual pre- cipitation already experience dehydration [18], and as drinking more water is a strategy goril- las use to cope with high temperatures [20], the combined impact of rising temperatures and reduced precipitation might lead to high levels of stress and result in a decline of body condi- tion and fecundity, and ultimately to population declines [16]. Exposure to droughts was projected only for few sites (mostly in West Africa) and there droughts could lead to a reduction in food sources. For forest elephants in Gabon, drier condi- tions led to lower encounter rates of ripe fruits and resulted in a decline in elephant body con- dition [27]. In contrast, some sites in savanna areas are projected to have an increase in precipitation, and thus projections show a lower proportion of area exposed to droughts in the long term, which is in line with updated model projections [66]. While there were extensive droughts in the 1970s and 1980s across the Sahel, rainfall has increased since the 1990s, which has been linked to changes in the West African monsoon [67]. In combination with the CO2 fertilization effect this could lead to a further greening of the Sahel [67,68] and potentially an increase in suitable habitat for apes. Our finding of an increase in the number of days with heavy precipitation at a majority of sites is in line with findings that rainfall patterns will become more erratic [69]. Heavy PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 12 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts precipitation can destroy ape nests [30]. At the same time, up to 90% of sites are projected to be exposed to river flooding, which can restrict animal movement, lead to splitting of social groups, make affected areas inaccessible to animals and can ultimately lead to higher mortality due to higher disease prevalence [32]. The high spatial exposure of ape sites to crop failures and wildfires can intensify forest frag- mentation and deforestation. Especially the combination of several stressors, such as drying, fires and deforestation could lead to a self-reinforcing process that could even lead to a tipping of the Congo rainforest into savanna [70] and thus a loss of ape habitat. The prevalence of exposure of ape sites to climate change impacts stresses the need to plan, for example, in conservation action plans, and implement conservation measures that will increase ape resilience to climate change. At sites facing water shortages, the creation of addi- tional water sources or the protection of such sources specifically for apes would be an impor- tant measure. In addition, measures that protect nesting and feeding trees and ape habitat in general, are needed to improve ape resilience. This can also include measures that prevent the unintentional spread of wildfires, for example, cutting fire breaks, as is implemented in Moyen-Bafing National Park in Guinea [46]. As we found a high projected prevalence of crop failures, interventions that support farmers in years of crop failure or supplementary income sources can contribute to avoiding deforestation. It has not yet been studied to which extent apes are able to track their climatic niche by shifting their range. However, dispersal velocities of primates are lower than for most other taxa [17,42]. Thus, to support adaptation to climate change impacts, the creation of corridors and new protected areas are needed to avoid isola- tion of ape populations. Limitations One limitation of this study pertains to uncertainties inherent in modelled climate data and simulated climate change impacts as discussed by Lange et al. [55]. However, the bias-adjust- ment implemented by ISIMIP reduces some of these uncertainties. To reduce bias, we imple- mented analyses separately for each GCM and then calculated the median across all four GCMs [49,55]. The choice of two emission scenarios allowed for estimating a possible corri- dor of future developments, as recent observations show that global greenhouse gas emis- sions are already exceeding the low-emission scenario RCP2.6. In addition, the climate data we used has a coarser resolution than other available data sources. We chose ISIMIP climate data because the same data was used to force the climate impact models that provided the input for estimating extreme event exposure [55]. This type of extreme event data, especially the inclusion of different types of impacts, is not available at higher resolution from other sources. Further, other sources of high-resolution climate data that are commonly used in biodiversity research (e.g., CRU [71] or WorldClim [72]) also have shortcomings, including the low and decreasing coverage of weather stations across Africa [73] or limitations in mountainous regions [74]. Similarly, we did not use CMIP6 climate data as the correspond- ing climate impact simulations are not yet available, and consequently not the respective extreme event data. Future research with climate and extreme event data based on CMIP6 will be useful to corroborate the findings of this study and to better understand modelling uncertainties, for example, regarding the ongoing discussion on whether a subset of CMIP6 models can be considered ‘too hot’ [75]. With the exception of the study by Zhang et al. [17] on the exposure of primates to past droughts and tropical cyclones, studies on the exposure of great apes to past extreme events are rare. Closing this research gap would be an important contribution to assessing the extent to which apes may be able to adapt to the projected prevalence of extreme events. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 13 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Conclusion Our study shows that African apes are and will be increasingly exposed to climate change impacts. However, the vulnerability of animals to the impacts of climate change, and in partic- ular to extreme events, remains poorly understood. Long-term research sites may be well placed to investigate how sensitive animals are to climatic stressors at physiological and beha- vioural levels. In addition, systematic data collection across sites with different climate change contexts would be important to better understand the mechanisms underlying climate change impacts on animals. Although large gaps remain, our study highlights the need to integrate cli- mate change adaptation into conservation action planning. Supporting information S1 Text. Supporting tables Table A in S1 Text. Annual mean temperature. Table B in S1 Text. Annual maximum temperature. Table C in S1 Text. Annual precipitation. Table D in S1 Text. Maximum number of consecutive dry days. Table E in S1 Text. Number of days with heavy precipitation. Table F in S1 Text. Projected exposure to crop failures. Table G in S1 Text. Pro- jected exposure to droughts. Table H in S1 Text. Projected exposure to heatwaves. Table I in S1 Text. Projected exposure to river floods. Table J in S1 Text. Projected exposure to tropical cyclones. Table K in S1 Text. Projected exposure to wildfires. Supporting figures Fig A in S1 Text. Anomaly of maximum daily temperature. Fig B in S1 Text. Projected exposure of African ape sites to changes in precipitation. Fig C in S1 Text. Projected exposure of African ape sites to droughts. Fig in S1 Text. Projected exposure of African ape sites to tropical cyclones Fig in S1 Text. Maps of projected exposure of African ape sites to crop failure for all four scenarios. Fig in S1 Text. Maps of projected exposure of African ape sites to droughts for all four scenar- ios. Fig in S1 Text. Maps of projected exposure of African ape sites to heatwaves for all four scenarios. Fig in S1 Text. Maps of projected exposure of African ape sites to river floods for all four scenarios. Fig in S1 Text. Maps of projected exposure of African ape sites to tropical cyclones for all four scenarios. Fig in S1 Text. Maps of projected exposure of African ape sites to wildfires for all four scenarios. (PDF) Acknowledgments Substantial part of this work emerged from the workshop “Training of young African academ- ics in using R to process, analyze and interpret wildlife survey data” that took place in Coˆte d’Ivoire (2022) and Rwanda (2023). We are grateful to the following organizations which orga- nized the training: the Senckenberg Museum of Natural History Go¨rlitz (Germany), the Ger- man Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (Germany), the Centre Suisse de Recherches Scientifiques—CSRS (Coˆte d’Ivoire), the Dian Fossey Gorilla Fund in Kinigi—DFGF (Rwanda), the IUCN SSC Primate Specialist Group—Section on Great Apes (PSG-SGA), Re:wild (USA) and the African Primatological Society (APS). We particu- larly thank Prof. Inza Kone and Dr. Winnie Eckardt for their special investment in the success of this workshop which enabled the collaborative work for this research. We thank Amanda Korstjens and Priyamvada Bagaria for helpful comments on earlier drafts of this manuscript. Author Contributions Conceptualization: Stefanie Heinicke. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000345 February 28, 2024 14 / 19 PLOS CLIMATEExposure of African ape sites to climate change impacts Data curation: Razak Kiribou, Paul Tehoda, Julie Ferreira, Tenekwetche Sop, Joana Carvalho, Stefanie Heinicke. Formal analysis: Razak Kiribou, Julie Ferreira, Stefanie Heinicke. Funding acquisition: Hjalmar S. Ku¨hl, Tenekwetche Sop, Matthias Mengel, Stefanie Heinicke. Methodology: Stefanie Heinicke. Project administration: Tenekwetche Sop. Supervision: Stefanie Heinicke. Validation: Razak Kiribou, Paul Tehoda, Onyekachi Chukwu, Godfred Bempah, Hjalmar S. 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10.1371_journal.pcbi.1011956	RESEARCH ARTICLE The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS- CoV-2 transmission model 1☯, You Chang1☯, Martijn SparnaaijID Bu¨ sra Atamer BalkanID Doris Boschma3, Yangfan LiuID Colin Teberg4, Kevin Schachtschneider4, Reina S. Sikkema5, Linda van Veen3, Dorine Duives2‡, Quirine A. ten BoschID 1¤, Yufei Yuan2, Winnie Daamen2, Mart C. M. de Jong1, 2, Berend Wouda3, 1‡ 1 Quantitative Veterinary Epidemiology, Wageningen University & Research, Wageningen, The Netherlands, 2 Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands, 3 Gamelab, Delft University of Technology, Delft, The Netherlands, 4 Steady State Scientific Computing, Chicago, Illinois, United States of America, 5 ViroScience, Erasmus Medical Center, Rotterdam, The Netherlands ☯ These authors contributed equally to this work. ¤ Current address: Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark ‡ These authors are joint senior authors on this work. * d.c.duives@tudelft.nl (DD); quirine.tenbosch@wur.nl (QtB) Abstract SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ inter- actions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of bench- mark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be con- sidered when assessing transmission risks upon different types of human interactions in a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Atamer Balkan B, Chang Y, Sparnaaij M, Wouda B, Boschma D, Liu Y, et al. (2024) The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model. PLoS Comput Biol 20(3): e1011956. https://doi.org/10.1371/journal. pcbi.1011956 Editor: Benjamin Althouse, University of Washington, UNITED STATES Received: July 17, 2023 Accepted: February 29, 2024 Published: March 28, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pcbi.1011956 Copyright: © 2024 Atamer Balkan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 1 / 39 PLOS COMPUTATIONAL BIOLOGYData Availability Statement: The development of PeDViS is part of a research project that develops decision support tools for practitioners to limit SARS-CoV-2 transmission inside their venues. An open-access web-based simulation environment was created, named the SamenSlimOpen App (SSO app: https://www.samenslimopen.nl/de-tool/ ). The PeDViS model is at the core of this app (Section C in S1 Text). All code for the PeDViS model and data to recreate the described experiments are openly available on Gitlab (https:// git.wur.nl/sso-public/pedvis). Funding: This publication is part of the project SamenSlimOpen (10430022010018, awarded to QAtB) of the research programme COVID-19 Programma, which is financed by the Dutch Research Council (NWO) and ZonMw. BAB, YC, MS, BW, DB, YY, WD, MdJ, LvV, DD, and QAtB received salary from this grant. CT and KS were hired as software developers and paid for by the grant. The funders did play no role in the study design, data collection and analysis, decision to publish, or the preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Multi-dimensional challenges of controlling indoor respiratory virus transmission indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions. Author summary With most infections happening indoors, indoor spaces played an important role in the spread and control of SARS-CoV-2. Indoor transmission and the impact of interventions targeted at these spaces are hard to predict due to the interplay of diverse inter-human interactions, host factors, virus characteristics, and the local environment. Mathematical models can help disentangle such complex processes. Here, we introduce a model that simulates viral spread in indoor spaces by combining models on detailed human move- ments and interactions with models that simulate the spread and uptake of viruses through direct and indirect transmission routes. We use a restaurant setting as a case- study and illustrate that, while common distancing measures hold for infection prevention during relatively short interactions, transmission may occur over longer distances if infected individuals spend more time in a space, particularly if poorly ventilated. The effects of intervention measures are tightly coupled to the transmission route they target and the relative importance of this route in a specific scenario. Uncertainty around the lat- ter should be considered when assessing transmission risks. The model can be adapted to different settings, interventions, levels of population immune protection, and to other virus variants and respiratory pathogens. It can help guide decision making on effective mitigation of virus transmission in indoor spaces. 1. Introduction With transmission estimated to be 18 times more likely to happen indoors than outdoors [1], indoor spaces played a focal role in the control of SARS-CoV-2 transmission [2–8]. This risk of transmission can however vary greatly across settings, depending on the context, indoor environment, variation in individual behaviours, infectiousness and susceptibility (e.g., due to immune protection) to the virus, as well as the level of adherence to intervention measures. Understanding how the interplay of human behaviour and viral spread in different envi- ronments affects SARS-CoV-2 transmission in indoor spaces is important for the design of effective mitigation strategies. The efficiency of indoor transmission of respiratory viruses depends on several factors that interact in non-straightforward ways. In general, the likelihood and extent of secondary infec- tions that result from a single introduction depend on three things: the contact structure, indi- vidual host and virus characteristics, and the environment in which the contacts take place. First, transmission is driven by the duration, closeness, and number of contacts the infectious individual has while visiting the indoor space [9,10]. Crowd monitoring tools have been used during the COVID-19 pandemic to record the frequency and duration of contacts and inform the topology of human interactions in different settings. These studies show, amongst other things, that the changes in the interaction patterns as a result of COVID-19 pandemic are very context dependent [11,12], and that habitual interaction patterns are difficult to change [13]. Second, how likely each of these contacts is to result in infection depends on the characteristics PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 2 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission of the infected individual (infectiousness, respiratory behaviours), the susceptibility of the con- tact individuals (as a result of immunity and other individual characteristics), and how effec- tively the virus spreads from one individual to the next. For respiratory viruses, transmission can generally happen through i) droplet spread (large viral-laden droplets that fall to the ground rapidly), ii) aerosol spread (small viral-laden droplets that have the potential to remain airborne for some duration of time), and iii) fomite transmission (i.e., when contaminated sur- faces act as intermediary vectors that result in virus exposure when individuals touch them). How effective each of these routes is, depends on the virus, the indoor conditions (i.e., temper- ature, ventilation, and humidity which may affect the persistence of viruses in their environ- ments), and the closeness, frequency, and duration of contacts. Many non-pharmaceutical interventions (NPIs) are targeted at public indoor spaces, including physical distancing, the use of face masks, hygiene measures, improved ventilation, and limiting crowding [2–5]. Although we know these NPIs to be effective in some spaces [14–19], predicting their impact in various settings and epidemiological contexts is not straightforward. In part, this is because NPIs differ by the main transmission route that they interfere with. While face masks mostly prevent droplet spread, improved ventilation predom- inantly interferes with the concentration of virus-laden aerosols. The impact of NPIs therefore depends on the context-specific relative contributions of different transmission routes. This also affects predictions on composite effects of NPIs, which is likely to be highest if combina- tions of NPIs are sought that affect complementary transmission routes, depending on the virus. Lastly, the level of compliance to the different NPIs may greatly determine their impact. It is this complex interaction between context-specific drivers of transmission, the choice and nature of NPIs, and the level of compliance thereof that make it challenging to preempt the success of intervention strategies. Mathematical models can help decipher these complex interactions. One can gain under- standing on the, often non-linear, relationships that drive the spread of pathogens by combin- ing mechanistic understanding of the transmission process on a population-level with knowledge on the distinct parts of the transmission process (typically on the individual or pathogen-level). Most mathematical models developed during the COVID-19 pandemic evalu- ate interventions at national or subnational levels [20–24]. Other efforts focus on smaller scale transmission, such as hospitals [25]; supermarkets [26]; educational settings [27,28] and work environments [29,30]. Due to the central role they play in transmission and the fact that most control strategies are targeted at these settings, indoor spaces have started to receive more attention from modellers for SARS-CoV-2 and other respiratory pathogens [31–35]. A particular goal of such indoor transmission models is to better understand how the het- erogeneity of encounters in indoor spaces affect transmission and influence the effectiveness of NPIs [36–39]. In one group of airborne transmission models, the Wells-Riley models, one assumes that infectious particles are well mixed in the indoor space. As a consequence, the amount of virus that individuals are exposed to is independent of their distance to the infec- tious individual and solely depends on the duration of this contact. Using these models, the effect of e.g., restricting occupancy and total event duration can be assessed [40]. Expansions of the Wells-Riley model have been proposed that allow for individual heterogeneity in infec- tiousness and respiratory activities [37,39,40], for spatial variation of the virus distribution in the environment [41,42], and the inclusion of multiple transmission routes [27,36]. The multi- route transmission models consider the transmission also via droplets and fomites and shed light on how the relative importance of transmission routes depends on the duration and dis- tance of infectious contacts [30,33,36]. A final class of indoor transmission models follow the computational fluid dynamics (CFD) principles and simulates the flow of particles in time and space [28,43,44]. (See Section A in S1 Text for an overview of indoor transmission models). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 3 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Most indoor transmission models described above assume simple, static interactions between individuals. Some recent advances have been made to incorporate the dynamic nature of human interactions and explore its impact on transmission [45,46]. The first of these models use descriptions of human behaviour such as contact duration [46,47] and couple these with simple rules on transmission risks, such as assuming a linear relationship between exposure duration and infection risk [26,46–50]. These parsimonious descriptions of pedestrian move- ment are helpful to build general understanding of transmission potential in crowded spaces, but are less useful to disentangle the impact of interventions that affect the contact structures (e.g., routing, distancing, cohorting) and associated transmission risks. Individual-based mod- els allow for the simulation of more realistic, diverse, context-based movements by including activity spaces (i.e., where and when do we spend our time) and pathfinding (i.e., how to reach a destination without colliding into objects or other individuals). Such models, when carefully calibrated to empirical observations, can contribute to our understanding of the relationships between human-building interactions and their potential impact on virus spread and exposure [51,52]. (See Section A in S1 Text for an overview of pedestrian models and their applications in infectious disease epidemiology). While great advancements have been made in the modelling of indoor respiratory virus transmission, challenges remain in linking simulated virus exposure to epidemiologically meaningful infection risks and doing so across the range of possible settings and human inter- actions. Models that combine context-based human activity (as determined by activity patterns and route choice of individuals present in an indoor space) with detailed SARS-CoV-2 spread, viral exposure, and consequent infection risks and enumerate the levels of uncertainty sur- rounding these outcomes, may form a valuable addition to the existing model ecosystem and help further guide recommendations on the safe use of public spaces. The objective of this paper is to examine how behavioural, viral, and the indoor environ- mental factors interplay in determining SARS-CoV-2 transmission risks and the relative impact of non-pharmaceutical interventions in indoor environments. To do so, we developed a combined Pedestrian Dynamics—Virus Spread model (PeDViS model) that combines an established pedestrian movement model and a multi-route spatially explicit viral transmission model. Recent insights from pedestrian modelling, virology, epide- miology, and IT-design are combined to develop this open-access software package to model the transmission of SARS-CoV-2 in indoor spaces. In particular, an expert-driven activity assignment model [53] is coupled with a force-based microscopic simulation model (NOMAD) and a virus spread model (Model for Quantifying Viruses in Environments, QVE- mod). Here, using a restaurant as a case study, we investigate how human interactions propa- gate transmission risks in indoor spaces and illustrate how these estimates are affected by differences in contact structures, the indoor environment, and the interventions in place. We highlight the importance of the efficiency of different transmission routes by illustrating how uncertainty surrounding their relative contributions affects our ability to model transmission risks and predict the impact of (the combined application of) NPIs. 2. Model overview In this research, we designed and implemented a combined model coined PeDViS. PeDViS chains an expert-driven strategic choice model with an existing microscopic pedestrian simu- lation model (NOMAD) [54,55] and an epidemiological model for Quantifying Viruses in Environments (QVEmod), see Fig 1. The first model in the modelling chain transforms user input regarding the context, spatial layout, population, and demand into a set of personalised activity schedules [53]. The strategic PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 4 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 1. Model chain to simulate SARS-CoV-2 transmission in indoor spaces. https://doi.org/10.1371/journal.pcbi.1011956.g001 choice model consists of multiple sub-models, which jointly determine the activity choices, destination choices, and departure time choices of each pedestrian in the simulation model. To derive the personal schedules, the strategic choice model also assigns personal characteris- tics to each agent. Here, user-specified settings that impact activity choices are taken into account: for example, in a restaurant environment, the model considers the availability of toi- lets and paying at the table. The second model (NOMAD) uses activity schedules and personalised characteristics to determine the operational movement behaviour of each individual. The operational move- ment behaviour features two sub-models, which determine the (best) route for each activity in a pedestrian’s activity schedule towards each destination and their corresponding walking dynamics (i.e. walking velocity and acceleration) along the route. Both route and operational walking dynamics models take user-specified measures to limit SARS-CoV-2 transmission into account: for instance, following the physical distancing rules has an impact on collision avoidance behaviour, which eventually impacts the operational walking dynamics. The result of the second model is detailed dynamic trajectory information for each individual within the space. The third model (QVEmod) uses these trajectories, combined with epidemiological attri- butes of the individuals (most notably the infectious status of individuals and respiratory behaviour), to simulate the spread of the virus in the environment and the extent to which sus- ceptible individuals concurrently (or shortly after) present in the same space get exposed to it. The infectious status of individuals can be randomly assigned or targeted towards specific agents depending on the design of the simulation experiment. How SARS-CoV-2 is distributed over time and in space is modelled as the accumulation of the virus in the environment, both within the airborne particles (i.e., droplets and aerosols) in the air and on contaminated sur- faces (fomites). This is informed by empirical data on the emission of the virus, the stability of both the virus and the airborne particles that carry it, and the uptake (i.e., through inhalation or by touching fomites) by individuals of virus through air and fomites. Susceptible individuals may get exposed to the virus by inhaling airborne particles or touching contaminated surfaces. This modelling step results in estimates of relative virus contamination at any location in the indoor space at each moment in time as well as individuals’ exposure to virus via each of three considered transmission routes: droplets, aerosols, and fomites. The final model, Risk Identification Model, assesses each individual’s risk of becoming infected with SARS-CoV-2 based on the total amount of virus they are exposed to by applying dose-response relationships. After calculating the infection risk at the individual level, we use Monte Carlo simulation to estimate the number of newly infected individuals. The details of model structure and equations are provided in the Methods section, and the details on model parametrization are presented in Section A in S1 Text. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 5 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission 3 Results 3.1 Virus spread between static contacts The model assesses individuals’ exposure over time and distinguishes the relative contribution of transmission routes to overall exposure in different settings as they arise from human inter- actions. To disentangle the interplay between the several factors that affect virus transmission, we first illustrate the working of QVEmod for various static contacts. We conduct simulation experiments (the term experiments used throughout the text refers to computer simulation experiments) to examine the three main factors of QVEmod namely the impact of i) the inten- sity of a contact (section 3.1.1), ii) respiratory activities (section 3.1.2), and iii) interventions implemented in the PeDViS model (section 3.1.3). The relationship between exposure and infection risk is likely to be non-linear (typically S-shaped) and different between routes (see details in 5.3.5). Relative differences in exposure should therefore not be interpreted as propor- tional to differences in infection risks. In the following static contact experiments, the results are presented relative to a benchmark contact. The benchmark contact is defined as a scenario where susceptible and infectious individu- als arrive concurrently in an indoor space and have a contact at a distance of 1.5 metres for 15 minutes, which is broadly used as an indicator of ‘a risky contact’ [4]. In that case, both infectious and susceptible individuals are assumed to talk and breathe both for 50% of the time each (akin to an interaction in a restaurant for instance), and the indoor space has an average ventilation rate of 3 air changes per hour (ACH). In section 3.1.1, we examined the impact of three determinants of contact intensity on exposure: duration, distance, and the time an infectious individual spent in the space prior to the contact. Then, in section 3.1.2, we examined the impact of different respira- tory activities, namely breathing, talking and singing on relative emission and exposure. Lastly, in section 3.1.3, we examined the impact of different ventilation levels and face masks on exposure in a benchmark contact. The details of experiment settings are provided in Section B in S1 Text. 3.1.1 The impact of contact intensity on exposure. First, we examine the impact of con- tact intensity on virus exposure resulting from a static contact. We examine three determinants of contact intensity: duration, distance, and the time an infectious individual spent in the space prior to the contact. We distinguish the exposure to three routes, where droplet trans- mission is considered a direct route, and aerosols and fomites are considered indirect routes as the buildup of virus in the environment via these routes can potentially contribute to exposure after the infectious individual has left. Contacts at shorter distance than 1.5m result in substan- tially larger exposures with a 3-fold increase at 1 metre (13-fold at 0.5 metre) (Fig 2A). Expo- sure at longer distances diminishes quickly, with exposure at 2 metres being 3-fold lower than the benchmark of 1.5m. At 1.5m distance, 78% of exposure is expected to be attributable to aerosolized virus (here defined as those particles smaller than 10 um) (Fig 2A). Exposure at short distance (0.5m) is dominated by droplet transmission routes, although short range aero- solized viruses may also contribute meaningfully to overall exposure. Prolonged contacts are associated with an increase in exposure. A static contact at 1.5 metres for 1 hour is expected to result in exposure 9-fold higher relative to a 15 minute contact (Fig 2B). The contribution of indirect transmission routes increases with contact duration, highlighting the impact of virus buildup in environments. In other words, the impact of contact duration on exposure is larger than what would be expected if only direct routes played a role in transmission. A similar effect is seen when the infectious individual has spent 3 hours in the space preceding the contact. In such a scenario, exposure following a benchmark contact would be 2.5-fold higher than in our default scenario (Fig 2A and 2C). This increase is driven by a buildup of viruses (aerosolized) in the environment. These particles make up 88% of the expected exposure versus 78% under the baseline scenario. As individuals in this experiment stand still and do not share any PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 6 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 2. Effect of contact intensity on exposure and the relative contribution to exposure of transmission routes. A) exposure for 15 minutes at increasing distance, B) exposure at 1.5 metres for an increasing duration, C and D) as A and B but when the infectious individual was present 3 hours prior to the contact occurring, allowing for a buildup of virus in the environment. Red dashed lines show the contact with 1.5 metres and 15 mins. Exposure is shown relative to this benchmark, in a scenario of concurrent arrival of the infectious and susceptible individuals (as shown in A and B). For instance, a relative exposure of 25 means that overall exposure is 25 times that of the exposure of a benchmark contact. Individuals do not share common surfaces in this experiment, thus exposure from the fomites routes is negligible. https://doi.org/10.1371/journal.pcbi.1011956.g002 common surfaces, the exposure from the fomites routes is negligible in Fig 2. These first analy- ses with QVEmod illustrate that RIVM (Dutch National Institute for Health and Environ- ment) guidelines regarding risky contacts, i.e., 1.5 metre distance and less than 15 minutes of exposure, provide good guidance to minimise exposure risks, provided infectious individuals have not convened in the same space for an extended period of time. 3.1.2 The impact of respiratory activities on exposure. The emission of virions per hour from talking and singing is assumed to be respectively 14 times and 16 times higher than from breathing [56] (Fig 3A). The make-up of the emitted particles (i.e., proportion aerosols and droplets) also varies depending on the respiratory behaviour, and are estimated to be 17% aerosols upon talking and 7% upon singing, relative to 98% upon breathing (Section E in S1 Text). Considering these factors in QVEmod, virus exposure upon 15 minutes of talking and singing was estimated to be about four times and eight times higher than upon breathing, respectively (Fig 3B). Notably, the contribution of aerosols to the overall exposure is estimated PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 7 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 3. Effect of different respiratory activities on exposure. A) The relative emission rate of virions and B) the relative exposure during talking and singing continuously for 15 mins, relative to breathing. C) The relative contribution of the three transmission routes to the individual’s exposure while breathing, talking and singing. Both the infectious and susceptible individuals are assumed to perform the respective respiratory activity. Individuals do not share common surfaces in this experiment, thus exposure from the fomites routes is negligible. https://doi.org/10.1371/journal.pcbi.1011956.g003 to be lower upon active respiratory activities (Fig 3C). However, due to different measuring methods and equipment, the quantity and partition of aerosols and droplets generated during different respiratory activities are inconsistent among studies [57,58] and may well differ between individuals of different age and gender [59,60]. 3.1.3. The impact of interventions on exposure. Beyond distancing measures, improved ventilation and wearing face masks are common interventions in indoor spaces. Here, we examined the impact of both intervention measures across a range of possible efficacies by simulating the impact of these two interventions on exposure upon a static benchmark contact. With an ACH as high as 24, ventilation can result in a maximum reduction of overall exposure of about 65% in this static example (Fig 4A). Increasing the ACH from a common level used in Dutch public indoor places (3 ACH, red line in Fig 4A) [61] to the recommended 6 ACH causes moderate effects and would reduce the exposure by aerosols with 20% (with the total exposure reduced from 81% to 65%). Under the assumption that face masks block most droplets, the aerosols route becomes the dominant source of virus exposure (99%), even at low filter efficiency (FE) (Fig 4B). Assuming 40% FE for aerosols (i.e., 60% of aerosols and 6% of droplets pass through the face masks) [62], masks reduce the overall exposure to 28% compared to exposure without masks (red line in Fig 4B). The near-perfect protection at the highest FE (>75%) can be attributed to the additive effect resulting from mask-wearing by both infectious and susceptible individuals, provided the masks are well used and fitted [62,63]. Effectiveness would differ upon longer exposure or in settings with poorer ventilation, for instance. How these intervention-induced reductions in exposure relate to infection risks is not straightforward and critically depends on the dose-response relationships of the several trans- mission routes. The impact of dose-response parameters on infection risks are further explored in a sensitivity analysis in section 3.2.3. 3.2. PeDViS application on a case study: simulating virus transmission at restaurants We demonstrate the use of PeDViS with a case study, namely the simulation of virus spread and exposure in a restaurant setting. For the case study, a small conceptual restaurant is PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 8 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 4. The impact of interventions on exposure after 15 minutes at a 1.5-metre distance. A) The impact of ventilation air change rate per hour (ACH) on exposure. The red dashed line shows the baseline ACH = 3 per hour indoors. B) The impact of mask-wearing by both infectious and susceptible individuals on virus exposure. The default filter efficiency is assumed to be 40% for aerosols (red dashed line). The exposure load for contact at 1.5m and 15 min without a mask and with poor ventilation (ACH = 0) is standardised to 1. https://doi.org/10.1371/journal.pcbi.1011956.g004 adopted, which has four tables, a bar and seating capacity of twenty people (Fig 5 and Fig B in S1 Text). The simulation lasts for 6 hours of service at a restaurant, in which some tables are used twice, and thirty two individuals in total enter the space. Only one infectious individual enters the simulation during its runtime, which is assigned at the beginning. The details of the case study setting are described in Section D in S1 Text. 3.2.3. Results of PeDViS simulation of restaurant case study. A case study in a restau- rant was provided to show how human interactions drive transmission outcomes. The model simulated virus exposure of individuals in the restaurant and the impacts of face masks and ventilation thereon. In a sensitivity analysis we explored different dose-response relationships to estimate the number of infected individuals and the relative contribution of transmission routes. In this section, first, the pedestrian movement dynamics are briefly discussed. Based on simulated movement trajectories, we present the viral spread through the restaurant’s environ- ment. The exposure to the virus for each of the individuals is then estimated. A sensitivity anal- ysis on the relation between infection risks and virus exposure is done to align simulated infection risks to literature. Lastly, we evaluate the impact of interventions on reducing infec- tions depending on the relative dose-response relationships assumed. Pedestrian movement dynamics in a restaurant setting. To examine how human movement influences the exposure and transmission indoors, PeDViS was used to simulate a real-life sce- nario. The individual trajectories of one run with PeDViS are shown in Fig 5. Due to the stochas- tic activity scheduler and randomly drawn characteristics of the individuals, each run with PeDViS results in different trajectories. In order to fully comprehend the impact of infectious pedestrians in one space, one has to consider multiple runs with PeDViS, the exact number depending on the setting, occupancy, and the amount of distinct activities individuals engage in. In the particular case visualised in Fig 5, the infectious individual (Individual 9) spent about 2 hours in total in this restaurant. It entered and sat at the middle table of the restaurant for 70 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 9 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 5. The trajectory, seat locations and the visiting duration of each individual in a simulation. (A) the trajectory of the infectious individual (ID = 9). (B) the trajectories of other 31 individuals with individual 25 sits in the same seat as Individual 9. (C) the visiting time of all individuals with the orange shade shows the visiting time of the infectious individual. https://doi.org/10.1371/journal.pcbi.1011956.g005 mins together with individuals 10, 11, and 12 (Fig 5C). Subsequently, Individual 9 went to the toilet for 4 mins and went back to their seat. Forty minutes later, individual 9 left the restau- rant. As one can see Fig 5A, the trajectories of Individual 9 are relatively straightforward and direct. Individual 9 has spent most of its time sitting or standing at static locations. Twenty- three individuals walked into the space before or after Individual 9 and spent part of their time in the same room as Individual 9 (Fig 5C). Eight individuals entered the space after Individual 9 had left and did not have any direct contact with Individual 9. Individual 25 sat at the same seat as Individual 9 after it left (Fig 5B). Other than the others at the same table as Individual 9, most other individuals have not come into close vicinity for an extended period of time with Individual 9 during their stay. The main corridor between the entrance and the toilet is highly frequented, as is the route between the table on the right and the toilet. Viral spread. The infectious individual’s whereabouts determines the virus distribution in the air and on fomites (Fig 6). The cumulative contamination in heatmaps represent the accrued virus contamination. The contamination load is represented as a relative value as the amount of virus that an average infectious individual emits per hour with 30 mins breathing and 30 mins talking activity is standardised to 1 unit. The three maps illustrate that the con- tamination is highest near the chair where the infectious individual spends most of its time. This is, as expected, particularly clear in droplets (Fig 6B) and fomites (Fig 6C) heatmaps. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 10 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 6. Cumulative virus contamination in the environment. (A) aerosols, (B) droplets, and (C) on fomites. Contamination is expressed as the virion quantity relative to an average infectious individual’s hourly emission. https://doi.org/10.1371/journal.pcbi.1011956.g006 While the changes in the concentration of virus in droplets over time is tightly linked to the presence of the infectious individual, aerosols and fomites can build up in their environment and may persist after the infectious person has left (see snapshot heatmaps in Fig C in S1 Text). Individuals’ exposure to virus. The cumulative exposure of thirteen individuals (i.e., Individ- uals 10–21, 25) surpassed that of the benchmark contact (15 min at 1.5m), despite the fact that eleven of those individuals (all but 11 and 12) were never within 1.5m of the infectious individ- ual (Fig 7). These thirteen individuals sat close to the infectious individual and overlapped suf- ficiently in time to get exposed to the virus or sat at the seat of Individual 9 after it left. Only the nearest neighbours (10 to 12) were exposed through droplet spread. Others were predomi- nantly exposed through indirect routes, mainly aerosols. Only Individual 25 who sits in the same seat that the infectious individual (9) had occupied has been exposed to fomite as they shared common surfaces. Uncertainty relationship between virus exposure and risk of infection. An individual’s cumu- lative virus exposure is indicative of someone’s risk of becoming infected, although the exact relation and how this differs by exposure route is uncertain. We applied exponential dose- response models, where the dose-response parameter k for each route determines the number of virions someone is exposed to that results in a 63% probability of getting infected (see sec- tion 5.3.5). The value of k varies between transmission routes due to different deposition loca- tion (eg. upper and lower respiratory tract) and deposition efficiency [64]. It is generally difficult to quantify k by experiments [65–67]. Molecular epidemiological studies estimated bottleneck estimates to be around 1000 (Dinf) [68]. We treat this as a lower limit for k, consid- ering that virions that contribute to an individual’s exposure load, still need to overcome sev- eral barriers prior to reaching the cells of the respiratory tract (croute). We performed sensitivity analyses by assessing the number of newly infected individuals expected to arise in this case study, assuming a range of proportional differences between the three routes (caerosols, cdroplets, cfomites), and assuming an average infectious person emits 106 viral particles per hour (ϕ) when spending half of its time breathing and half of its time speaking (see details in Section B in S1 Text). This sensitivity analysis also captures the uncertainty around bottleneck esti- mates, which may well be tighter than 1000 [69–73]. The latter generally does not affect the results, as c and ϕ scale linearly to exposure, with c used to tune the results to epidemiologically reasonable outcomes. Substantial uncertainty in ϕ and k should be considered when interpret- ing the estimates of c. The number of infected individuals arising from this restaurant is most sensitive to the effi- ciency of aerosol transmission, with the mean number of infected individuals varying from 5.4 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 11 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 7. Susceptible individuals’ exposure load. Exposure load is expressed as the virion quantity relative to an average infectious individual’s hourly emission and is partitioned by transmission route. The exposure of susceptible individuals with the red dashed line showing the exposure for a benchmark contact of 1.5m for 15min. https://doi.org/10.1371/journal.pcbi.1011956.g007 with caerosols at 100% to 0.02 when caerosols is 0.1% (Table 1). The efficiency of the fomite trans- mission route (cfomites) has little impact on the number of infected individuals in this specific case study due to limited sharing of surfaces between individuals. We considered a mean of 0.8 infections as a default, plausible scenario, in agreement with outbreak clusters data in similar social settings (mean secondary infections was 0.8, under the assumption that pairs with no Table 1. Sensitivity analysis on the impact of route specific dose-response relationships on the number of infected individuals (default in bold). Dinf 1,000 caerosols: cdroplets: cfomites 100%: 100%: 100% 100%: 100%: 10% 100%: 10%: 100% 10%: 100%: 100% 10%: 100%: 10% 10%: 10%: 100% 100%: 10%: 10% 10%:10%10% 10%: 10%: 1% 10%: 1%: 10% 1%: 10%: 10% 1%: 10%: 1% 1%: 1%: 10% 10%: 1%: 1% 1%: 1%: 1% 1%: 1%: 0.1% 1%: 0.1%: 1% 0.1%: 1%: 1% 5th percentile mean 95th percentile 3 3 3 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 5.4 5.3 5.3 1.4 1.3 1.0 5.1 0.81 0.80 0.76 0.15 0.14 0.10 0.75 0.09 0.08 0.08 0.02 8 8 8 3 3 3 8 2 2 2 1 1 1 2 1 1 1 0 https://doi.org/10.1371/journal.pcbi.1011956.t001 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 12 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission setting reported were proportionally distributed over the settings) [74]. As our default we use the efficiency estimates that give the best agreement with these empirical outcomes, which is when the most efficient exposure route has a croute is no larger than 10% (Table 1). We further adopt equal efficiency between routes (caerosols = 10%, cdroplets = 10%, cfomites = 10%) for our default dose-response relationship. Going forward, these route efficiency relationships are assumed, unless stated otherwise. The sensitivity of our model results to these assumptions is presented in the final part of this section, 3.2.3. Impact of interventions on exposure and infections. Intervention measures differ in the trans- mission routes that they predominantly target. Here, as an illustrative example, we investigated how the combined effect of ventilation and face masks can reduce the distribution of virus in indoor spaces, the exposure of susceptible individuals, and ultimately the number of infected individuals. We compared five scenarios: (A) a ‘worst case’ scenario in which no interventions are applied and ventilation is poor (ACH = 0), (B) a baseline scenario with no interventions and with typical ventilation (ACH = 3), (C) with no interventions and with ventilation at rec- ommended levels (ACH = 6) [75], (D) like (B) but with individuals wearing face masks when walking into and through the restaurant, and (E) like (D) but with increased ventilation (ACH = 6). In a poorly ventilated restaurant (ACH = 0), the virus-laden aerosol concentration becomes higher than the baseline scenario (ACH = 3) (Fig DAa and Fig DBa in S1 Text). This increased aerosol concentration is sufficient to expose more people to the virus: thirteen additional indi- viduals had exposures higher than that of a benchmark contact (IDs 1, 6, 7, 22–32) and only 5 individuals had exposure lower than a benchmark contact (ID 2–5, 8) (Fig 8A). The mean number of infected individuals in a poorly ventilated indoor space is estimated to be 1.59 times higher than in our baseline scenario (2.1 vs 0.81) (Fig 9Aa and 9Ab). Increasing ventila- tion to Dutch government recommendations (ACH = 6), the virus-laden aerosol contamina- tion is reduced compared to the baseline scenario (Fig DCa in S1 Text), resulting in an estimated 41% reduction in the mean number of infected individuals (0.48 vs 0.81) (Fig 9Ac and 9Ab). There is a 61% chance of zero individuals getting infected, compared to 42% under the baseline scenario. With a mere 1.2% reduction in infections (0.80 vs 0.81) relative to the baseline scenario, the impact of face masks was negligible (Fig 8B and 8D). This is due to the assumption that face masks are only worn while walking, as per Dutch guidelines that were in place. As a conse- quence, the only location where face masks have a notable effect on exposure is near the bath- room, and particularly for droplet spread (Fig DBb and Fig DDb in S1 Text). However, in this scenario, the risk of infection is low in these locations due to the short time people spend there. Including face masks to a scenario with increased ventilation has a similar effect, with an estimated 2.9% reduction in the estimated mean number of infections. Indeed, the impact of both interventions is compounded, owing to the different pathways that ventilation and face masks act on (aerosol vs droplet spread respectively). Sensitivity to route-specific infection efficiency. Here we examine the impact of different assumptions on the dose-response curves on the impact of interventions. Specifically, we con- sidered the infectious dose (Dinf) and its relation to the average emission rate known and vary the proportion of virions someone is exposed to reaching the cells of the respiratory tract target cells (croute) (Fig 9). We consider four scenarios: i) virions have equal probability of reaching the respiratory tract target cells, irrespective of the exposure route, ii) like i but virions that someone is exposed to through fomites have lower c iii) like ii but with droplets having a lower c than aerosols, and iv) like ii but with aerosols having a lower c than droplets. We examined the mean number of infections that may have arisen from the described case study. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 13 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 8. The impact of face masks and ventilation on virus exposure in the case study. (A,B,C) a scenario where individuals do not wear face masks and an ACH is 0 (A), 3 (B), and 6 (C) per hour in the restaurant, (D, E) a scenario where people wear face masks when moving and an ACH of 3 (D) and 6 (E). The dashed red line indicates the expected exposure of a benchmark contact of 1.5m for 15 minutes. https://doi.org/10.1371/journal.pcbi.1011956.g008 Considering the baseline scenarios (Fig 9Ab–9Db) of no intervention (i.e., no face masks) and average ventilation (ACH = 3), the mean number of infections ranges from 0.81 to 0.1, depending on assumptions on the relative transmission efficiencies of the different routes. Infection estimates are lowest when aerosol spread is assumed less efficient (mean = 0.1, 87.5% PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 14 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 9. The density distributions of the expected number of infected individuals in the case study for varying route-specific transmission efficiency. Each row shows a parameter setting for croute: (A) croute is the same for all routes (caerosols:cdroplets:cfomites is 10%:10%:10%). (B) croute is smaller for fomites (caerosols: cdroplets:cfomites is 10%:10%:1%). (C) croute is smaller for fomites and droplets (caerosols:cdroplets:cfomites is 10%:1%:1%). (D) croute is smaller for fomites and aerosols (caerosols:cdroplets:cfomites is 1%:10%:1%). Each column shows an intervention scenario: (a) poor ventilation scenario, ACH = 0, (b) baseline scenario, ACH = 3, (c) scenario with recommended ventilation, ACH = 6, (d) baseline scenario with face masks worn while moving, (e) scenario with recommended ventilation and with face masks worn while moving. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respective intervention scenario. Fig 9Ab (in bold border) shows the baseline scenario. https://doi.org/10.1371/journal.pcbi.1011956.g009 reduction relative to the default of (caerosols = 10%, cdroplets = 10%, cfomites = 10%) (Fig 9Db). Assuming less efficient transmission through fomites or through fomites and droplet exposure results in a smaller reduction (mean = 0.79 or 0.74, 2.7% or 8% reduction relative to the default) (Fig 9Bb and 9Cb). Due to the wider spatial distribution of aerosols, more individuals get exposed through this route. The total number of infections is therefore most sensitive to the aerosol specific dose-response relationship. Whereas aerosols (short and long range) would be accountable for 90% of infections under the default assumption (caerosols = 10%) (Fig EAb in S1 Text), this is reduced to 55% if aerosol transmission is assumed less efficient (caerosols = 1%) (Fig EDb in S1 Text). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 15 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission The sensitivity on the assumed dose-response relationship further becomes apparent when comparing the impact of ventilation on the estimated number of infections. Whereas, under default assumptions and relative to average ventilation (ACH = 3), poor ventilation (ACH = 0) would be associated with a 2.44-fold increase in the number of infections, (Fig 9Aa and 9Ab), this difference would be diminished if virions in aerosols would infect an order of magnitude less efficiently than those in droplets. (Fig 9Db and 9Dc). Since the use of face masks while walking was not found to substantially affect individuals’ virus exposure, the total number of infections averted is less sensitive to assumptions on the dose-response relationships. The largest impact is seen in a scenario in which droplet spread is the most efficient route of transmission (Fig 9Cb and 9Cd). 4. Discussion Although SARS-CoV-2 continues to circulate at high levels around the world, COVID-19 is no longer considered a global health emergency. Experiences from the COVID-19 pandemic are now being used to inform response plans for future pandemics by virulent, immune-escap- ing SARS-CoV-2 variants or other pathogens. Interventions targeted at reducing transmission in indoor spaces will constitute an important part of these plans, particularly for pathogens for which no pharmaceutical interventions are (yet) available. Here, we presented the hybrid sim- ulation model PeDViS, a tool that can contribute to the improved understanding of indoor transmission and guide preparedness efforts. It simulates the interplay between pedestrian’s choice and movement dynamics, in the specific context of indoor spaces, and the spread of respiratory viruses. We introduced this new model framework and demonstrated its use in identifying where and when at-risk contacts occur in real life scenarios in indoor spaces. We illustrate how this information can be used to inform intervention measures, and demonstrate that the impact of combined intervention strategies crucially depends on the efficiency of dis- tinct transmission routes. Many interventions in indoor spaces aim at reducing the number of proximate contacts vis- itors have. However, not all proximate contacts constitute a real risk for transmission. We aimed to get a better understanding of what constitutes a risky contact and how this differs depending on the setting in which this contact takes place. The explicit modelling of the spatial distributions of virions in the environment allowed for the exploration of how virus exposure may relate to the duration and distance of potentially infectious contacts. Specifically, we simu- lated an exponential decay in virus exposure over distance, with little exposure beyond the commonly used benchmark of 1.5m, provided the contact is of short duration. Longer contact durations are expected to be associated with buildup of virus in the environment, increasing virus exposure, also beyond 1.5m. The buildup of virus in environments can further contribute to elevated virus exposure when an infectious person has spent a substantial amount of time in that same space, before the contact takes place. Whether and how often such indirect transmis- sion events occur, is hard to verify from epidemiological surveillance data, but has been dem- onstrated to be possible in animal experiments [76]. We used PeDViS to assess the frequency and intensity of contacts that take place in a spe- cific setting, based on the activities performed in a space and typical pedestrian dynamics (i.e., as they arise from route choices and collision avoidance). This part of the modelling relies on the well-established pedestrian model NOMAD, which has been updated for this work for use in small-scale settings. While it allows for the inclusion of physical distancing, crowd monitor- ing data gave little support for a substantial effect of distancing methods and were therefore not included here. The NOMAD model gives the PeDViS framework the ability to construct contact networks for a wide range of settings and, through pairing with QVEmod, tie this to PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 16 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission exposure risks. These exposure risks cannot be easily related to a single benchmark contact (here within 1.5m for at least 15 minutes), due to the intricacies of indirect, airborne transmis- sion. For instance, the case study shows that, of the thirteen individuals whose cumulative exposure surpassed that of a benchmark contact, ten had never been within 1.5m of the infec- tious individual. Yet, their visits had overlapped sufficiently in time with the infectious individ- ual to accrue virus that had built up and was distributed in the environment. Modelling efforts, such as the ones performed by PeDViS, can help assess the added risks associated with such indirect exposure routes (i.e., aerosols and fomites) by accounting for the impact of individuals sharing spaces, even if not (entirely) concurrent in time. We examined the relative reductions in virus exposure that results from different interven- tion measures and showed that the impact of these measures may well be context specific. While in poorly ventilated spaces, by increasing ventilation to an average level, great reduc- tions in virus exposure can be achieved, increasing ventilation beyond this level has a smaller accrued effect. Similarly, face masks by the guests likely have little impact if not worn while seated, as this is when longer, static contacts occur. However, one incentive of such masking orders is to reduce the risk of contacts with individuals outside of one’s own social circle (i.e., those not seated at the same table). For the restaurant setting explored, we postulate that the encounters whilst walking to and from one’s dining table are sufficiently short to pose a minor risk to other people. The role of masking of personnel was not explored in this study but is expected to be more effective due to the frequent encounters they have with guests and col- leagues and the long duration they spend in the space. Future iterations of the model will include the additional activity models for personnel required for examining this question. How the route specific exposure to the virus relates to infection risks remains an open ques- tion [65,67]. This question both relates to the challenges involved with investigating and quan- tifying the biological processes in laboratory settings as well as the need for model validation based on epidemiological data. Beyond the challenges of estimating and validating the emis- sion and spread of viruses in environments, empirically measuring the rates at which virus is inhaled and/or picked up and subsequently reaches the respiratory tract target cells typically relies on indirect estimations [77,78]. Subsequently, as different target cells present different populations of receptors [79], the infection success of a virion may well depend on where in the respiratory tract it deposits. The mucous layer also likely differs in terms of permeability and clearance mechanisms across the respiratory tract [80]. We captured these different levels of uncertainty and variability in a single parameter c, which determines what proportion of virions, after exposure, successfully reaches the respiratory tract target cells [68,81]. The order of magnitude was scaled such that the distribution of cases matches that of a large infector-pair study in restaurant settings [74]. While this was not intended as a formal calibration, it should result in a rough ballpark estimation that harbours realistic numbers of infections. For this and other indoor transmission models, future efforts should include formal validation exercises that assure that the emerging properties of from the bottom up parameterized modelling sys- tems align with fine-scaled epidemiological outbreak data. The main purpose of this effort, examining the relative impact of intervention measures, is particularly sensitive to the assumed magnitudes and differences in transmission efficiency between routes (Fig E in S1 Text). In particular, the uncertainties in the efficiency of aerosol transmission affect the impact of interventions. As aerosols can both disperse and accumulate over time, they may contribute to transmission over distances longer than 1.5m, especially if the infectious person is present in the space for a prolonged duration. Superspreading events associated with poorly ventilated spaces are indicative of a role for aerosols in transmission [8,82–86]. The extent to which aerosols contribute to transmission in spaces with adequate ventilation depends on the efficiency of this route (Fig E in S1 Text) and will differ between PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 17 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission settings [78,87]. Similarly, the case study examined did not present a large contribution of fomites to transmission. In many infectious diseases, particularly those whose transmission through surfaces plays a major role such as Ebola or chicken pox, shared surfaces can be an important infection transmission route. PeDViS can simulate the virus transmission mecha- nism through surfaces along with the aerosols and droplets and is thereby generic in represent- ing all transmission routes relevant to respiratory pathogens. However, for COVID-19, evidence shows that the virus mainly spreads through respiration [88–90], and transmission through surfaces may be limited [91–95]. It can, however, not be ruled out and might play a role in specific scenarios. Experimental studies in cats, for example, have shown that SARS-- CoV-2 can be transmitted through the environment [76]. This transmission is primarily asso- ciated with the accumulation of the virus in the environment over prolonged time in a shared space rather than being linked to high-touch surfaces. Alternatively, simulation studies [96] illustrate that high-touch surfaces could potentially play a role in crowded settings such as train carriages, where some surfaces are potentially shared by many different individuals. For this particular case study, this low probability transmission route is considered only through the main activity areas of the customers, which are their tables and chairs. However, other sce- narios with conditions more favourable to fomite transmission, including the possible trans- mission through other high-touch surfaces such as door handles, coat rack or pay register, can be examined to better understand the potential for contribution by this route, for SARS-CoV- 2 and other pathogens. For instance, the study on controlled transmission in cats enumerated that, in that specific scenario, one third of transmission could be attributed to indirect, envi- ronmental transmission [76], highlighting that, albeit not the major source of transmission, SARS-CoV-2 has the potential to be transmitted through fomites. There are limitations to this study. Some parameters are hard to quantify empirically, are setting-specific, and/or vary greatly between individuals. For others, data is too sparse to draw strong conclusions. The model presents what we believe to be the currently available empirical evidence and shall be updated whenever new, valuable data become available. It can further be adapted to reflect different variants. While many of the model parameters may affect the abso- lute virus exposure, predicted infection risks were found to be robust to changes in most of the parameters explored (see details in Figs F-L in S1 Text). Infection risks are most sensitive to different levels of emission rates. Here, these are assumed proportional to individual viral loads, which are known to be highly heterogeneous, both between individuals as over the course of the infection [97]. Heterogeneity in infectiousness may, among other factors, be an important source of heterogeneity in observed outbreak sizes. In a sensitivity analysis on the emission rates, the average number of secondary infections varied from 0.09 to 5.40 (Fig F in S1 Text), reflecting that, while most individuals will on average not contribute to onward transmission, some may affect many [74]. PeDViS can be used to further disentangle the sources of heterogeneity that together result in the highly overdispersed outbreak sizes observed for this pathogen. Further, the division between droplets and aerosols is somewhat arbitrary [98]. We used the conventional discrete cut-off size to classify droplets and aerosols (d = 10um), so as to align with the definitions in public health guidance [98]. In addition, the airflow (i.e., the diffusion of air) is modelled to be homogeneous across the space and follow the same mechanism in all directions. Hence, the diffusion rate is independent of any external effects (e.g., temperature, ventilation, space occupancy). This simplification is intentional and should provide generic results. However, more directed airflows could alter transmission risks by resulting in increased exposure in some places and reductions in others. In future efforts, this model will be paired with more detailed airflow models. In this specific exercise, we did not present the full expected variation in outcomes but rather demonstrated the model application here with a single NOMAD replication of our case PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 18 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission study restaurant. Both the movements of the guests and the infectiousness of the infected per- son were identical between runs, as was the assignment of the infectious person (who is always seated at the middle table). As such, the simulation experiments could be regarded as a repeti- tion of a single evening in a restaurant that takes place under a select set of scenarios (Fig 5). This allowed us to make direct comparisons between runs and single out the impact of inter- ventions or uncertainty in parameter values. While these specific runs thus do not account for the several sources of stochasticity that underlie the indoor transmission events, the model and accompanying application are set up to do so. One can readily expand the types and configura- tions of restaurants and compare findings over large sets of iterations including several sources of randomness. For instance: the activity scheduling and NOMAD sections of the model simu- late randomness in guests’ entrance and leave times, walking speed, and the probability of vis- iting the toilet. In QVEmod, the assignment of infectious agents is randomised as well as whether a specific virus exposure results in infection. Further, sources of individual-level het- erogeneity, such as in infectiousness and respiratory activities, can be examined towards a bet- ter understanding of the drivers of superspreading events. Lastly, in current simulations, only guests to the restaurant are simulated. Guests have rather similar activity schedules when visit- ing a restaurant, resulting in a relatively easy, tangible example in which the index case is among guests, which are mostly stationary. The numerous short range contacts made by potentially infected personnel and the longer time spent in a space will result in different dynamics of spread and consequently a different set of interventions. Next iterations of the model will aim to address these questions. Many intervention measures applied during the pandemic relied on behavioural changes in response to non-pharmaceutical interventions (NPIs) that aim to reduce infectious contacts in public indoor spaces. The population-level impact of such measures depends on the contribu- tion of specific settings to overall transmission, which follows from i) the time people spend in specific settings and ii) the by-setting risk for an infected individual to infect other people while there. PeDViS is developed to help inform the latter. The use of fine scale pedestrian modelling allows for the characterisation of the human interactions that emerge in various indoor settings. It is the frequency and intensity of these interactions, coupled with the envi- ronmental factors that affect the efficiency of transmission, that determines the setting-specific risk of transmission. Here, we worked with an estimate of on average 0.81 infections arising from the infectious individual, in line with empirical estimates [74]. This estimate should be regarded as one component of the individual reproduction number, as it denotes the number of new infections caused by a specific infected individual during part of its infectious period. The full estimate being derived from adding up the infections estimated to arise from each set- ting visited over the course of one’s infectious period. The reproduction number for the popu- lation can be derived from the individual reproduction numbers, while accounting for the individual-level probabilities of getting infected. To reduce population-level transmission, intervention measures focused at indoor spaces should aim to reduce the reproduction num- ber to below one, by either reducing time spent in spaces with high by-setting transmission risk or by reducing the risk in such settings. Here, one should also consider that the reason for visiting a setting could affect one’s contribution. For instance, personnel are expected to have contact structures that are markedly different from guests. Also, personnel have a larger proba- bility of visiting a restaurant setting multiple times during their infectious period. This could increase their importance to restaurant transmission and possibly to overall transmission. The latter also depends on their risk of acquiring infection, which could, due to having a profession with frequent proximate contacts, be higher than the general population. However, such quan- tifications would require a more complete understanding of how people spend their time before and over the course of their infectious period [99]. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 19 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission The PeDViS model can be readily adapted to different SARS-CoV-2 variants and respiratory viruses and to populations with different levels of immunity. Owing to the modular set up of PeD- ViS, it can be used to characterise the infection risks in other types of indoor spaces, with different human movement and behaviour characteristics, and with a wide range of possible interventions. While the uncertainties surrounding many of the model parameters limits the ability to estimate actual numbers of infections arising from a scenario, estimating relative changes in response to interventions is robust for most scenarios and can help guide public health decision making. 5. Methods This section presents the details of modelling methodology. First, Section 5.1 details the high- level pedestrian activity choice behaviour models, which comprise of an activity, destination, and departure choice assignment models. Section 5.2 continues with a description of the opera- tional movement model, in particular NOMAD. The last section (5.3) provides an overview of the virus spread and risk identification models that form the last part of the modelling chain. 5.1 Activity scheduler model There is limited work featuring the modelling of activity choice behaviour in buildings (See more detailed literature review in Section A in S1 Text). Most activity assignment models are very specialised for certain types of buildings, predominantly offices or require extensive data. Thus, the authors have decided to develop a new pragmatic activity assignment model, in this case one specifically tailored to restaurants. The main design features of the new model are that it can create a variety of activity behaviours whilst requiring few and simple inputs. Below, the inputs and the model are further detailed. 5.1.1. Activity scheduler inputs. Based on consultation with people in the restaurant industry a number of inputs have been identified. These inputs are a combination of those nec- essary for the model to create realistic activity patterns and those that can be easily and realisti- cally provided by restaurant owners. The selected input are: 1. The restaurant layout: This includes the number of tables and number of chairs per table and their location, the location and amount of toilets (if they are present), the location of a coat rack (if present) and the location of a register (if present). 2. The time period that should be simulated. 3. The demand pattern: This input divides the overall time period into smaller time slots and for each of those defines how many groups will visit the restaurant during that time. 4. The expected average duration of guest visits. Together these inputs provide the activity model with the information it needs to create the activity schedules for each individual guest. 5.1.2. The activity choice and scheduling model. The activity choice and scheduling model uses a two-step approach to create the activity schedule of each individual guest. The first step involves scheduling the visit of all groups of guests. This step results in the start and end time of the visit of each group, the table to which they are assigned during their visit and the group size. The second step then creates an activity schedule for each individual of each group. In the first step, the model first creates a provisional schedule that ensures that each group, which is scheduled to visit the restaurant according to the demand pattern, is assigned a table and a provisional start and end time. The start and end time are chosen such that: • The start time of each group falls within the time slot provided by the demand pattern. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 20 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission • The visit duration (the difference between the end and start time) is at least the expected average duration provided by the input. • Any table is only occupied by one group at a time. Next, it computes the actual start and end time of each group’s visit by taking the provi- sional start and end time and adding some variation. This ensures that groups within the same time slot have slightly different visit durations and arrival times. In the second step, the model takes the visit start time, the visit end time, and the group size of each group to create an activity schedule for each individual of the group. The schedule of each individual guest consists of a number of mandatory activities, some optional activities and some conditional activities. These are the following (in order): • Enter the restaurant: This is always the first activity and a mandatory activity • Hang coat at the coat rack: This is an optional activity performed after entering the restau- rant provided a coat rack is available and the guest chooses to use it given a certain probability. • Sit at the table: A mandatory activity performed after entering the restaurant or using the coat rack • Go to the toilet: An optional activity provided a toilet is available and the guest chooses to use it given a certain probability. Afterwards the guest returns to the table. • Pay at the register: A conditional activity assigned to only one member of a group provided the payment is not performed at the table. • Pick up coat from the coat rack: A conditional activity provided the guest chose to hang their coat at the coat rack when entering the restaurant. • Leave the restaurant: The last activity and a mandatory one. All individuals of the same group will enter the restaurant at roughly the same time and will leave at the same time. By adapting the different probabilities and durations of the activities a range of activity schedules can be produced that fit different restaurants. For a more detailed description of the activity model, see [53]. 5.2. Operational model—NOMAD NOMAD is a microscopic simulation model that simulates the operational movement dynam- ics of individuals. In particular, the walker model is implemented in PeDViS (see Eqs 1–6). The result of NOMAD is a set of trajectories pertaining to the coordinates and velocity of each individual in the simulation at each timestep of the simulation. 5.2.1 Routing model—NOMAD. The routing model of NOMAD is utility-based and developed by Hoogendoorn and Bovy [54] and makes use of the minimum walking cost prin- ciple. In essence, individuals balance their desire to move towards their destination with other needs, for instance travel time, physical effort, closeness to attractive sights. In this implemen- tation of NOMAD, only the need to avoid static obstacles in their surroundings is accounted for. Using a floor field approach, the walking costs are computed for the complete walkable area of the pedestrian infrastructure. In particular, a grid of rectangular cells (0.1x0.1m) is adopted, each of which contains a cost value. Based on the static cost map, the desired direc- tion of an individual in the centre point of each cell can be determined using the steepest descent method. Here, individuals are walking orthogonal to the equi-cost lines. A continuous representation of the desired direction can accordingly be calculated on the fly by means of PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 21 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission linear interpolation between the actual location of an individual and the four nearest locations for which the desired direction was already computed. See Fig 10 for an illustration of two tra- jectories that could be the result of this routing model. 5.2.2. Operational dynamics—NOMAD. Underneath, the main elements of this model are briefly introduced. For an in-depth discussion of the walker model and its calibration one is referred to [101]. ! r ! p tð Þ ¼ v p tð Þ ! v ! p tð Þ ¼ a tð Þ d dt d dt ! !ðtÞ ¼ a a ! cðtÞ þ a ! pðtÞ þ x ð1Þ ð2Þ ð3Þ Fig 10. Illustrative NOMAD floor field with two resulting trajectories ([100]). The white rectangle (on the top) represents the entrance, grey rectangles represent the tables which also act as obstacles, the transparent rectangles around the tables are chairs that represent the destinations, coloured areas in the middle show how the walking cost fields are shaped over the space, and the red and blue lines are examples of the preferred path a pedestrian would follow. https://doi.org/10.1371/journal.pcbi.1011956.g010 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 22 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission ! a ! cðtÞ ¼ a ! sðtÞ þ a ! OðtÞ þ a pqðtÞ ! a s tð Þ ¼ ! ðv0ðtÞ � e gÞ (cid:0) t !ðtÞ v ! a ! pq tð Þ ¼ (cid:0) e pq � A0 � e (cid:0) dpq di ! a ! OðtÞ ¼ (cid:0) e O � AO 8 >>< >>: X o2O 1 1 (cid:0) ðdpO (cid:0) d0Þ 0 for 0 < dpO < d0 for d0 < dpO < 2d0 for dpO > 2d0 ð4Þ ð5Þ ð6Þ ð7Þ Within NOMAD, the movement of pedestrians is assumed to be accelerations that are caused by signals and forces that pedestrians are subjected to. These accelerations are partly ! cðtÞ and partly uncontrolled a ! controlled a accelerations, which simulates the natural fluctuations of pedestrian movements. Together these three acceleration reactions shape the acceleration of an individual. ! pðtÞ. A noise term x comprises the last part of the cðtÞ is the result of the individual’s desire for a certain velocity ! sðtÞ represents the path straying component, a ! The controlled reaction a 0ðtÞ (i.e. speed and direction), the physical interaction with other pedestrians, and surround- ! v ! ing objects. Here, a ! action component and a AO, A0, di, and d0 represent parameters that respectively determine the strength of the pedes- trian interaction and obstacle interaction forces. Please note, the parameters of NOMAD do not influence the movement dynamics of the simulated crowd to a similar extent, since not all forces are always present. Forces with respect to obstacles and pedestrians are only significant if the pedestrian resides within range of obstacles or pedestrians. OðtÞ represents the obstacle interaction component (see Eqs 4–7). pqðtÞ the pedestrian inter- Path straying. When walking, individuals have a desired velocity (combination of speed and direction) that is aligned along the optimal route and speed towards the destination of the pedestrian. NOMAD assumes that deviations from the optimal speed and/or direction incur increasing costs. Therefore, pedestrians always attempt to return to their optimal velocity ! v ! moving e time that individuals require to alter their speed and direction. 0ðtÞ. Tau represents the relaxation term, which identifies the desire of pedestrians to keep g towards their goal along their intended global path. The smaller τ, the longer the Interaction with other pedestrians. NOMAD models the collision avoidance behaviour by means of a non-cooperative game theory strategy [102]. Pedestrians minimise walking costs by anticipating the movement of others and themselves. Besides that, NOMAD’s reaction to other pedestrians is anisotropic. That is, pedestrians have a limited ellipse area in which they interact with other pedestrians and obstacles. The interaction costs of an interaction between two individuals is the inverse of their heart-to-heart distance. Thus, the closer individuals are, the larger the collision avoidance forces, which are pointing in the direction opposite of the interaction. Here, A0 identifies the interaction strength, di interaction distance, dpq the antici- ! pated distance and e pq the unit vector pointing in the direction of the other pedestrian. Interactions with obstacles. The strength of the interaction with obstacles is dependent on the distance to the obstacle dpO, the interaction strength of objects in general AO and the direc- ! tion of the nearest obstacle e O. Here, a step-based approach is used, where obstacles nearby PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 23 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Table 2. Parameters for activity scheduler and pedestrian model. Attribute parameters τ A0 di AO d0 Desired speed customers Pedestrian radius Toilet visit probability Toilet visit duration Coat rack visit duration Register visit duration Pay at table duration Range Value 0.5 [s] 2.0 [m/s2] 0.4 [m] 1.5 [m/s2] 0.1 [m] N(0.9, 0.2) [m/s] [0.4, 1.4] [m/s] 0.15 [m] 0.6 [–] N(120, 60) [s] 20 [s] N(30, 10) [s] 60 [s] [100, 240] [s] [20, 50] [s] https://doi.org/10.1371/journal.pcbi.1011956.t002 have a very large influence and obstacles outside a range of influence d0 not influence individu- als’ movement dynamics at all. Two distance thresholds (d0 and 2d0) are used to govern the gradual linear decline of the obstacle avoidance force. As a result of the formulation, agents within NOMAD only react to obstacles when they are really close to the obstacle. This is an advantage in case of the modelling of indoor spaces, where lots of obstacles are present. The parameter values used in NOMAD are depicted in Table 2. 5.2.3 Parameters setting in NOMAD. The output of NOMAD is detailed data on the movements and activities of all agents in the model. For each agent, the position is recorded every 0.1 seconds resulting in a detailed trajectory per agent. These outputs are converted into the inputs of the Virus Spread Model:QVEmod in the form of a script for each agent after the conversion of the time step from 0.1 seconds to a configurable user-defined value (default = 0.5 minutes). 5.3 Virus Spread Model: QVEmod A spatially explicit agent-based model was developed that simulates emission of viruses by infectious individuals, how these subsequently spread in space and over time within an environment, and eventually may get picked up by susceptible individuals. The model distinguishes seven processes (Fig 11): i. An infectious individual emits virus into the air through virus-laden aerosols and virus- laden droplets (further referred to as aerosols and droplets, depending on their size). ii. Droplets deposit onto surfaces. iii. Viruses lose infectivity at a rate depending on their state in the environment (airborne or on surfaces). iv. Viruses in droplets and aerosols diffuse in the air. v. Susceptible individuals can get exposed to viruses through inhaling air with viral-laden droplets and aerosols. vi. The infectious individual contaminates surfaces by touching objects in the space (e.g., tables, chairs, and menus). vii. Susceptible individuals can be exposed to viruses by touching contaminated surfaces (fomites). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 24 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Fig 11. Schematic of processes in the epidemiological model. https://doi.org/10.1371/journal.pcbi.1011956.g011 A description of the state variables and initialization processes is provided in sections 5.3.1– 5.3.2. The equations associated with the seven core processes and the parameterisation of the model are described in detail below in sections 5.3.3. The dose-response model used for calcu- lating the probability of becoming infected in relation to the virus exposure is provided in 5.3.4, and all the parameters in QVEmod are listed in Table 3. 5.3.1 State variables and scales. QVEmod has two classes, the agents (individuals) and the environment. Both classes acquire virus over the course of a simulation. Individuals have 4 state variables: virus contamination on hands (Vhand), and the accumulated virus exposure via aerosols, droplets, and fomites (Eaerosols, Edroplets, Efomites). The environment is composed of two air layers and one surface layer, all of which are divided into equally sized two-dimensional grid cells, the size of which is set to 0.25 m2: a proximate of the space occupied by a single person. Each layer has a coordinate variable and a state variable to record the virus contamination in space (Vaerosols, Vdroplets, Vfomites). The seven processes are evaluated each time step, which is configurable and set to the default value of 0.5 minutes. The default value of the time step is selected considering the rate of the processes in the model (e.g., for a given grid cell size, the time step should be small enough to capture the airflow between grid cells), and the model validation tests conducted with even smaller time step values show negligible differences in infection risk results. 5.3.2 Input and initialisation. QVEmod needs input for individuals’ identifiers and movement scripts, both of which are generated by the activity scheduler and NOMAD model. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 25 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission Table 3. Parameters for the transmission model. Attribute parameters Emission rate (ω) Emission quantity by an average infectious individual (ϕ) Respiratory activity scaler (δ) Individual Infectiousness scaler (σ) Proportion of viruses emitted in the form of aerosols (paerosols) Value Scaled to 1 unit per hour (Typical infectious individual, half breathing and half talking) 106 RNA copies per hour (used for informing dose- response relationships) 0.14 breathing 2.4 singing 1.86 talking (relative to the baseline) 1 (A typical infectious individual) 0 (Susceptible individual) 0.978 (Breathing) 0.0652 (Singing) 0.171 (Talking) Proportion of pathogen excreted to hands (η) Transfer efficiency between hands and surfaces (θ) Ratio of finger pads size to the cell size (π) 0.15 0.25 per touch 0.0196 Source [103] [56] [97] [27,104] [105,106] Calculated based on [107,108,109] (see details in Section E in S1 Text) Tables: 15 touches per hour [110] Surface touching frequency (γ) Fractional transfer rate from hands to facial membranes (ε) Unit decay rate of viruses in aerosols (μaerosols) Unit deposition rate of droplets (μdroplets) Diffusion coefficient (D) Unit decay rate of viruses on surfaces (μsurfaces) Inhalation rate (ρ) 0.01 per hour 1.5 per hour 37.93 per hour 0.0016 m2/sec Wood: 0.969 per hour 288 L per hour (breathing, talking) 432 L per hour (singing) Volume of a cell (L) Infectious dose (Dinf) The proportion of virions reaching respiratory cells caerosols, cdroplets, cfomites 125 L 1000 RNA copies 10% (aerosols) 10% (droplets) 10% (fomites) Calculated based on various references (see details in Section E in S1 Text) Set based on [111,112,113,114] (see details in Section E in S1 Text) [115,116] [117] [118] [119,120,121] [68] Set in this paper (analysis results presented in Table 1) Air change rate (ACH) Face mask filter efficiency aerosols (FEaerosols) Face mask filter efficiency droplets (FEdroplets) https://doi.org/10.1371/journal.pcbi.1011956.t003 Air in a room is replaced 3 times per hour 40% 94% [61] [62] [62] The latter contains the whereabouts and actions of each individual at each simulated time step, hence containing the duration of stay for each individual. In addition, the individuals’ infec- tiousness status is generated randomly. Under the default setting, only one infected individual enters a simulation with an infectiousness scaler set to unity. Super shedders can be included as well, through the generation of a higher infectiousness scaler. By default, an individual’s emission rate is based on breathing and talking at equal proportions, but other respiratory activities can be incorporated as well by the respiratory activity scaler. The size of the indoor space (width and length), and the location, size, and material of objects in the environment are user-defined inputs. In addition, interventions such as wearing masks, cleaning surfaces, and 1.5-meter physical distancing can be included as input to the model, which incurs changes in the activity scheduler, environment variables or NOMAD model parameters, respectively. All state variables are initialised at zero, both for the environment (Vaerosols, Vdroplets, Vfomites) and for individuals’ exposure to the virus through either of the three transmission routes (Eaerosols, Edroplets, Efomites). The superscripts i and s will be used to identify infectious agents and PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 26 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission susceptible agents when differentiation between agent groups is required for some variables. Susceptible individuals are initialised with virus contamination of zero on their hands (Vs hands) is initialised as a proportion of their emission rate, which is detailed in the following sections. All parameters used in the QVE model and their reference sources are presented in Table 3. hands), whereas the contamination on infectious individuals’ hands (Vi 5.3.3 Processes and agent-based state calculations. Here, we describe the details of each of the seven processes (Fig 11). All these seven processes are continuous events and calculated for each time step (Δt) throughout the simulation. Infectious individuals emit virus into the air. Infectious individuals emit viral-laden parti- cles by speaking, coughing, or sneezing. As a result of virus emission, it is assumed that a portion η of the pathogen is excreted to infectious agents’ hands, whereas the rest is emitted to the air. The total amount of virus emitted and the partition of aerosols and droplets emit- ted to the air varies by respiratory activity (section 3.1.2). In this model, we assumed that aerosols are buoyant aerosols (d < 10um) and droplets constitute the rest of the particles (d > 10um). Infectious individuals are assumed to emit viruses at a constant rate. The unit of viral quantities used in this model follows from the typical emission of one typical infec- tious individual per time unit (default: hour). The virus emission calculation is triggered only for the cell (x,y) in which the infectious agent is at time t, otherwise, it is 0. The virus emission rate that infectious agent i shed into the air per time distributed over aerosols (ri emission-aerosols) and droplets (ri emission-droplets) are: ri emission(cid:0) aerosols ¼ oð1 (cid:0) ZÞdspaerosolsð1 (cid:0) FEaerosolsÞDt ri emission(cid:0) droplets ¼ oð1 (cid:0) ZÞdspdropletsð1 (cid:0) FEdropletsÞDt: ð8Þ ð9Þ Here, ω represents the rate at which a typical infectious individual emits virus under half time breathing and talking condition, and is scaled to 1 per hour. η represents the proportion of pathogen secreted to hands, therefore (1-η) represents the proportion emitted to the air. δ represents the activity infectiousness scaler for scaling the heterogeneity in emission rates dur- ing different respiratory activities, which scales the emission rate relative to the emission rate under half breathing and half talking condition. The infectiousness scaler, σ, scales different infectiousness levels of individuals relative to a typical emitter. pi represent the proportion of viruses emitted in the form of aerosols and droplets, where the two proportions (paerosols, pdro- plets) add up to 1. FEi represent the filter efficiency of face masks for droplets or aerosols. Viral-laden droplets fall onto surfaces. Viral-laden droplets can fall onto surfaces through sedimentation. The resulting contaminated surfaces are called fomites. We assume surfaces can acquire viruses from droplets. On surfaces, viruses are assumed to be stationary and evenly distributed within the grid cells. The rate of viruses transferring from droplets onto fomites (rsedimentation) for cell (x,y) at time t is modelled as rsedimentationðx; y; tÞ ¼ Vdropletsðx; y; tÞmdropletsDt; ð10Þ where μdroplets represents the unit deposition rate of viral-laden droplets. Virus decay in the air and on surfaces. SARS-CoV-2 viruses are assumed to decay exponen- tially in the environment, the rates of which vary in aerosols and on different surface materials. Viruses-laden aerosols lose infectivity at a constant rate while floating in the air, and air change rate (ACH) indoors has an increasing impact on their decay. Conversely, viruses-laden drop- lets are assumed to fall onto surfaces rapidly (Eq 10), so the decay in the droplet layer is assumed to be negligible. On fomites, viruses decay at a constant rate which depends on the fomite’s material. The aerosols decay (rdecay-aerosols) and fomites decay (rdecay-fomites) equations PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 27 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission for cell (x,y) at time t is identified below where μaerosols and μfomites represent the unit decay rate of viruses in aerosols and on fomites respectively: rdecay(cid:0) aerosolsðx; y; tÞ ¼ Vaerosolsðx; y; tÞð1 (cid:0) e(cid:0) maerosolsDt(cid:0) ACHDtÞ rdecay(cid:0) fomitesðx; y; tÞ ¼ Vfomitesðx; y; tÞð1 (cid:0) e(cid:0) mfomitesDtÞ: ð11Þ ð12Þ Virus-laden aerosols and droplets diffuse in the air. To simulate the diffusion of virus-laden particles in the air, we solve two-dimensional diffusion equations for the number of virions in aerosols and droplets. We assume that all particles are well-mixed in the volume of the grid cell, after which the aerosols start to diffuse in x,y directions (see Eqs 13–14). Δx and Δy repre- sent the length unit of the cell (both 0.5m in the default). Here, D is the diffusion coefficient, indicating the unit diffusion rate per time (m2/sec). The diffusion-induced rate of change in cell (x,y) at time t in aerosols (rdiffusion-a(x,y,t)) and droplets (rdiffusion-d(x,y,t)) are calculated with the equations below (for convenience in the representation, “aerosols” and “droplets” are abbreviated here as “a” and “d” respectively): ð rdiffusion(cid:0) a x; y; t Þ ¼ D ðVaðx (cid:0) Dx; y; tÞ þ Vaðx þ Dx; y; tÞ þ Vaðx; y (cid:0) Dy; tÞ þ Vaðx; y þ Dy; tÞ (cid:0) 4Vaðx; y; tÞÞDt DxDy ð13Þ ð rdiffusion(cid:0) d x; y; t Þ ¼ D ðVdðx (cid:0) Dx; y; tÞ þ Vdðx þ Dx; y; tÞ þ Vdðx; y (cid:0) Dy; tÞ þ Vdðx; y þ Dy; tÞ (cid:0) 4Vdðx; y; tÞÞDt DxDy :ð14Þ Susceptible individuals inhale air with viral-laden droplets and aerosols. Susceptible individ- uals get exposed to the virus from aerosols and droplets by inhaling a portion of airborne viruses accumulated in the air (Vaerosols(x,y,t) and Vdroplets(x,y,t)) in the cell (x,y) they are in at time t. For each susceptible agent s, we calculate the inhaled amount of viruses per time step via aerosols and droplets by rs again for each susceptible agent s, the accumulated virus exposure via aerosols and droplets, Es droplets(T) are calculated by the summation of the inhaled amount of viruses up to time T. The inhalation of virus in the forms of aerosols and droplets is the ratio of human tidal volume per time step over the cell volume (L), where ρ represents the unit inhala- tion rate, which depends on the respiratory activities of an individual. FEi represents the filter efficiency of face masks against aerosols or droplets. inhalation-droplets(t), respectively. Then, inhalation-aerosols(t) and rs aerosols(T) and Es rs inhalation(cid:0) aerosols tð Þ ¼ Vaerosols x; y; t ð Þ r L ð 1 (cid:0) FEaerosols ÞDt rs inhalation(cid:0) droplets tð Þ ¼ Vdroplets x; y; t ð Þ � 1 (cid:0) FEdroplets � Dt r L Es aerosolsðTÞ ¼ Es dropletsðTÞ ¼ XT t¼0 XT t¼0 rs inhalation(cid:0) aerosolsðtÞ rs inhalation(cid:0) dropletsðtÞ ð15Þ ð16Þ ð17Þ ð18Þ Infectious individuals contaminate surfaces. Infectious people can contaminate surfaces by interacting with them. It is assumed that virus on infectious people’s hands, Vi hand, can be PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 28 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission transferred to surfaces. Surfaces, such as tables and chairs in cell (x,y) are assumed to be touched by proximate individuals at a constant rate if there is a surface area within the reach- able distance (0.5 m) of the infectious agent, i. For a grid cell (x,y) containing surface elements, the touching frequency (γ), transfer efficiency (θ), and the ratio of finger pads surface relative to the reachable surface area (π) determines the surface contamination rate in a time step, ri con- tamination(x,y,t). Vi hand is initialised at t = 0 as a proportion of emission rate, where η represents the proportion of pathogen excreted to hands. It is assumed that the decrease rate of the virus on the infectious agent’s hands (due to decay or transfer) is similar to its replenishment rate, then the change in the virus amount on the infectious agent’s hands is negligible. Hence, Vi is assumed to be constant throughout the event: hand ri contaminationðx; y; tÞ ¼ V i handðtÞgypDt V i handðtÞ ¼ V i handð0Þ ¼ oZ: ð19Þ ð20Þ Susceptible individuals touch virus on the surfaces. Susceptible individuals’ exposure to the virus from fomites is the amount of virus on fomites being picked up by their hands and sent to their facial membranes. It is assumed that, first, the virus transfer from surfaces to hands occurs when susceptible people touch the contaminated surface at cell (x,y), and the virus accumulates in each susceptible agents’ hand, Vs hand. Then, again for each susceptible agent s, the individual exposure from fomites route up to time T, Es fomites(T), is calculated as a propor- tion of viruses on hands that are assumed to be transferred from hands to facial membranes, ε. Similar to the surface contamination process, the touching frequency (γ), transfer efficiency (θ), and the ratio of finger pads relative to the reachable surface area (π) are used to calculate the virus pick up rate. rs pick(cid:0) upðx; y; tÞ ¼ Vfomitesðx; y; tÞgypDt rs pick(cid:0) upðtÞ ¼ X x;y rs pick(cid:0) upðx; y; tÞ V s handðt þ DtÞ ¼ V s handðtÞ þ rs pick(cid:0) upðtÞ Es fomitesðTÞ ¼ XT t¼0 V s handðtÞεDt ð21Þ ð22Þ ð23Þ ð24Þ 5.3.4 Environmental state calculations. As a result of the processes explained above, the state variables in the environment Vaerosols, Vdroplets, Vfomites are calculated and updated for each grid cell (x,y) in each Δt. In each time step Δt, Vaerosols is decreased by the inhaled amount by the susceptible agents in grid cell (x,y), updated by the diffused amount of particles, decreased by the decay of viruses and increased by the virus emission if there exists an infectious agent in cell (x,y) at time t: Vaerosolsðx; y; t þ DtÞ ¼ Vaerosolsðx; y; tÞ (cid:0) emission(cid:0) aerosols: þ ri X s rs inhalation(cid:0) aerosolsðtÞ þ rdiffusion(cid:0) aerosolsðx; y; tÞ (cid:0) rdecay(cid:0) aerosolsðx; y; tÞ ð25Þ Similarly, Vdroplets is decreased by the inhaled amount by the susceptible agents in grid cell (x,y), updated by the diffused amount of particles, decreased by the sedimentation of viruses from air layer to surface layer, and increased by the virus emission if there exists infectious PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 29 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission agent in cell (x,y) at time t: Vdropletsðx; y; t þ DtÞ ¼ Vdropletsðx; y; tÞ (cid:0) emission(cid:0) droplets: þ ri X s rs inhalation(cid:0) dropletsðtÞ þ rdiffusion(cid:0) dropletsðx; y; tÞ (cid:0) rsedimentationðx; y; tÞ ð26Þ In the surface layer, Vfomites is decreased by the picked-up amount by the susceptible agents within the reachable distance to grid cell (x,y), increased by the sedimentation of viruses from air layer to surface layer, decreased by the decay of viruses on the surfaces and increased by the virus contamination if there exists an infectious agent within the reachable distance to grid cell (x,y) at time t: Vfomitesðx; y; t þ DtÞ ¼ Vfomitesðx; y; tÞ (cid:0) X s rs pick(cid:0) upðx; y; tÞ þ rsedimentationðx; y; tÞ (cid:0) rdecay(cid:0) fomitesðx; y; tÞ þ ri contaminationðx; y; tÞ: ð27Þ aerosols, Es droplets, and Es 5.3.5 Estimating infection risks. QVEmod calculates each individual’s exposure via three routes Es fomites. Recall that the magnitude of Es variables are scaled since the unit emission rate ω is initially scaled to 1 for computational purposes. Therefore, the num- ber of viral particles someone is exposed to is rescaled as a product of Es variables and ϕ, the emission rate by an average infectious individual (see Table 3). The relationship between the number of viral particles someone is exposed to, and the risk of acquiring infection is likely to differ between transmission routes, because of different depo- sition locations (faces, lower and upper respiratory tract) and the viability of the virus, among others [122,123]. Accordingly, we modelled the relationship between the three exposure routes and the infection risk using an exponential dose-response relationship [124] as below: � (cid:0) Ps ¼ 1 (cid:0) e �Es �Es ðTÞ þ droplets kdroplets ðTÞ �Es þ ðTÞ fomites kfomites aerosols kaerosols � : ð28Þ droplets(T), Es where Ps represents the susceptible individual’s probability of getting infected, Es Es fomites(T) the individual’s scaled accumulated exposure via the three transmission routes, ϕ the emission rate by an average infectious individual, and kaerosols, kdroplets, kfomites the route specific exposure parameter, which corresponds to an exposure level resulting in 63% chance of getting infected via an individual route. The kroute depends on the infectious dose Dinf, for which we consider recent estimates of the founding virus population size required to cause infection in a recipient host [68] and the proportion of viral particles someone is exposed to that reach the respiratory tract cells (croute) and thus contribute to the founding population: aerosols(T), kroute ¼ Dinf croute : ð29Þ Here, croute is an unknown parameter and particularly hard to estimate. We therefore explore a range of different options in the Results section. We then used the calculations for individual exposures to estimate the number of infected individuals that occurred during a specific event: • Using each individual’s cumulative exposure, the dose-response model provides an estimate for infection risk: Ps, the probability that the susceptible individual s acquired an infection during their stay. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 30 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission • Then, for each susceptible individual in the simulation, a random number from the uniform distribution [0,1] is drawn, and this random number is compared to the individual’s infec- tion probability. If the individual’s infection probability was larger than the number drawn, then it is assumed that an infection is realised. • The total number of new infections that occurred during a specific scenario was estimated by the summation of infections realised. • We repeated this 10,000 times to obtain a distribution of the number of infections that may have occurred. • The mean of this distribution can be regarded as the event-specific reproduction number R: the average number of new infections that arose from one specific event with one infectious individual present. The parameter values used in QVEmod are depicted in Table 3. These reflect the most recent insights about SARS-CoV-2 characteristics, and can be configured with respect to new information available. For a detailed description of the parameterization, the reader is referred to Section E in S1 Text. Supporting information S1 Text. Supporting Information. Section A. Background information on indoor move- ment and transmission models. Section B. Experiment setting for static contacts. Section C. Description of SamenSlimOpen tool. Section D. Case study description. Section E. Parame- ter description in QVE-MOD. References for supporting information. Fig A. Screenshots of the SamenSlimOpen tool. A) introduction screen, B) scene selection screen, C) scene development screen, D) developed scenario. Fig B. The case study restaurant layout. The green rectangles and round brown circles signify the seats, the green arrows the entrances, the blue toilets the entrance to the toilets and the brown rectangles the tables. Fig C. The snapshot contamination map in the case study. Virus contamination in the environment in aerosols, droplets, and on fomites over time in minutes. Contamination is expressed as the virion quantity relative to an average infectious individual’s hourly emission. Fig D. The contamination maps in the case study for ventilation and face mask scenarios. (A,B,C) are the scenarios where individuals do not wear face masks and ACH is 0 per hour in the res- taurant in (A), 3 in (B), and 6 in (C). (D, E) are the scenarios where people wear face masks while moving and ACH is 3 per hour in the restaurant in (D) and 6 in (E). Within each sce- nario, the impact of intervention on viral spread is presented: (a, b, c) show virus concentra- tion in the aerosols, droplets, and fomites, respectively. Fig E. The analysis of relative contribution of transmission routes in the case study. Each row shows a parameters set- ting for croute (A) croute is the same for all routes (caerosols:cdroplets:cfomites is 10%:10%:10%). (B) croute is smaller for fomites (caerosols:cdroplets:cfomites is 10%:10%:1%). (C) croute is smaller for fomites and droplets (caerosols:cdroplets:cfomites is 10%:1%:1%). (D) croute is smaller for fomites and aerosols (caerosols:cdroplets:cfomites is 1%:10%:1%). Each column shows an intervention sce- nario: (a) poor ventilation scenario, ACH = 0, (b) baseline scenario, ACH = 3, (c) scenario with recommended ventilation, ACH = 6, (d) baseline scenario with face masks worn while moving, (e) scenario with recommended ventilation and with face masks worn while mov- ing. Fig F. Sensitivity analysis of emission rate. The distributions of the expected number of infected individuals in the case study with different emission quantities ϕ. (A) to (E) show the results for changing ϕ values from 10^5 to 10^7. This may reflect the heterogeneity in viral load of the index patients. The black solid lines indicate the mean value of the infected PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 31 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission number in the baseline scenario and the dashed lines show the mean value corresponding to each respective scenario. Fig G. Sensitivity analysis of proportions of aerosols. The distri- butions of the expected number of infected individuals in the case study with different pro- portions of virus emitted in the form of aerosols. In the baseline scenario, paerosols is 22.91%. (A) to (E) shows the results for from 50% lower to 50% higher (namely 11.45%, 17.18%, 22.91%, 28.63%, 34.37%) representing the heterogeneity due to respiratory activities or indi- vidual variation. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respec- tive scenario. Fig H. Sensitivity analysis of virus decay rate on surfaces. The distributions of the expected number of infected individuals in the case study with different virus decay rates on surfaces. In the baseline scenario μsurfaces for wood is 0.969 per hour. (A) to (E) shows the results for changing μsurfaces from 90% lower to 90% higher (namely 0.0969, 0.4845, 0.969, 1.4535, 1.8411 per hour) representing the heterogeneity due to different sur- face materials. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respec- tive scenario. Fig I. Sensitivity analysis of virus transfer rate between hand and surface. The distributions of the expected number of infected individuals in the case study with dif- ferent diffusion rates D and virus decay rates in aerosols μaerosols. Each row shows a parame- ter setting for diffusion: (A) Diffusion rate is 0.000278 m2/s, 6 times smaller than the baseline scenario. (B) Diffusion rate is at the baseline scenario 0.0016m2/s. (C) Diffusion rate is 0.01 m2/s as an upper bound from literature (Kudryashova et al. 2021), 6 times larger than the baseline scenario. Each column shows a parameter setting for virus decay rate in aerosols μaerosols. (a) Decay rate is 0.755/hour, 50% lower than the baseline scenario (b) Decay rate is 1.51/hour as the baseline scenario. (c) Decay rate is 2.27/hour, 50% higher than the baseline scenario. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respective scenario. Fig J. Sensitivity analysis of fractional virus transfer rate from hand to facial membranes. The distributions of the expected number of infected individuals in the case study with different diffusion rates D and deposition rates μdroplets. Each row shows a parameter setting for diffusion: (A) Diffusion rate is 0.000278 m2/s, 6 times smaller than the baseline scenario. (B) Diffusion rate is at the baseline scenario 0.0016m2/s. (C) Diffusion rate is 0.01 m2/s as an upper bound from literature (Kudryashova et al. 2021), 6 times larger than the baseline scenario. Each column shows a parameter setting for deposition: (a) Depo- sition rate is 18.97/hour, 50% lower than the baseline scenario (b) Deposition rate is 37.93/ hour as baseline scenario. (c) Deposition rate is 56.90/hour, 50% higher than the baseline scenario. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respective sce- nario. Fig K. Sensitivity analysis of diffusion rate and deposition rate. The distributions of the expected number of infected individuals in the case study with different virus transfer rates between hand and surface (θπγ). The baseline transfer rate between hand and surface is 0.0735 (0.01960.2515) per hour. (A) to (E) shows the results for changing transfer rates from 75% lower to 75% higher (namely 0.0184, 0.0368, 0.0735, 0.1103, 0.1286 per hour) rep- resenting the heterogeneity of touching surface behaviour. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corresponding to each respective scenario. Fig L. Sensitivity analysis of diffu- sion rate and virus decay rate in aerosols. The distributions of the expected number of infected individuals in the case study with different fractional virus transfer rates from hand to facial membranes ε. The baseline transfer rate from hand to facial membranes is 1%. (A) to (E) shows the results for changing ε from 0.1%, 0.5%, 1%, 5% and 10% representing the PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011956 March 28, 2024 32 / 39 PLOS COMPUTATIONAL BIOLOGYMulti-dimensional challenges of controlling indoor respiratory virus transmission heterogeneity of touch face behaviour. The black solid lines indicate the mean value of the infected number in the baseline scenario and the dashed lines show the mean value corre- sponding to each respective scenario. (PDF) Acknowledgments We thank Bas Dado, Wim van der Poel, Rineke de Jong, Marion Koopmans, Sander Herfst, Alexander Verbraeck, Yilin Huang, and Els van Daalen for their discussions and support in both research and acquisition. Moreover, we thank the testers of the SSO app for their enthusi- asm and critical notes. Author Contributions Conceptualization: You Chang, Linda van Veen, Dorine Duives, Quirine A. ten Bosch. Data curation: Bu¨sra Atamer Balkan, You Chang, Yangfan Liu. Funding acquisition: Reina S. Sikkema, Linda van Veen, Dorine Duives, Quirine A. ten Bosch. Methodology: Bu¨sra Atamer Balkan, You Chang, Martijn Sparnaaij, Berend Wouda, Doris Boschma, Yangfan Liu, Mart C. M. de Jong, Colin Teberg, Kevin Schachtschneider, Reina S. Sikkema, Dorine Duives, Quirine A. ten Bosch. Software: Bu¨sra Atamer Balkan, Martijn Sparnaaij, Berend Wouda, Doris Boschma, Colin Teberg, Kevin Schachtschneider. Supervision: Winnie Daamen, Linda van Veen, Dorine Duives, Quirine A. ten Bosch. Visualization: You Chang. Writing – original draft: Bu¨sra Atamer Balkan, You Chang, Dorine Duives, Quirine A. ten Bosch. Writing – review & editing: Martijn Sparnaaij, Yufei Yuan, Winnie Daamen, Mart C. M. de Jong, Reina S. Sikkema, Linda van Veen. References 1. Bulfone TC, Malekinejad M, Rutherford GW, Razani N. Outdoor Transmission of SARS-CoV-2 and Other Respiratory Viruses: A Systematic Review. 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10.1371_journal.pmed.1004341	RESEARCH ARTICLE Tropical cyclone-specific mortality risks and the periods of concern: A multicountry time- series study 1, Zhengyu YangID Wenzhong HuangID Ben ArmstrongID Antonio GasparriniID Tobias Geiger9, Yue Leon GuoID Farnaz Pourzand13, Shih-Chun PanID 1, MCC Collaborators¶ Yuming GuoID 3, Wenhua Yu1, Rongbin XuID 1, Yiwen ZhangID 1, Thomas VogtID 2, 1, 1, Pei Yu1, Yanming LiuID 3,4,5, Samuel Hundessa1, Eric Lavigne6,7, Tomas Molina8, 10,11,12, Christian Otto2, Simon Hales13, 11, Ke Ju1, Elizabeth A. RitchieID 14,15, Shanshan Li1, 1 Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia, 2 Potsdam Institute for Climate Impact Research, Potsdam, Germany, 3 Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom, 4 Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom, 5 Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, United Kingdom, 6 Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada, 7 School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada, 8 Department Applied Physics, Universitat de Barcelona, Barcelona, Spain, 9 Deutscher Wetterdienst (DWD), Regional Climate Office Potsdam, Potsdam, Germany, 10 Department of Environmental and Occupational Medicine, National Taiwan University (NTU) and NTU Hospital, Taipei, Taiwan, 11 National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan, 12 Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan, 13 Department of Public Health, University of Otago, Wellington, New Zealand, 14 School of Earth Atmosphere and Environment, Monash University, Melbourne, Australia, 15 Department of Civil Engineering, Monash University, Melbourne, Australia ¶ Membership of MCC Collaborators is provided in Supporting Information file S1 Text * shanshan.li@monash.edu (SL); yuming.guo@monash.edu (YG) Abstract Background AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: More intense tropical cyclones (TCs) are expected in the future under a warming climate scenario, but little is known about their mortality effect pattern across countries and over decades. We aim to evaluate the TC-specific mortality risks, periods of concern (POC) and characterize the spatiotemporal pattern and exposure-response (ER) relationships on a multicountry scale. Methods and findings Daily all-cause, cardiovascular, and respiratory mortality among the general population were collected from 494 locations in 18 countries or territories during 1980 to 2019. Daily TC exposures were defined when the maximum sustained windspeed associated with a TC was �34 knots using a parametric wind field model at a 0.5˚ × 0.5˚ resolution. We first esti- mated the TC-specific mortality risks and POC using an advanced flexible statistical frame- work of mixed Poisson model, accounting for the population changes, natural variation, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Huang W, Yang Z, Zhang Y, Vogt T, Armstrong B, Yu W, et al. (2024) Tropical cyclone- specific mortality risks and the periods of concern: A multicountry time-series study. PLoS Med 21(1): e1004341. https://doi.org/10.1371/journal. pmed.1004341 Received: August 8, 2023 Accepted: January 3, 2024 Published: January 22, 2024 Copyright: © 2024 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All mortality data used in our study were obtained from a collaborative research network under a data sharing agreement and the authors are not permitted to directly share the third-party raw data used in the analyses. For information on data access, readers are asked to contact Dr Sharon Harrison (sharon.harrison@monash.edu) for information on each country’s data providers. Annual gridded population was obtained from the Global Carbon Project (https://www.cger.nies.go. jp/gcp/population-and-gdp.html). Historical information on the temporal dynamics of cyclone events across the globe was collected from the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 1 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern seasonal and day of the week effects. Then, a mixed meta-regression model was used to pool the TC-specific mortality risks to estimate the overall and country-specific ER relation- ships of TC characteristics (windspeed, rainfall, and year) with mortality. Overall, 47.7 mil- lion all-cause, 15.5 million cardiovascular, and 4.9 million respiratory deaths and 382 TCs were included in our analyses. An overall average POC of around 20 days was observed for TC-related all-cause and cardiopulmonary mortality, with relatively longer POC for the United States of America, Brazil, and Taiwan (>30 days). The TC-specific relative risks (RR) varied substantially, ranging from 1.04 to 1.42, 1.07 to 1.77, and 1.12 to 1.92 among the top 100 TCs with highest RRs for all-cause, cardiovascular, and respiratory mortality, respec- tively. At country level, relatively higher TC-related mortality risks were observed in Guate- mala, Brazil, and New Zealand for all-cause, cardiovascular, and respiratory mortality, respectively. We found an overall monotonically increasing and approximately linear ER curve of TC-related maximum sustained windspeed and cumulative rainfall with mortality, with heterogeneous patterns across countries and regions. The TC-related mortality risks were generally decreasing from 1980 to 2019, especially for the Philippines, Taiwan, and the USA, whereas potentially increasing trends in TC-related all-cause and cardiovascular mortality risks were observed for Japan. Conclusions The TC mortality risks and POC varied greatly across TC events, locations, and countries. To minimize the TC-related health burdens, targeted strategies are particularly needed for different countries and regions, integrating epidemiological evidence on region-specific POC and ER curves that consider across-TC variability. IBTrACS data (https://www.ncei.noaa.gov/ products/international-best-track-archive). Funding: WH and KJ were supported by China Scholarship Council funds (nos.202006380055 and 202006240087); ZY and WY were supported by a Monash Graduate Scholarship and a Monash International Tuition Scholarship; YZ was supported by NHMRC e-Asia Joint Research Program Grant (GNT2000581); TV and CO acknowledged support from the German Federal Ministry of Education and Research (BMBF) under the research project QUIDIC (01LP1907A); RX was supported by VicHealth Postdoctoral Research Fellowships 2022; SL was supported by an Emerging Leader Fellowship of the Australian National Health and Medical Research Council (GNT2009866); YG was supported by Career Development Fellowship (GNT1163693) and Leader Fellowship (GNT2008813) of the Australian National Health and Medical Research Council; AG was supported by the Medical Research Council UK (grant ID MR/R013349/1), the Natural Environment Research Council UK (grant ID NE/ R009384/1), and the EU’s Horizon 2020 project, Exhaustion (grant ID 820655); SiH and FP are supported by the Health Research Council of New Zealand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: YG is a member of the Editorial Board of PLOS Medicine. All other authors declare no competing interests. Author summary Why was this study done? Abbreviations: AU : Theabbreviationlisthasbeenupdatedforthoseusedinthetext:Pleaseverifythatallentriesarecorrect: AIC, Akaike information criterion; CDC, Center for Disease Control and Prevention; CI, confidence interval; df, degrees of freedom; ER, exposure-response; IBTrACS, International Best Track Archive for Climate Stewardship; ICD, International Classification of Diseases; IDI, Integrated Data Infrastructure; MCC, Multi-Country Multi-City; ND-GAIN, Notre Dame Global Adaptation Index; POC, periods of concern; RR, relative risk; SA3, Statistical Area Level 3; SD, standard deviation; SIM, Sistema de Informac¸ão sobre Mortalidade; SE, standard error; TA, territorial authority; TC, tropical cyclone; USA, United States of America. • Tropical cyclones (TCs), among the most destructive and costliest climate extreme events, are expected to be more intense due to climate change. • Despite the widely acknowledged hazards, a consistent and quantitative assessment of the mortality risks of TC across countries is lacking. Such quantitative and comparable evidence across countries is urgently required to better understand the health effects and respond to the potentially increasing hazards. • No previous studies have characterized the periods of concern (POC), exposure- response (ER) relationship, and temporal trends of the TC health risks, directly relevant to more precise and effective preparedness and mitigation strategies. What did the researchers do and find? • Using mortality data from 494 TC-exposed locations in 18 countries or territories, we quantified the TC-specific mortality risks and POC of the 382 TC events that affected PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 2 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern these locations. The ER relationships and temporal trends were then characterized for each country or territory. • TC exposure was associated with a prolonged elevated risk of all-cause, cardiovascular, and respiratory mortality, with an overall average POC of around 20 days. • The TC mortality risks and POC varied greatly across TC events, locations, and countries. • Overall, the mortality risks increased approximately linearly with increasing TC-related maximum sustained windspeed or cumulative rainfall. • Most studied countries or territories witnessed a decreasing TC-related mortality risks from 1980 to 2019, especially for the Philippines, Taiwan, and the USA, while potentially increasing TC-related all-cause and cardiovascular mortality risks were observed for Japan. What do these findings mean? • TC events can exhibit significant variations in their risk patterns, and future risk assess- ments may need to better account for this large across-TC variability. • Targeted and evidence-based disaster management and preparedness strategies need to be developed for different countries to more effectively mitigate the TC hazards. • Key study limitations include potential exposure misclassification errors, residual con- founding, and limited generalizability. Introduction Tropical cyclones (TCs), including hurricanes, typhoons, and tropical storms, dominate weather-related disaster damages [1] and pose a major threat to our society and health [2]. It has been estimated that TCs exposed 150 million people [3] and caused billions of US dollars in damages [4,5] annually worldwide. With continued growth in coastal populations and global warming, the impacts of TCs are expected to worsen due to the increasing exposed pop- ulation and proportion of very intense TCs (e.g., the warmer surface ocean is likely fueling more powerful TCs with higher windspeed and precipitation) [6–8]. These indicate that TCs will likely remain an important public health concern. Quantifying their spatiotemporal health risks has important implications for understanding the health effects and helps develop strate- gies to mitigate and respond to the foreseen health burden. Emerging evidence suggests an increased risk of adverse health outcomes, mostly all-cause hospitalizations or mortality, associated with TC exposure [9–16]. Except for the immediate physical impacts such as drowning and injuries, TCs also have been found to introduce persist- ing or delayed elevated mortality and morbidity risks, partially attributable to medical support disruptions, environmental contamination, and psychosocial stress [17]. These indirect and longer-term effects of TC could increase the cardiovascular and respiratory mortality and mor- bidity, which consist of a major and important part of the disease burden indirectly PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 3 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern attributable to TCs. However, previous studies on TC epidemiology largely focused on a single TC event (mostly Hurricane Katrina, Sandy) restricted to a single year or area (mostly in the United States of America [USA]) [13] and focused on all-cause mortality. For example, 8 stud- ies assessed the excess mortality in Puerto Rico after Hurricane Maria [18–25], but varied greatly in the estimated number of excess mortality (point estimates of all-cause excess mortal- ity ranged from 514 to 4,645) due to the different utilized designs (e.g., various timeframes) and tools (e.g., survey versus mortality registration). Therefore, the results of single-TC studies may not be comparable and generalize well given the high heterogeneity in TC and population characteristics, study period/design, infrastructure, and modeling approaches across studies. To compensate for the limitation on generalizability, several more recent studies have included multiple TCs spanning more than a decade and estimated the average health effects of TC exposures at county level in the USA [9–11,26]. While these studies revealed fundamen- tal features of TC epidemiology in the USA, the multi-TC average health effects do not account for the across-TC variability [27]. The TC-specific health effects can vary greatly depending on the characteristics of the TC events and the population’s social structure and vulnerabilities. Additionally, very few studies have estimated the temporal trends in TC-related health risks, the exposure-response (ER) relationships, as well as the periods of concern (POC) of TCs, which were important aspects of strategic disaster management and resource allocation. For example, identifying patterns in the risk magnitude and the concerned periods after TCs with diverse characteristics across regions offers valuable evidence for efficiently allocating resources, optimizing preparedness efforts, and better understanding TC health effects. How- ever, there is an overall knowledge gap in consistently exploring the spatiotemporal mortality risks associated with TCs across countries over a long timeframe. To address these knowledge gaps, we aim to employ a recently proposed flexible statistical framework within the framework of a two-stage analysis based on a global dataset of multiple TCs and locations over long timeframes [24]. This advanced approach could account for the TC-specific POC and mortality risks, and has been shown higher accuracy and statistical power (i.e., stronger ability to detect small and persistent increases in mortality) compared to the Farrington model currently implemented by the US Center for Disease Control and Pre- vention (CDC) [24]. Specifically, we aim to consistently estimate the TC-specific POC and mortality risk across TCs, locations and countries, and characterize the spatiotemporal pattern of ER relationships of mortality risk with TC characteristics. Beyond all-cause mortality, we also included 2 other leading mortality outcomes, cardiovascular and respiratory mortality, to comprehensively capture and understand the health effects of TC, including the indirect effects that were largely unclear. Method Data collection Based on the most updated Multi-Country Multi-City (MCC) Collaborative Research Network database, we integrated a global dataset of 1,914 locations from 44 countries or territories. Among these 1,914 locations, 494 locations from 18 countries or territories that experienced at least 1 TC during the data collection period were included in the study (Table A in S2 Text). The details of the MCC dataset have been described in our previous work [28,29]. Specifically, for each location in the MCC network, daily counts of all-cause mortality were collected and non-external causes (International Classification of Diseases [ICD], 9th Revision codes 0–799 or ICD-10 codes A0–R99) mortality were alternatively collected when all-cause mortality was unavailable. Two major and distinct causes of death, cardiovascular (ICD-10 codes I00–I99) and respiratory (ICD-10 codes J00–J99) mortality were also collected for each PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 4 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern location. Cardiovascular and respiratory mortality, largely attributable to the indirect conse- quences of TCs, such as property loss, resource depletion, and disruptions in medical support, constitute a major part of the TC-associated mortality burden [17]. Except for the MCC data, we also collected data on all individual deaths (date, cause, and location of death) in Australia between 2009 and 2017 from the Australian Cause of Death Unit Record File [30], in New Zea- land between 2000 and 2018 from the New Zealand Ministry of Health [31], in Brazil between 1996 and 2019 from the Brazil Mortality Information System (Sistema de Informac¸ão sobre Mortalidade, SIM) [32], and in Canada between 1986 and 2015 from the Vital Statistics Deaths Database of Statistics Canada [33]. For a time-series analysis, we aggregated these individual death data at location and daily level based on the administrative boundary with a proper area size for each country (Statistical Area Level 3 [SA3] for Australia [n = 316], territorial authority [TA] for New Zealand [n = 63], immediate region for Brazil [n = 510], and second-level administrative divisions [regions or districts within the provinces and territories] for Canada [n = 288]). MCC locations in Australia, New Zealand, Brazil, and Canada were thus excluded to avoid duplication. Consequently, the integrated global dataset covers 1,914 locations from 44 countries or territories, of which 494 locations from 18 countries or territories that experi- enced at least 1 TC during the data collection period were included. Among these 494 loca- tions, the all-cause mortality data in 13 locations (2.6%) was represented by non-external mortality. To estimate the annual population for each location, the annual gridded population per 10 years between 1980 and 2100 at a spatial resolution of 0.5˚ × 0.5˚ (about 55 km grid), derived by ensemble learning technique and models (R-squared values ranged from 0.81 to 0.84), was also collected from the Global Carbon Project [34]. The population data were first interpolated with a natural spline function of the available values to each year for each grid [29]. The annual population of each location was then calculated as the sum of the population of the grid cells in that year covered by that location. Exposure assessment We used the improved Holland wind field model [35] to estimate the global historical tempo- ral dynamics of the windspeed associated with TCs, which has been successfully applied in pre- vious studies [8,36,37]. The methodology of this model has been described in detail in our previous work [3]. Briefly, we first obtained historical information on TCs including the posi- tion (i.e., center latitude and longitude coordinates), surface central pressure, radius, and the maximum sustained windspeed from the International Best Track Archive for Climate Stew- ardship (IBTrACS), a collection of best track data of TCs from sources worldwide [38]. The above variables served as inputs for the Holland wind field model as implemented within the CLIMADA Python package, an open-source impact modeling framework available on GitHub (https://github.com/CLIMADA-project/climada_python) [39]. We generated the daily wind profile (i.e., the grid-level daily 1-min sustained wind speeds associated with the cyclone) for each cyclone event in IBTrACS from January 1st, 1980 to December 31st, 2019, at a spatial res- olution of 0.5˚ × 0.5˚. The estimated global historical TC-related windspeed showed a good agreement in the validation analysis of reported wind fields in the regional dataset (Pearson correlation of r = 0.86). For each location, we defined TC exposure days as days with a TC- related maximum sustained wind speed �34 knots (17.5 m/s, 63 km/h, 39 mph; gale-force wind on the Beaufort scale) for the grid cell of the location [9,10]. For each TC in each location, we defined the TC hit day, t0, as the first day with a sustained wind speed �34 knots for that TC in that location. We obtained the cumulative rainfall (mm) at t0 for each location from the ERA-5 reanalysis data, which is created by assimilating historical weather data from numerous PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 5 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern platforms (e.g., satellite, ground-based stations, radar, boats, airplanes, buoys) using sophisti- cated data assimilation models [40] and has been widely used in previous studies [41–45], as an additional metric of TC exposure besides the maximum sustained windspeed. Statistical analysis We adopted a two-stage analytical approach to characterize the association between TC expo- sure and mortality. In the first stage, we implemented a flexible statistical framework that per- mitted both the surveillance of concerning increases in mortality rates and careful characterization of the effect of a past event to estimate the POC and excess mortality for each TC in each location [24,46,47]. The theoretical framework and methodological details have been well described elsewhere [24]. Briefly, the daily death counts were modeled with the fol- lowing mixed Poisson model with a log-link: Yitjεit � Poissonðmit½1 þ fiðtÞ�εitÞ mit ¼ offsetðNiyÞeaiðtÞþsiðtÞþwiðtÞ ð1Þ ð2Þ In the core formula of Eq (1), Yit and μit represent the observed and expected deaths on day t in location i, respectively; (t) refers to the relative increase in mortality on day t in location i due to a TC and (t)*100 is the percent increase; and εit is a time series of auto-correlated ran- dom variables representing natural variability on day t in location i. The expected deaths, μit, can be further decomposed according to Eq (2), where Niy is the population on year y in loca- tion i and the log of population is treated as an offset to account for the change in population size; αi(t) is a smooth function of time that accounts for the secular changes (i.e., a slow-mov- ing trend); and wi(t) and si(t) are day-of-week effects and a yearly periodic function represent- ing a seasonal trend, respectively. During the periods without TC exposure (i.e., the control periods), we assumed (t) = 0. When different from 0, (t) was assumed to be smooth enough to be represented by a smoothing cubic spline that provides enough flexibility to detect unusual mortality fluctuation due to an extreme event like a TC. To estimate the component of interest, (t), the μit and correlation structure of εit were first estimated based on the mortality during the control periods. Then, the fi(t) and the standard error (SE) were estimated using the Cen- tral Limit Theorem approximation that assumed ^fi ðtÞ followed a normal distribution and accounts for the uncertainty in the expected mortality rate. Using estimates of ^fi ðtÞ and the SE, the POC was defined as a post-TC period during which a percent increase of 0 is not in the 95% confidence interval (CI) for ^fi ðtÞ (i.e., the lower limit of the 95% CI of the estimated excess mortality is greater than 0). We permitted a discontinuity on the TC hit day, t0, to account for a sudden direct effect and fitted a smoother spline, with 6 knots per year in the main model, to provide more power to detect subtle indirect effects [24]. If a location was exposed to multiple TCs during the data collection period, we excluded the two-month (60 days) post-TC periods to exclude the effects of other TCs. The final results for the first stage were presented as the POC and excess mortal- ity (with 95% CI) for each TC in each location. The relative risk (RR) was also calculated as the observed deaths divided by the expected deaths (i.e., observed deaths minus excess deaths) for each TC. Sensitivity analyses by excluding a different length of post-TC period (30 days, 90 days) were conducted to test the robustness of the results. In the second stage, with the TC-specific RRs (with 95% CI) from the first stage, we further used a mixed meta-regression model, accounting for the hierarchical structure of the RRs (a location could have several TC-specific RRs for multiple TCs, i.e., TCs nested within PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 6 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern locations), to characterize the ER relationships of TC-related mortality risks with TC charac- teristics. Specifically, we built a univariate meta-regression model between the TC-specific RRs and the TC-related maximum sustained wind, cumulative rainfall, and the calendar year of TC hit, respectively, to examine the univariate ER relationship. All of these terms were included in the meta-regression models with a natural spline function of 2 degrees of freedom (df), as determined by a minimum Akaike information criterion (AIC), to allow for nonlinear ER relationships. All data organization and analyses were conducted in R software, version 4.0.3 (Foundation for Statistical Computing, Vienna, Austria) [48]. The first- and second-stage analysis were con- ducted using the “excess_model” function from the “excessmort” package [49] and the “mix- meta” function from the “mixmeta” package [50], respectively. Results Deaths, TC, and periods of concern The spatial distribution of the included 494 locations and a summary of these locations (e.g., study periods, number of deaths, and TCs) are shown in Fig 1 and Table A in S2 Text, respec- tively. In total, 47.7 million all-cause deaths, 15.5 million cardiovascular deaths, and 4.9 million respiratory deaths were included in the analyses. Each location contributed an average of 21 years (standard deviation [SD]: 9.4) of data. A total of 382 TC events that hit these 494 loca- tions during the study period were included, with an average of 7 TC events for each location (Fig 1). The number of exposed TCs per decade varied substantially by location, ranging from 1 to 55. Locations in Taiwan (e.g., Taipei, Kaohsiung), Japan (e.g., Naha, Okinawa), the Philip- pines (e.g., Manila, Valenzuela) experienced TC most frequently (average number of TC per Fig 1. The spatial distribution and number of exposed TCs of the 494 study locations. AU : AbbreviationlistshavebeencompiledforthoseusedinFigs1to6:Pleaseverifythatallentriesarecorrect: domain Natural Earth project (source: https://www.naturalearthdata.com/downloads/; terms of use: www.naturalearthdata.com/about/terms-of-use/). TC, tropical cyclone. . The base layer of the world map was imported from the public https://doi.org/10.1371/journal.pmed.1004341.g001 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 7 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern decade �14), whereas the lowest number (1–2) were mainly observed in locations from Brazil, New Zealand, and Canada. Overall, the average length of POC after a TC for all-cause mortal- ity was 22 days (SD: 51.3), with great variations across and within countries (Table A in S2 Text). The average POC were relatively longer for the study locations in the USA, Brazil, and Taiwan (>30 days), while shorter for the study locations in Vietnam, Mexico, Australia, and New Zealand (�10 days). Similar overall average POC and great variations across and within countries were also observed for TC-related cardiovascular and respiratory mortality (Table A in S2 Text). The estimated overall and country-specific POC were robust to sensitivity analy- ses by excluding a different length of post-TC period (30, 60, and 90 days) (Table B in S2 Text). TC-related excess mortality and risk The TC-location-specific excess mortality for the 100 TCs with the highest excess deaths is shown in Fig 2, with each point indicating a location and each tick on the x-axis representing a TC (a TC with a unique ID recorded in the IBTrACS). Large variations are observed for the TC-related excess deaths within and across TCs. For example, the TC of “1999253N17124” had the highest TC-related excess deaths, which hit 1 location (Hong Kong) in China Main- land in 1999 with a maximum sustained windspeed of 40 to 45 knots and caused around 1,076 deaths (Fig 2). Despite that the TC events that contributed most respiratory and cardiovascular deaths were different from those contributed most all-cause deaths, similar patterns of large inter- and intra-TC variability were also observed (Fig 2). The TC-specific RRs varied substan- tially, with a point estimate ranging from 1.04 to 1.42, 1.07 to 1.77, and 1.12 to 1.92 among the 100 TCs with highest RRs for all-cause, cardiovascular, and respiratory mortality, respectively (Fig 3). A maximum RR of 1.42 (95% CI [1.09, 1.86], p = 0.009), 1.77 (95% CI [1.76, 1.78], p < 0.001), and 1.92 (95% CI [1.07, 3.44], p = 0.028) for all-cause, cardiovascular, and respira- tory mortality were observed for TC “2011023S16147” (Australia, 2011), “2014209N16134” (Japan and South Korea, 2014), and “2011020S13182” (New Zealand, 2011), respectively. At country level, relatively higher and statistically significant TC-related mortality risks were observed in Guatemala for all-cause mortality, Brazil, Vietnam, and South Korea for cardiovas- cular mortality, and New Zealand and Australia for respiratory mortality (Fig 4). The country- specific RR was generally robust to sensitivity analyses by excluding a different length of post- TC period (Table C in S2 Text). ER curve of TC-related windspeed with mortality by country or territories When characterizing the associations of TC-related maximum sustained windspeed with mor- tality risk, we observed an overall monotonically increasing and non-threshold curve with approximately linear shape for the ER relationships for all-cause, cardiovascular, and respira- tory mortality (Fig 5). At country level, we found generally significant, positive linear or supra-linear ER curves of TC-related maximum sustained windspeed with mortality in Japan, South Korea, Taiwan, and the USA for all-cause mortality; Japan, Taiwan, and the USA for cardiovascular mortality; and Japan, Taiwan, and the USA for respiratory mortality (Fig 5). The positive ER curves were consistently observed for Japan, Taiwan, and the USA. However, there is insufficient evidence to support significant and positive ER curves between TC-related maximum sustained windspeed and mortality in other countries or regions including China Mainland, Mexico, and Thailand (Fig 5). Sensitivity analysis by excluding a different length of post-TC period showed robust overall and country-specific ER relationships of TC-related maximum sustained windspeed with mortality, with a similar shape to that in the main model (Fig A in S2 Text). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 8 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern Fig 2. The top 100 TCs with highest excess deaths from all-cause, CVDs, and RDs. Each point in the figure indicates a location, and each tick on the X-axis represents a TC, which is identified by its IBTrACS event ID. A boxplot was fitted for the location-specific TC-related excess deaths within each TC. Each box represents the IQR of the excess deaths of each TC, with the middle bolded black line in the box representing the median value. The whiskers extending from the box indicate a range of 1.5 times the IQR. CVD, cardiovascular disease; IQR, interquartile range; RD, respiratory disease; TC, tropical cyclone. https://doi.org/10.1371/journal.pmed.1004341.g002 ER curve of TC-related precipitation with mortality by country or territories We observed similar patterns for the overall ER relationships of TC-related cumulative rainfall with mortality, with consistently significant and positive linear curves for all-cause, cardiovas- cular, and respiratory mortality (Fig B in S2 Text). Positive linear or supra-linear and mono- tonically increasing ER curves were detected in Japan, the Philippines, South Korea, Taiwan, and the USA for all-cause mortality; the Philippines, Taiwan, and the USA for cardiovascular PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 9 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern Fig 3. The top 100 TCs with highest RR for all-cause, CVDs, and RDs mortality. The RRs were estimated by comparing the deaths on TC-exposed days with those on non-exposed days, after adjusting for population changes, natural variation, seasonal, and day of the week effects. CVD, cardiovascular disease; RD, respiratory disease; RR, relative risk; TC, tropical cyclone. https://doi.org/10.1371/journal.pmed.1004341.g003 mortality; and Taiwan and the USA for respiratory mortality. Consistent monotonically increasing ER curves were consistently observed for Taiwan and the USA. No sufficient evi- dence of a significant and positive ER curve between TC-related cumulative rainfall and mor- tality in other countries or regions including China Mainland, Vietnam, Mexico, and Thailand. The estimated overall and country-specific ER relationships were robust to sensitiv- ity analyses by excluding a different length of post-TC period (Fig C in S2 Text). Temporal trends of TC-related mortality by country or territories When considering the temporal variations of the TC-related mortality, an overall decreasing trend was found for all-cause and respiratory mortality, while not for cardiovascular mortality (Fig 6). At country level, an overall slightly increasing trend in TC-related all-cause mortality PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 10 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern Fig 4. Country or territory-specific overall RR with 95% CI for all-cause, CVDs, and RDs mortality associated with TC exposure. The RRs indicated the mortality risks in TC days compared to non-TC days. CI, confidence interval; CVD, cardiovascular disease; RD, respiratory disease; RR, relative risk; TC, tropical cyclone. https://doi.org/10.1371/journal.pmed.1004341.g004 risk was observed in Japan and Taiwan, while decreasing trends in the Philippines, South Korea, and the USA; an increasing trend in TC-related cardiovascular mortality risk was observed in Japan and Mexico, while overall decreasing trends were observed in the Philip- pines, South Korea, and the USA; a decreasing increasing trend in TC-related respiratory mor- tality risk was observed in Japan, New Zealand, the Philippines, and the USA, while a potentially increasing trend was found for Taiwan (Fig 6). For the remaining countries includ- ing Australia, Canada and Vietnam, Thailand, Mexico and New Zealand, no sufficient Fig 5. The exposure-response relationship of the RR for all-cause, CVDs, and RDs mortality with TC-related maximum sustained windspeed (knots) by countries or territories. The RRs indicated the mortality risks in TC days compared to non-TC days. CVD, cardiovascular disease; RD, respiratory disease; RR, relative risk; TC, tropical cyclone. https://doi.org/10.1371/journal.pmed.1004341.g005 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 11 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern Fig 6. The temporal trends of the RR for all-cause, CVDs, and RDs mortality by countries or territories from 1980 to 2019. The RRs indicated the mortality risks in TC days compared to non-TC days. CVD, cardiovascular disease; RD, respiratory disease; RR, relative risk; TC, tropical cyclone. https://doi.org/10.1371/journal.pmed.1004341.g006 evidence to detect a temporal trend. Generally, similar overall and country-specific temporal trends were observed in sensitivity analyses by excluding a different length of post-TC period (Fig D in S2 Text). Discussion Our large-scale population-based study estimated the TC-specific POC and mortality risks, with substantial variations in TC-related mortality risk within and across TCs. Additionally, we characterized the ER relationships and found an overall monotonically increasing, non- threshold and approximately linear curve of TC-related maximum sustained windspeed and cumulative rainfall with all-cause and cardiopulmonary mortality risks. An overall decreasing trend was observed for TC-related all-cause and respiratory mortality risk from 1980 to 2019. Further heterogeneous patterns of the ER relationships and temporal trends were revealed at the country level, such as the increasing trend in TC-related all-cause mortality risk in Japan, yet a decreasing trend in the Philippines, South Korea, and the USA. As expected, we found that the TC-related mortality risks varied considerably across and within TCs, evidencing the necessity to account for the across-TC variability when assessing the health effects of TCs. However, among the limited epidemiological studies that systemati- cally assessed the health effects of multiple TC events, most estimated the multi-TC average effects [9–11,26,27]. These studies also utilized a wind field model to quantify the TC exposure for a large number of TCs and found an elevated risk of mortality [9,27], hospitalization [10,11], and preterm birth [26] associated with TCs in the USA for the past decades. To our knowledge, only 1 study has estimated the TC-specific health risks, which included all TCs in the USA from 1999 to 2015 and also observed large variations in the TC-related excess mortal- ity across and within TCs [27]. However, this study did not estimate the TC-specific POC, but instead used an 11-day post-TC period as a hypothetical POC and calculated the excess deaths within this period for all TCs. TCs could impact public health through both direct and indirect pathways. Direct impacts including physical trauma (e.g., injury and drowning) during expo- sure could be more immediate, while indirect impacts such as the socio-psycho environmental stress, poor or mal-nutrition due to TCs (e.g., loss of property and resources, evacuation, inter- ruption of medical and social support) could manifest at a longer-term to increase the mortal- ity and morbidity. Therefore, a POC of several days may not be able to sufficiently capture the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 12 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern health impacts of TCs, especially for the indirect impacts, as indicated by a POC of more than 1 month for some countries/TCs in our results. Additionally, TCs could vary substantially in terms of their POC due to their different physical characteristics (e.g., windspeed, rainfall, and duration), as well as the population vulnerability. Such variability was reflected in our findings of a large standard deviation of the estimated POC across TCs, even within the same country. The self-specified identical exposure window (i.e., hypothetical POC) commonly used in previ- ous multi-TC studies may introduce potential exposure misclassification and not be able to well capture the TC-related health effects. To our knowledge, this study is the first to examine the ER relationships between TC char- acteristics and risk of mortality on a multicountry and multi-TC scale. Based on the estimated mortality risk and characteristics for each TC, we characterized the overall and country-spe- cific ER curves of TC-related maximum sustained windspeed and cumulative rainfall with dif- ferent mortality outcomes. We observed an overall monotonically increasing, non-threshold and approximately linear curve of TC-related maximum sustained windspeed and cumulative rainfall with all-cause, cardiovascular, and respiratory mortality risks. Only 1 prior study has estimated the ER curves of TC characteristics with health outcomes and observed a similar monotonically increasing ER relationship with an approximately linear shape between TC- related maximum sustained windspeed and all-cause mortality, hospitalizations for respiratory diseases, chronic obstructive pulmonary diseases, and cardiovascular diseases in the USA [27]. Stronger TCs with higher windspeed and rainfall were more likely to induce adverse events such as flood, displacement and power outage, there having higher risks causing mortality and morbidity. Prior epidemiological studies on multiple TCs generally assessed the potential haz- ard of TCs only based on a binary variable of exposure (exposed versus unexposed) in terms of the maximum sustained windspeed [9–11,26,27,41,51]. The established ER curves with TC- related windspeed and rainfall in the current study could inform the potential risks of various mortality outcomes associated with different TC intensities. Considering that more intense TCs are expected in the future under a changing climate, it is critical to incorporate the epide- miological evidence such as the ER curves in the early warning system to accurately evaluate the potential hazards of a landfalling TC and develop strategies accordingly to minimize the health burdens [52]. Heterogeneous TC-related mortality risks and patterns of the ER relationships across coun- tries and mortality outcomes were further revealed in our study. Populations in countries like Guatemala, Brazil, Vietnam, and South Korea appear to be especially vulnerable to TC-related elevated mortality risks compared to those in other countries or regions such as Canada, Mexico, and Australia. Many factors, such as topography, economics, disaster management practices, and population characteristics, can impact the susceptibility to natural disasters like TCs [53–55]. However, there is very little evidence on systematic assessment of the vulnerabil- ity to TCs across countries and no clear explanation for such differences has yet been pro- posed. The higher susceptibility to the elevated mortality risks of TCs in these regions could be partially attributed to the higher frequency of high-amplitude TCs, while the relatively fewer strong TCs or study locations in other countries including Mainland China and Mexico hin- dered us from detecting a significant and positive ER curve [56–59]. Additionally, Clark and colleagues proposed a Notre Dame Global Adaptation Index (ND-GAIN) and attributed the differences in the overall vulnerability to climate change across countries to 6 country-level life-supporting sectors—food, water, health, ecosystem service, human habitat, and infrastruc- ture [60]. Countries with more reliable water and food supply (e.g., higher fresh water with- drawal rate), better health and ecosystem services (e.g., less slum population and dependency on natural capital), and improved infrastructure and human habitat (e.g., less population living under 5 m above sea level and smaller age dependency ratio) could be more resilient to the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 13 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern adverse impacts of climate change. The heterogeneous patterns of the ER relationships could also be explained by differences in these factors across countries. Overall, current epidemiolog- ical evidence on the potential contributing factors or effect modifiers on the associations between TCs and health is still very limited and inconclusive. More studies are required to bet- ter elucidate this issue and the underlying contributors. We observed an overall decreasing trend for TC-related all-cause and respiratory mortality risks, with great heterogeneity across countries or territories. Despite the temporal change of the TC characteristics (e.g., tracks, frequency, intensity, duration) has been well characterized [56–59,61,62], no studies have yet estimated the temporal trend of TC-related mortality risks. To our knowledge, only 1 study examined the temporal change in the risk of homelessness, casualties, and property losses induced by TCs in South Korea and found a decreasing trend over 1979 to 2010, which is similar to our observed ER curve for TC-related all-cause mortality in South Korea [63]. Additionally, the mostly decreasing TC-related mortality risk across countries highlights the effectiveness and progress of the disaster management measures and devoted prevention efforts, especially for the Philippines, Taiwan, and the USA. While the intensity and duration of landfall TCs have been increasing [64,65], the improved early warn- ing system and disaster preparedness practices can significantly reduce the related health risks [66–68]. However, it should also be noted that a potentially increasing trend in TC-related all- cause and cardiovascular mortality risk was observed in Japan. This may be partially attributed to the considerably increasing proportion of the elderly population and the prevalence of car- diovascular diseases over the past decade in this country [69,70]. Further studies are highly warranted to elucidate the underlying mechanisms and formulate targeted approaches to reverse the increasing trends. This study had 4 main strengths. To the best of our knowledge, this is the first and largest global investigation of the mortality risk attributed to TCs. Compared with most previous studies confined to single or several TC events within a limited region or timeframe, we col- lected representative death data from countries or territories of the USA (including the loca- tions from the territories in the Caribbean [i.e., Virgin Islands and Puerto Rico]), Japan, South Korea, Canada, Brazil, Taiwan, Australia, and New Zealand. We also developed TC exposure data based on a collection of representative and best track data of TCs from official sources worldwide (i.e., IBTrACS), which has been widely used to analyze TC ecology and subsequent events (e.g., flood) [62,71–73]. A final of 382 TC events in 494 locations from 18 countries or territories during 1980 to 2019, which were characterized by different climates, socioeco- nomics, demographics, public health service development, and TC features. This allowed us to characterize the spatiotemporal pattern of the TC-related mortality risks, and to reduce poten- tial selection-related biases and ensure the high-quality and generalizability of our findings. Compared to the Farrington Model currently implemented by the CDC for evaluating related disaster-attributable deaths [74–76], we employed an advanced flexible statistical framework that has higher power to detect the small and persistent increases in death rate introduced by such effects as one contiguous period [24]. Additionally, this modeling technique enabled us to account for the great across-TC variability and evaluated TC-specific POC and mortality risks, which tends to provide more precise effect estimates than the traditional multi-TC aver- age estimates based on an identical exposure window. Finally, apart from providing the TC- related mortality risk estimates like in most prior studies, we further estimated the POC, the ER curves between TC characteristics and mortality outcomes, as well as the temporal trends of mortality risks, which were important aspects for developing or adjusting disaster manage- ment policies and public health interventions to mitigate the adverse impacts of TCs. There are also some limitations in our study. The possibility of residual confounding cannot be completely excluded. Despite our application of an advanced methodology to first attempt PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 14 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern to estimate the TC-specific POC across countries, some uncertainty in the estimates caused by the limitations of this methodology must be acknowledged. We involved a predetermined wash-out period by excluding a certain length of post-TC period for other TCs (if any) when estimating the individual mortality risks for each TC event. The impacts of other TCs may still exist and bias our results. However, we believed the impact of this issue could be modest as evi- denced by the robust results in sensitivity analyses by excluding different lengths of post-TC period. Furthermore, the determination of POC relied on statistical significance (i.e., the lower limit of the 95% CI > 0), potentially influenced by factors irrelevant to TCs, such as sample size. However, a series of simulation studies indicated that the impact of this issue on results may be modest, with consistent and reliable estimates in different scenarios [24]. Nonetheless, more studies are needed in the future to work on this challenging topic and improve the esti- mates of POC across TCs. In addition, mandatory pre-TC evacuation orders and population displacement during TC could be important influential factors in the health impacts of TCs. However, to our knowledge such data have never been systematically compiled and available on a multi-TC scale [24]. To minimize the health threats of climate and weather-related disas- ters, it is highly warranted to collect, compile, and incorporate richer data on these events in future studies. In this study, we estimated the yearly population size for each location and included it as an offset in the model to account for the long-term variations over time and across space due to the unavailability of daily population size in each location. Population characteristics such as the age and sex structure were associated with the vulnerability to TC hazards. Based on a classical two-stage modeling approach, we accounted for the temporal var- iations within a location by the control of temporal trends in the first stage, and the spatial var- iations across locations by using a mixed meta-regression model in the second stage. However, due to the lack of these data (e.g., age- or sex-specific daily mortality) at daily level and on a multicountry scale, we were thus unable to assess the potential risk differences across subpopu- lations such as different age and sex subgroups. Moreover, the TC-specific excess deaths for some TCs could be underestimated for some countries or territories without nationwide data such as China Mainland, Vietnam, and Mexico. The limited locations in China Mainland, Vietnam, and Mexico also increased the uncertainty of our results and prevented us from detecting significant findings for those countries and regions. We were also not able to esti- mate the TC-related mortality risk in the countries or territories without study locations but with a potentially high TC-related health burden (e.g., Bangladesh, Myanmar). These issues warrant further exploration with more comprehensive data and should be lessened in the future as the MCC network expands. Finally, land conditions could significantly influence the speed and direction of surface winds. The widely used improved wind field model by Holland incorporated an attenuation factor, the ratio between the distance to the center and the radius of maximum winds, to resemble surface friction effects [8,35–37]. This does not explicitly account for the surface friction-induced wind speed reduction [77] or motion-induced asym- metry [78]. Post-landfall, TC wind fields could become very noisy due to interaction with com- plex land surfaces, posing challenges in accounting for uncertainties when assessing the ER relationship with health outcomes [79]. However, neglecting inhomogeneous wind conditions over land is expected to minimally impact the results, given the study’s focus on a binary TC exposure variable (exposed versus unexposed). Conclusion To conclude, TCs show great variation in the POC and elevated mortality risks globally. The overall ER relationships of TC-related windspeed and rainfall with all-cause and cardiopulmo- nary mortality exhibited a monotonically increasing, non-threshold and linear curve, with a PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004341 January 22, 2024 15 / 20 PLOS MEDICINETropical cyclone-specific mortality risks and the periods of concern heterogeneous pattern across regions. An overall decreasing trend was observed for the TC- related all-cause and cardiovascular mortality risk from 1980 to 2019. The TC-related mortal- ity risks were generally decreasing in most of the study countries, especially for the Philippines and the USA, while potentially increasing trends in TC-related all-cause and cardiovascular mortality risks were observed for Japan. Further targeted actions and in-depth explorations of TC epidemiology in the countries with high and increasing TC-related mortality burdens are particularly needed. Supporting information S1 Text. MCC Collaborators. (DOCX) S2 Text. Supplementary Material. (DOCX) Acknowledgments We thank relevant institutes/agencies who provided data on mortality and weather conditions. Author Contributions Conceptualization: Wenzhong Huang, Shanshan Li, Yuming Guo. Data curation: Wenzhong Huang. Formal analysis: Wenzhong Huang. Funding acquisition: Yuming Guo. Investigation: Wenzhong Huang. Methodology: Wenzhong Huang, Thomas Vogt, Elizabeth A. Ritchie. Resources: Yuming Guo. Software: Wenzhong Huang. Supervision: Yuming Guo. 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10.1371_journal.pgen.1010503	RESEARCH ARTICLE Subscaling of a cytosolic RNA binding protein governs cell size homeostasis in the multiple fission alga Chlamydomonas Dianyi Liu1,2, Cristina Lopez-Paz1¤a, Yubing Li1¤b, Xiaohong Zhuang1¤c, James UmenID 1* 1 Donald Danforth Plant Science Center, St. Louis, Missouri, United States of America, 2 University of Missouri—St. Louis, Cell and Molecular Biology Program, St. Louis. Missouri, United States of America ¤a Current address: GAT BIOSCIENCES S. L, Parc Cientı´fic de Barcelona, Spain ¤b Current address: Foundation for Applied Molecular Evolution, Alachua, Florida, United States of America ¤c Current address: Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China * jumen@danforthcenter.org Abstract Coordination of growth and division in eukaryotic cells is essential for populations of prolifer- ating cells to maintain size homeostasis, but the underlying mechanisms that govern cell size have only been investigated in a few taxa. The green alga Chlamydomonas reinhardtii (Chlamydomonas) proliferates using a multiple fission cell cycle that involves a long G1 phase followed by a rapid series of successive S and M phases (S/M) that produces 2n daughter cells. Two control points show cell-size dependence: the Commitment control point in mid-G1 phase requires the attainment of a minimum size to enable at least one mitotic division during S/M, and the S/M control point where mother cell size governs cell division number (n), ensuring that daughter distributions are uniform. tny1 mutants pass Commitment at a smaller size than wild type and undergo extra divisions during S/M phase to produce small daughters, indicating that TNY1 functions to inhibit size-dependent cell cycle progression. TNY1 encodes a cytosolic hnRNP A-related RNA binding protein and is produced once per cell cycle during S/M phase where it is apportioned to daughter cells, and then remains at constant absolute abundance as cells grow, a property known as sub- scaling. Altering the dosage of TNY1 in heterozygous diploids or through mis-expression increased Commitment cell size and daughter cell size, indicating that TNY1 is a limiting fac- tor for both size control points. Epistasis placed TNY1 function upstream of the retinoblas- toma tumor suppressor complex (RBC) and one of its regulators, Cyclin-Dependent Kinase G1 (CDKG1). Moreover, CDKG1 protein and mRNA were found to over-accumulate in tny1 cells suggesting that CDKG1 may be a direct target of repression by TNY1. Our data expand the potential roles of subscaling proteins outside the nucleus and imply a control mechanism that ties TNY1 accumulation to pre-division mother cell size. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Liu D, Lopez-Paz C, Li Y, Zhuang X, Umen J (2024) Subscaling of a cytosolic RNA binding protein governs cell size homeostasis in the multiple fission alga Chlamydomonas. PLoS Genet 20(3): e1010503. https://doi.org/10.1371/journal. pgen.1010503 Editor: Susan K. Dutcher, Washington University School of Medicine, UNITED STATES Received: November 16, 2022 Accepted: February 27, 2024 Published: March 18, 2024 Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: This work was funded by National Institutes of Health R01GM092744, National Institutes of Health R01GM126557, and National Science Foundation MCB 1515220 to JU, and by National Science Foundation DBI 2018962 to Donald Danforth Plant Science Center. The funders had no role in study design, data collection and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 1 / 28 PLOS GENETICSanalysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Subscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Author summary Size control is a fundamental property of cells which requires balancing cell growth with cell division, but the mechanisms used by cells to achieve this balance are only partly understood. The best-characterized mechanisms for size control to date involve fixed amounts of nuclear-DNA-bound inhibitory factors which repress cell division until cells grow past a minimum size threshold to overcome the inhibition. The unicellular green alga Chlamydomonas and many other algae and protists use a non-canonical cell cycle where cells can grow by many-fold in size before dividing, and then undergo multiple fis- sion which involves successive rapid divisions to produce a uniform-sized population of daughters. In Chlamydomonas an unknown size homeostasis mechanism couples mother cell size to division number such that larger mother cells divide more times than smaller mother cells. Here, we identified and characterized a key factor governing size control in Chlamydomonas, a cytoplasmic RNA-binding protein and division inhibitor, TNY1, that is produced in a fixed amount in daughter cells and does not increase with cell growth, a property called subscaling. We found that TNY1 represses production of a cell cycle acti- vator, CDKG1, during multiple fission to control daughter cell size. TNY1 is the first example of a cytosolic cell cycle inhibitor that does not depend on nuclear DNA binding to govern subscaling. Introduction Size homeostasis is a fundamental property of proliferating cells and is achieved through mechanisms that balance cell growth with cell division. However, how cells sense and control size remain unexplored in most eukaryotic lineages. Active size control mechanisms have been characterized in several eukaryotes including budding yeast, mammalian tissue culture cells, and Arabidopsis meristems [1–3]. In each case, a titration mechanism operates where a cell cycle inhibitor is produced at a fixed absolute amount per cell in each cell cycle, a property known as subscaling, while an activator accumulates as cells grow [1,4]. At their critical size, cells have accumulated enough activator to overcome the inhibitor and allow cell cycle pro- gression. The details of which proteins acts as the inhibitor or the activator differ in each spe- cies, but there are some systems-level similarities in several taxa including G1-S control with a nuclear-localized and/or chromatin associated factor as the subscaling inhibitor [5,6]. Chro- matin or nuclear DNA content is a naturally subscaling component of cells that has been exploited in Arabidopsis to ensure that the absolute amount of the inhibitor protein KRP4 apportioned to daughters is independent of birth size [3,4]. In yeast and mammalian cells, chromatin-bound cell cycle inhibitor proteins, Whi5 and Rb respectively, are also subscaling and have been hypothesized to act as limiting inhibitors of cell cycle progression [7,8]. The unicellular green alga Chlamydomonas reinhardtii (Chlamydomonas) is a microbial model for plant cell cycles and for non-canonical multiple fission cell cycles that are used by many algae and other protists [9,10]. Multiple fission cell cycles partially uncouple cell growth and cell division: during a prolonged G1 phase, cells can grow more than ten-fold in size. Upon exiting G1, mother cells undergo (n) rapid alternating rounds of DNA synthesis and mitosis (S/M) and produce 2n daughters within a common mother cell wall. Upon mitotic exit, the daughters hatch and enter either G0 or G1 phase due to nutrient availability [9,10]. The Chlamydomonas multiple fission cell cycle has two size control points or checkpoints. The Commitment point occurs in G1 phase, and is operationally defined by the transition from growth-dependence to growth-independence for completing at least one cycle of S/M. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 2 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Cells must reach a minimum size to pass Commitment, and may continue to grow after Com- mitment for 5–7 hours, but this additional growth is optional for completing at least one cycle of S/M [9–11]. Consequently, mother cells can begin S/M within a very large size range between two and twenty times the modal daughter size [9–11]. A second critical size check- point operates during the S/M phase and ensures that larger mother cells divide more times than smaller mother cells so that daughter sizes are in a uniform range regardless of the start- ing sizes of the mother cell population [9–11]. Thus, multiple fission incorporates a size con- trol mechanism that is conceptually somewhat different than a simple gating mechanism used to control size in binary fission cell cycles. Previous studies identified mutants that disrupted cell size homeostasis, including mutants affecting each subunit of the Chlamydomonas retinoblastoma tumor suppressor complex (RBC), MAT3/RBR, E2F1, and DP1 [12,13]. Interestingly, both Commitment size and the S/M size checkpoint were changed in these mutants [12,13]. Loss of function mutations in the MAT3/RBR gene caused cells to pass Commitment at a smaller size than wild type, and to divide too many times producing small daughters [12]. In contrast, loss of function mutations in the DP1 gene suppressed the mat3/rbr phenotype and caused cells to pass Commitment at a larger size and to divide too few times leading to large daughters [13]. Unlike the proposed model for size control in mammalian cells where the RB protein subscales [7], RBC subunits do not show this subscaling behavior in Chlamydomonas [14,15]. cdkg1 was isolated in an insertional screen for size control defects. The mutant caused a large daughter cell phenotype and was found to act upstream of the RBC [15]. CDKG1 encodes a D-cyclin dependent kinase (CDK) that phosphorylates the MAT3/RBR subunit of the RBC and is a limiting factor in mitotic size control. While loss of the protein in cdkg1 mutants caused too few divisions and large cells, over-production of CDKG1 caused extra divisions leading to smaller daughter cells [15]. CDKG1 protein is synthesized just before S/M begins with larger mother cells producing a higher nuclear concentration of CDKG1 than smaller mother cells. Nuclear CDKG1 concentration decreases with each round of cell division. Upon mitotic exit CDKG1 protein becomes undetectable and remains so until the S/M phase of the next cell cycle [15]. It is unknown how CDKG1 mRNA abundance and CDKG1 protein levels are modulated to control cell division number. Here, we identified and characterized a Chlamydomonas heterogeneous nuclear ribonu- cleoprotein (hnRNP) related protein, TNY1, that acts as a cytosolic repressor in the size con- trol pathway upstream of CDKG1 and the RBC. A loss of function mutation in the TNY1 locus altered Commitment and S/M size control leading to production of small daughters. TNY1 protein was produced once per cell cycle during S/M phase and apportioned to daughter cells where its absolute abundance stayed constant during G1 phase. Gene dosage alteration and mis-expression experiments with TNY1 both supported its role as a limiting regulator of mitotic size control. At least one key target of TNY1 repression is CDKG1, whose mRNA and protein abundance were negatively regulated by TNY1. TNY1 was found to be part of a ribo- nucleoprotein complex in vivo, and was able to bind the unusually long and uridine-rich 3’ untranslated region of the CDKG1 mRNA in vitro. TNY1 is a novel example of a non-nuclear subscaling inhibitor which governs size control in Chlamydomonas. Results TNY1 is a negative regulator of cell division upstream of CDKG1 tny1-1 mutants were discovered in a forward insertional mutagenesis screen using a paromo- mycin antibiotic selection marker (paroR) with direct screening for size defects of plate-grown gametes using a Coulter Counter. tny1-1 gametes arrested in early G1 phase showed a small PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 3 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 1. Identification of TNY1 as a regulator of cell size in the retinoblastoma pathway. (A) Upper panel, schematic of TNY1 locus with location of an inserted paromomycin resistance marker (paroR in blue) in exon 1 that produced the tny1-1 allele. Black rectangles, exons; dark gray rectangles, untranslated regions; narrow gray lines, introns and intergenic regions. Lower panel, Differential Interference Contrast (DIC) images of daughter cells from wild type parent strain CC-124 and tny1-1. Scale bar = 10 μm. (B) Left panel, size distributions of daughter cells from tny1-1 (median size 44 μm3/modal size 40 μm3), wild type CC-124 (median size 66 μm3/modal size 70 μm3), cdkg1-2 (median size 117 μm3/modal size 113 μm3), and cdkg1-2 tny1-1 (median size 113 μm3/modal size 117 μm3). Median size of wild type > tny1-1 (p<0.01, Student’s t-test) (S1 Table). Median sizes of tny1-1 and tny1-1 cdkg1-2 are not different (p>0.1, Student’s t-test) (S1 Table). Right panel, epistasis diagram showing positive (arrows) and negative (bars) regulators of size-dependent cell division. TNY1 functions upstream of CDKG1. (C) Size distributions of daughter cells from tny1-1 (median size 44 μm3/modal size 40 μm3), wild type CC-124 (median size 69 μm3/ modal size 74 μm3), tny1-1 rescued strains gTNY1::tny1-1 (median size 72 μm3/modal size 75 μm3) and gTNY1-HA::tny1-1 (median size 66 μm3/modal size 70 μm3). Median sizes of daughter cells of wild type, gTNY1::tny1-1, and gTNY1-HA::tny1-1 are not significantly different (p>0.1) by one way ANOVA testing (S1 Table). (D) Immunoblots of SDS PAGE separated protein lysates from daughter cells of indicated genotypes using α-HA, α-TNY1, or α-histone H3 (internal loading control). https://doi.org/10.1371/journal.pgen.1010503.g001 size phenotype and the mutant was re-tested under more controlled vegetative growth condi- tions to assess daughter cell size (Fig 1A). Wild type parental strain CC-124 and tny1-1 cultures were synchronized under a diurnal cycle and daughter cell sizes were measured. tny1-1 daugh- ter cells had a modal cell size of ~45 μm3 compared with ~75 μm3 for wild type daughters (Fig 1B and S1 Table), with both strains passing Commitment and entering S/M with similar timing (S1A Fig), though with tny1-1 populations always at a smaller size than the control pop- ulation when undergoing these two transitions (S1B Fig). The time interval between Commit- ment and entering S/M was the same in wild type and tny1-1 mutants, so the small size defects of tny1-1 strains are not attributable to a shortened cell cycle duration (S1A and S1B Fig). We next generated populations of wild type and tny1-1 mother cells and compared cell division numbers using a Commitment assay (Methods). Synchronized tny1-1 and wild-type strains were sampled at the same time points in mid- or late-G1 phase and had similar division num- ber profiles (S1C Fig), despite the wild-type cells mother cells being 50% to 60% larger PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 4 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas (S1B Fig). In experiments where mother cell size distributions were matched between the two strains (modal size ~230 μm3), tny1-1 mother cells underwent an average of 2.8 rounds of mul- tiple fission versus 1.4 rounds for wild type (S1D Fig). Besides causing a shift in cell size set points, size control mutants can also increase size distribution variance due to weakened coupling between cell size and division control mechanisms as we recently observed in mat3/rbr mutants [11]. The variance of an approxi- mately log-normal distribution found in synchronized Chlamydomonas populations can be described using log transformed size bins which preserve symmetry around the mean of the distribution and are more intuitive to interpret than the same data described in linear space. Using this transform we compared standard deviation (SD) and coefficient of variance (CV) in daughter populations of wild-type, tny1-1, cdkg1, and tny1-1 strains expressing res- cuing transgenes (S2A Fig). CVs of cell populations are sensitive to small deviations in syn- chrony and had some variability between replicates, but the best-synchronized populations of tny1-1 and cdkg1-2 daughter cells had CVs similar to wild type controls (S2 Fig). This finding suggests that TNY1 is needed for establishing the relationship between mother cell size and division number during S/M, but that in its absence separate or redundant mecha- nisms govern the strength of coupling between cell size and division behavior. In summary, the overall timing of cell cycle events is normal in tny1-1 mutants, but the minimum Com- mitment cell size and S/M phase size control of tny1-1 cells are both mis-regulated in a man- ner consistent with TNY1 acting as a negative regulator for size-dependent cell cycle control points. We next used epistasis experiments to determine the relationship of tny1-1 to other cell size regulators. CDKG1 functions upstream of the RBC, and cdkg1-2 null mutants cause a large-cell phenotype [15]. cdkg1-2 tny1-1 double mutants had nearly identical sizes as cdkg1-2 single mutants indicating that TNY1 functions upstream of CDKG1 and the RBC and does not appear to control cell size homeostasis through an independent mecha- nism (Fig 1B and S1 Table). Commitment sizes for cdkg1-2 and cdkg1-2 tny1-1 (~200 μm3) are very similar to the Commitment size (~200 μm3) of a wild-type strain (S3A and S3B Fig), indicating that cdkg1-2 suppresses both the Commitment and the S/M size defects of tny1-1. The tny1-1 strain was found to contain a single insertion of the paromomycin (paroR) marker in the first exon of Cre07.g330300 [16] (Fig 1A). tny1-1 was back-crossed to wild- type strain CC-125 and random progeny were selected and scored for gamete cell size, mat- ing type, and paromomycin resistance (paroR) or sensitivity (ParoS). The paroR segregants produced small gametes, while the paroS segregants were wild-type size indicating linkage between the paromomycin cassette insertion and the tny1-1 phenotype (Methods, S3C–S3E Fig). Rescue of the tny1-1 small cell defect was performed by transforming constructs that contained either a full-length genomic fragment of wild type Cre07.g330300 (gTNY1) or a version with a C-terminal triple hemagglutinin epitope tag (gTNY1-3xHA). In both cases, normal daughter cell sizes were restored in a fraction of transformants while no rescue was observed in control transformants bearing an empty vector (Fig 1C and S1 Table). Rescue efficiency with either of the two constructs was somewhat low (~2%) but not atypical for Chlamydomonas rescues. Immunoblotting of SDS-PAGE separated proteins from wild type, tny1-1, and rescued tny1-1 strains using polyclonal antibodies raised against recombi- nant TNY1 protein or α-HA antibodies detected proteins of the expected migration (~48 kDa) in wild type and rescued strains showing that TNY1 expression was restored in those rescued lines (Fig 1D). Together these experiments confirm that disruption of Cre07. g330300 causes the tny1-1 phenotype. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 5 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas TNY1 is predicted to encode a putative hnRNP A-related RNA binding protein TNY1 is predicted to encode a protein with two N-terminal RNA recognition motifs (RRMs) and a low complexity glycine-rich C-terminus (Figs 2A and S4). This structure is found in eukaryotic heterogeneous nuclear ribonucleoproteins (hnRNPs) and other related RNA bind- ing proteins that have diverse roles in nucleic acid regulation and metabolism, functioning as Fig 2. TNY1 encodes a hnRNP-related RNA binding protein. (A) Schematic of predicted TNY1 protein domain structure from N to C terminus. Two RNA binding motifs (RRM1 and RRM2, orange bars) are followed by a glycine- rich region and a short, conserved motif (CM) at the C-terminus. (B) Maximum likelihood phylogeny TNY1 and related hnRNP related proteins in indicated taxonomic groups. Species abbreviations are followed by protein names and NCBI protein IDs. Cr, Chlamydomonas reinhardtii. Ts, Tetrabaena socialis. Gp, Gonium pectorale. Vc, Volvox carteri. Ce, Chlamydomonas eustigma. Ds, Dunaliella salina. Cs, Coccomyxa subellipsoidea. Mn, Monoraphidium neglectum. Mc, Micractinium conductrix. Os, Oryza sativa. Zm, Zea mays. At, Arabidopsis thaliana. Sm, Selaginella moellendorffii. Kn, Klebsormidium nitens. Sv, Setaria viridis. Atr, Amborella trichopoda. Pp, Physcomitrella patens. Mp, Marchantia polymorpha. Gs, Galdieria sulphuraria. Dr, Danio rerio. Dm, Drosophila melanogaster. Ce, Caenorhabditis elegans. Hs, Homo sapiens. Sr, Salpingoeca rosetta. https://doi.org/10.1371/journal.pgen.1010503.g002 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 6 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas RNA or DNA binding proteins [17,18]. BLAST searching in different taxa was used to identify proteins related to TNY1 in animals, plants, and algae. These sequences were curated and used to estimate a maximum likelihood phylogeny which placed TNY1 in a clade of green algal TNY1-like homologs, and this TNY1 clade was sister to a larger grouping of plant tandem RRM hnRNP-like proteins suggesting a common origin at the base of the Viridiplantae (Meth- ods, Fig 2B). While Chlamydomonas encodes other hnRNP-like proteins, these are grouped outside of the green algal TNY1 clade which may have originated in the crown Chlorophytes (Chlorophyceae/Trebouxiophyceae/Ulvophyceae). No close matches to TNY1 were found in predicted proteomes of earlier diverging prasinophycean grade Chlorophytes including Micromonas and Ostreococcus which both have reduced genomes and may have lost ancestral TNY1-related genes. TNY1 is localized in the cytosol To determine the subcellular localization of TNY1, a genomic TNY1 construct with a C-termi- nal fusion of Chlamydomonas codon-optimized mCherry was used to rescue tny1-1 mutants and generate gTNY1-mCherry::tny1-1 strains with fusion protein expression detected by immunoblotting (S5A Fig), and confirmed with a rescued size phenotype (S5B Fig and S1 Table). Live cell confocal fluorescence microscopy revealed TNY1-mCherry signal in the cyto- sol throughout the vegetative cell cycle (Figs 3 and S5C). Indirect immunofluorescence using α-HA antibodies targeting tagged TNY1-HA confirmed the cytosolic location and showed exclusion of TNY1 protein signal from the nucleus (S5D Fig). TNY1 regulation and subscaling throughout the cell cycle To determine the accumulation pattern of TNY1 mRNA during the cell cycle, wild-type cul- tures were synchronized under a standard diurnal cycle (12hr:12hr light:dark) and RNA sam- ples were prepared from cells at different time points and used for quantitative RT-PCR. TNY1 mRNA was present at very low levels during G1 phase and rose sharply to a peak toward the middle/end of S/M phase, and then declined in the dark phase after division (Fig 4A). This experiment largely reproduced the results of previous genome-wide expression studies [19,20], where the timing of TNY1 mRNA accumulation coincided with that of many late mitotic and cilia-related genes. The trigger for TNY1 mRNA accumulation is likely to be entry to S/M phase, but we could not rule out diurnal control or the light-to-dark transition as signals for TNY1 expression. To distinguish these possibilities, we used two alternative diurnal regimes where the light-to-dark transition was shifted forward or backward by three hours, but the timing of S/M phase [15] and TNY1 peak expression were unaffected (Fig 4A). The accumulation pattern of TNY1 protein throughout the cell cycle was determined by quantitative immunoblotting of samples taken from wild-type cultures synchronized under the standard 12hr:12hr light:dark diurnal regime (S6 Fig). Samples were loaded either by equal protein per lane which reflects TNY1 concentration in cells (Figs 4B and S7A–S7E, upper blots) or by equal culture volume per lane which reflects amount of TNY1 per cell in G1 phase samples (Figs 4B and S7A–S7E, lower blots). Plots of TNY1 signal during the cell cycle (Figs 4C and S7F and S2 Data) showed a constant amount per cell during G1 phase as cells increased in size by around six-fold, and an increase during S/M phase as cells divided. The complemen- tary curve of TNY1 concentration shows it is highest in early G1 daughter cells, and drops as cells grow during G1 phase, and restored at cell division. In summary, cells are born with a fixed amount of TNY1 protein that is steadily diluted during G1 phase as cells grow, reaching PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 7 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 3. TNY1 is localized in the cytosol. Confocal fluorescence images of live cells at different cell cycle phases (left side labels) expressing a functional TNY1-mCherry fusion protein. TNY1-mCherry signal (mCherry, pseudo colored cyan). Chlorophyll fluorescence (Chl, pseudo colored red). Differential Interference Contrast (DIC). Merged fluorescent images (Overlay). Scale bar = 10 μm. https://doi.org/10.1371/journal.pgen.1010503.g003 its minimum concentration just prior to S/M during which its mRNA is transcribed and the protein is replenished in new daughters (Figs 4C and S7F and S2 Data). We next determined how TNY1 gene expression scaled with mother cell size during S/M phase. We compared mean mother cell size in samples from the three regimes in Fig 4A (S7G Fig) to the TNY1 mRNA expression peak height and found they are correlated, suggesting that mother cell size or numbers of daughter nuclei may control TNY1 mRNA production (Fig 4D). TNY1 is limiting for size control To determine if subscaling of TNY1 is controlled by feedback from size control regulators, we examined its levels in cell size mutants. TNY1 protein levels were determined in dark-shifted PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 8 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 4. Cell cycle control of TNY1 mRNA and TNY1 protein accumulation. (A) qRT-PCR data time series for TNY1 mRNA accumulation in synchronous wild type cultures with light phase (white bar) and dark phase (black bar), and cell cycle phasing cartooned above. Cultures were synchronized under a standard diurnal regime (dark grey line, 12hr:12hr light:dark), in parallel with two modified diurnal regimes of early dark (light grey line, 15hr:9hr light:dark) or extended light (black line, 9hr:15hr light:dark). Under all three different diurnal regimes >80% of cells divide between ZT 12 hrs ZT 15 hrs. TNY1 transcripts were normalized against an internal control GBLP transcripts (Methods) and plotted as the average and SD (error bars) of three biological replicates. (B) Representative immunoblots with whole cell lysates from synchronized wild-type cultures under a 12hr:12hr light:dark regime with sampling at indicated time points. Each image set shows total protein signal (top) and α TNY1 signal (bottom). Upper set was loaded with equal protein in each lane, and lower set with equal culture volume per lane (with equal cell number in G1 samples). (C) Plot of TNY1 abundance across the cell cycle to show TNY1 concentration (red curve) or amount per cell (blue curve) in arbitrary units from three biological replicates. Error bars: SD of the average of three biological replicates. (D) Mean mother cells size (grey) and TNY1 mRNA transcript abundance (blue) for three diurnal regimes shown in panel (A) at ZT 15 hrs. Error bars: SD of three biological replicates. https://doi.org/10.1371/journal.pgen.1010503.g004 daughter populations (equivalent to ZT 0 hrs in our light:dark regime) produced from wild type, mat3-4/rbr, dp1-1 and cdkg1-2 cells (S8A Fig and S1 Table). Interestingly, daughter popu- lations with different mean sizes contained the same amount of TNY1 on a per cell basis sug- gesting that subscaling of TNY1 is independent of daughter cell size and its levels may instead be controlled by limiting factors that scale invariantly with cell size such as genomic template for TNY1 transcription (Fig 5A and S8B Fig). If so, then TNY1 abundance may be sensitive to PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 9 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 5. TNY1 is limiting in cell size control. (A) Immunoblots with whole cell lysates from wild type or mutant daughter cells (ZT 0hr under standard conditions) with two replicates each (reps 1 and 2). Each gel was probed with α-TNY1, α-Histone H3 and α-Tubulin. Bar graphs show signals in arbitrary units for each replicate band with the strongest band in each blot set to 1. (B) Size distributions of diploid daughter cells of indicated genotypes. tny1/tny1 (median size 111 μm3/modal size 96 μm3), TNY1/tny1 (median size 124 μm3/modal size 113 μm3), and TNY1/TNY1 (median size 154 μm3/modal size 166 μm3). Median size of TNY1/TNY1 > TNY1/tny1 (p< 0.01, one-tailed t-test). Median size of TNY1/tny1 > tny1/tny1 (p< 0.01, one-tailed t-test) (S1 Table). (C) Immunoblots with whole cell lysates from indicated diploid daughter cells with two replicates each (reps 1 and 2). Immunoblots were loaded and processed similar to those in Fig 5A. Quantitation of the immunoblot signals were plotted below as described in panel A. (D) Size distributions of synchronous daughter cells of two independent RPL23:TNY1::tny1-1 rescued strains (#1 median size 98 μm3/modal size 98 μm3 and #2 median size 89 μm3/modal size 95 μm3), a control strain transformed with resistance marker only Aph7:tny1-1 (median size 44 μm3/modal size 40 μm3), and a gTNY1::tny1-1 rescued strain (median size 74 μm3/modal size 80 μm3). Median sizes of four independent RPL23:TNY1::tny1 transformants > wild type (p< 0.05, one-tailed t-test) (S1 Table). https://doi.org/10.1371/journal.pgen.1010503.g005 gene dosage. To test gene dosage effects, we created a set of isogenic diploid strains with geno- types TNY1/TNY1, TNY1/tny1-1, and tny1-1/tny1-1 (Methods). Size profiles of daughters from synchronized diploid cultures of each strain were compared and found to differ based on TNY1 dosage, with heterozygote daughter size in between that of wild type and homozygous mutants (Fig 5B and S1 Table). TNY1 protein abundance in daughter cells of TNY1/tny1 het- erozygous daughters was also reduced compared with homozygous TNY1/TNY1 strains (Fig 5C). We also examined cell sizes from haploid meiotic progeny of crosses between tny1- 1::TNY1 (or tny1-1::TNY1-HA) and wild type where progeny could inherit, 0, 1 or 2 copies of TNY1. As with the diploid dosage series, the progeny that inherited two copies of TNY1 were PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 10 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas larger than progeny with a single copy (S8C Fig). Besides altering gene dosage, we also gener- ated a TNY1 transgene driven by a constitutive promoter/terminator from the Chlamydomo- nas RPL23 gene [21]. This RPL23:gTNY1:RPL23 construct was transformed into a tny1-1 strain and transformants were tested for size phenotypes along with control transformants that received an empty vector [22]. Among 24 independent RPL23:gTNY1::tny1-1 transformants, ~ 20% showed a large daughter cell phenotype with a modal cell size > 80 μm3 that was never observed in controls or wild-type rescues (Fig 5D). The large-cell transformants appeared to progress through the cell cycle with similar kinetics as wild type and tny1 mutants but were larger at each transition (S8D and S8E Fig). Taken together, these data indicate that dosage and expression level of TNY1 impact mitotic cell size control and are consistent with the sub- scaling behavior observed for TNY1 expression being an important contributor to size-depen- dent cell cycle control. TNY1 inhibits the accumulation of CDKG1 mRNA and protein in postmitotic cells Because the cdkg1 large cell phenotype is epistatic to the tny1 small cell phenotype we investi- gated a possible antagonistic relationship between TNY1 and CDKG1 where TNY1 might limit production of CDKG1. In post-mitotic tny1-1 daughter cells we detected a three-fold increase in CDKG1 mRNA compared with wild type (Fig 6A). To test the impact of TNY1 on CDKG1 protein abundance tny1-1 was crossed into a rescued cdkg1 strain expressing an HA epitope tagged allele HA-CDGK1, and expression was assessed by immunoblotting [15]. In mitotic cells we did not consistently see a difference in HA-CDKG1 signal between wild type and tny1-1 strains, likely due to opposing and non-linear effects of i) mother cell size—which would amplify the CDKG1 signal in wild-type mother cells over the smaller mother cells of tny1-1 cells (S9A Fig)—and ii) the tny1-1 mutation—which could increase CDKG1 abundance over what it would have been for a similar-sized wild type cell, but not necessarily over that of the matched control strain with larger mother cells. We instead focused on post-mitotic cells where we consistently observed more HA-CDKG1 in tny1-1 versus wild-type cells (Figs 6B and S9B). Indirect immunofluorescence (IF) was also used to detect HA-CDKG1 in mitotic and post-mitotic cells [15], where a clear HA-CDKG1 signal was present in around 70% of tny1-1 daughters (97/145 cells) but never in the TNY1 control strain (0/133 cells) (Figs 6C and S9C). It is unclear whether the tny1-1 daughters without a CDKG1 IF signal were truly negative or below the detection limit of the IF experiment; but the high proportion of HA-CDKG1 pos- itive staining post-mitotic cells in tny1-1 (but not TNY1) strains was reproducible in two inde- pendent staining experiments for each genotype. Together these data show that TNY1 limits the accumulation of both CDKG1 mRNA and CDKG1 protein in post-mitotic cells. TNY1 is part of an RNP complex and can bind to the 3’UTR of CDKG1 mRNA The finding that cytosolic TNY1 could inhibit accumulation of nuclear-localized CDKG1 pro- tein suggests a mechanism which might involve direct interaction of TNY1 with CDKG1 mRNA. We first used native electrophoresis of whole cell extracts, and immunoblotting to determine if TNY1 might be part of a ribonucleoprotein complex (RNP). On native gels, TNY1 migrated near the 158 kDa marker, but shifted to a slower migrating complex (>450 kDa) when treated with ribonuclease A (RNAse) or micrococcal nuclease (MNase), but not deoxyribonuclease (DNase). These results suggest that TNY1 is associated with RNA in vivo as an RNP, and that the RNA component may contribute significantly to the negative charge state of the complex leading to faster migration when present (Fig 6D). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 11 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 6. TNY1 inhibits the accumulation of CDKG1 mRNA and CDKG1 protein. (A) qRT-PCR quantitation of average CDKG1 mRNA level in daughter cells of wild type and tny1-1 normalized to internal control gene GBLP (Methods). Three biological replicates (averaged value of two technical replicates) are plotted for each genotype with the mean wild-type signal set to 1. Wild-type and tny1-1 were significantly different by t-test (, p<0.01). (B) Immunoblots with HA-CDKG1-expressing daughters loaded with equal protein per lane and probed with α-HA, α- Tubulin or α-Histone H3. (C) Brightfield and immunofluorescence microscopy images of representative HA-CDKG1:: cdkg1 and HA-CDKG1::cdkg1 tny1 cells. Synchronous mitotic and post-mitotic cells were probed for HA-CDKG1 (α- HA, pseudo-colored green) and stained with DAPI (pseudo-colored red). Note that some of the α-HA pixels were saturated, but all images were taken with identical settings. Scale bar = 10 μm. (D) Native gels were loaded with whole cell lysates from a gTNY1-HA::tny1-1 strain, fractionated and immunoblotted using α-HA. Lysates were pre-treated with different nucleases prior to loading as indicated above each lane. RNase used at different concentrations is indicated by the triangle from lowest to highest 0.01 mg/mL, 0.1mg/mL, and 1mg/mL. The lower image is the same membrane stained with Ponceau S as a protein loading control. (E) Blots containing recombinant GST-TNY1 or GST probed with 32P labeled CDKG1 3’UTR or CDKG1 5’UTR and CDS (Methods). The total protein input was visualized by Ponceau S staining and the 32P signal by film-based autoradiography. https://doi.org/10.1371/journal.pgen.1010503.g006 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 12 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas A simple model for regulation of CDKG1 by TNY1 is direct binding of TNY1 to the CDKG1 mRNA which has an unusually long (1.5kb) and uridine-rich (28%) 3’ UTR—both rel- atively rare features in Chlamydomonas mRNAs that tend to have shorter 3’ UTRs (median length 677 bp) and low uridine content (22% ± 3.3% mean and SD) (Methods). We attempted to detect TNY1 binding to CDKG1 mRNA in vivo using RNA crosslinking and immunopre- cipitation (RIP) [23] but were unable to amplify an enriched signal due to high background. Instead, we developed an in vitro assay where radiolabeled CDKG1 mRNA fragments were used as a probe for binding to GST-TNY1 fusion protein or GST immobilized on a membrane (Methods) [24]. Radiolabeled CDKG1 mRNA was synthesized in two fragments, with the 5’ region including the 5’UTR and CDS in one fragment, and the 3’ UTR in a second fragment. After incubation of radiolabeled RNA with membrane-bound GST1-TNY1 or GST1 and washing, the signal was detected only for the 3’ UTR fragment binding to GST1-TNY1 (Fig 6E). These data indicated that TNY1 protein can bind RNA with sequence specificity, including sequences in the 3’ UTR of its likely target gene CDKG1. Discussion In this study we identified a new Chlamydomonas sizer protein, TNY1, a hnRNP-related cyto- solic RNA binding protein which functions as a negative regulator of cell size in a dosage- dependent manner. Like other size mutants in Chlamydomonas, tny1-1 mutant cells retain rel- atively normal cell cycle progression kinetics but do so with altered cell size checkpoints for Commitment and for division number during S/M. As in other systems, size control in Chla- mydomonas has at least two components—a size setpoint which governs the optimal target size of daughters measured as the median or modal size of their distribution, and a noise or variance component which describes how accurately cells adhere to a theoretical two-fold size window as the best achievable accuracy for producing daughters by multiple fission [10,11]— and these components are not necessarily the same. In Arabidopsis, budding yeast and mam- malian cells, subscaling inhibitor proteins KRP4, WHI5 and RB, respectively, control the size threshold of the G1➔S phase transition and consequently the amount of size variance at this transition [4,5,7,8,25] (S10 Fig). For example, when the Arabidopsis KRP4 subscaling mecha- nism is genetically disabled, the variance in cell size at G1➔S is not reduced the same as in wild type. At the same time, absolute levels of KRP4 also govern the size setpoint for G1➔S [4]. In contrast, tny1-1 mutants had altered size setpoints governing Commitment and mitotic size control, but tny1-1 daughters had similar distribution variance as wild type (S2 Fig) mean- ing that mechanisms which control variance or noisiness may still operate in tny1-1 mutants. To date, only mat3/rbr mutants seem to have increased noise in daughter size distributions caused by unregulated activity of the cell cycle activator E2F1/DP1 [11] (S1 Table and S2A Fig). However, a definitive analysis of how TNY1 subscaling might influence stochastic pro- cesses during cell division will require more in-depth analysis of single cells. TNY1 mRNA and TNY1 protein are synthesized once per cell cycle during S/M phase, and TNY1 protein is at its highest concentration in newborn daughters (Fig 4). During G1 phase TNY1 absolute abundance remains constant, meaning that its cellular concentration drops as cells grow. This subscaling behavior appears to be important for size homeostasis since increased or decreased TNY1 dosage or expression impacted mitotic size control (Fig 5). The cell cycle activator and size regulator CDKG1, a D-cyclin dependent RBR kinase is a likely direct target of TNY1 repression since ectopic accumulation of CDKG1 protein and mRNA was observed in tny1-1 mutants, and TNY1 protein could interact spe- cifically with the 3’UTR of the CDKG1 mRNA, possibly as a translational repressor or desta- bilizing factor (Fig 6). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 13 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Fig 7. Model for subscaling TNY1 as a regulator of size-dependent cell cycle progression. Left panel, in wild-type cells during early G1 phase (top half) cytosolic TNY1 binds the 3’UTR of CDKG1 mRNA and possibly other targets and prevents premature expression. Prior to and during early S/M phase (bottom half) CDKG1 mRNA and other target mRNAs outnumber TNY1 protein which is at its lowest concentration. Translation of CDKG1 drives size-dependent cell cycle progression through phosphorylation of RBR by CDKG1/D-type cyclins and other mitotic kinases in the nucleus [15]. Right panel, in tny1 mutants some CDKG1 is inappropriately produced in early G1 phase (top half) and may prematurely push cells to Commitment at a smaller size through ectopic phosphorylation of RBR. During S/M phase (bottom half) the absence of TNY1 allows extra CDKG1 to accumulate causing an imbalance in size sensing and more cell divisions than in equivalent-sized wild-type mother cells. https://doi.org/10.1371/journal.pgen.1010503.g007 Together these data suggest a model where TNY1 controls cell division by modulating the accumulation of a limiting activator protein, CDKG1, and possibly other limiting cell cycle regulators (Fig 7). This modulation might occur in at least two ways. During G1 phase, CDKG1 is not detectable and does not seem to play a normal role in cells passing Commit- ment [15], but in a tny1-1 mutant its inappropriate expression in G1 phase could change the Commitment threshold size by contributing to the premature inactivation of the RBC which controls Commitment cell size [11,13,14]. Just prior to S/M phase, the absence of TNY1 may cause the production of extra CDKG1 leading to increased division number during S/M, or it may cause extra divisions by preventing the timely removal of CDKG1 which normally PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 14 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas accompanies mitotic exit (Fig 7). Future experiments based on quantitative detection of CDKG1 in single cells should help resolve whether its abundance is increased in mitotic cells of tny1-1 cells or whether the postmitotic mis-expression of CDKG1 in tny1-1 mutants described here (Fig 6) is enough to cause extra cell divisions. In vivo binding studies to determine the timing of when TNY1 associates with CDGK1 mRNA, and to identify other direct RNA targets of TNY1 will be useful for testing the direct repression model for cell size control. Evidence for cell size checkpoints based on some form of protein subscaling has been found in different eukaryotic taxa, including fungi, animal cells and plant meristems (S10 Fig) [4,5,7,26]. An appealing property of subscaling proteins is their absolute abundance can act as a ruler for perceiving changes in cell size by titrating against an antagonist that remains at constant concentration as cells grow. In the examples cited above (S10 Fig), sub- scaling is directly tied to DNA or chromatin [3,6]. In budding yeast, Whi5 protein binds to and inhibits the DNA bound transcription factor SBF, a key activator of S phase transcrip- tion. While some regulation of Whi5 abundance may occur based on synthesis of Whi5, it is also limited by chromatin binding [5,26,27]. Similar findings were made for the RB protein in mammalian cells which is a functional analog of Whi5 for S phase transcription [7]. In plants, chromatin binding by the CDK inhibitor KRP4 coupled with elimination of excess unbound KRP4 allows daughter cells to be apportioned with a fixed amount of KRP4 that acts as a concentration dependent inhibitor of the cell cycle in the subsequent G1 phase and ensures that S phase entry occurs at a constant average cell size regardless of daughter cell sizes [4]. Here we found that subscaling can also occur for a cytosolic protein, TNY1, that has no direct connection to the nucleus or chromatin. This finding raises the question of how TNY1 synthesis is controlled and how its levels can be modulated so that daughters always contain the same amount of TNY1. One way to achieve a fixed dose of TNY1 per cell would be if production of TNY1 mRNA is limited by TNY1 gene copy number in daughters and not influenced by cell size related factors (e.g. transcription factor abundance, co-acti- vator abundance) [28], but this remains to be determined. Supporting this idea, TNY1 abso- lute abundance in daughters was not influenced by cell size mutants that caused production of large or small daughters (Fig 5A). To date, TNY1 is the only cell cycle regulatory protein in Chlamydomonas known to subscale. The RB complex is downstream of TNY1 in Chla- mydomonas, but MAT3/RBR increases in abundance during G1 phase [14,15] and does not show dosage sensitivity for size control as its mammalian homolog RB and its yeast counter- part Whi5 do [5,7]. Thus, the systems-level target for subscaling of size control is not con- served across taxonomic groups. Interestingly, TNY1 shares some similarity to budding yeast Whi3, an RNA binding protein and negative cell cycle regulator that functions in part by restricting expression of the limiting G1 cyclin Cln3 [29,30]. In budding yeast, Whi3 represses the function of Cdc28-Cln3 by retaining Cdc28-Cln3 complexes in the cytoplasm in G1 phase [31]. Whi3 does not impact the abundance of Cdc28 but does represses CLN3 mRNA abundance and translational efficiency [32]. In Chlamydomonas, TNY1 functions upstream of CDKG1 and appears to repress the accumulation of CDKG1 mRNA and CDKG1 protein (Fig 6A–6C). Unlike Whi3, cytosolic TNY1 does not impact the nuclear localization of CDKG1. Musashi proteins (MSIs) are meta- zoan hnRNPs that play a role in stem cell maintenance and proliferation [33]. While the targets of MSIs are not fully defined, they primarily bind to 3’ UTRs of mRNAs and regulate mRNA stability and/or translation [33,34]. Future work aimed at systems-level understanding of cell size regulatory networks may reveal additional parallels for RNA binding proteins such as TNY1 in governing cell size and cell cycle progression. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 15 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Methods Chlamydomonas strains and growth conditions Strains were maintained on Tris-acetate-phosphate (TAP) + 1.5% agar plates (https://www. chlamycollection.org/methods/media-recipes/tap-and-tris-minimal/). For synchronous growth, strains were cultured at 25˚C in Sueoka’s High-Salt-Media (HSM) liquid media [35] with diurnal cycles as indicated and 300 μE total LED light intensity (150 μE blue at 465 nm and 150 μE red at 625 nm) bubbling with 1% CO2 in air. Diurnal light regimes used are described in figure legends and text. Gamete generation, mating, and zygote germination were performed following standard protocols [36–38]. Segregation analysis was done with randomly selected progeny from mat- ing. Dark-shift experiments, Commitment assays, and size distribution measurements with a Coulter Counter (Beckman Multisizer 3) were conducted as described previously [39]. Cell size distribution statistics (mean, median, mode) were determined in data ranging from 20 μm3 to 2000 μm3. Particle sizes above and below this range are rare, and mostly consist of small debris or large clumps. Chlamydomonas transformation Cells were cultured asynchronously at 25˚C in TAP liquid media with constant light 100 uE total light intensity (50% Blue:50% Red– 50 μE blue at 465 nm and 50 μE red at 625 nm LED lights) bubbling with filtered air [40]. Cells were transformed using electroporation as previ- ously described [21]. Transformants were plated on TAP agar plates with either 15 μg/mL of paromomycin or 25 μg/mL of hygromycin depending on selection markers. Forward genetic screen for size mutant and mapping of tny1-1 Wild type strain CC-124 was subject to an insertional mutagenesis using vector pSI103 [22] linearized with NotI and transformed using the glass bead method [41] with selection on TAP agar plates containing 15 μg/mL paromomycin. Transformants were picked and re-grown in individual wells of 96 well plates, stamped onto TAP agar plates using a 48-pin replicator tool and grown on a light shelf at 25˚C for 6 days. Approximately 1/3 of each stamped spot was removed with a toothpick and resuspended in nitrogen-free HSM in a new 96 well plate to cre- ate a gamete suspension. Gametes made by growing cells on agar plates for a week provide a less labor-intensive estimate of early G1 phase cells compared with liquid cultures under synchro- nous growth conditions described above. Gametes were then checked for cell size using a Coul- ter Counter. Confirmed mutants were then crossed to wild type strain CC-125, and progeny were tested for linkage of the suppressor phenotype to the pSI103 insertion. The tny1-1 inser- tion site was determined by sequencing junction fragments from ligation mediated PCR [42]. The insertion site was confirmed using genotyping oligos for TNY1 and tny1-1 (S2 Table). Diploid generation Diploid selection was done by plating crosses (described below) shortly after mating on double selection plates to select for both parental markers. Wild type TNY1/TNY1 vegetative diploids were generated by a mating between wild type CC-1039 (Sager’s 21 gr) (NIT1 NIT2 MT+) and wild type CC-124 transformed with pKS-aph7”-lox [43] (MT-, hygromycin resistant, nit1 nit2) with selection on 25 μg/mL hygromycin and nitrate as the only nitrogen source. Heterozygous TNY1/tny1 vegetative diploids were generated by a mating between wild type CC-1039 and tny1-1 (MT-, paromomycin resistant) with selection on paromomycin with nitrate as the only nitrogen source. Homozygous tny1/tny1 vegetative diploids were generated by mating between a PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 16 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas tny1 MT+ Nit+ segregant from a cross with CC-1039 and tny1 transformed with pKS-aph7”-lox [43], with selection on plates with 25 μg/mL hygromycin and nitrate as the only nitrogen source. Diploid candidates validated by genotyping with mating-type locus oligos (S2 Table) [44]. Rescue of tny1-1 A 3.4 kb fragment containing the full-length genomic region of TNY1 was amplified from geno- mic DNA using primers TNY KpnI/TNY NdeI listed in S2 Table. The amplified fragment was digested with KpnI/NdeI and ligated into KpnI/NdeI digested vector pHyg3 (https://www. chlamycollection.org/product/phyg3/) to generate tny1 rescue construct pTNY1. A triple hemag- glutinin epitope tag (3xHA) was inserted into pTNY1 just before the stop codon into a BgllI site created by overlapping PCR with two fragments amplified with oligos TNYKpnI/ TNYBglIIRev and TNY BglIIF/TnyNdeIIF (S2 Table) with Phusion polymerase and GC buffer. A triple HA epi- tope tag (3xHA) was amplified from 3xHA-MAT3 [14] with oligos HABglII-F/HABglIIR cut with BglII and inserted at the BglII site just before the translation stop codon to generate pTNY1- 3xHA. pTNY1 or pTNY1-3xHA were transformed into tny1 by electroporation as described above with selection on TAP agar with 30 μg/mL hygromycin. Individual transformants were picked into 96 well plates and screened for gamete cell sizes as described above for screening insertional mutants. Rescue of the TNY1 protein was confirmed by immunoblotting (see below). Mis-expression of TNY1 To generate mis-expression construct pRPL23-TNY1, full genomic TNY1 fragment between the start and stop codons was amplified with primers BamHI TNY1 F and Xho1 TNY1 R (S2 Table) with Phusion polymerase and GC buffer from tny1 rescue construct pTNY1. The ampli- fied TNY1 fragment was digested with BamH1 and Xho1 and inserted into pRPL23:Luc:RPL23 [21], then recombined with plasmid pKS-aph7”-lox [43] to generate pRPL23-TNY1-aph7. pRPL23-TNY1-aph7 or pKS-aph7”-lox (negative control) were transformed into tny1-1 by elec- troporation (see above). Transformants were selected on TAP agar plates containing 25 μg/mL hygromycin. Phylogenetic analysis of TNY1 and hnRNP proteins BLAST searching was done within NCBI or on Phytozome [16] using Chlamydomonas TNY1 protein sequence as a query to find high-scoring hits in plants, green algae and holozoans. Tandem RNA binding domain proteins are found in most eukaryotes, with several representa- tives besides TNY1 within Chlamydomonas. However, the top BLAST hits for TNY1 were found outside of Chlamydomonas as single best hits within other species of green algae, including three representative volvocine algal species (Gonium pectorale, Tetrabaena socialis, Volvox carteri). The sequences were aligned using MAFFT within Guidance2 [45], and the well-supported portion of the alignment of 158 residues containing the RNA binding domains was retained for phylogenetic analysis. Some duplicates and very closely related sequences were removed to reduce redundancy, with a final group of 39 proteins used for phylogenetic reconstruction. Evolutionary models were tested using Modeltest-NG [46], with the best model being LG+G(1.46)+I(0.08). A maximum likelihood phylogeny was estimated using W-IQ-tree [47] with approximate likelihood ratio testing of branch support. 3’UTR analysis Data on 3’ UTR length and nucleotide composition were extracted from the v5.6 genome assembly and gene models available on Phytozome [16]. 3’ UTR sequences from predicted PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 17 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas primary transcripts at each protein coding locus were used to determine length distributions and nucleotide composition. The length data were comparable to those from a prior analysis done with an earlier version of the genome assembly and gene models [48]. TNY1 antibody generation A full length TNY1 cDNA was amplified with primers TNY1-1F and TNY1-1R (S2 Table) from cDNA prepared using RNA from wild-type strain CC-124 and inserted into pGEM-T easy vector (Promega) to generate pGEM-TNY1. After verification by Sanger sequencing the TNY1 cDNA fragment was released by digestion with NdeI and XhoI (NEB) and inserted into vector pET28a (Sigma-Aldrich) digested with NdeI and XhoI. The construct was transformed into E.coli strain BL21 codon plus-RIL (DE3) (Agilent technologies). Induction of recombi- nant TNY1 expression in E. coli and purification of insoluble 6xHis-TNY1 was performed under denaturing conditions as described previously [14]. Purified 6xHis-TNY1 was cut out from a Coomassie blue stained SDS-PAGE gel and sent to Cocalico Biological Inc. to generate rabbit polyclonal anti-sera. Polyclonal antibodies were affinity purified with AminoLink Plus Resin (Thermo Fisher) coupled to purified GST-TNY1 (see below). Protein extraction Chlamydomonas cultures were grown as described above and harvested by centrifugation at 4000g for 5 min after adding Tween-20 to a final concentration of 0.005%. Pellets were washed in PBS and resuspended in lysis solution (1xPBS pH 7.4, 1x Sigma plant protease Inhibitor, 5 mM Na3VO4, 1 mM NaF, 1 mM Benzamidine, 500 mM PMSF, 1 μM ALLN, 1 μM MG-132) to a final concentration of 5x108 cells/mL, and immediately frozen in liquid nitrogen. Pellets were thawed quickly and placed on ice. Resuspensions were then processed with a Covaris ultrasonicator (peak power 150, duty factor 150, cycle 200, treatment 120 sec) to generate pro- tein lysates. Immunoblotting Lysate quantity for loading in each lane was determined from measuring cell number from each sample prior to preparation and protein concentration of the lysate (see below). Between 9 and 18 ug total protein were loaded per lane for equal protein loading, and 5x104 synchro- nous G1 phase cells (or same volume of culture for mitotic time points) for equal cell number loading. Protein lysates were mixed 5:1 with 6X SDS protein loading buffer and boiled for 10min. Lysates were cleared by centrifugation at 12,000 g for 10 min. Total protein was sepa- rated on 12% SDS-PAGE gels and wet-transferred to PVDF membranes at 50 Volt for 1 hr. When TotalStain Q (PVDF) was used, membranes were stained according to manufacturer’s instructions immediately after transfer. After quantitation of total protein using TotalStain Q staining (see below), membranes were blocked in PBS containing 9% nonfat dry milk for 1 hr at RT, then incubated overnight for 16hrs at 4˚C with primary antibodies (1:5000 α-TNY1, 1:10,000 Roche α-HA high affinity 3F10, 1:50,000 Sigma-Aldrich α-Tubulin, or 1:50,000 Invi- trogen α-Histone H3) in 5% non-fat dry milk. Membranes were then washed in PBS contain- ing 0.1% Tween 4 x 15 min, incubated at room temperature with secondary antibodies coupled to horseradish peroxidase (1:20,000 Thermo Fisher goat-anti-rabbit, or 1:20,000 Milli- pore Sigma goat-anti-rat in 5% nonfat dry milk). Membranes were washed again in PBS con- taining 0.1% Tween 4 x 15 min, then subject to chemiluminescent detection using autoradiographic film or a Bio-Rad quantitative imaging system (Chemi Doc XRS+ Imaging System) for quantitative experiments (see below). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 18 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Total protein quantification Protein input for cell lysate was determined using a Pierce BCA Protein Assay Kit (Thermo Fisher Scientific) with bovine serum albumin as a standard. TotalStain Q (PVDF) (Azure Bio- systems) staining was performed for total protein input quantification following the manufac- turer’s protocol using a Sapphire FL Biomolecular Imager (Azure Biosystems). Fluorescent signal in a range of 9 to 75 μg of input lysate used for quantitative experiments was close to lin- ear (S6A and S6B Fig). An image with the longest exposure settings yet without pixel satura- tion was taken. All the lanes were then automatically detected, along with an empty lane on the same blot to represent the background signal. The TotalQ signal in each lane was computed as (total signal-background). All quantitative blots were arbitrarily rescaled with a maximum value of 1. TotalStain Q (PVDF) is compatible with subsequent immunoblotting and chemilu- minescence detection. Immunoblotting signal quantification Bio-Rad Image-Lab (PC version) software was used for image capture and processing. “High Sensitivity ChemiBlot” setting was used with accumulated exposure time (300 seconds/100 images) to take a stack of 100 raw images with different exposure times. The image with the longest exposure time but no pixel saturation was chosen for signal measurement. Under “Vol- ume Tool,” “Rectangular” boxes were drawn to outline each band, along with a control region above or below the band to control for background signal. The signal for each lane was the computed as (boxed band signal-background signal). For some experiments histone H3 was used as a control for cell numbers. Tubulin or TotalStain Q were used as loading controls for protein input. Immunoblot signals were relatively linear using α-TNY1, α-Tubulin, and α-His- tone H3 antibodies in a range of 2–16 μg total protein per lane (S6C Fig). qRT-PCR Total RNA samples were extracted at different time points from synchronized strains using a Trizol-like reagent following the method of [13] then digested with RNase-free Turbo DNase following the manufacturer’s protocol. 4 μg total RNA was reverse transcribed with oligo dT and random hexamers (9:1) using Thermo Script Reverse Transcriptase at 25˚C for 10 min, 42˚C for 10 min, 50˚C for 20 min, 55˚C for 20 min, 60˚C for 20 min, 85˚C for 5 min. SYBR- Green based qPCR reactions in two technical duplicates of two biological replicates were per- formed and quantitated in a Bio-Rad CFX384 system. Each 10 μL reaction contained 0.1 μL cDNA, 1x Invitrogen Taq buffer, 3.5 mM MgCl2, 0.5x SYBR Green I, 0.05% Tween 20, 0.05 mg/mL BSA, 5% DMSO, 200 μM dNTPs, 0.3 μM primers, and 5U of Invitrogen Taq DNA polymerase. Expression was normalized against GBLP (GenBank NC_057009.1) as an internal control. The melting curve was examined for each reaction to ensure that no primer dimers or non-specific PCR products were present. qPCR experiments were performed targeting CDKG1, TNY1, and GBLP (S2 Table). Light microscopy Chlamydomonas cells were fixed in 0.2% glutaraldehyde final concentration. Cells were mounted on slides and imaged with a Leica DMI 6000 B microscope with a 63x oil objective (NA 1.40) and DIC optics with images taken using a Photometrics Coolsnap HQ2 CCD camera. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 19 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Immunofluorescence microscopy Wild type CC-125, TNY1-HA::tny1, HA-gCDKG1:: cdkg1-2 [15], or HA-gCDKG1:: cdkg1-2 tny1-1 strains were synchronized as described above on a 14hr light: 10hr dark diurnal cycle. S/M phase cells were collected at ZT 15 hrs and daughter cells at ZT 23 hrs. Cells were centri- fuged and collected in an Eppendorf tube, fixed with 2% paraformaldehyde in PBSP (1x PBS pH7.4, 1 mM DTT, 1x Sigma plant protease inhibitor cocktail) for 30 min on ice. Fixed cells were extracted in cold methanol 3 x 10 min at -20˚C and rehydrated in PBSP for 30 min on ice. Cells were blocked for 30 min in blocking solution I (5% BSA and 1% cold water fish gela- tin in PBSP) and 30 min in blocking solution II (10% goat serum, 90% blocking solution I). Cells were incubated overnight with primary antibody α-HA Roche HA high affinity 3F10 (1:1000 dilution in 20% blocking solution I) at 4˚C, then washed 3 x 10 min in 1% blocking solution I at room temperature. Cells were then incubated with 1:1000 Alexa Fluor 568 conju- gated goat anti-mouse IgG in 20% blocking solution I for 1 hr at 4˚C and then incubated with 4’,6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI) at a final concentration of 5ug/mL for 5 min. Cells were washed in 1 x PBS for 3 x 10 min. Cells were mounted in 9:1 Mowiol: 0.1% 1, 4-phenylenediamine (PPD), and imaged with a Leica DMI 6000 B microscope with a 63x oil objective (NA 1.40) and a Photometrics Coolsnap HQ2 CCD camera. Fluorescence illumination was provided by a metal halide lamp (Prior Lumen 200 Fluorescence Illumination Systems) using a Leica A4 filter cube (ex 360/40; em 470/40) for DAPI imaging and TX2 filter cube (ex 560/40; em 630/75) for detection of HA-TNY1 or HA-CDKG1. Confocal Immunofluorescence microscopy HA-gCDKG1:: cdkg1-2 or HA-gCDKG1:: cdkg1-2 tny1-1 cells were stained and mounted in microscopy slides as described above. Cells were imaged using a Leica SP8-X confocal micro- scope equipped with a white light laser and a 405 nm diode laser using 63x/1.20 water objec- tive. DAPI DNA staining was detected using a Leica HyD detector with 405 nm excitation and a 440–470 nm emission window. HA-CDKG1 was detected using 578 nm excitation and a and a 580–620 nm emission window. Frame average = 1. Line average = 16. Frame accumula- tion = 3. Line accumulation = 1. Bright Field images were captured using a PMT trans detector. Construction of a TNY1-mCherry expressing strain To generate a fluorescence protein-tagged tny1 complemented strain, a pTNY1: gTNY1-GFP-TNY1 3’ UTR construct was generated first. Chlamydomonas codon optimized GFP fragment (SpeI-SacI-BamHI-GFP-Xba-Xho-EcoR-NcoI) was amplified from pMF124cGFP [49] and digested by SpeI and NcoI, followed by insertion into RPL23:Luc: RPL23 which is digested by XbaI and NcoI. A fragment of pTNY1:gTNY1, including the pro- moter region, 5’UTR, and exons and intron of genomic TNY1, was amplified and digested with SacI and BamHI, and inserted into the above modified GFP plasmid. TNY1 3’UTR and terminator region was amplified and digested with XbaI and EcoRI, followed by insertion into the above pTNY1:gTNY1-GFP backbone. Chlamydomonas codon-optimized mCherry was amplified using a primer set of BamH1 mCherry F and XbaI mCherry R (S2 Table) from pLM006 [50], digested with BamH1 and Xba1, then used to replace GFP in the plasmid pTNY-GFP digested with BamHI and XbaI to create plasmid pTNY1-mCherry. pTNY1-m- Cherry was transformed into tny1-1 and rescued transformants were identified by measuring gamete sizes as described above and then confirmed by immunoblotting with α-TNY1 and measuring sizes of daughter cells. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 20 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Confocal live cell fluorescence microscopy pTNY1-mCherry expressing transformants were synchronized and harvested throughout the cell cycle. Live cells were immobilized on a very thin layer of TAP agar on a glass slide, and topped with a coverslip, which was sealed with PicoDent following the manufacturer’s instruc- tions (https://www.picodent.de/). Cells were imaged using a Leica SP8-X confocal microscope equipped with a white light laser and a 405 nm diode laser using 63x/1.20 water objective. TNY1-mCherry was detected using a Leica HyD detector with 570 nm excitation and a 550– 650 nm emission window. Frame average = 1. Line average = 16. Frame accumulation = 4. Line accumulation = 1. Fluorescence lifetime gating 0–4.9 ns was used to remove most of the chlorophyll background/bleed-through signals. Chlorophyll was detected using 405 nm excita- tion and a 676–704 nm emission window. Bright Field images were captured with a PMT trans detector. Native gel separation and detection of TNY1 RNP complexes 50 mL samples from Chlamydomonas cultures at 106 cells/mL were mixed with Tween-20 to a final concentration of 0.005% and collected by centrifugation at 4000 g for 5 min. Pellets were washed in PBS and resuspended in lysis solution (1xPBS pH 7.4, 1x Roche plant protease Inhibitor, 1 mM PMSF) to a final concentration of 5x108 cells/mL, and immediately frozen in liquid nitrogen. Pellets were thawed on ice and centrifuged at 12,000 g for 10 min at 4˚C. For RNA binding assays, 20 μL of supernatant was incubated with different RNase dilutions 1:10, 1:100 or 1:1000 (stock 10 mg/mL, NEB) or with 1:10 DNase I (stock 2 U/μL, Roche), and micrococcal nuclease (stock 2000 U/μL, NEB). 6 X SDS protein loading buffer without DTT nor SDS was added to samples before loading into a precast native 4–12% tris glycine gel (Invi- trogen) without SDS in Tris-Glycine running buffer. A mixture containing aldolase, BSA and ferritin was used as a molecular weight marker. Native PAGE gels were transferred to nitrocel- lulose membranes in 25 mM Tris, 192 mM glycine, 20% methanol. Blots were blocked in 1x PBS with 5% non-fat dry milk for 1h at room temperature and incubated with 1:2500 anti- TNY diluted in PBST (PBS + 0.05% Tween-20) with 3% dry milk at 4˚C overnight. After wash- ing in PBST for 3 10 min, the blot was incubated with horseradish peroxidase (HRP) conju- gated goat-anti-rabbit-IgG (1:5000, Pierce ECL) for 1hr at RT, then washed in PBST for 3* 10 min, and processed for chemi-luminescence (Luminata forte, Millipore). 32P RNA radio-labeling CDKG1 DNA for in vitro transcription was amplified from genomic DNA with oligos contain- ing a T7 promoter (S2 Table). 32P labeled RNA was generated/transcribed in vitro using a Maxiscript kit in the presence of α-32P-CTP (NEN Radiochemicals) according to manufac- turer instructions. Each 25 μL reaction had the following components: DNA template 0.5ug, 10x Transcription buffer 2 μL, 0.5 mM ATP, 10mM GTP 1 μL, 10mM UTP 1 μL, 500uM CTP 1 μL, 32P-CTP 2 μL (10 mCi/mL), 2 μL T7 RNA polymerase. After 1 hr reaction at 30˚C, the mixture was treated with DNaseI (ambion) and purified with Sigma post reaction clean-up columns SigmaSpin to remove unincorporated nucleotides. RNA integrity was visualized by separating a sample of the RNA on a urea denaturing 4% polyacrylamide gel followed by autoradiography. GST-TNY recombinant protein expression The TNY1 cDNA coding sequences were cloned into the Gateway pDEST15-GST (glutathione S-transferase) plasmid using the procedures recommended by the manufacturer (Invitrogen) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 21 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas with oligos listed in S2 Table. GST-TNY constructs were transformed into E.coli BL21 codon plus-RIL strain (Agilent Technologies). Cells were grown in LB media and induced for 5 hrs at 30˚C with 0.5 mM isopropyl-β-d-thiogalactopyranoside (IPTG) when cultures reached an O. D.600 of 0.5. After induction, cells were harvested by centrifugation and dry cell pellets stored at -80˚C. Frozen cells were thawed on ice and resuspended in 1/10th original culture volume of EB (100 mM Tris-HCl, pH 8.0, 500 mM NaCl, and 10 mM imidazole), sonicated eight times for 2 min each on ice with a Branson sonicator (50% power with a duty cycle of 0.5s on and 0.5 s off) followed by supernatant clearance by centrifugation at 12,000g for 10 min. GST-TNY recombinant proteins were purified from the soluble fraction using Glutathione Sepharose beads (Amersham) following the product manual. TNY1 RNA binding assay Equal amounts of GST purified proteins estimated based on Ponceau S staining were separated by SDS-10% PAGE and transferred to a nitrocellulose membrane (0.22-m pore size) and stained with homemade Ponceau S. The membrane was incubated at 4˚C overnight with rena- turation buffer: 50 mM tris-HCl pH 7.5, 100 mM KCl, 1% Triton X-100 and 10% glycerol. After renaturation, the membrane was incubated for 1 hr with reactivation buffer (Tris-HCl pH 7.5, 0.1% triton X-100, 10% glycerol) at room temperature, blocked for one hour with yeast tRNA (80 μg/mL) in reactivation buffer followed by incubation with 32P labeled RNA in reacti- vation buffer for 3 hrs. Membranes were washed 4X with reactivation buffer and exposed to X- ray film for 2 days at -80˚C before development. Supporting information S1 Fig. Characterization of tny1-1 and rescued tny1-1 strains. (A) Plot showing passage through Commitment (Commitment %, solid lines) and mitotic index (fraction dividing %, dashed lines) of synchronous tny1-1, wild type CC-124, and a tny1-1 rescued strain gTNY1:: tny1-1 collected at indicated time points during a synchronous diurnal cycle. Grey dotted line marks the time when 50% of the cells had passed Commitment (~ZT 6 hrs). (B) Plot of modal cell sizes for cultures in panel (A). Grey dotted line marks at ZT 6hrs, ~50% of the cells had passed Commitment in all the genotypes. Commitment sizes for each genotype: tny1 ~ 80 μm3, wild type and gTNY1::tny1-1 ~ 200 μm3. (C) Division number profiles of tny1-1 and wild type CC-124. Cells from synchronized cultures were collected at indicated times, plated on minimal media, incubated in the dark, and scored for cell division number (see Methods). ~100 clusters were scored for each genotype at each time point. Two independent repeats were plotted side by side (rep1 and rep2). (D) Division number profiles of size-matched G1 phase cultures of tny1-1 and wild type cells (~230 μm3) taken at different time points in G1 to enable tny1-1 cultures to reach the same size as wild type. A summary of the results is presented in the table. (TIF) S2 Fig. Characterization of tny1-1 and rescued tny1-1 strains (continued-1). (A) Statistics on log2 transformed size histogram data for synchronous daughter cells (ZT 0 equivalent) of size mutants and wild type. (B) Statistics on log2 transformed size histogram data for synchro- nous tny1-1 and wild type CC-124 in G1 phase at different ZT hrs. (TIF) S3 Fig. Characterization of tny1-1 and rescued tny1-1 strains (continued-2). (A) Plot show- ing timing of Commitment for indicated genotypes similar to panel S1A Fig. Grey dotted lines mark Commitment timing of cdkg1-2 or tny1-1 cdkg1-2 and wild type. (B) Plot of modal cell PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 22 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas sizes for cultures in panel (S3A Fig). Grey dotted lines mark cell sizes of strains showing that cdkg1-2 and tny1-1 cdkg1-2 have similar Commitment sizes as wild type. cdkg1-2 and tny1-1 cdkg1-2 pass Commitment at an earlier ZT. Commitment sizes for each genotype: tny1 ~ 80 μm3; wild type, gTNY1::tny1-1, tny1-1 cdkg1-2, and cdkg1-2 ~ 200 μm3. (C) Linkage between paromomycin insertion (Fig 1A) and small size phenotype. Each data point represents the modal size of a population derived from an independent meiotic progeny of tny1-1 crossed to wild-type strain CC125 and grouped according to their paroR (tny1-1 insertion) or paroS (TNY1) phenotypes. Box and whisker plots of modal gamete sizes for paroS (n = 44) or paroR (n = 46) progeny. Boxes enclose the second quartile of data with horizontal lines showing median values, and whiskers enclose the 10th - 90th percentiles. Outliers are plotted as individ- ual data points. The size distributions were significantly different in a Student’s t-test (, p<0.01). (D) Validation of genotyping primers for tny1-1, TNY1, and mating type loci (mating type minus, mt-; mating type plus, mt+) (see S2 Table). (E) Growth on selective media for tny1-1 (paromomycin resistance marker; Paro) and tny1-1 with rescuing constructs (with hygromycin resistance markers, Hyg). (TIF) S4 Fig. Multiple sequence alignment of green algal TNY1 orthologs. Peptide alignments for subset of proteins from Fig 2: Chlamydomonas reinhardtii TNY1 (Cre07.g330300), Volvox car- teri (Vocar.0031s0001), Chromochloris zofingiensis (Cz12g11070), and Dunaliella salina (Dusal.0065s00006). Gene IDs are from Phytozome [16]. Alignment is shaded to show con- served residues. Positions of RNA recognition motifs 1 and 2 (RRM1, RRM2) and a conserved C-terminal motif (CM) are marked. The inverted black triangle shows the position of the single intron found in TNY1 orthologs in the green algal subclade. (TIF) S5 Fig. Detection of TNY1-mCherry expression in gTNY-mCherry:tny1-1 strains. (A) Immunoblots with whole cell lysates of daughter cells from indicated genotypes. The gel was loaded with equal protein per lane, fractionated by SDS PAGE, and immunoblotted using α- TNY1 (upper panel). Coomassie blue (CBB) staining is shown in the lower panel as a loading control. (B) Size distributions of daughter cells from tny1-1 (median size 55 μm3/modal size 46 μm3), a tny1 rescue strain gTNY1:tny1-1 (median size 79 μm3/modal size 70 μm3), and two independent mCherry tagged rescue TNY1-mCherry::tny1 strains (strain c2.2 median size 81 μm3/modal size 83 μm3, strain c2.6 median size 86 μm3/modal size 79 μm3). Median sizes of TNY1-mCherry::tny1 transformants and gTNY1:tny1-1 rescued strains are not different (p>0.1, Student’s t-test) (S1 Table). (C) Fields of TNY1-mCherry::tny1 cells, along with gTNY1::tny1 cells as the negative control under the same detection settings. Annotation is the same as Fig 3. Scale bar = 20 μm. (D) DIC and immunofluorescence microscopy images of wild type CC-124 and gTNY1-HA::tny1-1. Daughter cells were fixed and immunostained for HA epitope (pseudo-colored green). DNA was stained with DAPI (pseudo-colored red). Merged fluorescence images (Overlay). Scale bar = 10 μm. (TIF) S6 Fig. Cell cycle and diurnal control of TNY1 mRNA and TNY1 protein accumulation. (A) and (B) Protein lysates were made using indicated cell numbers of wild-type daughter cells. Protein quantity was determined using a standard BCA kit with two replicates for each standard concentration. TotalStain Q staining signal across a range of loading amounts are documented with two replicates per sample. The band of the highest signal was set to be 1 in each blot. (A) Protein loading range used for most experiments with 9–30 μg/lane. (B) Expanded total protein loading dilution series with 9–75 μg/lane. The grayscale images were of PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 23 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas the longest exposures without any saturated pixels. Linear regression lines (grey) are plotted for each data series. (C) Immunoblots with protein lysates made using indicated cell numbers of wild type daughter cells. Over the normal protein loading range the signals of α-TNY1, α- Histone H3, α-Tubulin are approximately linear. The band with the highest signal was set to 1 in each plot. Two independent replicates were plotted side by side (rep1 and rep2) with linear regression plotted from the average of the two repeats (black dots) for each antibody. (D) Rep- resentative size distributions of a synchronous wild type strain CC-124 at different ZT time points throughout a standard 12hr:12hr light:dark cycle. Protein lysates at each ZT were col- lected for immunoblots. (TIF) S7 Fig. Cell cycle and diurnal control of TNY1 mRNA and TNY1 protein accumulation (continued). (A)–(E) Immunoblot repeats as described in Fig 4B. Three biological replicate sets with two technical repeats are included as follows: biological replicate set 1—S7A and S7B Fig; biological replicate set 2—Figs 4B and S7C; biological replicate set 3—S7D and S7E Fig. (F) Data were plotted as in Fig 4C with the inclusion of total protein (grey bars) from Total- Stain Q staining with the ZT1 value set to 1. Bar values/dots represent the average of three bio- logical repeat sets with two technical replicates each. Error bars: standard deviation of three biological replicates. (G) Size distributions of mitotic populations at ZT 15 under different diurnal regimes in Fig 4A. Standard regime at ZT 15, mean cell size 511 μm3. Early dark regime at ZT 15, mean cell size 341 μm3. Extended light regime at ZT 15, mean cell size 581 μm3. (TIF) S8 Fig. Dosage sensitivity of TNY1. (A) Daughter size distributions (ZT 0 equivalent) of dark-shifted size mutants compared with a wild-type strain. mat3-4/rbr (median size 37 μm3/ modal size 28 μm3), wild type (median size 73 μm3/modal size 75 μm3), dp1-1 (median size 111 μm3/modal size 123 μm3), and cdkg1-2 (median size 114 μm3/modal size 108 μm3). (B) Immunoblot of samples in Fig 5A with gel loading by equal protein per lane with signal quan- titation shown in bar plots below. Annotation is the same as Fig 5A. (C) Left panel, box and whiskers plots of modal gamete sizes of populations derived from a back-cross between wild type CC124 and rescued strains gTNY::tny1-1 (left side) or gTNY::tny1-1 (right side). Each data point represents the modal size of a gamete population derived from an independent mei- otic progeny. Numbers of progeny for each genotype sampled are listed in the table above each plot. Boxes enclose the second quartile of data with horizontal lines showing median values, and whiskers enclose the 10th - 90th percentiles. Outliers are plotted as individual data points. Comparisons among the four genotypes were done using a one-way ANOVA with post-hoc Tukey HSD Test. , samples are different at p < 0.01; n.s., samples are not significantly differ- ent (p>0.05). (D) Plot showing timing of passing Commitment for indicated genotypes, simi- lar to S1A Fig. Grey dotted lines mark Commitment timing of tny1-1, wild type, and a RPL23: TNY tny1-1 strain with a large size phenotype. Plot of modal cell sizes for cultures in panel (D). Grey dotted lines mark cell sizes of strains in panel (E) showing that tny1-1, wild type, and the RPL23:TNY tny1-1 strain pass Commitment at about the same ZT. Commitment sizes for each genotype: tny1 ~ 80 μm3, wild type ~ 200 μm3, RPL23:TNY tny1-1 #1 ~ 250μm3. (TIF) S9 Fig. TNY1 inhibits the accumulation of CDKG1 protein. (A) Size distributions of syn- chronous mitotic (ZT 13) and post-mitotic (ZT 1) populations of indicated strains. (B) Immu- noblots using synchronized strains of indicated genotypes loaded with equal numbers of cells per lane and probed with α-HA to detect HA-CDKG1 or stained with Coomassie blue (CBB). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 24 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas (C) Immunofluorescence images of HA-CDKG1::cdkg1 and HA-CDKG1::cdkg1 tny1 post- mitotic cells (ZT 1) as described in Fig 6C. Scale bar = 10 μm. (TIF) S10 Fig. Systems level comparison of cell size control across taxa. Cell cycle inhibitors sub- scaling with cell size in G1 phase are highlighted in bold red. (TIF) S1 Table. Size distribution statistics for selected strains used in this study. (XLSX) S2 Table. Oligonucleotides used in this study. (XLSX) S1 Data. Coulter Counter size distribution files for Figs 1B, 1C, 5B, 5D, S1C, S1D, S2A, S2B, S5B, S6D, S7G, S8A, and S9A. (XLSX) S2 Data. Immunoblot quantification for Figs 4B and S7A–S7E. (XLSX) Acknowledgments We thank Tuya Wulan, Fuqin Sun, Richard Davenport, Thomas Connell, Kerri Husa, Nazifa Hoque, Jie Li, Dylan Wetzel, Hunter Draffen, Brooke Harris, Zach Jaudes, Ashley Cloud, and Chris Reynolds for laboratory support. We thank Dr. Rebecca Bart, Ke Ke, Marisa Yoder, Dr. Dmitri Nusinow, and Dr. He Huang for the training on Bio-Rad Image-Lab software and Chemi Doc XRS+ Imaging System. We thank Dr. Dmitri Nusinow and Dr. He Huang for the training on BioRad CFX Manager Software and BioRad CFX384 qPCR machine. We thank Dr. Keith Slotkin and Dr. Yu-Hung Hung for the training on Sapphire FL Biomolecular Imager IS4000 and TotalStain Q (PVDF) kit. We thank Dr. Kirk Czymmek, Dr. Anastasiya Klebanovych, and Dr. Howard Berg for the guidance and assistance on microscopy. We thank Dr. Mao Li for the suggestions and assistance on statistical analyses and Dr. Peipei Sun for assistance with genomic statistics. Author Contributions Conceptualization: Dianyi Liu, Cristina Lopez-Paz, James Umen. Data curation: Dianyi Liu. Formal analysis: Dianyi Liu, James Umen. Funding acquisition: James Umen. Investigation: Dianyi Liu, Cristina Lopez-Paz, Yubing Li, Xiaohong Zhuang. Methodology: Dianyi Liu, Cristina Lopez-Paz, Yubing Li, James Umen. Project administration: Dianyi Liu, James Umen. Supervision: James Umen. Validation: Dianyi Liu, Cristina Lopez-Paz. Visualization: Dianyi Liu, Cristina Lopez-Paz, James Umen. Writing – original draft: Dianyi Liu. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1010503 March 18, 2024 25 / 28 PLOS GENETICSSubscaling of a cytosolic cell cycle inhibitor governs size homeostasis in Chlamydomonas Writing – review & editing: Dianyi Liu, Cristina Lopez-Paz, Yubing Li, Xiaohong Zhuang, James Umen. References 1. Amodeo A A, Skotheim J M. Cell-Size Control. Cold Spring Harb Perspect Biol. 2016 Apr 1; 8(4): a019083. https://doi.org/10.1101/cshperspect.a019083 PMID: 26254313 2. Facchetti G, Chang F, Howard M. Controlling cell size through sizer mechanisms. Curr Opin Syst Biol. 2017; 5:86–92. https://doi.org/10.1016/j.coisb.2017.08.010 PMID: 32984663 3. D’Ario M, Sablowski R. Cell Size Control in Plants. Annu Rev Genet. 2019 Dec 3; 53(1):45–65. https:// doi.org/10.1146/annurev-genet-112618-043602 PMID: 31430180 4. D’Ario M, Tavares R, Schiessl K, Desvoyes B, Gutierrez C, Howard M, et al. 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10.1371_journal.pcbi.1011775	RESEARCH ARTICLE Inferring country-specific import risk of diseases from the world air transportation network Pascal P. KlamserID Clara Jongen1,2, Frank Schlosser1,2, Dirk BrockmannID 1,2, Adrian ZachariaeID 1,2, Benjamin F. MaierID 1,2,7* 1,2,3,4, Olga Baranov5,6, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Klamser PP, Zachariae A, Maier BF, Baranov O, Jongen C, Schlosser F, et al. (2024) Inferring country-specific import risk of diseases from the world air transportation network. PLoS Comput Biol 20(1): e1011775. https://doi.org/ 10.1371/journal.pcbi.1011775 Editor: Yamir Moreno, University of Zaragoza: Universidad de Zaragoza, SPAIN Received: May 3, 2023 Accepted: December 21, 2023 Published: January 24, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pcbi.1011775 Copyright: © 2024 Klamser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The software “ImportRisk-v1.0.0” to compute the import risk is available under the Zenodo repository https://doi. org/10.5281/zenodo.7852476. 1 Department of Biology, Institute for Theoretical Biology, Humboldt-Universita¨t zu Berlin, Berlin, Germany, 2 Robert Koch Institute, Berlin, Germany, 3 DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark, 4 Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark, 5 Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany, 6 German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany, 7 Center Synergy of Systems (SynoSys), Center for Interdisciplinary Digital Sciences, Technische Universita¨t Dresden, Dresden, Germany * dirk.brockmann@tu-dresden.de Abstract Disease propagation between countries strongly depends on their effective distance, a mea- sure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country—essentially, the likeli- hood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the ‘import risk’ model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak’s origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model’s precision increases for countries with stronger connections within the WAN, and it reveals a geo- graphic distance dependence that implies a pull- rather than a push-dynamic in the distribu- tion process. Author summary For the spread of a contagious disease, human mobility puts distant places in proximity and geographically closer targets may be effectively much further away. The worldwide flight network is crucial for long distance travels and the previously proposed ‘effective PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 1 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Funding: B.F.M received funding through Grant CF20-0044, HOPE: How Democracies Cope with Covid-19, from the Carlsberg Foundation and was supported as an Add-On Fellow for Interdisciplinary Life Science by the Joachim Herz Stiftung. P.P.K, A.Z, F.S received funding through Grant D81870, COVID-19 Lockdown-Monitor, from Germany’s Federal Ministry of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors declare no competing interests. distance’ translates this mobility into a distance measure that correlates with the disease arrival time. We use the effective distance to generate a bottom-up and thus parameter- free distribution process of passengers on the flight network, which takes into account all possible flight routes. This allows us to determine the import probability of a disease. Our ‘import risk’ model outperforms or matches established mobility models, some of which require calibration with scarce or costly data. In contrast, our approach relies on minimal flight network data, that is the number of planes between airports and their passenger capacities, but not on passenger data. Its bottom-up approach enables future studies on country-specific measures for controlling and containing infected passengers, a challenge with existing models. Thus, the ‘import risk’ model’s strength lies in its data simplicity, this relevance to pandemics, and parameter-free design. Introduction The recent decades have seen a considerable increase in mobility: The worldwide number of passenger cars in use increased by an average of about 4% each year between 2006 and 2015, reaching approximately 1 billion in 2015 [1]. This growth is comparable to the yearly increase in the number of sea containers shipped [2], and the global scheduled air passenger count also experienced an annual growth of about 6% between 2004 and 2019 [3] In essence, the world is becoming increasingly interconnected in terms of passenger mobility, both on a small scale (cars) and a large scale (air traffic), as well as in the import and export of goods. This height- ened connectivity facilitates the distribution of goods and people, as demonstrated by the dis- tribution of over 400 invasive species through agricultural imports, which is best predicted by the global trade network [4]. A prime example of unwanted side effects of well-connected regions is the potential for pandemics, accompanied by death, economic damage and the potential stigmatization of survivors, migrants and minorities [5–7]. Already the first plague pandemic that started AD 541 in the Nile Delta of Egypt spread in 8 years across the territories (Mediterranean, Northern Europe and Near East) of 2 affected empires because of the intense commerce in the Roman Empire [6]. Nowadays, the intensified exchange reduces the time until a pandemic reaches all parts of the world to months as for the 2009 H1N1 virus that spread from Mexico in 5 months to all continents [8, 9] or the recent COVID-19 pandemic whose variants spread within a few months across the globe [10–13]. The connection strength between world regions is only partly explained by their geographic proximity. Instead, due to historic geopolitical relations [14, 15] pandemics spread rather along an effective distance that is derived from the world air transportation network (WAN) [16–19], or, if applied on a smaller scale, also from other means of transportation [16, 20]. According to the effective distance, region B is closest to region A if the passenger flow from A to B is greater than to other destinations. An intriguing extension is the multipath effective dis- tance, which enhances the prediction of disease arrival times by considering all paths taken by a random walker on the WAN [17]. The effective distance is regularly used to analyze the impact of mobility on the spread of diseases, as for example for MERS [21], Ebola [22], Zika [23] and most recently COVID-19 [20, 24–26]. While it enables a qualitative estimation of dis- ease arrival times, its applicability is severely restricted when it comes to describing the impor- tation of infected passengers from a specific source to a target. However, these import events are highly relevant for political decision-makers and to enable modeling predictions. In this work, we describe these import events via the “import probability” p(B\|A), which is equivalent to the origin-destination (OD) matrix whose element TBA represents the number of PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 2 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN trips from A to B, with the difference that the probability is normalized by all trips starting in A, i.e. p(B\|A) = TBA/TA. There are mobility models that fit the OD matrix, requiring a reference OD matrix as seen in the gravity model [27–31]. Additionally, some models integrate OD matrix-fitted models on a smaller scale with the OD matrix of the global air transportation net- work, creating a multiscale mobility network to represent all modes of transportation [32, 33]. Note that the multiscale mobility model has been successfully employed to analyze past pan- demics [34–36]. Yet, it can be extremely difficult to obtain the OD matrix and most often it is estimated by small surveys [37] or alongside a census [38]. Even for the air transportation net- work derived from a booking system, the OD is only an approximation since passengers increasingly book directly at the airlines (in 2015 30% of all Lufthansa flights were booked directly which increased to 52% in 2018 [39]) and not via the big GDS (global distribution sys- tems) from which most OD-estimates are derived [40, 41]. This means that to exactly compute the air transportation OD matrix, bookings of all GDSs and about 900 airlines must be pur- chased/estimated and combined. Thus, models that do not rely on an existing reference OD matrix are important and those either assume an underlying decision process without integrat- ing traffic information as the radiation model [42, 43] or they apply a maximum entropy approach to distribute the unknown OD trips along possible routes of a known traffic network [30, 44, 45]. However, none of the above approaches use the effective distance with its qualita- tive link to disease propagation and none is based on a mechanistic distribution process on a traffic network. To our understanding, a mechanistic process mimics the detailed movement behavior of the passengers on the traffic network, and neither uses only quantities of and between the locations (gravity and radiation model) nor relies on principles of system in ther- modynamic equilibrium (maximum entropy model), in other words it is a bottom-up approach. This approach grants us a mechanistic understanding of the observed patterns, enabling us to investigate how modifications impact passenger distribution. For instance, we can analyze how containment interventions along distribution routes reduce the import prob- ability of infected passengers. In this work, we introduce the import risk model, based on a distribution process following the shortest path tree of the WAN based on effective distance. This process is combined with a random walker that explores all potential paths within the WAN. We are using WAN data from the year 2014 and compare it to the Global Transnational Mobility Dataset from 2014 [40], as a ground truth baseline. Additionally, we investigate the discrepancy to the import risk and alternative mobility models as the gravity [27, 31] and radiation model [43] through multi- ple comparison measures. We find that the import risk model outperforms the alternative models and improves only slightly when it includes not only WAN information but also the geodetic distance between airports. Lastly, we evaluate the quality of import probability estima- tion for specific countries and assess if and how the geodesic distance is encoded in the import risk estimate. Results Relating the WAN, OD-probability and the effective distance In this work, we introduce the import risk, which estimates the probability of a passenger departing from airport A to conclude their journey at any airport worldwide, even those not directly connected to the origin airport. The estimation is based on the traffic flow of airplanes and the respective maximal passenger capacity between airports, a.k.a. the world air transpor- tation network (WAN), provided by the Official Airline Guide (OAG) [46]. This inference- problem is intriguing because it is much easier to monitor the origin and destination of air- planes, than of passengers with possibly multiple connecting flights until their final PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 3 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN destination. In our study, we use the WAN from 2014 (Fig 1A) and compare the derived import probabilities to a reference dataset. The reference import probability is based on the Global Transnational Mobility Dataset (GTN) from 2014 [40, 47], which combines an origin- final-destination dataset from a major global distribution system (GDS) with a tourism dataset from the World Tourism Organization (Fig 1B, see Material and methods for more details on the data). Before introducing the import risk model, we contrast the two datasets, introduce the effective distance [16] and quantify its potential as the base metric for our proposed model. By comparing the world air transportation network (WAN) with the country-specific refer- ence import probability from the GTN (compare Fig 1A and 1B), we see that the airports con- nected via direct links belong to countries that also have a high import probability. Nevertheless, due to physical constraints and logistical optimization, not all countries with non-zero import probabilities are directly connected to airports in the source country; instead, they are reached via connecting flights. In the context of import probability, estimates based on geodesic distance and the population of the target country are useful but exhibit limitations in certain scenarios. For instance, the import probability for Italy is approximately 1.4 times greater than that for Germany, even though Germany is geographically closer to Canada and Fig 1. The relation between WAN, OD-probability, SPT and effective distance. A: The world air transportation network (WAN) represents the direct flight connections and maximal seat capacities between airports in 2014, here shown for flights starting from five selected countries. It is based on flight-schedule-data. The lines are bundled and do not represent the specific flight route, but illustrate the links to airports abroad. B: The reference import probability from Canada to all countries, based on the OD matrix (Origin-Destination) of the Global Transnational Mobility Data set [40, 47] in 2014. It combines origin and final-destination trips between countries from the SABRE and the World Tourism Organization (UNWTO). The lines illustrate the connection to the common source country. C: Based on the effective distance deff = d0 − ln(p) a shortest path tree (SPT) is constructed with the largest Canadian airport as source (YYZ: Toronto Pearson International Airport). The link color and thickness shows the hop distance, i.e. number of connecting flights. D: exponential decay of the reference import probability (as in B but for all countries as source) with the effective distance deff (derived from the SPT (C) of the WAN (A)). Each dot represents a country-country link, the lines are medians including either all source countries or only from a specific continent. Maps are created with geopandas [48]. https://doi.org/10.1371/journal.pcbi.1011775.g001 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 4 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN has a larger population. The effective distance is an alternative network-based distance mea- sure that does not rely solely on direct connections and geographic information [16–19]. Instead, it is based on the passenger flow Fij from j to i and its relationship to the outflow Fj through the transition probability Pij = Fij/Fj. Together with a constant distance offset d0, the effective distance between directly connected airports is deffðij jÞ ¼ d0 (cid:0) lnðPijÞ : ð1Þ The effective distance between airports without direct connection is the cumulative distance along the shortest path tree (SPT) derived from deff, as illustrated for the largest Canadian air- port (Toronto Pearson Airport, YYZ) in Fig 1C. Note that a distance offset of d0 = 0 would make two routes indistinguishable as long as the product of the transition probabilities along each route is the same, but with d0 > 0 the one route with fewer connecting flights is effectively shorter. Previous studies have demonstrated that the arrival time of diseases in countries exhibits a linear dependence on their effective distance [16–19]. We show that the import probability also correlates with deff (Fig 1D), whereby the correlation is higher than for other distance measures (see Fig A in S1 Text). In fact, the import probability decays exponentially with effective distance (linear decay on a semi-log scale in Fig 1D) which can be reproduced in a simplified model for a passenger that travels at a constant effective speed and has a constant exit rate. Therefore, the effective distance seems to be a good representation of the underlying distribution process, and is a promising candidate for the base of our proposed import risk model, to directly estimate the import probability. Import risk model The idea behind the import risk model is a combination of two elements: (i) a random walk with an exit probability of the walker to finish its travel at the current node and (ii) a distribu- tion mechanism derived from the deff SPT (Fig 2). The use of a random walk is motivated by Iannelli et al. [17] who could improve the arrival-order prediction of deff by including all possi- ble paths. The exit probability enables us to combine the random walk with a distribution mechanism that assigns the likelihood of each node being the final destination, as explained in detail in the second step. In the first step, we use the transition network representation of the WAN and let a random walker start at source n0 and after each step it either exits at the current node i with exit probability qi or continues to walk. Let us define the walker’s probability to continue walking to node n given it was at node n − 1 before and originally started in n0 by Sn;n(cid:0) 1ðn0Þ ¼ Pn;n(cid:0) 1ð1 (cid:0) qn(cid:0) 1ðn0ÞÞ ; ð2Þ with Pn,n−1 as the transition probability from n − 1 to n. Now the probability to walk along a path Γ starting at n0 and exiting at n is the probability to continue walking Si,j along each link (i, j) that is part of the path times the exit probability of the final node pðGÞ ¼ qn Y ði;jÞ2G Si;j ; ð3Þ where we omitted the explicit dependence on the source n0. Our goal is to describe all possible paths the walker can take from n0 to n. We will use the matrix S, whose elements are the proba- bilities to continue walking Si,j. The element (i, j) of the product of the matrix with itself S � S = S2 sums over all paths of length l = 2 that end at i and start at j. Next, we can define the probability of a walker to exit at n after traversing all paths of length l as plðnjn0Þ ¼ qnðSlÞn;n0 : PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 ð4Þ 5 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Fig 2. Import risk scheme. Starting from the transition network (left) the shortest path tree is computed based on the effective distance (center bottom). Based on the shortest path tree, the exit probabilities q� = q(�\|?) are computed. In the formula, the geometric symbols represent the estimated population of the respective node, which can also be distance-weighted (depending on the exact model). A random walk-process with exit probability is defined (top): at each step, the walker either exits the node with prob. q� = q(�\|?), or continues walking with prob. (1 − q�). The import risk p1(�\|?) (right) is the probability of a walker to exit at node � given it started at node ? under consideration of all possible paths. https://doi.org/10.1371/journal.pcbi.1011775.g002 Finally, the import risk is the probability to exit at n given all paths of all lengths p1ðnjn0Þ ¼ qn ! X1 Sl l¼1 n;n0 ¼ qnððI (cid:0) SÞ(cid:0) 1 (cid:0) IÞn;n0 ; ð5Þ where we used the convergence of the geometric series with identity matrix I. In the second step, we approximate the exit probability qi(n0) that we used above, but did not specify yet. Thereby, we assume that passengers start at source airport n0, travel along the SPT and exit at node i with an exit-probability qiðn0Þ ¼ NðiÞ NðiÞ þ NðOðijn0ÞÞ ð6Þ with N(i) as the population at airport i and O(i\|n) as the set of all offspring nodes downstream of i on the SPT centered at source n0. Hence, the exit probability at node i is determined by the ratio of the population at node i to the combined populations of all downstream nodes of i on the SPT, inclusive of node i. We estimate the population at airport i using its outflow on the WAN, denoted as N(i) = Fi. To aggregate the import probabilities at the country level, we sum the targets and apply a weighted average to the source airports, with population serving as the weighting factor. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 6 / 26 PLOS COMPUTATIONAL BIOLOGY Infer import risk from the WAN To elucidate how additional information about the geographic distance between nodes influences p1, we explore two variations of the import risk model: In the variation with “geo- desic distance weighted” exit probability the populations in Eq 6 are substituted with ^N ðijn0Þ ¼ NðiÞ=di;n0 is the geodesic distance between i and n0. To control for increasing model complexity, we study the “effective distance weighted” exit probability, where ^N ðijn0Þ ¼ NðiÞ=deffðijn0Þ, i.e. no geographic information is used, but the model struc- ture is equivalent. , where di;n0 Alternative models. Numerous alternative models estimate the OD-matrix, from which the import probability can be derived [30, 31, 42, 43, 49–52]. Among those, the gravity [27] and the intervening opportunity [42, 43] model are most widely used. A recent variant of the latter is the radiation model [43]. Although past studies have found that the gravity model out- performs the radiation model at small scale [38, 53, 54], especially the radiation model’s good performance at the large scale [38, 54] makes it an interesting model for mobility on the WAN. It was originally conceptualized for commuter flows [43] where the surrounding populations serve as a proxy for possible job opportunities. By estimating an airport’s population based on its outflow, we adjust the concept from job opportunities to tourism opportunities. Its deriva- tion from a mechanistic decision process makes it parameter free, and therefore similar and a good comparison to our model. However, it only requires information on the population den- sity and does not integrate flight data. We compare our model to the gravity model with an exponential and power-law distance dependence and the radiation model (see Material and methods for definitions). These models solely rely on the outflow data from the WAN to estimate the node’s population and the geo- graphic locations. To incorporate structural information of the WAN [55], the alternative mod- els are also implemented with the geodesic path distance (the geodesic distance along the SPT) and the effective distance, i.e. there are in total nine alternative models: the radiation model, the gravity model with exponential and with power-law distance decaying function, and each implemented with geodesic, geodesic path and effective distance. The exponents of the six grav- ity models are fitted to the reference import probability by assigning the best fitting exponent to each of the six comparison measures (Pearson correlation, root-mean-square error, common part of commuters, Kendalls rank correlation and the correlation and RMSE of the logarithmic measures, all defined in Material and methods) and taking their mean value (see Figs B and C in S1 Text). As comparison measures, we have chosen three measures that are related to the absolute error and three that are related to the relative error between estimate and reference. Symmetry by returning visitors. Each of the twelve models provides an estimate for the import probability p(i\|n0), which is used to compute the OD-matrix T through multiplication with the corresponding source population N(n0). By comparing the symmetry of T with the reference OD-matrix ^T, we find a much higher and qualitatively different symmetry in the ref- erence data (see Supplementary Note B, Fig D in S1 Text). The high symmetry is likely due to visitors (family, business, tourism, etc.) that dominate the international travel. They return to their home-location after a limited period [56] and only the minority of the travelers are migrants, i.e. stay permanently at the destination. Interestingly, the import risk model has the highest symmetry, but is still less symmetric than the reference data by a factor of 4. Therefore, before conducting a detailed comparison of the estimates, we rectify the import probability estimates by symmetrizing their OD-matrix (by extracting the symmetric part and recalculat- ing the import probability; for further details, refer to Material and methods and Supplemen- tary Note B in S1 Text). This correction can be seen as an alternative version of a doubly constrained model where normally the constraints on in- and out-flow are ensured by an itera- tive proportionate fitting [31]. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 7 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Model comparison In the subsequent analysis, we evaluate the import probability estimates against the reference data through four approaches: (i) a direct comparison and assessment of their medians to identify potential systematic errors, (ii) the application of six distinct goodness-of-fit metrics to assess the individual model’s rank and relative performance, (iii) a classification task identi- fying countries with the highest import risk, particularly relevant in the context of a pandemic and (iv) a correlation study of the arrival time of 20 diseases and SARS-CoV-2 variants. Qualitative comparison. In Fig 3 the import probability estimate p(i\|n0) of each model is compared to the reference import probability ^pðijn0Þ. The gravity models exhibit the closest agreement with the reference data when the effective distance is employed, as indicated by the medians (Fig 3, first and second columns). In contrast, the median values of the radiation and import risk models are relatively stable and less influenced by variations in distance metrics or their associated weighting (third and fourth columns). All models overestimate the lowest median import probability (leftmost orange dot in Fig 3), since the estimated import probabil- ity is always nonzero, but a large proportion of the lowest reference import probabilities are zero due to the limited observation period and/or an insufficient number of departing passen- gers. The overestimation of the median import probability is observed up to p(i\|n0) � 10−4 for both the gravity and import risk models. However, this overestimation is notably absent in the case of the gravity model with an exponential distance decaying function and the effective Fig 3. Estimates of import probability by the gravity model with exponentially (1st column) and power law (2nd column) decaying distance function, the radiation model (3rd. column) and by the import risk model (4th column). The first three models (1st-3rd column) use as distance the geodesic (1st row), geodesic path (2nd row) and the effective (3rd row) distance. The import risk model is computed from the WAN with the geodesic distance (D) or the effective distance (L) as a weight for the exit probabilities or without weighting (H), i.e. in the last two cases (H, D) only WAN information is used. The orange line depicts the median and the gray line is y = x and illustrates perfect mapping. https://doi.org/10.1371/journal.pcbi.1011775.g003 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 8 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN distance metric (Fig 3I), where the median demonstrates the closest alignment with the refer- ence data. The radiation models (third column) systematically overestimates the highest import probabilities (p(i\|n0) >� 10−1) and consequently underestimates the lower import probabilities. Goodness of fit by multiple measures. We compared each model with the reference import probability via the Pearson correlation, the root-mean-square error (RMSE), and the common part of commuters. These measures are more sensitive to strong links, i.e. large import probabilities, which is important when the emphasis is placed on the countries that are most likely to import passengers. However, if the focus is to get a fair comparison including all links, logarithmic versions of the above measures or rank correlations are more appropriate. Thus, we also quantify the agreement by the correlation and the RMSE of the logarithm of the measures and by Kendall’s rank correlation. The three import risk model variations outper- form the other models in all but one measure, whereby the variation employing the geodesic Fig 4. Rank and relative performance of import risk estimation models. The different import probability models are compared via their rank (A) and relative performance (B), with the highest values representing the best approach. The rank and relative performance are shown for each (black dots) of the six comparison measures (corr, logcorr, RMSE, logRMSE, cpc, τKendall) the box illustrates the interquartile range, the horizontal line the median and the red triangle the mean. The colors of the boxes illustrate the different distance measures in use. The outlier measure of the import risk models (I.R.) is the logRMSE, where the gravity models with effective distance are performing best. See Material and methods for definitions of comparison measures and Figs E, F in S1 Text for absolute and detailed relative performance. https://doi.org/10.1371/journal.pcbi.1011775.g004 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 9 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN distance weighted exit probability performs best (Fig 4A). Following the import risk models, the two gravity models based on effective distance also exhibit strong rankings. In contrast, the remaining models lack consistent high rankings across all six measures and are more evenly distributed within the lower half. This model categorization also holds for the relative perfor- mance of the models (Fig 4B), with linear scaling of values in between (see Eq 22). In contrast to the rankings, the median relative performance shows a notable improvement when the gravity models incorporate effective distance. However, among the import risk models, the dif- ference in median relative performance remains marginal. The only measure where the import risk models are outperformed by the gravity models with effective distance is the logRMSE (Figs E, F in S1 Text). It is expected from the gravity models’ good agreement in median import probability with the reference data over wide ranges and the overestimation of low import probability by the import risk model. This overes- timation can be reduced by model-modifications that introduce parameters favoring the exit at nodes with large-populations (for details, see Supplementary Note C and Figs G, H in S1 Text). However, we refrain from adding complexity to the model, since its generic nature is its key aspect. Classification of ten top risk countries. In a pandemic context, it is of specific interest to identify the countries with the highest import probability. We analyzed how well the twelve proxy models can classify, if a country is among the ten countries with the highest import probability. Again, the import risk models outperform the other models and the one with geo- desic distance-weighted exit probabilities is the top predictor with a sensitivity of 71.1% (Fig 5D). All effective distance-based models have a high sensitivity (>� 65%), including the radia- tion model with 66.8% that had the lowest relative performance and second-lowest mean rank (Fig 5I–5K). For these high import probabilities, the import risk models now outperform the Fig 5. Classification of the 10 countries with the highest import probability by the gravity model with exponentially (1st column) and power law decaying (2nd column) distance function, the radiation model (3rd. column) and by the import risk model (4th column). A true or false positive (T. Pos. or F. Pos.) means that the country is or is not among the 10 countries with the highest reference import probability ^p. A false negative (F. Neg.) means that it belongs to the reference set but was not detected by the respective model. The pie chart illustrates the sensitivity of the models. https://doi.org/10.1371/journal.pcbi.1011775.g005 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 10 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Fig 6. Correlation analysis: Disease arrival time vs. the effective model distance. Each model’s import probability is converted to an effective distance dM(i\|n0) = −ln(p(i\|n0)) with n0 as the outbreak country of the respective disease. The correlation results C(tA, dM) with the arrival time tA(i) of the disease in the target country i are grouped by model (A) and by the disease (B). As comparison distances, the correlation of the geodesic, geodesic path (on the effective shortest path tree) and the effective distance with tA are shown. Each dot represents a correlation result of the 21 considered outbreaks (H1N1 in 2009, COVID-19 in 2020 and the spread of 18 of its variants in the years 2020–2022). https://doi.org/10.1371/journal.pcbi.1011775.g006 other models also in terms of RMSE and logRMSE, i.e. the 10 countries at highest risk are not only classified best by the import risk model, but also quantitatively assessed best. Disease arrival time. In our final comparison, we evaluate the correlation between disease arrival times and the estimated import probability from the outbreak country of the disease. Note that the effective distance, which is the base of the import risk model, already has the clear relation to disease arrival times and the import risk model is developed to extend this qualitative relation to a quantitative number of passengers imported, as done in a recent study on the pandemic potential of SARS-CoV-2 variants [11]. However, a qualitative comparison to arrival time is of course possible via the negative logarithm of the import probability for each model, which we refer to as effective model distance, which linearly relates [16, 19] to the arrival time tA(i\|j) of a disease dMðijjÞ ¼ (cid:0) lnðpijÞ / tAðijjÞ ð7Þ with j as the disease outbreak country. The arrival time tA(i\|j) is the number of days between the disease outbreak and the day the first case is reported in the target country i. We evaluated the correlation C(tA, dM) for the H1N1 pandemic starting 2009 [8], the COVID-19 pandemic starting 2019 [57] and 18 of its variants. Additional to the import probability models, the cor- relations of the geodesic, geodesic path and effective distance with tA are included. Our analysis reveals that models employing the effective distance as the distance measure consistently out- perform those relying on the geodesic or geodesic path distance (Fig 6A). Interestingly, the gravity model with a power-law decaying distance function consistently performs well, regard- less of the specific distance measure employed. We do not observe a specific model that excels exclusively for certain diseases. Instead, we observe similar correlation values for the same dis- ease across models (Fig 6B), which suggests that there is considerable noise on the arrival time tA that varies between diseases. The noise could be related to the disease specific spreading speed: our assumption, that the outbreak country is the sole source, gets increasingly violated the slower the disease spreads, because other countries become secondary sources. A simple linear regression of the mean correlation hC(tA, dM)i and the mean arrival time htai supports this hypothesis (r = −0.44, p = 0.055, Fig K in S1 Text). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 11 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Import risk of countries and regions Having quantified the performance of the import risk model, we now focus on (i) country spe- cific differences in its prediction quality, (ii) possible limitations due to no concept of adminis- trative units (e.g. countries) whose airports are more interconnected and (iii) how the geodesic distance is encoded in the import risk model, i.e. how a distance dependence emerges from WAN information only. Country specific performance. In the import risk approach, we assume minimal knowl- edge of the system, i.e. only the WAN is known. Consequently, we differentiate countries only via their network properties, one of which is the degree of a node, or more precisely the node strength, since the WAN is a weighted network. It is the simplest metric that is also easily adjustable for the country-level perspective. At the country level, the node strength corre- sponds directly to the flow out of country C FC ¼ X X n2C m=2C Fmn : ð8Þ This country-specific characteristic signifies a country’s potential to influence the network’s structure, since flows from small-outflow countries are diluted by large-outflow countries. From an ecological point of view, the outflow is strongly correlated with the gross domestic product of a country (Fig N in S1 Text). The correlation (logcorr) between the logarithms of the import risk p1 and the reference import probability ^p1 improves with the outflow of the source country (Fig 7), as illustrated by Great Britain (GB) as the country with the largest out- flow in the WAN and Eritrea (ER) as one of the countries with the lowest outflow. The predic- tion improvement with the country’s outflow suggests that the WAN is dominated by large- outflow countries and therefore predictions worsen for countries with lower WAN outflow. However, the prediction improvement is also present in model alternatives that do not use WAN information at all (e.g. gravity with geodesic distance, Fig M in S1 Text). We rule the explanation out that the alternative models show this improvement due to preferential fitting of strong links—and therefore of large-outflow countries—since the models are fitted to the reference data by their import probabilities, which ensures equal weighting among countries. It rather suggests that the mobility behavior in low outflow regions is different, also supported by the sudden performance saturation for countries with a WAN outflow of FC >� 106 (Fig 7 and Fig M in S1 Text). Possibly, their passenger distribution is constrained by additional fac- tors and is limited to the regions in proximity. There are clear exceptions where the import risk estimation is worse compared to outbreak countries with a similar WAN outflow, as Australia (AU), Israel (IL) and Macao (MO). These countries are connected due to historical relations to specific regions that are either not in their direct neighborhood (European countries for AU and IL) or that are more important than the bare neighborhood would suggest, as Macao that is a special administrative region of China. For Macao the import risk to China is underestimated, which consequently overesti- mates the import to other countries, and for AU and IL Europe is underestimated which over- estimates other regions (Fig 7). AU, IL, and MO serve as examples illustrating that the WAN may not fully encapsulate all relevant information accessible to the import risk model. Another concept that is missing in our methodological approach is the idea of a country or another administrative unit. Instead, it treats airport pairs uniformly, disregarding their country affilia- tions. Since we know the international flights leaving a specific country from the WAN, we can run a self-consistency analysis, i.e. without the need of reference import probability data. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 12 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Fig 7. Source countries’ prediction quality and WAN outflow. The correlation between the logarithm of the import risk and the reference import probability logcorr ¼ corrðlogðp1Þ; logð^pÞÞ improves with the outflow of the respective source country (top). Examples of source countries with particularly low (ER, Eritrea) and high (GB, Great Britain) outflow and log_corr are shown with their import risk and reference import risk to target countries (middle row). Countries with exceptionally low log_corr measures compared to source countries with a comparable outflow are either historically linked to specific regions as Australia (AU) and Israel (IL) to European countries (lower right panel) or politically as Macao (MO) as a special administrative region of China. https://doi.org/10.1371/journal.pcbi.1011775.g007 We can estimate the outflow leaving the country C by the import risk model by X X TC ¼ p1ðmjnÞNn : n2C m=2C ð9Þ If we compare it to FC the WAN flow out of country C (see Eq 8), it turns out that the import risk model systematically overestimates the flow out of a country (Fig I panel A in S1 Text). In fact, the relative error increases with the number of airports belonging to the country (Fig I panel B in S1 Text). Possible explanations for this overestimation include the absence of a country-specific concept within the import risk model and the unintentional inclusion of tran- sit passengers in the population count of airport catchment areas (since we use the outflow as a proxy for the population). However, we can easily correct for this overestimation on country- level analysis, by normalizing the airport population such that the WAN country outflow is recovered. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 13 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Fig 8. Import risk aggregated on regional level “to target” vs. “from source” and its geodesic distance dependence. The geodesic distance between regions predicts the import risk p1 to a single target from all sources (A, B) better than from a single source to all targets (D, E) as can be seen by the p-values (C) of the power law fit p1(d) = c�d−α that is illustrated for each selected examples by a grey line (A, B, D, E). The fitted exponent α of the import risk to a single target decreases with the respective regional WAN flow out of the target region (F), i.e. the more connected a region, the weaker the import risk decays with distance. The dashed horizontal lines show the average import risk of a single target (A, B) or a single source (D, E). The color of the dots corresponds to the depicted world regions (right). Maps are created with geopandas [48]. https://doi.org/10.1371/journal.pcbi.1011775.g008 Geodesic distance dependence. The import risk model estimates import probabilities without explicit geodesic-distance information (excluding the variant with distance weighted exit probability). Since classical models have proven distance to be a good predictor for human mobility, we assume that it is encoded in the WAN structure and by consequence in the import risk estimate [58]. To enhance clarity, we aggregate the import risk data across twenty- two world regions. We observe that the import risks to individual targets decrease in a manner resembling a power-law as the geodesic distance to the sources increases (Fig 8A and 8B and Fig L in S1 Text). When we change our perspective and examine the distance-dependence from a single source to all target regions (Fig 8D and 8E), the observed dependence is less con- sistent with a power-law fit of the form p1 ¼ c � d(cid:0) a import risk is computed via a source-centric view (by computing the exit probability from the shortest path tree originating at each source), which suggests that the distance dependence should be best from one source to its possible targets. A possible explanation is that each target possesses its own attractiveness independent of the source region. This suggests that the distri- bution dynamics may resemble a pull mechanism rather than a push mechanism. Indeed, we find that the fitted exponent α from the power-law fit decreases as the WAN flow out of the target region increases, which can serve as a proxy for the attractiveness of a region (Fig 8F). In other words, the more attractive a region, the larger the import risks from more distant source regions. The fitted exponent c has a high rank correlation with α (τKendall = 0.89), i.e. also the coefficient is dependent on the attractiveness of the region. (Fig 8C). This is surprising, since the ij PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 14 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Discussion and conclusion Motivated by the import probability’s strong dependence on the effective distance, we imple- mented the import risk model based on the effective distance shortest path tree’s exit probabil- ity in combination with a random walk on the WAN. As a result, we can infer the passenger trip distribution within the traffic network of their transport vehicle (WAN). When we com- pare our parameter-free model to variations of established mobility models, we observe that it surpasses the alternatives in most comparison measures. The only exception is where the two parameter-fitted gravity models with effective distance perform the best. The import risk model is the most accurate in determining countries with the highest import probability and is one of the models that correlate best with the time of arrival of 20 diseases, showcasing its importance for epidemic-related problems. However, it systematically overestimates low import probabilities and its performance worsens for countries with a passenger outflow below a million per year. Despite the lack of any explicit geodesic distance information, the import risk model recovers a geodesic distance dependence. This distinction is more promi- nent when considering all sources to a single target compared to the reverse scenario. We attri- bute this phenomenon to a target’s specific attractiveness, which we estimate using its node strength, i.e. the target’s passenger outflow. The only measure where the gravity models with effective distance outperform the import risk models is the logRMSE. This is likely due to their good agreement over wide ranges of the import probability (Fig 3I and 3J). The import risk model performs poorly with respect to the logRMSE due to its systematic overestimation of low import probabilities. Note, that the sec- ond parameter free model, the radiation model, systematically underestimates low import probabilities in the same way as the import risk model does. This is expected, since deviation from the assumptions cannot be corrected by any parameter adjustment. We identified several ways to reduce the import risk’s overestimation of low import probabilities by introducing an additional parameter that scales the population of the respective airport, changes the exit prob- ability along the shortest path tree or only the exit probability of specific nodes (for details, see Supplementary Note C and Figs G, H in S1 Text). In conclusion, we find that introducing modifications that enhance the probability of exiting at airports or nodes with large popula- tions mitigates the issue of overestimation. However, we leave this as a possible extension of our model and highlight that it outperformed the other models in all correlation measures, illustrating its high potential. The radiation model’s poor performance can likely be attributed to its initial design, which focused on small-scale commuter flows driven by work opportunities [43], which shows that bottom-up approaches are often limited to their specific use case but can be adapted, such as the extended radiation model [59], which is no longer parameter-free and has similar perfor- mance to the gravity model [54]. Interestingly, the radiation model is the only one that does not improve with inclusion of flight network information via the geodesic path or the effective distance (Fig 4). The radiation model’s insensitivity to network information can be attributed to the fact that it only extracts rank information from the distance data, resulting in a signifi- cant loss of information. The rank representation has the problem that airports that directly follow in their rank with respect to a source airport could be separated by a mountain range or ocean, i.e. the rank difference is minimal but the actual distance immense. This argument holds for any distance information. We corrected the import probability by the symmetrization of the respective OD-matrices which corresponds to a specific form of a doubly-constrained model. Normally, the constraints only ensure that the out- and inflow of each location corresponds to the observations [31, 52, 54], in contrast, we assume that both equal each other because of returning visitors. We PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 15 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN repeated the model comparison without the correction: it reduced the agreement with the ref- erence data for all but five of the seventy-two model-measure combinations (Fig F in S1 Text), which is in agreement with previous studies that report a better performance of doubly con- strained models [54]. Importantly, the import risk model still outperforms the other models if the import probability estimates are not corrected (compare Fig 4 with Fig J in S1 Text). It’s crucial to note that the assumption of returning visitors is applicable when visitors and tourists dominate while migrants can be disregarded. However, this assumption may not hold for links between low- and high-income countries or conflict regions. In the disease arrival time analysis, all models that use the effective distance perform simi- larly well, including all gravity models with power-law distance decay. The disease arrival time tA correlates with the logarithm of the estimated import probabilities, i.e. the results should be in agreement with the logcorr goodness of fit results. The models with effective distance vary only by maximal 0.07 in their logcorr measures and these are based on 183 countries as poten- tial source (Fig J in S1 Text). However, the 20 diseases in the arrival time analysis have only 10 unique outbreak countries. Additionally, due to factors like varying testing rates between countries, the uncertainty in arrival times, and other factors, the sample size is likely insuffi- cient to recover the logcorr results. In order to decrease the noise on tA, we repeated the analy- sis by extrapolating the arrival time via a logarithmic fit on the early cases, i.e. assuming an initial exponential growth (see Supplementary Note D in S1 Text). As a result of this proce- dure, some countries with insufficient data for extrapolation had to be excluded, which in turn led to the exclusion of more diseases. Nevertheless, the results are consistent with the tA esti- mation by 1st count (compare Fig 6 and Fig P in S1 Text). We found that without providing any geodesic distance information to the import risk model, a distance dependence is recovered that is stronger for import probabilities to a single target, than from a single source, even if the import probability is computed from a source- centric view. Since the WAN is spatially embedded and has a network dimension of three [58], its connections reflect up to a certain degree the characteristics of the embedding space. This explains the import risk model’s ability to capture distance dependence in general. That dis- tance is a better predictor in the target-centric view aligns well with a previous study in which a target-specific human-mobility model collapses mobility data to multiple targets by assigning each target a specific attractiveness that is proportional to the target’s population [51]. The import risk model predictions worsen for countries with a small outflow on the WAN, and since the country’s WAN outflow is proportional to its gross domestic product, the model performs less good for countries with a lower GDP, i.e. small population and/or low to middle income countries. This is unfortunate, as our model derives Origin-Destination (OD) infor- mation (costly to directly monitor) from cost-effective traffic flow monitoring, making it par- ticularly valuable for regions with limited resources. However, we find that the model alternatives (gravity, radiation) also perform poorly for low-outflow countries and that the pas- senger distribution of the latter is most likely constrained by the GDP and thus limited to the target-regions in effective proximity. To circumvent this problem, one could aggregate neigh- boring low-outflow countries until the conglomerate crosses the outflow threshold of FC = 106 above which we observe a performance saturation (Fig 7 and Fig M in S1 Text). Of course, this compromise comes with a lower spatial resolution and we emphasize the need for future research in this direction. While we have assessed the model’s performance on the world air transportation network, its applicability extends to other modes of transportation such as subway systems, cars, buses, and trains. Future research will explore the specific conditions under which this model can be effectively applied. Furthermore, there is room for improvement in the basic estimation of the traveling population within an airport’s catchment area based solely on its outflow. This PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 16 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN estimation does not currently account for the significant role of hubs and the missing informa- tion about transit passengers. The simple framework that only relies on the traffic network is appealing, but in certain scenarios its prediction can be refined by using information about the GDP, Gini-coefficient or population density. Our comparison focused on the parameter-free radiation model and the fitted gravity model, but we acknowledge the existence of promising variations and alternative models that were not included in this study [30, 31, 54, 59]. However, the gravity model is widely applied and has been shown to perform equally well [59] or better than alternatives [54]. There are exceptions, e.g. an iterative computation of a gravity-like model outperforms the common gravity model in cases where the complete mobility network is not available [29]. Additionally, the radiation model outperforms the gravity model for long-distance connections [38, 54]. Still, the simplicity of the gravity model and its adaptability by parameter adjustment make it a strong counterpart. The model alternatives make use of the WAN-structure information by using the effective distance as done in e.g. Ren et al. [60] where the radiation model with time- distance was better than the travel-distance on the road network to predict the traffic on each link. Similarly, we observed that the effective distance, which is related to the arrival time of diseases, outperforms geodesic path-distance in predicting import probabilities. The import risk model is fundamentally different from classic approaches that estimate OD trips from traffic data, because the latter find the OD trips that best reproduce the traffic data [28, 30, 44, 45], while our model runs a distribution process on the traffic data network. Thus, our model is a mechanistic bottom-up approach, while the classic approaches either fit and require the knowledge of the reference trip data [28, 30] or are based on the assumption that the trip distribution across the links follows the maximum entropy principle, i.e. the OD trips are considered as most likely that can be realized by the largest number of microstates [44, 45]. Note that maximum entropy approaches require an estimation of routes and their alternatives between each OD pair, while we allow all routes to be taken by the random walker. To the best of our knowledge, our model stands as unique in its mechanistic nature, enabling the study of modifications to its underlying distribution process. This includes strategies for containment aimed at slowing or restricting a pandemic, for instance. A straight forward implementation could be the testing of a fraction of passengers Ci � 1 at every transit airport i, which corre- sponds to reducing the probability to continue walking of an infected passenger (Eq 2) to ~Sn;n(cid:0) 1ðn0; CÞ ¼ ð1 (cid:0) Cn(cid:0) 1Þ � Pn;n(cid:0) 1ð1 (cid:0) qn(cid:0) 1ðn0ÞÞ : With C = [C1, C2, . . .] one could allow for a varying testing capacity between the airports. Material and methods Data sources The WAN provided by OAG (Official Airline Guide) [46] contains the number of flights and the respective maximum seat capacity Fi,j between airports i and j aggregated for the year 2014. The reference import probability ^pðmjnÞ ¼ ^T mn= ^T n is based on the “Global Transnational Mobility Dataset” [40, 47] that assigns the number of trips in 2014 ^T mn from country n to m worldwide by combining the world air transportation origin-final-destination data set from the company SABRE, and cross-boarder visits with an overnight stay from the UNWTO (World Tourism Organization). Thus, ^pðmjnÞ represents not only the mobility via air travel but also via other means (sea, road, rail). However, air travel dominates long distance trips which makes it a fair reference set of the air transportation origin-final-destination matrix. For details on how the data sets were combined, see Supplementary Note A in S1 Text. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 17 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Alternative models The gravity model states that the number of trips between regions n and m increase with their population sizes (Nn and Nm) and decrease with distance dnm Tmn ¼ On Nn Nm f ðdnmÞ ; ð10Þ with f(d) as a function that grows monotonically with distance d, most often chosen as either a power-law f(d) = dγ or an exponential f(dnm) = eγd. In the radiation model, the trips from n to m depend on their respective population sizes Nn, Nm (or other measures as job opportunities) and on the number of people smn that are in a circle with radius rmn centered around location n including Nn and Nm: Tmn ¼ On Nn Nm ðsmn (cid:0) NmÞsmn : ð11Þ The import probability of both models is computed by normalizing the trips with respect to the source-region pðmjnÞ ¼ TmnP jTjn ¼ Tmn Tn : ð12Þ Trip-symmetrization We correct the import probability via symmetrizing the OD-matrix by (i) compute the esti- mated OD-matrix m;n ¼ pð0ÞðmjnÞNn Tð0Þ from the import probability estimate, (ii) correct it by computing its symmetric part S ¼ ðT þ T>Þ=2 and (iii) compute the corresponding corrected import probability via pð1ÞðAjBÞ ¼ SAB=SB : ð13Þ ð14Þ ð15Þ By going through these steps, the asymmetry is reduced heavily but still persists. Thus, we repeat steps (i) till (iii) until p(3)(A\|B), which returns for all models a comparable asymmetry in mean and median to the reference data (see Supplementary Note B in S1 Text for details). Comparison measures We compare the import probability models with the reference data via the Pearson correlation corrðx; yÞ ¼ E½ðx (cid:0) �xÞðy (cid:0) �yÞ� sxsy ; with E½x� � �x as average, the root-mean-square error RMSEðx; yÞ ¼ q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi E½ðx (cid:0) yÞ2� ; PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 ð16Þ ð17Þ 18 / 26 PLOS COMPUTATIONAL BIOLOGYthe common part of commuters [59] cpcðx; yÞ ¼ P 2 P ij minðxij; yijÞ P ijxij þ ijyij Infer import risk from the WAN ; ð18Þ which is 1 if all links are identical and 0 if none of them agrees. All the above measures are more sensitive to strong links, i.e. large import probabilities. However, if the focus is to get a fair comparison including all links, we are more interested in logarithmic versions of the above measures or rank correlations. Thus, we compare the logarithm of the import probabili- ties via correlation logcorrðx; yÞ ¼ corrðlogðxÞ; logðyÞÞ ; root-mean-square error logRMSEðx; yÞ ¼ RMSEðlogðxÞ; logðyÞÞ ; and use the Kendall rank correlation coefficient tKendall ¼ q C (cid:0) D ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðC þ D þ TxÞðC þ D þ TyÞ ; ð19Þ ð20Þ ð21Þ with C and D as the number of concordant and discordant pairs and Tx and Ty as ties only in x and y, respectively. To simplify and generalize the comparison we combine the six above defined measures by computing the mean rank of each model, i.e. the best correlating model has the highest (12) and the worst the lowest (0) rank and the mean rank of one model is the average of all six ranks. To quantify the mean difference between the models we define the relative performance of one model M as rel:perf:ðf ðxM; yÞÞ ¼ f ðxMÞ (cid:0) worstðf ðxkÞ; kÞ bestðf ðxkÞ; kÞ (cid:0) worstðf ðxkÞ; kÞ ; ð22Þ with f(xM) = f(xM, y) as the specific comparison function and best( f (xk), k) and worst( f (xk), k) as the best and worst performing value of all models using this comparison function. Note, that best(. . .) = max(. . .) apart for the rmse-measures, where it is min(. . .) (analog for worst (. . .)). Disease arrival times The disease arrival time tA(i) in country i is estimated by the date of the first reported case for H1N1 and SARS-CoV-2. For the SARS-CoV-2 variants we use the first sequenced sample in this country. However, for certain variants some sequenced samples appear in the statistics month before the outbreak date declared by the WHO [61], which we treat as misclassifica- tions, discard them and use instead the first sample after the WHO listed outbreak for the respective country (see Supplementary Note D for details and Fig O in S1 Text). For each of the diseases/variants we used the WAN that we have access to and that is closest to the respec- tive outbreak date (see Table B in S1 Text) and as outbreak country we used the one listed by the WHO as first country with first sequenced sample of the respective variant [61]. For the H1N1 outbreak in 2009 we used the case data provided by FluNet [62, 63] (the column AH1N12009), for the COVID-19 cases we use the WHO COVID-19 dashboard [64] accessed PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 19 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN through ourworldindata.org, the number of sequenced samples was accessed through GISAID [65–67] using the file gisaid_variants_statistics.json. Supporting information S1 Text. Supplementary Note A: Origin-destination data (“Global Transnational Mobility Dataset”). Supplementary Note B: Symmetrized flows. Supplementary Note C: On the overes- timation of low import probabilities. Supplementary Note D: Disease arrival time analysis. Table A. Filtering criteria for the log-cases fit to extrapolate the arrival times tA. A country is excluded if (C0:) the detection is too sparse before peak-0 (less than 6 weeks of data), (C1:) the number of cases at peak-0 is below 30 (otherwise the signal is too noisy), (C2:) the extrapolated arrival time is before the WHO-outbreak date. N is the number of countries for which case data could be generated. NC0 and NC1 are the countries that pass criteria C0 and C1. NC0 & C1 and NC0 & C1 & C2 are the numbers of countries that pass multiple criteria. Table B. Disease and SARS-CoV-2 Variant outbreak information and WAN date. For each disease/variant the outbreak country and the date of the WAN used to compute the import probability esti- mates with the different models is displayed. Note that we only have the WAN from the years 2014 and 2019 in a yearly resolution and from 2020–2022 in monthly resolution. We repeated the analysis for COVID with the WAN from the month 2020–01-01, instead of using the yearly WAN from 2019, which gave comparable results. Fig A. Import probability dependence on the geographic distance (A), the effective distance (B) and the geographic path distance (C). The orange line represents the median and C(x, y) is the correlation between the two measures either log-transformed or not. The geographic distance between countries is averaged over all airport pairs. The geographic path distance is the geographic distance along the shortest path derived from the WAN using deff, i.e. it is a combination of geographic and network informa- tion. The axis scale corresponds to the one with the highest correlation, i.e. log-log for distance and path distance (A, C) and y-log for the effective distance (B). Fig B. Gravity model scans. Parameter dependence of measures that compare the model estimated import probability with the reference import risk ^pðijn0Þ. Thereby is “corr” the correlation, “cpc” the common part of commuters, “log_corr” the correlation on log-scale, “rmse” the root mean squared error and “kendalltau” the rank correlation via Kendalls tau. Two versions of the gravity model are shown with an exponentially decaying distance function f(d) = e−γd (left column: A, C, E), and a power law decaying distance function f(d) = d−β (right column: B, D, F). As distance the geo- desic distance (first row: A, B), the geodesic path distance (second row: C, D) and the effective distance (third row: E, F) are used. The dotted horizontal lines show the comparison measure with the import risk as model and have the same respective color. Fig C. Mean optimal parameters for gravity models. For each gravity model with exponentially and power law decaying distance function and with one of the three different distance measures (geodesic dis- tance, geodesic path distance and effective distance), the exponent γ or β that results in the best fit to the reference import risk is shown. The comparison is quantified via the correlation (corr), correlation between the log-transformed import risks (log_corr), root mean square error (rmse), root mean square error of the log-transformed import risks (log_rmse), Kendall rank correlation (kendalltau) and the common part of commuters (cpc). The mean optimal parameter for each model is marked by a horizontal line and their values are γ = [6.71, 6.41] * 10−4 for geographic and geo. path distance and γ = 0.84 for the effective distance, and β = [1.90, 1, 95, 5.10] for geo., geo. path, and effective distance, respectively. Fig D. Symmetry check for OD-matrix. Each dot represents the number of passengers that travel between 2 countries and back. The OD-matrix is computed by the radiation model (1st. column), gravity model with exponentially (2nd column) and power law decaying (3rd column) distance PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 20 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN function and by the import risk model (4th column). The OD-matrix of the models is com- puted by multiplying the import probability with the source-outflow. The reference trips and return trips have the highest symmetry (5th column, M). The orange line depicts the median and the gray line is y = x and illustrates perfect symmetry. The mean (AVG(asym)) and median (MED(asym)) asymmetry of the flows, computed according to Eq. C in S1 Text., are shown in each panel. The reference trips (M) show the lowest asymmetry, especially for large passenger flows. Fig E. Relative comparison measures for the import probability estimates. The rank (A) and the relative performance (B) for the different import probability estimation models. The model that agrees best (worst) with the reference import risk according a specific measure has the highest (lowest) rank and a relative performance of one (zero). The relative perfor- mance is then a linear interpolation between the best and worst model. The comparison mea- sures are the correlation (corr), correlation between the log-transformed import risks (log_corr), Root-mean-square error (rmse), Root-mean-square error of the log-transformed import risks (log_rmse), Kendall rank correlation (kendalltau) and the common part of com- muters (cpc). As exponents of the gravity models the mean optimal parameter is used (hori- zontal lines in Fig C in S1 Text.). Fig F. Absolute comparison measures for the import probability estimates. The comparison measures are the correlation (corr), correlation between the log-transformed import risks (log_corr), Root-mean-square error (rmse), Root- mean-square error of the log-transformed import risks (log_rmse), Kendall rank correlation (kendalltau) and the common part of commuters (cpc). As exponents of the gravity models the mean optimal parameter is used (horizontal lines in Fig C in S1 Text.). The colors depict the 4 different models. The solid circles are the models with corrected import probability by symmetrizing their OD-matrix, and the transparent squares are the non-corrected import probabilities of the respective model. Fig G. Import risk comparison and its deviation from a linear relation. Scatter plot (left) and only median and IQR with an exponential fit (right). Fig H. Variations of the import risk model to investigate how additional parameters influ- ence the relation between the import risk and the reference import risk. A: the flow scaling exponent ν that estimates the travelling population N(i) of the airport i depending on its WAN outflow Fi via NðiÞ ¼ Fn i (default: ν = 1). B: the effective distance offset d0 that penalizes larger hop-distances in the effective distance deff(i\|n0) = d0 − ln(Pij) when creating the shortest path tree (default: d0 = 1). C: the descendant fraction introduced in the shortest path exit probabil- ity, where 0.5 is the default value and values larger than 0.5 mean that the exiting at the descen- dant (or offspring) nodes compared to the current node becomes more likely. D: different weight options introduced for the shortest path tree exit probability. Per default, the node pop- ulations are not weighted. The weight is the inverse of either the geodesic or the effective dis- tance. E: manually set shortest path exit probability of leaf nodes (dead-end nodes). Per default, the exit probability is 1. A decrease to 0.9 or 0.8 does not visually change the median. Fig I. Country outflow reconstruction by import risk. The flow in the WAN leaving a coun- try FC is estimated by the import risk model by TC = ∑n2C ∑m=2C p1(m\|n)Nn. Both measures are directly compared (A) and the relative error is computed depending on the number of airports in the respective country Narpts (B). The import risk model does not include the concept of a country which partly explains the overestimation for larger airports. Another explanation is the overestimation of the respective airport population Nn = Fn by the WAN outflow for the import risk model (the true population is smaller because of the transit passengers that need to be excluded). Note that the WAN is used here, i.e. we check for self-consistency of the model and no reference data is included. Fig J. Uncorrected models: rank and relative performance. Same analysis as in the main text in Fig 4), however, here the uncorrected model predictions are used, i.e. without symmetrizing the OD-matrix. Fig K. Mean correlation between arrival time and effective model distance vs. the speed of the disease estimated by the mean arrival PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 21 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN time htA(C)iC, averaged over all countries C. The correlation C(tA, dM) between arrival time tA and effective model distance dM is averaged over all models. The size of the datapoints illus- trates the number of countries that were reached by the disease. Fig L. Import risk between world regions to a specific target region. In contrast to its derivation the import risk is dis- played in a target-centric view, i.e. each panel displays the import probability to a single target region from all source regions. The distance between world regions is the mean distance between their airport locations. The grey line represents a power-law fit p1 = c � d−α. The mean import risk is marked for each world region by a horizontal dashed line. The 22 target- world-regions are sorted according to their mean import risk. Maps are created with geopan- das [48]. Fig M. Source countries prediction quality and WAN outflow for two gravity models. Same model-result representation as in Fig 7 but here instead of the import risk model, the gravity model with power-law distance decaying function using the geodesic dgeo (left) or effective deff (right) distance is applied. Also for these models the logcorr between import probability estimates p(i\|n0) and the reference data ^pðijn0Þ improves for countries with a larger outflow in the WAN. Fig N. WAN flow out of countries vs. population and GDP The WAN flow out of a country is best mapped by its gross domestic product (GDP, C) com- pared to its population (A) or per capita GDP (B). The linear double-logarithmic regression results are shown in the lower part of each panel (r- and p-value). The size of each country cor- responds to its population (A) and the color codes its continent. GDP is taken from the World Bank Dataset for the year 2014 [69]. Fig O. Variant outbreak detection and fraction of sequenced samples for each of the considered variants. To illustrate the spread of the variant and how often it occurs worlwide the fraction of the variant in all sequenced probes is plotted, i.e. if it reaches 1, all sequenced probes are the respective variant. The official WHO outbreak date [61] is highlighted as red dotted vertical line. We estimated an outbreak date by 45 days before the fraction of sequenced samples reached 2.5% of its world-wide peak. The orange ver- tical lines (lower row of lines) show for each country the arrival of the variant, estimated by the first sequenced probe (“count1”). The black vertical lines (upper row of lines) show the arrival times after the outbreak which are used in the main text. Fig P. Correlation analysis with log- cases estimated arrival time. Each model’s import probability is converted to an effective dis- tance dM(i\|n0) = −ln(p(i\|n0)) with n0 as the outbreak country of the respective disease. The cor- relation results C(tA, dM) with the arrival time tA(i) of the disease in the target country i are grouped by model (A) and by the disease (B). As comparison distances, the correlation of the geodesic, geodesic path (on the effective shortest path tree) and the effective distance with tA are shown. Each dot represents a correlation result of the 10 considered outbreaks (H1N1 in 2009, COVID-19 in 2020 and the spread of 8 of its variants in the years 2020–2022). For the analysis only those diseases/variants were used with more than 10 datapoints (see Table A in S1 Text.). Fig Q. New case numbers of the Alpha variant for countries that passed the selec- tion criteria for the log-cases fit to extrapolate the arrival time tA in the attempt to reduce noise. The vertical dashed line marks the outbreak as listed by the WHO [61], the yellow star is the extrapolated arrival time from the log-cases fit that is illustrated by a yellow line. To deter- mine the peak-0 (marked by a vertical line) we used a difference analysis on the smoothed new-cases data. Fig R. New case numbers of the Alpha variant for countries that failed the selection criteria for the log-cases fit to extrapolate the arrival time tA in the attempt to reduce noise. The vertical dashed line marks the outbreak as listed by the WHO [61]. Those countries that passed the criteria C0 and C1 (see Table A in S1 Text. for details) show the log-cases fit. Note that the latter have an extrapolated tA before the outbreak date listed by the WHO. To determine the peak-0 (marked by a vertical line) we used a difference analysis on the smoothed new-cases data. (PDF) PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011775 January 24, 2024 22 / 26 PLOS COMPUTATIONAL BIOLOGYInfer import risk from the WAN Acknowledgments We acknowledge Marc Wiedermann for insightful comments. Author Contributions Conceptualization: Pascal P. Klamser, Dirk Brockmann. Data curation: Pascal P. Klamser, Adrian Zachariae, Benjamin F. Maier, Olga Baranov, Clara Jongen, Frank Schlosser. Formal analysis: Pascal P. Klamser, Adrian Zachariae, Benjamin F. Maier. Funding acquisition: Dirk Brockmann. Methodology: Pascal P. Klamser, Benjamin F. Maier, Frank Schlosser, Dirk Brockmann. Software: Pascal P. Klamser, Adrian Zachariae, Benjamin F. Maier. Visualization: Pascal P. Klamser. Writing – original draft: Pascal P. Klamser. Writing – review & editing: Pascal P. Klamser, Adrian Zachariae, Olga Baranov, Clara Jon- gen, Dirk Brockmann. References 1. Carlier M. Number of passenger cars and commercial vehicles in use worldwide from 2006 to 2015; 2021. 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10.1371_journal.pcbi.1011796	RESEARCH ARTICLE Vision-based collective motion: A locust- inspired reductionist model David L. KrongauzID 1, Amir AyaliID 2, Gal A. Kaminka1 1 Computer Science Department, Bar-Ilan Univeristy, Israel, 2 School of Zoology and Sagol School of Neuroscience, Tel Aviv University, Israel kingkrong@gmail.com Abstract Naturally occurring collective motion is a fascinating phenomenon in which swarming indi- viduals aggregate and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities, and ignore the underlying perception pro- cesses, particularly for agents relying on visual perception. Specifically, biological vision in many swarming animals, such as locusts, utilizes monocular non-stereoscopic vision, which prevents perfect acquisition of distances and velocities. Moreover, swarming peers can visually occlude each other, further introducing estimation errors. In this study, we explore necessary conditions for the emergence of ordered collective motion under restricted condi- tions, using non-stereoscopic, monocular vision. We present a model of vision-based collec- tive motion for locust-like agents: elongated shape, omni-directional visual sensor parallel to the horizontal plane, and lacking stereoscopic depth perception. The model addresses (i) the non-stereoscopic estimation of distance and velocity, (ii) the presence of occlusions in the visual field. We consider and compare three strategies that an agent may use to interpret partially-occluded visual information at the cost of the computational complexity required for the visual perception processes. Computer-simulated experiments conducted in various geometrical environments (toroidal, corridor, and ring-shaped arenas) demonstrate that the models can result in an ordered or near-ordered state. At the same time, they differ in the rate at which order is achieved. Moreover, the results are sensitive to the elongation of the agents. Experiments in geometrically constrained environments reveal differences between the models and elucidate possible tradeoffs in using them to control swarming agents. These suggest avenues for further study in biology and robotics. Author summary Swarm collective motion is a wide-ranging phenomenon in nature, with applications in multi-agent, multi-robot systems. In most natural swarming species, individuals rely on monocular, non-stereoscopic vision as the key sensory modality for their interactions. For example, the desert locust (Schistocerca gregaria) displays large swarms of individuals, moving in alignment and relying solely on non-stereoscopic visual perception. Inspired by these locust swarms, we have developed a monocular, non-stereoscopic vision-based a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Krongauz DL, Ayali A, Kaminka GA (2024) Vision-based collective motion: A locust-inspired reductionist model. PLoS Comput Biol 20(1): e1011796. https://doi.org/10.1371/journal. pcbi.1011796 Editor: Ricardo Martinez-Garcia, Center for Advanced Systems Understanding (CASUS), GERMANY Received: February 20, 2023 Accepted: January 3, 2024 Published: January 29, 2024 Copyright: © 2024 Krongauz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data and code used for running experiments and plotting is available on a GitHub repository at https://github. com/kronga/vision-based-collective-model. Funding: This work was supported by Israel Science Foundation (ISF) (#2306/18 to GAK,AA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: No competing interests. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 1 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model model that achieves synchronized motion in a swarm of two-dimensional agents, even with inaccurate estimates of distances and velocities, particularly in the presence of occlu- sions. We explore three general strategies for handling occlusions, which differ in the requirements they place on the complexity of the visual perception process. We show that strategies may reach a highly ordered motion state but differ in their rate of convergence to this ordered state. 1 Introduction Swarms composed of large groups of individuals can engage in coordinated collective motion, without centralized or group-wide control, global perception, or global communications. This coordinated collective motion (which we henceforth term flocking, but is also known as school- ing, or swarming) describes the emergence of a common heading for the motion of agents in the swarm. Flocking can arise from disordered initial conditions, where initial headings and positions are arbitrary, despite the restricted locality of the perception and action of any indi- vidual agent in the swarm. In nature, flocking is ubiquitous in birds [1, 2], fish [3, 4], insects [5, 6], bacteria [7], and human crowds [8–12]. It is a phenomenon that has been of interest to the scientific commu- nity for decades, inspiring modeling efforts (e.g., [13, 14]) and bio-mimetic technologies in graphics [13, 15]), simulations (e.g., [16, 17]), and robotics (see [18] for a recent survey). The leading paradigm underlying models of collective motion is that it results from repeated local (myopic) interactions among individual swarm members (see, e.g., [19, 20]). The control procedure of each single agent translates its perception of the local physical and social (nearby conspecifics) environments into a decision regarding its next action. The indi- vidual decisions made by each agent, based on their interactions with others, lead to the group eventually forming an ordered state. In this state, all agents move in a common direction, which can dynamically change. Commonly, flocking agents are modeled as self-propelled parti- cles (SPP) that are continuously subjected to the mutual steering forces caused by their neigh- bors [21]. These mutual force interactions feed into the agents’ decision-making, changing their motion [14, 22, 23]. Under appropriate conditions, this generates flocking [24–26]. Traditional models of flocking abstract away from the real limitations of perceptual pro- cesses. They rely on idealized perceptual capabilities that allow agents to determine their neighbors’ distances, headings, and velocities (see, for instance, [13, 14, 19, 20, 27]). This ignores the sensory and computational limitations inherent to physical agents in nature or in a robotics laboratory: limited effective sensing regions (width and range of the sensory field of view), systematic perceptual ambiguities, computational resources required for sensor infor- mation processing, and sensitivity to occlusions of some neighbors by others (common in flocking) [28]. In those that use vision as a primary sensory modality, the underlying sensory structure and processing abilities of the agent places multi-faceted constraints on the possible visual per- ception processes that may be employed. The position of the eyes/visual sensors, and the angu- lar and range limitations on their fields of view, constrain the perception strategies that can be used to provide the information needed for flocking. These strategies vary in accuracy, failure modes, and computational/cognitive complexity they demand of the individual brain [29–31]. For example, when an agent has two or more sensors that have intersecting fields of view, stereopsis (stereoscopic vision) can be used to estimate distance accurately, but the intersecting field of view is relatively narrow, and its effective range is short [32]. In contrast, when one or PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 2 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model more eyes generate monocular (non-stereoscopic) images, distances may be inferred by matching conspecific visual templates, by integrating images over time to compute optical flow, or by other strategies [33–40], all of which vary in computational requirements and accu- racy. The tradeoffs involved, their biological plausibility, their potential computational costs, and the opportunities they offer for robots are currently not well understood. Marching locust nymphs [41–43] offer an inspiring example to challenge our understand- ing of vision-based collective motion. The individual locust nymph lacks binocular depth per- ception, though its two eyes offer an almost-perfect omni-directional visual field. Both field and laboratory studies indicate that the robust locust collective motion emerges from the inter- actions between individuals [26, 44–46]. It is largely accepted that non-stereoscopic vision is the key sensory modality underlying these local interactions. With limited processing power, and having no depth perception, the individual locust makes motion decisions based on visual information that lacks precision in measurement of its neighbors’ proximity, headings, or velocities. Despite these limitations, locusts display impressive flocking, involving large num- bers of individual agents. Models that ignore the visual perception processes lack the explana- tory power to capture how this is achieved. Recent studies of monocular vision-based flocking have investigated some relevant related mechanisms. Studies of natural swarms (often in vertebrates) [3, 28, 47–50] and robot swarms [51–53] have suggested strategies for forming dynamic sensory networks, by which agents remain connected to each other while attending to only a subset of their neighbors at any given time. These are useful both in cases of a limited field of view, and in handling the occlu- sions that limit the ability to recognize and track neighbors that are only partly visible. Other studies have focused on the mechanisms used by the individual for visual processing, given a specific morphology of agents [54–59]. The different studies all reveal important insights but often make assumptions (e.g., that agents are circular, or that they can sense the orientation of visible neighbors), that may not be relevant to the locust body morphology or its perception capabilities (we discuss these investigations in more depth in Section 5). Inspired and challenged by the marching locust phenomenon, we have developed a reduc- tionist model of monocular, non-stereoscopic, vision-based collective motion in locust-like agents (Section 2). The model builds on the geometrical characteristics of locust body mor- phology and visual perception (elongated shape, wide field of view, monocular images), but reduces the visual inputs to the bare minimum perceivable in two dimensions (i.e., no height information is used; objects are perceived only along the horizontal sensory plane). We present a control algorithm that employs only the information accessible via the agent’s visual field (Section 2.1). We then propose several general strategies that the agent might employ when assessing partially obstructed neighbors (Section 2.2). From these restricted capabilities, the control algorithm synthesizes flocking under various environment conditions and occlusion- handling strategies. Experiments performed via computer simulation (Section 3) explored the emergence of ordered (flocking) movement under various conditions: varying group sizes, range of the visual field, body lengths, and strategies for handling occlusions. The experiments were per- formed in various simulated arenas, that differed in their border periodicity constraints and area. Our goal was to elucidate strategies that organisms—and robot builders—can use to trade computation or cognitive complexity for reliable ordered collective motion. The results (Sec- tion 4) reveal that in many cases, the swarm’s order parameter, which characterizes the level of alignment between the agents in the swarm, reaches high values regardless of occlusion-han- dling strategy or environment constraints. However, in highly constrained arenas, different strategies for handling occlusions differ in the rate and degree of emerging order. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 3 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Furthermore, the body morphology (specifically, body elongation) impacts the rate in which order is achieved, when using the different strategies. Section 5 presents an in-depth discussion of the results, their relation to previous models, and their implications for future research efforts. 2 A reductionist, non-stereoscopic model of visual perception for collective motion We present a reductionist model of non-stereoscopic vision-based collective motion, from the perspective of a locust-like agent. First, in Section 2.1, we present the restricted visual percep- tion mechanisms, and the vision-based algorithm governing the agents’ movement. Next, in Section 2.2, we discuss the potentially harmful effects of occlusions on perception. We then present three alternative strategies allowing the algorithm to interpret and deal with partially occluded visual information. 2.1 The principal monocular vision-based flocking model We begin with the basic geometry of the agent. We consider a group of N identical rectangular agents with width w and length l, moving in a two-dimensional environment at velocity vi, parallel to their length axis. The elongation of the agents is measured by the ratio of length to width (l/w), such that the ratio is � 1, i.e., a square is the shortest agent. The position coordinates xi of agent i are updated at discrete time steps according to the motion equation, xiðt þ DtÞ ¼ xiðtÞ þ viðtÞ � Dt; ð1Þ with velocity vi(t) updated at each time step, causing the agent to steer towards a desired veloc- ity with steering-parameter factor η, viðt þ DtÞ ¼ viðtÞ � ð1 (cid:0) ZÞ þ vdesiredðtÞ � Z ð2Þ vdesired is calculated based on the decision algorithm of the Vicsek Model [14]. Assuming agent i has a set of neighbors Ji, its desired velocity averages the velocities of the neighbors j 2 Ji at each time t: vdesired tð Þ ¼ 1 jJij X j2Ji ~v j tð Þ; ð3Þ where ~v j is the estimated velocity of a neighbor j. The question, of course, is how the velocities of neighbors are estimated based on visual information. To explore this in-depth, we first dis- cuss the geometry of locust-like visual perception. The Geometry of Locust-Like Vision. We model each agent’s visual field of view by an idealized omnidirectional sensor covering 360 degrees around the observing agent (hereafter, the focal agent). This wide field of view is consistent with the nearly omni-directional field of view of locust nymphs [60, 61]. The range of the sensing, i.e., the maximum distance at which it can detect other agents, is denoted R, a parameter of the model (which we examine in the experiments). Fig 1a presents the basic geometry and notation for a focal agent o heading “up”, with veloc- ity vector vo. The focal agent has a single neighbor j moving with velocity vj and located at a distance rj < R measured between the focal agent and the neighbor j along the line of sight (LOS) connecting the center-of-mass (COM) of the neighbor (COMj) and the COM of the focal agent (COMo). We denote the displacement vector of neighbor j equals rj = COMj − PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 4 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 1. (a) A schematic depiction of a neighbor’s observed geometrical features and notation used. The bearing angle βj defines the angle between the heading of the focal agent (vo) and the line of sight (LOS, as defined in the text). The circle represents the idealized sensor of the focal agent. The subtended angle αj is defined as the angle between the edge rays directed towards the extremities of the neighbor. The distance from the focal agent to the center of neighbor j is denoted rj. The neighbor’s velocity vj, is composed of two orthogonal components: the radial component vj,r is parallel to LOS, and the tangential component vj,t is perpendicular to LOS. The unit vectors ^u j;r; ^uj;t are equal to vj,r, vj,t but with magnitude fixed to 1; they are not shown in the figure. (b) Geometry of finding the subtended angle αj. Edge rays are denoted with green lines. Edge rays pass at two corners of the neighboring agent, and the segment between those points we define as the ‘effective edge’ d (here, dA for neighbor A, dB for neighbor B, etc.). Depending on the relative orientation of the neighbor with respect to the focal agent, the effective edge may be either the neighbor’s diagonal (see neighbor A), its shorter side (neighbor B), or its longer side (neighbor C). https://doi.org/10.1371/journal.pcbi.1011796.g001 COMo, while the scalar distance to j is rj = krjk. The velocity vj is composed of the tangential velocity vj,t and radial velocity vj,r components, relating to the line of sight (LOS). The angular position of the neighbor j relative to the heading direction is denoted as (bearing; βj), and the angle subtended on o’s visual sensor is denoted as αj. This angle is calculated as an angle between the edge rays from the focal agent to the observed corners of the neighbor [62]. The edge rays mark the maximally-distant observable pair of the neighbor’s corners. The line seg- ment connecting these two corners is called the effective edge. Fig 1b illustrates the edge rays for three neighbors observed by the focal agent. Taking a reductionist approach, we only assume the single omni-directional sensor can measure subtended angles and—over multiple frames taken in successive time—angular dis- placements of tracked objects (Fig 2a and 2b). It does not measure the orientation or heading of the observed neighbor, since identifying orientation requires depth perception ability. As a result, inferring inter-agent distances from the angular projection of a neighbor is generally impossible, as different distances can produce equal projections (Fig 2b). This also raises a challenge for estimating the velocity vector vj for neighbor j, as different actual velocity vectors can be projected to identical observed angular displacements (Fig 2c; see also [35]). The elon- gated morphology of the agents is a crucial factor in the accuracy of this process: when agents are circular, the projected subtended angle allows for accurate estimation of the distance, and thus to the precise knowledge of displacements and velocity (see Fig 2d, and an extended dis- cussion in Section 5). Estimating neighbors radial and tangential velocities. We start the estimation of neigh- bor’s j velocity vj by separately estimating its two components vj,r (radial velocity) and vj,t PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 5 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 2. Sensing pointing angles and angular displacements. The blue circle represents an idealized 360-degree visual sensor of the focal agent. Positions at time t are marked by x(t). The elongated shape of the neighbor agent leads to ambiguity in computing its kinematic parameters when using solely angular data. In contrast, for circular agent morphology, the angular data are sufficient to extract complete and exact kinematic data of the neighbor. (a) Angular velocity _bj is computed from Δβj, i.e., from the change of the LOS direction. (b) Distance rj to the neighbor j is estimated from the angle αj subtended by the neighbor, using Eq 5. Different distances to the neighbor (green and purple lines) can have the same subtended angle αj due to the different orientations of the neighbor with respect to the LOS. (c) A related source of ambiguity lies in the impossibility of computing the components of the neighbor’s velocity accurately when using only angular information from a single visual sensor. As shown: many different endpoints produce equal Δβ. (d) In contrast: when agents are circular, the angular information αj and Δβj suffices for an exact computation of distance and velocity, because the distance rj is uniquely obtained from αj alone. https://doi.org/10.1371/journal.pcbi.1011796.g002 (tangential velocity) (illustrated in Fig 1a). Both components are estimated on the basis of the instantaneous vectorial distance rj. We make two assumptions in computing this estimate, with respect to the orientation and size of the observed neighbor, as discussed below. First, since the orientation of the neighbor is unknown to the observer, we use a simplifying assumption that the neighbor’s effective edge (d, in Fig 1b) is perpendicular to the LOS. Com- monly, this effective edge would be the diagonal of the observed rectangle (neighbor A in Fig 1b), as observing the rectangle edges occurs only in rare cases of perfectly parallel or head-on PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 6 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model motion. The triangle comprised of the focal agent’s COMo and the two vertices of the effective edge d (see Fig 1a) is then taken to be equilateral (see Fig A in S1 Text). Under this assumption, the LOS constitutes both median and altitude to the effective edge, and a bisector of the sub- tended angle αj, and therefore, the scalar distance rj is given by rj ¼ 1 2 kdk cot aj 2 and the vectorial distance rj is given by rj ¼ rj^uj;r ð4Þ ð5Þ where ^uj;r is the unit vector pointing toward the neighbor j along the LOS to it (see Fig 1a, and Fig B in S1 Text). The distance estimation is based on a second assumption, as also made by other researchers [63–65] that animals can possess knowledge of the typical size of its conspecifics, especially in homogeneous swarms. In our case, this translates into an assumption that the effective edge kdk used in Eq 4 is a known constant for the agents. Combining this constant d with the angle (αj), one can estimate the distance vector (Eq (5)). This estimate has been used in earlier stud- ies in the context of loom calculations [61, 63, 64, 66]. We emphasized that rj, as given by Eq 4 is an inaccurate estimate of the actual distance to the neighbor j, because it is based on the assumption that the effective edge d is always perpen- dicular to the LOS, which is not true in general, and is of given constant length (typical of con- specifics). In reality, the effective edge depends on the specific instantaneous orientation of the observed neighbor, as shown in 1b, and on its actual size. Relying on the two assumptions above, the radial velocity is computed by differentiating Eq (5) with respect to time t, � � @ @t rj vj;r ¼ ^uj;r ¼ (cid:0) 1 4 d _aj � � ^uj;r aj 2 sin2 ð6Þ where _a denotes the time derivative of the subtended angle. Expressing d ¼ 2rj tan from Eq (4) and substituting into Eq (6) results in the radial velocity vj,r, (the derivation is detailed in S1 Text): (cid:0) � aj 2 vj;r ¼ (cid:0) _aj sin aj rj ð7Þ The negative sign means that when the subtended angle increases, the velocity of the neighbor is towards the focal agent, and vice versa; see Fig 1a for the intuition. While the radial velocity is estimated only from the projected subtended angle and its rate of change, the tangential velocity requires additional components: the bearing angle β (which is generally known), and its derivative over time _bj (also known as the instantaneous angular velocity): from which we can deduce _bj ¼ kvj;tk rj vj;t ¼ _bj rj^uj;t; PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 ð8Þ ð9Þ 7 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model where ^uj;t is the unit vector of the tangential direction, i.e., perpendicular to the radial unit vec- tor ^uj;r. Combining the two components, we obtain the full velocity vector of neighbor j, vj = vj,r+ vj,t. This process is repeated for all the neighbors, and the mean vj, (vdesired of the focal agent) is computed by the formula in Eq 3. We emphasize that this is a baseline model. It assumes that all the neighbors are fully visible and does not account for possible obstructions of sight. In other words, the agents are pre- sumed to be transparent, in the sense that they do not occlude more distant neighbors. Because this assumption clearly ignores fundamental limitations of visual perception in nature or in robots, we explore general strategies to address it in the next section. 2.2 Addressing occlusions: Three approaches Occlusions present an inherent challenge to the use of visual modality in both natural and syn- thetic agents. Flocking swarms, whether natural or artificial, are often dense [67]. Conspecifics located closer to the observing animal are inevitably blocking, partially or entirely, the animals standing behind them (Fig 3). Complete and partial occlusion of neighbors not only reduces the information available to the focal agent but can also introduce large estimation errors. Neighbors that are completely occluded are not taken into account in the collective motion model. Partially-occluded neigh- bors introduce errors, as the projected area of their subtended angle, used as a proxy for dis- tance, is smaller than it should be. For example, suppose a neighbor is partially occluded, such that only a small portion of it is observed, and thus it is initially perceived to be distant: if the occluding animal moves to uncover it, its full length will now be revealed, and within a very short time it will be seen as being close, implying high radial velocity towards the observer and a potential collision. The accumulation of such frequent errors may disturb the stability of the swarm. We posit there are three general strategies that may be applied (illustrated in Fig 4). Suppose the focal agent may be able to recognize peers and thus differentiate between entirely-visible Fig 3. A schematic illustration of the visual social environment from the perspective of the individual locust in a swarm. https://doi.org/10.1371/journal.pcbi.1011796.g003 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 8 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 4. (a) OMID—partially occluded neighbor (orange) is omitted from the field of view. (b) COMPLID—orange neighbor is completed from the seen segment. (c) PARTID—partially seen segment is regarded as a neighbor. https://doi.org/10.1371/journal.pcbi.1011796.g004 individuals and parts (partially-occluded individuals that are not recognized as conspecifics). This allows it to ignore partially-visible neighbors (Fig 4a). It may also be able to cognitively extrapolate parts to a whole, inferring the position and orientation of the partially-occluded peer from its visible parts (Fig 4b). Alternatively, without being able to recognize peers, the focal agent may still be able to perceive any visible part of a neighbor as a distinct whole indi- vidual. These different strategies place very different requirements on the cognitive-computa- tional processes of visual perception in the focal agent, as discussed in detail below. Approach 1: Omission of the Occluded (OMID). The first approach disregards any visual information originating in partially occluded agents (see Fig 4a). This requires the ani- mal to possess a dedicated peer recognition mechanism, i.e., to be able to recognize fully- imaged conspecifics (and ignore anything else). Mechanisms of selective attention in visual perception are known to exist in humans and are achieved in the human brain in multiple stages of perception [68, 69]. Neurobiological studies have shown the existence of selective attention mechanisms also in insects’ visual processes [70, 71]. However, it is not known whether locust visual perception mechanisms are able to recog- nize peers. Experiments reported by Bleichman et al. [36] have shown that an individual locust responds by walking when exposed to visual images composed of randomly-moving dots that are projected via computer screens to both eyes. As the dots are positioned randomly and do not mimic the shape or the colors of locust nymphs, these results seem to indicate that the motion is triggered in the individual devoid of any dedicated peer recognition mechanism. Nevertheless, such visual processing may, in principle, be applied during collective motion and constitute a plausible approach that exists in nature. Approach 2: Completion of the Occluded (COMPLID). In the second approach, par- tially occluded agents are “completed” as if they are fully visible to the observer. In other words, a neighbor that presents even the smallest visible segment from the focal agent’s per- spective would be treated as if no occlusion is present when processing its visually extractable information. COMPLID utilizes peer recognition as in OMID, while also requiring that the agents will be able to assess the obscured part of a neighbor (if needed) based on its visible part. This completion assumes an agent’s visual extrapolation that reconstructs neighbors’ out- lines using their visible features. Completing partially visible targets obscured by other objects is a long-studied process in visual perception. The filling-in of details and image regions partially obscured by interceding objects [72, 73] is an established neurophysiological process that gives the organism an ability to identify a complete form based upon observed parts of the contour and is described by the term “visual completion” [74]. This mechanism produces an internal representation called “illusory contour”, which extrapolates the physical stimulus to the full geometrical shape of the PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 9 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model object [29, 75, 76]. Visual completion of occluded objects has been shown in varied and phylo- genetically distant species: birds, fishes, cephalopods, bees, etc., and is accepted as one of the fundamental components of vision in nature [75, 77, 78]. Approach 3: Every Part is a Full Agent (PARTID). The third approach treats all visual stimuli related to a neighbor as if they represent a full-body conspecific. Contrary to OMID and COMPLID, this approach makes no assumption of peer recognition capabilities. Rather, the visual field is divided into segments, with each segment containing the same optical flow vectors. The agent assumes that each segment represents a different neighbor. In other words, any visual information is taken completely at face value without any additional interpretation. Hence, other than the ability to accurately extract optical flow vectors, no further advanced visual perception mechanisms are required. Since the optical flow is essentially the vectorial difference between two consecutive frames and does not consist in any form of object recogni- tion by itself, PARTID would be the least computationally demanding approach if imple- mented in real life. However, in this approach, the potential error in the assessment of the environment is the largest, in comparison to OMID and COMPLID, since partially occluded agents occupy less area on the visual field, which translates to a significantly larger distance estimation. The same applies to velocity estimations, which are tightly dependent on the distance. Although in this approach, an agent does not possess with object recognition abilities, it is assumed that the observable parameters ða; _a; b; approach requires relatively limited visual processing and is easier to implement in robotic systems. _b Þ are still fully available and extractable. As noted, this PARTID takes its inspiration from biological mechanisms, in which an organism performs an action based on visual stimuli originating from an object that is not recognized. For exam- ple, locusts possess a pair of visually-sensitive neurons that encode looming stimuli and cause the locust to produce escape behaviors [61]. The visual stimuli affect the behavior of the indi- vidual directly and without passing through object recognition mechanisms [36]. 2.3 Summary We summarize the different mechanisms introduced in this section. First, we derived esti- mates for the velocities of visible neighbors, such that these velocity vectors can be aggregated in a Viscek flocking mechanism for determining individual velocity at any given moment. These estimates rely on assumptions with respect to the background knowledge available to the individual (the typical size of conspecifics), as well as on the orientation of the observed agents (parallel to the line of sight). We refer to this base reductionist model as the principal model. We then discuss strategies for addressing occlusions, which can further degrade the accu- racy of estimated velocities. All three strategies ignore completely occluded agents (unlike the principal model) but differ in how they treat partially occluded neighbors. They are summa- rized in Table 1 below. 3 Methods In order to evaluate swarm behavior using different occlusion-handling approaches, we devel- oped a two-dimensional (2D) collective motion simulator based on a basic simulation engine [79] (see Fig 5). The agents’ movement in two dimensions is simulated by updating their coor- dinates at each iteration in accordance with their current velocities and the position update control laws presented in Section 2.1. The location and orientation of each rectangular agent are computed from the coordinates of its COM. It is assumed in our model that velocity PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 10 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Table 1. A summary of the differences and similarities between the different reductionist models. Rows present different occlusion conditions with respect to the neighbor in question. Columns contrast the various models in how they respond to these conditions. See also Fig 4. Neighbor visible? Completely occluded Not occluded Partially-occluded Principal Fully visible Fully visible Fully visible OMID Ignored Fully visible Ignored COMPLID Ignored Fully visible Fully visible PARTID Ignored Fully visible Part is neighbor https://doi.org/10.1371/journal.pcbi.1011796.t001 heading is always along the long axis of the body. The velocity magnitude can vary between 0 and a fixed maximal speed value vmax, i.e., the agents can accelerate up to a maximal speed. Together with the steering parameter η, this reduces the sharpness of turns and accelerations in the agents’ motions. The agent’s motion decisions are based on the neighbors’ velocities. These velocities, in turn, are derived from the angular measurements of each perceived neighbor: their subtended angle and the angular velocity. These inputs serve as the agent’s subjective perception. 3.1 Simulating perception We compare the emergent collective motion resulting from the different occlusion-handling approaches. The perception of each agent is simulated. The exact values stored in the simula- tion, are used as the basis for emulated perceptual processes, and the effects of occlusions. Each simulated focal agent is given the information it would have perceived in the 2D environ- ment, in the principal model, and in the three occlusion-handling strategies. Simulating the Principal Model. The α angle is calculated using the neighbor’s vertices of the edge that subtends the largest angle on the agent, regardless of occlusion. The angle between the two vectors pointing from the focal agent’s COM to the respective vertices equals α. The β angle is simply the angle between the focal agent’s velocity vector and the neighbor’s Fig 5. (a) Toroidal arena snapshot at t = 10[frames]. Agents are initialized at random positions and random velocities. The purple-colored agent is an arbitrarily marked focal agent with its respective neighbors colored green. (b) Toroidal arena snapshot at t = 2000[frames]. An apparent flocking behavior is displayed, with all agents moving roughly in a single direction. https://doi.org/10.1371/journal.pcbi.1011796.g005 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 11 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model COM, again regardless of occlusions. The focal agent receives visual parameters of all the neighbors, including those that are completely occluded by others. Simulating OMID. All completely occluded or partially occluded neighbors are ignored. The effective α and β for each completely-visible neighbor are taken from the subtended angle as before, and only those are used in computing vdesired. Simulating COMPLID. We simulate this capacity by means of calculation, taking the same measurements as in the principal model. We then remove from consideration all neighbors fully occluded by others. Simulating PARTID. We iterate over the neighbors, from the closest to the furthest. Each neighbor’s effective edge is calculated and then checked against an array of edges. If a partial overlap occurs with the current edge and one or two of the already checked edges, the effec- tive α is calculated using only the non-overlapping segment: that is, the subtended angle from any visible part of a neighbor is taken to be a neighbor, and its center of mass is taken to be the angular midpoint. 3.2 Controlled (independent) simulation variables The simulator enabled control of the many variables. The population size N controls the num- ber of agents in the simulated swarm. The body length-to-width ratio determines the elonga- tion of the agent, and thus is assumed-constant effective edge size d. The effective range of the sensor, R is measured in body lengths ([BL] units), and determines the range within which the agent is able to perceive neighbors, without occlusions. The steering parameter η sets the weight of vdesired relative to the current velocity (vi) of an agent. vmax, which caps the maximal speed attainable by agents and was arbitrarily set to 1[BL]/frame. We utilized different areas (arenas) in the simulation experiments: a square arena with peri- odic boundaries, an infinite corridor where only one axis has periodic boundaries and a circu- lar arena (with no periodic boundaries). Where a period boundary occurs, once an agent’s COM passes the maximal/minimal coordinates or the X/Y axes, it reappears on the other side, respectively. Where a non-periodic bound is reached by an agent, it is repelled with varying repelling force, depending on the size of the radial velocity component (relative to the arena center), i.e., an agent traveling to the external circular boundary will be repelled from it with a force proportional to the size of the agent’s radial velocity component. Our choice to incorporate both ring and corridor arenas in our simulations draws inspira- tion from laboratory studies of collective motion, which often use bounded environments for practical reasons. These settings are advantageous for aligning simulation results with experi- mental data. In nature, although environments may appear open, they are often restricted by various topographical features such as valleys, crests, and boulders, as well as diverse vegeta- tion, all of which significantly influence the movement patterns of swarms [80]. In our study, the corridor arena represents an approximation of such a naturally restricted environment, akin to an endless path with one direction of movement. The ring arena, with its curved boundaries, serves as a variation of this concept, emulating a continuous corridor but with a circular layout. These designs are intended to reflect the adaptive behavior of locusts in avoiding collisions with boundaries, as observed in natural settings. In contrast, the torus arena, while useful for simulating unbounded environments, does not accurately represent the boundary-limited conditions typically found in natural settings. Both the ring and corridor arenas are illustrated in Fig 6, providing visual representations of these simulation environments. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 12 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 6. (a) Snapshot of corridor arena. The vertical boundaries are repelling, while the horizontal ones are periodic. (b) Ring arena snapshot. https://doi.org/10.1371/journal.pcbi.1011796.g006 3.3 Measured (dependent) simulation outcome: Flocking order The ideal flocking is a situation in which all agents are synchronously moving in the same direction. Over the years, various measures of order have been proposed and utilized in differ- ent settings. As we essentially extend the Vicsek-based collective motion model to account for visual perception, we chose the polarization measure of order, denoted ϕ and used in other investigations of Vicsek-based collective motion [14, 20, 81–83]. It is defined by � � � � � 1 N X i2N vi kvik � � � � � � ¼ ð10Þ where N is the population size, and vi, kvik correspond to the velocity and speed (resp.) of agent i. ϕ measures the degree of global alignment by averaging the normalized velocities of the agents (i.e., headings). It is a scalar value representing at any given time the degree of global order in the system. For a random disordered group state, ϕ is approximately 0, while for a fully ordered flock, with all agents moving with an identical heading, it approaches a value of 1. 4 Results Two sets of experiments were conducted to evaluate the presented approaches. The first set, presented in Section 4.1, uses the principal model to set baseline parameter ranges for various controlled settings. The second set of experiments, presented in Section 4.2, then uses the established parameters to contrast the performance of the three occlusion strategies alongside the principal model in different arenas, whose geometry and bound periodicity are varied. Unless otherwise stated, the experiments comprised 50 independent trials, each with its own randomized initial conditions (individual velocities, including headings), such that the swarm was unordered (ϕ close to 0). The figures present the mean over the trials, with error PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 13 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model bars (or shaded envelopes around the solid lines) showing margins defined by the standard error. This enables the distinction of significant differences between different models. The primary measure in our flocking system analysis is the polarization order ϕ, ranging from close to 0 (no order) to 1 (high order). We study both the time evolution of ϕ and its steady state value for large t: one tracing ϕ over simulation frames t = 1 to 3000, and another showing ϕ at frame t = 3000. The former assesses whether ϕ stabilizes or varies, indicating the swarm’s convergence dynamics, while the latter provides a quick view of the swarm’s order at the simulation’s end. 4.1 Flocking using the principal model: Baselines We begin by testing the principal model in a toroidal arena, with independent simulation vari- ables chosen in accordance with observed locust characteristics. The goal is to establish base- line responses to various settings, such as the visual range R, the steering parameter η, etc. As simulation measurements are artificial, we use a standard length unit, [BL], which is the agent’s default body length, with a length-to-width ratio of 3. For the experiments reported in this sec- tion, we used an arena of size 20 × 20 [BL2]. 4.1.1 Determination of steering parameter η. The first experiment sought to determine an appropriate steering parameter, η empirically. Initial settings were based on observations of locust marching bands: a population size N = 120 within the arena resembles reported march- ing locust density in nature [67] (see below for other values of N). Similarly, the sensing radius R = 3 body lengths ([BL]) was set according to empirical observations of locust nymphs not reacting to visual stimuli located farther than 2–3 [BL] [26]. The agent elongation (body length ratio) was set to 3 (i.e., agent length is three times its width; see Section 4.1.3 for discussion). We experimented with different values of the steering parameter η. Fig 7a shows the mean order measure ϕ as it changes over time, measured in simulation frames (t = 1. . .3000), for four values of η. It can be seen that smaller values of η cause the swarm to converge towards a higher order, while larger values do not. Fig 7b examines a more extensive set of η values in terms of the order measurement at time t = 3000. It can be seen that a value of η = 0.01 yields the maximal value of the order parameter, approaching 1. This is where the swarm is nearly fully aligned. Notably, no convergence occurs for smaller values, meaning that the agents are apathetic to the environment and retain their original heading directions. In contrast, a significant drop can be seen in the order parameter magnitude for large η values, i.e., the agents’ convergence fails due to over-sensitivity to the external steering parameter. Further analysis of these large η values is provided in Fig 7c. It shows that agents aggregate in small and tight clusters and constantly change their headings, unable to reach either a local or a global uniform moving direction. Based on these findings, we fixed the steering-parameter factor parameter as η = 0.01 for the rest of the experiments reported in this study. 4.1.2 Influence of vision radius R. A second series of experiments examined the role of the visual sensory range (distance-wise). Initial settings, based on empirical observations of locusts, have set the range R at 3[BL]. In this subsection, we examine other values. Fig 8a and 8b, present the evolution of order ϕ over time, and its long-term values, for dif- ferent visual ranges 0.67 � R � 3.67 [BL] for a swarm of size N = 100. Fig 8a shows the order developing over time for different values. Fig 8b shows long-term mean values of ϕ at the end of the simulation (t = 3000). As expected, for R smaller than 1[BL], the progress toward the ordered group state is weak and very slow. The reason for that is for such short range of visibil- ity, most neighbors are unobserved. Thus, vision does not provide sufficient information about the neighbors to the focal agent. For larger values of R, the long-term ϕ slightly increases PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 14 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 7. Steering parameter sensitivity analysis, over 10 independent runs. (a) Mean order (ϕ) for different η, t = 1. . .3000. The solid line shows the mean order parameter of the swarm for each t, with standard error margins shown in the envelope. (b) Long-term (t = 3000) mean order (ϕ) for varying η values. (c) Cluster pattern of agents moving under high η values. https://doi.org/10.1371/journal.pcbi.1011796.g007 Fig 8. (a) Time-dependent and (b) long term sensitivity analysis for visual range R, measured in body lengths [BL], in the Torus arena. Means and standard errors shown for 50 trials. https://doi.org/10.1371/journal.pcbi.1011796.g008 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 15 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model with larger radii. Interestingly, lab experiments and observations of locusts have estimated their visual range to be 2–3[BL]. For the remainder of the experiments, we set R = 2.67[BL]. 4.1.3 Other influences on flocking order. Interconnections clearly exist between the dif- ferent parameters. For instance, it is possible that for visual ranges that dramatically differ from those we used in our experiments, different values of η will yield different results. Simi- larly, varying the type of environment used can influence the rate of convergence or even its existence. As we sought to explore the model as inspired by nature (in particular, locust), we set out to experiment with settings in ranges that approximate locust swarms and used the toroidal arena and principal model, as we believe these to be the least constraining, and least sensitive to parameters that are external to the model itself. The population size N is a clear factor regarding the emergence of order, as varying N while maintaining a fixed arena area (or inversely, varying the arena size while maintaining a fixed value of N) impacts the swarm density. This in turn influences the likelihood of occlusions, the ability—given limits on R—to observe neighbors, etc. In the different arenas we set values of N that we had experimentally determined to be informative, in that they reveal differences between the different strategies. Fig C in S1 Text, shows how this procedure was carried out for the torus arena. We took similar steps to determine N in the other arenas. We now turn to discussing the body length ratio, which measures the elongation of the agent. We used a length-to-width ratio of 3 [BL] unless otherwise noted, as this approximates the observed dimensions of typical locust nymphs in our laboratory, which inspired this research. This is a critical issue, as some existing models of vision-based flocking use non-elon- gated (circular) agents. While locusts, and many other swarming species, are clearly elongated, it is important to establish whether the elongation (as measured by the body length ratio) influ- ences the results. Otherwise, non-elongated agents—circular or squares—could equally serve as a model for locusts or other elongated agents. Fig D in S1 Text provides an empirical exploration of the influence of the length-to-width ratio on convergence, in various environments, and in all flocking models (principal, OMID, COMPLID, PARTID). Briefly, the results show that convergence to an ordered state is highly sensitive to the length-to-width ratio, and thus setting its value to model locust body dimen- sions is critical. As these results complement the main results for the occlusion-handling strat- egies that we report below, we advise the reader to examine them after the main body of results is presented. We also address this issue in the Discussion (Section 5). 4.2 Comparison of the three occlusion strategies and the principal model Having established the baseline parameter and experiment settings, we now turn to investigate the emerging order ϕ of swarms, utilizing different strategies. The three occlusion-handling strategies are evaluated in comparison with the principal model (which does not account for occlusions). A summary of the commonalities and differences between the models is provided in Table 1. 4.2.1 Experiments in the Torus arena. We begin with the experiments in the Torus arena, which we had utilized (above) for establishing the baseline parameter values. Fig 9 shows the evolution of the order ϕ over time for all four strategies. The graphs show the mean order parameter for each point in time. Three population sizes of N = 60, 120, 180 are shown; in all experiments R = 3[BL], η = 0.01, and length-to-width ratio is 3. At higher densities (shown in Fig 9c) the rate of convergence of all three perceptive approaches lags behind the principal model. At higher densities, rates of convergence become steeper. At the same time, the long-term order parameter remains very close for all the meth- ods, and even different densities. Finally, the ranking of the rates of convergence at N = 180 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 16 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 9. ϕ evolving over time t = 1 . . . 3000, for different strategies, in the torus arena. Plots show the mean order parameter of the swarm at each simulation frame, with standard error margin for different population sizes–(a), (b), (c) 60, 120, 180 agents respectively–over 50 independent trials. Larger N generally leads to a slightly steeper transition to a flocked state. https://doi.org/10.1371/journal.pcbi.1011796.g009 indicates that COMPLID converges faster than OMID. Completing parts of neighbors, rather than omitting them, leads to an effectively larger number of neighbors, which leads to stronger alignment. Fig 10 complements Fig 9 above. It shows the long-term mean order at the end of the simu- lation t = 3000. It is evident that all three occlusion approaches, alongside the original model, reach similar long-term order-parameter values (ϕ * 0.9), indicating they are reaching similar degrees of ordered flocking. That said, when we consider the result of PARTID at N = 180, and also examine its behavior in Fig 9b and 9c, we see that PARTID has a slower rate of conver- gence, and slightly lower long-term order (note the separation defined by the standard error bars for PARTID when N = 180, in Fig 10). This can be interpreted as additional evidence that PARTID may generate excessively noisy perception. 4.2.2 Experiments in bounded arenas. The torus arena is fully periodic: agents moving towards an edge are not repelled by it nor blocked. Rather, they move through it to appear on Fig 10. Long-term order of the four strategies: Principal, OMID, COMPLID, PARTID. The plots shows the mean (and standard error) long-term order ϕ for t = 3000, for different N values (50 independent trials). Long-term ϕ values are practically indistinguishable. https://doi.org/10.1371/journal.pcbi.1011796.g010 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 17 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model the opposite side of the arena. Likewise, agents close to one edge can visually sense neighbors that are on the “other side of the edge”, i.e., on the opposite side of the arena. While this is a common arena model in theoretical studies of swarms, its abstract nature distances it from the geometrical constraints of realistic environments, which have bounds and obstacles that impose limits on the movement of the agents. We therefore switched to experiments in the infinite corridor (periodic on one side, but not the other) and the ring (non-periodic) arenas, described in Section 3. Three versions of each arena type were tested: wide, intermediate, and narrow. The geometry of the arenas is charac- terized by the arena width to single agent body-length, i.e., arena width in in [BL] units. For the infinite corridor, the distance between the periodical boundaries (length) was 20 [BL] for all the experiments. The widths were: 10, 20, 30 [BL] respectively. For the ring arena the radius of the inner circle was 1.66 [BL] and the outer circle radii tested were: 5, 8.33, 11.66 [BL]. In the experiments below, N = 100 (empirically selected; see Fig C in S1 Text). Figs 11 and 12 combine to show that narrow bounded environments (i.e., higher densities, and perturbations caused by bounds pushing the agents back into the arena) cause distinguish- able differences in the convergence rate (and success) of the different flocking, when utilizing different strategies for handling occlusions. In particular, while all strategies show a rise in the ordering parameter, the behavior under each strategy is distinct. A potential reason for this is the fact that in narrow arenas, interactions with the boundaries are much more frequent. In the corridor, the principal model and the COMPLID strategy converge significantly faster and to a higher long-term value than OMID and PARTID. In the ring, all four strategies are clearly distinguished. A commonality to both arenas, in all settings, is that the PARTID strategy is generally slower than the others (with the possible exception of wide corridor and intermediate ring). Fig 11. Mean and standard error of the order measure ϕ, as it changes over time t = 1. . .3000, in 50 trials. (a),(b), (c) Infinite corridor arena. Wide / Intermediate / Narrow arena dimensions are 20 × 30 / 20 / 10 [BL]). (d),(e),(f) Ring arena. Wide / Intermediate / Narrow ring external border radii are 11.66 / 8.33 / 5, respectively. The internal ring border is constant for all three types and equals 2.5. https://doi.org/10.1371/journal.pcbi.1011796.g011 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 18 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 12. Mean and standard error of the long-term order measure ϕ, at t = 3000, in 50 trials. In both (a),(b), the horizontal axis marks the width of the arena (wide/intermediate/narrow, as above). (a) shows the results for the infinite corridor. (b) shows the results for the ring arena. https://doi.org/10.1371/journal.pcbi.1011796.g012 We believe that given that PARTID is a-priori more likely to suffer from noise in the observa- tions, the geometrical bounds, which repel or push agents into the arena (sudden orientation changes) are particularly detrimental to convergence when PARTID is used as a strategy. 5 Discussion The reductionist approach we have taken in this study is intended to shed light on the neces- sary (minimal) mechanisms that can generate an ordered collective motion based on visual processing. This goal stands in contrast to the clear variety of sufficient mechanisms that can be used at a computational (cognitive complexity) cost and/or mechanical-physiological requirements. Even when disregarding energy- and computation-hungry sensors and pro- cesses used in robots (e.g., LIDAR sensors and associated processing [84]), distance estimates could still be generated from visual information in a number of ways, albeit demanding more capabilities from the agent, compared to the approach we have taken here. For example, ste- reoscopic vision is a well-understood mechanism for reliable distance estimation [32] in both natural and artificial systems. However, the requirement for overlapping fields of view of each eye (camera) narrows the perceived angle. While multi-lateral distance estimation is required for most traditional models of flocking, quite literally, carrying out the stereoscopic vision in a backward direction would require an additional pair of eyes at the back of the agent’s head [55]. We introduced a non-stereoscopic (monocular) vision-based flocking model for elongated agents whose motion and perception are situated in two-dimensional flat worlds. The goal was to explore the generation of ordered collective motion with the bare minimum of information that may be perceived through a monocular vision. The models utilize geometrical aspects of vision, such as subtended visual angle, observable angular velocity, and other derived parame- ters, but does not otherwise rely on complex visual processes. The model departs from previous theoretical models [13, 14, 81] that ignore the inherent limitations of the visual sensing modality (and rely on direct measurement of inter-agent dis- tances or velocities). Rather, the model estimates distances and velocities from observed angles and their rates of change, measures which are biologically plausible in non-stereoscopic vision. Also, we avoid the assumption of circular agent shapes which is made in some previous inves- tigations of vision-based flocking, and specifically allow for elongated agents. Finally, we also depart from most studies of vision-based flocking by explicitly considering different perceptual strategies for handling occlusions, and their effect on the resulting movement. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 19 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Below, we highlight specific issues and explore the questions raised by the results, in partic- ular also with respect to previous investigations of collective motion. 5.1 The plausibility of different strategies for handling occlusions We tested and compared three different general strategies for addressing occlusions in differ- ent arenas: • The first strategy (COMPLID) completes the outline of a partially hidden neighbor. Such abilities are present in various species, and there is some evidence that these include insects [75, 78]. However, this approach is the most complex of the three (cognition and computa- tion-wise) as it requires the recognition of conspecifics combined with extrapolation capabilities. • The second strategy (OMID) entirely ignores any partial information. This requires differen- tiating between fully vs. partially observed neighbors, which implies using recognition of conspecifics. However, it is somewhat simpler than COMPLID since it only filters out erro- neous visual stimuli rather than computing the correct stimuli. • The last strategy (PARTID) treats each segment of a neighbor as if it represents a full-length body. Hence, it is the simplest of the three since it requires minimal cognitive processing from the individual. It does not rely on dedicated conspecific recognition mechanism but instead clusters distinguishable visual features and regards each cluster as a neighbor, a rela- tively simple process. However, the same simplicity also results in PARTID providing the most erroneous percep- tion of the surrounding agent, as parts of neighbors’ segments change in their degree of visi- bility due to closer neighbors revealing or occluding them, which in turn is perceived as neighbors moving–quickly—away from or towards the focal agent (i.e., large absolute mag- nitude of the vj,r component). The difference in the required computational power under the different approaches is very significant, as in nature, organisms demonstrating collective motion are very often limited in this respect (small brains, simple neuronal substrates). Hence, finding the least computation- ally demanding algorithm that is still capable of reaching flocking can potentially explain the actual mechanisms involved in the flocking of these relatively simple species. From this per- spective, PARTID has the least requirements for visual information processing, while COM- PLID has the most requirements. In the torus arena, all three perception approaches of occlusions have successfully demon- strated the flocking transition from a disordered initial state to an ordered collective state. However, a detailed analysis reveals slight differences in convergence rates, where PARTID consistently appears to be slower to converge than the other strategies. This deficiency of PAR- TID is more pronounced at a higher density of neighbors, where occlusions are more frequent, and thus PARTID makes more errors. It is important to emphasize that there is no intrinsic algorithmic advantage to faster con- vergence in collective motion models. However, there may very well be a functional advantage, in the sense that faster or slower convergence is advantageous to the swarm, and thus in this sense, an algorithm displaying faster convergence may be considered better. In the context of natural swarms such as locusts, there could be a potential advantage for faster convergence. Observations from biological studies (e.g., [6, 21]) indicate that locust swarms exhibit daily activity patterns transitioning from a state of disorder to organized movement. In their natural behavior, locusts spend early hours inactive on vegetation, gradually moving to ground-level PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 20 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model activities, and then, as temperatures and activity levels rise, they shift towards collective motion. This daily cycle, sometimes repeating within a day due to temperature variations, emphasizes the ecological benefit of a rapid transition from disorganized to coordinated move- ment. A higher convergence rate of the swarm’s alignment implies a quicker formation of an ordered swarm, facilitating efficient resumption of marching and migration, crucial for the survival and functioning of the swarm. When we evaluated the models in constrained environments (non-periodic bounds; corri- dor and ring), the general following conclusion emerges: at best, PARTID converges as quickly as others; most often, its rate of convergence to an ordered state is consistently less rapid com- pared to other strategies, and has lower long-term order parameter values. Note that such con- strained environments are common in nature. The topography of natural terrain has creeks, valleys, ridges, and other lateral constraints resulting in effectively constrained geometry. As is well established (see [44, 80] and references within), marching locust bands successfully main- tain flock formation despite such constraints. This presents an intriguing challenge for our understanding of collective motion. On the one hand, an occlusion-handling method (PARTID) that is computationally cheap, and employs mechanisms whose existence in insects is generally accepted. However, it is brittle and generally inferior to others exactly in the type of settings in which natural swarms, and in particular locust swarms, excel. On the other hand, in terms of order evolution over time, as well as order value at the end of the simulation, COMPLID appears to be superior to the others in most cases in its convergence rate and long-term value and inferior to none. However, COM- PLID implies complex capabilities for recognizing conspecifics and for being able to extrapo- late complete neighbor outlines from partial visual clues. While there is some limited evidence that insects are able to carry out such tasks (e.g., to extrapolating environment contours [75, 78]), recent laboratory studies of locust nymphs have demonstrated that they move in response to simulated movement of random visual patterns, which do not necessarily need to be recog- nized as other locust [36]. Considering our results from constrained arenas, it is tempting to declare that PARTID is an oversimplification of the perceptive mechanisms in locust vision, and that advanced computational capabilities are required for coping with partially-occluded neighbors, as assumed by the proposed OMID or COMPLID approaches. However, examining related investigations offers other possibilities, as we discuss below. 5.2 Reliable distance estimation, revisited The critical weakness of all the models under the restricted perceptual capabilities we allow, is in the estimation of distance to neighbors. The geometry of the visual image denotes a single subtended angle α, parallel to the horizontal plane of motion (the plane on which the agent is moving) as the basis of distance estimation. As detailed in Section 2.1, we assume no informa- tion is given to the agent about the neigbor’s orientation. Lacking this information, the model assumes it is heading in a direction perpendicular to the LOS. Violations of this assumption insert errors into the distance estimations. Without occlusions, their effects on the emerging flocking order is clear, but not dominant to the degree it prohibits flocking (consider the results in low-density arenas, for instance). In the presence of partial occlusions, the errors caused by the assumptions of the model may gravely affect the result. COMPLID relies on (assumed) complex capabilities of the agent to extrapolate the true dimensions of partially-occluded neighbors from visible parts. As a result, its distance estimates with respect to partially-occluded neighbors are the same as with fully-visible neighbors; it is therefore relatively robust to occlusions, in the sense that its PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 21 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model performance should not change much as they become more frequent. In contrast, PARTID, which considers every visible part as an agent by itself, is gravely affected by partial occlusions. A small visible part of an occluded agent would be considered a distant neighbor. If the part grows—more of the occluded agent becomes visible, e.g., because the occluding agent in between is moving—then that same perceived distance agent now captures a much wider sub- tended angle, and would suddenly be perceived as close. In other words, methods for reliable distance measurement in monocular images (other than those implied by COMPLID and OMID) can help avoid the failures of PARTID. The complexity and biological plausibility of such methods should be considered vis-a-vis the pro- cesses assumed by strategies we already discuss above: neighbor contour extrapolation (COM- PLID) and conspecifics recognition (COMPLID and OMID). Several studies touch on the critical relationship between the agent morphology and dis- tance estimation. Ignoring occlusions, visual perception of circular agents avoids the errors introduced by incorrect interpretation of α, as discussed in Section 2.1 and in S1 Text. Indeed, Moshtag et al. [58] and Berlinger et al. [85] demonstrated vision-based collective motion in physical robots, treating them as circles. Still, partial occlusions may cause rapid changes to α, and would make distance estimation unreliable under such conditions. Bastien et al. [56] (and later Qi et al. [57]) demonstrated that under the assumption of circu- lar agents, a completely different control approach can be taken, which avoids identifying indi- vidual neighbors or estimating the distance and heading of neighboring agents altogether. Rather, the agents only mark the projected blocking of the visual field by neighbors, without tracking them individually; angular segments in the field of view, blocked by neighbors, are marked as such, without a measurement of distance or identification of the neighbor. As a result, this approach is not sensitive to the occlusions in the same manner as the models intro- duced here. While robots may be built to be circular in shape, natural swarming animals are most often elongated—with locusts being an example. As we were initially motivated by the behavior of natural swarms, the experiments above were tested using elongated simulated agents. None- theless, the reduction in errors offered by assuming a circular shape raises the question of the importance of the agent’s morphology to the presented models. To address this question, we experimented with different length-to-width ratios. The analysis (Fig D in S1 Text) reveals that the performance of the different models varied widely when the body length ratio was changed, both in the rate by which order increases, as well as in long-term order values. Moreover, the qualitative relationships between models varied as well. In other words, the elongation of the agent has a dramatic effect on the emergence of ordered collective motion. The models presented in this study exhibit a dependency on several parameters, notably the body length ratio, visual range R, and steering parameter η. This dependency may limit their applicability across different scenarios. Future research could benefit from integrating our findings with the methodologies employed by Bastien et al. [56] and Qi et al. [57], which could lead to the development of more robust models for collective motion. Such integration has the potential to enhance the accuracy of distance estimation in swarms of elongated agents, partic- ularly in overcoming the challenges posed by occlusions. There are additional strategies that may be applicable. Because the agent’s shape is a given property in nature, how else might an agent overcome the errors introduced into its distance estimates by the variance in α (esp. with partial occlusions)? First, we may infer the orientation of the agent, to improve the distance estimate. This method can be applied by monitoring the position of each individual over a potentially brief period. This allows for the inference of their orientation based on their movement trajectory. This necessitates persistent labeling the individuals over the interval, despite occlusions, and PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 22 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Fig 13. Hypothetical example of how recognized template distortions may be used to infer orientation of visible and partially-visible neighboring locust nymphs based on its black patterning alone (i.e., not fully-detailed conspecific recognition). (a) Template distortions due to different headings. (b) Template distortions due to partial occlusion. https://doi.org/10.1371/journal.pcbi.1011796.g013 their relatively uniform appearance. Such tracking is considered to be very challenging from a computational perspective, even more so when the observer itself is moving [86–89]. Another approach is to infer orientation from enriched visual information. For instance, this may be done by matching distortions of known visual features to compute the orientation. Fig 13 illus- trates a hypothetical example of how this might work with locust nymphs. Note that this type of process is still possible with flat 2D sensing (no height), as the distortions are revealed as dis- tance changes between visual features of the known template. Second, independently, we may remove the artificial restriction on perception of a flat world, and consider the more realistic view that the agent views three-dimensional objects. Visible neighbors would then be characterized by two subtended angles, one measuring the horizontal dimension of the neighbor (the familiar α subtended angle), and one measuring its vertical dimension, i.e., its height (let us call it γ). Note that for elongated agents moving on the horizontal plane, α depends heavily on the orientation of the observed agent, but γ does not. For example, in Fig 13a, note how the subtended angle of the neighbor α changes with its head- ing, much more than its height γ. Integrating this information enables much more robust dis- tance estimations, and as both natural agents and robots move in three-dimensional worlds, it is commonly applicable [54, 85]. The use of γ can alleviate the errors caused by partial occlu- sions considerably when neighbors’ height is visible while their horizontal dimension is par- tially hidden. Third, we may attempt to generate depth information from monocular images taken over time. In computer science, this is called structure from motion (SfM), a complex process that generates depth information (and thus, estimated distance) from multiple images taken by a single moving camera, at different (close) times [90, 91]. While this is typically carried out in a static environment (i.e., the agent is localized with respect to static objects), it is theoretically possible, in principle, to apply this also to moving neighbors. However, it is considered very challenging, and in many ways an open problem for computer vision (see above for a brief dis- cussion of the challenges involved in tracking, which would be a subset of the challenges for SfM). Below, we also discuss the analogous (simpler) case for optical flow generation. None of the approaches discussed above for distance estimation from monocular images, completely solves the problem raised by partial occlusions. However, independently or in com- bination, they may alleviate it to an extent that enables computationally-simpler mechanisms to perform as well as those requiring complex processes. Indeed, more generally, allowing for rich visual projected information allows more robust measurements, based on many visual fea- tures, including shading, 3D shapes, color and spectral data, texture, etc. [31]. Even relatively simple combinations of visual features can be very useful. For example, Wang et al. [59] PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 23 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model demonstrated implicit cooperation between robotic swarm members using visual inputs. Uti- lizing specific schemes for positioning poles holding specifically-placed sets of LED lights, the robots were able to estimate the relative positioning, velocity, state, and other features of their neighbors. Royer et al. [39] and Dong et al. [34] survey the progress in this direction in robot- ics. There has also been great interest recently in applying machine learning approaches to the challenge of estimating depth from monocular images, utilizing data containing rich visual information [37, 92]. These studies, rooted in robotics and engineering, may inspire investiga- tions into biologically-plausible counterparts. For real-world robot swarms, it is crucial to consider the available computational resources against the requirements of the reductionist algorithmic models discussed above, which serve as baselines. In realistic settings, there are additional mechanisms competing for computa- tional resources on one hand (e.g., manipulation and basic processing of panoramic images, processing of additional sensor modalities, etc.), but also offering opportunities for greater accuracy and robustness, on the other—as discussed above. Thus although the algorithms are designed to minimize computational load, their practical application in swarm robotics needs to be considered alongside the opportunities and challenges offered by physical robot compu- tational and perceptual resources. 5.3 Reliable velocity estimates A common attractive component in all the models we presented is their reliance on optical flow as a key step in measuring _a and _b. Optical flow is a widely recognized technique employed by numerous species in nature, such as insects [33, 38], and is also an important method utilized in robotics for navigation and perception tasks [35, 93, 94]. One of the most difficult challenges to the use of optical flow in crowded environments, even ignoring the issue of occlusions, is that it is difficult to compute when the agent’s social environment is moving independently of its own movement. In other words, distinguishing the optical flow of observed agents that are moving in the vicinity of the observer, while the observer itself is moving, is computationally difficult, prone to errors, and sometimes impossi- ble (this challenge also arises for SfM processes, discussed above) [35, 94–97]. As we conducted simulation experiments in which estimations were produced by a simu- lated process, we could ignore this complexity. Employing the reductionist model in robots— or investigating its potential use in nature—would require tackling this computation; neither animals nor robots can side-step this issue. We note that computing optical flow when either the observer is moving (and others are standing still), or when the observer halts (and others are moving) is relatively easy [35, 96]. However, for the purposes of employing the model we present here, neither simplified variant would appear sufficient, as agents move while observing. In this context, it is important to note that previous work has established that the Pause-and-Go motion scheme plays a role in the repeated decision-making of locusts in a swarm [21, 26, 98], i.e., a representation of the local environment, utilized for deciding whether and in what direction to move, is constructed by the locusts when standing. Assuming that locusts utilize optical flow for decision-making in collective motion, it is plausible they adopt a two-stage approach to manage the computational challenge posed by calculating optical flow while in motion. This hypothesis suggests that locusts first pause to accurately compute the optical flow of their social environment, focusing on estimating the motion of neighbors. Then, in the movement phase, they estimate their own velocity vector, simplifying the task by treating the environment as static. This pause-and-go pattern poten- tially reduces the computational load involved in processing optical flow during active PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 24 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model movement, a task known for its complexity. This aspect of locust behavior and its application in robotic models merits further exploration, particularly regarding the efficiency of optical flow computation in dynamic settings. The research presented above has studied different aspects of vision-based collective motion in swarms. The biological inspiration was to study visual, non-stereoscopic inputs, without direct distance measurements and while accounting for occlusions. Our primary quantitative “lens” for this investigation is the polarization measure of order (defined in Eq 10), which is commonly and frequently used in studies of collective motion research [14, 20, 81–83]. Using this order measure, we have shown that the reductionist model is sufficient in many cases to achieve ordered collective behavior in a swarm. It is possible that perhaps some other types of measures of order could reveal additional information about these different cases. This study illuminates how a swarm’s behavior can leverage simple monocular visual cues to facilitate robust collective movement, influenced significantly by the agents’ physical form and field of view, as well as by the specific strategies employed to manage occlusions. The implications of this research extend beyond visual perception, potentially affecting other sen- sory systems and their role in coordinated group behaviors. Ultimately, our findings aim to deepen the understanding of the intricate connections between an agent’s shape, the algo- rithms governing collective motion, and sensory processing. Ultimately, our findings strive to enrich the comprehension of how an agent’s physical configuration interacts with collective motion algorithms and sensory perception, setting the stage for future research to unravel these complex dynamics and their applications in both natural and artificial swarming systems. Supporting information S1 Text. Supplementary materials. (PDF) Acknowledgments We would like to thank Dr. Michael Krongauz for his invaluable contribution to this research and the development of the model. Thanks to K. Ushi. Author Contributions Conceptualization: David L. Krongauz, Amir Ayali, Gal A. Kaminka. Data curation: David L. Krongauz. Formal analysis: David L. Krongauz. Funding acquisition: Amir Ayali, Gal A. Kaminka. Investigation: David L. Krongauz, Amir Ayali, Gal A. Kaminka. Methodology: David L. Krongauz, Amir Ayali, Gal A. Kaminka. Project administration: Amir Ayali, Gal A. Kaminka. Resources: Amir Ayali, Gal A. Kaminka. Software: David L. Krongauz. Supervision: Amir Ayali, Gal A. Kaminka. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011796 January 29, 2024 25 / 30 PLOS COMPUTATIONAL BIOLOGYVision-based collective motion: A locust-inspired reductionist model Visualization: David L. Krongauz. Writing – original draft: David L. Krongauz, Amir Ayali, Gal A. Kaminka. Writing – review & editing: David L. Krongauz, Amir Ayali, Gal A. Kaminka. References 1. Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Pro- ceedings of the National Academy of Sciences of the United States of America. 2008; 105(4):1232– 1237. https://doi.org/10.1073/pnas.0711437105 PMID: 18227508 2. 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10.1371_journal.pcbi.1011795	RESEARCH ARTICLE Mutational signature dynamics indicate SARS- CoV-2’s evolutionary capacity is driven by host antiviral molecules Kieran D. LambID T. Phan3,4, Matthew Cotten1,3,4,5, Ke YuanID 1,2☯, Martha M. Luka1,2☯, Megan Saathoff1, Richard J. Orton1, My V. 2,6,7, David L. RobertsonID 1 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Medical Research Council - University of Glasgow Centre for Virus Research, School of Infection and Immunity, Glasgow, Scotland, United Kingdom, 2 School of Computing Science, University of Glasgow, Glasgow, Scotland, United Kingdom, 3 Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda, 4 College of Health Solutions, Arizona State University, Phoenix, Arizona, United States of America, 5 Complex Adaptive Systems Initiative, Arizona State University, Scottsdale, Arizona, United States of America, 6 School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, United Kingdom, 7 Cancer Research UK Scotland Institute, Glasgow, Scotland, United Kingdom ☯ These authors contributed equally to this work. * Ke.Yuan@glasgow.ac.uk (KY); David.L.Robertson@glasgow.ac.uk (DLR) OPEN ACCESS Citation: Lamb KD, Luka MM, Saathoff M, Orton RJ, Phan MVT, Cotten M, et al. (2024) Mutational signature dynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules. PLoS Comput Biol 20(1): e1011795. https://doi.org/10.1371/journal.pcbi.1011795 Editor: Roger Dimitri Kouyos, University of Zurich, SWITZERLAND Received: July 12, 2023 Accepted: January 3, 2024 Published: January 25, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pcbi.1011795 Copyright: © 2024 Lamb et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Computational code is available at https://github.com/kieran12lamb/ SARS-CoV2_Mutational_Signatures GISAID data accessions are available at doi.org/10.55876/gis8. Abstract The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to more efficient human infection since the emergence of this new human coronavirus in late 2019. Here, we use machine learning models to identify shared signatures, i.e., common underly- ing mutational processes and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and virus properties were associated with the waves of regional SARS- CoV-2 reported infection numbers and this impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity and the effectiveness of government stringency. To assess underlying evolutionary change, we used non-negative matrix factorisation and observed three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signa- tures 1, 2 and 3 were biased to C!T, T!C/A!G and G!T point mutations. We hypothe- sise assignments of these mutational signatures to the host antiviral molecules APOBEC, ADAR and ROS respectively. We observe a shift amidst the pandemic in relative mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. This could represent changes in how the virus and the host immune response interact and indicates how SARS-CoV-2 may continue to gener- ate variation in the future. Linkage of the detected mutational signatures to the VOC-defining amino acids substitutions indicates the majority of SARS-CoV-2’s evolutionary capacity is PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 1 / 26 PLOS COMPUTATIONAL BIOLOGY221201qs, doi.org/10.55876/gis8.230406qg and doi.org/10.55876/gis8.230406fb. likely to be associated with the action of host antiviral molecules rather than virus replication errors. Mutational processes and SARS-CoV-2’s evolutionary capacity Funding: The authors acknowledge funding from the Medical Research Council (MRC, MC_UU_12014/12 to DLR, MC_UU_00034/5 to DLR and a Doctoral Training Programme in Precision Medicine studentship for KDL, MR/ N013166/1 to KY and DLR), the Wellcome Trust (220977/Z/20/Z to MC, KY, DLR), the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement (MC_PC_20010 to MC), Engineering and Physical Sciences Research Council (EPSRC, EP/R018634/ 1 to KY), and the European Union’s Horizon 2020 research and innovation programme project PANCAIM (101016851 to KY). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Author summary We show that both public health measures and virus properties are associated with the rise and fall of regional SARS-CoV-2 reported infection numbers with regional differences attributable to the extent of vaccine usage and the effectiveness of public health measures. In our mutational signature analysis, using non-negative matrix factorisation, we detected three distinct mutational signatures that can be putatively attributed to the action of spe- cific host antiviral molecules. Interestingly, we observe a shift in mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. These mutation patterns influence SARS-CoV-2’s evolutionary capacity, the available genetic variation that selection can act on, and so can be linked to the mutations defining the variants of concern responsible for the distinct SARS-CoV-2 infection waves. The dominant types of nucleotide substitutions involved indicate that much of the mutation and hence variation come from the action of the host immune response rather than replication errors since the virus has an error correction system. Introduction The COVID-19 pandemic began in late 2019 following a zoonotic spillover event of a SARS- related coronavirus, subsequently named SARS-CoV-2, in Wuhan, China [1, 2]. The extensive and rapid global spread of this new human coronavirus and its detrimental impact on human health has rendered it among the most significant pandemics in recent history [3]. Different geographical regions of the world have reported varied infection patterns that are attributed to differences in population demographics and health care systems, diverse government responses [4, 5], the emergence of more transmissible variants [6, 7] and other viral, human and population factors. Since its emergence, SARS-CoV-2 has undergone significant genetic change such that numerous variants, i.e., distinct genotypes, have been identified [8], many with altered phenotypic properties [9]. The World Health Organization (WHO) and other public health bodies have broadly classi- fied variants that pose an increased risk to global public health (due to increased transmissibil- ity, increased virulence or decrease in the effectiveness of public health measures relative to 2019/early 2020 SARS-CoV-2 variants) as variants of concern (VOCs) and variants of interest (VOIs) [10]. The early SARS-CoV-2 variants to emerge in 2019 and the more transmissible +S:D614G variant followed by the VOCs (Alpha, Beta, Gamma, Delta and currently Omicron) have driven significant and sequential “waves” of SARS-CoV-2 infections internationally. The emergence of each variant showing a clear geographical link [11–13]. Viral mutations arise from a diverse set of processes (principally viral polymerase replica- tion errors and host anti-viral editing processes), which can be identified by the characteristic mutational signatures that they leave on the genome [14, 15]. Such characterisation of domi- nant mutational processes is routinely used in cancer genomics [16]. The catalogue of SARS- CoV-2 nucleotide changes show distinct mutational patterns suggestive of a role for host antiviral mutational processes in introducing changes in the viral RNA [17, 18]. These PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 2 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity processes potentially dominate in SARS-CoV-2 evolution because point mutations introduced in replication are mostly corrected by the action of a proofreading enzyme. The generation of virus diversity, the key to virus persistence by generating novel variation and thus evolutionary capacity, is multi-faceted [19], yet our understanding of the relative importance of underlying mutational processes linked to the action of host anti-viral mole- cules is still very limited. Given that SARS-CoV-2 continues to develop new variants, many associated with sets of previously observed (convergent) and novel mutations [9], it is critical that we improve our understanding of the mechanisms and sources of evolutionary change. Along with routine surveillance of SARS-CoV-2 infections, there has been an unprece- dented global sequencing effort resulting in databases containing many millions of genome sequences, in particular GISAID [20]. Here we examined this data to describe the global molecular epidemiology and evolution of SARS-CoV-2. Using regression models we first examined how viral properties and public health measures have influenced the magnitude of infection waves in different geographic locations. Satisfied that SARS-CoV-2 variants have been an important driver of infections we then used non-negative matrix factorisation to char- acterise the mutational processes involved in the generation of variants and their changing pat- terns of activity over time. Results Characterising the SARS-CoV-2 waves regionally This first part of the study reports on global SARS-CoV-2 data from 24/12/2019 to 28/01/ 2022 only as limited public health measures were in place after this time. We observed 1,544 distinct SARS-CoV-2 lineages from 7,348,178 sequences. 88% of the infections in the global pandemic during this time frame were caused by a subset of 13 Pango and WHO variants (S1 Table). While there are geographical differences there is a clear dominance of a subset of variants and replacement of these through time (Fig 1). This “wave” infection pattern was evident in all geographic locations. Although biased by testing rates, Europe and the Ameri- cas had the highest infection rates, reporting up to 450 cases per million population per day (Fig 1). The emergence or introduction of VOCs coincided with a steep increase in infection rates globally. For example, cases in Asia showed a steep rise in February 2021, which peaked in May 2021 (Fig 1, panel Asia). During this period, Alpha and Delta comprised greater than 75% of the SARS-CoV-2 cases identified in the sequence data. Africa and Oceania on the other hand displayed overall sustained low case numbers. Despite this, Beta dominated the second wave in parts of Africa while Alpha dominated the third Oceanic wave. After its emergence in March 2021, Delta spread to become the predominant variant across all conti- nents. The Omicron variant of concern was first identified in South Africa in late November 2021 and, by January 2022, it had rapidly become the predominant cause of infections world- wide (Fig 1). Covariates of the waves We investigated the degree to which public health measures and viral properties explain conti- nent-specific reported cases of infection. Correlation analysis at the global level showed a sig- nificant correlation between infection rates and the predictor variables: government stringency, vaccination, previous infection burden, virus diversity and fitness (S2 Table). Regression analysis revealed that the impact of the predictor variables on the magnitude of reported cases were found across all continents. We classified significance levels as follows: no significance for p-values greater than 0.05, weak significance for p-values between 0.05 and 0.001, and high significance for p-values less than 0.001. Our findings indicated that PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 3 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 1. Continent-level SARS-CoV-2 lineage dynamics and pandemic curves. Lines show a 14-day rolling average of reported SARS-CoV-2 cases. Bars show the biweekly proportions of common lineages and are coloured by lineage. The white space shows the proportion of sequences from other (non-majority) lineages. https://doi.org/10.1371/journal.pcbi.1011795.g001 government stringency had a weakly significant impact in Asia, Europe, and South America, but a strongly significant impact in Africa, Oceania, and North America. Virus fitness, previ- ous infection burden, and vaccination demonstrated a strongly significant impact across all continents. Virus diversity was strongly correlated with high infection numbers in Europe and PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 4 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity North America, with a weaker association in Africa, Asia, Oceania, and South America. The R squared values, indicating the proportion of variance explained by our model, were greater than 0.5 for all continents, ranging from 0.66 in Oceania to 0.79 in Africa (S3 Table). Gener- ally, our predictions closely resembled the rise and fall of SARS-CoV-2 infection case numbers (Fig 2). For country-level analysis, we included 29 countries from six continents based on the com- pleteness of data (availability of sequence data in every 14 day bin). Pandemic plots were visu- alised using biweekly bins and multiple linear regression was fitted using the same approach. Different countries had varying lineage dynamics as illustrated in S1 Fig. The five predictor variables had varying impacts on infection rates across countries (S2 Fig). Despite some differ- ences related to the population level processes investigated here, there is a clear variant replace- ment process taking place. As the generation of novel variants is fundamentally a mutation dependent process we next investigated the underlying patterns of mutations being generated through time. The goodness of fit varied among countries, with the R squared varying from 0.28 (Japan) to 0.96 (Australia), with a median of 0.69 (S4 Table). Though our model success- fully captured the general infection wave patterns in many countries, it struggled to capture short-term data spikes in specific instances, such as in Belgium (November 2020), India (May 2021), Indonesia (August 2021) and Japan (September 2021) (S2 Fig). Fig 2. Association of SARS-CoV-2 infection rates and predictor variables globally. A. Pearson’s correlation matrix of infection rate and predictor variables. Positive correlations are denoted in orange and negative correlations in blue and colour intensity is directly proportional to coefficient value. B. Model fitting using multiple linear regression. Black solid lines show a 14-day rolling average of adjusted SARS-CoV-2 cases. Pink solid lines show fitted mean response values of infection rates with predictor values as input. https://doi.org/10.1371/journal.pcbi.1011795.g002 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 5 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Identifying putative mutational processes contributing to changes in SARS-CoV-2 New variants of concern have displaced viral lineages that were previously dominant in the population in different geographical regions and in some cases globally (Fig 1). This behaviour has been observed with the original variants of concern (Alpha, Beta and Gamma) and then globally with the Delta and Omicron lineages. We investigated whether these variant wave events (periods of time where infections are dominated by a single variant) were linked to the activity of specific mutational processes. Each of the variants of interest/concern has evolved independently such that detecting the patterns of mutations in the SARS-CoV-2 sequence data allows us to observe which processes are most active and could be contributing to the emer- gence of variants. Mutations were called using inferred references for each of the Pango lineages, which we call tree-based referencing (S3 Fig). The SARS-CoV-2 alignment of 13,278,844 sequences up to 26/10/2022 was used. Of these 13 million sequences 2,195,182 sequences were selected as they contained 5,726,144 newly arisen mutations. Cytosine to thymine mutations (C!T) were the most common and were the primary substitution category for most weeks where sequences were recorded. Note, SARS-CoV-2 has an RNA genome but we refer to uracil as a thymine to match pre-existing DNA mutational signature notations. Three signatures were identified with distinct substitution patterns using non-negative matrix factorisation (NMF) (Fig 3 and S5 Fig). Signature 1 is heavily biased towards C!T mutations. Signature 1 had a high probability of ACA, ACT and TCT contexts (adjacent nucle- otides in the 5’ and 3’ direction of the mutated site), consistent with what was earlier reported by Simmonds et al. [17] as highly mutated contexts for C!T substitutions in SARS-CoV-2. Signature 2 is predominantly adenine to guanine (A!G), guanine to adenine (G!A) and thy- mine to cytosine (T!C) mutations. The proportion of A!G and T!C mutations is approxi- mately equal in this signature, which is indicative of a double-stranded mutational process. SARS-CoV-2 mutations at adenine positions on the negative strand will be counted as thymine mutations due to the negative strand being used to replicate positive sense RNA, with the mutated A!G now pairing with a cytosine on the +sense RNA and replacing the original thy- mine [21, 22]. Signature 3 is predominantly composed of guanine to thymine (G!T) substitutions. The dynamics of mutational processes through the pandemic By using the available SARS-CoV-2 sequences we can measure the mutational signature activ- ity across time as long as our samples are aggregated using time series annotations. Signature exposures (Fig 4) show that Signature 1 remained the most prominent signature throughout the pandemic, although following the emergence of Signature 2 its activity reduced propor- tionally. Absolute exposure values (Fig 4B) show that Signature 1 does not appear to reduce its exposure, rather Signature 2 increases its exposure. Signature 2 establishes itself as a substantial signature after December 2020. It continues to expand after October 2021, just prior to the emergence of the Delta VOC. Signature 3 is by far the least active of the three signatures but remains consistent until after January-February 2022 when it begins to drop towards zero. This is around the time Omicron began to emerge as the dominant VOC. Combined signature activity reached a peak between July and October 2021 (Fig 4B) coin- ciding with the peak number of unique mutations (Fig 5A and 5B). This is around the time the mutational signature dynamics appear to be shifting, with Signature 2 contributing more unique mutations. We can see that this also coincides with the Delta VOC wave, which, between May 2021 and January 2022, was the lineage group showing the greatest number of PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 6 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 3. Mutational signatures extracted from the SARS-CoV-2 genome sequences by non-negative matrix factorisation. Signatures are patterns of probabilities for each category of substitution in a three nucleotide context. Each bar represents a context and is coloured by the substitution category of the mutation that occurs there. Each signature may represent a distinct mutational process. Signature 1 is heavily biased towards cytosine to thymine (C!T) mutations, particularly in 3’ CpG contexts TCG, CCG and ACG. Signature 2 from SARS-CoV-2 is predominantly adenine to guanine (A!G), guanine to adenine (G!A) and thymine to cytosine mutations (T!C). Signature 3 is strongly guanine to thymine (G!T), a pattern that is thought to be caused by the action of guanine oxidation by reactive oxygen species. Signatures are shown normalised against the tri-nucleotide composition of the SARS-CoV-2 genome. Non-normalised forms in the context of the SARS-CoV- 2 genome composition are shown in S5 Fig. https://doi.org/10.1371/journal.pcbi.1011795.g003 newly acquired mutations (Fig 5). Delta was the first VOC to dominate on a global scale, out- competing other VOCs like Alpha, Beta and Gamma in their regions of circulation. Omicron similarly repeated this phenomenon, almost entirely replacing Delta globally within weeks of its emergence (Fig 5B). We also see a marked decrease in the activity of Signature 3 following Omicron’s establishment as the dominant variant. A similar decrease in G!T mutations was PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 7 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 4. Signature exposure plots showing the activities of the extracted mutation signatures over the duration of the COVID-19 pandemic. A. Shows the percentage activity of the signatures during a given week of the pandemic, with each colour representing a different signature. B. Shows the signature activities as their absolute values at each epidemic week. https://doi.org/10.1371/journal.pcbi.1011795.g004 also observed by Bloom et al. [23] and Ruis et al. [24]. This is different to Delta, where there was an increase in Signature 3 following its emergence. These Signature 3 changes become par- ticularly apparent when we begin to look at signature activities within variant-defined subsets of the data. Signature dynamics spatially and by variant After observing changes in signature activity during transitions between dominant variants, we next investigated the differences between signature activities in variant-defined subsets of the data as well as in continent-defined subsets. We used the globally extracted signatures to extract exposures from the subsets using a non-negative least squares regression to retain the non-negativity constraint. This allowed for the measurement of signature activity in each of the subsets of interest. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 8 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 5. A. Counts of unique SARS-CoV-2 mutations for each epidemic week, with colours representing which continent the mutations came from. B. Counts of unique mutations per week that are part of the mutational signature substitution-context features (i.e., no indel mutations included). Colours represent which lineage/group of lineages the mutations belong to. C. Ridgeline plot showing the exposure of mutational signatures in SARS-CoV-2 variant-defined subsets. Exposures are coloured by the signature they have been attributed to. D. Ridgeline plot showing the exposure of mutational signatures in SARS-CoV-2 continent-defined subsets. https://doi.org/10.1371/journal.pcbi.1011795.g005 Signature 1 was the most active in almost all the variant-defined subsets as was expected from the global activity. Signature 3 was most active in the Delta subset as well as during the Delta wave in the continent-defined subsets (Fig 5). The non-VOC, Beta and Omicron subsets appear to be the least impacted by Signature 3 with almost zero activity in Omicron. Signature 2 also shows low activity in the non-VOC subset but is very active in the other VOC subsets, in particular Alpha, where it appears to be the most active, overtaking the Signature 1 process. Continent-defined subsets of the data also consistently showed the high activity of Signa- ture 1. Signature 2 begins to consistently appear in all continents after 2020, with only small bursts of activity being detected before this (Fig 5D), again consistent with what we see in the global data. Signature 3 activity also follows the pattern of the global activity, appearing most prominently during the Delta wave. Bridging the gap between mutation signatures and amino acid substitutions Stratifying non-synonymous nucleotide substitutions by their association with mutational sig- natures should provide insights into how these mutational processes affect viral proteins. Exposures were calculated by stratifying nucleotide mutations by whether they were synony- mous or non-synonymous substitutions for each dataset (Fig 6A). The unattributed exposure PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 9 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 6. A. Exposures for each of the SARS-CoV-2 mutational signatures for both synonymous and non-synonymous stratified datasets. Synonymous exposures are below 0 on the y-axis, while non-synonymous exposures are above 0. Each area represents signature exposures across epidemic weeks, with colours representing which signature the exposures are attributed to. B. Non-synonymous and synonymous mutations in the tree-based references of identified variants of concern. Signature 1 produces the majority of both synonymous and non-synonymous substitutions in all lineages. Signature 3 mutations are more often non-synonymous substitutions in the lineages of concern, with most lineages having few to no changes. Signature 2 non-synonymous mutations appear to have increased in the Omicron lineages (BA.1 and BA.2). C. Variant of concern associated non-synonymous mutations coloured by the mutational signature with the greatest likelihood of causing the change. D. Variant of concern synonymous mutations coloured by the putative mutational process that caused the change. https://doi.org/10.1371/journal.pcbi.1011795.g006 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 10 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity was calculated using the model error for mutational categories not contained within any of the extracted mutational signatures. The majority of non-synonymous substitutions can be described by the observed mutational signatures. Signature 1 likely produces most of the non- synonymous mutations, however, Signature 3 is an almost exclusively non-synonymous signa- ture, with particularly high activity during the Delta wave of infections. Signature 2 appears to produce predominantly synonymous mutations. Using the tree-based references, we can also look at individual lineage reference sequences to observe which mutational processes have probably produced their specific amino acid sub- stitution set. The tree-based references were used since they are equivalent to a high-quality representative sequence and because many of the early real sequences contain sequencing errors. For each variant of concern, mutations were assigned to a signature by calculating the maximum likelihood of the mutation and its context being produced by each of the three extracted signatures. Using the trinucleotide context C[C ! T]G as an example, the likelihood function is P(C[C ! T]G j Signature), which corresponds to the probability bars for CT-CCT in the extracted signatures. Mutations that contained substitution-context pairs not found within any of the mutational signatures were labeled as “unattributed”. The Alpha VOC tree-based reference sequence contains eleven Signature 1 changes, six Sig- nature 2 changes and a single Signature 3 change. Signature 1 changes account for 39% of all substitutions within the Alpha tree-reference sequence, with 75% of these mutations being non-synonymous substitutions. Signature 1 was frequently active prior to the Alpha VOC’s emergence. The activity plots (Fig 4) show that this was the case for much of the pandemic, particularly prior to the Alpha’s emergence around September 2020. It should be noted that while Signature 1 mutations are by far the most frequent, only one is found within the Spike protein (producing the S:T716I change). Signature 3 only had one change, which was non-syn- onymous appearing in ORF:8. Signature 2 mutations were non-synonymous substitutions 83% of the time, with three Spike mutations relating to the process including S:D614G, which is present within all known variants of concern. The Beta VOC emerged around the same time as Alpha (Autumn 2020) and is defined by a smaller set of mutations. A greater proportion of Signature 1 mutations are non-synonymous substitutions in Beta (66%). Signature 2 mutations resulted in S:D215G and S:E484K, the latter reported to help the virus evade neutralising antibodies [25]. Signature 3 mutations most likely produced S:K417N in spike, which is also reported to aid in antibody evasion [25, 26] similar to S:E484K. Gamma also emerged in Autumn 2020 and has 33 different defining substitutions. Signa- ture 1 mutations account for 11 of these with 54% being non-synonymous. Four are present in Spike including S:L18F, S:P26S, S:H655Y and S:T1027I. Signature 2 mutations resulted in six amino acid substitutions, with only 75% of changes being non-synonymous. Three of the five mutations in non-synonymous substitutions occurred in Spike. Signature 3 mutations in the Gamma lineage were all non-synonymous except for a single synonymous substitution in ORF1a/b. Delta was the first VOC to dominate worldwide and replace almost every other lineage in all regions. The initial Delta sequence (Pango lineage B.1.617.2) contains six Signature 1 muta- tions. 66% of these changes were non-synonymous and none occurred within Spike. Signature 2 mutations were all non-synonymous and displaced throughout the virus ORFs including ORF1a/b, S and M. Signature 3 mutations in Delta are found in non-coding regions and N, with the N mutations both being non-synonymous. Omicron is the most recent VOC to emerge, quickly replacing Delta globally. Omicron dif- fers from earlier VOCs with a much greater number of Spike mutations relative to the other ORFs. The first identified Omicron variant B.1.1.529 has 40 substitutions of which 32 are non- PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 11 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity synonymous changes. This is almost double that of Delta, which only had 18. Seven of these substitutions were Signature 1 changes, two were Signature 3 and ten were Signature 2 changes. There are four non-synonymous ORF1a/b mutations despite this ORF being substan- tially longer than SARS-CoV-2’s other ORFs. Only one Spike substitution was synonymous out of the 21 total changes. This number is even greater when looking at the major Omicron variants BA.1 and BA.2. BA.1 had 31 non-synonymous substitutions in Spike alone while BA.2 had 28. Between these three Omicron variants, only two Spike substitutions are non-synony- mous out of a total of 40. Nine of the 40 changes are from Signature 1, 2 are from Signature 3 and 12 are from Signature 2. This means 23/40 of the changes appear to come from these three mutational processes. 20 of the 40 substitutions observed in these variants were present in the receptor-binding domain (RBD) of Omicron, with nine of these changes thought to help Omi- cron evade the immune response or increase its transmissibility [27]. Of these beneficial RBD changes, three are potentially the result of Signature 1 activity, 9 are Signature 2 and one is from Signature 3. The high density of Signature 2 RBD amino acid changes in a variant that has emerged as Signature 2 exposure increased suggests that the mutational process behind Signature 2 may have contributed to the emergence of the Omicron variant. Signature exposures and highly mutated sequences in wastewater data Similar trends over time in exposures are seen when the mutational signatures are applied to publicly available wastewater data. Although the trend is seen at a lower resolution than global data, Signature 1 and Signature 3 are gradually replaced by Signature 2 (Fig 7A). Although, Sig- nature 2 is not quite as strong as in the global data (Fig 4). This suggests trends in mutational processes can be monitored using wastewater, not only sequencing of the infected population. Additionally, at time periods where a high level of virus diversity is expected, there are highly mutated sequences present in the wastewater (Fig 7C). This suggests cryptic sequences in wastewater may be used to observe potential upcoming variants, similar to how known sequences have been back-traced to particular buildings using wastewater [28]. As chronic SARS-CoV-2 infections are implicated as a major contributor to VOC evolution [29, 30], it may be possible to parse highly-mutated cryptic sequences of interest from chronic infections out of wastewater data in the interest of detecting potential VOCs. Unfortunately, this is problematic to deconvolve as sequencing data for immunocompromised and chroni- cally infected individuals is sparse. When sequences from known chronic infections are exam- ined, the distribution of mutation types is consistent with global data, with Signature 1 mutations dominating as expected for samples from January 2022 (Fig 7B). Although, due to the low number of chronic infections for comparison this result is not very conclusive, it does demonstrate how mutational patterns can be potentially detected in this type of data. Studying these types of infections, and underlying mutational processes, will be important to under- stand better the origins of the sets of mutations that contribute to the generation of VOCs. Discussion In this study, we investigated SARS-CoV-2 lineage dynamics and identified temporal variables that are associated with increased numbers of infection cases. Both public health measures and virus properties were associated with the sequential waves of regional SARS-CoV-2 infections cases. These predictors have varying impact in different geographical locations. As more of the global population’s immune system becomes sensitised to existing SARS-CoV-2 variants, either through previous infection or vaccination, the virus has and will continue to undergo changes that enable reinfections. The continued emergence of new variants is thus expected. In some regions, government stringency had limited significant impact on patterns of PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 12 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Fig 7. A. Signature exposures per month from wastewater sequences show similar trends in mutational processes as the global data, although at a lower resolution and, interestingly, with a lower Signature 2 exposure. B. Substitutions in SARS-CoV-2 consensus sequences from infections of immunocompromised individuals contain mutation types corresponding with patterns observed in the distinct signatures. Of note, there are more synonymous mutations present in the chronic infection data than in the global sequences, although it is important to note the sample size for immunocompromised infections is low. C. Mutation counts in wastewater sequences for bi-yearly time periods. Highly mutated sequences cluster to the right especially during the 2021 July-December time period, as would be expected when Omicron was emerging. https://doi.org/10.1371/journal.pcbi.1011795.g007 infection. This could be due to differences in implementation strategies and support, other competing predictor variables, as well as behavioural changes in citizens as a response to the restrictions. Our analysis highlights the significant role of vaccination in influencing reported COVID- 19 case patterns across all continents, even in regions with lower vaccination coverage like Africa. Despite Africa’s lower vaccination rates, the continent has seen a relatively low-level of sustained transmission. This phenomenon might be attributed to factors such as the younger median age of the population, lower population density, immune priming due to prevalent PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 13 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity infectious diseases, and limited testing capacity [31]. The weak impact of viral diversity on reported cases in Asia and South America may be explained by the emergence and dominance of variants such Delta and Gamma in the regions, respectively. For instance, the Delta variant, initially identified in Asia, quickly became the predominant strain, overshadowing other line- ages before spreading globally. Overall, the predictor variables significantly contributed to explaining the rise and fall of infection numbers across different continents, accounting for more than half of the variance in reported cases. The differences in the regression effectiveness can be attributed to intrinsic differences among continents, such as variations in vaccine cov- erage, testing and sequencing capabilities, and the effectiveness of government stringency measures. While our model effectively captured the general trends of infection waves, it struggled to accurately represent peaks within short time-frames in some countries. This discrepancy might be attributed to the omission of certain predictor variables, like mass gatherings, which are known to contribute to viral super-spreading events [32]. In utilizing the OWID and OxCGRT datasets, which are arguably among the most compre- hensive for addressing our research objectives, we note some limitations. First, there were dis- crepancies in parameter definitions, such as varying case classifications across regions. Second, positive tests are commonly labeled based on their reporting date rather than “date-of-event” [33]. Lastly, the cases reported in these datasets may not be fully representative of the actual disease burden. Although the Human Development Index (HDI) of a country can act as a proxy to bridge the gap between reported cases and the true disease burden, it does not fully capture the entire complexity. The extracted signatures from the global SARS-CoV-2 dataset show clear and distinct pat- terns describing mutational processes acting on the viral genome. The most prominent of these signatures, Signature 1 (Fig 3 and S5 Fig), shows a marked bias towards C!T mutations, a signal indicative of the APOBEC family of cytidine deaminases [17, 18]. APOBEC enzymes have been shown to cause extensive C!T editing of DNA and RNA in human and viral genomes. However, it is not yet clear whether they are the cause of this pronounced C!T bias in SARS-CoV-2 despite a number of other studies also observing other APOBEC-like muta- tional patterns [34–37]. Cytosines flanked by either an adenine or thymine in both the 3’ and 5’ direction appear to be the most pronounced targets of Signature 1. APOBEC editing was shown to have contexts outside of the traditional TpC when structural features of the nucleic acid such as hairpin loops are present [38]. Outside of structural features, APOBEC3A is thought to be the predominant cause for TpC changes and is found to be expressed in lung tis- sue [39]. ApC changes are considered to be caused by APOBEC1, which in cell models was shown to efficiently edit SARS-CoV-2 RNA [39]. APOBEC1 is found predominately in the liver and small intestine, tissues reported to be infected by SARS-CoV-2 [39, 40]. 3’ CpG nucle- otide contexts are the most targeted, in particular TCG, CCG and ACG. CpG suppression is a well-known dinucleotide bias. In RNA viruses, this appears to be a result of selective pressures exerted from the presence of host CpG sensing molecules such as Zinc-finger Antiviral Protein (ZAP). ZAP relies on host CpG suppression to allow it to specifically target non-host genomic material (such as viral RNA) with higher CpG content [41]. This allows viruses with lower CpG content to better evade restriction by ZAP since it more closely resembles the host CpG composition. While ZAP does not induce C!T changes, it may help explain why C!T sites in a CpG 3’ context are preferentially edited relative to other 3’ contexts. ZAP has been shown to restrict SARS-CoV-2 despite pre-existing CpG depletion [42]. ZAP isoforms have been shown to prevent necessary translational frame-shifting for SARS-CoV-2 ORF1b protein pro- duction. [43]. The non-normalised form of Signature 1 (S5 Fig) shows that when tri-nucleotide bias is not accounted for 3’ CpG’s are lower than the normalised signatures, yet 5’ TpC and PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 14 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity ApC contexts remain the most prevalent(S5 Fig). The most targeted contexts do shift to ACA, ACT and TCT, likely reflecting their comparatively high abundance within the SARS-CoV-2 genome relative to 3’CpG contexts. These non-normalised contexts are consistent with what was earlier reported by Simmonds et al. [17]). Signature 2 (Fig 3 and S5 Fig) has a nearly identical proportion of A!G and T!C muta- tions. These are a known target of the ADAR family of adenine deaminases. ADAR enzymes typically operate on double-stranded RNA and convert adenine into inosine [21, 22]. Inosine forms base pairs with cytosine, which after another round of replication causes guanine to replace the inosine and complete the A!G change. As ADAR operates on both strands of dsRNA, the mutational signature resulting from the process is expected to contain an equal proportion of A!G and T!C mutations, which is the case for Signature 2 [21]. Signature 2 also contains a number of G!A mutations, which could be caused by low-level C!T activity on the negative sense RNA strand. Due to the cellular strand biases present between the posi- tive and negative sense RNA [36], C!T mutational processes acting on ssRNA are much less likely to produce a mutation on the negative strand (resulting in G!A substitutions) than C!T changes on the positive strand. The negative strand will only be present during the repli- cation phase of the virus while the positive strand will be present both on cell entry and on exit as the new viral particles are packaged to infect further cells. This could explain why the nega- tive sense Signature 1 changes are present in Signature 2, since it may be operating at a similar level to Signature 2 on the negative strand. The non-normalised form of Signature 2 (S5 Fig) does have different targeted contexts, just as with Signature 1. However, the main attribute of Signature 2 is its equal contributions of A!G and T!C substitutions, which still remain equal. Signature 3 (Fig 3 and S5 Fig) is dominated by G!T substitutions. A putative mechanism for this is Reactive Oxygen Species(ROS) in the cell. Increases in oxidative stress as part of a ROS ‘burst’ have been associated with viruses during the early stages of infection [34, 44]. Gua- nine nucleotides are known to be vulnerable to oxidation, with the product 7,8-dihydro-8-oxo- 2’-deoxyguanine (oxoguanine) pairing with adenine bases rather than cytosine [44, 45]. Similar to inosine causing A!G changes, this change to oxoguanine will result in a G!T mutation after a replication cycle. The lack of C!A changes in the signature also suggests that the mech- anism is most active on the positive single-stranded RNA rather than the negative single- stranded RNA. The initial positive single-stranded RNA is found in the cytoplasm, meaning it can be easily accessed by ROS and other mechanisms of mutation. Viral replication is thought to take place within membrane-bound environments that aim to protect the RNA. The pres- ence of double-stranded RNA within these environments strongly suggests that this is the case [46] and may explain the relative lack of negative strand mutations in SARS-CoV-2 signatures. The non-normalised G!T signature (S5 Fig) seems to display a context preference of TpG and ApG nucleotides, although this contextual bias is changed to CpG and ApG following normali- sation. These contextual biases mean that the signature could be some other as yet unknown editing mechanism on the viral RNA, although normalisation changing this context so heavily suggests that this bias perhaps has more to do with genome composition. The increased CpG context shift post-normalisation could also be another ZAP-induced effect, where CpG deple- tion is selected for to help the virus evade ZAP. Curiously, this G!T bias has been observed in other coronaviruses, but not widely among RNA viruses [47]. ROS has a verified cancer muta- tional signature [15, 48] although the context preferences do not match the signatures (normal- ised or non-normalised) observed here. However, there are a multitude of differences between viral RNA and human DNA that make these signatures difficult to compare. It is important to note that while SARS-CoV-2 does have an error correction mechanism resulting in fewer replicase-induced errors, this mechanism will not catch all changes. A PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 15 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity number of the mutations picked up from the set of sequences (and included in our mutational signatures) will be derived from replication errors. However, the clear and repeatable extrac- tion of the signatures indicates that despite this potential contamination, the extracted signa- tures do appear to be predominantly other mutational processes. While a replication error- associated mutational signature may be identified in future, this signature is too diffuse to identify as a distinct process. Similarly, a high proportion of mutations are not accounted for by the extracted mutational signatures. These mutations were not present in large enough quantities to enable effective extraction from the data. Future methods may be able to tease out the more subtle mutational mechanisms that almost certainly exist to induce these less com- mon mutation types. Signature activities clearly change in both the global dataset and in the various subsets of the data for VOCs and continents. In the global data (Fig 4) Signature 1 is dominant through- out the pandemic. Signature 2 only begins to appear around November 2020, after which it appears consistently active for the remainder of the pandemic. This is approximately when variant of concern lineages began to emerge, as well as the beginning of the first vaccine roll- outs. This is particularly apparent in the Alpha subset where Signature 2 is the most highly active mutational process (Fig 5), with a large depletion of Signature 1 activity as well. Alpha was shown to increase sub-genomic RNA expression of several immune-antagonist viral proteins including nucleocapsid (N), ORF9b and ORF6 [49–52]. N is thought to shield dsRNA from detection by RNA sensors, which trigger downstream antiviral response path- ways [49, 52–54]. ORF9b antagonises TOM70, a protein required for the activation of mito- chondrial antiviral-signalling proteins (MAVS) [49] while ORF6 inhibits the transportation to the nucleus of inflammatory transcription factors [55]. Combined, the cumulative immune inhibition may have resulted in an observable change in the mutational processes that we observe within the Alpha lineage. Beta and Gamma (both VOCs that emerged around the same time as Alpha) gained amino acid substitutions that helped evade the immune system primarily via antigenic change. Alpha’s reliance on attenuating immune pathways rather than antibody binding may be why we see a different signature exposure pattern in this VOC rela- tive to the others. This could be due to the attenuated pathways being involved in signalling for the mutational processes behind Signatures 1 and 3, while not inhibiting Signature 2 as much. This Alpha pattern is not observed in the other VOC datasets, although Delta and Omicron have a high level of Signature 2 exposure as well, despite Signature 1 remaining the dominant process in those subsets. Signature 3 appears to be most prominently found in the Delta subset and remains consistently at low levels in the global data until January 2022 when it appears to disappear almost entirely. The Omicron subset has little to no exposure for Signature 3 and this happens to be the VOC almost exclusively circulating after January 2022. Why Omicron appears to have so little Signature 3 exposure is unclear, although unlike previous VOCs, Omi- cron differs in its preference of cell entry mechanism. Previous variants of the virus typically enter the cell using membrane fusion, where the viral membrane fuses with the cell membrane via the action of ACE-2 receptor binding and TMPRSS2 cleavage of the spike protein. Omi- cron instead favours an endosomal route of entry whereby the viral particle binds to the cell using ACE-2 and is enveloped by endocytosis into the cell. Cleavage of the spike protein then occurs via the action of Cathepsin L, which allows for the release of the viral RNA into the cytoplasm of the now-infected cell [56, 57]. Signature transitions from Signature 1 to Signature 2 changes occur from December 2020 onwards in the global dataset and appears consistently in the VOC and continent-defined sub- sets around this time point as well. Alpha underwent a major shift to Signature 2 mutations early in its time as a VOC, although Signature 1 returned as the predominant set of changes towards the end of its wave of infections. The non-VOC subset appears to be the least impacted PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 16 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity by Signature 2 changes. However, this can mostly be explained by the number of non-VOC sequences quickly declining after the emergence of the VOC lineages. Delta underwent a dra- matic increase in Signature 2 and Signature 3 exposure from July 2021, with Signature 2 becoming the predominant signature towards the end of Deltas wave. Signature 2 changes continue into Omicrons introduction, although it does decrease after the initial BA.1 wave from December 2021 to March 2022. It seems clear that while Signature 1 mutations have dominated in contributing to the evolutionary capacity of SARS-CoV-2 throughout the pan- demic, this mutational environment is beginning to change. Such shifts in mutational pro- cesses are potentially evidence of changing interactions between the viruses and the immune systems of the hosts they circulate within. For example, changes in population-level immunity via vaccination or previous infections may influence the mutations that we observe in the data. Changing mutational process activity in consensus sequences from infections is unlikely to fully reflect the true activity of each process, but they are likely to show which processes are contributing mutations that eventually make it into circulating viruses. All variants of concern we assessed show predominantly non-synonymous mutations and all mutational signatures are associated with more non-synonymous than synonymous changes. More synonymous substitutions in the lineage references were found in ORF1a/b, which is expected due to it being the longest ORF. However, this pattern is not observed with non-synonymous mutations as these are mainly located in the spike protein (Fig 6C and 6D). This is consistent with spike being under intense immune pressure since it is the main glyco- protein for SARS-CoV-2. As such, spike must change in order to escape the host immune response, while maintaining its main function of binding and entry into host cells. Signature 1 changes are the predominant source of mutations in all SARS-CoV-2 VOCs that we analysed, followed by unattributed mutations, Signature 2 changes and Signature 3 changes. Signature 3 changes were unlikely to be synonymous mutations with only Beta, Gamma and Delta con- taining very few such changes (Fig 6D). This is also reflected in the global synonymous/non- synonymous exposures where Signature 3 appears completely inactive in the synonymous mutation subset (Fig 6A). Signature 2 exposure appears the most likely to be synonymous mutations (Fig 6A) but this does not seem to be observed in the VOC lineages where most Sig- nature 2 changes are non-synonymous mutations (Fig 6B). In conclusion, mutational signature analysis reveals important processes contributing to SARS-CoV-2 genetic variation and serves as a tool to track the dominant changes over time and to generate hypotheses about the main mechanistic processes in play. Specifically, host antiviral molecules as opposed to replication errors appear to be a the main generator of muta- tions (confirming earlier computational studies), a result that requires experimental confirma- tion. Despite limitations in potential biases, our findings contribute to a better understanding of the complex dynamics driving the evolution of SARS-CoV-2 and the emergence of VOCs. Methods Data The findings of this study are based on metadata associated with 13,281,213 sequences avail- able on GISAID up to October 26, 2022 and accessible at doi.org/10.55876/gis8.221201qs. Sequences were filtered to remove records from non-human hosts, with lengths less than 20,000 nucleotides, non-assigned lineages, with greater than 30% unknown bases, sequences reported to be collected before 24/12/2019 and those with excessive mutations/deletions. The cutoff for filtering out hypermutated sequences was 175 mutations in coding regions or more than 69 different deletions, the cutoffs were manually determined after evaluation of the PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 17 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity mutation/deletion distribution and selecting the point where sequence counts were consis- tently observed in single digits, this resulted in 1,852 sequences being filtered out. Publicly available daily SARS-CoV-2 cases, tests performed and total vaccinations per capita were obtained from OWID [58] in September 2022. Prior to February 2023, the OWID data was piped from the Johns Hopkins University COVID-19 dashboard [33, 59]. Country-level government stringency indices were downloaded from OxCGRT [60]. Government stringency indices are composed of nine indicators: school closure, workplace closure, cancellation of public events, stay at home order, public information campaigns, restrictions on public gather- ings, public transport, internal movement and international travel. The index on a given day ranges from 0 to 100 and is calculated as the mean of the nine indicators, with higher indices indicating stricter regulations. If responses vary at sub-national levels, the index at the strictest level is used [60]. Wastewater findings are based on metadata associated with 1,343 sequences available on GISAID and accessible at doi.org/10.55876/gis8.230406qg. Wastewater sequences were down- loaded from the ‘wastewater data’ section of GISAID in December 2022. Sequences for immunocompromised individuals were downloaded from GISAID in November 2022. Analysis of this was based on the metadata associated with 34 sequences avail- able on GISAID and accessible at doi.org/10.55876/gis8.230406fb. Sequences were chosen based on the known list of sequences used in [30]. Sequences were aligned to the COVID refer- ence genome before use. Design Predictors of SARS-CoV-2 reported cases were explored using a linear model at both country and continent levels. We collected continuous dependent variables reported on a daily basis. These were classified into two groups: (i) public health measures (government stringency, test- ing capacity and vaccination), (ii) viral properties (diversity and fitness). We examined the data for completeness of predictive variables. In instances of missing vaccination data, we interpreted this as no vaccinations having been given. This was a reasonable assumption for periods prior to the vaccine rollouts in the respective countries. With the exception of vaccina- tions, variables with less than 70% of the countries reporting data were not included. The num- ber of SARS-CoV-2 diagnostic tests performed was excluded as a predictor due to missing data. We determined the previous burden by summing the adjusted new cases per capita over the past 90 days. Prior infection significantly reduces the risk of a subsequent infection, with a reduction in risk of up to 95% in the initial three months [61]. This was included as a predictor variable in the linear model. Amino acid substitutions were defined against the Wuhan-Hu-1 sequence. Building on findings from Obermeyer et al., we extracted a list of previously identified fitness-associated mutations [62]. Each fit mutation within a sequence was counted and the counts were normal- ized to the number of sequences per geographical location. Virus fitness was therefore defined as the sum of the frequencies of previously identified [62] amino acid substitutions that increase SARS-CoV-2 fitness divided by the sum of total genomes and the log of total muta- tions per location. Virus Fitness ¼ weekly sum of fit mutations total seqs per week þ logðtotal mutations per weekÞ Diversity was calculated by dividing distinct lineages by the total number of genomes in a given week. Sequences reported in GISAID were assumed to be representative of the diversity of infections for that continent/country. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 18 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Linear model We employed a linear regression model, described by Heo et al. [63], to adjust reported cases per country using the Human Development Index (HDI), which encompasses not just eco- nomic growth but also reflects a country’s capacity for per capita testing. Countries with higher HDI levels, typically high-income nations, conducted more tests per million people, often lead- ing to more confirmed cases compared to nations with lower HDI levels. Adjusted daily cases were smoothed using a 14-days rolling average to limit possible noise and identify simplified changes over time. For continent-level analysis, data from all contributing countries was used to fit the linear model. To ensure that countries with a large number of cases didn’t artificially inflate the results, each country’s influence on the continent-level OxCGRT index was adjusted based on its percent contribution to the continent’s 14-day average daily case tally. Pearson’s correlation was used to test for correlation among the variables. Multiple linear regression was fitted to evaluate the relationship between infection rate (adjusted daily cases per capita) as the outcome and the public health measures and viral properties as predictors within the different continents. The regression models were fitted on data from 01 April 2020 onwards, as (sequence) data addition remained stable after this. The country-level analysis was carried out for countries with less than 50 days of missing genome data using a similar approach. Pandemic plots Case numbers and sequence data were aggregated by their respective continents, a 14-day roll- ing average was used to smooth out daily infection rates and categorical variables were sum- marised by counts. Proportions of lineages were calculated in 14-days bins and the most common lineages were visualised per continent. Tree-based referencing The rapid evolution of SARS-CoV-2 means that the majority of viral sequences are distinct from the early pandemic reference genome Wuhan-Hu-1 [64]. Continuing to count mutations against the early reference sequence can result in mutations being allocated the wrong substi- tution category (i.e., A!T instead of a C!T) where sites have mutated multiple times. Azgari et al. [35] tackled this issue by building a tree of clustered sequences to remove ancestral muta- tions. However, we utilise the available SARS-CoV-2 tree generated as part of the Pango [8] nomenclature to generate a reference sequence for each defined lineage. This means that sequences from the lineage B.1 are compared against a generated reference sequence for the B lineage rather than the Wuhan-1 sequence (See S3 Fig for diagrammatic description). One reference sequence was generated for each of the Pango lineages in the alignment. A nucleotide was included in the generated Pango reference if it exceeded a frequency threshold of greater than 75% of the samples from the lineage. If this threshold was not reached, the ref- erence nucleotide of the nearest parental lineage was used (i.e., if a mutation in B.1 is ambigu- ous, the nucleotide from the B lineage reference at that position is used). Building intermediate references also meant that counting inherited mutations could be avoided. Since mutations were identified relative to their nearest parental Pango lineage, inherited mutations are not counted because, relative to this sequence, there hasn’t been a mutation. Mutations are also only counted once per lineage set of sequences so that mutations that are observed many times due spread of the virus rather than acquisition by a mutational process are not over-counted. This means that convergent amino acid substitutions can be observed between lineage sets, although they may be undercounted within a lineage. However, this is necessary since it is very difficult to identify convergence within similar sequences (especially at a global scale). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 19 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Overcounting of the mutations results in mutational signatures that reflect the circulating pre- dominant lineages rather than the mutational processes producing the mutations in those lineages. Pseudo-sampling Mutations were binned into categories composed of their substitution type (e.g., cytosine ! thymine = CT) and their mutation context. The mutation context is the mutated base and the nucleotides at the 5’ and 3’ positions of the mutated base. There are a total of 192 types of substitution-context matchings that can appear (12 possible single nucleotide changes x four possible nucleotide 5’ x four possible nucleotide 3’). Every sequence produces a single count vector of mutation category counts, with the total count matrix becoming the muta- tional catalogue of the virus. On average, a single SARS-CoV-2 genome sequence has very few new mutations. As extracting mutational signatures when mutation counts are low is unlikely to produce meaningful results, we define each sample as a time-point (all of the sequences collected in an epidemic week) and decompose signatures from the counts at each time-point rather than from each sequence. This shrinks the mutational catalogue of the virus from millions of samples down to less than 200 samples, one for each Epidemic Week. Non-negative matrix factorisation NMF (non-negative matrix factorisation) [65, 66] was used to split the mutational catalogue into two sub-matrices. One matrix represents the mutational signatures, the other matrix rep- resents the exposure of the signatures. These matrices were used to reconstruct the original mutational catalogue with some degree of error. To verify the validity of the identified signa- tures, NMF was performed 100 times for each value of N, with N representing the number of signatures to extract from the mutational catalogue. For this analysis, N was set to 2, . . ., 10. For each NMF run, a new mutational catalogue was generated using bootstrap re-sampling of the original matrix and removal of any mutational categories that did not account for more than 0.5% of mutations. Mutational categories are pseudo-sampled down into epidemic week matrices that NMF was run on. The signatures were then clustered together using K-means clustering, with the cluster means forming the new signatures. Clusters were then assessed using the silhouette score to determine the clustering quality. Clusters with high silhouette scores are well separated from other clusters and are dense and well-formed. Cosine similarity was used to determine if the signature was reliably extracted from the cluster. The cosine simi- larity was calculated between signatures extracted from the whole mutational catalogue and the cluster means of the signature clusters. A higher cosine similarity indicates that the cluster mean shows a similar pattern to the initial mutational signature. Following the best practices in Islam et al. [66], an N value of three was selected due to the reduction of the reconstruction error plateauing around three and the marked decrease in silhouette score for signatures greater than 3. The average cosine similarity between signatures and clusters was consistently above 0.95 for each cluster and had an average of 0.98 for all three clusters when clustering was repeated 100 times. Silhouette scores for each cluster were above 0.95, suggesting excellent sep- aration and density of clusters (S5 Table and S9 Fig). Signatures can therefore be reliably extracted from the bootstrapped catalogues, are robust and thus are unlikely to be artefacts. Counts of mutations were normalised by the tri-mer composition of the SARS-CoV-2 refer- ence sequence (dividing the counts by the number of contexts in the reference sequence). Composition biased versions of the signatures were then produced by rescaling the signatures using tri-mer composition. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 20 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Non-negative least squares regression A non-negative least squares (NNLS) Regression was used to produce positive exposure weights for each of the signatures in each of the datasets. The non-negativity of the regression ensures that the weights of the signatures continue to represent an additive process. The NNLS weights can then represent the exposures of the signatures on each dataset. Consensus lineage and continent signatures Mutational catalogues were constructed for each continent and each of the Variant of Concern (VOC) lineages (Alpha, Beta, Gamma, Delta and Omicron). The global signatures were then used to extract exposures for each of the mutational catalogues to determine how processes varied between each mutational catalogue subset. VOC sequence sets were filtered so that weeks with fewer than 100 sequences were excluded. Supporting information S1 Fig. Country-level SARS-CoV-2 lineage dynamics. Solid bars show the biweekly propor- tions of the common lineages. Bars are coloured by lineage and white space shows the propor- tion of sequences from other lineages. The countries included in this analysis is based on temporal data completeness. (TIF) S2 Fig. Model-fitting of country-level SARS-CoV-2 reported cases. Black solid lines show a 14-day rolling average of adjusted SARS-CoV-2 cases. Pink solid lines show fitted mean response values of infection rates with predictor values as input and grey shaded areas high- light the confidence intervals. The countries included in this analysis is based on temporal data completeness. (TIF) S3 Fig. Diagrammatic depiction of how tree-based referencing works. Each Pango lineage has a reference generated for it. Arrows show which sequences use which reference sequence, with the arrow tip indicating the reference. For example, sequences from the B.1 lineage are compared against the reference for the B lineage so that B.1 lineage-defining mutations can be counted. (TIF) S4 Fig. Graphical description of the methods for NMF extraction of mutational signatures. For every value of N signatures, the mutational signatures are extracted 100 times for boot- straped and pseudo-sampled datasets. Once this has been completed, signatures are clustered into N clusters and the stability and density of those clusters are evaluated using the silhouette score. Signatures that have silhouette scores above 0.95 are evaluated as stable signatures. The cluster means become the extracted signatures. The best set of N signatures is selected by pick- ing the value of N that best minimises the reconstruction error and has the best silhouette score (with a minimum of 0.95). A further evaluation is the cosine similarity of the clustered signa- ture means with the signatures extracted by completing NMF on the original pseudo-sampled dataset. Again, signatures must have a cosine similarity of at least 0.95 to be considered. (TIF) S5 Fig. Non-normalised mutational signatures for SARS-CoV-2. Signatures were extracted using normalised counts calculated by dividing the mutation counts by the count of the tri- nucleotide context of the mutation context (Fig 4). These signatures were then multiplied post-analysis by the tri-nucleotide composition of the reference sequence to produce the non- PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 21 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity normalised signatures shown here. (TIF) S6 Fig. Counts of unique substitutions per week of the pandemic. Areas are coloured by substitution category. (TIF) S7 Fig. Counts of unique substitutions per week of the pandemic for each VOC category. Areas are coloured by substitution category. (TIF) S8 Fig. Counts of unique substitutions per week of the pandemic for each continent cate- gory. Areas are coloured by substitution category. (TIF) S9 Fig. Signature evaluation metrics. The number of signatures was selected at N = 3 since this produced an “elbow” for the reconstruction error while having a suitable silhouette score greater than 0.95. (TIF) S1 Table. Proportion of common lineages/variants globally. (XLSX) S2 Table. Correlation between infection rate and predictor variables across different conti- nents. (XLSX) S3 Table. Effect of public health measures (government stringency and vaccination) and viral properties (diversity and fitness) on infection rates at continent level. (XLSX) S4 Table. Effect of public health measures (government stringency and vaccination) and viral properties (diversity and fitness) on infection rates at national levels. (XLSX) S5 Table. Evaluation Results for Signature with N = 3. (XLSX) Acknowledgments We gratefully acknowledge all data contributors, i.e., the authors and their originating labora- tories responsible for obtaining the specimens and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. We thank Spyros Lytras, Francesca Young, Sejal Modha, Andres Gomez and Procheta Sen for their help- ful comments throughout the process of writing and preparing this manuscript. Author Contributions Conceptualization: Kieran D. Lamb, Ke Yuan, David L. Robertson. Data curation: Richard J. Orton. Formal analysis: Kieran D. Lamb, Martha M. Luka, Megan Saathoff. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011795 January 25, 2024 22 / 26 PLOS COMPUTATIONAL BIOLOGYMutational processes and SARS-CoV-2’s evolutionary capacity Funding acquisition: Matthew Cotten, Ke Yuan, David L. Robertson. Investigation: Kieran D. Lamb, Martha M. Luka, Ke Yuan, David L. Robertson. Methodology: Kieran D. Lamb, Ke Yuan. Resources: Kieran D. Lamb. Software: Kieran D. Lamb. Supervision: My V. T. Phan, Matthew Cotten, Ke Yuan, David L. 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10.1371_journal.pgen.1011201	RESEARCH ARTICLE Spoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Riccardo PianezzaID Robert KoflerID 1* 1,2☯, Almorò Scarpa1,2☯, Prakash Narayanan3, Sarah Signor3, 1 Institut fu¨ r Populationsgenetik, Vetmeduni Vienna, Vienna, Austria, 2 Vienna Graduate School of Population Genetics, Vetmeduni Vienna, Vienna, Austria, 3 Biological Sciences, North Dakota State University, Fargo, North Dakota, United States of America a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 ☯ These authors contributed equally to this work. sarah.signor@ndsu.edu (SS); rokofler@gmail.com (RK) Abstract OPEN ACCESS Citation: Pianezza R, Scarpa A, Narayanan P, Signor S, Kofler R (2024) Spoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s. PLoS Genet 20(3): e1011201. https://doi.org/10.1371/journal. pgen.1011201 Editor: Ce´dric Feschotte, Cornell University, UNITED STATES Received: November 28, 2023 Accepted: February 27, 2024 Published: March 26, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgen.1011201 Copyright: © 2024 Pianezza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The consensus sequence of Spoink as well as the sequences of the six PCR amplicons are available at https://github. com/rpianezza/Dmel-Spoink/tree/main/ During the last few centuries D. melanogaster populations were invaded by several trans- posable elements, the most recent of which was thought to be the P-element between 1950 and 1980. Here we describe a novel TE, which we named Spoink, that has invaded D. mela- nogaster. It is a 5216nt LTR retrotransposon of the Ty3/gypsy superfamily. Relying on strains sampled at different times during the last century we show that Spoink invaded worldwide D. melanogaster populations after the P-element between 1983 and 1993. This invasion was likely triggered by a horizontal transfer from the D. willistoni group, much as the P-element. Spoink is probably silenced by the piRNA pathway in natural populations and about 1/3 of the examined strains have an insertion into a canonical piRNA cluster such as 42AB. Given the degree of genetic investigation of D. melanogaster it is perhaps surpris- ing that Spoink was able to invade unnoticed. Author summary Horizontal transfer of transposable elements (TE) is a major factor driving genome evolu- tion. Yet well documented cases of such horizontal transfer events are rare. Most evidence is indirect, relying on sequence similarity of TEs between species. Based on strains sam- pled during the last decades we provide direct evidence that the retrotransposon Spoink was absent in worldwide D. melanogaster populations before 1983 but present in popula- tions after 1993. We suggest that the Spoink invasion was triggered by a horizontal transfer from a Drosophila species of the willistoni group. Introduction Transposable elements (TEs) are short genetic elements that can increase in copy number within the host genome. They are abundant in most organisms and can make up the majority of some genomes, i.e. maize where TEs constitute 83% of the genome [1]. There are two classes of TEs which transpose by different mechanisms—DNA transposons which replicate by PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 1 / 25 PLOS GENETICSreleasedseqs. The tool LTRtoTE is available on GitHub (https://github.com/Almo96/LTRtoTE). The analysis performed in this work have been documented with RMarkdown and have been made publicly available, together with the resulting figures, at GitHub (https://github.com/rpianezza/ Dmel-Spoink; see *.md files). Funding: This work was supported by the National Science Foundation Established Program to Stimulate Competitive Research grants NSF- EPSCoR-1826834 and NSF-EPSCoR-2032756 to SS, and by the Austrian Science Fund (FWF) grants P35093 and P34965 to RK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Spoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s directly moving to a new genomic location in a ‘cut and paste’ method, and retrotransposons which replicate through an RNA intermediate in a ‘copy and paste’ method [2–4]. From humans to flies, more genetic variation (in bp) is due to repetitive sequences such as transpos- able elements than all single nucleotide variants combined [5]. Although some TEs, such as R1 and R2 elements, may benefit hosts [6, 7] most TE insertions are thought to be deleterious [8, 9]. Host genomes have therefore evolved an elaborate system of suppression frequently involv- ing small RNAs [10]. Suppression of TEs in Drosophila relies upon small RNAs termed piRNA, which are cognate to TE sequences [11–13]. These small RNAs bind to PIWI clade proteins and mediate the degradation of TE transcripts and the formation of heterochromatin silencing the TE [11, 14–19]. However, while host defenses quickly adapt to new transposon invasions, TEs can escape silencing through horizontal transfer to new, defenseless, genomes [20–23]. This horizontal transfer allows TEs to colonize the genomes of novel species [20, 23– 26]. The first well-documented instance of horizontal transfer of a TE was the P-element, which spread from D. willistoni to D. melanogaster [27]. Following this horizontal transfer the P-element invaded natural D. melanogaster populations between 1950 and 1980 [28, 29]. It was further realized that the I-element, Hobo and Tirant spread in D. melanogaster populations earlier than the P-element, between 1930 and 1960 [29–31]. The genomes from historical D. melanogaster specimens collected about two hundred years ago, recently revealed that Opus, Blood, and 412 spread in D. melanogaster populations between 1850 and 1933 [21]. In total, it was suggested that seven TEs invaded D. melanogaster populations during the last two hun- dred years where one invasion (the P-element) was triggered by horizontal transfer from a spe- cies of the willistoni group and six invasions by horizontal transfer from the simulans complex [21, 27, 31–34]. It was, however, widely assumed until now that the P-element invasion, which occurred between 1950–1980, was the last and most recent TE invasion in D. melanogaster [21, 29, 31, 35, 36]. Here we report the discovery of Spoink, a novel TE which invaded worldwide D. mela- nogaster populations between 1983 and 1993, i.e. after the invasion of the P-element. Spoink is a LTR retrotransposon of the Ty3/gypsy group. We suggest that the Spoink invasion in D. mel- anogaster was triggered by horizontal transfer from a species of the willistoni group, similarly to the P-element invasion in D. melanogaster. In a model species as heavily investigated as D. melanogaster it is perhaps surprising that Spoink was able to invade undetected. Materials and methods Discovery of the recent Spoink invasion We identified TE insertions in different long-read assemblies using RepeatMasker [37] and the TE library from [5]. When comparing the TE composition between strains collected in the 1950’s and 1960’s [38, 39] and more recently collected strains (� 2003 [40] we noticed an ele- ment labeled ‘gypsy-7_DEl’ which was only present in short degraded copies in the older genomes but was present in full length copies in the more recent genomes (S1 Table). Structure and classification of Spoink To generate a consensus sequence of Spoink we extracted the sequence of full-length matches of ‘gypsy-7_DEl’ plus some flanking sequences from long-read assemblies [Ten-15, RAL91, RAL176, RAL732, RAL737, Sto-22; [40]] and made a consensus sequence by performing mul- tiple sequence alignment (MSA) with MUSCLE (v3.8.1551) [41] and then choosing the most abundant nucleotide in each position of the MSA with a custom Python script (MSA2consensus). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 2 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s The consensus sequence of the LTR was used to identify the TSD with our new tool LTRtoTE (https://github.com/Almo96/LTRtoTE). We used LTRdigest to identify the PBS of Spoink [42]. We picked several sequences from each of the known LTR superfamily/groups using the consensus sequences of known TEs [2, 43] (v9.44). We performed a blastx search against the NCBI database to identify the RT domain in the consensus sequences of the TE [44]. We then performed a multiple sequence alignment of the amino-acid sequences of the RT domain using MUSCLE (v3.8.1551) [41]. We obtained the xml file using BEAUti2 [45] (v2.7.5) and generated the trees with BEAST (v2.7.5) [45]. The maximum credibility tree was built using TreeAnnotator (v2.7.5) [45] and visualized with FigTree (v1.4.4, http://tree.bio.ed.ac.uk/ software/figtree/). Distribution of Spoink insertions Genes were annotated in each of the 31 genomes from [40] using the annotation of the refer- ence genome of D. melanogaster (6.49; Flybase) and liftoff 1.6.3 [46, 47]. The 1kb regions upstream of each gene were classified as putative promotors. The location of canonical D. mel- anogaster piRNA clusters was determined using CUSCO, which lifts over the flanks of known clusters in a reference genome to locate the homologous region in a novel genome [48]. The location of Spoink insertions within genes or clusters was determined with bedtools intersect [49]. To determine if genic insertions were shared or independent, the sequence of the inser- tion was extracted from each genome along with an extra 1 kb of flanking sequence on each end. Insertions purportedly in the same gene were then aligned, and if the flanks aligned they were considered shared insertions. To determine if cluster insertions were shared the flanking TE regions were aligned using Manna, which aligns TE annotations rather than sequences, to determine if there was any shared synteny in the surrounding TEs [50]. Abundance of Spoink insertions in different D. melanogaster strains We investigated the abundance of Spoink in multiple publicly available short-read data sets [31, 40, 51–53]. These data include genomic DNA from 183 D. melanogaster strains sampled at different geographic locations during the last centuries. For an overview of all analysed short-read data see S5 Table. We mapped the short reads to a database consisting of the con- sensus sequences of TEs [43] (v9.44), the sequence of Spoink and three single copy genes (rhi, tj, RpL32) with bwa bwasw (version 0.7.17-r1188) [54]. We used DeviaTE (v0.3.8) [55] to esti- mate the abundance of Spoink. DeviaTE estimates the copy number of a TE (e.g. Spoink) by normalizing the coverage of the TE by the coverage of the single copy genes. We also used DeviaTE to visualize the abundance and diversity of Spoink as well as to compute the fre- quency of SNPs in Spoink (see below). To identify Spoink insertions in 49 long-read assemblies of D. melanogaster strains collected during the last 100 years we used RepeatMasker [37] (open-4.0.7; -no-is -s -nolow). For an overview of all analysed assemblies see S6 Table [39, 40, 48, 56]. For estimating the abundance of Spoink in the long-read assemblies we solely considered canonical Spoink insertions (> 80% of length, < 5% sequence divergence). Population frequency of Spoink insertions For every putative Spoink insertion (including degraded ones) in the eight long-read assem- blies of individuals from Raleigh [40], we extracted the sequence of the insertion plus 1 kb of flanking sequence with bedtools [49]. The sequence of the Spoink insertion was removed with seqkit [57] and the flanking sequences were mapped to the AKA017 genome (i.e. the common PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 3 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s coordinate system) with minimap2 allowing for spliced mappings [40, 57, 58]. The mapping location of each read was extracted and if they overlapped between strains they were consid- ered putative shared sites. Regions with overlapping reads were visually inspected in IGV (v2.4.14) and if the mapping location was shared they were considered shared insertions sites [59, 60]. PCR To validate whether Spoink is absent in old D. melanogaster strains but present in recent strains we used PCR. We designed two primers pairs for Spoink and one for vasa as a control. We extracted DNA from different strains of D. melanogaster (Lausanne-S, Hikone-R, iso-1, RAL59, RAL176, RAL737) using a high salt extraction protocol [61]. We designed two primers pairs for Spoink (P1,P2) and one for the gene vasa (P1 FWD TCAGAAGTGGGATCGGGCTCGG, P1 REV CAGTAGAGCACCATGCCGACGC, P2 FWD ATGGACCGTAATGGCAGCAGCG, P2 REV ACACTCCGCGCCAGAGTCAAAC, Vasa FWD AACGAGGCGAGGAAGTTTGC, Vasa REV GCGATCACTACATGGCAGCC). We used the following PCR conditions: 1 cycle of 95˚C for 3 minutes; 33 cycles of 95˚C for 30 seconds, 58˚C for 30 seconds and 72˚C for 20 seconds; 1 cycle of 72˚C for 6 minutes. Small RNAs To identify piRNAs complementary to Spoink we analysed the small-RNA data from 10 GDL strains [62]. The adaptor sequence GAATTCTCGGGTGCCAAGG was removed using cuta- dapt (v4.4 [63]). We filtered for reads having a length between 18 and 36nt and aligned the reads to a database consisting of D. melanogaster miRNAs, mRNAs, rRNAs, snRNAs, snoR- NAs, tRNAs [64], and TE sequences [43] with novoalign (v3.09.04). We used previously devel- oped Python scripts [65] to compute ping-pong signatures and to visualize the piRNA abundance along the sequence of Spoink. UMAP We used the frequencies of SNPs in the sequence of Spoink to compute the UMAP. This fre- quencies reflect the Spoink composition in a given sample. For example if a specimen has 20 Spoink insertions and a biallelic SNP with a frequency of 0.8 at a given site in Spoink than about 16 Spoink insertions will have the SNP and 4 will not have it. The frequency of the Spoink SNPs was estimated with DeviaTE [55]. Solely bi-allelic SNPs were used and SNPs only found in few samples were removed (�3 samples). UMAPs were created in R (umap package; v0.2.10.0 [66]). Origin of horizontal transfer To identify the origin of the horizontal transfer of Spoink we used RepeatMasker [37] (open- 4.0.7; -no-is -s -nolow) to identify sequences with similarity to Spoink in the long-read assem- blies of 101 drosophilid species and in 99 different insect species [67, 68] (S8 Table). We included the long-read assembly of the D. melanogaster strain RAL737 and of the D. simulans strain SZ129 in the analysis [23, 40]. We used a Python script to identify in each assembly the best hit with Spoink (i.e. the highest alignment score) and than estimated the similarity between this best hit and Spoink. The similarity was computed as s = rmsbest/rmsmax, where rmsbest is the highest RepeatMasker score (rms) in a given assembly and rmsmax the highest score in any of the analysed assemblies. A s = 0 indicates no similarity to the consensus sequence of Spoink whereas s = 1 represent the highest possible similarity. To generate a PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 4 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s phylogenetic tree we identified Spoink insertions in the assemblies of the 101 drosophilid spe- cies and RAL737 using RepeatMasker. We extracted the sequences of full-length insertions (> 80% of the length) from species having at least one full-length insertion using bedtools [49] (v2.30.0). A multiple sequence alignment of the Spoink insertions was generated with MUS- CLE (v3.8.1551) [41] and a tree was generated with BEAST (v2.7.5) [45]. Results Previous work showed that at least seven TE families invaded D. melanogaster populations during the last two hundred years [21, 29, 31]. To explore whether additional, hitherto poorly characterised TEs could have invaded D. melanogaster, we investigated long-read assemblies of recently collected D. melanogaster strains [40] using a newly assembled repeat library [5]. Interestingly we found differences in the abundance of “gypsy_7_DEl” between the reference strain Iso-1 and more recently collected D. melanogaster strains (S1 Table). To better charac- terize this TE, we generated a consensus sequence based on the novel insertions and checked if this consensus sequence matches any of the repeats described in repeat libraries generated for D. melanogaster and related species [5, 40, 43, 69, 70]. A fragmented copy of this TE, with just one of the two LTRs being present, was reported by [40] (0.13% divergence; “con41_- UnFmcl001_RLX-incomp”; S2 Table). The next best hits were gypsy7 Del, gypsy2 DSim, micro- pia and Invader6 (18–30% divergence; S2 Table). Given this high sequence divergence from previously described TE families and the fact that this novel TE belongs to an entirely different superfamily/group than gypsy7 (see below), we decided to give this TE a new name. We call this novel TE “Spoink” inspired by a Poke´mon that needs to continue jumping to stay alive. Spoink is an LTR retrotransposon with a length of 5216 bp and LTRs of 349 bp (Fig 1A; for coordinates of the analysed insertions see S3 Table). At positions 4639–4700 Spoink contains a poly-A tract, which length may differ by a few bases between insertions. Spoink encodes a 695 aa putative gag-pol polyprotein. Ordered from the N- to the C-terminus, the conserved domains of the polyprotein are: reverse transcriptase of LTR (e-value = 2.2e − 59; CDD v3.20 [71]), RNase HI of Ty3/gypsy elements (e-value = 1.65e − 48;) and integrase zinc binding domain (e-value = 4.81e − 16). Spoink lacks an env. The order of these domains, with the inte- grase downstream of the reverse transcriptase, is typical for Ty3/gypsy transposons [72]. A phylogeny based on the reverse transcriptase domain of different TE families suggests that Spoink is a member of the gypsy/mdg3 superfamily/group of LTR retrotransposons (Fig 1B; [2]). As expected for members of the Ty3/gypsy superfamily, Spoink generates a target site duplication of 4 bp and it has an insertion motif enriched for ATAT (Fig 1A; [2, 73]). A gag- pol polyprotein as encoded by Spoink was observed for some Ty3/gypsy transposons [74, 75] but not for others [72]. However, Spoink differs from what is expected for the Ty3/gypsy super- family in two ways. First, the predicted primer binding site of Spoink directly follows the LTR, whereas typically for Ty3/gypsy there is a shift of 5–8nt (Fig 1A; [2]). Second, the LTR motif is TG. . .TA which is different from the TG. . .CA motif usually reported for gypsy TEs [2]. Finally we investigated the genomic distribution of Spoink insertions in long-read assem- blies of D. melanogaster strains collected � 2003 [40]. In total, these assemblies contains 481 full-length (> 80% length with at least one LTR) insertions of Spoink (on the average 16 per genome). Unlike the P-element which has a strong insertion bias into promoters, Spoink inser- tions are mostly found in introns and intergenic regions (S1 Fig). 54% of the Spoink insertions are in 201 different genes. Interestingly we found 7 independent Spoink insertions in Myo83F. To summarize we characterized a novel LTR-retrotransposon of the Ty3/gypsy superfamily in the genome of D. melanogaster that we call Spoink. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 5 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Fig 1. Spoink is a novel TE of the Ty3/gypsy superfamily. A) Overview of the composition of Spoink. Features are shown in color and the alignments show the sequences around the main features of Spoink for two insertions in each of three different long-read assemblies of D. melanogaster. B) Phylogenetic tree based on the reverse-transcriptase domain of pol for Spoink and several other LTR retrotransposons. Multiple families have been picked for each of the main superfamilies/groups of LTR transposons [2]. Our data suggest that Spoink is a member of the gypsy/mdg3 group. https://doi.org/10.1371/journal.pgen.1011201.g001 Spoink recently invaded worldwide D. melanogaster populations To substantiate our hypothesis that Spoink recently invaded D. melanogaster we used three independent approaches: Illumina short read data, long-read assemblies, and PCR/Sanger sequencing. First we aligned short reads from a strain collected in 1958 (Hikone-R) and a strain PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 6 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Fig 2. Spoink invaded D. melanogaster. A) DeviaTE plots of Spoink for a strain collected in 1954 (Hikone-R) and a strain collected in 2015 (Ten-15). Short reads were aligned to the consensus sequence of Spoink and the coverage was normalized to the coverage of single-copy genes. The coverage based on uniquely mapped reads is shown in dark grey and light grey is used for ambiguously mapped reads. Single-nucleotide polymorphisms (SNPs) and small internal deletions (indels) are shown as colored lines. The coverage was manually curbed at the poly-A track (between dashed lines). B) Insertions with a similarity to the consensus sequence of Spoink in the long-read assemblies of Oregon-R (collected around 1925) and the more recently collected strain RAL737 (2003). C) PCR results for two Spoink primer pairs (for location of primers see sketch at bottom) and one primer pair for the gene vasa. Spoink is absent in old strains (Lausanne-S, Hikone-R and Iso-1) and present in more recently collected strains (RAL59, RAL176, RAL737). D) Population frequency of Spoink insertions in long-read assemblies of strains collected PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 7 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s in 2003 from Raleigh [40]. Note that highly diverged insertions are largely segregating at a high frequency while canonical Spoink insertions mostly segregate at a low frequency. https://doi.org/10.1371/journal.pgen.1011201.g002 collected in 2015 (Ten-15) [31, 40] to the consensus sequence of Spoink using DeviaTE [55]. DeviaTE estimates the abundance of Spoink insertions by normalizing the coverage of Spoink to the coverage of a sample of single-copy genes. Furthermore, DeviaTE is useful for generat- ing an intuitive visualization of the abundance and composition (i.e. SNPs, indels, truncations) of Spoink in samples. We found that only a few degraded reads aligned to Spoink in the 1950’s strain (Hikone-R) whereas many reads covered the sequence of Spoink in the more recently collected strain Ten-15 (Fig 2A). There were also very few SNPs or indels in the recently col- lected strain suggesting that most insertions have a very similar sequence (Fig 2A). This obser- vation holds true when multiple old and young D. melanogaster strains are analysed (S2 Fig). Next we investigated the abundance of Spoink in long-read assemblies of a strain collected in 1925 (Oregon-R) and a strain collected in 2003 (RAL737). We found solely highly diverged and fragmented copies of sequences with similarity to Spoink in Oregon-R (Fig 2B). These degraded fragments were mostly found near the centromeres of Oregon-R. Investigating the identity of these degraded fragments of Spoink in more detail we found that they largely match with short and highly diverged fragments of Invader6, micropia and the Max-element (S4 Table). In addition to these degraded fragments, the more recently collected strain RAL737 also carries a large number of full-length insertions with a high similarity to the consensus sequence of Spoink (henceforth canonical Spoink insertions; Fig 2B). The canonical Spoink insertions are distributed all over the chromosomes of RAL737 (Fig 2B). This observation is again consistent when several long-read assemblies of old and young D. melanogaster strains are analysed (S3 Fig). Finally we used PCR to test whether Spoink recently spread in D. melanogaster. We designed two PCR primer pairs for Spoink and, as a control, one primer pair for vasa (Fig 2C; bottom panel). The Spoink primers amplified a clear band in three strains collected 2003 in Raleigh but no band was found in earlier collected strains, including the reference strain of D. melanogaster, Iso-1 (Fig 2C). We sequenced the fragments amplified by the Spoink primers using Sanger sequencing and found that the sequence of the six amplicons matches with the consensus sequence of Spoink (S4 Fig). Finally we investigated the population frequency of canonical and degraded Spoink inser- tions. Using the long-read assemblies of eight strains collected in 2003 in Raleigh we computed the population frequency of different Spoink insertions. We found that canonical Spoink inser- tions (< 5% divergence) are largely segregating at a low population frequency, as expected for recently active TEs (Fig 2D). While several degraded fragments that were annotated as Spoink are private, there were many at a higher population frequency as expected for older sequences (Fig 2D). In summary our data suggest that Spoink recently spread in D. melanogaster and that degraded fragments with some similarity to Spoink are present in heterochromatic regions of the centromeres of all investigated D. melanogaster strains. These degraded fragments may be the remnants of more ancient invasions of TEs sharing some sequence similarity with Spoink. Timing the Spoink invasion Next we sought to provide a more accurate estimate of the time when Spoink spread in D. mel- anogaster. First we generated a rough timeline of the Spoink invasion using D. melanogaster strains sampled during the last two hundred years. We estimated the abundance of Spoink in PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 8 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Fig 3. Spoink invaded D. melanogaster between 1983 and 1993 after the invasion of the P-element. A) Rough timeline of the Spoink and P-element invasion based on different strains sampled during the last two hundred years. The numbers represent the estimated copy number of Spoink and P-element based on DeviaTE. B) Timeline of the Spoink and P-element invasion based on 183 strains sampled between 1960 and 2015. The intensity of the color varies due to overlapping dots C) Abundance of canonical Spoink insertions (> 80% length and < 5% divergence) in long- read assemblies of D. melanogaster strains collected between 1925 and 2018. https://doi.org/10.1371/journal.pgen.1011201.g003 these strains using DeviaTE [55]. As reference we also estimated the abundance of the P-ele- ment, which is widely assumed as to be the most recent TE that invaded D. melanogaster popu- lations [28, 31]. Spoink was absent from all strains collected �1983 but present in strains collected �1993 (Fig 3A). By contrast our data suggest that the P-element was absent in the strains collected � 1962 but present in strains collected �1967 (Fig 3A). This is consistent with previous works suggesting that the P-element invaded D. melanogaster between 1950 and 1980 [21, 29, 35, 36]. Our data thus suggest that Spoink invaded D. melanogaster after the P-ele- ment invasion. To investigate the timing of the invasion in more detail we estimated the abun- dance of Spoink in short-read data of 183 strains collected between 1960 and 2015 from PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 9 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s different geographic regions using DeviaTE (S5 Table; data from [31, 40, 51–53]). The analysis of these 183 strains supports the view that Spoink was largely absent in strains collected � 1983 but present in strains collected � 1993 (Fig 3B). However there are two outliers. Spoink is pres- ent in one strain collected in 1979 in Providence (USA), which could be due to a contamina- tion of the strain. On the other hand Spoink is absent in one strain collected in 1993 in Zimbabwe (Fig 3B). As Spoink was present in six other strains collected in 1993 from Zimba- bwe, it is feasible that Spoink was still spreading in populations from Zimbabwe around 1993. The strains supporting the absence of Spoink prior to 1983 were collected from Europe, Amer- ica, Asia and Africa while the strains supporting the presence of Spoink after 1993 were col- lected from all five continents (S5 Table). Finally we estimated the abundance of Spoink in 49 long-read assemblies of strains collected during the last 100 years (S6 Table; [39, 40, 48, 56]). We used RepeatMasker [37] to estimate the abundance of canonical Spoink insertions (> 80% length and < 5% divergence) in these strains. Canonical Spoink insertions were absent in strains collected before 1975 but present in all long- read assemblies of strains collected after 2003 (Fig 3C). The strains of the assemblies supporting the absence of canonical Spoink insertions were collected from America, Europe, Asia, and Africa whereas the strains showing the presence of Spoink were largely collected from Europe, though genomes from North America and Africa are also represented (S6 Table). In summary we conclude that Spoink invaded worldwide populations of D. melanogaster approximately between 1983 and 1993. Moreover, the Spoink invasion is more recent than the P-element invasion. Geographic heterogeneity in the Spoink sequence variation Previous work showed that the composition of TEs within a species may differ among geo- graphic regions [21, 31]. Such geographic heterogeneity could result from founder effects occurring during the geographic spread of a TE. For example, a TE spreading in a species with a cosmopolitan distribution such as D. melanogaster may need to overcome geographic obsta- cles such as oceans and deserts. The few individuals that overcome these obstacles, thereby spreading the TE into hitherto naive populations, may carry slightly different variants of the TE than the source populations. These distinct variants will then spread in the new population. Such founder effects during the invasion may lead to a geographically heterogeneous composi- tion of a TE within a species. For example, for the retrotransposon Tirant, individuals sampled from Tasmania carry distinct variants [31], while for 412 and Opus individuals from Zimba- bwe are distinct from the other populations [21]. To investigate whether we find such geo- graphic heterogeneity we analysed the Spoink composition in the Global Diversity Lines (GDL), which comprise 85 D. melanogaster strains sampled after 1988 from five different con- tinents (Africa—Zimbabwe, Asia—Beijing, Australia—Tasmania, Europe—Netherlands, America—Ithaca; [51]). Except for a single strain from Zimbabwe all GDL strains harbour Spoink insertions (S5 Fig). We estimated the allele frequency of SNPs in Spoink, where a SNP refers to a variant among dispersed copies of Spoink. The allele frequency estimate thus reflects the composition of Spoink within a particular strain. To summarize differences in the compo- sition among the GDL strains we used UMAP [76]. We found that the composition of Spoink varies among regions where three distinct groups can be distinguished: Tasmania, Bejing/Ith- aca and Netherlands/Zimbabwe (S5 Fig). It is interesting that clusters are formed by geograph- ically distant populations such as Bejing (Asia) and Ithaca (America). We speculate that human activity, where flies might for example hitchhike with merchandise, could be responsi- ble for this pattern. In summary, we found a geographically heterogeneous composition of Spoink which is likely due to founder effects occurring during the spread of this TE. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 10 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Fig 4. A piRNA based defence against Spoink emerged in D. melanogaster A) piRNAs mapping to Spoink in a strain sampled 1938 (Lausanne-S) and 2004 (I17). The transposon HMS Beagle is included as reference. Solely the 5’ positions of piRNAs are shown and the piRNA abundance is normalized to one million piRNAs. Sense piRNAs are shown on the positive y-axis and antisense piRNAs on the negative y-axis. B) Ping-pong signature for the piRNAs mapping to Spoink and HMS Beagle in the D. melanogaster strain I17 (2004). https://doi.org/10.1371/journal.pgen.1011201.g004 Spoink is silenced by the piRNA pathway in natural populations The host defence against TEs in Drosophila is based on small RNAs termed piRNAs. These piRNAs bind to PIWI clade proteins and silence a TE at the transcriptional as well as the post- transcriptional level [11, 12, 14, 77]. To test whether Spoink is silenced in D. melanogaster pop- ulations we investigated small RNA data from the GDL lines [62]. Small RNA were sequenced for 10 out of the 84 GDL lines such that two strains were picked from each of the five conti- nents [62]. We find piRNAs mapping along the sequence of Spoink in the GDL strain I17 which was collected in 2004 but not in the strain Lausanne-S which was sampled around 1938 (Fig 4A; [78]). piRNAs mapping to Spoink were further found for all 10 GDL strains (S6 Fig). An important feature of germline piRNA activity in D. melanogaster is the ping-pong cycle [11, 12]. An active ping-pong cycle generates a characteristic overlap between the 5’ positions of sense and antisense piRNAs, i.e. the ping-pong signature. Computing a ping-pong signature thus requires several overlapping sense and antisense piRNAs. Since the amount of piRNAs was too low we could not compute a ping-pong signature for the strain Lausanne-S (collected in 1938; see above). However we found a pronounced ping-pong signature in all 10 GDL sam- ples (Fig 4B and S6 Fig). It is an important open question as to which events trigger the emergence of piRNA based host defence. The prevailing view, the trap model, holds that the piRNA based host defence is initiated by a copy of the TE jumping into a piRNA cluster [17, 25, 79–81]. If this is true we expect Spoink insertions in piRNA clusters in each of the long-read assemblies of the recently collected D. melanogaster strains [40]. We identified the position of piRNA clusters in these PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 11 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s long-read assemblies based on unique sequences flanking the piRNA clusters [48]. Interest- ingly, we found an extremely heterogeneous abundance of Spoink insertions in piRNA clus- ters, where some strains (e.g. RAL176) have up to 14 cluster insertions whereas 18 out of 31 strains did not have a single cluster insertion (S7 Table). Three of the cluster insertions were into 42AB, which usually generates the most piRNAs [11, 69]. It is an important open question whether such a heterogeneous distribution of Spoink insertions in piRNA clusters is compati- ble with the trap model [82, 83]. In summary we found evidence that Spoink is silenced by the piRNA pathway but the number of Spoink insertions in piRNA clusters is very heterogeneous among strains. Origin of Spoink The invasion of Spoink in D. melanogaster was likely triggered by horizontal transfer from a different species. To identify the source of the horizontal transfer we investigated the long- read assemblies of 101 Drosophila species [67] (and D. simulans strain SZ129) and of 99 insect species [23, 67, 68] (S8 Table). We did not consider short-read assemblies, as TEs may be incompletely represented in them [48]. Apart from D. melanogaster we found insertions with a high similarity to Spoink in D. sechellia, in one out of two D. simulans assemblies (in SZ129 but not in 006), and species of the willistoni group, in particular D. willistoni (Fig 5A). In agree- ment with this, a sequence from D. willistoni with a high similarity to Spoink can be found in RepBase (Gypsy-78_DWil; I: 99.73% similarity, LTR: 93.54% similarity [84]). Spoink insertions with a somewhat smaller similarity were found in D. cardini and D. repleta. No sequences sim- ilar to Spoink were found in the 99 insect species (S7 Fig). To further shed light on the origin of the Spoink invasion we constructed a phylogenetic tree with full-length insertions of Spoink in D. melanogaster, D. sechellia, D. simulans (SZ129) D. cardini and species of the willistoni group (Fig 5B and for a star phylogeny see S8 Fig). We did not find a full-length insertion of Spoink in D. repleta. This tree reveals that Spoink insertions in D. sechellia and D. simulans have very short branches. Furthermore, in D. simulans just one out of the two analysed assem- blies has Spoink insertions. We thus suggest that the Spoink invasion in these two species is also of recent origin (manuscript in preparation). However, Spoink insertions in D. melanogaster are nested within insertions from species of the willistoni group (Fig 5B). Our data thus suggest that, similar to the P-element invasion in D. melanogaster [27], the Spoink invasion in D. melanogaster was also triggered by horizontal transfer from a species of the willistoni group. The synonymous divergence of Spoink is lower than for any of 140 single copy orthologous genes shared between D. melanogaster and D. will- istoni, further supporting the recent horizontal transfer of Spoink (S9 Fig) [20, 85, 86]. Species of the willistoni group are Neotropical, occurring throughout Central and South America [87– 89]. Therefore horizontal transfer of Spoink only became feasible after D. melanogaster extended its habitat into the Americas approximately 200 years ago [90–92]. Insertions of D. cardini are next to species of the willistoni group, suggesting that D. cardini also acquired Spoink by horizontal transfer from the willistoni group, likely independent of D. melanogaster (Fig 5B). D. cardini is also a Neotropical species and its range overlaps many species of the will- istoni group, thus horizontal transfer between the species is physically feasible [93, 94]. In summary, similarly to the P-element, horizontal transfer from a species of the willistoni group likely triggered the Spoink invasion in D. melanogaster. Discussion Here we suggest that the LTR-retrotransposon Spoink invaded D. melanogaster populations between 1983 and 1993, after the spread of the P-element. Similarly to the P-element, the PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 12 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Fig 5. The Spoink invasion in D. melanogaster was likely triggered by a horizontal transfer from a species of the willistoni group. A) Similarity of TE insertions in long-read assemblies of diverse drosophilid species to Spoink. The barplots show for each species the similarity between Spoink and the best match in the assembly. For example, a value of 0.9 indicates that at least one insertion in an assembly has a high similarity (� 90%) to the consensus sequence of Spoink. B) Bayesian tree of Spoink insertions in the different drosophilid species. Only full-length insertions of Spoink (> 80% of the length) were considered. Node support values are posterior probabilities estimated by BEAST [45]. Note that Spoink insertions of D. melanogaster are nested in insertions from the willistoni group (blue shades). https://doi.org/10.1371/journal.pgen.1011201.g005 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 13 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Spoink invasion was likely triggered by horizontal transfer from a species in the willistoni group. Horizontal transfer of a TE is usually inferred from three lines of evidence: i) a patchy distribution of the TE among closely related species, ii) a phylogenetic discrepancy between the TE and the host species and iii) a high similarity between the TE of the donor and recipient species, which is frequently quantified by the synonymous divergence of the TE [95, 96]. All of these three lines of arguments support a horizontal transfer of Spoink in D. melanogaster, with a species of the willistoni group being the likely donor. First we found a patchy distribution among species of the melanogaster group (for D. simulans we even have a patchy distribution among different strains; Fig 5A). Second Spoink insertions of D. melanogaster (and other spe- cies that may have gotten Spoink recently) are nested within species of the willistoni group (Fig 5B), a clear phylogenetic discrepancy. Third we found that the synonymous divergence of Spoink is lower than for all orthologous genes in D. melanogaster and D. willistoni (S9 Fig). In addition to this classical but indirect lines of evidence, we have however more direct and thus more compelling evidence for the horizontal transfer of Spoink. Based on strains collected dur- ing the last hundred years from all major geographic regions we showed that Spoink insertions were absent in all strains collected before 1983 but present in all strains collected after 1993 (using Illumina short read data, long-read assemblies, and PCR/Sanger sequencing). This makes Spoink one of the best documented cases of a recent horizontal transfer of a TE, simi- larly to the P-element where also strains collected during the last 100 years support the recent horizontal transfer [28, 29]. The abundance of sequencing data from strains collected at different time points during the last century allowed us to pinpoint the timing of the invasion in a way that would not have been previously possible. Spoink appears to have rapidly spread throughout global populations of D. melanogaster between 1983 and 1993. The narrow time-window of 10 years is plausible as studies monitoring P-element invasions in experimental populations showed that the P-ele- ment can invade populations within 20–60 generations [65, 97, 98]. Assuming that natural D. melanogaster populations have about 15 generations per year [99], a TE could penetrate a nat- ural D. melanogaster population within 1–3 years. Given this potential rapidness of TE inva- sions it is likely that Spoink spread quickly between 1983 and 1993. Since there is a gap between strains sampled at 1983 and 1993 we cannot further narrow down the timing of the invasion. Furthermore, the strains used for timing the invasions were sampled from diverse geographic regions and Spoink likely spread at different times in different geographic regions. If horizontal transfer from a willistoni species triggered the invasion, as suggested by our data, then Spoink will have first spread in D. melanogaster populations from South America (the habitat of willistoni species), followed by populations from North America and the other conti- nents. It is also feasible that Spoink invaded D. melanogaster indirectly, for example using D. simulans as intermediate host, in which case the Spoink invasion in D. melanogaster may have been triggered in almost any geographic region (both, D. simulans and D. melanogaster, are cosmopolitan species [100]). Unfortunately, we cannot infer the timing of the geographic spread of the Spoink invasion in different continents as D. melanogaster strains were not sam- pled sufficiently densely from different regions. Our work thus highlights the importance of efforts such as DrosEU, GDL and DrosRTEC to densely sample Drosophila strains in time and space [51, 101, 102]. It is also interesting to ask as to which extent human activity (e.g. traffick- ing of goods) contributed to the rapid spread of Spoink. Given that our analysis of the Spoink composition shows that geographically distant populations (Bejing/Ithaca or Netherlands/ Zimbabwe) cluster together, human activity may have played a role. Increasing human activity could also explain why Spoink (invasion 1983–1993) seems to have spread faster than the P- element (1950–1980). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 14 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Our investigation of Spoink insertions in different drosophilid species suggests that the Spoink invasion in D. melanogaster was triggered by horizontal transfer from a species of the willistoni group. Although it is possible that we did not analyse the true donor species, we con- sider it unlikely to be a species outside of the willistoni group given the wide distribution of Spoink in all species in the willistoni group. In addition, the phylogenetic tree of Spoink has deep branches within the willistoni group, suggesting that Spoink is ancestral in this group (S10 Fig). A related open question is when Spoink first entered D. melanogaster populations. Since a TE may initially solely spread in some isolated subpopulations there could be a considerable lag time between the horizontal transfer of a TE and its spread in worldwide population. The presence of Spoink in a strain collected around 1979 in Providence (USA; Fig 3B) could be due to this lag time (or contamination). Nevertheless, the horizontal transfer of Spoink must have happened between the spread of D. melanogaster into the habitat of the willistoni group, about 200 years ago, and the invasion of Spoink in worldwide populations between 1983 and 1993. In addition to the P-element, Spoink is the second TE that invaded D. melanogaster populations following horizontal transfer from a species of the willistoni group. Species from the willistoni group are very distantly related with D. melanogaster (about 100my [103]) and we were thus wondering whether it is a coincidence that a species of the willistoni group is again acting as donor of a TE invasion in D. melanogaster. The recent habitat expansion of D. melanogaster into the Americas resulted in novel contacts with many species, in addition to species of the willistoni group, that might have acted as donors of novel TEs such as D. pseudoobscura or D. persimilis [104]. Why is again a species of the willistoni group and not one of these other spe- cies acting as donor of a novel TE? Apart from mere chance, there are several, not mutually exclusive, hypotheses for this observation. First, it is feasible TEs of the willistoni group are exceptionally compatible with D. melanogaster at a molecular level. Second, some parasites tar- geting both D. melanogaster and species of the willistoni group could be efficient vectors for horizontal transfer of TEs. Third, the physical contact between D. melanogaster and some spe- cies of the willistoni group might be unusually tight, facilitating horizontal transfer of TEs by an unknown vector. D. willistoni is a common drosophilid in South American forests [105]. Habitat fragmentation caused by human deforestation may thus generate intensive contacts between human commensal species, such as D. melanogaster, and abundant forest species like D. willistoni. Fourth, species of the willistoni group might be exceptionally numerous resulting in elevated probability for horizontal transfer of a TE. The Spoink invasion is the eighth TE invasion in D. melanogaster that has occurred during the last 200 year. As we argued previously, such a high rate of TE invasions is likely unusual during the evolution of the D. melanogaster lineage since the number of TE families in D. mel- anogaster is much smaller than what would be expected if this rate of invasions would persist [21]. It is possible that the high rate of TE invasions continues beyond the past 200 years since many LTR transposons in D. melanogaster are likely of very recent origin (possibly < 16.000years [85, 106]). One possible explanation for this high rate of recent TE invasions is that human activity contributed to the habitat expansion of D. melanogaster. Due to this habitat expansion D. melanogaster spread into the habitat of D. willistoni which enabled the horizontal transfer of Spoink. This raises the possibility that other species with recent habi- tat expansions also experienced unusually high rates of TE invasions. It is also interesting to ask whether the rate of TE invasions differs among species. For example cosmopolitan species, such as D. melanogaster, may generally experience higher rates of horizontal transfer than more locally confined species. The cosmopolitan distribution will bring species into contact with many diverse species, thereby increasing the opportunities for horizontal transfer of a TE. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 15 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s The Spoink invasions also opens up several novel opportunities for research. First, the broad availability of strains with and without Spoink will enable testing whether Spoink activity induces phenotypic effects, similarly to hybrid dysgenesis described for the P-element, I-ele- ment and hobo, but not for Tirant [31, 107–109]. Second, it will be interesting to investigate whether some Spoink insertions participated in rapid adaptation of D. melanogaster popula- tions, similar to a P-element insertion which contribute to insecticide resistance [110]. Third, it will enable studying Spoink invasions in experimental populations, shedding light on the dynamics of TE invasions, much as other recent studies investigating the invasion dynamics of the P-element [97, 98, 111]. Fourth, investigation into the distribution of species that have been infected with Spoink will shed light on the networks of horizontal transfer in drosophilid species. Fifth, the Spoink invasion provides an opportunity to study the establishment of the piRNA-based host defence [similar to [24, 65]]. For example we found that none of the piRNA cluster insertions are shared between individuals, suggesting there is no or solely weak selec- tion for piRNA cluster insertions. Furthermore we found an extremely heterogeneous abun- dance of Spoink insertions in piRNA clusters where we could not find a single cluster insertions of Spoink in several strains. It is an important open question whether such a hetero- geneous distribution is compatible with the trap model [83]. One possibility is that a few clus- ter insertions in populations are sufficient to trigger the paramutation of regular (non- paramutated) Spoink insertions into piRNA producing loci [16, 112, 113]. These paramutated Spoink insertions may then compensate for the low number of Spoink insertions in piRNA- clusters [112]. Paramutations could thus explain why several studies found that stand-alone insertions of TEs can nucleate their own piRNA production [69, 83, 114, 115]. The war between transposons and their hosts is constantly raging, with potentially large fit- ness effects for the individuals in populations. Over the last two hundred years there have been at least eight invasions of TEs into D. melanogaster, each of which could disrupt fertility for example by inducing some form of hybrid dysgenesis. TEs are responsible for > 80% of visible spontaneous mutations in D. melanogaster, and produce more variation than all SNPs com- bined [116–118]. In the long read assemblies considered here, more than half of insertions of Spoink were into genes [40]. The recent Spoink invasion could thus have a significant impact on the evolution of D. melanogaster lineage. Supporting information S1 Fig. Abundance of Spoink and P-element insertions in different genomic features. TE insertions were identified in 31 long-read assemblies of D. melanogaster [40] and the reference annotation was lifted to each assembly with liftoff [46, 47]. Note that the P-element has a pro- nounced insertion bias in promoters (defined as 1000bp upstream of the first exon) whereas Spoink insertions are largely found in introns and intergenic regions. (AI) S2 Fig. DeviaTE plots of six D. melanogaster strains collected during the last century. The short reads were aligned to the consensus sequence of Spoink and the coverage was normalized to the the coverage of single-copy genes. The coverage was manually curbed at the poly-A track (indicated by dashed lines). Note that very few reads of old strains (� 1975) align to Spoink whereas a contiguous coverage of reads along Spoink is observed for more recently col- lected strains (� 1993). (AI) S3 Fig. Abundance of Spoink insertions in six long-read assemblies of D. melanogaster strains collected during the last century. Note that all strains contain fragmented and PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 16 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s diverged insertions of Spoink, while solely recently collected strains (�2003) contain canonical Spoink insertions (i.e. full-length insertions with little divergence from the consensus sequence). (AI) S4 Fig. The Sanger sequence of the six PCR amplicons matches with the consensus sequence of Spoink. The Sanger sequences of the amplicons of P1 (red) and P2 (green) have been aligned to the consensus sequence of Spoink (blue, top) and the coordinates of the align- ments are indicated. The D. melanogaster strain and the sequence similarity between the Sanger sequence and the consensus sequence of Spoink are provided next to each matching region. (SVG) S5 Fig. Abundance and composition of Spoink insertions in the GDL. A) Abundance of Spoink in the GDL. Note that one strain from Zimbabwe does not have any Spoink insertion. B) UMAP summarizing the composition of Spoink among the GDL. Note that Spoink shows a pronounced population structure, where three main clusters can be discerned: Tasmania, Bej- ing/Ithaca and Netherlands/Zimbabwe. (SVG) S6 Fig. A piRNA based defence against Spoink is active in the 10 GDL strains. Two strains are analysed for each continent (Bxx Beijing/Asia, Ixx Ithaca/America, Nxx Netherlands/ Europe, Txx Tasmania/Australia, ZWxx Zimbabwe/Africa; the second strain from Ithaca (I17) is shown in the main manuscript). A) piRNAs mapping to the sequence of Spoink. Solely the 5’ positions of piRNAs are shown and the piRNA abundance is normalized to one million piR- NAs. Sense piRNAs are shown on the positive y-axis and antisense piRNAs on the negative y- axis. B) ping-pong signature of Spoink. (SVG) S7 Fig. Barplots show the similarity between the consensus sequence of Spoink and the best match in each of 99 long-read assemblies of diverse insect species. As a reference, two D. melanogaster assemblies (red) were included, where D.mel.RAL176 has canonical Spoink insertions while D.mel.Iso1 solely has degraded fragments of sequences having some similarity with Spoink. (AI) S8 Fig. Star phylogeny of Spoink insertions in the different drosophilid species. Only full- length insertions of Spoink (> 80% of the length) were considered. (SVG) S9 Fig. Distribution of synonymous divergence for Spoink and 140 single copy orthologous genes shared between D. melanogaster and D. willistoni (red). For Spoink we used the shared part of the longest ORF (green). The red dashed line is the 2.5% quantile of nuclear genes [85]. Note that the dS of Spoink is lower than the dS of any of the orthologous genes shared between D. melanogaster and D. willistoni, consistent with a horizontal transfer of Spoink between the two species. The genes were obtained with the software BUSCO [119]. The predicted proteins were aligned using Clustal Omega [120]. The codons information from the protein alignment was used for the nucleotide alignment using PAL2NAL [121]. The dS was calculated using the software PAML. (PNG) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 17 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s S10 Fig. Average distance between 100 pairs of Spoink insertions randomly sampled within either the melanogaster group (i.e D. melanogaster, D. simulans, D. sechellia) or the willis- toni group. Distances within the willistoni group are significantly longer than the distances in the melanogaster group (t = −6.31, df = 193.88, p = 1.762e − 09). Note that this test accounts for the phylogenetic information of the tree using the distances of the insertions within the two groups. (PNG) S1 Table. Differences in the abundance of Gypsy_7_DEl between the reference genome Iso1 and a long-read assemblies from a more recently collected strain. The best ten matches for Gypsy_7_DEl and the consensus sequence of Spoink are shown for both assemblies. Matches were identified with RepeatMasker [37]. Note that the discrepancy between Iso1 and TOM007 is more pronounced when the consensus sequence of Spoink is considered. (XLSX) S2 Table. Similarity between Spoink and other TEs in the different repeat libraries gener- ated for D. melanogaster. For each repeat library the best five hits are shown. Solely matches with a minimum overlap with Spoink of at least 30% are considered. subst. substitions in per- cent between Spoink and the TE, len. fraction of the length of a TE aligning with Spoink; a [40], b [43], c [5], d [69], e [70]. (XLSX) S3 Table. Coordinates of Spoink insertions in the strains RAL091, RAL176 and RAL732 used for Fig 1A of the main manuscript. (XLSX) S4 Table. Identity of sequences in Oregon-R having some similarity with the consensus sequence of Spoink. Solely sequences having a divergence of �25% and minimum overlap of at least 10% with Spoink are considered. The sequences were extracted from the assembly of Oregon-R (chromosome:start-end) and aligned against the TE library of D. melanogaster using blastn [43, 122]. Most of these sequences match TARTC and DMDM11. (XLSX) S5 Table. Overview of the short-read data analysed in this work. Data are from [31, 40, 51– 53]). (XLSX) S6 Table. Overview of the long-read assemblies of D. melanogaster strains analysed in this work. For each strain we show the assembly ID, the strain, the sampling location and the sam- pling date. a [38, 39], b [48], c [56], d [40], e [123]. (XLSX) S7 Table. Spoink insertions in piRNA clusters of long-read assemblies of different D. mela- nogaster strains [40]. Note that for several strains we could not find a single Spoink insertion in a piRNA cluster. On the other hand, some strains, like RAL176, have multiple Spoink inser- tions in piRNA clusters. (XLSX) S8 Table. Overview of the long-read assemblies of diverse insect species analysed in this work. (XLSX) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011201 March 26, 2024 18 / 25 PLOS GENETICSSpoink, a LTR retrotransposon, invaded D. melanogaster populations in the 1990s Acknowledgments We thank Matthew Beaumont for the idea to call the here described transposon Spoink. We thank Silke Jensen for comments. We thank Neda Barghi and Claudia Ramirez Lanzas for pro- viding fly strains used for PCR. SS would like to thank J. B. Signor for helpful comments on the manuscript. RK, RP, and AS thank all members of the Institute of Population Genetics for feedback and support. Author Contributions Conceptualization: Sarah Signor, Robert Kofler. Data curation: Riccardo Pianezza, Almorò Scarpa, Robert Kofler. Formal analysis: Riccardo Pianezza, Almorò Scarpa, Sarah Signor, Robert Kofler. Funding acquisition: Sarah Signor, Robert Kofler. Investigation: Almorò Scarpa, Prakash Narayanan, Sarah Signor, Robert Kofler. Methodology: Riccardo Pianezza. Project administration: Sarah Signor, Robert Kofler. Resources: Sarah Signor, Robert Kofler. Software: Riccardo Pianezza, Robert Kofler. Supervision: Sarah Signor, Robert Kofler. Visualization: Riccardo Pianezza, Almorò Scarpa, Sarah Signor, Robert Kofler. Writing – original draft: Riccardo Pianezza, Almorò Scarpa, Sarah Signor, Robert Kofler. 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10.1371_journal.pclm.0000339	RESEARCH ARTICLE A panel data study on the effect of climate change on life expectancy Amit RoyID 1,2* 1 Department of Economics, Shahjalal University of Science and Technology, Sylhet, Bangladesh, 2 Department of Economics, The New School for Social Research, New York, New York, United States of America * amit-eco@sust.edu Abstract The life and health of billions of people is endangered by climate change today. Life expec- tancy is generally used as the best metric for assessing the population health status of a nation. Against this backdrop, this paper investigates the effect of climate change on life expectancy using the panel data model. To do so, imprimis, this paper develops a concep- tual framework linking direct and indirect pathways by which climate change affects health. The direct pathways are through weather variables and natural disasters. The indirect path- ways are mediated through economic systems and ecosystems. Then this paper estimates the effect of climate change on life expectancy using cross-national data from 191 countries covering the period 1940–2020 and employing the fixed-effect method. The finding of this study suggests that if the annual average temperature increases by 1˚C, then the life expec- tancy at birth will decline by 0.44 years. Moreover, the temperature rise will further nega- tively impact life expectancy by interacting with the rainfall cycle. If the composite climate change index, an index of the geometric mean of temperature and rainfall, increases by 10 points, the life expectancy at birth will decline by 0.50 years. Moreover, climate change will disproportionately reduce the life expectancy of females more than the life expectancy of males. A negative relationship between a composite climate change index and life expec- tancy underscores the urgency of addressing climate change as a public health crisis. Miti- gation efforts to reduce greenhouse gas emissions and adapt to changing conditions are essential to minimize the health risks associated with climate change. Thus, countries should come forward with prompt initiatives to contain global temperature rise and protect the health of the population on the verge of climate change. 1. Introduction The global climate is changing rapidly [1]. Long-term shifts in temperature, rainfall, and other fundamental properties of climate are evident now which is globally recognized as “climate change”. The impacts of climate change include but are not limited to warming temperatures, abnormalities in precipitation as well as increases in the frequency and intensity of extreme weather events. These impacts threaten our health by affecting the food we eat, the water we a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Roy A (2024) A panel data study on the effect of climate change on life expectancy. PLOS Clim 3(1): e0000339. https://doi.org/10.1371/ journal.pclm.0000339 Editor: Bharath Haridas Aithal, Indian Institute of Technology Kharagpur, INDIA Received: April 28, 2023 Accepted: December 15, 2023 Published: January 18, 2024 Copyright: © 2024 Amit Roy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are publicly available for download from the following refereed sources. 1. Data on GDP Percapita, LDC and Life Expectancy: World Bank. World Development Indicators 2023. The World Bank; 2023 March 01. https://databank.worldbank.org/source/world- development-indicators 2. Data on Temperature and Rainfall. World Bank. The Climate Change Knowledge Portal (CCKP) 2023. The World Bank; 2023 March 30. https://climateknowledgeportal. worldbank.org/. Funding: The author received no specific funding for this work. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 1 / 21 PLOS CLIMATECompeting interests: The authors have declared that no competing interests exist. Effect of climate change on life expectancy drink, the air we breathe, and the weather we experience. Between 2030 and 2050, climate change is expected to cause approximately 250,000 deaths annually and cost US$4 billion of global income loss per year [2]. One of the most noticeable effects of climate change is the increase in global average tem- peratures. Over the past century, the Earth’s surface temperature has risen, with the most sig- nificant warming occurring in recent decades. It is splendent that the global surface temperature increased above 1.1˚C in 2011–2020 from 1850–1900 [2]. This change is primarily driven by human activities, particularly the emission of greenhouse gases such as carbon diox- ide (CO2), methane (CH4), and nitrous oxide (N2O). These gases trap heat in the Earth’s atmo- sphere, leading to a phenomenon known as the greenhouse effect, which results in rising global temperatures. Higher temperature increases the polar ice shields melting and resulting in the sea level rising which is catastrophic for coastal countries. Moreover, higher tempera- tures increase water evaporation, which increases the risk of severe droughts in landlocked countries. The potential future effects of escalating temperatures also include more frequent wildfires and tropical cyclones [3]. Moreover, higher temperatures will result in severe costs to society including a fall in output, reduced productivity, damage to food security, and increased mortality [4]. In addition to a higher temperature, more intense and frequent abnormal rainfall is observed [5]. Rainfall anomalies are defined as the deviations of rainfall from longrun aver- ages. Precipitation extremes–in terms of both excess and deficient rainfall—have serious con- sequences. On the one hand, increased rainfall over extended periods leads to coastal flooding, which results in soil erosion, fatalities, injuries, drownings, crop damage, increased risk of undernutrition resulting from diminished food production, waterborne diseases, and other flooding-related effects on health [6]. On the other hand, the decline in rainfall leads to drought which results in a scarcity of drinking water and irrigation facilities for agriculture production. This can result in rising food prices and food insecurity which ultimately effect of nutrition and health of people. Many of the world’s poorest countries which have a dispropor- tionately high dependence on agricultural employment are now experiencing strong negative feedback variability of rainfall including job losses, reduced income for farmers, and decreased health status in affected regions. Disruption of our climate system along with rising temperature and abnormal rainfall is brought by associated natural calamities like extreme heat waves, powerful storms, hurricanes, tropical cyclones, flooding, droughts, wildfires, and rising numbers of insect and vector-borne diseases [7]. These events can have far-reaching and often devastating consequences for the economy, ecosystem, health, and human society. For instance, rising temperatures can result in more frequent and intense heat waves which can lead to heat-related illnesses and higher mortality. Likewise, droughts can impact water availability for agriculture, industry, and households, leading to water scarcity, crop failures, food shortages, and a fall in expected years of living. Flash floods and river floods can damage infrastructure, displace communities, and result in casualties. Warmer ocean temperatures can fuel the development and intensification of tropical storms, leading to more powerful hurricanes and cyclones. These storms can bring widespread destruction to the lives and livelihoods of people. Rising temperatures, prolonged droughts, and changes in vegetation patterns can also create conditions conducive to wildfires. These fires can devastate forests, destroy homes, and have serious air quality and health impli- cations. Ultimately, these effects of climate disruption are fundamentally health issues and they pose existential risks to all of us which include but are not limited to death, disability, illness and loss or disruption in health care delivery [8]. Life expectancy is the best metric for assessing population health which captures the mortal- ity along the entire life course [9]. Climate change can have a significant impact on life PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 2 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 1. Conceptual relationship between climate change and life expectancy. Source: Authors’ build-up based on [10, 11]. https://doi.org/10.1371/journal.pclm.0000339.g001 expectancy, both directly and indirectly, through a variety of mechanisms. These effects can vary depending on factors such as geographic location, socioeconomic status, and access to resources. Fig 1 draws the conceptual link between climate change and life expectancy showing two primary exposure pathways by which climate change affects health (following [10, 11]). These impacts are diverse and multifaceted, affecting various aspects of human health and well-being. The direct pathway is through weather variables and natural disasters which relate primarily to changes in the frequency of extreme weather including heat, drought, storms, floods, heavy rain, etc. [12]. Natural disasters can have significant and complex effects on life expectancy. Natural disasters and extreme weather events, rising sea levels, changes in precipi- tation resulting in flooding and droughts, and intense hurricanes can directly cause injury, ill- ness, and even death and decrease in expected years of living. The impact depends on several factors, including the type and severity of the disaster, the preparedness and resilience of the affected community, access to resources, and the response to the disaster. Natural disasters often cause injuries, some of which can be severe and lead to long-term disabilities. Natural disasters can damage healthcare facilities, disrupt the supply of medications, and strain health- care systems. This can hinder access to medical care, exacerbate existing health conditions, and lead to preventable deaths. Moreover, crowded shelters and unsanitary conditions in the aftermath of disasters can increase the risk of disease outbreaks, such as cholera, dysentery, and respiratory infections. These outbreaks can lead to additional deaths. The effects of climate change can also indirectly affect health through alterations to the environment. The indirect pathways are heavily mediated through economic systems such as food security, household assets, income loss, population displacement, conflict over depleted resources, such as water, fertile land, and fisheries, and ecosystems such as disease vectors and pollution [13]. For example, climate change can disrupt agricultural systems, leading to crop failures and reduced food availability. This can result in malnutrition, lack of access to clean drinking water and sanitation, and related health problems among vulnerable populations [14]. Changes in temperature and rainfall can alter the survival, distribution, and behavior of PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 3 / 21 PLOS CLIMATEEffect of climate change on life expectancy insects and other species which can lead to changes in infectious diseases [15]. Climate change can alter the distribution and behavior of disease-carrying vectors like mosquitoes and ticks. This can lead to the spread of diseases such as malaria, dengue fever, Zika virus, and Lyme dis- ease into new regions [16]. Increases in precipitation, storm surge, and sea temperature can lead to more water-related illnesses. Prolonged exposure to extreme heat can result in heat- related illnesses, such as heat exhaustion and heatstroke, which can be fatal [17]. Climate change can also worsen air quality by increasing the frequency and severity of wildfires, dust storms, and air pollutants. Poor air quality can exacerbate respiratory problems and cardiovas- cular diseases and cause plummeting life expectancy [18]. Climate change can also affect food safety, exposing people to contaminated foods that can result in foodborne illnesses [19]. In addition, climate change can affect mental health and well-being. Survivors who experience extreme weather events, such as hurricanes, floods, wildfires, or heatwaves, can suffer from direct trauma, including injuries and loss of loved ones or property. The emotional toll of such events can lead to post-traumatic stress disorder (PTSD), anxiety, depression, and other men- tal health issues and increase the rate of suicides [20]. Economic losses resulting from climate change, such as damage to property, crop failures, and job losses in affected industries, can contribute to financial stress, which can, in turn, impact mental health. Climate change-related disasters can damage healthcare infrastructure and disrupt healthcare services, making it diffi- cult for individuals to access medical care when needed and result in premature death [21]. Resource scarcity driven by climate change can lead to the displacement of communities and conflicts over resources; as a result, the displaced populations would face health risks due to inadequate shelter, sanitation, and clean water, access to healthcare [22]. All these basic path- ways lead to how climate change reduces the life expectancy of nations by affecting hunger, nutrition status, diseases, mental health, and premature death profile. However, there is no empirical research has yet been conducted to estimate the effect of cli- mate change on life expectancy. Against this backdrop, this paper attempts to fill up the knowl- edge vacuum by empirically analyzing the effect of temperature and rainfall variability on life expectancy using panel data and the fixed effect method. Moreover, it introduces a novel com- posite climate change index and then estimates the overall impact of climate change on life expectancy. The rest of the paper is organized as follows: section 2 describes the materials and econometric methods used in this study. The next section discusses the results and empirical findings of the study. Finally, section 4 concludes this study with policy suggestions. 2. Materials and methods 2.1 Data and variables description To study the effect of climate change on the population health of countries, this paper employs cross-national panel data from 191 countries covering the period 1940–2020. The dependent variable of the study is the life expectancy at birth. To check the gender sensitivity of the results, this study further uses the life expectancy at birth of males and life expectancy at birth of females as dependent variables on separate regressions. Data on life expectancy at birth, life expectancy at birth of males, and life expectancy at birth of females are taken from World Bank World Development Indicators 2023 [23]. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life [23]. The major explanatory climate variables of the analysis are temperature and rainfall. Data on temperature and rainfall are collected from the World Bank Climate Change Knowledge Portal 2023 [24], where the temperature is defined as the annual average temperature of a country in a year in degrees Celsius and rainfall is defined as the annual average rainfall of a PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 4 / 21 PLOS CLIMATEEffect of climate change on life expectancy country in a year in millimeters. Moreover, in order to determine the overall impact of climate change on life expectancy, this study introduces a novel composite climate change index by taking the geometric mean of temperature and rainfall. The geometric mean is used instead of the arithmetic mean in order to avoid perfect substitutability between temperature and rainfall (following [25]). Moreover, the geometric mean has an advantage over the arithmetic mean in that it is less affected by extreme values in a skewed distribution. Climate Change Indexit ¼ p 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Temperatureit � Rainfallit A climate change index is a valuable tool for several reasons, as it serves multiple important purposes in understanding, addressing, and communicating the challenges and impacts of cli- mate change. A climate change index provides a quantitative way to measure and track prog- ress in addressing climate change. It can assess whether efforts to reduce greenhouse gas emissions and mitigate climate change are effective over time. This information is crucial for evaluating the success of policies and initiatives. A climate change index can also serve as a public awareness tool. It helps convey the urgency and severity of climate change to the general public, policymakers, businesses, and other stakeholders. By providing a clear and easily understandable metric, it can motivate individuals and organizations to take action. It helps identify areas where policy interventions are most needed and assess the effectiveness of exist- ing policies. This can guide the allocation of resources and the development of more targeted and impactful climate policies. Moreover, a climate change index can help prioritize resource allocation by identifying countries that are most vulnerable to climate change or where mitiga- tion efforts can have the most significant impact. In addition, climate change indices allow for international comparisons of climate performance. This can promote healthy competition among nations to reduce emissions and adapt to climate change, as well as foster collaboration in addressing global challenges. Moreover, the introduction of a novel composite climate change index, achieved through the strategic combination of temperature and rainfall metrics using the geometric mean, marks a significant advancement in climate science. This innovative approach transcends tra- ditional univariate indices by capturing the intricate interplay between multiple climatic vari- ables. The utilization of the geometric mean, as opposed to more conventional methods, brings forth a nuanced measure that encapsulates both the magnitude and proportional changes in temperature and rainfall. This specialized significance lies in the index’s ability to provide a more comprehensive and accurate representation of climate conditions, enabling a deeper understanding of the evolving climate dynamics. In the realm of climate change research, the introduction of this composite index addresses the limitations of single-variable indices, which may oversimplify the complexity of climate systems. By incorporating both temperature and rainfall in a geometric mean, the index offers a holistic perspective, acknowl- edging the interconnected nature of these climatic components. This methodological innova- tion is particularly crucial in a time where the impacts of climate change manifest in multifaceted ways. Thus, the specialized significance of this novel index lies in its capacity to enhance the precision of climate assessments, fostering more informed decision-making in various sectors that rely on accurate climate data. In the existing empirical literature, GDP per capita is the most important determinant of life expectancy [26]. GDP per capita increases life expectancy through increased economic growth and development in a country and thus leads to the prolongation of longevity. How- ever, an increase in per capita income does not directly translate into higher life expectancy if it is not utilized for provisioning nutrition, clean drinking water, sanitation, and other public health goods. Thus, we incorporate GDP per capita as the control variable in the study. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 5 / 21 PLOS CLIMATEEffect of climate change on life expectancy Table 1. Descriptive statistics. Variables Observations Life Expectancy at Birth Life Expectancy at Birth of Males Life Expectancy at Birth of Females GDP Percapita (GDPPC) Temperature Rainfall Climate Change Index LDC (Dummy) https://doi.org/10.1371/journal.pclm.0000339.t001 9,685 9,685 9,685 8,733 11,135 11,135 10,971 14,970 Mean 64.4342 62.0579 66.9415 10567.68 19.0099 100.3906 40.9900 - S.D. 11.3322 10.9119 11.8774 17378.35 8.1231 71.0110 21.5718 - Min 11.9950 10.054 14.008 144.0314 -9.2166 1.16667 4.69e-08 0 Max 84.6156 82.6 87.74 191193.7 29.3666 417.3917 104.9058 1 Moreover, to distinguish between developed and developing countries against the least devel- oping countries (LDC), this study includes a dummy variable representing the latter. Data on GDP per capita and LDC are collected from World Bank World Development Indicators 2023 [23]. Descriptive statistics of the variables under study are reported in Table 1. The global average life expectancy increased significantly between 1960 and 2020 from 55 years to 72 years [Fig 2]. At the same time, the global average life expectancy of males increased significantly from 48 years to 70 years and the global average life expectancy of females increased significantly from 52 years to 74 years. This longevity is due to access to plentiful and more nutritious food, clean water, better hygiene, and advanced medical care along with innovations in antibiotics and vaccines [27]. However, the due to Covid-19 pandemic, global life expectancy decreased slightly in 2020 [28]. The global average GDP percapita has tripled in the meantime from US$5,000 (PPP) to US$15,000 (PPP) [Fig 3]. Fig 2. Global average life expectancy from 1960–2020. Source: Authors’ plot in STATA based on [17] data. https://doi.org/10.1371/journal.pclm.0000339.g002 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 6 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 3. Global average GDP percapita from 1960–2020. Source: Authors’ plot in STATA based on [17] data. https://doi.org/10.1371/journal.pclm.0000339.g003 The global mean annual temperature increased sharply between 1940 and 1990 from 15˚C to 21˚C [Fig 4]. The rising temperatures, also known as global warming, are the major exter- nalities of human activities where the overuse of fossil fuels caused the increase in the concen- tration of greenhouse gases (GHGs) in the atmosphere [29]. However, since 1990 it has declined by 1˚C. The slight reduction in global mean annual temperature is the result of miti- gation action taken by countries to reduce GHG emissions by introducing GHG taxes or adopting an emission trading system [30]. In the meantime, the global mean annual rainfall has remained steady at 100±10 millimeters with an increasing trend [Fig 5]. The computed composite climate change index reveals alarming changes in global climate between 1940 and 1990 which are still persisting [Fig 6]. However, a significant decrease in the value of the index is apparent in the twenty-first century owing to the global effort to combat climate change. 2.2 Econometric method To examine the effect of climate change on life expectancy, this paper employs the panel fixed effect model. The fixed effects model is a statistical method used in econometrics and social sciences to analyze panel data, which is data collected on the same entities (e.g., individuals, firms, countries) over multiple time periods. This model is a type of linear regression model that accounts for both time-invariant and unobserved heterogeneity across the entities in the panel. The panel fixed effects model is particularly useful when you want to control for unob- served time-invariant characteristics that may be correlated with your explanatory variables and affect the dependent variable. By including entity-specific fixed effects, you can better iso- late the relationship of interest and make more accurate inferences about the impact of the explanatory variables on the dependent variable while accounting for this unobserved hetero- geneity. The panel fixed effects model estimates the parameters (coefficients) of the model using various estimation techniques, such as ordinary least squares (OLS) or the within- PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 7 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 4. Global average yearly temperature from 1940–2020. Source: Authors’ plot in STATA based on [18] data. https://doi.org/10.1371/journal.pclm.0000339.g004 Fig 5. Global average yearly rainfall from 1945–2020. Source: Authors’ plot in STATA based on [18] data. https://doi.org/10.1371/journal.pclm.0000339.g005 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 8 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 6. Global average annual climate change index from 1945–2020. Source: Authors’ plot in STATA based on calculated data. https://doi.org/10.1371/journal.pclm.0000339.g006 transformation (also known as the fixed effects estimator). The within-transformation sub- tracts the entity-specific means from the data, effectively eliminating the fixed effects. This paper employs the latter method of estimation as it is more efficient than OLS [31]. For i = 1,2,. . .,N countries and t = 1,2,. . .,T time period, a fixed effect model can be expressed as: yit ¼ ai þ bixit þ εit Where yit is the dependent variable. αi captures the fixed effect, that is, the country specific heterogeneity. xit is the vector of explanatory variables. βi are the parameters to be estimated. εit is the independently and identically distributed error term. In this analysis, the dependent variable is the life expectancy at birth (Life Expectancyit). xit is the vector of climate variables, such as, temperature (Temperatureit) and rainfall (Rain fallit) as well as control variable GDP percapita (GDPPCit). The previous equation can be rewritten in the form as: Life Expectancyit ¼ ai þ bi 2 6 4 GDPPCit Temperatureit Rainfallit 3 7 5 þ εit where Life Expectancyit denotes the life expectancy of birth of ith country on t time and so on. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 9 / 21 PLOS CLIMATEEffect of climate change on life expectancy We also use the composite climate change index as an explanatory variable in separate regression which takes the form as follows: Life Expectancy it ¼ ai þ bi 2 6 4 GDPPCit Climate Change Indexit GDPPCit�Climate Change Indexit 3 7 5 þ εit We also introduce interaction terms between climate and control variables during the empirical analysis in order to determine how much effect climate change has on the effect of GDP percapita on life expectancy. If the interaction coefficient is positive, then the effect of GDP percapita on life expectancy increases as a climate variable increase, if negative the oppo- site. If the interaction coefficient is zero, then the effect of GDP percapita on life expectancy is independent of the climate variable. 3. Results and discussion 3.1 Effect of climate change (Temperature and rainfall separately) on life expectancy The results of the empirical analysis are reported in Tables 2–7. Column 1 presents the vari- ables under study. All the models are found statistically significant as the F-statistics exceeds the critical value at 1% level of significance in all cases. Moreover, R-squared reports the overall goodness of fits of the models. Table 2. Panel fixed effect result. Dependent Variable: Life Expectancy at Birth. Variables GDPPC Temperature Rainfall GDPPC × Temperature GDPPC × Rainfall Temperature × Rainfall LDC Constant Observations F-statistic R-squared Fixed Effect (1) 0.0003* (0.0000) -0.4382* (0.0121) 0.0255* (0.0012) 68.3576* (0.2695) 8,465 2030.68* 0.4203 Yes (2) 0.0002* (0.0000) -0.2836* (0.0102) 0.0133* (0.0010) -10.2977* (0.1620) 71.6373* (0.2275) 8,465 3264.55* 0.6085 Yes (3) 0.0001* (0.0000) -0.7798* (0.0213) -0.0178* (0.0066) 0.0000* (0.0000) 0.0000* (0.0000) 0.0019* (0.0003) 75.0641* (0.4792) 8,465 1233.79* 0.4685 Yes (4) 0.0001* (0.0000) -0.4571* (0.0188) -0.0040 (0.0056) 0.0000* (0.0000) 0.0000 (0.0000) 0.0008* (0.0002) -9.5914* (0.1661) 74.5729* (0.4056) 8,465 1953.28* 0.6195 Yes Note: (1) *, , * denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t002 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 10 / 21 PLOS CLIMATETable 3. Panel fixed effect result of sensitivity analysis. Dependent Variable: Life Expectancy at Birth of Males. Variables GDPPC Temperature Rainfall GDPPC × Temperature GDPPC × Rainfall Temperature × Rainfall LDC Constant Observations F-statistic R-squared Fixed Effect (5) 0.0003* (0.0000) -0.3646* (0.0116) 0.0236* (0.0012) 64.6406* (0.2599) 8,465 1977.21* 0.4139 Yes (6) 0.0002* (0.0000) -0.2209* (0.0100) 0.0123* (0.0010) -9.5709* (0.1589) 67.6888* (0.2230) 8,465 3030.39* 0.5907 Yes Note: (1) , , denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t003 Effect of climate change on life expectancy (7) 0.0001* (0.0000) -0.6535* (0.0206) -0.0066 (0.0064) 0.0000*** (0.0000) 0.0000* (0.0000) 0.0014* (0.0003) 70.1948* (0.4649) 8,465 1172.49* 0.4558 Yes (8) 0.0001* (0.0000) -0.3517* (0.0185) 0.0062 (0.0055) 0.0000* (0.0000) 0.0000 (0.0000) 0.0004* (0.0002) -8.9721* (0.1634) 69.7353* (0.3989) 8,465 1796.15* 0.5996 Yes Table 2 reports the result of the baseline panel fixed effect estimation. From Model (1), we find that GDP percapita (GDPPC) and climate variables have statistically significant on life expectancy individually at 1% level of significance. For instance, the coefficient of GDPPC is 0.0003, which tells us that if the GDP percapita increases by $1000, the life expectancy at birth will increase by 0.3 years or by 4 months, holding all other factors remain the same. The coeffi- cient of temperature is -0.44, which tells us that if the annual average temperature increases by 1˚C, the life expectancy at birth will decline by 0.44 years or by 5 and half months, holding all other factors remain the same. The coefficient of rainfall is 0.03, which tells us that if the annual average rainfall increases by 10 millimeters, the life expectancy at birth will increase by 0.30 years or by 4 months, holding all other factors remain the same. The intercept term of the model is 68.36 which reveals that if all the explanatory variables of the model become zero, the life expectancy at birth would be 68.36 years. To control for the least developing countries (LDC) against the developed and developing countries, we introduce the LDC dummy variable in fixed effect regression (2). LDC coefficient is -10.30 which tells us that if a country is an LDC, its life expectancy at birth is 10.30 years lower than that of the developed and developing world, holding all other factors remain the same. The sign and significance of all variables remain the same in Model (2). Model (3) incorporates interaction terms between climate and control variables. In this case, the coefficient of GDPPC is reduced to 0.0001, which tells us that if the GDP percapita increases by $1000, the life expectancy at birth will increase by 0.1 years, holding all other fac- tors remain the same. The negative effect of temperature rise on life expectancy has now almost doubled to -0.78 years, holding all other factors remain the same. The interaction terms PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 11 / 21 PLOS CLIMATETable 4. Panel fixed effect result of sensitivity analysis. Dependent Variable: Life Expectancy at Birth of Females. Effect of climate change on life expectancy Variables GDPPC Temperature Rainfall GDPPC × Temperature GDPPC × Rainfall Temperature × Rainfall LDC Constant Observations F-statistic R-squared Fixed Effect (9) 0.0003* (0.0000) -0.5102* (0.0128) 0.0276* (0.0013) 72.1242* (0.2847) 8,465 2037.13* 0.4211 Yes (10) 0.0002* (0.0000) -0.3432* (0.0107) 0.0144* (0.0011) -11.1218* (0.1693) 75.6664* (0.2376) 8,465 3392.12* 0.6176 Yes Note: (1) *, , * denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t004 Table 5. Panel fixed effect result of overall effect of climate change. Dependent Variable: Life Expectancy at Birth. Variables GDPPC Climate Change GDPPC × Climate Change LDC Constant Observations F-statistic R-squared Fixed Effect (13) 0.0004* (0.0000) -0.0107 (0.0043) 61.9920* (0.2225) 8,388 1962.57* 0.3204 Yes (14) 0.0002* (0.0000) -0.0155* (0.0034) -11.4713* (0.1644) 68.1949* (0.1978) 8,388 3697.43* 0.5713 Yes Note: (1) , , denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t005 (11) 0.0001* (0.0000) -0.9119* (0.0223) -0.0284* (0.0069) 0.0000* (0.0000) 0.0000* (0.0000) 0.0025* (0.0003) 80.0757* (0.5026) 8,465 1274.63* 0.4766 Yes (15) 0.0002* (0.0000) -0.0511* (0.0051) 0.0000* (0.0000) 63.4307* (0.2407) 8,388 1413.41* 0.3375 Yes (12) 0.0001* (0.0000) -0.5662* (0.0196) -0.0137 (0.0058) 0.0000*** (0.0000) 0.0000* (0.0000) 0.0013* (0.0002) -10.2737* (0.1728) 79.5495* (0.4217) 8,465 2057.77* 0.6317 Yes (16) 0.0001* (0.0000) -0.0330* (0.0041) 0.0000* (0.0000) -11.2763* (0.1656) 68.7138* (0.2079) 8,388 2808.57* 0.5744 Yes PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 12 / 21 PLOS CLIMATEEffect of climate change on life expectancy Table 6. Panel fixed effect result of overall effect of climate change. (Sensitivity Analysis). Dependent Variable: Life Expectancy at Birth of Males. Variables GDPPC Climate Change GDPPC × Climate Change LDC Constant Observations F-statistic R-squared Fixed Effect (17) 0.0004* (0.0000) -0.0045 (0.0041) 59.4077* (0.2110) 8,388 2090.11* 0.3343 Yes (18) 0.0002* (0.0000) -0.0090* (0.0033) -10.5456* (0.1586) 65.1100* (0.1909) 8,388 3606.00* 0.5651 Yes (19) 0.0002* (0.0000) -0.0403* (0.0048) 0.0000* (0.0000) 60.6831* (0.2286) 8,388 1486.73* 0.3489 Yes (20) 0.0002* (0.0000) -0.0236* (0.0039) 0.0000* (0.0000) -10.3814* (0.1600) 65.5470* (0.2009) 8,388 2731.02* 0.5676 Yes Note: (1) , , denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t006 between GDPPC and temperature and rainfall are zero and statistically significant at 1% level of significance which posits the climate variables have no effect on life expectancy through increasing GDPPC. Most surprisingly, the effect of rainfall on life expectancy now becomes negative and statistically significant at 1% level and the interaction term between temperature and rainfall is positive and statistically significant at 1% level which implies the negative effect of temperature rise on life expectancy is compounding through the effect of temperature rise on water cycle and rainfall. In model (4), we reintroduce the LDC dummy and now the effect of rainfall on life expectancy becomes statistically insignificant though the negative effect of Table 7. Panel fixed effect result of overall effect of climate change. (Sensitivity Analysis). Dependent Variable: Life Expectancy at Birth of Females. Variables GDPPC Climate Change GDPPC × Climate Change LDC Constant Observations F-statistic R-squared Fixed Effect (21) 0.0004* (0.0000) -0.0165* (0.0046) 64.6566* (0.2380) 8,388 1819.56* 0.3042 Yes (22) 0.0002* (0.0000) -0.0218* (0.0036) -12.4837* (0.1740) 71.4068* (0.2094) 8,388 3678.80* 0.5701 Yes (23) 0.0002* (0.0000) -0.0604* (0.0054) 0.0000* (0.0000) 66.2203* (0.2573) 8,388 1319.05* 0.3222 Yes (24) 0.0001* (0.0000) -0.0407* (0.0043) 0.0000* (0.0000) -12.2720* (0.1753) 71.9699* (0.2201) 8,388 2796.33* 0.5734 Yes Note: (1) *, , * denote 1%, 5% and 10% levels of significance respectively. (2) Numbers in parentheses are robust standard errors. https://doi.org/10.1371/journal.pclm.0000339.t007 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 13 / 21 PLOS CLIMATEEffect of climate change on life expectancy temperature rise on life expectancy persists. In sum, the effect of temperature rise on life expec- tancy is consistent and negative, on the other hand, the effect of rainfall variation on life expec- tancy is ambiguous. The test the robustness of our findings, we employ two sets of separate fixed effect estima- tions based on the gender sensitivity of males and females. Table 3 reports the fixed effect esti- mation for the life expectancy at birth of males. Similar to the previous findings, we find that the increase in GDP percapita has a statistically significant positive effect on the life expectancy at birth of males, holding all other factors constant. Moreover, the temperature rise has a statis- tically significant negative effect on the life expectancy at birth of males. The coefficient of tem- perature is -0.65 (Model-7), which tells us that if the annual average temperature increases by 1˚C, the life expectancy at birth of males will decline by 0.65 years or by around 8 months, holding all other factors remain the same. However, the effect of rainfall on the life expectancy at birth of males is ambiguous and statistically insignificant in some cases. Table 4 reports the fixed effect estimation for the life expectancy at birth of females. Similar to the previous findings, we find that the increase in GDP percapita has a statistically signifi- cant positive effect on the life expectancy at birth of females, holding all other factors remain the same. Moreover, the temperature rise has a statistically significant negative effect on the life expectancy at birth of females. The coefficient of temperature is -0.91 (Model-11), which tells us that if the annual average temperature increases by 1˚C, the life expectancy at birth of females will decline by -0.91 years or by around 10 months, holding all other factors remain the same. However, the effect of rainfall on the life expectancy of birth of females is ambiguous but statistically significant. 3.2 Effect of composite climate change index on life expectancy It is observed in the above findings that temperature rise has a negative effect on life expec- tancy. Moreover, temperature rise influences rainfall and brings about further negative effects on life expectancy. To test the overall effect of temperature and rainfall variation on life expec- tancy, we introduce the composite climate change index. Table 5 reports the result of the base- line panel fixed effect estimation for the effect of the climate change index. From Model (15), we find that GDP percapita (GDPPC) and climate change index have statistically significant on life expectancy individually at 1% level of significance. For instance, the coefficient of GDPPC is 0.0002, which tells us that if the percapita income increases by $1000, the life expec- tancy at birth will increase by 0.2 years, holding all other factors remain the same. The coeffi- cient of the climate change index is -0.05, which tells us that if the annual climate change index increases by 10 points, the life expectancy at birth will decline by 0.50 years or by 6 months, holding all other factors remain the same. The interaction term between GDPPC and climate change index is zero and statistically significant at 1% level which posits that climate change has no effect on life expectancy through increasing GDPPC. We reintroduce the LDC dummy variable in fixed effect regression (16) where the LDC coefficient is -11.28 which tells us that if a country is an LDC, its life expectancy at birth is 11.28 years lower than that of the developed and developing world, holding all other factors remain the same. The test the gender sensitivity on life expectancy, we estimate the effect of the composite cli- mate change index on the life expectancy of males and the life expectancy of females, and the results are produced in Tables 6 and 7 respectively. The sign and significance of our findings are robust to gender sensitivity. However, results suggest that climate change would reduce the life expectancy of birth of females more than males. For instance, from Model (19), we find that if the annual climate change index increases by 10 points, the life expectancy at birth of males will decline by 0.40 years or by 5 months, holding all other factors remain the same. By PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 14 / 21 PLOS CLIMATEEffect of climate change on life expectancy contrast, from Model (23), we find that if the annual climate change index increases by 10 points, the life expectancy at birth of females will decline by 0.60 years or by 7 months, holding all other factors remain the same. 3.3 Effect of climate change on life expectancy: Graphical findings Our above findings are supported by the post-regression fitted line plotted in STATA. For example, Fig 7 depicts a sharp negative tradeoff between temperature and life expectancy. Higher temperatures would bring about higher mortality. However, it is also observable from the graph that there are many regions of the world where increasing temperatures have a posi- tive effect on life expectancy. It implies that higher temperatures can have both positive and negative effects on life expectancy, and the impact depends on several factors, including the magnitude and duration of the temperature increase, geographic location, and the ability of communities to adapt to changing conditions. Higher temperatures can positively influence life expectancy by reducing cold-related mortality during winter seasons in cold extreme coun- tries [32]. Moreover, in some agricultural regions, higher temperatures and longer growing seasons can lead to increased agricultural productivity, hence enhancing food security, reduc- ing malnutrition, and positively impacting public health. Moderate increases in temperature may improve overall comfort and well-being for some individuals, potentially reducing stress- related health issues associated with extreme cold. It’s important to note that the health impacts of higher temperatures are not uniform and can vary by geographic region, socioeco- nomic factors, and individual health status. Vulnerable populations, such as the elderly, chil- dren, and those with limited resources, are often more severely affected by extreme heat. Efforts to mitigate the negative health effects of higher temperatures include heat action plans, improved healthcare infrastructure, public education campaigns, and efforts to reduce Fig 7. Regression line between temperature and life expectancy. Source: Post Estimation Plot in STATA. https://doi.org/10.1371/journal.pclm.0000339.g007 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 15 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 8. Regression line between rainfall and life expectancy. Source: Post Estimation Plot in STATA. https://doi.org/10.1371/journal.pclm.0000339.g008 greenhouse gas emissions to combat climate change. Adaptation and resilience-building mea- sures are also crucial to protect human health in a warming world. Fig 8 reveals a flat line between rainfall and life expectancy which is commensurate with findings of the ambiguous effect of rainfall on life expectancy. The relationship between rain- fall and life expectancy is complex and can be influenced by numerous factors, including geo- graphic location, temperature variability, socioeconomic conditions, and healthcare infrastructure. It is not uncommon to find mixed or ambiguous findings when studying this relationship, and a "flat line" or lack of a clear correlation is one possible outcome. For exam- ple, in regions with arid or semi-arid climates, an increase in rainfall may have a positive effect on agriculture, food production, and water availability, potentially leading to improved health outcomes. Conversely, in regions with high and consistent rainfall, too much rainfall can lead to flooding, waterborne diseases, and other health risks. Access to clean water, sanitation, healthcare, and nutrition can mitigate the negative health effects of inadequate or excessive rainfall. In areas with poor infrastructure and limited resources, the impact of rainfall on health may be more pronounced. Fig 9, on the other hand, demonstrates a significant negative relationship between the com- posite climate change index and life expectancy. Higher degrees of climate change, if left unchecked, have the potential to reduce global life expectancy significantly. As temperatures rise and extreme weather events become more frequent and severe, the health risks amplify. A significant negative relationship between a composite climate change index and life expectancy suggests that as the composite climate change index worsens or indicates more severe climate change impacts, life expectancy tends to decrease. This finding implies that the adverse effects of climate change, such as extreme weather events, temperature extremes, changes in disease patterns, and environmental degradation, are having a detrimental impact on human health PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 16 / 21 PLOS CLIMATEEffect of climate change on life expectancy Fig 9. Regression line between climate change index and life expectancy. Source: Post Estimation Plot in STATA. https://doi.org/10.1371/journal.pclm.0000339.g009 and longevity. Such a relationship aligns with the growing body of research that highlights the harmful health consequences of climate change. Considering the evident inverse relationship between life expectancy and both the climate change index and temperature, along with the consistently unchanging dynamic between rain- fall and life expectancy, can we infer that temperature holds a more pronounced influence on life expectancy compared to precipitation. The observed negative correlation between life expectancy and the climate change index, along with the clear association with temperature trends, prompts a compelling question about the relative impact of these climatic factors. The data reveals a distinct pattern, where as temperatures increase or the climate change index rises, life expectancy tends to decrease. Moreover, the consistent flat line observed in the rela- tionship between rainfall and life expectancy suggests that precipitation might not be as influ- ential a factor in determining life expectancy as temperature. This inference is supported by the empirical evidence showcasing a more apparent and consistent influence of temperature on life expectancy. The negative correlation indicates that higher temperatures, often associ- ated with climate change, are linked to a reduction in life expectancy. However, the unaltered relationship between rainfall and life expectancy implies that variations in precipitation might not play a significant role in impacting life expectancy levels. Further exploration and nuanced analyses would be instrumental in comprehensively understanding the intricate interplay between climate variables and their consequences on life expectancy. 4. Conclusion Climate change is indeed considered one of the most significant global health threats facing humanity today. Its impact on human health is multifaceted and can be far-reaching. Climate PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 17 / 21 PLOS CLIMATEEffect of climate change on life expectancy change is already impacting health in a myriad of ways through direct and indirect mecha- nisms. This paper develops a framework to identify and integrate these mechanisms. More- over, this paper estimates the effect of climate change on life expectancy using cross-national panel data from 191 countries covering the period 1940–2020. Our findings suggest that tem- perature rise will negatively affect life expectancy. Moreover, an increase in temperature will further negatively impact life expectancy by interacting with the rainfall cycle. Moreover, our findings reveal that climate change disproportionately reduces the life expectancy of females more than the life expectancy of males. Thus, countries should come forward with prompt ini- tiatives to contain global temperature rise and protect the health of the population on the verge of climate change. To address these health impacts, it is crucial to mitigate climate change by reducing greenhouse gas emissions and adapting to the changes that are already occurring [33]. Additionally, public health measures, disaster preparedness, and healthcare infrastructure improvements can enhance resilience and reduce the health risks associated with climate change. Recognizing the interconnectedness of climate and health is essential for developing effective strategies to protect human well-being in a changing climate. Otherwise, climate change threats may also accumulate over time, leading to longer-term changes in resilience and health. Moreover, our findings suggest that climate disruption and its associated natural calamities are not evenly distributed globally. Some regions, particularly, the least developed countries (LDC) are more vulnerable due to their geographic location, socioeconomic factors, and existing levels of resilience and preparedness [34]. Mitigating the impacts of climate change and implementing adaptation strategies are crucial steps in reducing these health risks and protecting human life expectancy [35]. This identified research gap opens up a new avenue for scholarly exploration, encouraging investigators to explore the nuanced relationships between climate-induced disasters and their potential repercussions on life expectancy. By expanding the scope to include a broader array of climate-related events, such as hurricanes, floods, and wildfires, researchers can obtain a more comprehensive understanding of the multifaceted impacts of climate change on human health. Furthermore, it is crucial to recognize that the effect of climate change on life expec- tancy is not universally uniform. Rather, it varies significantly based on local conditions, vul- nerabilities, and geographical specifics. To unravel these complexities, researchers frequently conduct region-specific analyses that carefully consider the unique characteristics of each area under investigation. The study’s emphasis on the regional nuances of the climate change-life expectancy relationship is a crucial recognition of the diverse impacts felt by different commu- nities. Future research endeavors could benefit from adopting a localized approach, tailoring investigations to the specific vulnerabilities, adaptive capacities, and environmental contexts of distinct regions. This targeted analysis would provide a more accurate and contextually rele- vant understanding of the intricate interplay between climate change and life expectancy. Moreover, it’s crucial to acknowledge certain limitations associated with estimating the cli- mate change index, particularly when considering various scenarios that encompass fluctua- tions in both temperature and rainfall. In instances where temperatures rise while rainfall experiences a decrease, the traditional index may face limitations in distinguishing between the individual contributions of each factor. The geometric mean might not fully encapsulate the nuanced impact of temperature increases and reduced rainfall on the overall climate change scenario. This limitation could potentially lead to underestimating the severity of cer- tain climatic changes. In situations where temperature decreases while rainfall increases, the geometric mean may not distinctly reflect the intricate dynamics at play. The index might not adequately convey the potential complexities arising from contrasting trends in temperature and rainfall. This limitation could result in an oversimplified representation of the climate change scenario, overlooking the intricate interplay between these two factors. When both PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000339 January 18, 2024 18 / 21 PLOS CLIMATEEffect of climate change on life expectancy temperature and rainfall remain within normal ranges, the geometric mean may not effectively discern the absence of extreme climatic variations. The index might not adequately capture the subtleties associated with stable climatic conditions, potentially leading to an overemphasis on the index’s sensitivity to extreme changes. This limitation highlights the need for additional indicators or adjustments when assessing scenarios of climatic stability. It is essential to recog- nize that the proposed composite climate change index, while innovative, may encounter chal- lenges in accurately capturing the diversity of climate change scenarios. These limitations underscore the importance of complementing the index with a comprehensive analysis that considers multiple indicators and accounts for the intricacies associated with varying tempera- ture and rainfall patterns. Addressing these limitations will contribute to a more nuanced and accurate understanding of the impact of climate change in diverse environmental contexts. All remaining errors are of the author and suggestions are welcomed. Author Contributions Conceptualization: Amit Roy. Data curation: Amit Roy. Formal analysis: Amit Roy. Funding acquisition: Amit Roy. Investigation: Amit Roy. Methodology: Amit Roy. Resources: Amit Roy. Supervision: Amit Roy. Validation: Amit Roy. Visualization: Amit Roy. Writing – original draft: Amit Roy. Writing – review & editing: Amit Roy. References 1. Baines PG, Folland CK. Evidence for a rapid global climate shift across the late 1960s. Journal of Cli- mate. 2007; 20(12):2721–44. https://doi.org/10.1175/JCLI4177.1 2. 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10.1371_journal.pgen.1011178	RESEARCH ARTICLE A natural bacterial pathogen of C. elegans uses a small RNA to induce transgenerational inheritance of learned avoidance Titas SenguptaID Renee J. SetoID 1,2* T. MurphyID 1,2, Jonathan St. Ange1,2, Rachel KaletskyID 1,2, Rebecca S. MooreID 1,2, 1,2, Jacob Marogi2, Cameron MyhrvoldID 2, Zemer Gitai2, Coleen 1 Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America, 2 Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America * ctmurphy@princeton.edu Abstract C. elegans can learn to avoid pathogenic bacteria through several mechanisms, including bacterial small RNA-induced learned avoidance behavior, which can be inherited transge- nerationally. Previously, we discovered that a small RNA from a clinical isolate of Pseudo- monas aeruginosa, PA14, induces learned avoidance and transgenerational inheritance of that avoidance in C. elegans. Pseudomonas aeruginosa is an important human pathogen, and there are other Pseudomonads in C. elegans’ natural habitat, but it is unclear whether C. elegans ever encounters PA14-like bacteria in the wild. Thus, it is not known if small RNAs from bacteria found in C. elegans’ natural habitat can also regulate host behavior and produce heritable behavioral effects. Here we screened a set of wild habitat bacteria, and found that a pathogenic Pseudomonas vranovensis strain isolated from the C. elegans microbiota, GRb0427, regulates worm behavior: worms learn to avoid this pathogenic bac- terium following exposure, and this learned avoidance is inherited for four generations. The learned response is entirely mediated by bacterially-produced small RNAs, which induce avoidance and transgenerational inheritance, providing further support that such mecha- nisms of learning and inheritance exist in the wild. We identified Pv1, a small RNA expressed in P. vranovensis, that has a 16-nucleotide match to an exon of the C. elegans gene maco-1. Pv1 is both necessary and sufficient to induce learned avoidance of Grb0427. However, Pv1 also results in avoidance of a beneficial microbiome strain, P. mendocina. Our findings suggest that bacterial small RNA-mediated regulation of host behavior and its transgenerational inheritance may be functional in C. elegans’ natural environment, and that this potentially maladaptive response may favor reversal of the transgenerational memory after a few generations. Our data also suggest that different bacterial small RNA-mediated regulation systems evolved independently, but define shared molecular features of bacterial small RNAs that produce transgenerationally-inherited effects. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Sengupta T, St. Ange J, Kaletsky R, Moore RS, Seto RJ, Marogi J, et al. (2024) A natural bacterial pathogen of C. elegans uses a small RNA to induce transgenerational inheritance of learned avoidance. PLoS Genet 20(3): e1011178. https://doi.org/10.1371/journal. pgen.1011178 Editor: Gregory P. Copenhaver, The University of North Carolina at Chapel Hill, UNITED STATES Received: October 16, 2023 Accepted: February 9, 2024 Published: March 28, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgen.1011178 Copyright: © 2024 Sengupta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Bacterial small RNA sequencing data are available at NCBI Bioproject PRJNA1062118. The numerical data for all the PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 1 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally plots in all the figures have been included in S1 Data. Funding: This work was supported by a Pioneer Award to CTM (NIGMS DP1GM119167), a Transformative R01 Award (1R01AT011963-01) to ZG & CTM., CDCP 75D30122C15113 to CM, T32GM007388 (NIGMS) support of RSM., a Damon Runyon Fellowship (DRG-2481-22) to TS, a Ford Foundation Predoctoral Fellowship to RS (https://www.nationalacademies.org/our-work/ ford-foundation-fellowships), and an NSF GRFP (DGE-2039656) predoctoral award to JS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: ZG is the founder of ArrePath. Author summary C. elegans can learn to avoid a pathogenic clinical isolate of Pseudomonas aeruginosa, PA14, for four generations after training, through ingestion and RNA interference pro- cessing of a bacterial small RNA, P11, that targets a C. elegans neuronal gene, maco-1, through a 17-nucleotide perfect match. We screened bacteria associated with C. elegans in the wild, and found that lab C. elegans as well as wild C. elegans strains can also learn (and remember) to avoid P. vranovensis, a wild Pseudomonas pathogen. P. vranovensis uses a different small RNA that we identified and named Pv1, which targets a different exon of maco-1 (through a different 16-nucleotide match) and downregulates maco-1 expression transgenerationally, resulting in transgenerational inheritance of learned P. vranovensis avoidance. These data suggest that this mechanism of learning and remembering patho- gen avoidance likely happens in the wild. Furthermore, the similarity in the “smell” of pathogenic and nutritious Pseudomonas (P. mendocina) may exert evolutionary pressure to forget the learned avoidance by the fifth generation, to prevent the worms from missing out on good food sources while avoiding pathogens. Introduction Plants and animals have evolved diverse mechanisms to adapt to constantly changing environ- mental stimuli. Some of these stimuli are encoded as molecular changes that do not involve changes in DNA sequence, but are instead epigenetic, that is, mediated through changes in non-coding RNAs, DNA modifications, histone modifications, and nucleosome positioning [1–6]. These changes can occasionally cross the germline and confer adaptive benefits to the first generation of progeny (intergenerational) [7–20] or more (transgenerational) [21–33]. Multigenerationally-inherited effects can provide adaptive advantages in changing environ- ments, particularly in organisms with short generation times [34–36]. Over the past decade, instances of multigenerational inheritance have been reported in vari- ous organisms [37–39]. We previously characterized an example of epigenetic inheritance in response to a physiological stimulus, highlighting its adaptive benefits in C. elegans: upon exposure to the pathogenic Pseudomonas aeruginosa strain PA14, worms learn to subsequently avoid the bacteria, then pass on this learned avoidance to four generations of progeny [27]. A single small RNA from PA14, P11, mediates this avoidance and its transgenerational inheri- tance through downregulation of the worm neuronal gene maco-1, which results in a switch from attraction to avoidance behavior [23,26]. These studies provided the first example of bac- terial small RNA-mediated regulation of a learned behavior and its transgenerational inheri- tance [23,26,27]. However, PA14 is a human clinical Pseudomonas isolate; whether bacteria in C. elegans’ natural environment elicit learned responses and multi-generational inheritance of learned responses through small RNAs is not known. Diverse bacterial species influence C. elegans physiology and life history traits [40–42]. Bac- terial species from C. elegans’ natural environment have been systematically characterized [43– 49]. These studies revealed multiple features of the microbiota in C. elegans natural habitat and their relationship to host physiology [50,51]. Studying bacterial species that are naturally asso- ciated with C. elegans might reveal processes that occur in the wild, and not in the laboratory, and vice versa; for example, bacteria from C. elegans’ natural environment suppress mortal germline phenotypes that wild worms exhibit on laboratory strains of E. coli [52]. Therefore, it is important to test the physiological relevance of laboratory experimental results under more natural conditions. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 2 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Bacterial species in the worm microbiome that induce stress and immune response report- ers are categorized as pathogenic [47], while species that promote increased worm growth rates are categorized as beneficial [47,49,53], and some of these species confer protection against pathogenic species [44,54]. Other species are beneficial in some contexts and patho- genic in others [55]. Therefore, it may be evolutionarily favorable for worms to have plastic responses to different bacterial classes that they naturally encounter. Worms are naively attracted to specific beneficial and neutral bacterial species (e.g., Pseudomonas mendocina and Proteus mirabilis, respectively) when given a choice between these bacteria and their laboratory diet E. coli HB101 [56]. Similarly, worms grown on the beneficial bacterial strain Providentia alcalifaciens prefer this bacterial species over their laboratory diet E. coli OP50 in a behavioral choice assay [57]. These beneficial bacterial species modulate C. elegans’ attraction towards several chemicals. Naïve or learned attraction in response to beneficial bacteria that worms encounter in their natural environment may have evolved as an evolutionarily favorable strat- egy. However, it is not known if worms can learn to avoid the various pathogenic bacterial spe- cies in their environment, or if they can inherit this learned avoidance. Additionally, whether bacteria in C. elegans’ natural environment can modulate the host nervous system through small RNAs and whether they can induce transgenerationally inherited effects are not known. Pseudomonas is one of the largest among the bacterial genera that constitute C. elegans’ nat- ural microbiome [47]. In this study, we examined C. elegans’ behavioral responses to a Pseudo- monad species present in its natural microbiome. We found that an isolate of Pseudomonas vranovensis can elicit learned avoidance and its transgenerational inheritance through a single small RNA that is both necessary and sufficient. However, this learned response to P. vrano- vensis also leads to avoidance of a beneficial bacteria also found in C. elegans’ environment, P. mendocina. Our work reveals a transgenerational effect in response to bacteria in C. elegans’ natural microbiome, underscoring the physiological relevance of transgenerational inheritance and its significance in the wild. We also identified a new small RNA that can induce a learned behavior in C. elegans, therefore expanding the repertoire of bacterial small RNA-mediated regulation of the host nervous system and helping to identify characteristics of small RNAs necessary for trans-kingdom signaling. Finally, the induced avoidance of a beneficial bacteria after pathogen training suggests that “forgetting” learned pathogen avoidance after a few gen- erations might benefit C. elegans, limiting maladaptive behaviors. Results Wild microbiome bacteria induce learned avoidance To examine if exposure to bacteria isolated from C. elegans’ natural environment can produce stereotypic behavioral responses and further, whether these could potentially be small RNA- mediated, we tested C. elegans’ response to strains that are present in its natural microbiome [47]. We chose nine different bacterial species, mostly from the CeMBio collection [50] to test. These include non-pathogenic bacteria that are beneficial, as they enhance worm growth rates or provide protection against pathogen infection, or have positive or neutral effects depending on the physiological context (Pseudomonas mendocina (MSPm1), Raoultella sp. (Jub38), Leliot- tia sp. (Jub66), and Acinetobacter guillouiae (Myb10), Ochrobactrum vermis (Myb71), Entero- bacter hormaechi (CEN2ent1), as well as three bacteria that are pathogenic or impair worm growth and development (Stenotrophomonas maltophilia (Jub19), Sphingobacterium multi- vorum (Bigb0170), and Pseudomonas vranovensis (GRb0427) [46,47,49,50]. Starting as late L4 animals, we exposed C. elegans for 24hrs either to OP50 E. coli (the standard lab cultivation strain) or to the test bacteria, and then assayed their preference to OP50 vs. the test strain (Fig 1A). In general, C. elegans prefer the wild strains—both beneficial and pathogenic— PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 3 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 1. Wild microbiome bacteria induce learned avoidance. (A) Worms trained for 24 hours on E. coli OP50 or a test wild microbiome bacterial strain are tested in a choice assay between OP50 and the test bacterial strain. (B-J) Choice assays before and after training on Pseudomonas mendocina MSPm1 (B), Raoultella sp. Jub38 (C), Leliottia sp. Jub66 (D), Acinetobacter guillouiae Myb10 (E), Ochrobactrum vermis Myb71 (F), Enterobacter hormaechei CEN2ent1 (G), Stenotrophomonas maltophilia Jub19 (H), Sphingobacterium multivorum Bigb0170 (I), Pseudomonas vranovensis GRb0427 (J). (K) Worms trained for 24 hours on E. coli OP50 or a microbiome bacterial strain are bleached to obtain eggs, which are allowed to grow to Day 1 adults on OP50 plates. These adult F1 progeny are tested in a choice assay between OP50 and the respective bacterial strain. (L-N) Choice assays with F1 progeny of OP50 and Myb71-trained (L), OP50 and CEN2ent1-trained (M), and OP50 and GRb0427-trained (N) animals. Each dot represents an individual choice assay plate. Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test, **p < 0.0001, p < 0.001, and p<0.05, ns, not significant. Schematic representation in (A) and (K) were created using Biorender. https://doi.org/10.1371/journal.pgen.1011178.g001 relative to the laboratory food E. coli OP50 (OP50-trained (gray bars), Fig 1A–1J). After 24hr exposure to wild bacteria (training), six of the bacteria showed no significant change in prefer- ence, despite the fact that two of those strains, Jub19 and Bigb0170, have detrimental effects on worms [47,50]; however, 24hr of exposure to three strains (Ochrobactrum vermis (Myb71), Enterobacter hormaechi (CEN2ent1)), and Pseudomonas vranovensis (GRb0427)) induced sig- nificant avoidance in the trained mothers (P0) (Fig 1F, 1G and 1J). That is, upon cultivation on a bacterial lawn for 24 hours, worms learn to robustly avoid the bacteria, as shown in a choice assay between OP50 and the test strain. (This P0 learned avoidance has been previously reported for Ochrobactrum vermis (Myb71) [58]). Longer exposure (36 hr) to the detrimental bacteria Sphingobacterium multivorum (Bigb0170) does not induce avoidance (S1 Fig). Thus, it seems that most wild strains are inherently attractive to C. elegans, whether they are PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 4 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally beneficial, neutral or pathogenic, only a subset of strains induce avoidance in the P0, and this first-generation avoidance does not correlate with pathogenicity of the strain. To determine whether this P0 learned avoidance is inherited by the next generation, trained mothers (P0) were bleached and their progeny (F1) were raised on OP50 E. coli until adult- hood, then were tested for their choice (with no F1 training) (Fig 1K). We observed that although neither Ochrobactrum vermis (Myb71) nor Enterobacter hormaechi (CEN2ent1)) progeny inherited the learned avoidance from their mothers (Fig 1L and 1M), progeny of Pseudomonas vranovensis (GRb0427)-trained mothers also avoided Pseudomonas vranovensis (GRb0427) (Fig 1N). Of the various bacteria we tested, only the pathogenic Pseudomonas vra- novensis (GRb0427) induces learned avoidance and inheritance of avoidance. C. elegans learn to avoid the natural bacterial pathogen, P. vranovensis Despite worms’ naïve attraction to Pseudomonas vranovensis, this bacterium is pathogenic to C. elegans: adult exposure to P. vranovensis (“GRb0427” hereafter) causes severe illness (Fig 2A) and significantly reduces survival to less than 2–3 days (Fig 2B), in contrast to C. elegans’ normal lifespan of 2–3 weeks. Exposure to P. aeruginosa PA14 causes gene expression changes in particular C. elegans sen- sory neurons; specifically, a 24-hour exposure to PA14 results in the induction of expression of the TGF-beta ligand DAF-7, as indicated by daf-7p::gfp, in the ASJ neurons and an increase in daf-7p::gfp expression in the ASI neurons [27,59]. PA14 small RNAs induce expression of daf- 7p::gfp in the ASI that persists in the F1-F4 progeny generations [23, 27], while the increase in ASJ daf-7 is caused by PA14 secondary metabolites phenazine-1-carboxamide and pyochelin [59], and does not persist beyond the P0 [27]. To determine whether altered daf-7 levels corre- late with the learned avoidance response to P. vranovensis, we examined daf-7p::gfp expression in GRb0427-trained animals (P0). Upon GRb0427 exposure, daf-7p::gfp levels significantly increase in the ASI neurons (Fig 2C and 2D), but no expression was observed in the ASJ neu- rons, in contrast to the response to PA14 training [27] (Fig 2E). Although daf-7p:GFP levels increase only in the ASI neurons upon GRb0427 exposure, this increase is comparable to that observed upon PA14 exposure (Fig 2F). Unlike other pathogenic bacteria, exposure to GRb0427 triggers a significantly milder innate immune response, as indicated by low expression of the innate immune response irg-1 (Infection Response Gene) promoter-GFP reporter, which is induced upon exposure to PA14 but not GRb0427 (Fig 2G and 2H). Lack of induction of phenazine-mediated ASJ daf-7p::gfp expression and only a mild induction of the irg-1 dependent innate immune pathway suggest that these innate immune pathways might not play a significant role in the neuronal response to the wild bacteria P. vranovensis GRb0427, even in the P0 generation, unlike the response to the clinical isolate PA14. The avoidance response to P. vranovensis is transmitted for four generations Since training on P. vranovensis resulted in an increase in P0 daf-7p::gfp levels in the ASI (Fig 2C and 2D), and the adult F1 progeny of GRb0427-trained mothers showed robust avoidance of P. vranovensis compared to the F1 progeny of the control (OP50-trained) mothers (Fig 1) we examined the expression of daf-7p::gfp in progeny of GRb0427-trained mothers: these F1 animals express higher levels of daf-7p::gfp in the ASI neurons (Fig 3A and 3B) compared to that in F1 animals from OP50-trained mothers. To examine if learned avoidance to GRb0427 is inherited transgenerationally (beyond the F1 generation), we first asked whether daf-7p::gfp expression in the ASI remains high in the PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 5 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 2. GRb0427, a natural Pseudomonad pathogen of C. elegans induces learned avoidance. (A) Representative images acquired after exposing Day 1 worms to OP50 (left) or GRb0427 (right) for 24 hours. GRb0427 is pathogenic and 24-hour exposure makes worms sick. (B) Worms have significantly lower survival on a GRb0427 lawn compared to an OP50 lawn (p<0.0001 by Log-rank (Mantel-Cox)). (C) Representative image of a 24-hour OP50-trained or GRb0427-trained worm expressing daf-7p::gfp, which is expressed in the ASI sensory neurons (blue arrowheads). The PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 6 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally dashed line indicates the outline of the worm head. Scale bar = 10 μm. (D) Quantification of mean ASI daf-7p::gfp intensities from OP50 and GRb0427 animals shows higher expression in GRb0427-trained animals. (E) Representative image of a 24-hour PA14-trained (top) or GRb0427-trained (bottom) worm. daf-7p::GFP is expressed in the ASI (blue arrowheads) and ASJ (orange arrowheads) sensory neurons (right panel) in the PA14-trained worm, but only in the ASI (blue arrowheads) in the GRb0427-trained worm. The dashed line indicates the outline of the worm head. Scale bar = 10 μm. (F) Quantification of mean ASI daf-7p::gfp intensities from OP50, PA14, and GRb0427 animals shows similar increase in daf-7p expression in PA14 and GRb0427-trained animals. (G) Expression of an irg-1p::gfp reporter in representative OP50-trained, PA14-trained and GRb0427-trained animals. Images are merged confocal micrographs of brightfield and GFP channels. Scale bar = 100 μm. (H) Mean fluorescence intensity of an irg-1p::gfp innate immune response reporter in OP50 (gray), Pseudomonas aeruginosa PA14 (blue), and GRb0427 (purple)- trained worms. Mean irg-1p::gfp reporter intensity is significantly lower in GRb0427-trained worms compared to PA14-trained worms. Each dot represents an individual neuron (D, F) or an individual worm (H). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test, **p < 0.0001 (D, F); one-way ANOVA with Tukey’s multiple comparison’s test, p<0.0001, p<0.01, p < 0.05, ns, not significant (H). For the survival assay in (B), *p<0.0001 (by Log-rank (Mantel-Cox) test for survival). https://doi.org/10.1371/journal.pgen.1011178.g002 grandprogeny and subsequent progeny of GRb0427-trained mothers; like F1, the F2 and F4 animals had higher levels of ASI daf-7p::gfp, and these levels return to baseline (similar to the OP50 control) in the F5 generation (Figs 3C, 3D and S2). We then tested the next generations of progeny for avoidance. The learned avoidance of GRb0427 lasts up to the F4 generation, but returns to naïve attraction to GRb0427 in the F5 generation (Fig 3E). Thus, GRb0427 training induces transgenerational inheritance of learned avoidance behavior, as we previously found for PA14. Notably, in contrast to the higher avoidance of PA14 in the P0 generation than in F1-F4 [27], the level of avoidance of P. vranovensis is constant across P0 through F4 (Fig 3F). This result is consistent with the ASI (but not ASJ) expression of daf-7p::gfp and lack of expres- sion of the innate immunity reporter irg-1p::gfp (Fig 2G and 2H) suggesting that innate immu- nity pathways may not contribute significantly to C. elegans’ avoidance of P. vranovensis, but rather that the major pathway of avoidance of P. vranovensis even in the first generation is through the same pathway as in F1-F4, rather than through classical innate immune pathways. This lack of an innate immune response to GRb0427 is particularly notable since P. vranoven- sis is found in C. elegans’ natural habitat [47], while PA14, the standard pathogen used for worm host-pathogen studies, is a human clinical isolate of P. aeruginosa. P. vranovensis avoidance requires the Cer1 retrotransposon and can be horizontally transferred We had previously shown that the Cer1 retrotransposon is required for the learned avoidance of PA14 and its transgenerational inheritance [26] and is proposed to be involved in the trans- mission of information from the germline to neurons [26]. Similarly, we found that learned avoidance of P. vranovensis and the transgenerational inheritance of this avoidance require Cer1 (Fig 3G–3I). We had also found that training worms on conditioned media from the progeny of PA14-trained mothers can induce avoidance [26]; similarly, conditioned media from progeny of GRb0427-trained mothers can also induce learned avoidance (Fig 3J), indi- cating that the learned information can be horizontally transferred. Wild C. elegans strains can learn to avoid P. vranovensis and transgenerationally transmit this information Wild C. elegans strains have been isolated all over the world [60,61], and can be helpful in dis- tinguishing lab N2-strain-specific phenomena from those that are likely to function in the wild. We tested JU1580, a wild strain, for its responses to P. vranovensis; we find that JU1580, like N2, is attracted to GRb0427, but learns to avoid it after 24hr of training (Fig 4A). Like N2, PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 7 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 3. GRb0427-mediated learned avoidance is inherited transgenerationally. (A) F1 progeny of GRb0427-trained animals have higher daf-7p::gfp expression in the ASI sensory neurons (blue arrowheads). Scale bar = 10 μm. (B) Quantification of mean ASI daf-7p::gfp intensities from F1 progeny of OP50-trained and GRb0427 animals shows higher expression in F1 progeny of GRb0427-trained animals. (C, D) Quantification of mean ASI daf-7p::gfp intensities shows higher expression in F2 (C), F4 (D), and F5 (D) progeny of GRb0427-trained mothers, compared to the respective OP50 controls. (E) Untrained F1-F4 progeny of GRb0427-trained P0 animals avoid GRb0427 relative to the progeny of OP50-trained control P0 animals. This avoidance is lost in the F5 generation. (F) Learning index (trained choice index - naive choice index) of generations P0–F5. Error bars represent mean ± SEM. (G-I) N2 (wild- type) worms trained on a GRb0427 bacterial lawn learn to avoid GRb0427, but Cer1(gk870313) mutant animals don’t exhibit learned avoidance (G). The learned avoidance is inherited by the F1 (H) and F2 progeny (I) of GRb0427-trained N2 mothers but not by the progeny of GRb0427-trained Cer1(gk870313) mothers (n = 1). (J) Worms exposed to conditioned medium from the F1 progeny of OP50- and GRb0427-trained mothers were tested in a choice assay between OP50 and GRb0427. Worms exposed to conditioned medium from the F1 progeny of GRb0427-trained mothers exhibit avoidance of GRb0427. Each dot represents an individual choice assay plate (E, G-J) or an individual neuron for fluorescence images (B-D). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test, p < 0.0001, p<0.001, p<0.05, ns, not significant (B-D, J); one-way ANOVA with Tukey’s multiple comparison’s test, **p<0.0001, p<0.01, p<0.05, PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 8 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally ns, not significant (E, F), Two-way ANOVA with Tukey’s multiple comparison’s test, p<0.05, **p<0.0001, ns, not significant (G-I). https://doi.org/10.1371/journal.pgen.1011178.g003 JU1580 worms inherit this learned avoidance through the F4 generation, then return to naïve attraction in the F5 (Fig 4B). We tested an additional wild strain, ED3040 [49], and found that it behaved similarly to N2 and JU1580 in its initial attraction to P. vranovensis and its learned avoidance after 24hr of exposure (Fig 4C). Thus, it is likely that many wild C. elegans strains are attracted to P. vranovensis, learn to avoid it after exposure, and can transmit this learned avoidance transgenerationally, as we have shown for the laboratory C. elegans strain, N2. P. vranovensis small RNAs drive learned avoidance Since learned avoidance to P. vranovensis is transgenerationally inherited, and transgenera- tional inheritance of avoidance of PA14 is driven by its small RNA, P11, we next asked if small RNAs made by P. vranovensis induce avoidance. Like PA14, when adult C. elegans were exposed for 24 hours to sRNAs isolated from P. vranovensis GRb0427, worms learned to avoid P. vranovensis (Fig 5A). Exposure to P. vranovensis sRNAs increased daf-7p::gfp expression in the ASI sensory neurons (Fig 5B and 5C). We next tested if P. vranovensis sRNA-induced learned avoidance is transgenerationally inherited. Indeed, as observed for P. vranovensis lawn exposure, P. vranovensis sRNA-induced avoidance is inherited up to the F4 generation and resets in the F5 (Fig 5D and 5E). Training on GRb0427 small RNAs also induces learned avoidance and transgenerational inheritance in JU1580 worms (Fig 5F and 5G). P. vranovensis sRNA treatment also induces avoidance of PA14 We next asked if learned avoidance induced by P. vranovensis small RNAs is species-specific. We trained worms on P. vranovensis sRNAs and tested avoidance of Fig 4. Wild C. elegans strains can learn to avoid P. vranovensis and transgenerationally transmit this information. (A) A wild strain of C. elegans, JU1580 (a natural C. elegans isolate), avoids GRb0427 following 24-hour exposure to a GRb0427 bacterial lawn. (B) Untrained F1-F4 progeny of GRb0427 bacterial lawn-trained P0 JU1580 animals avoid GRb0427 relative to the progeny of OP50 bacterial lawn-trained control P0 JU1580 animals. This avoidance is lost in the F5 generation. (C) ED3040 (another natural isolate of C. elegans) also learns to avoid GRb0427 after a 24-hour exposure to a GRb0427 lawn. Each dot represents an individual choice assay plate. Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t-test (A, C), p<0.0001; one-way ANOVA with Tukey’s multiple comparison’s test, p<0.0001, p<0.01, ns, not significant (B). https://doi.org/10.1371/journal.pgen.1011178.g004 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 9 / 33 24 h OP50 trained24 h GRb0427 trainedP0 JU1580GRb0427-0.8-0.6-0.4-0.20.00.20.40.60.8**GenerationGRb0427choice assay24 h OP50 lawn trained24 h GRb0427 lawn trainedJU1580 Transgenerational Inheritance**************nsP0F1F2F3F4F524 h OP50 trained24 h GRb0427 trainedP0 ED3040 GRb0427-0.8-0.6-0.4-0.20.00.20.40.60.8GRb0427preferenceOP50preference-1.0-0.50.00.5Choice indexGRb0427preferenceOP50preferenceChoice indexGRb0427preferenceOP50preferenceChoice indexABCPLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 5. GRb0427 small RNAs induce learned avoidance and its transgenerational inheritance. (A) Worms trained on GRb0427 small RNAs exhibit learned avoidance of GRb0427 in an OP50-GRb0427 choice assay. (B) GRb0427 sRNA-trained animals have higher daf-7p::gfp expression in the ASI sensory neurons (blue arrowheads). Scale bar = 10 μm. (C) Quantification of mean ASI daf-7p::gfp intensities from OP50 sRNA-trained and GRb0427 sRNA-trained animals shows higher expression in GRb0427 sRNA-trained animals. (D) Untrained F1-F4 progeny of GRb0427 sRNA-trained P0 animals avoid GRb0427 relative to the progeny of OP50 sRNA-trained control P0 animals. This avoidance is lost in the F5 generation. (E) Learning index (index - naive choice index) of generations P0–F5. Error bars represent mean ± SEM. (F) GRb0427 sRNA-trained JU1580 animals avoid GRb0427 relative to OP50 sRNA-trained JU1580 animals. (G) F1-F4 progeny of GRb0427 sRNA-trained JU1580 animals avoid GRb0427 relative to the respective controls. This avoidance is lost in the F5 generation. Each dot represents an individual choice assay plate (A, D, F, G) or an individual neuron for fluorescence images (C). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test (A, C, F), p<0.001, *p<0.0001; one-way ANOVA with Tukey’s multiple comparison’s test, p< 0.0001, p<0.001, p<0.01, p<0.05, ns, not significant (D, E, G). https://doi.org/10.1371/journal.pgen.1011178.g005 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 10 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally PA14; P. vranovensis sRNA training induces avoidance to PA14, similar to training on PA14 sRNAs (Fig 6A). Previously, we found that a specific PA14 small RNA, P11, with 17nt of perfect match to the C. elegans neuronal homolog of the human nervous-system-specific ER membrane protein Macoilin, maco-1 [62], is required for learned avoidance [23]. Intriguingly, worms exposed to bacteria expressing only the PA14 sRNA P11 also induces avoidance to P. vranovensis (Fig 6B), suggesting that the underlying mechanism in P. vranovensis sRNA- induced avoidance is like that of PA14-induced avoidance. P. vranovensis treatment decreases maco-1 transcripts through F4 Consistent with the idea of a conserved mechanism between PA14 and P. vranovensis-induced avoidance, loss of maco-1, either by mutation (maco-1(ok3165)) (Fig 6C) or by RNAi treat- ment (Fig 6D) significantly reduces the strong naïve preference for GRb0427. Similarly, expo- sure to P. vranovensis small RNAs does not further increase maco-1 mutants’ avoidance of P. vranovensis (Fig 6C), indicating that maco-1 loss phenocopies GRb0427 sRNA treatment. Quantitative RT-PCR showed a decrease in relative maco-1 transcript abundance in GRb0427-trained animals (Fig 6E), as well as in the F2 and F4 progeny of GRb0427-trained mothers (Fig 6F), but levels of maco-1 return to the same levels as untrained animals in the F5 generation (Fig 6F). That is, the decrease in maco-1 transcript levels after P0 treatment on P. vranovensis persists from F0 through F4 generation, mirroring the change in avoidance. This observation indicates that learned avoidance induced by P. vranovensis targets maco-1, as PA14’s P11 small RNA does. We also examined differentially-expressed genes between P. vra- novensis-treated and E. coli HB101-treated P0 adult worms in RNA sequencing data reported in Burton et al., 2020 [8]; consistent with our results, maco-1 expression is downregulated in P. vranovensis-treated adult animals (log2fold change = -0.2837998, padj = 0.027) in this indepen- dent analysis [8]. (Our previous experiments suggest that we should only see changes in adult animals with fully-developed germlines [27]; the Burton dataset only provided adult data for the P0 generation [7,8]) We next examined whether P. vranovensis might encode a P11-like small RNA. We first analyzed the recently-sequenced genome of P. vranovensis [8], but we did not find any geno- mic region with sequence homology to P11. In fact, there is no region analogous to the operon that contains P11 in the P. vranovensis genome. That is, while P11 can induce similar avoid- ance of GRb0427 as it does for PA14, and GRb0427 induces avoidance through a small RNA, GRb0427 does not appear to encode a small RNA with a P11-like sequence in its genome; therefore, we needed to determine whether GRb0427 expresses a different sRNA that induces learned avoidance and inheritance of this avoidance. While a P11-like sRNA cannot account for the learned avoidance of GRb0427, our small RNA maco-1 experiments suggested that an sRNA with similarity to maco-1 might be involved. Therefore, we searched the P. vranovensis genome for similarity to the maco-1 sequence; we found five perfect matches to the maco-1 coding region in the P. vranovensis genome, but only one of these, a 16nt match, lies in an intergenic region that would be likely to encode a small RNA (Fig 7A–7C). Interestingly, this sequence identity lies in a different exon of maco-1 (Exon 1) from P11’s 17nt perfect match (Exon 8; Fig 7A). The P. vranovensis intergenic region containing this 16-nucleotide sequence match to maco-1 is flanked by bacterial protein-coding genes with predicted functions in the iron metabolism and sugar transport pathways (Fig 7B). To test whether the region containing this match might mediate P. vranovensis-induced avoidance, we expressed a 347 bp region of this intergenic sequence (“IntReg”; Fig 7C) in E. coli, and found that training on IntReg induces avoidance to P. vranovensis (Fig 7D). The avoidance induced by E. coli expressing the PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 11 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 6. GRb0427 sRNA-induced avoidance requires maco-1 and GRb0427 training heritably reduces maco-1 transcripts. (A) Worms were trained on OP50 (gray), Pseudomonas aeruginosa PA14 (blue), or GRb0427 (purple) small RNAs, and tested for their PA14 preference in a bacterial choice assay between OP50 and PA14. Worms treated on GRb0427 small RNAs not only avoid GRb0427 (Fig 3A), but also avoid PA14 in an OP50-PA14 choice assay. (B) Worms trained on the PA14 small RNA P11 avoid GRb0427 in an OP50-GRb0427 choice assay. (C) N2 (wild-type) worms trained on GRb0427 small RNAs avoid PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 12 / 33 -0.6-0.4-0.20.00.20.40.60.81.0Choice indexsmall RNAtraining******n.s.24 h OP50 small RNA24 h PA14 small RNA24 h GRb0427 small RNAPA14preferenceOP50preferenceGRb0427preferenceOP50preference24 h E. coli control lawn24 h GRb0427 lawn24 h E. coli expressing P11 lawn -0.8-0.6-0.4-0.20.00.20.40.60.8Choice index****n.s.ABGRb0427preferenceOP50preferenceC24 h OP50 small RNA24 h GRb0427 small RNAChoice indexGRb0427preferenceOP50preference24 h control RNAi24 h maco-1 RNAi-1.0-0.50.00.5D-1.0-0.50.00.5Choice index****nsN2maco-1E0.00.51.0Expression (relative toact-1)0.00.51.0Expression (relative toact-1)ns0.00.51.0Expression (relative toact-1)FP0F2F4F524h OP50 lawn trained 24h GRb0427 lawn trained24h OP50 lawn training in P024h GRb0427 lawn training in P00.00.51.0Expression (relative toact-1)**maco-1 qPCRmaco-1 qPCRPLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally GRb0427, relative to Worms trained on OP50 small RNAs. maco-1(ok3165) loss-of-function mutant worms naively avoid GRb0427, relative to wild type worms, and do not show increased avoidance upon exposure to GRb0427 sRNAs. (D) Upon downregulation of maco-1 by RNAi, wild type worms exhibit higher naïve avoidance of GRb0427 compared to control RNAi- treated wild-type worms. (E) Fold change (2^(-ΔΔCt) of maco-1 transcript levels in GRb0472-trained P0 (E), F2, F4, and F5 animals (F) relative to the respective OP50-trained controls (act-1 was used as the housekeeping gene for reference). Each data point represents an independent biological replicate, and 3 technical replicates were performed for each biological replicate. Each dot represents an individual choice assay plate (A-D) or a biological replicate in a qPCR assay (E,F). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values, One-way ANOVA with Tukey’s multiple comparison’s test, p<0.0001, ns, not significant (A,B); Two-way ANOVA with Tukey’s multiple comparison’s test, p<0.01, **p<0.0001, ns, not significant (C); Unpaired, two-tailed Student’s t test, p<0.001, p<0.01, p<0.05 (D-F). https://doi.org/10.1371/journal.pgen.1011178.g006 intergenic region persists for four generations after parental exposure and is lost by the F5 gen- eration (Fig 7E), similar to the transgenerational inheritance of learned avoidance induced by a P. vranovensis lawn or small RNA exposure. Like P. vranovensis treatment, exposure of the wild strain JU1580 to the IntReg clone also induced avoidance of P. vranovensis (Fig 7F). A specific P. vranovensis sRNA induces learned avoidance We next investigated if the intergenic region encodes a small RNA. While the genome of P. vranovensis is published, no information on small RNAs were publicly available, so we sequenced the small RNAs produced by P. vranovensis (see Methods), that is, the total small RNA pool that induces learned avoidance and its transgenerational inheritance. Indeed, we detected a small RNA that maps within the intergenic region that contains the 16-nucleotide sequence match to maco-1 (Fig 8A). This small RNA, which we named “Pv1”, is 124 bp long, and its homology to maco-1, like P11, lies in a predicted stem loop (Fig 8B). We next asked whether exposure to Pv1 expressed in E. coli would be sufficient to induce avoidance to P. vranovensis; indeed, training on E. coli-Pv1 induces avoidance in the mother generation (P0; Fig 8C), as well as the inter (F1)- and transgenerational (F2) inheritance of this learned avoidance (Fig 8D). To determine if Pv1’s sequence identity to maco-1 is necessary for the learned avoidance to P. vranovensis and its transgenerational inheritance, we constructed a mutant P. vranovensis bacterial strain lacking the 16-nucleotide match to maco-1, Δ16. This Δ16 mutant GRb0427 strain is equally attractive to worms as wild-type GRb0427, and C. elegans prefer Δ16 to OP50 just as they prefer GRb0427 to OP50 (Fig 8E–8G), suggesting that the bacteria do not “smell” different to the worms. Additionally, Δ16 is similarly pathogenic to worms as wild-type GRb0427 (Fig 8H). However, Δ16 is unable to induce transgenerational inheritance of learned avoidance (Fig 8I), nor can Δ16 induce daf-7p::gfp expression in ASI neurons (Fig 8J). Finally, mismatch of four of the nucleotides in the loop of Pv1 within the 16nt maco-1 match (but that still retains its predicted stem-loop structure, Fig 8K) removes the ability of Pv1 to induce avoidance (Fig 8L). Together, our data suggest that a specific P. vranovensis small RNA, Pv1, is sufficient to induce avoidance, and Pv1’s 16nt match to the maco-1 sequence is necessary for sRNA-mediated learned avoidance of GRb0427 and its transgenera- tional inheritance. Mechanism: RNAi components are required for Pv1-induced avoidance Previously, we showed that components of the RNA interference pathway are required for PA14- and P11-induced learned avoidance and its transgenerational inheritance [23,27]. These components included the SID-2 dsRNA transporter, the DCR-1 (Dicer) endoribonu- clease, and the SID-1 RNA transmembrane dsRNA transporter. To determine whether the P. vranovensis sRNA Pv1 is processed through a similar mechanism, we tested mutants of these PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 13 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 7. An intergenic region in the GRb0427 genome contains a 16-nucleotide perfect match to maco-1 and is sufficient for learned avoidance of GRb0427. (A) The PA14 small RNA P11 contains a 17-nucleotide perfect match to the worm neuronal gene maco-1 (Exon 8). We found an intergenic region in the GRb0427 genome with a 16-nucleotide perfect match to a stretch of Exon 1 of maco-1. (B, C) The GRb0427 genome has an intergenic region, flanked by iron metabolism operon and sugar transport operons, containing a 16-nucleotide perfect match to maco-1 (B). This region is represented as a schematic in (C). 347 bp of this intergenic region (“IntReg”, shown in green) was cloned into E. coli for testing. IntReg contains the 16-nt match to maco-1 (indicated in purple). (D) Training worms on E. coli expressing the intergenic region (IntReg) with the match to maco-1 induces avoidance of GRb0427. (E) Untrained F1-F4 progeny of worms trained on E. coli expressing the intergenic region with the match to maco-1 exhibit higher avoidance of GRb0427 compared to controls. This higher avoidance is lost in the F5 generation. (F) Training of JU1580 worms on E. coli expressing the intergenic region (with the match to maco-1) induces avoidance of GRb0427. Each dot represents an individual choice assay plate (D-F). Box plots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test, p<0.01, p<0.001 (D, F); one-way ANOVA with Tukey’s multiple comparisons test, p<0.01, p<0.05 ns, not significant (E). https://doi.org/10.1371/journal.pgen.1011178.g007 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 14 / 33 Iron Metabolism OperonSugar Transport OperonHeme degradation proteinUncharacterized iron-regulated proteinOMe receptor for Fe3+ dictrate5,10-methylene-tetrahydrofolate dehydrogenaseABS-type sugar transport system componentSmall integral membrane proteinABC-type sugar transport, permease componentABC-type sugar transport, permease componentABC-type sugar transport, ATPaseABC-type sugar transport, ATPasesigma-54 dep. Fis family transcriptional regulatorPenicillin tolerance protein......347 nt intergenic regionC. elegans maco-1Choice indexGRb0427preferenceOP50preference347 bp IntReg (intergenic region with maco-1 match)BD16-nt match to maco-1E. coli + empty vectorE. coli + IntReg GRb0427preferenceOP50preference-1.0-0.50.00.5*******nsP0F1F2F3F4F5P0EE. coli + empty vectorE. coli + IntReg GRb0427preferenceOP50preferenceJU1580Choice indexE. coli + empty vectorE. coli + IntReg -1.0-0.50.00.5FC-0.8-0.6-0.4-0.20.00.2Choice index *......Iron Metabolism OperonSugar Transport Operon.........Exon 1Exon 8homology to GRb0427 genomehomology to PA14 genome (P11 sRNA)APLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 8. Pv1, a GRb0427 sRNA, is necessary and sufficient for transgenerational inheritance of learned avoidance. (A) Alignment of GRb0427 small RNA sequencing reads to the GRb0427 genome. The peaks obtained from this alignment and the Sapphire small RNA promoter prediction software indicated the presence of three small RNAs (shown in dark gray, green, and light gray) in the intergenic region between the two predicted operons described in Fig 7. Of the 3 predicted sRNAs, the sRNA marked in green contains the 16-nucleotide match to maco-1 (purple), and we named it Pv1. (B) mFold structure prediction for Pv1; the maco-1 region is in a predicted stem loop of Pv1 (the boxed region). (C) Training worms on E. coli expressing just the Pv1 small RNA induces avoidance of GRb0427 (compared to training on E. coli expressing a control empty vector). (D) E. coli-Pv1 induced learned avoidance of GRb0427 is transgenerationally inherited. Untrained PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 15 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally F1 and F2 progeny of E. coli-Pv1 trained P0 worms also exhibit higher GRb0427 avoidance compared to controls. (E-G) Naïve worms do not exhibit a preference towards either wild type GRb0427 or GRbΔ16nt strain in a GRb0427-GRbΔ16nt choice assay (E), while preferring both GRb0427 and GRbΔ16nt with respect to OP50 (F, G). (H) Survival of worms on lawns of GRb0427 and GRbΔ16nt are not significantly different. (I) F2 progeny of GRbΔ16nt-trained worms do not exhibit learned avoidance of GRb0427. (J) Mean ASI daf-7p::GFP intensities in GRbΔ16nt-trained worms are comparable to that of OP50-trained worms, in contrast to worms trained on GRb0427 where the mean ASI daf-7p::GFP intensities are higher than that of OP50-trained worms. (K) An E. coli strain expressing Pv1 containing 7 mismatches (4 of which lie within the 16- nucleotide match to maco-1) is predicted to have identical secondary structure to wild type Pv1, but the mismatches disrupt the sequence homology to maco-1. (L) Worms trained on E. coli-Pv1 learn to avoid GRb0427, while worms trained on E. coli expressing the Pv1 with mismatches fail to learn avoidance. Each dot represents an individual choice assay plate (C-G, I, L), or an individual neuron (J). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Unpaired, two-tailed Student’s t test, p<0.01, *p<0.001, (C, D); One-way ANOVA with Tukey’s multiple comparison’s test, p<0.01, *p<0.001, p<0.0001, ns, not significant (I, J, L). For the survival assay in (H), ns–not significant (by Log-rank (Mantel-Cox) test for survival). https://doi.org/10.1371/journal.pgen.1011178.g008 components. We found that sid-2(qt42) [63,64], dcr-1(mg375) [65,66], or sid-1(qt9) [67] ani- mals were unable to learn P. vranovensis avoidance after Pv1 training (Fig 9A–9C). Moreover, mutants of hrde-1(tm1200), the nuclear Argonaute that binds 22G RNAs, cannot learn to avoid P. vranovensis after training on Pv1 (Fig 9D), indicating that HRDE-1 is also required. Together, our data suggest that the mechanism that we had previously identified for PA14-P11 sRNA processing is shared with P. vranovensis-Pv1 processing, and involves bacterial sRNA uptake (sid-2), dsRNA processing (dcr-1), 22G sRNA binding (hrde-1), dsRNA transport (sid- 1), maco-1 transcript reduction, Cer1-mediated germline-to-neuron communication, daf-7 expression increase in the ASI, and then behavioral switching from attraction to avoidance (Fig 9E). C. elegans avoid the beneficial bacterium Pseudomonas mendocina following exposure to GRb0427 or Pv1 In the wild, worms experience a range of temperatures, and bacterial species can be differen- tially pathogenic at different temperatures. In the case of P. aeruginosa PA14, pathogenicity decreases at lower temperatures [23,68], and the PA14 small RNA, P11 is not expressed at lower temperatures [23]. We wondered if this was also the case for P. vranovensis/GRb0427 and Pv1, or if other factors, including other members of the microbiome, might influence the cessation of avoidance. We first asked if temperature affects the pathogenicity of GRb0427, as it does for PA14. Although less pathogenic to worms than 25˚C-grown GRb0427, 15˚C-grown GRb0427 still kills all the worms in the population before the control (OP50)-grown worms have started dying (Fig 10A). Consistent with these results, the Pv1 small RNA is expressed in total RNA pools from both 25˚C GRb0427 and 15˚C GRb0427 (S3 Fig), unlike the differential expression of PA14’s P11 sRNA at different temperatures [23]. Consistent with Pv1 being expressed under both temperature conditions, worm populations trained on 25˚C and 15˚C GRb0427 small RNAs both avoid GRb0427 compared to worms trained on control (OP50) small RNAs (Fig 10B). Thus, it seems unlikely that temperature-dependent changes and Pv1 sRNA expres- sion in GRb0427 pathogenicity drive the loss of memory of learned avoidance. We next asked if GRb0427-induced learned avoidance might alter worms’ responses to other bacteria. Several bacterial species in the worm microbiome are beneficial; that is, they increase worm growth rates and progeny production and extend lifespan [47]. In fact, all the wild bacteria we tested are more attractive than OP50 (Fig 1). This preference likely evolved as Pseudomonas bacteria are sources of nutrition to C. elegans in the wild [47]. Therefore, we asked if exposure to the pathogenic P. vranovensis/GRb0427 might cause worms to also avoid beneficial bacteria in their microbiome. Some of the beneficial bacterial species in the worm PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 16 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 9. Genes involved in sRNA processing are required for Pv1 sRNA-mediated learned avoidance. (A-D) sid-2 (qt42) (A), dcr-1(mg375) (B), sid-1(qt9) (C), and hrde-1(tm1200) (D) do not learn to avoid GRb0427 in response to Pv1 training. (E) Model of Pv1 uptake, processing, and induced avoidance of P. vranovensis (created using Biorender). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. Two- way ANOVA with Tukey’s multiple comparison’s test, p<0.01, *p<0.001, ns, not significant (A-D). https://doi.org/10.1371/journal.pgen.1011178.g009 microbiome, e.g., Pseudomonas mendocina, belong to the same genus as GRb0427 [50]; C. ele- gans are attracted to Pseudomonas mendocina [56], and prefer it to OP50 (Figs 1, S4). As we showed above, worms trained on a strain of P. mendocina from the worm’s microbiome, MSPm1, do not exhibit an altered response to P. mendocina compared to control (E. coli OP50)-trained worms (Fig 1B), as might be expected for beneficial bacteria. Consistent with PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 17 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Fig 10. Pv1-induced learned avoidance causes avoidance of P. mendocina, a beneficial natural bacterial species. (A) Worms have significantly lower survival on a 25˚C-grown GRb0427 lawn compared to a 15˚C-grown GRb0427 lawn, but in both these conditions, all worms die before worms on an OP50 lawn have started dying, indicating that both 25˚C and 15˚C-grown GRb0427 are pathogenic. (B) Training worms on sRNAs from both 25˚C and 15˚C-grown GRb0427 induce learned avoidance. (C) Untrained worms have similar preference for GRb0427 and MSPm1 in a choice assay. (D) Training worms on a GRb0427 lawn reduces attraction to P. mendocina compared to OP50-trained control worms. (E) Training worms on E. coli expressing Pv1 reduces attraction to P. mendocina. (F) Naïve F2 progeny of GRb0427-trained P0 animals exhibit reduced attraction to P. mendocina relative to F2 progeny of OP50-trained P0 animals. (G) Schematic outlining the findings of the study. Prior to exposure to pathogenic P. vranovensis, worms are naively attracted to both P. vranovensis, and the non-pathogenic P. mendocina. Exposure to P. vranovensis or to the Pv1 small RNA of P. vranovensis induces learned avoidance of P. vranovensis in worms and four generations of progeny. Exposed worms not only avoid the pathogenic P. vranovensis, but also the non- pathogenic P. mendocina. Schematic was created using Biorender. Each dot represents an individual choice assay plate (B-F). Boxplots: center line, median; box range, 25th–75th percentiles; whiskers denote minimum-maximum values. One-way ANOVA with Tukey’s multiple comparison’s test, p<0.01, ns, not significant (B); Unpaired, two-tailed Student’s t test, p<0.01, p<0.001 (D-F). For the survival assay in (A), **p<0.001 (by Log-rank (Mantel-Cox) test for survival). https://doi.org/10.1371/journal.pgen.1011178.g010 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 18 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally this logic, we found that the P. mendocina genome does not encode the Pv1 small RNA used by GRb0427 to elicit avoidance, and the intergenic regions in the genome do not contain any significant contiguous matches to the Pv1 target maco-1. We next tested the effect of GRb0427 exposure on worms’ responses to P. mendocina. In their naïve state, C. elegans exhibits no preference for GRb0427 or MSPm1 over one another (Fig 10C). However, worms trained on P. vranovensis/GRb0427 for 24 hrs have significantly reduced attraction to P. mendocina compared to worms grown on the control E. coli OP50 (Fig 10D)–that is, although they have never been exposed to P. mendocina, and it is a beneficial food source they are naively attracted to (Figs 1B and S4), exposure to pathogenic P. vranoven- sis induces avoidance of the beneficial bacteria. Similarly, training on E. coli expressing the Pv1 small RNA is sufficient to reduce attraction of worms to the beneficial P. mendocina (Fig 10E). Since Pv1 mediates transgenerational inheritance of learned GRb0427 avoidance, we next tested if altered P. mendocina preference upon GRb0427 training is inherited for multiple gen- erations. Indeed, F2 progeny of GRb0427-trained animals continue to exhibit reduced attrac- tion to P. mendocina (Fig 10F). Taken together, our results suggest Pv1 small RNA-mediated learned avoidance induces both avoidance of pathogenic P. vranovensis but also the avoidance of beneficial P. mendocina bacteria that may be nutritious food source for C. elegans. Therefore, it may be beneficial to reverse this memory after a few generations, when the worms may no longer encounter the original pathogen. Discussion C. elegans’ wild environments are rife with diverse bacterial species, and recent studies have provided comprehensive descriptions of C. elegans’ natural microbial environments [43,44,47]. The effects of these bacterial species on C. elegans’ nervous system and behavior have been largely unexplored, and whether these bacteria can exert transgenerational effects on worms was previously unknown. Here we show that a small RNA from Pseudomonas vra- novensis GRb0427, a pathogenic bacterial species found in C. elegans’ natural environment, induces learned avoidance in worms. This learned avoidance persists for four generations, similar to the learned avoidance induced by the P11 small RNA expressed in the clinical isolate of P. aeruginosa, PA14. This wild bacterial species, P. vranovensis, however, does not express the P11 small RNA; instead, it uses a distinct small RNA, which we identified and named Pv1, to induce avoidance and its transgenerational inheritance. The Pv1 small RNA, like P11, regu- lates avoidance by targeting the neuronal gene maco-1 –but in a different exon—and subse- quently upregulating the expression of the TGF-beta ligand daf-7, leading to the switch from attraction to avoidance (Fig 9E). Our results also indicate that the RNAi pathway is required for both the PA14/P11 and P. vranovensis/Pv1 mechanisms of bacterial sRNA-induced avoid- ance, as SID-2, DCR-1, and SID-1, as well as the 22G Argonaute, HRDE-1, are all required for Pv1-induced avoidance. Our study provides an example of small RNA-mediated cross-kingdom signaling between bacteria and their animal hosts, adding to the diverse instances of RNA-based trans-kingdom signaling [23,69,70]. The identification of Pv1 expands the repertoire of bacterial small RNAs that can affect host physiology, and begins to identify common principles of bacterial small RNA-mediated regulation of C. elegans behavior. Both Pv1 and P11 target maco-1, a conserved ER-localized calcium regulator [62], indicating that the sRNA/maco-1/daf-7 axis may be important for regulation by multiple bacterial small RNAs. Pv1, like P11, has a short perfect match to its putative target, maco-1, indicating that bacterial small RNAs require perfect sequence complementarity for sRNA-mediated effects; moreover, these matches are present in PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 19 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally stem loops of predicted secondary structures of these sRNAs (Fig 8B). However, Pv1 and P11 contain sequence matches to different exons of the maco-1 gene sequence, suggesting that the P11-matched region of maco-1 is not the key element. Although P11 and Pv1 are similar in secondary structure and length, they have very low sequence homology, suggesting that these two small RNAs likely evolved independently. The odds of a Pseudomonas small RNA having a perfect 16 nt match to an exon of maco-1 by chance are low (less than 0.03, which perhaps is an overestimate; see Methods). Therefore, although the 16 nucleotide length is short, the per- fect match requirement between a bacterial small RNA and a potential C. elegans transcript may sharply limit the possibilities. The fact that the maco-1 sequence matches are present in predicted stem loops may also suggest that there may be convergent evolution towards a par- ticular length or secondary structure. This supports the idea that several other bacterial sRNA- based regulation systems exist that may target the same or distinct C. elegans genes and may be yet to be discovered. Characterizing sRNAs from additional bacterial species that induce heri- table behaviors in worms may help define molecular and structural features of sRNAs that can induce learning and inheritance, and may even allow us to predict which naturally existing exogenous sRNAs, including sRNAs from animal gut microbiomes, have the potential to induce heritable effects. These findings establish bacterial small RNA-mediated regulation of host physiology as a phenomenon that may occur in the wild and in C. elegans’ interaction with multiple different bacterial species. In fact, our data indicate that small RNAs may play a significant role in the neuronal response of C. elegans to wild bacteria: the behavioral response to GRb0427 is almost entirely mediated by small RNAs. In fact, the secondary metabolite-induced neuronal response observed upon Pseudomonas aeruginosa PA14 exposure (daf-7 induction in ASJ neurons) [27,59] is not observed upon GRb0427 exposure, and the innate immunity gene irg-1 [71] is not greatly induced, either, suggesting that the sRNA-mediated response, rather than classical innate immunity pathways, may be the major mechanism of avoidance of this pathogen. The sRNA-mediated learned response is also mechanistically distinct from a previously-described intergenerational effect on progeny (embryo) survival upon P. vranovensis exposure, which does not appear to use bacterial small RNAs or continue beyond the F1 generation [8], or a P0 avoidance effect induced by O. vermis [58]. Nor is there any overlap with the general response to pathogens [72], which is mediated by translational inhibition; for example, vhp-1, which plays a key role in the general response, has the opposite effect on pathogen avoidance [23]. The transgenerational effect we observe is species-specific; in fact, of the pathogenic species we tested here (Fig 1), and in previous work (S. marcescens [27]), only PA14 and GRb0427, two Pseudomonas pathogens, induce transgenerational inheritance of pathogen avoidance. Although not every Pseudomonas species induces avoidance (the beneficial P. mendocina does not, for example), of the pathogens we have tested, only Pseudomonas pathogens seem to induce a small RNA-mediated transgenerational avoidance response. Further work will deter- mine how broadly C. elegans employs this mechanism to avoid Pseudomonas pathogens. Transgenerational inheritance of learned avoidance of both PA14 and GRb0427 lasts for four generations, suggesting that there may be some benefit to “forgetting” learned avoidance of a pathogen. Here we ruled out temperature-dependent changes in pathogenicity and result- ing fluctuations in Pv1 expression as factors. Instead, our results suggest that avoidance of pathogenic P. vranovensis may also cause (mistaken) avoidance of beneficial Pseudomonas spe- cies, such as the non-pathogenic food source, P. mendocina. Taken together, our results sug- gest that in the wild, Pv1 small RNA-mediated learned avoidance may protect worms and their progeny from the pathogenic Pseudomonas, but since this might also result in avoidance of beneficial bacteria, it may be advantageous to reverse this memory after a few generations, when the worms may no longer encounter the original pathogen. Reversal of this memory PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 20 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally may have evolved to protect worms from maladaptive avoidance of other beneficial bacterial species once the pathogen threat has passed. Since most bacterial families in C. elegans’ micro- biome include beneficial as well as detrimental members that may present similar odors to the worms, reversible transgenerational responses to multiple other bacterial species may have evolved as an adaptive strategy. Together, our results suggest that the “interpretation” of bacterial small RNA signals may in fact be one reason that C. elegans developed such robust RNA interference mechanisms: in addition to endogenous RNAi systems used to silence errant germline transcripts [73–75], mechanisms to regulate and process exogenous sRNAs from the worm’s environment and bac- terial food sources may be critical for avoidance of pathogens in C. elegans’ environment. The wild C. elegans strain JU1580 has a mutation in the viral RNAi Dicer-like homolog, drh-1, dis- abling the viral RNA interference pathway; these results suggest that C. elegans detects wild bacterial sRNA through the canonical RNAi pathway, not through the viral pathway. Together, our results suggest that environmental sRNAs–that is, sRNAs produced in the bacteria that live in C. elegans’ habitat–can be taken up and processed by C. elegans’ RNA interference path- way. Considering the worms’ requirement for bacteria as its food and the broad spectrum of microbes in their environment, the ability to interpret bacterial sRNA signals may be a power- ful mechanism of adaptive immunity. If C. elegans constantly surveys its bacterial environ- ment, but also needs to cease avoidance after a few generations to prevent accidentally avoiding beneficial food sources, the bacterial small RNA/RNA interference pathway may pro- vide an ideal adaptable, resettable response mechanism. More examples will be necessary to determine whether the system is restricted to Pseudomonad species and small RNAs that target maco-1, as well as to better define the characteristics of key bacterial small RNAs. Our work provides proof of principle that transgenerational inheritance of learned avoid- ance is likely to benefit C. elegans in the wild. Although bacterial small molecules have been widely implicated in bacteria-host interactions [76], the potential roles of bacterial small RNAs in regulating host physiology are largely understudied. Identifying different bacterial small RNAs that can interact with host organ systems will lead to a better understanding of this new dimension of bacteria-host interactions. Methods Resource availability Further information and requests for resources and reagents should be directed to and will be fulfilled by Coleen T. Murphy (ctmurphy@princeton.edu). Materials availability Bacterial and C. elegans strains generated in this study are available on request. Experimental model and subject details Bacterial strains. The GRb0427 and Jub38 strains were a generous gift from Dr. Buck Samuel’s lab. OP50 was obtained from the C.G.C. The PA14 strain was a gift from Prof. Zemer Gitai’s lab. Control (L4440) and maco-1 RNAi were obtained from the Ahringer RNAi library, and the respective sequences were verified. MSPm1, Jub66, Myb10, Myb71, CEN2ent1, Jub19, and Bigb0170 are part of the CeMbio [50] collection and was obtained from CGC. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 21 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Engineered bacterial strains IntReg. E. coli expressing the GRb0427 347-nt intergenic region containing the 16-nucle- otide match to maco-1 was constructed by Gibson Assembly. The entire intergenic region was cloned out of GRb0427 and ligated into pZE27GFP using a double restriction digestion to open the plasmid and a single fragment Gibson assembly to ligate. This plasmid was then transformed into MG1655 E. coli using a standard transformation protocol. GRb0427Δ16. The deletion of the 16-nt region of Pv1 (that matches to C. elegans maco-1) from the GRb0427 genome was constructed by two-step allelic exchange using plasmid pEXG2. Briefly, ~600 bp fragments directly upstream and downstream of Pv1 sequence were amplified from GRb0427 gDNA using primer pairs (Pv1-KO1, Pv1-KO2) and (Pv1-KO3, Pv1-KO4), respectively. Overlap-extension PCR, with primer pair (Pv1-KO1, Pv1-KO4), was used to fuse together the upstream and downstream fragments. The final fragment was cloned into pEXG2, which was PCR linearized with primer pair (pEGX2-Lin1, pEGX2-seq2). The pEXG2 plasmid was integrated into GRb0427 by conjugation from donor strain E. coli s17 and exconjugants were selected on gentamycin 30 μg/mL and irgasan 100 μg/mL. Mutants of inter- est were counterselected on 15% sucrose and the proper deletion was confirmed via PCR, using primers (Pv1-seq1, Pv1-seq2), and sequencing of PCR products. Primer details: Pv1-KO1—CGCACCCGTGGAAATTAATTGCTTCAGTGAAGGGCGG Pv1-KO2—TTCAGCATGCTTGCGGCTCGAGCACAGAAAGCAGATTAAATATGCGC Pv1-KO3—CTCGAGCCGCAAGCATGCTGAATTTTAGCCGTACCGAACAAGC Pv1-KO4—CCGGAAGCATAAATGTAAGCGTCCTTGTCGGGGC pEGX2-Lin1—CTTTACATTTATGCTTCCGGCTCGTA pEGX2-Lin2—AATTAATTTCCACGGGTGCGCATG Pv1. E. coli expressing the Pv1 small RNA was constructed in a similar method, using primers specific to the Pv1 region: Pv1-seq1—CGCACCCGTGGAAATTAATTGCTTCAGTGAAGGGCGG Pv1-seq2 –CCGGAAGCATAAATGTAAGCGTCCTTGTCGGGGC The region encoding Pv1 was cloned out of GRb0427 and ligated into pZE27GFP using a double restriction digestion to open the plasmid and a single fragment Gibson assembly to ligate. This plasmid was then transformed into MG1655 E. coli using a standard transforma- tion protocol. Pv1 mismatch. E. coli expressing the Pv1 small RNA with disrupted maco-1 sequence homology, but with intact secondary structure, was constructed using the Pv1-expressing plas- mid. Four mismatches were introduced into the maco-1 match sequence such that the sequence homology is disrupted but the secondary structure is maintained. The mismatches were introduced in the PCR primers, Gibson assembly was used for ligation, and the plasmid was transformed into MG1655 as previously described. The primers used for cloning the plas- mid containing the Pv1 sequence with the mismatches are: PV1 (sequence disrupted/secondary structure maintained) 5’ AGCTTACTGTGACGTTTGCTAATAAACTTTTAGCCGTACCGAACAAGCT PV1 (sequence disrupted/secondary structure maintained) 3’ ATTAGCAAACGTCACAGTAAGCTGATAAAATATGCGCCCGTAGCTCAGCT C. elegans strains The following strains were used in this paper: N2 (wild type), AU133: agls17[Pmyo-2::mCherry + Pirg-1::gfp], FK181: ksIs2 [Pdaf-7p::gfp + rol-6(su1006)], CQ759: maco-1(ok3165), CQ667: Cer1(gk870313), HC196: sid-1(qt9), YY11: dcr-1(mg375) III, CQ738: hrde-1(tm1200), HC271: PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 22 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally ccIs4251 [(pSAK2) Pmyo-3::GFP::LacZ::NLS + (pSAK4) Pmyo-3::mitochondrial GFP + dpy-20 (+)] I; qtIs3 [Pmyo-2::GFP dsRNA hairpin] sid-2(qt42) III; mIs11 [Pmyo-2::GFP + Ppes-10::GFP + gut-promoter::GFP] IV, and JU1580 and ED3040 (wild type, natural C. elegans isolates). Cultivation of bacterial strains E. coli OP50, P. vranovensis GRb0427, P. vranovensis GRb0427 Δ16 nt, Raoultella sp. Jub38, the CeMbio strains, and P. aeruginosa PA14 were grown in LB (10 g/l tryptone + 5 g/l yeast extract + 10 g/l NaCl in distilled water) in a shaker at 250 rpm. E. coli expressing the Pv1 sRNA were grown in LB supplemented with 50 μg/mL Kanamycin. RNAi bacteria were grown in LB sup- plemented with 100 μg/ml carbenicillin and 12.5 μg/ml tetracycline. General maintenance of C. elegans strains Worm strains were maintained at 20˚C on high-growth medium (HGM) plates (3 g/l NaCl, 20 g/l bacto-peptone, 30 g/l bacto-agar in distilled water, with 4 mL/L cholesterol (5 mg/mL in ethanol), 1 mL/L 1 M CaCl2, 1 mL/L 1 M MgSO4 and 25 mL/L 1 M KPO4 buffer (pH 6.0) added to molten agar after autoclaving) on E. coli OP50. Method details Training plate preparation Training plates were prepared by pipetting 800 uL of bacteria onto NGM (3 g/L NaCl, 2.5 g/L Bacto-peptone, 17 g/L Bacto-agar in distilled water, with 1 mL/L cholesterol (5 mg/mL in etha- nol), 1 mL/L 1M CaCl2, 1 mL/L 1M MgSO4, and 25 mL/L 1M potassium phosphate buffer (pH 6.0) added to molten agar after autoclaving) or HG plates. Pathogenic bacteria such as PA14 or GRb0427 were prepared on NGM plates to avoid overgrowth, while sRNA producing MG1655 were prepared on HG plates. For sRNA training, 200 μl of OP50 was spotted in the center of a 6-cm NGM plate. Plates were stored at 25˚C for 48hrs. RNAi bacteria were pre- pared on HG plates (supplemented with 1 mL/L 1M IPTG, and 1 mL/L 100 mg/mL carbenicil- lin) and kept at room temperature for 48 hrs. Plates were then taken out of the incubator and allowed to cool to room temperature before moving animals onto them. Bacterial choice assay plate preparation Overnight bacterial cultures were diluted in LB to an OD600 = 0.5, and 25 μl of each bacterial suspension was spotted onto one side of a 60-mm NGM plate to make bacterial choice assay plates. These plates were incubated for 2 days at 25˚C. Preparation of bacteria for small RNA isolation GRb0427 and E. coli OP50 bacteria were cultured for 16 hours overnight. 1 ml of either bacte- rial culture was diluted to an OD = 0.5, plated on 100mm NGM plates and grown at 25˚C for 48 hours. For 15˚C GRb0427 small RNA isolation, overnight (37˚C) cultures of GRb0427 bac- teria were centrifuged for 10 min at 5,000g. The supernatant was removed, and the remaining pellet was resuspended in 5 ml of fresh LB. Washed bacteria were used to inoculate (1:500) fresh LB to grow at 15˚C for 36 hrs. 1 ml of this bacterial culture was then diluted to an OD = 0.5, plated on 100mm NGM plates and grown at 15˚C for 48 hours. Bacterial lawns were collected from the surface of the plates using a cell scraper. 1 ml of M9 buffer was applied to the surface of the bacterial lawn, and the bacterial suspension obtained by scraping was transferred to a 15-ml conical tube. E. coli OP50 from 15 plates, or GRb0427 from 10 plates were pooled in each tube and pelleted at 4500g for 8 min. The supernatant was PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 23 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally discarded, and the bacterial pellet was resuspended in 900 uL of Trizol LS for every 100 μl of bacterial pellet. The pellet was resuspended by vortexing and the tubes containing the bacterial pellet were frozen at −80˚C. Bacterial small RNA isolation To isolate small RNA, bacterial pellets-Trizol suspensions were first incubated at 65˚C for 10 min with occasional vortexing. Debris were pelleted at 4500g for 5 min. The supernatant was transferred to 1.5 mL tubes (1 mL in each tube) and 200uL chloroform was added. Samples were mixed by inverting and centrifuged at 12,000g at 4˚C for 10 min. The aqueous phase obtained was used as input for small RNA extraction using the mirVana miRNA isolation kit. Extraction was done as per the manufacturer’s instructions for small RNA (<200 nt) isolation. Purified small RNA was used immediately in aversive learning assays or for sequencing, or fro- zen at −80˚C until further use. Training C. elegans on bacterial lawns and small RNAs Wild-type N2 animals were synchronized by bleaching and grown until larval stage 4 (L4) on standard HG plates seeded with OP50. At L4 stage, they were transferred to training plates. After 48 hrs at 25˚C the training plates were left on the bench top for 30 mins to allow them to reach room temperature. For sRNA training, 100 ug of sRNA was added to the OP50 spot on the sRNA training plates. For RNAi, 200 μL of 0.1M IPTG was spotted onto seeded RNAi plates and left to dry at room temperature before adding worms. Larval stage 4 (L4) worms were washed off plates using M9 and left to pellet on the bench top for 2–3 min. Then, 5 μl of worms were placed onto sRNA-spotted training plates, 10 μl onto OP50 plates, or 20 μl onto RNAi plates, E. coli expressing Pv1, GRb0427, or GRb0427Δ16 training plates. Worms were incubated on training plates at 20˚C in separate containers for 24 hours. After 24 hours, worms were washed off plates using M9 and washed an additional 2–3 times to remove excess bacteria. Trained worms were tested in the aversive learning assay. Aversive learning assay On the day of the assay, bacterial choice assay plates were left at room temperature for 1 h before use. To start the assay, 1 μl of 1 M sodium azide was spotted onto each respective bacte- ria spot to be used as a paralyzing agent during choice assay. Worms were then washed off training plates in M9, allowed to pellet by gravity, and washed 2–3 additional times in M9. Using a wide orifice pipet tip, 5 μl of worms were spotted at the bottom of the assay plate, mid- way between the bacterial lawns. The assay plates were incubated at room temperature for 1 h. After that, the number of worms on each bacterial spot were counted. Plating a large number of worms (>200) on choice assay plates was avoided, because the worms clump at bacterial spots, making it difficult to distinguish individual worms during counting, and also because high densities of worms can alter behavior. For experiments testing behavior of the F1 generation, day 1 worms from parental (P0) training were bleached and eggs were placed onto HG plates and left for 3 days at 20˚C. After 3 days, the F1 worms (Day 1–72 hours) were washed off HG plates with M9. Some of the pooled worms were subjected to the aversive learning assay, and the remaining worms were bleached to obtain eggs. The eggs were then placed onto HG plates, which were left at 20˚C. After 3 days the F2 progeny were tested, and the same steps were followed for subsequent progeny generations. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 24 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Annotation of the GRb0427 genome The GRb0427 genome was obtained from [8] and run through an annotation pipeline in python. This pipeline searches for all open reading frames across the genome on both strands and makes a temporary gene list. It then filters genes by both size and overlap with other genes to give a final predicted gene list. Our pipeline predicted 4952 genes in the genome. Multiple genes were then selected and run through NCBI BLAST to confirm their identities. After identifying the genes in the genome, we used Standalone BLAST, specifically, BLAST- nShort, to identify regions of homology between maco-1 and the GRb0427 genome. This yielded 5 hits: 4 hits of 16 nucleotides and 1 of 20 nucleotides. Next, these hits were run through a python function to determine if they lie in an intergenic region, and this filtered our list down to a singular 16 nucleotide hit. This hit lies within a predicted 347nt intergenic region. Bacterial small RNA sequencing Prior to sRNA sequencing, each sample of GRb0427 sRNA was tested for C. elegans behavior. The size distribution of sRNA samples was examined on a Bioanalyzer 2100 using RNA 6000 Pico chip (Agilent Technologies). The sRNA sequencing protocol was similar to the protocol used in [23]. Briefly, around 300 ng of sRNA from each sample was first treated with RNA 50 pyrophosphohydrolase (New England Biolabs) at 37˚C for 30 min, then converted to Illumina sequencing libraries using the PrepX RNA-seq library preparation protocol on the automated Apollo 324 NGS Library Prep System (Takara Bio). The treated RNA samples were ligated to two different adapters at each end, then reverse-transcribed to cDNA and amplified by PCR using different barcoded primers. The libraries were examined on Bioanalyzer DNA High Sen- sitivity chips (Agilent) for size distribution, quantified by Qubit fluorometer (Invitrogen), and then pooled at equal molar amount and sequenced on Illumina NovaSeq 6000 S Prime flowcell as single-end 122-nt reads. The pass-filter reads were used for further analysis. Bacterial small RNA sequencing data analysis Four replicates of GRb0427 small RNA and three replicates of E. coli OP50 small RNA were sequenced. Reads were mapped to the sequenced GRb0427 genome [8] using RNA STAR [77]. Default settings were used for the RNA STAR mapping. The resulting BAM files were then loaded into IGV genome browser [78] for analysis of the intergenic region containing the 16-nucleotide sequence match to maco-1. The peaks and read strands indicated that 3 small RNAs lie in the intergenic region of interest, and one spans the 16nt region of match to maco- 1. Using the Sapphire promoter analysis software for Pseudomonas species [79], we found highly confident predicted promoters that were consistent with the sequencing data. Of the three small RNAs, one contains the 16 nt homology to maco-1, and this small RNA was named Pv1. The boundaries of Pv1 were determined based on the depletion of mapped reads at the same genomic positions across all four GRb0427 small RNA sequence datasets. sRNA data available at NCBI BioProject PRJNA1062118. Determination of the operon context of Pv1 We examined if Pv1 lies in a GRb0427 operon. Using the Operon Mapper operon detection software [80], we found that while Pv1 is flanked by operons for iron metabolism and sugar transport, Pv1 itself is not part of an operon. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 25 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally Pv1 structure prediction The small RNA that has homology to maco-1 was identified by our sequencing data to be 124 nucleotides in length. Using the mFold sRNA secondary structure prediction tool on the UNAFold webserver [81], we found that this RNA contains a long stem loop structure with the sequence match to maco-1 beginning in a stem and ending after the turn of a loop. Inter- estingly P11’s sequence match to maco-1 has a similar secondary structure context. Imaging and image analysis daf-7p::gfp images of OP50, PA14, and GRb0427 worms were taken on a Nikon Eclipse Ti microscope. Worms were prepared and treated as described in ‘Worm preparation for training’. Worms were mounted on 2% agar pads on glass slides and immobilized using 1 mM levamisole. Z-stack multi-channel (DIC and GFP) images of day-1 adult GFP-trans- genic worms were acquired at 60X magnification. Maximum intensity projections of head neurons were built using Fiji. Quantification of mean fluorescent intensity was done using NIS Elements software. Average pixel intensity was measured in each worm by drawing a Bezier outline of the neuron cell body for 2 ASI head neurons. For imaging of daf-7p::gfp in each generation, exposure times were adjusted to prevent oversaturation., For control and treatment groups imaged on the same day, the same exposure time and camera settings were used. For irg-1p::gfp quantification, worms were prepared as described in ‘Worm preparation for training’ and imaged at 20× magnification on a Nikon A1 R confocal microscope. Image anal- ysis was done with Fiji, where ROIs were drawn around each animal in the field of vision and mean intensity values for all regions of interest were recorded and plotted. C. elegans Survival assay Survival assay on GRb0427 and GRb0427Δ16 lawns: GRb0427 and GRb0427Δ16 bacteria were grown in liquid culture overnight (37˚C) and diluted 1:4 to an OD = 0.5. 750 μL of diluted GRb0427 or GRb0427Δ16 was spread to completely cover six 6-cm NGM plates for each bacterial genotype. Plates were incubated for 2 days at 25˚C to allow bacterial growth. Plates were equilibrated to 20˚C before adding worms (84 hours post-bleach) to plates. Survival assays were performed at 20˚C. The assay plates were counted every 6–9 h. Every 48h, worms were moved onto new plates. Survival assay on 25˚C-grown and 15˚C-grown GRb0427 lawns: Preparation of 25˚C-grown GRb0427 survival assay plates—Overnight (37˚C) cultures of GRb0427 bacteria were diluted in LB to (OD600) = 0.5 and used to fully cover six 6-cm nema- tode growth medium (NGM) plates. The plates were incubated for 2 days at 25˚C. Preparation of 15˚C-grown GRb0427 survival assay plates—Overnight (37˚C) cultures of GRb0427 bacteria were centrifuged for 10 min at 5,000g. The supernatant was removed, and the remaining pellet was resuspended in 5 ml of fresh LB. Washed bacteria were used to inocu- late (1:500) fresh LB to grow at 15˚C for 36 hrs. Cultures were diluted in LB to an OD600 = 0.5 and used to seed NGM plates. The plates were incubated at 15˚C for 2 days. Assay procedure—Plates were equilibrated to 20˚C before adding Day 1 (72 hours post- bleach) worms to plates. Survival assays were performed at 20˚C. The assay plates were counted every 6–9 h. Every 24h, worms were moved onto new plates of 25˚C-grown and 15˚C-grown GRb0427 (prepared as described above). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 26 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally PCR detection of the Pv1 small RNA Total RNA and small RNA were extracted from 25˚C-grown and 15˚C-grown GRb0427 using the mirVana miRNA isolation kit, and reverse-transcribed to DNA (using SuperScript III First Strand Synthesis System). A 62 bp region and an 88 bp region of Pv1 were identified using the following PCR primers: Pv1(62bp)Fwd: CTGTGACGATTACAAATTAAC Pv1(62 bp)Rev: GCCGTACCGAACAAG Pv1(88 bp)Fwd: GCCTAGCACTGGTTAG Pv1(88bp)Rev: CTGTGACGATTACAAATTAAC Quantification of maco-1 gene expression by qPCR Worms were trained for 24 hours on E. coli OP50 and GRb0427 (as described in the ‘Training C. elegans on bacterial lawns and small RNAs’ section). After training, the worms were collected in M9 and washed several times to remove excess bacteria. Worm pellets were crushed in liquid nitrogen and transferred to an appropriate volume of Trizol LS (100 μL of worm pellet in 900 μL of Trizol). Total RNA was extracted from sample-Trizol suspensions using chloroform extraction, isopropanol and ethanol precipitation, and cleanup using the RNeasy mini kit. cDNA was made from 1 ug of RNA using the Superscript III First-strand system for RT-PCR. The extracted cDNA was used as input for qPCR reactions using the Power SYBR green qPCR master mix and protocol and run on a Viia7 Real-time PCR system. The qPCR primers used are listed below: maco-1 Forward: GTGTCACGACAATTGCC maco-1 Reverse: CACATAGGTAGTGGCGAG act-1 Forward: GGCCCAATCCAAGAGAGGTATC [82] act-1 Reverse: CAACACGAAGCTCATTGTAGAAGG [82] Statistical analysis Survival assays were assessed using Log-rank (Mantel-Cox) tests. For the comparison of choice indices between more than two genotypes, one-way ANOVA with Tukey’s multiple compari- sons test was used. For comparisons of choice indices between genotypes and between condi- tions (naïve vs learned), two-way ANOVA with Tukey’s multiple comparisons test was used. Unpaired t tests were performed for comparisons between two groups. Experiments were repeated on separate days with separate populations, to confirm that results were reproducible. Prism 9 software was used for all statistical analyses. Perfect match calculation There are approximately 500 small RNAs in P. aeruginosa [83] with an average length of 188 nt, constituting a total of ~86,000 16-nt windows within these small RNAs. The length of the maco-1 coding sequence is approximately 2700 nucleotides, and so contains ~1,350 semi-inde- pendent 16-nt windows (allowing up to 90% overlap between neighboring 16-nt windows). The product of these two numbers is ~116,100,000 pairs of potentially matching windows. Dividing by 4^16 possible 16-nt sequences yields an estimated probability of ~0.027. It should be noted that this number is likely an overestimate, as it assumes that the 86,000 windows in the small RNAs are independent of one another, which is not the case. Supporting information S1 Fig. 36-hour training of worms on Bigb0170 do not induce learned avoidance of Bigb0170. Each dot represents an individual choice assay plate. Boxplots: center line, median; PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 27 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally box range, 25th–75th percentiles; whiskers denote minimum-maximum values; ns, not signifi- cant; Unpaired, two-tailed Student’s t test. (PDF) S2 Fig. F2 progeny of GRb0427-trained animals have higher daf-7p::gfp expression in the ASI sensory neurons (blue arrowheads), compared to F2 progeny of OP50-trained ani- mals. Scale bar = 10 μm. (PDF) S3 Fig. (A) 62 bp and 88 bp of Pv1 amplified by two different primer sets (set 1 and set 2) from total RNA pool extracted from 25˚C and 15˚C-grown GRb0427 (indicating expression of the Pv1 small RNA under both temperature conditions). (B) 62 bp and 88 bp of Pv1 amplified by the same primer sets as in (A) (also see Methods) from small RNA pool extracted from 25˚C and 15˚C-grown GRb0427. +RT and -RT indicate presence and absence respectively of the reverse transcriptase enzyme. (PDF) S4 Fig. (A-C) Naïve worms do not exhibit a preference towards either P. vranovensis GRb0427 or P. mendocina MSPm1 in a GRb0427-MSPm1 choice assay (A) while showing preference for GRb0427 and MSPm1 with respect to OP50 (B,C). (PDF) S1 Data. Source data files for all figures. (XLSX) Acknowledgments We thank Buck Samuel for sharing the GRb0427 and Jub38 bacterial strains, the C. elegans microbiome resource CeMbio for the remaining bacterial strains, and the C. elegans Genetics Center for worm strains; W. Wang, J. Arly Volmar, and J. Miller (Genomics Core Facility, Princeton University); the Murphy lab for discussion and feedback; and J. Ashraf and W. Keyes for assistance. Author Contributions Conceptualization: Titas Sengupta, Rachel Kaletsky, Rebecca S. Moore, Zemer Gitai, Coleen T. Murphy. Formal analysis: Titas Sengupta. Funding acquisition: Cameron Myhrvold, Coleen T. Murphy. Investigation: Titas Sengupta, Jonathan St. Ange, Rachel Kaletsky, Rebecca S. Moore, Renee J. Seto, Jacob Marogi, Cameron Myhrvold. Methodology: Titas Sengupta, Rachel Kaletsky, Rebecca S. Moore, Jacob Marogi. Project administration: Zemer Gitai, Coleen T. Murphy. Software: Jonathan St. Ange. Supervision: Cameron Myhrvold, Zemer Gitai, Coleen T. Murphy. Validation: Titas Sengupta, Renee J. Seto. Writing – original draft: Titas Sengupta, Jonathan St. Ange, Rachel Kaletsky. Writing – review & editing: Cameron Myhrvold, Zemer Gitai, Coleen T. Murphy. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011178 March 28, 2024 28 / 33 PLOS GENETICSWild pathogens use sRNAs to induce C. elegans bacterial avoidance transgenerationally References 1. Brzezinka K, Altmann S, Czesnick H, Nicolas P, Gorka M, Benke E, et al. Arabidopsis FORGETTER1 mediates stress-induced chromatin memory through nucleosome remodeling. Weigel D, editor. eLife. 2016 Sep 28; 5:e17061. https://doi.org/10.7554/eLife.17061 PMID: 27680998 2. Burton NO, Greer EL. Multigenerational epigenetic inheritance: Transmitting information across genera- tions. Semin Cell Dev Biol. 2022 Jul; 127:121–32. https://doi.org/10.1016/j.semcdb.2021.08.006 PMID: 34426067 3. Cecere G. Small RNAs in epigenetic inheritance: from mechanisms to trait transmission. 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10.1371_journal.pbio.3002453	RESEARCH ARTICLE Cell size homeostasis is tightly controlled throughout the cell cycle Xili Liu1, Jiawei Yan2, Marc W. KirschnerID 1* 1 Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America, 2 Department of Chemistry, Stanford University, Stanford, California, United States of America * marc@hms.harvard.edu Abstract AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: To achieve a stable size distribution over multiple generations, proliferating cells require a means of counteracting stochastic noise in the rate of growth, the time spent in various phases of the cell cycle, and the imprecision in the placement of the plane of cell division. In the most widely accepted model, cell size is thought to be regulated at the G1/S transition, such that cells smaller than a critical size pause at the end of G1 phase until they have accu- mulated mass to a predetermined size threshold, at which point the cells proceed through the rest of the cell cycle. However, a model, based solely on a specific size checkpoint at G1/S, cannot readily explain why cells with deficient G1/S control mechanisms are still able to maintain a very stable cell size distribution. Furthermore, such a model would not easily account for stochastic variation in cell size during the subsequent phases of the cell cycle, which cannot be anticipated at G1/S. To address such questions, we applied computation- ally enhanced quantitative phase microscopy (ceQPM) to populations of cultured human cell lines, which enables highly accurate measurement of cell dry mass of individual cells throughout the cell cycle. From these measurements, we have evaluated the factors that contribute to maintaining cell mass homeostasis at any point in the cell cycle. Our findings reveal that cell mass homeostasis is accurately maintained, despite disruptions to the nor- mal G1/S machinery or perturbations in the rate of cell growth. Control of cell mass is gener- ally not confined to regulation of the G1 length. Instead mass homeostasis is imposed throughout the cell cycle. In the cell lines examined, we find that the coefficient of variation (CV) in dry mass of cells in the population begins to decline well before the G1/S transition and continues to decline throughout S and G2 phases. Among the different cell types tested, the detailed response of cell growth rate to cell mass differs. However, in general, when it falls below that for exponential growth, the natural increase in the CV of cell mass is effec- tively constrained. We find that both mass-dependent cell cycle regulation and mass-depen- dent growth rate modulation contribute to reducing cell mass variation within the population. Through the interplay and coordination of these 2 processes, accurate cell mass homeosta- sis emerges. Such findings reveal previously unappreciated and very general principles of cell size control in proliferating cells. These same regulatory processes might also be opera- tive in terminally differentiated cells. Further quantitative dynamical studies should lead to a better understanding of the underlying molecular mechanisms of cell size control. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Liu X, Yan J, Kirschner MW (2024) Cell size homeostasis is tightly controlled throughout the cell cycle. PLoS Biol 22(1): e3002453. https:// doi.org/10.1371/journal.pbio.3002453 Academic Editor: Jonathon Pines, The Institute of Cancer Research, UNITED KINGDOM Received: September 15, 2023 Accepted: November 28, 2023 Published: January 5, 2024 Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data supporting the findings of this manuscript are available on the Open Science Framework at osf.io/3kyvw. Funding: This work was funded by the National Institute of General Medical Sciences (5RO1GM26875-42 to MWK, 5R35GM145248 to MWK) and National Institute on Aging (1R56AG073341 to MWK, 5R01AG073341 to MWK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 1 / 34 Cell size homeostasis is tightly controlled throughout the cell cycle Introduction AIC, Akaike information criterion; Abbreviations: AU : Anabbreviationlisthasbeencompiledforthoseusedinthetext:Pleaseverifythatallentriesarecorrect: BI, bilinear; ceQPM, computationally enhanced quantitative phase microscopy; CV, coefficient of variation; DA std, standard deviation of Division Asymmetry; DMEM, Dulbecco’s Modified Eagle Medium; ERA, ergodic rate analysis; FBS, fetal bovine serum; PFS, Perfect Focus System; Rb, retinoblastoma; SE, sub-exponential; SLBP, stem-loop binding protein. The size distribution of a population of proliferating cells is accurately maintained over many generations, despite variability in the growth rate and the duration of the cell cycle in individ- ual cells, as well as the imprecision in the equipartition of daughter cells at mitosis. Each of these factors is known to contribute to a dispersion in cell size within a population [1]. It has long been evident that there must be some “correction” mechanism that would act within indi- vidual cells to counteract the combined effects of all the sources of random variation and thereby ensure a stable size distribution in the population over many generations [2]. Studies on mammalian and yeast cell size up to now have focused on 1 attractive and plausible mecha- nism for size homeostasis: a dependence of the G1 length inversely with cell size. Theoretically, such a mechanism should allow small cells to “catch up” with larger cells by spending a longer time growing in the G1 phase. Such a process would be expected to reduce cell size variation by normalizing size at the point of S phase entry [2–9]. Several molecular players in this pro- cess have been suggested, such as the dilution of retinoblastoma (Rb) protein [6,9,10] and the activation of p38 MAPK kinase [11,12]. However, such a mechanism, while attractive for its simplicity, cannot in principle fully explain the constancy in the cell size distribution over many generations. Specifically, if G1 length regulation were the only operative mechanism, cells would have no way to anticipate the random variation introduced during the subsequent nonG1 cell cycle phases, a period longer than G1 in most proliferating cell types. Nevertheless, most proliferating cell populations, regardless of their surrounding environment and genetic background, manage to achieve highly accurate size homeostasis [13]. In 1985, Zetterberg and colleagues reported that the variation of G1 length in mouse fibro- blast cells accounted for most of the variation in cell cycle length when cells switched from qui- escence to proliferation [14]. However, a later study in several cell lines found the G1, S, and G2 phase lengths had comparable variability and were all positively correlated with the cell cycle length in normal cycling populations [15], implying a dependency of cell cycle phase lengths on cell size outside of G1. Furthermore, regulation of the S and G2 lengths is known to make a con- tribution to size homeostasis in lower eukaryotic organisms, such as budding and fission yeasts [16–18]. However, evidence of size-dependent regulation outside of G1 has seldom been reported in mammalian cells [4,7]. Little is known about whether the nonG1 phases play an appreciable role in maintaining mammalian cell size homeostasis or whether variation in cell size introduced in the nonG1 phases is somehow fully compensated at the next G1/S transition. An alternative approach for regulating cell size, other than regulating it at S phase entry or in the length of other cell cycle phases, would be to regulate cell growthAU : PerPLOSstyle; italicsshouldnotbeusedforemphasis:Hence; allitalicizedwordshavebeenchangedtoregulartextthroughoutthearticle: [1,19]. A few studies have suggested various types of size-dependent growth rate modulation in cultured cells. For example, Cadart and colleagues found that the slope of volume growth rate versus cell volume decreases for large cells at birth [7]; Neurohr and colleagues found that volume growth rate slows down in excessively large senescent cells [20]; and Ginzberg and colleagues found that nuclear area, an approximate proxy for cell size, is negatively correlated with growth rate at 2 points during the cell cycle [8]. Though such observations have been noted, there has been lit- tle said about their quantitative importance. Furthermore, it is hard to evaluate the various types of growth modulation, as they were discovered in different systems using different physi- cal proxies for cell size, such as cell volume and nuclear area. Hence, little can be concluded about whether these processes coexist in the same cell, are specific to certain cell types, or are only reflected in certain types of cell measurement. Compared to studies on cell cycle control, cell growth control has received little attention. In keeping with a previous study in bacteria [21], we wish to distinguish between “size con- trol” and “size homeostasis.” We will use the term “size control” to refer to the regulation of PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 2 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle the mean size, such as when the mean size in a population of cells responds to a change of envi- ronment or when cells differentiate into a different cell type; whereas, we reserve the term “size homeostasis” for the control of the variance around the mean size of a population in a defined steady-state condition. Though these 2 processes may turn out to be mechanistically related, we cannot assume that they share the same mechanism. In this study, our focus is on the less well studied but perhaps more common process of size homeostasis. We used cultured cell lines because primary cells can take a very long time to reach a stable cell size in culture, whereas cell lines are much more stable and reproducible. Furthermore, cell lines have been well characterized; hence, observations from different laboratories can be readily compared and experiments can be easily replicated. Finally, we expect that size regulation would occur in all cell types, normal and transformed, embryonic and differentiated. Like other general cellu- lar mechanisms, such as mitosis, DNA replication, and protein secretion, it is highly likely that underlying general mechanisms are conserved. To test this generality, we have studied size reg- ulation during the cell cycle in several human cell lines of diverse origins, cultured under dif- ferent conditions. Cell size can be expressed either in terms of mass or volume. Cell volume tends to be a more passive response than mass to the mechanical and osmotic conditions occurring during the cell cycle and differentiation [22–25]. Hence, we have chosen to focus on cell mass homeo- stasis. There are excellent experimental means to measure cell mass in suspension culture [26], but it is much harder to measure cell mass accurately when cells are attached to a substratum, which is closer to the physiological context for most mammalian cell types. This single experi- mental limitation has thwarted the study of cell mass homeostasis and growth rate control in the most well-studied systems. Measuring the mass of a single cell on a culture dish accurately is surprisingly difficult. Furthermore, determining the growth rate from the time derivative of the mass is even more challenging [27,28]. The study of cell mass growth rate regulation in attached cells with sufficient precision to distinguish between different models of growth con- trol required the development of new methods. To this end, we recently developed computa- tionally enhanced quantitative phase microscopy (ceQPM), which measures cell dry mass (the cell’s mass excluding water) by the refractive index difference between cell and medium to a precision of better than 2% [29]. To describe statistically significant features of cell mass and growth rate regulation, we tracked single-cell growth and the timing of cell cycle events at a scale of thousands of cells per experiment. Using this improved technology, we could investi- gate the process of cell mass accumulation relative to cell cycle progression throughout the cell cycle. From these improved measurements, we could derive new understandings of cell mass homeostasis during the cell cycle in several cultured cell lines. The results challenge existing theories of cell mass (or, more colloquially, cell size) homeostasis and suggest further mecha- nistic experiments. Results Cell mass variation is tightly controlled and largely independent of the state of the G1/S circuitry “Cell mass homeostasis” can be strictly defined as the maintenance of a stable distribution of cell mass over generations in a population of proliferating cells. Expressed mathematically, at homeostasis, the coefficient of variation (CV) of cell mass at division, CVd, should be lower than the CV of cell mass at birth, CVb. And, the two should fulfill the equation adapted from Huh and colleagues [30]: PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 CV2 b ¼ CV2 d þ Q2; ðEq1Þ 3 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle where Q denotes the partition error, with Q2 ¼ <ðm1(cid:0) m2Þ2> <m1þm2>2 ; m1 and m2 are the birth masses of the 2 daughter cells of the same mother cell, respectively. By monitoring the proliferation and growth of HeLa cells by ceQPM, we found that the cells were indeed at such a homeostatic state, as the difference between the left- and right-hand sides of Eq 1 was negligible (S1 Fig). To explore this homeostasis further, we considered an abstract model of how the cell mass variation of a cell population evolves with cell cycle progression (Section 1 in S1 Text). If there were no operative controls and cell mass grew exponentially (dm ¼ am) (Fig 1A), the cell mass dt CV would be expected to increase super-exponentially as the cells traverse the cell cycle due to the variation of the growth exponent, α, among cells (Fig 1B). Furthermore, the variation in cell cycle length and the partition error would further contribute to the cell mass variation (quantified by the birth mass CV) at each generation (Fig 1C). To maintain cell mass Fig 1. Cell mass variation is tightly controlled in mammalian cell lines and is robust to perturbations in G1/S (A–C) An abstract model of cell mass homeostasis at different G1 regulation strengths, regulation or growth rate. AU : AbbreviationlistshavebeencompiledforthoseusedinFigs1to5:Pleaseverifythatallentriesarecorrect: represented by the slope of G1 length vs. birth mass correlation. The corresponding model and simulation parameters are in the Section 1 in S1 Text. In the model, we assume cells grow exponentially, and the G1 length control is the only mechanism to reduce cell mass variation. (A) Correlations between G1 length and birth mass. Blue: no G1 length control; red: with strong G1 length control; yellow: with weak G1 length control. (B) Cell mass CV changes with cell cycle progression during 1 cell cycle with the corresponding G1 length regulation in (A). (C) Birth mass CV changes across generations with the corresponding G1 length regulation in (A). (D–G) The mean birth mass (D), birth mass CV (E), division mass CV (F), and DA std. (G) for different cell lines. (H–K) The mean birth mass (H), birth mass CV (I), division mass CV (J), and DA std. (K) for RPE-1 and U2OS cells in normal culture medium, medium with 50 nM palbociclib, and medium with 100 nM rapamycin at cell mass homeostasis. Error bars in (D–K) indicate the standard deviation of 3 or more measurements. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. CV, coefficient of variation; DA std., standard deviation of Division Asymmetry. https://doi.org/10.1371/journal.pbio.3002453.g001 PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 4 / 34 BirthG1/SDivisionCell cycle progression0.10.150.20.25Cell mass CV200400600800Birth mass0102030G1 length5101520GenerationBirth mass CV0.10.20.30.60.70.8HT1080HeLaRPE-1U2OSSaos-20200400Mean birth mass (pg)HT1080HeLaRPE-1U2OSSaos-200.10.20.3Birth mass CVHT1080HeLaRPE-1U2OSSaos-200.10.20.3Division mass CVHT1080HeLaRPE-1U2OSSaos-200.020.040.06DA Std.DEFGABCControl50nM Palb100nM Rapa0200400600Mean birth mass (pg)Control50nM Palb100nM Rapa00.20.4Birth mass CVControl50nM Palb100nM Rapa00.20.4Division mass CVControl50nM Palb100nM Rapa00.020.040.06DA Std.RPE-1U2OSHIJKPLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle homeostasis, these accumulated discrepancies must be offset by a reduction of variability by some processes during the cell cycle. If, as suggested in both in vitro and in vivo systems [4,6], the G1/S checkpoint were the principal “size control checkpoint” (Fig 1A), we would expect the reduction in cell mass variation to occur before or at the G1/S transition. The cell mass CV would then be expected to increase super-exponentially after G1/S due to the lack of any oper- able size control processes in the nonG1 phases. Therefore, the CV reduction before G1/S would have to greatly undershoot the birth mass CV to anticipate and compensate for the cell mass variability that would accumulate during the nonG1 phases (Fig 1B). If the G1/S control were weakened by genetic mutation or pharmacological perturbation (Fig 1A), the reduction in cell mass CV before G1/S would be expected to decrease, and the uncorrected error would cause an increase in the division mass CV (Fig 1B). Such a population would eventually reach a new homeostatic state with higher birth and division mass CVs in order for Eq 1 to be ful- filled (Fig 1C). Therefore, the birth mass CV at homeostasis can be used as an indicator of the stringency of the control on cell mass homeostasis. To investigate how different forms of G1/S control might affect cell mass homeostasis, we compared various human cancer cell lines, each with different G1/S deficiencies, and RPE-1, a cell line with a wild-type G1/S transition [7,12,31] (S1 Table). To evaluate the stringency of the control mechanisms regulating cell mass homeostasis, we measured the birth and division mass CVs of live cell populations from short-term videos using ceQPM. We define the Divi- sion Asymmetry, DA ¼ m1;2 , where m1 and m2 represent the birth masses of the 2 daughter m1þm2 cells, and m1,2 denotes the mass of either of the daughter cells. For a population that divides with perfect symmetry, the distribution of DA should be precisely at 0.5 without any disper- sion. But if either daughter cell were larger or smaller than half the mother cell mass, its DA would deviate from 0.5. The standard deviation of DA (DA std.) quantitatively represents the fidelity of cytokinesis, and it is more commonly used than the partition error Q in Eq 1 [17,32]. Despite the considerable variation in cell mass across the different cell lines (the mean birth mass of the largest cell line, HT1080, is 1.85-fold greater than the smallest cell line, Saos- 2) (Fig 1D), the difference in birth mass CV is less than 15% for each cell line (Fig 1E); the divi- sion mass CV and DA std. for these cell lines were also comparable (Fig 1F and 1G). Note that the measurement error of ceQPM is negligible (less than 2%) compared to the birth and divi- sion mass CVs. To assess the robustness of the birth mass CV to perturbations in the G1/S transition, we perturbed G1/S regulation in both RPE-1 and U2OS cells using a well-characterized CDK4/6 inhibitor, palbociclib [33]. Although U2OS cells have intact Rb proteins, which have been reported to govern the G1/S transition [4,6,34], they carry deficiencies in other G1/S regulators (S1 Table) and are much less sensitive to palbociclib than RPE-1, which has intact G1/S cir- cuitry (S2A and S2B Fig). Both cell lines were examined at a low dose of palbociclib, where there was a delay in G1/S but no arrest of the cell cycle (11). We measured the dry mass of RPE-1 and U2OS cells after being cultured for more than 1 week in palbociclib, at which point the mass distribution of each cell line had reached a new steady state. It had been shown previ- ously that a low dose of palbociclib weakens the negative correlation between birth size and G1 length (like the yellow curve in Fig 1A) [11]. Thus, if G1 regulation were essential for cell mass homeostasis, we would expect the birth mass CV to increase with palbociclib treatment (like the yellow curve in Fig 1C). Surprisingly, although the mean mass at birth had increased by 1.68-fold and 1.13-fold, respectively, in RPE-1 and U2OS cells (Fig 1H), the birth mass CV for either cell line hardly changed and in fact slightly decreased (a 4% and 3% reduction for RPE-1 for U2OS cells, respectively) (Fig 1I). Similarly, the division mass CV and the standard devia- tion of Division Asymmetry, DA std., also hardly changed after exposure of both cell lines to PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 5 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle palbociclib (Fig 1J and 1K). These very small changes in mass CVs indicate that the control of mass homeostasis still operates accurately, despite strong perturbation of the G1/S transition. Since disruption and delay of the cell cycle at G1/S did not appear to affect mass homeosta- sis, we examined the inhibition of cell growth for effects on cell mass variability. We used rapa- mycin, a specific inhibitor of mTOR [35], which has pervasive knock-on effects on protein synthesis and degradation [36]. When RPE-1 and U2OS cultures were exposed to rapamycin, the steady-state birth mass decreased by 27% and 20%, respectively (Fig 1H). However, there were no significant changes in the birth mass CV, division mass CV, or DA std. (changes less than 8% were observed) (Fig 1I–1K). Therefore, it appears that mass homeostasis is strongly buffered, even when mass is greatly perturbed. Cell mass variation is regulated throughout the cell cycle Using ceQPM, we can now ask at what points during the cell cycle variation in cell mass occurs and at what points it is suppressed. We used the CV as a metric of cell mass variation and mea- sured it throughout the cell cycle in live RPE-1 and HeLa cells. To correlate the CV with the state of the cell cycle, we utilized fluorescently tagged geminin degron as the cell cycle marker. Geminin is a protein that regulates DNA replication. Possessing a destruction sequence like cyclin B, geminin is degraded precisely at mitosis and begins to accumulate precisely at the G1/S transition (S3A Fig) [37]. We aligned individual cell mass trajectories (S3B Fig) by nor- malizing the length of the G1 segment to 0–0.5 and that of the nonG1 segment to 0.5–1 and then calculated the CV of these normalized cell mass trajectories with cell cycle progression. In RPE-1 cells, the cell mass CV was found to be reduced throughout the cell cycle (Fig 2A), whereas in HeLa cells, the cell mass CV increased in the G1 phase before declining in the nonG1 phases (Fig 2B). Neither cell line exhibited a minimum cell mass CV at the G1/S transi- tion, as would be predicted by conventional G1 length control models (Fig 1B). To examine the regulation of cell mass variation further in various cell lines and under dif- ferent conditions, we calculated the cell mass CV profile as a function of cell cycle progression from fixed cells, which provided much higher throughput than our live cell measurements. Using ergodic rate analysis (ERA) (38), we defined a cell cycle mean path and divided it into 13 to 14 segments evenly spaced in time, based on measurements of DNA content and fluores- cently tagged geminin degron. We applied this analysis to hundreds of thousands of fixed cells (S4A Fig). By definition DNA replication occurs exclusively in the S phase, whereas geminin accumulation starts at the G1/S transition (S4B–S4E, S4H, and S4J Fig) [38]. Though these 2 markers provide good resolution in late G1 and S phases, they have poor temporal resolution in the early G1 and G2-M phases due to inaccuracy in cell cycle stage identification (S4F Fig). Therefore, we focused our analyses exclusively on the cell mass CV in the late G1 and S phases, employing large numbers of fixed cells. We applied this approach to 4 cell lines: RPE-1, HeLa, U2OS, and HT1080. The cell mass CV profiles in fixed RPE-1 and HeLa cells (Fig 2C and 2D) were similar to what we had previ- ously found in the live cell trajectories (Fig 2A and 2B), further validating the use of fixed cells to extract cell mass CV profiles. We found that in RPE-1 and U2OS cells, the cell mass CV declined in late G1 (Fig 2C and 2E), as would be expected from conventional models where regulation of the G1 length is thought to be the sole means for normalizing cell size. However, we were surprised to find that the CV of cell mass then continued to decrease progressively through S phase. Most strikingly, in HeLa and HT1080 cells, there was virtually no reduction in cell mass CV in late G1; the major decrease only took place in S phase (Fig 2D and 2F). These quantitative differences in cell mass CV profiles may depend on the status of the G1/S circuitry in these cell lines (S1 Table). These observations are completely at odds with the G1/S PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 6 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Fig 2. Cell mass variation is regulated throughout the cell cycle. (A, B) Cell mass CV change with cell cycle progression measured in live RPE-1 (n = 89) (A) and HeLa cells (n = 223) (B). The red solid lines denote the cell mass CV of the population; the pink shadows show the 95% confidence interval; the dashed line indicates the G1/S transition. (C–H) The profiles of how cell mass CV changes with cell cycle progression at cell mass homeostasis measured in fixed RPE-1 (C), HeLa (D), U2OS (E), and HT1080 (F) cells, as well as RPE-1 cells that had reached the new cell mass homeostasis with 50 nM palbociclib (G) or 100 nM rapamycin (H). The cell cycle stages were identified by DNA content and log(mAG-hGeminin) as illustrated in S4B–S4F, S4H, and S4J Fig; the late G1 and S phases are indicated by areas shaded in purple and orange, respectively; error bars are the standard error of CV, (CV= where n is the cell number at the corresponding cell cycle stage (n > 135 for all conditions). The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. CV, coefficient of variation. ffiffiffiffiffi 2n p ), https://doi.org/10.1371/journal.pbio.3002453.g002 transition playing the dominant role in cell size control, although it may remain a critical point for cell cycle regulation [1,19,34]. Note that the decrease in cell mass CV cannot be explained by a reduction in noise because even if noise went to zero at some point, the CV would remain at its previous value. We believe that a very strong conclusion can be drawn from these phenomenological measurements: there must be feedback between cell mass and cell growth rate or between cell mass and cell cycle outside of the G1 phase. The effect of this feedback would be to effectively reduce existing variation in the population in nonG1 phases of the cell cycle. Since palbociclib and rapamycin had little or no effect on the birth and division mass CVs (Fig 1I and 1J), we wondered whether they affected the timing of mass CV regulation during the cell cycle. Consequently, we carefully measured the cell mass CV profiles in fixed RPE-1 cells that had reached new cell mass homeostasis with either drug. Both drugs altered the PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 7 / 34 0.20.40.60.8Cell cycle progression0.10.120.140.16Cell mass CVHeLa0.20.40.60.8Cell cycle progression0.150.20.250.3Cell mass CVRPE-1G1/SCell cycle progression0.10.20.30.4Cell mass CVRPE-1G1/SCell cycle progression0.150.20.25Cell mass CVHeLaG1/SCell cycle progression0.10.20.30.4Cell mass CVU2OSG1/SCell cycle progression0.10.20.30.4Cell mass CVHT1080Late G1SABCDEFGHG1/SCell cycle progression0.20.30.4Cell mass CVRPE-1 50 nM PalbG1/SCell cycle progression0.10.20.30.4Cell mass CVRPE-1 100 nM RapaPLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle duration of the cell cycle phases and particularly extended the G1 phase (S2 Table). As we had done above with untreated cells, we computed the cell cycle mean path of treated cells and examined their cell mass CV as a function of cell cycle progression (S4G–S4J Fig). Strikingly, we found that disrupting the G1/S transition with palbociclib led to a slight increase in cell mass CV in late G1, followed by a much greater reduction in cell mass CV during the S phase (Fig 2G). Conversely, inhibiting cell growth with rapamycin caused a greater reduction of cell mass CV in late G1, and the reduction in S phase became smaller (Fig 2H). These results sug- gest that the regulation of mass CV during S phase can compensate for the mass CV reduction in late G1. Thus, when there is an insufficient or excessive reduction in mass CV in late G1 due to the inhibition of the G1/S transition or growth, respectively, there is a corresponding change in the mass CV in S phase, which acts to maintain the mass CV reduction at division at the same level. Feedback by cell mass not only acts on the duration of G1, but also on the durations of S and G2 phases To investigate further cell mass regulation outside of the G1 phase, we needed to better opti- mize the resolution of the cell cycle markers we had employed. We therefore adopted 2 cell cycle markers for live cells that bracketed S phase: mAG-hGeminin [37] and mTurquoi- se2-SLBP [39]. The APCCdh1 substrate, geminin, starts to accumulate in the nucleus at S phase entry [40], whereas the histone mRNA stem-loop binding protein, SLBP, is rapidly degraded at the end of the S phase [41] (S5A Fig). Unlike the conventional PCNA or DNA ligase I mark- ers, which label replication foci during the S phase [42,43], geminin and SLBP are diffusive in the nucleus and more suitable for the relatively low spatial resolution of the QPM camera. With these 2 markers, we could accurately quantify the durations of G1, S, and G2-M phases. Since the duration of M phase is remarkably constant [15], we attributed most of the variation in G2-M duration to the G2 phase itself. We verified that the timing of S phase, as identified by geminin and SLBP, was consistent with the timing of S phase identified by the DNA ligase I foci (S5B and S5C Fig). None of the markers affected the length of any of the cell cycle phases nor did they affect the mass-dependent regulation of the duration of the cell cycle phases (S3 Table). Moreover, the identification of the cell cycle phases (G1, S, and G2-M) using geminin and SLBP exhibited a similar variability in their lengths as those shown using PCNA as a marker of S-phase by Araujo and colleagues [15] (S2 Table). Therefore, we could be confident that the geminin and SLBP markers faithfully reported the cell cycle phase durations and did not change the physiology of these processes. Using this approach, we confirmed the well-established existence of cell size-dependent reg- ulation of G1 length with ceQPM. Consistent with previous findings [3,4,6–8,12], we found that the G1 length was negatively correlated with birth mass in both non-transformed and transformed cell lines, RPE-1 (Fig 3A) and HeLa (Fig 3E), respectively. The correlation was stronger in RPE-1 than in HeLa cells (Fig 3A and 3E and S4 Table). We also investigated the mass-dependent regulation of the durations of both S and G2 phases. S and G2-M phase lengths negatively correlated with the initial mass of the corresponding periods in both RPE-1 and HeLa cells (Fig 3B, 3C, 3F, and 3G). For RPE-1 cells, the correlations of cell cycle phase length with initial mass in S and G2 were weaker than that in G1, yet they were significant (Fig 3A–3C and S4 Table), demonstrating that regulation of cell mass variation can occur through regulating the durations of S and G2 phases in non-transformed cells with an intact cell cycle network, including an intact G1/S transition. This contrasts to the conventional models that would have predicted G1 length to vary inversely with mass while leaving other phases unaf- fected. We also found in HeLa cells that the negative correlation between cell cycle phase PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 8 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Fig 3. The negative regulation of the durations of the G1, S, and G2 phases by cell mass. (A–D) The correlations between the lengths of the G1 (A), S (B), G2-M phases (C), and the full cell cycle (D) and the initial mass of the corresponding period in RPE-1 cells. The bottom panels indicate the correlation; the top panels are the distributions of the initial mass. Each gray dot in the bottom panels is an observation; R is the correlation coefficient of the gray dots; black squares indicate the average of each cell mass bin; error bars are the SEM; solid black line is the best fit of the black squares (S4 Table). The red shaded area in the top panel indicates the cell mass range that is affected by the minimal cell cycle phase length limit, with the text indicating the percentage of affected cells in the distribution. (E–H) The correlations between the length of the G1 (E), S (F), G2-M phases (G), and the full cell cycle (H) and the initial mass of the corresponding period in HeLa cells. (I, J) The correlations between birth and division masses in RPE-1 (I) and HeLa (J) cells. Each gray dot is an observation; black squares are the average of each cell mass bin; error bars are SEM. The solid black line is the best linear fit of the gray dots; the text indicates the function of the best fit; the red line is the prediction of the best fit in (D) or (H), respectively, assuming that cells grow exponentially (Materials and methods). The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3002453.g003 length and mass was much stronger in the S phase, with a correlation coefficient of −0.29, compared to that in the G1 phase, which had a correlation coefficient of −0.20 (Fig 3E and 3F and S4 Table). It is worth noting that although RPE-1 has more stringent G1/S control than HeLa, the overall dependency of cell cycle length on cell mass was not stronger (Fig 3D and 3H and S4 Table). These studies challenge the G1/S checkpoint model, as mass-dependent cell cycle regulation is not restricted to the change in the length of G1 phase as predicted [2,44,45], but rather it is accompanied by changes in the lengths of the other phases of the cell cycle. Upon closer examination of the binned correlations, we observed a fixed minimum limit for the length of nearly every phase of the cell cycle, as well as the length of the entire cell cycle in RPE-1 and HeLa cells (Fig 3A, 3B, and 3D–3H). These limits are not further reduced in large cells. To summarize these findings, we employ 2 graphical representations for these cor- relations: a linear model and a bilinear model, comprised of 2 line segments. With these, we fit the binned correlations of mass and cell cycle phase lengths. We found that a bilinear model PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 9 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle provided a better fit for all phases of RPE-1 and HeLa cells, with the exception of the G2-M phase in RPE-1 cells (Fig 3A–3H and S4 Table). This graphical relationship implies that regula- tion of the durations of cell cycle phases cannot effectively control the mass of large cells. To illustrate the impact of the minimal cell cycle length on cell mass variation, we conducted sim- ulations to observe the mean and CV of cell mass within a cell population across generations, while varying the fraction of cells affected by the minimal length limit (Section 2 in S1 Text). The simulations show that as the minimal cell cycle length applies to more and more cells, the homeostatic birth mass CV increases. The system eventually loses homeostasis when the mini- mal cell cycle length is imposed on more than 40% of the cell population (S6 Fig). In these experiments, we found that the slope of a graph of birth masses versus division masses was close to 1 in both RPE-1 (Fig 3I) and HeLa cells (Fig 3J), consistent with the adder- like behavior seen previously [7]. The adder model is interpreted as a behavior where cells add a constant amount of mass during the cell cycle regardless of their birth mass. Furthermore, we found in our measurements that each cell cycle phase exhibited an adder-like behavior (S7 Fig), making the full cell cycle a sequential adder. Such behaviors challenge the interpretation that, in mammalian cells, mass regulation arises from a combination of a G1 sizer and a nonG1 timer [19]. Rather, the present findings strongly suggest that each cell cycle phase, except for M phase, contributes to cell mass homeostasis. Moreover, the fitted function of birth mass and cell cycle length correlation cannot fully explain the adder behavior. This is par- ticularly the case for large cells, under the assumption of exponential growth (Fig 3I and 3J). This discrepancy is at least partially due to the existence of a minimal cell cycle phase length. These new results underscore the need for a process of non-exponential growth (or what we term “growth rate modulation”) to maintain cell mass homeostasis in the mammalian cells we have studied, rather than relying solely on processes of cell cycle regulation. Mass-dependent growth rate modulation reduces the CV of cell mass during cell cycle progression The simplest mathematical model for cell growth kinetics, which requires no size sensing or feedback mechanisms, is an exponential model where the growth rate is proportional to size. This has been particularly successful in describing growth in bacteria and can be rationalized by a process of ribosome-dependent ribosome biosynthesis [26,46]. This simple exponential model, however, causes variation in cell size to amplify as cells progress through the cell cycle (Fig 1B and Section 3.1 in S1 Text). Contradicting this model, several studies have found that although large cells generally grow faster than small cells, growth is not precisely exponential in mammalian cells [7,26,29]. Such a lack of exponential growth might in itself lead to a reduc- tion in cell size variation. Various previous studies suggested the dependency of growth rate on cell size changes with cell size and cell cycle stage [7,8,20,47–50]. Recent studies by us and others have found growth rate oscillations [29,51], where a cell alternates between increases and decreases in growth rate. To explore the dependence of growth rate on cell mass in proliferating cells, we measured the growth rate in a 3-h time window and computed its correlation with cell mass at time zero. We examined how growth rate correlated with cell mass in 18,000 HeLa cells and found that the relation of mass to growth was close to exponential, except for a slight depression for large cells (S8A Fig). Nevertheless, when we segregated the cells into 4 cell cycle phases, we uncov- ered distinct cell cycle dependencies in such correlations, which were originally masked by pooling all cells for analysis (S8B Fig). An even closer look at the data, with cells categorized into 14 equally divided cell cycle stages, revealed positive-to-negative correlation transitions at various points in the cell cycle (S8C Fig). The slope of the linear relation between cell mass and PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 10 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle growth rate for cells in different stages of the cell cycle indicated stronger modulation (greater deviation from the expected slope of exponential growth) in the late G1 and G2-M phases (S8D Fig), consistent with S8B Fig and previous studies [8,38]. However, the proportionality is sub-exponential in most of the cell cycle stages (S8D Fig), suggesting a global process that inherently limits the growth of large cells. When we investigated the mass versus growth correlations in finely divided cell cycle stages, we found subtle features. Yet, such studies require very large numbers of cells and very accurate growth rate measurements. Coarser cell cycle discrimination leads to a loss of this kind of infor- mation on subtle changes in the growth rate, it nevertheless adds greater statistical power to conclusions about overarching aspects of mass-dependent growth regulation. Therefore, there is a practical tradeoff between high cell cycle resolution of the growth analyses and the statistical reliability of the findings. In the following analyses, we aimed for stronger statistical significance and therefore partitioned cells more crudely into the G1 and nonG1 phases, focusing on the most salient features of growth rate modulation. This level of resolution was sufficient to reveal previously undiscovered features, which serve to correct our current understandings. Measuring the correlation between cell mass and growth rate in 5 different cell lines, we found that each cell line behaved somewhat differently. In RPE-1 cells, growth was propor- tional to cell mass, but the proportionality was much less than exponential, with a significant nonzero intercept (Fig 4A). In HeLa cells, the proportionality between growth and cell mass is much closer to, but slightly less than exponential in both G1 and nonG1 phases (Fig 4B). The observed mass versus growth correlations in short-term measurements in RPE-1 and HeLa cells were consistent with their long-term growth trajectories (S9 Fig), showing nearly linear growth in RPE-1 and a slight deviation from exponential growth in HeLa cells. Therefore, we could confirm that the observed deviation from exponential growth is not due to inspection or sampling bias caused by the short-term measurement [52], but truly signifies the inherent growth law of the cells. In U2OS cells, the correlation was close to exponential for all cells in nonG1 and most cells in G1 phase, but it was abruptly negative for the 15% largest cells in G1 phase (Fig 4C). In HT1080 cells, growth was close to exponential for small cells but transi- tioned to nearly linear growth in large cells during both G1 and nonG1 phases (Fig 4D). A bilinear model provided a significantly better fit than a simple linear model for cells in the nonG1 phase, indicating the significance of this transition in mass versus growth correlation as cells became larger (S5 Table). In Saos-2 cells, growth was exponential except for a slight deviation for large cells in nonG1 phase (Fig 4E). Taken together, these results indicate that the mathematical description of growth rate is not simply exponential in the cell lines we have investigated, and that different cell lines display different characteristics of mass dependency at different phases of the cell cycle. To better compare the behaviors of different cell lines, we normalized the mass versus growth correlations, using the means of birth mass and cell cycle length (S6 Table). Since DNA copy number affects the correlation intercepts (Fig 4A–4D), we focused solely on the slope of the correlations. We could distinguish 2 general types of growth rate modulation (Fig 4F and S6 Table). In the first type, growth is linearly related to cell mass, but with a slope lower than exponential growth (RPE-1 and HeLa). We refer to this as sub-exponential (SE) modulation. In the second type, the slope of the mass versus growth correlation is close to exponential for small cells but becomes less positive or even negative for large cells (U2OS G1, HT1080, and SaoS-2 nonG1). We refer to this as bilinear (BI) modulation. For U2OS cells in the nonG1 phase and SaoS-2 cells in the G1 phase, the correlation slope is not significantly different from exponential growth, suggesting minimal regulation. Other studies had proposed that growth rate modulation contributes to cell size homeosta- sis [1,7,8,19,38]. However, most of these claims were speculative and lacked sufficient PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 11 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Fig 4. Growth rate dependence on mass differs in different cell lines, and growth rate modulation can effectively reduce cell mass CV during the cell cycle. (A–E) Correlations between cell mass and growth rate in the G1 (blue) and nonG1 (red) phases for RPE-1 (A), HeLa (B), U2OS (C), HT1080 (D), and Saos-2 (E) cells. Filled squares represent the median growth rate of each bin; error bars show SEM. The black dashed lines indicate the expected behavior for exponential growth. The solid blue and red lines are the best fit of the filled squares (S5 Table). (F) The observed conditions were categorized into 3 types: sub-exponential, bilinear, and no modulation. (G) Contour plot illustrating the change in cell mass CV during the entire cell cycle for SE growth rate modulation, as a function of the mean and CV of α0 and β0, obtained from numerical simulations (Section 3.1 in S1 Text). (H, I) Contour plots illustrating the change in cell mass CV during the G1 (H) and nonG1 phases (I) for BI growth rate modulation, as a function of the means of γ0 and m0 t, obtained from numerical simulations (Section 3.2 in S1 Text). These simulations assumed a 20% CV in α0. Solid circles in (G–I) indicate the corresponding positions in the contour plots when adopting parameter values from the experimental data. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. BI, bilinear; CV, coefficient of variation; SE, sub- exponential; SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3002453.g004 quantitative support. The work by Cadart and colleagues in 2018 stands out as an exception, as it quantitated the correlation between birth size and growth rate [7]. Accurate and quantitative correlations between growth rate and cell size are essential for a thorough assessment of the impact of growth rate regulation. Nevertheless, due to the scarcity of high-quality experimental data, most theoretical investigations into cell size homeostasis have disregarded growth rate regulation completely and focused solely on the regulation of cell cycle length, often assuming exponential growth [53–56]. In this study, we addressed this gap in previous studies by investi- gating theoretically whether the types of growth rate modulation we observed could effectively reduce cell mass variation. Using stochastic models and simulations, we focused on the influ- ence of growth rate modulation and growth rate noise on the cell mass CV over 1 cell cycle. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 12 / 34 200400600Cell mass (pg)05101520Growth rate (pg/hr)Saos-2200400600Cell mass (pg)0102030Growth rate (pg/hr)RPE400600800Cell mass (pg)10152025Growth rate (pg/hr)HeLa 200400600800Cell mass (pg)05101520Growth rate (pg/hr)U2OS4006008001000Cell mass (pg)10203040Growth rate (pg/hr)HT1080ABCDEFIGHG1nonG1ExponentialCV(0.5)2-CV(0)2-0.03-0.025-0.02-0.015-0.01-0.00500HT1080U2OS11.52m'-3-2-10'(cid:2)Type 1Type 1 Condi(cid:2)on Modula(cid:2)on type RPE-1 HeLa Sub-exponen(cid:2)alType 1 U2OS G1 Bilinear U2OS nonG1 None HT1080 G1 Bilinear HT1080 nonG1 Bilinear SaoS2 G1 None SaoS2 nonG1 Bilinear Sub-exponen(cid:2)alCV(1)2-CV(0)2-0.03-0.02-0.01000.010.020.030.04HeLaRPE-100.20.40.6'00.10.20.30.4CV'=CV'CV(1)2-CV(0.5)2-0.006-0.004-0.002000.0020.0040.0060.008HT1080Soas-211.52m'00.10.20.30.4'(cid:2)PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Initially for convenience, we assumed that all cells divided at the same cell cycle length. Subse- quently in more comprehensive models, we incorporated cell cycle regulation and noise, as discussed in a later section. In the absence of any growth rate modulation, we might imagine that cell mass should accu- mulate exponentially, as has been found in bacteria [26]. This would cause the cell mass CV to increase super-exponentially due to stochastic variation in growth rate (Section 3.1 in S1 Text). When growth rate modulation is in the SE form (Fig 4F and S6 Table), the slope of the correlation between cell mass and growth rate is lower than that of exponential growth. This can be described by the equation: dm0 terms: α0m0 represents the part of growth rate proportional to cell mass, whereas β0 represents the part independent of cell mass. Here, m0 and t0 are the cell mass and cell cycle progression time normalized by the means of birth mass and cell cycle length, respectively (Section 3.1 in S1 Text). According to the definition of sub-exponential growth, the mean of α0 is smaller than ln2 and greater than 0, and the mean of β0 is determined by α0 when assuming that the mean division mass is twice the mean birth mass, a requirement for maintaining mass homeostasis. For simplicity, we first assumed that α0 and β0 have the same CV, but we also examined how the CV of either parameter affected the results in the Supporting information (S10 Fig). dt0 ¼ a0m0 þ b0, where the growth rate is composed of 2 During the initial stages of the cell cycle, the cell mass CV consistently decreases, with the rate of decrease negatively correlated with the mean of α0 and independent of the CVs of α0 and β0 (S10A, S10C, and S10F Fig and Section 3.1 in S1 Text). As the cell cycle progresses, the rate of mass CV reduction slows down, and the mass CV may even increase during the later period of the cell cycle (S10B, S10D, and S10G and Section 3.1 in S1 Text). The overall change in the cell mass CV throughout the cell cycle depends on both the mean of α0 and the CVs of α0 and β0. The smaller mean of α0 and lower CV of α0 and β0 result in a more significant reduc- tion in the cell mass CV (Figs 4G, S10E, and S10H). In summary, growth rate variability (char- acterized by the CVs of α0 and β0) amplifies cell mass variation, while strong growth rate modulation (small α0) can reduce cell mass variation throughout the cell cycle. To assess whether growth rate modulation in RPE-1 and HeLa cells can cause cell mass CV reduction throughout the cell cycle, we derived the parameters from the experimental data. The mean of α0 was determined based on the mean correlations in Fig 4A and 4B (S6 Table). To estimate the variation in α0, we used long-term live-cell growth trajectories. The CV of α0 was found to be independent of cell mass (S11A and S11B Fig). The variability in α0 arises from 2 sources: stochastic partitioning of cellular contents during cell division (intercellular variability) and intrinsic fluctuations in biochemical reactions (intracellular variability) [57]. The former, determined at birth, is a major contributor to cell mass variation, while the effect of the latter gradually cancels out over time, exerting minimal impact on cell mass variation. Therefore, we focused on the intercellular variability and estimated it by calculating the varia- tion among the means of individual growth trajectories (S11C Fig). The CV of α0 was esti- mated to be 0.33 for RPE-1 and 0.23 for HeLa cells, respectively. It is challenging to isolate the variation in β0 from measurement error, thus we conducted simulations with β0 having the same CV as α0 or with the CV of β0 being equal to zero. Using these parameters, we found that both RPE-1 and HeLa cells could reduce the cell mass CV after 1 cell cycle (Figs 4G, S10E, and S10H). Since the minimal requirement for cell mass homeostasis is to have a lower cell mass CV at division than that at birth, we concluded that growth rate modulation alone is sufficient to maintain cell mass homeostasis in RPE-1 and HeLa cells. When a plot of growth rate versus mass is in the BI form (Fig 4F and S6 Table), the slope of the mass versus growth correlation is close to exponential for small cells and becomes less posi- tive or even negative in large cells. This can be described by the equation: PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 13 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle t t (cid:0) (cid:0) � � � t (cid:0) g0m0 m0 � m0 t þ g0m0 þ a0m0 (cid:0) dm0 , where the mean of α0 is close to ln2 dt0 ¼ a0m0 m0 < m0 and the mean of γ0 is smaller than ln2 (Section 3.2 in S1 Text). The first term on the right side of the equation represents the exponential portion of the mass versus growth rate correlation, while the second term describes the part where growth rate modulation takes effect. Here, γ0 0 signifies the cell mass at which this modulation indicates the strength of modulation and mτ begins to take effect. Both γ0 and mτ mass, respectively. Our findings indicate that the increase in cell mass CV is primarily driven by the CV of α0 (S12A–S12D Fig). Additionally, we investigated the impact of the means of γ0, 0 on the change in cell mass CV throughout the cell cycle. We found that the smaller the and mτ means of γ0 and mτ cells it affects, the greater the cell mass CV reduction (Figs 4H and 4I and S12). 0, which means the stronger the modulation on growth rate and the more 0 are normalized by the means of cell cycle length and birth To assess whether the growth rate modulation on its own in U2OS, HT1080, and SaoS-2 cells can also lead to a reduction in cell mass CV, we simulated the changes in cell mass CV during the G1 or nonG1 phase using values of γ0 and mτ 0 obtained from the experimental data. When assuming a 20% CV for α0, growth rate modulation was found to decrease the cell mass CV in the G1 phase for U2OS and HT1080 cells (Fig 4H), as well as in the nonG1 phase for HT1080 cells (Fig 4I). However, it was not sufficient to reduce the cell mass CV in the nonG1 phase for SaoS-2 cells (Fig 4I). As the CV of α0 increases, the reduction in cell mass CV becomes less pronounced (S12E–S12H Fig). Eventually, all 3 cell lines fail to reduce cell mass CV at a 40% CV for α0 (S12G and S12H Fig). Notably, despite U2OS G1 cells exhibiting a greatly negative γ0 value, which indicated an exceptionally strong growth rate modulation, its effectiveness in reducing cell mass CV was lower than that of HT1080 G1 cells due to a smaller 0. proportion of affected cells in U2OS, represented by a larger mτ In summary, we found diverse patterns of correlation between cell mass and growth rate in different cell lines, and within the same cell line measured at different cell cycle stages. We developed stochastic models to explore the impact of different mass versus growth correlations on the change in cell mass CV throughout the cell cycle. These models are representations of the data itself and not contrived schemes. They suggest strongly that in many cases sub-expo- nential growth, either for all cells or even for a subset of cells, can be an effective means of reducing cell mass CV and can ensure cell mass homeostasis. Regulation of the cell cycle and regulation of growth rate compensate for each other to maintain cell mass homeostasis Both mass-dependent regulation of the progression through the cell cycle and mass-dependent regulation of growth rate are used by cells to reduce cell mass variation. To evaluate the relative importance of these processes in maintaining cell mass homeostasis, we have tried to perturb each mechanism individually in RPE-1 cells. To disrupt mass-dependent regulation of G1 length, we slowed entry into S phase using pal- bociclib, an inhibitor that specifically blocks the activation of Cdk4,6, which is required for entry into S phase [33]. As discussed, low concentrations of palbociclib increased the mean cell mass and, as expected, prolonged the cell cycle length by elongating the G1 phase (Fig 1H and S2 Table). However, once treated cells reached a new homeostatic state, the CV of birth mass remained unchanged compared to untreated cells (Fig 1I). When we analyzed the duration of each cell cycle phase as a function of cell mass, we found a reduced impact of cell mass on G1 phase length coupled with an enhanced impact on S phase length, characterized by the slopes and the correlation coefficients of the correlations between cell mass and the durations of these phases (Fig 5A, 5B, and 5K). Additionally, the mass-dependent regulation of G2 phase was diminished, yet still statistically significant (p = 0.0057) (Fig 5C and 5K). These opposite PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 14 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle changes in G1 and S phase regulation suggest that the mass-dependent regulation of S phase had effectively compensated for a weakened impact of cell mass on G1 length regulation. Hence, in specific circumstances such as palbociclib treatment, S phase can become the pri- mary period responsible for reducing cell mass variation (Fig 2G). Nevertheless, such compen- sation ultimately proves insufficient, resulting in a diminished cell mass-dependent regulation of the entire cell cycle length (Fig 5D and 5K). To maintain the birth mass CV at the same level as untreated cells, additional regulation of growth rate is required to further reduce cell mass variation during the cell cycle. Indeed, we found that the correlations between cell mass and growth rate in palbociclib-treated cells were even closer to linear growth compared to untreated cells (Fig 5E), implying a stronger growth rate modulation and a greater reduction in cell mass variation through growth rate regulation. The unchanged CV of birth mass when cells are treated with the G1/S inhibitor, palbociclib (Fig 1I), is a collective result of the inter- play between mass-dependent cell cycle regulation and mass-dependent growth rate regula- tion. Thus, the cell mass CV is maintained despite a significant increase in the mean birth mass (Fig 1H). In a converse experiment, we specifically perturbed cell growth rate. We treated cells with rapamycin to inhibit mTOR activity. Treatment with rapamycin resulted in an elongation of the cell cycle (S2 Table) and a decrease in mean cell mass (Fig 1H). Similar to the results with palbociclib treatment, rapamycin treatment left the birth mass CV unchanged (Fig 1I). Cell mass-dependent feedback on G1 length was enhanced in the presence of rapamycin (Fig 5F and 5K), while feedback on the S and G2-M phases were weakened (Fig 5G, 5H, and 5K). Additionally, the minimal lengths of all cell cycle phases were slightly increased compared to untreated cells (Fig 5F–5H). In the presence of rapamycin, the cell mass fed back more strongly on the entire cell cycle length, as indicated by the more negative slope and correlation coeffi- cient of the mass versus cell cycle length correlation (Fig 5I and 5K). Furthermore, the relative strengths of correlations between cell mass and cell cycle phase lengths aligned with the reduced cell mass CV in the corresponding phases: for example, cell mass CV was primarily reduced in the G1 phase with rapamycin treatment (Fig 2H), consistent with the strengthened cell cycle regulation in the G1 phase. On the other hand, we found that the slopes of the mass versus growth correlations in both the G1 and nonG1 phases closely resembled that of expo- nential growth (Fig 5J), suggesting a weaker role of growth rate regulation in maintaining cell mass homeostasis when growth rate is inhibited by rapamycin. From the experiments described above, mass-dependent cell cycle regulation and mass- dependent growth rate modulation must interact with each other to maintain the birth mass CV at a consistent level even when the G1/S transition or cell growth rate is perturbed, result- ing in significant changes in the mean birth mass. After studying the feedback of cell mass on cell cycle length and growth rate under many different circumstances, we felt a need for a new way to compare the response of each under different conditions. We have found it convenient to define a new parameter to represent the strength of this linkage. We utilized the normalized slope of birth mass versus cell cycle length correlation as the parameter λ0, which quantifies the strength of mass-dependent cell cycle regulation. The value of λ0 is always negative. A more negative value of λ0 indicates stronger regulation. Additionally, since the slopes of the cell mass versus growth rate correlations in the G1 and nonG1 phases were similar in RPE-1 and HeLa cells, we found it useful to calculate the average slope of these phases and normalized it by the mean doubling time to represent the strength of mass-dependent growth rate regulation, which we denoted as α0. The value of α0 is smaller than or equal to ln2, which represents expo- nential growth. A smaller value of α0 indicates a greater deviation from exponential growth and thus a stronger modulation of growth rate. We found an inverse correlation between λ0 and α0 across all the conditions we have investigated (Fig 5L and S7 Table), suggesting a PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 15 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Fig 5. The compensatory roles of mass-dependent cell cycle regulation and mass-dependent growth rate regulation in maintaining cell mass homeostasis. (A–D) The correlations between the lengths of the G1 (A), S (B), G2-M phases(C), and the full cell cycle (D) and the initial mass of the corresponding period in RPE-1 cells treated with 50 nM palbociclib. Each gray dot is an observation; black squares indicate the average of each cell mass bin; error bars are SEM; solid black line is the best fit of the black squares; solid red lines are the corresponding correlations in untreated RPE-1 cells. (E) Correlations between cell mass and growth rate in the G1 (blue) and nonG1 (red) phases for RPE-1 cells treated with 50 nM palbociclib. Filled squares represent the median growth rate of each bin; error bars show SEM. The black dashed lines indicate the expected behavior for exponential growth. The solid blue and red lines are the best fit of the filled squares. (F–I) The correlation between the lengths of the G1 (F), S (G), G2-M phases (H), and the full cell cycle (I) and the initial mass of the corresponding period in RPE-1 cells treated with 100 nM rapamycin. (J) Correlations between cell mass and growth rate in the G1 (blue) and nonG1 (red) phases for RPE-1 cells treated with 100 nM rapamycin. (K) Kendall rank correlations between the duration of indicated cell cycle phase and cell mass at the initiation of the respective phase, in untreated RPE-1 cells, RPE-1 treated with 50 nM palbociclib, and RPE-1 treated with 100 nM rapamycin. (L) The correlation between the normalized slope of birth mass vs. cell cycle length correlation, λ0, and the normalized slope of cell mass vs. growth rate correlation, α0, depicted for untreated HeLa and RPE-1 cells, as well as RPE-1 cells treated with palbociclib or rapamycin. The values of λ0 and α0 used in this plot are listed in S7 Table. (M) The contribution of each control mechanism shown as the reduction in the simulated division mass CV when the respective control mechanism is included compared to that without any control mechanisms. Simulation parameters were obtained from experimental data measured in untreated HeLa and RPE-1 cells, as well as RPE-1 cells treated with palbociclib or rapamycin. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. CV, coefficient of variation; SEM, standard error of the mean. https://doi.org/10.1371/journal.pbio.3002453.g005 compensatory effect between the regulation of cell cycle and growth rate (i.e., the strengths of these regulatory processes tend to change in opposite directions). For example, when cell cycle regulation was inhibited (e.g., by palbociclib), the modulation of growth rate became stronger, and conversely, when growth rate regulation was inhibited (e.g., by rapamycin), the modula- tion of cell cycle length became stronger. These findings highlight the compensatory roles played by these 2 processes in maintaining cell mass homeostasis. To illustrate further the compensatory roles of regulation on cell cycle and growth rate, we developed a stochastic model to simulate changes in cell mass variation throughout the cell cycle (Section 4 in S1 Text). In this model, we considered 3 factors that could contribute to the increase of cell mass variation: variability in cell cycle length, variability in growth rate, and noise in cell mass partition during mitotic division. For simplicity, we only considered PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 16 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle intercellular noise as the source of growth rate variability, which is due to stochasticity in the partitioning of cellular contents during cell division, as previously discussed (S11C Fig and Section 3.1 in S1 Text). As control mechanisms, we considered mass-dependent regulation of the duration of G1 and nonG1 phases separately, and we also considered mass-dependent growth modulation throughout the entire cell cycle. We chose all the parameters in this model from our actual experimental data and evaluated the impact of each control mechanism by comparing the cell mass CV at division with and without these control mechanisms. Notably, we observed some discrepancies between the simulated division mass CV, incorporating all 3 control mechanisms, and the values measured in experiments (S8 Table). These may arise from the simplification of variability in growth rate (S11C Fig and Section 4 in S1 Text), which effectively influences cell mass variation (S13 Fig) but is quite challenging to estimate accu- rately from experimental data. Nevertheless, these simulations largely reflect the relative signif- icance of each control mechanism in maintaining cell mass homeostasis. The model results indicate that in RPE-1 cells, the regulation of G1 length plays a slightly greater role compared to nonG1 length regulation, but both are overshadowed by the modula- tion of growth rate (Fig 5M). When the G1/S control is inhibited by palbociclib, the contribu- tion of G1 length regulation slightly decreases, the contribution of nonG1 regulation slightly increases, and the role of growth rate modulation becomes even more dominant (Fig 5M). On the other hand, inhibiting growth with rapamycin leads to an increase in the dominance of G1 length regulation, with its contribution now comparable to that of growth rate modulation, while the impact of nonG1 regulation becomes smaller (Fig 5M). In HeLa cells, the cell mass variation is considerably smaller than that in RPE-1 cells (S8 Table) when not including any control mechanisms, due to the lower variation in growth rate in HeLa cells. It is worth noting that HeLa cells possess a mutated G1/S network. Its ranking of contributions from the 3 mech- anisms is similar to the scenario observed in RPE-1 cells treated with palbociclib, which dis- rupts the G1/S transition. Specifically, in HeLa cells, the contribution of growth rate modulation outweighs that of nonG1 length regulation, which, in turn, outweighs that of G1 length regulation (Fig 5M). These findings collectively reveal compensatory roles of cell cycle and growth rate regula- tion in reducing cell mass variation, particularly distinguishing the regulation of G1 length and the regulation of growth rate. Generally, growth rate modulation, rather than cell cycle regulation, is the more dominant mechanism. When one feedback process is hindered, other mechanisms become relatively stronger to maintain cell mass variation at a similar level. Growth rate modulation, rather than cell cycle regulation, consistently plays the predominant role in reducing cell mass CV, regardless of whether or not the cells possess an intact G1/S cir- cuit. In the most extreme case, we studied when the growth rate is inhibited by rapamycin, the contribution of growth rate modulation is on par with that of G1 length regulation. These observations contradict the conventional size control models [1,14,19,44,58–62], which predict that G1/S control is the primary contributor to size homeostasis in mammalian cells. Other explanations for how a population of cells might reduce its cell mass variation We evaluated additional processes that could potentially contribute to the reduction in cell mass CV but were not accounted for in our stochastic model. In principle, any process that affects the likelihood of cell division or cell viability differentially in large and small cells could influence the distribution of cell mass within a population. To estimate the importance of such effects, we examined the rate of cell death and cell cycle arrest through long-term measure- ments of cell growth and proliferation. During the 48 to 72-h duration of our cell PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 17 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle measurements, we defined cell cycle arrest events as instances where a cell remained in the same cell cycle phase while its mass continued to increase throughout the experiment. Further- more, cell death was identified by a sudden and drastic decrease in cell dry mass, suggesting cell membrane permeabilization. We found events of cell cycle arrest or cell death in the culture affected no more than 2% of cells in all the conditions that were studied (S9 Table). In particular, neither cell cycle arrest nor cell death occurred frequently enough to contribute significantly to cell mass homeostasis in any of the experiments that we have described. It is worth noting that the remarkably low frequency of cell cycle arrest in cells treated with rapamycin and palbociclib at the drug con- centrations used in this study suggests that these drugs at low concentrations do not induce quiescence or senescence at the population level (S9 Table). Furthermore, the concentrations of these drugs did not appear to be toxic enough to cause significant cell death (S9 Table). One intriguing observation was that some large RPE-1 cells treated with palbociclib experienced a partial loss of cytoplasm during mitosis (S9 Table and S1 Movie). This cytoplasmic loss could be attributed to incomplete cortical contraction during mitotic rounding [63]. The amount of mass loss appeared to be random. Notably, these rare events, accounting for approximately 0.5% of cells, did not have a significant impact at the population level on cell mass homeostasis in the presence of palbociclib. It is worth noting that although these mechanisms were of negligible importance in the spe- cific experimental setting of our study, they might still play a significant role in a tissue setting, for example, during wound healing, regeneration, aging, and/or disease. A picture of cell mass homeostasis in proliferating cells Homeostasis refers to the maintenance of a balance between inherent noise in cellular pro- cesses and feedback control mechanisms that correct for them. In proliferating cells, this noise arises from stochastic variation in growth rate, cell cycle length, and cell mass partitioning dur- ing mitosis. To reduce cell mass variation, mass-dependent regulation can occur through the control of cell cycle progression, growth rate, or both. To illustrate mass regulation graphically as a balance between noise and control mecha- nisms, we have depicted the concept of cell mass homeostasis as a “teeter-totter” (Fig 6). Sto- chastic noise and feedback control mechanisms are represented as opposing forces on either side of the lever’s fulcrum; the sizes of the icons represent the importance of the processes, as determined from the stochastic models (S8 Table). When these effects are balanced, the system reaches a steady state. In cell lines like RPE-1, where the G1/S circuit is intact, the relative importance of the control mechanisms can be ranked from greatest (heaviest on the teeter-tot- ter) to smallest (lightest on the teeter-totter) as follows: growth rate modulation, G1 length reg- ulation, and nonG1 length regulation. A perturbation of the system leads to changes in the stochastic nature of the processes and affects the operation of specific control mechanisms. When this happens, other control mechanisms compensate for these changes and restore the balance. For example, when G1/S control is inhibited, either through pharmacological inhibi- tors, such as palbociclib, or genetic mutations in the G1/S circuitry, as seen in HeLa cells, the contribution of G1 length regulation is reduced. In response, nonG1 length regulation and growth rate modulation become more significant. Conversely, when growth rate modulation is inhibited, such as by rapamycin, G1 length regulation becomes more important, and growth rate modulation contributes less. Overall, the teeter-totter of cell mass homeostasis is robustly balanced through the compen- satory interactions of these different control processes within the cell. It is likely that the coor- dination and adjustment of these compensatory mechanisms at the molecular level are crucial PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 18 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Fig 6. The teeter-totter model of cell mass homeostasis. Cell mass homeostasis requires a balance between stochastic noise and control mechanisms. In unperturbed cells with an intact G1/S circuitry, the weights of control mechanisms from the heaviest to the lightest are the growth rate modulation, G1 length regulation, and nonG1 length regulation. When G1/S control is perturbed, the impact of the G1 length regulation becomes smaller, and the nonG1 length regulation and growth rate modulation become larger to compensate. When the growth rate modulation is suppressed, the G1 length regulation plays a more prominent role in compensating for the reduced impact of growth rate modulation. https://doi.org/10.1371/journal.pbio.3002453.g006 for cellular survival under changing conditions. While our understanding of how these mecha- nisms achieve balance has advanced, further study is needed to elucidate how they coordinate and adapt their compensation at the molecular level to maintain balance under changed condi- tions and how this plays out in health, disease, aging, etc. Discussion To summarize: in examining cell mass homeostasis, we found that stochastic variation in cell mass in proliferating cells is tightly controlled throughout the cell cycle (Fig 2) via mass-depen- dent regulation of cell growth rate (Fig 4) and mass-dependent regulation of cell cycle progres- sion (Fig 3). Generally speaking, among the cell lines and cell cycle and cell growth inhibitors that we have employed (including those previously studied and analyzed), we conclude that the G1/S transition does not appear to be a privileged place where cell mass regulation is imposed. Rather mass regulation occurs throughout the cell cycle phases. The compensation that keeps stochastic variation of mass in check emerges from an interplay of these mecha- nisms and results in effective cell mass regulation. Not only is homeostasis maintained, but it is also maintained at high stringency, as indicated by the narrow distribution of cell mass at birth (Fig 1). Furthermore, cell mass homeostasis is robust to changes in genetic background and is resistant to manipulations of the G1/S transition or perturbation of mTOR activity (Fig 1). The birth size CVs measured in many proliferating bacterial, yeast, mammalian, and plant cells fall in a relatively small range (from 11% to 25%) (S10 Table), which is comparable to the birth weight CV of a human fetus [64]. Although it is not clear whether such strict control is explicitly selected for during evolution or merely a by-product of some other selection [65,66], cell size homeostasis appears to be highly regulated and presumably important. Though we PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 19 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle focused on cultured human cell lines in this study, the mechanisms underlying cell size homeostasis, just as the mechanisms underlying the cell cycle itself, are likely to be conserved. In this study, we utilized ceQPM [29] as a means of measuring cell dry mass, providing a complementary approach to previous studies that focused on cell volume as an indicator of cell size [3,7]. We found that many aspects of the behavior of cell mass, as directly measured by ceQPM, were consistent with studies of cell volume, particularly those reported by Cadart and colleagues, who obtained high-quality cell volume data [7]. For example, in line with their observations, we also identified inverse correlations between initial mass and cell cycle phase duration in both the G1 and nonG1 phases in HeLa cells (Fig 3), the existence of a minimal duration of the G1 phase (Fig 3), the “adder”-like correlation between the birth and division masses (Fig 3), and the coordination between mass-dependent cell cycle regulation and growth rate modulation in maintaining cell mass homeostasis (Fig 5). This consistency is further sup- ported by our recent findings that cell volume usually changes proportionally with cell mass in cultured proliferating cells, except during mitosis, resulting in a narrow distribution of cell mass density [67]. However, we were able to observe more detailed discrepancies in the regula- tion of mass and volume growth. For example, while Cadart and colleagues reported that vol- ume growth rate is dependent on cell volume at birth [7], we found that mass growth rate is related to cell mass at any point of the cell cycle, and this relationship varies across different cell cycle stages (Figs 4 and S8). Moreover, the noise in mass growth rate appears to affect the slope of the correlation (S11 Fig), in contrast to Cadart and colleagues’ findings of noise pri- marily impacting the intercept of volume growth rate [68]. These discrepancies may be attrib- uted to inherent differences in the factors affecting mass or volume and the speed and mechanisms by which cells respond to perturbations or fluctuations in mass or volume [69]. Aside from confirming previous discoveries, our findings took a significant step forward in exploring mechanisms underlying cell mass homeostasis. Extensive data collection on large populations of cells was possible thanks to the high-throughput of ceQPM [29]. From these extensive measurements, we derived reliable correlations between cell mass, the durations of cell cycle phases, and the growth rate. We studied these across multiple cell lines and under various pharmacologic perturbations. We were able to fit such data to simple functions (Figs 3, 4, and 5), which facilitated our ability to derive quantitative models. These models, in turn, facilitated our interpretation of the underlying cellular responses. For example, we showed how G1, S, and G2 phases are each under negative regulation by cell mass in both transformed and non-transformed cells (Fig 3). A particularly noteworthy discovery was the identification of a minimum length for each phase of the cell cycle in large cells, which explains the limited impact of cell cycle regulation on very large cells, leaving the underlying process to growth rate modulation (Fig 3). We further demonstrated that growth rate is modulated differently in dif- ferent cell types or cell lines (Fig 4). Such comprehensive characterization of growth regulation was not previously possible without the extensive and precise measurements of cell mass and growth rate by ceQPM [29]. When we perturbed cells by inhibiting the G1/S transition or sup- pressing the growth rate (Fig 5), ceQPM enabled us to go beyond the qualitative phenomena observed in previous studies [8,11,12]. It allowed us not only to determine the average changes in cell mass, cell cycle phase duration, and growth rate but also to measure these qualities at the single-cell level, tracking the individual cells over time. This enabled us to derive important quantitative correlation functions. These functions in turn allowed us to write deterministic equations, incorporate stochastic noise, and ultimately develop a stochastic model. With this model, we could estimate the relative weight of each of the regulatory mechanisms employed in maintaining cell mass homeostasis and finally deduce how the weights of these separate mechanisms depend on each other (Fig 5). PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 20 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle One simple finding stands out. It has been generally assumed, and widely cited in review articles and textbooks of biology, that G1 length regulation is the predominant or even the sole mechanism controlling cell size during the cell cycle [1,14,19,44,58–62]. There was always an appeal of this simple mechanism, as it made perturbation of the cell cycle at G1/S the whole process for cell size control. We now can say that this is clearly not the case. Our current highly quantitative studies involving at least hundreds of cells per condition demonstrated that, at least for the cell lines we employed, the impact of G1 length regulation on constraining cell mass CV within a proliferating cell population is much less significant than the modulation of cells’ mass accumulation (growth) rate (Fig 5). This holds true for non-transformed cells with intact G1/S control. Furthermore, even in the presence of growth inhibition induced by rapa- mycin, the contribution of growth rate modulation to cell mass CV reduction is no less than that of G1 length regulation. Why would there be size-dependent growth rate regulation if regulation of cell cycle pro- gression were sufficient to control cell size? With so many essential genes in the genome, it seems like a weak argument to claim that having 2 separate mechanisms provides increased security for survival. We propose instead that they serve 2 separate functions. Control of the G1 length might be used primarily to set the cell size for a given cell type. In this view, the G1/ S transition is hard-wired into developmental pathways like the MAP kinase pathway or the BMP pathway through proteins like TGFβ. By contrast, control of cell growth might be pri- marily used for a different purpose: maintaining cell size homeostasis of any given cell type against environmental or stochastic variation. It makes more sense that the targeted mean size of a given cell type is controlled by a few key molecular players downstream of specific hor- monal or nutrient signals or cellular differentiation. Those molecular players (such as CDK4/6 or other CDK inhibitors) were described as a cell size “dial” in a previous model by Tan and colleagues [11]. However, once cells are programmed to adopt a defined size in their new state, they would still require a mechanism to maintain size homeostasis around that new mean by buffering against environmental or internal stochastic fluctuation. Consistent with the work presented here (Fig 1) and studies in budding and fission yeasts [13,17], deletion or overex- pression of the G1/S inhibitors change the mean size dramatically but have only limited effects on the variation of cell size. Furthermore, systems that only act at a single gate for size variation would fail to provide continuous feedback on size variation and would have difficulty correct- ing noise introduced after that gate operates, which in this case is early in the cell cycle [70]. By contrast, growth rate regulation, particularly sub-exponential growth, where growth rate is proportional to cell mass but exhibits a slope smaller than that of exponential growth, proves to be a highly effective means for reducing cell mass variation throughout the cell cycle (Fig 4 and Section 3 in S1 Text). The effectiveness of this mechanism is bolstered by its operation throughout the entire cell cycle and in the whole cell size range. This form of regulation would be more effective than growth rate modulation restricted to short periods of the cell cycle and only in large cells, as suggested by previous studies [1,8,20,38,49]. Unraveling the determinant factors that underlie the sub-exponential scaling between growth rate and cell mass will likely shed light on the coordination between size-dependent biomass synthesis, nutrient transporta- tion, and macromolecule destruction [71]. We can imagine that pathological conditions, such as aging related diseases, may target growth rate regulation and therefore affect cells at differ- ent stages of cell cycle or even non-growing cells. Aside from the mass-dependent regulation on the G1 length and cell growth rate, the regu- lation of nonG1 phase lengths also contributes significantly to the reduction of cell mass varia- tion (Fig 5). This is presumably due to the fact that cell cycle phases outside of G1 have non- negligible negative correlations with cell mass (Fig 3) and often occupy a larger portion of the cell cycle than the G1 phase (S2 Table). The mechanisms regulating G2 length have been PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 21 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle mainly studied in fission yeast, where the G2/M transition acts as the major size control check- point [17,72–74]. Mammalian cells share homologous components of this G2/M regulation with fission yeast [75,76], suggesting that similar mechanisms might function during this stage in mammalian cells. However, further investigation beyond citing simple homology will be needed to confirm this possibility. The regulation of S phase length as a means for controlling cell size in mammalian cells has been rarely explored. One potential mechanism of size-depen- dent S phase length regulation could involve the control of the number of replication com- plexes. If the number of forks were proportional to the total cell size, so that small cells made fewer forks, this could serve to lengthen S phase [77]. If cell size were to affect the number of active origins or DNA replication speed, it might also affect the level of DNA damage due to the under-replicated regions [77–79]. Replication stress is not uncommon in normal cycling populations, as evidenced by the presence of DNA lesions in more than 20% of G1 cells in non-transformed cell lines [80]. If the occurrence of replication stress were influenced by cell size and if it led to forms of DNA damage that could be resolved, it could potentially drive tumorigenesis or senescence in a cell size-dependent manner, resulting in heterogeneous behavior in a genetically uniform population. This scenario might hold clinical significance and thus deserves further investigation. Additional research is needed to establish the relation- ship between the probability of replication stress and cell size during S phase. Furthermore, the actual mechanism of S phase length regulation could be more complicated than the size- dependent replication fork number. The negative correlation between cell mass and S phase length is strengthened in palbociclib-treated RPE-1 cells compared to untreated cells (Fig 5), suggesting more complex crosstalk between the G1 and S phase regulation that cannot be fully explained by the size-dependent replication fork number. In line with previous research [7], we found that both RPE-1 and HeLa cells exhibit adder- like behaviors (Fig 3I–3J). More specifically, they demonstrate sequential adder behaviors, wherein each phase of their cell cycles can be mathematically expressed as an adder, with the correlation between the masses at the beginning and the end of each phase having a slope close to one (S7 Fig). Our focus in this study is not on their adherence to an adder model. Rather, we emphasize the existence of size control mechanisms across all cell cycle phases. Such regula- tion could manifest at multiple cell cycle checkpoints by controlling the duration of individual cell cycle phases, operate throughout the cell cycle through continuous monitoring and adjust- ing the rate of mass accumulation, or more likely, be a combination of both. If the time resolu- tion of the measurements were sufficiently high, we might be able to observe that each fine segment of the cell cycle follows an adder behavior. Such a mechanism would require that a cell continually “knows” how large it is and how large it should be at any point of the cell cycle. How might cells sense their size relative to a changing standard that changes with cell cycle progression? How would such a mechanism respond differently in different cell types, differ- ent nutrient conditions, and to pharmacological perturbations? A proposed mechanism of cell size sensing relies on some form of disproportionality of molecular components or signals to cell size. For example, cells might sense size through the sub-scaling of inhibitors or super-scal- ing of activators to regulate their cell cycle length [6,10,44,81]. Cell mass accumulation requires nutrient provision, transcription, translation, and degradation; any rate-limiting step might serve as a size sensor. It has also been proposed that cells may sense size and modulate growth rate by DNA limitation, cytoplasmic dilution, surface-to-volume ratio, sublinear proportional- ity between metabolic rate and cell size, transport efficiency, and other such mechanisms [20,70,82–84]. We have found that different cell lines modulate their growth rates differently. It is of course plausible that each cell line we investigated employs a distinct size-sensing mech- anism and a distinct mode of response of mass accumulation. However, it seems more likely that all cell lines share a universal mechanism that allows various forms of growth rate PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 22 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle modulation under particular conditions. One potential candidate for this universal mechanism would be the mTOR pathway, which governs biomass synthesis and responds to various upstream signals [36,85]. Therefore, we suggest that an investigation of how the mTOR path- way responds to cell size could be informative. Additionally, growth rate regulation exhibits cell cycle-specific patterns and even intrinsic oscillations [29,48,50,51]. The likely coexistence of multiple forms of regulation could complicate any investigation. Future studies might bene- fit from isolating each mechanism, perhaps by identifying conditions where only one of the processes is dominant. Situations such as cell cycle arrest and size enlargement (so called cellu- lar senescence) triggered by DNA damage or other stresses are of particular interest in this regard [44,86]. Such phenomena may help us disentangle size-dependent growth regulation from other forms of cell cycle-dependent growth regulation, thus allowing us to focus on the effects of cell size on growth rate using the methods we employed in this study. In summary, the use of ceQPM to quantify single-cell dry mass, mass growth rate, and cell cycle progression has provided the currently most accurate, complete, and quantitative description of cell mass homeostasis in mammalian cells. In this paper, we have also showcased the often-underappreciated power of phenomenological descriptions. Such descriptions have been proven to be inherently powerful in physics and chemistry. The observed reduction in the coefficient variation of cell mass within a proliferating population throughout the cell cycle unequivocally rules out the possibility that cells control mass solely or principally by control- ling the length of the G1 phase at the G1/S transition. While this result is far from a complete answer to the problem of cell size homeostasis and does not yet provide specific molecular mechanisms, it nevertheless can serve as a guide for future investigation. It redirects our focus away from the G1/S transition or any specific cell cycle transitions in cell size homeostasis. We propose instead focusing on the molecular-level mechanisms governing size-dependent regu- lation of growth rate, as this appears to be the predominant player and holds greater promise in elucidating how cells maintain a stable size distribution. Our findings, which reveal com- pensatory responses to perturbing size, suggest the existence of previously underappreciated regulatory pathways in cell size regulation. Specifically, we suggest that there is a need to exam- ine how cell size feeds back on the anabolic or proteostatic machinery. As of now, we are still in the early stage of describing the phenomenon of size homeostasis in quantitative terms. These efforts prove that we have much to learn about the regulatory cir- cuits that tell a cell how large it is and how large it should be at any given time or in any given circumstance. Studying cell size homeostasis in cultured cells can lay the groundwork for future investigations into size control in vivo and its implications for disease, thereby expand- ing our understanding of cell physiology. Materials and methods Cell culture and chemical treatment HeLa mAG-hGem, RPE-1 mAG-hGem, HT1080 mAG-hGem mKO2-hCdt1, and HeLa mAG-hGEM DNA-ligase-dsRed cells were made in previous studies by our laboratory [38,87]. U2OS mAG-hGem and Saos-2 mAG-hGEM cells were generated by lentivirus infection in this study. Lentivirus carrying mTurquoise2-SLBP was purchased from Addgene (83842-LV) to make HeLa mAG-hGem mTurquoise2-SLBP, RPE-1 mAG-hGem mTurquoise2-SLBP, and HeLa mAG-hGEM DNA-ligase-dsRed mTurquoise2-SLBP. Single clones of stable expression were selected for each cell line. Cells were incubated at 37˚C with 5% CO2 in Dulbecco’s Modi- fied Eagle Medium (DMEM) (11965; Thermo Fisher Scientific) with 25 mM HEPES (15630080; Thermo Fisher Scientific) and 10 mM sodium pyruvate (11360070; Thermo Fisher Scientific), or McCoy’s 5A Medium (16600082; Thermo Fisher Scientific). Both media were PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 23 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle supplemented with 10% fetal bovine serum (FBS) (16000044; Thermo Fisher Scientific) and 1% penicillin/streptomycin (15140122; Thermo Fisher Scientific). Palbociclib was purchased from Selleckchem (PD-0332991) and rapamycin was purchased from LC Laboratories (R- 5000). Live cell imaging Cells were imaged at 10× magnification by an Eclipse Ti microscope with the Perfect Focus System (PFS) (Nikon, Japan) and an SID4BIO camera (Phasics, France). Nikon NIS-Elements AR ver. 4.13.0.1 software with the WellPlate plugin was used to acquire images. A home-made incubation chamber was used to maintain a constant environment of 36˚C and 5% CO2 dur- ing imaging. Cells were seeded on 6-well glass bottom plates (P06G-1.5-14-F; MatTek) at a density of 1,500 cells/cm2 3 h before long-term imaging or 3,500 cells/cm2 16 h before short- term imaging. Before time-lapse imaging was started, mineral oil (M8410; Millipore Sigma) was added into each well to prevent media evaporation. In the long-term experiments studying the cell cycle regulation, cells were monitored for 48 or 72 h. In the short-term experiments studying growth rate modulation, cells were monitored for 3 h. For all experiments, the phase images were acquired every 30 min, and the fluorescence images were acquired every 1 h. Cell fixation and cell cycle identification After the short-term time-lapse imaging, the mineral oil was gently removed by aspiration. Cells were fixed with 4% paraformaldehyde (RT 157–8; Electron Microscopy Sciences) and stained with Hoechst 33342 (62249; Thermo Fisher Scientific) at a final concentration of 1 μm. The cells were then imaged by QPM again to identify their cell cycle stages. QPM image processing and data analysis The QPM images were processed by the ceQPM method developed previously [29] and con- ducted on the O2 high-performance computing cluster at Harvard Medical School. To test the significance of the minimal cell cycle phase length, we fitted the binned correla- tions between the initial mass and cell cycle phase duration in Figs 3 and 5 with 2 alternative models. A linear model y = a1x+b1, and a bilinear model y ¼ a2x þ b2ðx � x0Þ; y ¼ a2x0 þ b2ðx > x0Þ, where y is the cell cycle phase length, x is the ini- tial mass, a1, b1, a2, b2, and x0 are the fitting parameters. We used the Akaike information crite- rion (AIC) to compare the goodness of fits. A smaller AIC indicates a better fit, and the relative likelihood p_linear or p_bilinear predicts the probability that the alternative model is a better fit when the linear or bilinear model has the smaller AIC [88]. Since the correlations between the initial mass and cell cycle phase duration were not linear, we utilized the Kendall’s rank correlation coefficient to represent the correlation strength. This coefficient is more suit- able for our data as it does not assume a linear relationship, unlike the widely used Pearson correlation coefficient [89]. To evaluate whether the cell cycle control could explain the adder behavior in Fig 3I and 3J, we assumed cells grow exponentially at the rate of α = ln(2)/DT, where DT is the averaged cell cycle length. The division mass could be predicted by md = mbeαT, T = f(mb), where f is the best-fitted function in the alternative models of cell cycle length versus birth mass. To fit the binned correlation between growth rate and cell mass in Figs 4 and 5, we employed 2 alternative models: a linear model dm dt dm dt Þ þ gm þ amt þ b (cid:0) gmt ¼ am þ b ð Þ m < mt ð ð ¼ am þ b and a bilinear model ð Þ m � mt Þ, where y is the growth rate, x is PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 24 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle the cell mass, α, β, a, b, γ, and mτ are the fitting parameters. We used the AIC to estimate the goodnesses of fits. Supporting information S1 Text. Models used in this study. (DOCX) d þ Q2; CV2 S1 Fig. The left- and right-hand sides of Eq 1 and their difference quantified in HeLa cells. d is indicated in black, Q2 is indicated in white; error bars are the In the term, CV2 standard deviation of 8 experiments. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S2 Fig. RPE-1 and U2OS sensitivity to palbociclib. The mean cell mass of the population (A) and the percentage of G1 cells quantified by low Geminin expression (B) after being treated in palbociclib at the indicated concentrations for 2 days. Dashed black lines show the concentra- tion (50 nM) chosen for the analyses in this study. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/ 3kyvw. (EPS) S3 Fig. mAG-hGeminin (A) and cell mass (B) trajectories of a representative HeLa cell. Dashed lines denote the timing of the G1/S transition identified by the initiation of geminin accumulation. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S4 Fig. Segregation of cells into stages along the cell cycle mean path. (A) The 2D plane of the logarithmic scale of mAG-hGeminin intensity, log(Geminin), and the intensity of Hoechst fluorescence, DNA, in asynchronous RPE-1 cells. Black contours indicate cell number density; the solid red line is the cell cycle mean path; filled red circles show the centroids of the chosen stages along the mean path; the stages are evenly separated in the time axis computed by the ERA method [38]. (B–E) The averages of log(Geminin) (blue) and DNA content (red) change with cell cycle progression in different cell lines. X-axes are calculated by the ERA method [38]. The cell cycle is segregated into 4 phases indicated by color-shaded areas: the early G1 phase from birth to the onset of geminin accumulation, the late G1 phase from the initiation of geminin accumulation to the onset of DNA replication, the S phase covering DNA replication, and the G2-M phase where geminin and DNA accumulation plateau. (F) Error in computed cell mass CV caused by inaccurate cell cycle stage identification. The cell dry mass and cell cycle markers data were from Fig 2D. We added 10% random Gaussian noise to each cell’s position in the log(Geminin)-DNA plane. The cells were reassigned to cell cycle stages accord- ing to their new positions, and the cell mass CV of each stage was computed. The solid black line and error bars indicate the mean and standard deviation of computed cell mass CVs of 100 simulations; the first and last stages were truncated due to having much higher cell num- bers and variations than other stages. (G, I) The 2D planes of log(Geminin) and DNA content in RPE-1 cells in 50 nM palbociclib (G) or 100 nM rapamycin (I). The red line and filled circles are the cell cycle mean path and centroids of stages calculated from the treated cells. (H, J) The averages of log(Geminin) (blue) and DNA content (red) change with cell cycle progression in RPE-1 cells in 50 nM palbociclib (H) or 100 nM rapamycin (J). The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 25 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle io/3kyvw. (EPS) S5 Fig. The geminin and SLBP markers faithfully report the timing and duration of S phase. (A) The trajectories of dsRed-DNA-ligase I foci, mAG-hGeminin, and mTurquoi- se2-SLBP in a representative HeLa cell. Open circles are the raw data; solid colored lines are the spline interpolations; dashed yellow and pink lines mark the S phase start and end, respec- tively. (B–D) Correlations between the S phase start (B), end (C), and duration (D) identified by the dsRed-DNA-ligase foci or mAG-hGeminin and mTurquoise2-SLBP combined. Each black dot is one observation; Solid red lines are the best linear fit. Texts indicate the functions of the solid red lines and the Pearson correlations of the black dots. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S6 Fig. The impact of minimal cell cycle length on cell mass homeostasis, indicated by the birth mass CV (A) and mean birth mass (B) changing with simulated generations. Differ- ent colors show the percentage of cells affected by the minimal cell cycle length in the popula- tion of the first generation of simulations. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S7 Fig. The sequential adder behavior in RPE-1 and HeLa cells. (A, D) The correlations between birth mass and mass at G1/S in RPE-1 (A) and HeLa (D) cells. (B, E) The correlations between mass at G1/S and mass at S/G2 in RPE-1 (B) and HeLa (E) cells. (C, F) The correla- tions between mass at S/G2 and division mass in RPE-1 (C) and HeLa (F) cells. Each gray dot is an observation; black squares are the average of each cell mass bin; error bars are the stan- dard error of means (SEMs). Solid black lines are the best linear fits of the gray dots; texts indi- cate the functions of the solid black lines. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S8 Fig. Growth rate modulation in HeLa cells. (A) The correlation between cell mass and growth rate in HeLa cells when pooling all cells together. Each gray dot is an observation in the 3-h measurements, n = 18,334. Black squares are the median growth rate of each mass bin; error bars are SEMs. The solid black line is the best fit of the black squares (S5 Table). The dashed black line indicates exponential growth. (B, C) The correlations between cell mass and growth rate in HeLa cells in 4 cell cycle phases (B) and one fine stage of the cell cycle (C). The stages were determined by log(Geminin) and DNA using the ERA method [38], as indicated in S4C Fig. Filled squares are the median growth rate of each mass bin; error bars are SEMs. The solid lines are the best fit of the filled squares (S5 Table). The dashed black line in (B) indi- cates exponential growth. (D) The slope of the linear relationship between cell mass and growth rate plotted against cell cycle progression. The short-dashed line indicates the expected slope for exponential growth. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S9 Fig. Specific growth rate changes with cell cycle progression in RPE-1 (A) and HeLa cells (B) in G1 (blue) and nonG1 (red) phases. Since the binned correlation could be affected by inspection bias [52], we investigated how the specific growth rate (growth rate divided by mass) changed with cell cycle progression from the long-term trajectories as recommended by PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 26 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle Kar and colleagues [52]. We arbitrarily assumed the G1 or nonG1 phase each occupies half of the cell cycle when normalizing the length of the growth trajectories. Solid blue and red lines are the means of the normalized growth trajectories of the G1 and nonG1 segments; the shaded areas indicate SEM. Dashed lines are the expected curves of exponential growth; short- dashed lines are the expected curves of linear growth, assuming the cells behave like an adder. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S10 Fig. Simulation results for the sub-exponential growth rate modulation. (A, C, F) Con- tour plots illustrating the rate of change in cell mass CV at the beginning of the cell cycle (t0 = 0) when assuming CVα0 = CVβ0 (A), CVβ0 = 0 (C), or CVα0 = 0 (F), respectively. Here, μα0 repre- sents the mean of α0. (B, D, G) Contour plots illustrating the rate of change in cell mass CV at the end of the cell cycle (t0 = 1) when assuming CVα0 = CVβ0 (B), CVβ0 = 0 (D), or CVα0 = 0 (G), respectively. (E, H) Contour plots illustrating the overall change in cell mass CV throughout the cell cycle when assuming CVβ0 = 0 (E), or CVα0 = 0 (H), respectively. Solid circles indicate the corresponding positions in the contour plots when adopting parameter values from the experimental observations of RPE-1 and HeLa cells. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/ 3kyvw. (EPS) S11 Fig. Estimating the variability in α0 for RPE-1 and HeLa cells. (A, B) The variability of the specific growth rate, defined as the growth rate divided by cell mass, does not change with cell mass for RPE-1 (A) and HeLa (B) cells. Blue squares and lines indicate the means and stan- dard deviations of live cell growth trajectories, which are binned by cell mass. The black lines show ð1 � �CV Þ �gr j, where �CV is the average CV in specific growth rate for all cell mass bins, and (cid:0) gr j is the average specific growth rate for each bin. (C) Schematic illustrating the defini- tions of intercellular and intracellular variability in α0. Solid lines are representative live cell growth trajectories. Dashes lines represent the means of each trajectory. Intercellular variabilty is defined as the variaiton among the means of each trajectories, while intracellular variability is defined as the fluctruation within individual trajectories. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/ 3kyvw. (EPS) t , when applying variability t (C), or equal variability to all 3 parameters (D). S12 Fig. Simulation results for the Bilinear growth rate modulation. (A–D) Three-dimen- sional (3D) volumetric plots showing how the change in cell mass CV throughout the cell cycle responds to the means of γ0 and m0 t, represented by μα0 and mm0 (CV) to only 1 parameter of α0 (A), γ0 (B), m0 The slice planes are orthogonal to the CV axis at CV = 0.2. (E, F) Contour plots illustrating the change in cell mass CV during the G1 (E) and nonG1 phases (F) with the means of γ0 and m0 t when assuming a 30% CV in α0. (G, H) Contour plots illustrating the change in cell mass CV during the G1 (G) and nonG1 phases (H) with means of γ0 and m0 t when assuming a 40% CV in α0. Solid circles in (E–H) indicate the corresponding positions in the contour plots when adopting parameter values from the experimental data. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/ 3kyvw. (EPS) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 27 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle S13 Fig. Impact of growth rate variability on division mass CV, CV(mi(Ti)), in the stochas- tic model. The stochastic model is described in Section 4, Scenario IX in S1 Text. All parame- ter values used in this simulation are listed in the table at the end of Section 4, with the exception of CVgr, which is varied in this simulation. Solid blue lines indicate the simulation results. Filled blue circles are the division mass CV when simulated with the CVgr estimated from experimental data. Dashed black lines represent the division mass CV measured in experiments. The data underlying this figure and the scripts used to generate the plots are available on the Open Science Framework at osf.io/3kyvw. (EPS) S1 Table. Characteristics of the human cell lines used in this study. (DOCX) S2 Table. The durations of cell cycle phases for HeLa, RPE-1, RPE-1 in 100 nM rapamycin or 50 nM palbociclib at cell mass homeostasis. MAD is the median absolute deviation, and nMAD is MAD normalized by the median in robust statistics. (DOCX) S3 Table. Comparing cell cycle phase durations and mass versus phase length correlations with and without the mTurquoise2-SLBP marker in HeLa cells. (DOCX) S4 Table. Comparison of the linear and bilinear fits for the cell mass vs. cell cycle phase length correlations. The significantly better fits (p_bilinear or p_linear < 0.05) and the signifi- cant negative correlations (p < 0.05) are highlighted. (DOCX) S5 Table. Comparison of the linear and bilinear fits for the cell mass vs. growth rate corre- lations. The significantly better fits (p_bilinear or p_linear < 0.05) are highlighted. (DOCX) S6 Table. The normalized fitting parameters for the cell mass vs. growth rate correlations for different cell lines. For correlations fitted better by the linear model, dm dt normalized parameters α0 and β0 are listed in the table, with α0 = α<T>, b0 ¼ b <T> <T> and <mb> are the means of cell cycle length and cell birth mass, respectively. For expo- nential growth, α0 = ln2 ~= 0.693. For correlations fitted better by the bilinear model, ¼ am þ b ð dm dt b0, γ0, and m0 The correlation slopes, α0, a0, and γ0, lower than 0.75 or higher than 1.25-fold (arbitrarily cho- sen thresholds) of ln2 were highlighted. SE and BI denote the type of growth rate modulation, where SE stands for sub-exponential and BI stands for bilinear. (DOCX) Þ þ gm þ amt þ b (cid:0) gmt t are listed in the table, with a0 = a<T>, b0 ¼ b <T> Þ, the normalized parameters a0, <mb> ; g0 ¼ g < T >; m0 <mb>. ¼ am þ b, the <mb>, where ð Þ m < mt ð Þ m � mt t ¼ mt ð S7 Table. The values of λ0 and α0 used in Fig 5L, for untreated HeLa and RPE-1 cells, as well as RPE-1 cells treated with 50 nM palbociclib or 100 nM rapamycin. (DOCX) S8 Table. Contribution of each factor to cell mass variation, as indicated by the division mass CV simulated using the stochastic model in Section 4 in S1 Text. The values reported in this table are the average of division mass CVs obtained from 50 simulations. (DOCX) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002453 January 5, 2024 28 / 34 PLOS BIOLOGYCell size homeostasis is tightly controlled throughout the cell cycle S9 Table. The frequencies of cell death, cell cycle arrest, and cytoplasmic loss observed in the long-term measurements in HeLa, RPE-1, RPE-1 in 100 nM rapamycin, and RPE-1 in 50 nM palbociclib when cells have reached cell mass homeostasis. (DOCX) S10 Table. Birth size CVs, division size CVs, and DA stds. reported in the literature. (DOCX) S1 Movie. Time-lapse quantitative phase images of RPE-1 cells in 50 nM palbociclib; the time interval is 30 min; the yellow arrow indicates the lost cytoplasmic mass of a mitotic cell (red arrow); the scale bar indicates 100 μm. (GIF) Acknowledgments We thank the Nikon Imaging Center at Harvard Medical School for sharing its resources. 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10.1371_journal.pcbi.1011164	RESEARCH ARTICLE Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Janne J. LuppiID 1,2,3, Cornelis J. Stam3, Philip Scheltens1,2, Willem de HaanID 1,2,3 1 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands, 2 Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands, 3 Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands j.j.luppi@amsterdamumc.nl Abstract Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer’s disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simu- lated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyra- midal neurons of the corresponding neural masses was modulated to simulate tDCS. Out- come measures were relative power spectral density (alpha bands, 8–10 Hz and 10–13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further dete- rioration. The best performing setup involved right parietal anodal stimulation, with a contra- lateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Luppi JJ, Stam CJ, Scheltens P, de Haan W (2024) Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer’s disease. PLoS Comput Biol 20(1): e1011164. https://doi.org/10.1371/journal. pcbi.1011164 Editor: Marcus Kaiser, University of Nottingham, UNITED KINGDOM Received: May 5, 2023 Accepted: December 19, 2023 Published: January 17, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pcbi.1011164 Copyright: © 2024 Luppi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper, its Supporting Information files, and on Zenodo (DOI: 10.5281/zenodo.7900141). The Brainwave software, which was used to PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 1 / 24 PLOS COMPUTATIONAL BIOLOGYgenerate and analyze the data is available at https:// github.com/CornelisStam/BrainWave. Funding: W.d.H. is a ZonMw Memorabel (733050518) and ZonMw TOP (40-00812- 9817043) grant recipient (https://www.zonmw.nl/ nl). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Model-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Author summary Patient-friendly and non-invasive forms of brain stimulation are being investigated as alternative or additional treatments to medication in Alzheimer’s disease, but there is still no general agreement on how to best perform them. Transcranial direct current stimula- tion (tDCS) is one of these techniques, in which a low electrical current is passed between electrodes placed on the scalp in order to regulate brain activity. In this study, we used a computer model of the Alzheimer’s disease brain to simulate the effects that tDCS would have on brain activity, with the aim of predicting where the electrodes should be placed to see the most beneficial changes in brain activity. We compared 20 different electrode placements, and discovered placing the positive electrode at the back of the head resulted in the best improvement. For example, we saw a general increase in the speed of brain activity and increase in connectivity between brain regions, both of which are reduced in Alzheimer’s disease. We believe that our approach can help guide non-invasive brain stimulation treatments in Alzheimer’s disease and potentially other disorders, while help- ing keep the burden on patients to a minimum. Introduction While amyloid-targeting interventions in Alzheimer’s disease (AD) are getting closer to obtaining clinically relevant treatment effects, therapies targeting neuronal activity and plastic- ity are also increasingly employed [1–8]. A recent clinical trial involving repeated transcranial magnetic stimulation (rTMS) of the precuneal region showed substantial delay of cognitive and functional decline [9]. Arguably, targeting neuronal activity is a relatively downstream and apparently less ‘disease modifying’ strategy, but may nonetheless be clinically meaningful [10,11]. This is supported by the fact that up till now the most successful pharmacological treatment of AD, i.e. cholinesterase inhibitors, act on neuronal signal transmission [7,12]. Fur- thermore, the structure-function relationship of the brain is bidirectional, and experimental studies have demonstrated that influencing neuronal activity not only promotes plasticity, but can actually diminish pathological burden [13–15]. Given this recent fundamental and clinical progress, and the patient-friendly and cost-effective nature of non-invasive brain stimulation, techniques such as tDCS deserve the attention of the AD research community [16]. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation tech- nique that can influence the activity of targeted neuronal populations by modulating excitabil- ity and plasticity [17]. It is targeted using relative positioning of scalp electrodes, determining the current spread [18,19]. Improvement of cognitive symptoms is presumably achieved by modulating pre-existing activity of specific parts of the brain, which in turn can exert influence on connected, unstimulated regions [20,21]. In AD, tDCS interventions have reported work- ing memory and general cognitive improvement [22–33]. In contrast, there are also studies that found little to no improvement in response to tDCS [34–36]. This variability may partly be due to methodological differences, since there is no consensus yet on optimal tDCS setup parameters such as stimulus intensity, duration, electrode position. In general, it appears that stimulation strengths around 2 mA represent a good balance between effectivity and avoiding side effects (skin sensations during stimulation), and that repeated stimulation blocks of around 20–30 minutes are required for longer-lasting effects [37]. Determining the optimal tDCS electrode position is more ambiguous, with many groups basing their targets on anatomical and pathophysiological knowledge, such the DLPFC due to the region’s role in working memory or the precuneus due to its vulnerability in AD [9,38,39]. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 2 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease However, many other choices can be made here, and trying out every conceivable electrode position in separate patient studies seems highly challenging, let alone incorporating personali- zation of stimulation strategies, e.g. based on individual anatomy or connectivity. Moreover, although it seems intuitively appealing to stimulate a vulnerable anatomical region, its embed- ding in a larger cerebral network with unpredictable, non-linear effects could in theory make an intuitive strategy actually counterproductive [40–43]. Stimulation studies focusing solely on cognitive outcome cannot address this issue. A more systematic modeling approach, sup- ported by neurophysiological data to explore changes in brain function associated with suc- cessful outcome, may help to deal with this variability and complexity [44,45]. In order to virtually explore optimal tDCS intervention parameters in AD, we employ an established computational model of AD neurophysiology [46–49]. Its output is comparable to electro- or magnetoencephalographical (EEG/MEG) data, but it also features lower-level descriptions of relevant neuronal parameters such as membrane potential and neuronal excit- ability. Moreover, the model features an activity-dependent degeneration (ADD) algorithm, which has been shown to resemble AD-like brain network damage, including early-phase neu- ronal hyperactivity and -connectivity and gradual oscillatory slowing and loss of connectivity [49]. By tuning the excitability of tDCS-targeted neural masses in the model, it can be used to systematically explore and make predictions of their effects on disrupted brain network activ- ity [50,51]. Using this setup, we can also examine whether treatment success is related to stim- ulating regions with specific network profiles, for example mainly stimulating hub regions, as suggested by existing literature [9]. The aim of the current study was to develop a method for incorporating virtual tDCS inter- ventions into a network model of the AD brain, and to consequently investigate the influence of tDCS electrode position. We focused on assessing the direction of change, meaning that if AD-damage in the model causes oscillatory slowing in the form of reduced alpha power, a suc- cessful strategy would need to counteract this change and increase alpha power. We hypothe- sized that there would be significant differences in outcome between different stimulation setups, and that strategy success would also depend on the network profile of the involved regions, such that stimulation of highly connected functional network hubs would be more effective. Results General observations The performance (composite score) of the different stimulation setups varied greatly (Table 1), with some resulting in significant improvements across all outcome measures, while others had little effect or even forced the outcome measures further away from the normal range. For example, as seen in Fig 1A, anodal stimulation of the parietal lobe with a contralateral supraor- bital cathode resulted in a consistent and significant increase in the relative power in the lower alpha band, shifting the values closer to healthy levels. In this case, reversing the hemisphere for the stimulation did not have a large effect on the outcome, but reversing the polarity reversed the effect, significantly decreasing the relative power in the lower alpha band even below the levels of the AD condition without intervention. Overall, stimulation setups in which the larger area of interest was stimulated anodally out- performed the cathodal variants, while reversing the hemisphere had less impact on the out- come. Pearson’s correlation between the ratio of anodally stimulated regions to cathodally stimulated regions and the composite score of success was 0.69 (p < 0.01). For all three power measures, virtual tDCS resulted in a consistent shift either towards or further away from healthy control values, and reversing the polarity either reduced or reversed this effect. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 3 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Table 1. Composite scores of all stimulation setups. All stimulation setups were given a composite score between -6 and 6 based on their performance in all outcome measures. Each stimulation setup is described by the 10–20 position of the electrodes. The first position denotes the anode position, followed by the cathode position. For a given category, each stimulation setup was given a score of -1 (deterioration), 0 (no change) or 1 (improvement). In this analysis, all outcome measures were weighed equally, although it is possible that in terms of cognitive improvement some might be more pivotal than others. Setup PO7a-AF4c PO8a-AF3c F7a-F4c F8a-F3c P5a-P6c P6a-P5c O1a-F3c O2a-F4c FC5a-FC6c FT10a-FC3c F4a-O2c F3a-F8c FC4a-FT9c FC3a-FT10c FC6a-FC5c F3a-O1c F4a-F7c FT9a-FC4c AF4a-PO7c AF3a-PO8c Score Alpha1 Alpha2 Total Power Peak Freq. PLI AEC 6 6 6 6 6 6 4 4 1 1 1 -1 -1 -1 -2 -2 -3 -3 -6 -6 1 1 1 1 1 1 1 0 -1 0 0 -1 0 0 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 -1 -1 -1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 -1 -1 1 1 1 1 1 1 1 1 0 1 0 -1 0 0 0 1 -1 1 -1 -1 1 1 1 1 1 1 0 0 0 -1 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 -1 -1 -1 -1 -1 -1 https://doi.org/10.1371/journal.pcbi.1011164.t001 However, reversing the polarity had more complicated effects on the peak frequency and the two functional connectivity measures, specifically at time points closely following stimulation onset, as seen in Figs 1 and 2. This did not change the overall performance, as cathodal setups were even in these case characterized by a more rapid deterioration back to the levels caused by the ADD algorithm. In general, tDCS-induced shifts began at the stimulation onset at t = 10, and lasted until approximately t = 20. However, the effects on the relative power in the lower alpha band and peak frequency appeared to still be present, if small, to the end of the simulation period. The best performing interventions Scoring of the virtual tDCS setups by performance revealed that the anodal variants of the con- tralateral parieto-frontal (PO7a-AF4c and PO8a-AF3c) and the contralateral temporo-frontal (F7a-F4c and F8a-F4c) setups resulted in the most consistent changes towards healthy control values across all outcome measures. The bilateral posterior setups likewise resulted in improve- ments in all outcome measures, but these changes were smaller in amplitude compared to the best four setups (P5a-P6c and P6a-P5c). In general, and in particular for the best performing anodal stimulation setups, reversing their polarity by switching the positions of the electrodes caused a clear shift from improvement to deterioration, as can be seen for example in the scores for AF4a-PO7c in comparison to PO7a-AF4c. Fig 1 displays results for all outcome measures for the contralateral parieto-frontal setups, while Fig 2 displays the same for the con- tralateral temporo-frontal setups. We will now evaluate the most successful strategies in more detail. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 4 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 1. The performance of the contralateral parieto-frontal setups across virtual time. All stimulation setups were begun at t = 10, in order to give the ADD algorithm time to cause intitial damage. Strategies were considered succesful if they resulted in a shift of the outcome measure values closer to the healthy control values (green) in comparison to the AD damage condition without intervention (orange). The brainplots show the electrode placement and current spread for all setups. (A-B) Anodal stimulation setups caused an immediate increase in relative power in the lower and upper alpha bands towards healthy control values. Reversing the polarity of the stimulation reversed the effect, with cathodal stimulation further shifting the mesures from healthy values. (C) Total power was very strongly reduced by the ADD algorithm, but anodal stategies resulted in a sizable shift towards healthy values, while cathodal setups had a slight worsening effect. (D) The anodal setups slightly but consistently increased peak frequency, keeping it closer to healthy values. Cathodal stimulation resulted in an intital sharp increase in peak frequency, followed by a drop to values below the AD condition wihtout intervention. (E-F) Anodal stimulation setups caused an initial decrease in both functional connectivity measures, followed by a leveling out and an eventual decline, which did sustain the measures closer to healthy values. Cathodal stimulation instead caused a rapid decline in functional connectivity, which was preceded by a transient increase in PLI but not in AEC. https://doi.org/10.1371/journal.pcbi.1011164.g001 When analyzing the spectral outcome measures for the anodal variants contralateral par- ieto-frontal (Fig 1) and the contralateral temporo-frontal setups (Fig 2), the results are overall similar, but slightly favor the parieto-frontal setups (see S1 and S2 Tables). While the initial effect on relative power in the lower and upper alpha bands as well as total power is similar and even favors the temporo-frontal setups, at around t = 15 the values for the temporo-frontal setups deteriorate more rapidly towards the ADD condition. As such, the beneficial shifts towards healthy values seem to be slightly more resilient in response to the parieto-frontal set- ups. The most distinct difference can be seen in the peak frequency, where the parieto-frontal setups result in an initial dip, even very transiently becoming lower than the ADD condition, PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 5 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 2. The performance of the contralateral temporo-frontal setup across virtual time. All stimulation setups were begun at t = 10, in order to give the ADD algorithm time to cause intitial damage. Strategies were considered succesful if they resulted in a shift of the outcome measure values closer to the healthy control values (green) in comparison to the AD damage condition without intervention (orange). The brainplots show the electrode placement and current spread for all setups. (A-B) Anodal stimulation setups caused an immediate increase in relative power in the lower and upper alpha bands towards healthy control values. Reversing the polarity of the stimulation reversed the effect, with cathodal stimulation further shifting the mesures from healthy values. (C) Total power was very strongly reduced by the ADD algorithm, but anodal stategies resulted in a shift towards healthy values, while cathodal setups caused a slight improvement. (D) The anodal setups slightly increased peak frequency, transiently even above healthy values, then leveling down while remaining slightly above AD values. Cathodal stimulation resulted in worsened peak frequency. (E-F) Anodal stimulation setups caused a sharp initial decrease in both functional connectivity measures, followed by a leveling out and an eventual decline, overall delaying the decline. Cathodal stimulation instead caused a rapid decline in functional connectivity. https://doi.org/10.1371/journal.pcbi.1011164.g002 followed by a stabilization towards the healthy values prior to deterioration starting at t = 20. In contrast, the temporo-frontal setup causes an initial rise in peak frequency that exceeds the healthy average, followed by a decline where it falls below healthy values likewise at t = 20. The response in the functional connectivity measures of PLI and AEC differed between the parieto-frontal and temporo-frontal setups with anodal stimulation. In all conditions with the ADD algorithm, an initial rise in functional connectivity can be observed in the initial part of the simulation, with a decrease at stimulation onset, which in some cases causes the values to drop well below healthy values. In the best performing strategies, the decrease at stimulation onset is followed by a leveled phase in which the decrease in PLI or AEC is slowed down com- pared to the ADD condition. The difference in stimulation setups is most clearly seen for the PLI, where both setups initially result in a reduction in functional connectivity, but this PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 6 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease reduction is much more drastic in the temporo-frontal condition, where it almost immediately begins to decline below the healthy control values. Therefore, while the initial shift close to healthy values could be considered beneficial, it is outweighed by the more gradual and slow decline of in PLI as seen in the parieto-frontal setup. For the AEC the contrast in intervention response is less evident, but the parieto-frontal setup is still supported by its more gradual and slow decline in AEC. Therefore, the contralateral parieto-frontal setups with anodal stimula- tion (PO7a-AF4c and PO8a-AF3c) performed the best in counteracting AD damage in all spectral and functional connectivity outcome measures. Comparison of the most successful strategies When comparing the outcomes of the successful PO7a-AF4c, PO8a-AF3c and F7a-F4c setups to the ADD condition using independent t-tests at virtual time points 10, 15 and 20, they all consistently showed a significant improvement towards the healthy control values (p < 0.001), with the following exceptions (see S1 Table for detailed results). PO7a-AF4c caused a decrease in peak frequency at t = 10, and non-significant effects in PLI and AEC. PO8a-AF3c resulted in a decrease in peak frequency at t = 10 and a non-significant effect at t = 15, but a significant increase at t = 20. F7a-F4c showed significant improvements in all outcome measures, except for the non-significant outcome in peak frequency at t = 10. Finally, the best performing anodal stimulation setups were compared to each other to investigate the amplitude of the improvement (S2 Table). The right hemispheric variant PO8-AF3c significantly outperformed PO7a-AF4c in total power at t = 10 and t = 20, and PLI at t = 10. While F7a-AF4c outperformed PO8a-AF4c at t = 10 in both bands of relative alpha power, peak frequency, and PLI, this effect was very brief. At t = 20 PO8-AF3c significantly outperformed F7a-AF4c in all outcome measures at t = 15 and t = 20, except for peak fre- quency at t = 15, where it remained significantly higher in F7-F4c and t = 20, where the effect was not significant. Therefore, the statistical analysis at the chosen virtual time points supports the right hemispheric variation of the contralateral parieto-frontal setup with anodal stimulation. Relation between strategy success and network profile While we hypothesized post hoc that hub stimulation would generally be more effective, we did not find a clear relationship between the connectivity profile of regions involved in a strat- egy and its success (Fig 3). Pearson’s correlations revealed a weak, significant correlation between average composite score and AEC (r = -0.31, p < 0.05), while no correlation was found between average composite score and DTI node degree (r = 0.14, p = 0.25). Discussion In this computational modelling study, we compared 20 tDCS setups to predict their effective- ness in counteracting disruptions of brain network activity in a neurophysiological model of Alzheimer’s disease. These interventions were considered successful if they managed to steer outcome measures of spectral activity and functional connectivity towards healthy control val- ues. We found that virtual treatment success varied greatly. Here, we discuss these observa- tions in more detail, as well as potential limitations to this study. The best performing tDCS setups The simulations of various virtual tDCS interventions in a neural mass model of the AD brain demonstrated clear differences in their effects on the outcome measures, with some PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 7 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 3. Correlation between connectivity and composite score of performance. For a given region, its functional connectivity as AEC and structural connectivity as DTI node degree were obtained. Additionally, for a given region, the composite scores of performance of each setup the region was anodally stimulated in were averaged. As assessed by Pearson’s correlation, (A) a weak but significant negative correlation was observed between averaged composite score and AEC. (B) No significant correlation was found between averaged composite score and DTI. https://doi.org/10.1371/journal.pcbi.1011164.g003 stimulation setups clearly outperforming others. In particular, the contralateral parieto-frontal setup with anodal stimulation (PO7a-AF4c and PO8a-AF3c), the contralateral temporo-fron- tal setup with anodal stimulation (F7a-F4c and F8a-F4c) and the bilateral posterior stimulation with both polarities (P5a-P6c and P6a-P5c) resulted in an overall improvement in all six out- come categories (Table 1 and Figs 1 and 2). Of these setups, the contralateral parieto-frontal setups with anodal stimulation resulted in the most benefit in terms of amplitude positive change, and were therefore considered the best performers. The strength and irreversibility of the ADD damage algorithm ensured that the outcome measures would eventually be driven back to the levels of the AD condition without interven- tion, but as can be seen in Figs 1 and 2, certain stimulation setups did cause clear shifts in out- come measures prior to this. Due to the nature of the virtual time in the simulations, making any connections to actual time or disease progress is not possible. Instead, the simulations are better interpreted as general effects towards of further away from healthy values. While tDCS-induced shifts in the spectral power results generally followed a pattern of a transient shift in a consistent direction compared to the ADD condition, the patterns tended to be more complicated for the posterior peak frequency and functional connectivity mea- sures. For example, as seen in Fig 1D, the cathodal parieto-frontal setups initially elevated the peak frequency sharply, but then proceeded to deteriorate rapidly even below the ADD condi- tion. In contrast, the anodal parieto-frontal setups caused a more modest initial increase that remained constantly above the ADD values. Additionally, Fig 2D displays that the anodal par- ieto-frontal setups caused both a sharp increase in peak frequency that remained steadily above the ADD values. While as a rule an increase in peak frequency would seem beneficial in AD, the shift caused by the temporo-frontal setups might in actuality be disruptive, as it clearly exceeds normal values, while the parieto-frontal setup results in values generally closer to the healthy control. This point and the fact that the both setups start declining away from the healthy values at the same time point favor the choice of the parieto-frontal setups. The functional connectivity patterns featured an initial decrease in PLI and AEC at stimula- tion onset, with the exception of PLI in the parieto-frontal setup (Figs 1E, 1F, 2E, and 2F). This decrease tended to be smaller in anodal setups compared to cathodal ones. It is not evidently PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 8 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease clear what causes this decrease, even in setups that slow down the reduction in functional con- nectivity overall. It is possible that it connected to a transient disruption in functional connec- tivity due to the introduction of a stimulus foreign to the network, or that it is primarily caused in contrast to the initial rise in functional connectivity in the first 10 epochs, that is also seen in the ADD condition. Additionally, an initial decrease in functional connectivity can actually bring the values closer to the healthy control. However, the ultimate aim of the inter- vention is to delay the inevitable reduction of functional connectivity, which can be observed in the anodal stimulation setups. Intervention success Four out of six of the best performing stimulation setups involved anodal stimulation of the posterior cortical regions around the temporo-parietal junction, which lead to significant shifts towards healthy values in all outcome measures. Interestingly, Marceglia et al. [52] found that anodal stimulation of the temporo-parietal region resulted in an increase of power in the alpha band, which was furthermore correlated with an improved working memory performance, as assessed by a word recognition task. The success of posterior interventions has plausible links to network disruptions and connectivity in AD. Firstly, the improvements we report could be related counteracting the slowing of the posterior dominant rhythm of the occipital lobe, a key feature in electrophysiology of AD. While not necessarily the case, direct intervention in the area with the most noticeable oscillatory slowing could very well be the optimal intervention. This could also be linked to amyloid pathology originating in these regions [53]. Another factor behind these results could be the inclusion of hub regions, especially but not limited to the precuneus, which were included in the stimulated areas in these setups. Hub vul- nerability is naturally the feature of AD that the rationale for the ADD algorithm is based on. As such, the hub regions near the temporo-parietal junction may be particularly responsive to the virtual tDCS intervention. This could also help explain why the ipsilateral parieto-frontal setups (O1a-F3c and F3a-O1c) only resulted in modest improvement across outcome mea- sures, as while they did stimulate posterior regions, they did not reach the precuneus. While we hypothesized post hoc that strategies stimulating hub regions would be the most successful ones, we did not find strong correlations between connectivity of anodally stimulated regions and intervention success (Fig 3). Somewhat surprisingly, stimulation setups targeting the frontal lobe showcased mixed results in term of change in the outcome measures. This is in contrast to the popularity of set- ups targeting the frontal lobe bilaterally, or specifically focusing on the DLPFC [26,27,33,38]. In fact, the two bi-frontal setups that included both DLPFC in their current spread (FC6a- FC5c and FC5a-FC5c) resulted in little improvement and even some deterioration in some of the outcome measures. In contrast, the temporo-frontal setup, which also affected the typical DLPFC target of one hemisphere but focused instead on stimulation of the lower DLPFC and temporal regions managed to improve all outcome measures, as stated previously. Deterioration in response to tDCS In addition to identifying the stimulation setups that performed the best, the simulation results also showed that certain setups resulted in a deterioration in some or all outcome measures. The possibility of a poorly designed tDCS intervention resulting in further deterioration of AD symptoms is not commonly discussed in literature, as the majority of studies report either improvement or no change in response to tDCS, with few exceptions. This scarcity of negative result could also be due to publication bias. A study by Das et al. [54] did report that a sham stimulation setup resulted in significant improvements in for example episodic memory PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 9 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease compared to baseline, while anodal stimulation resulted in a non-significant trends towards worsening of episodic memory compared to baseline. While rarely reported, it stands to reason that if beneficial effects can be achieved with tDCS, the reverse would also be possible, leading to a necessity to investigate possibly causes for such responses. Our results of worsened AD network disruptions in response to some interventions support this notion. In our data, worsening tended to occur in the setups with a reversed polarity, which had a larger area of cathodal stimulation in contrast to anodal. How- ever, other studies have also reported cases where cathodal stimulation outperformed anodal stimulation [26]. Ultimately, understanding the causes for possible deterioration in response to tDCS interventions in AD is essential for patients. Limitations By definition, modelling the activity of the brain requires simplifying concepts, structures and interactions. This can lead to arbitrary choices, whereas our goal was to conduct the simula- tions as systematically as possible. For example, converting the CFM results to targeted neural masses in the model was done by matching the current spread to the anatomical boundaries of the AAL regions the corresponded to the neural masses. However, the size and shape of the AAL regions vary, and the current spread naturally does not follow their borders. As such, we chose to include neural masses in the targeted population if there was current spread visible on approximately half or more of the associated AAL regions. Due to this complexity, we also chose to make binary choices on whether a neural mass was considered to be targeted or not. Ideally, it would be optimal to instead have a gradient for the response, with stronger current spread resulting in larger excitability and vice versa. Both of these problems could be alleviated by using an atlas with smaller or preferably even uniform regions of interest. However, we do not think that this would have a large effect on the results, given the relatively large areas that tDCS interventions of this type affect. Another improvement would be to include subcortical structures of the AAL atlas used in the modeling. While the effects of tDCS mainly reach the cortex, this does not mean that there is no relevant interaction between the stimulated areas and subcortical regions. A more detailed incorporation of the subcortex (for example by using all subcortical ROIs of the full AAL atlas) in the model could improve its accuracy. Regarding model dynamics, the Lopes da Silva neural mass model does feature subcortical (thalamic) input as an important parameter to obtain realistic thalamo-cortical feedback loops for oscil- latory behavior. Future directions True validation of our model prediction requires replication in AD patients. This is planned to be done in the following clinical validation phase of this project, in which early-stage, bio- marker positive AD patients will undergo a simultaneous tDCS-MEG session, using our best performing stimulation setup. Here, we will focus on the short-term effects of tDCS during and minutes after stimulation, in preparation for more extensive studies investigating long- term effects and plasticity. Implementing plasticity in the virtual model would also be a very valuable step, if challenging [55]. For example, the adaptive brain model described by [56] could prove useful for studying plasticity in response to stimulation. It would also be worth investigating to what extent varying the stimulation onset influences the outcome. While the nature of the virtual time in the model does complicate this, it would be interesting to try and stimulation onset at the proposed early phase of hyperactivity in AD versus the later hypoac- tive phase [57,58]. Finally, recent studies have emphasized the importance of personalizing tDCS interventions [59,60]. We plan to improve this aspect of our model by implementing PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 10 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease individual structural connectivity. The goal of the clinical study will be to assess whether our model predictions match what is seen in real-time MEG of AD patients, and whether personal- ized approaches outperform a generalized one. Conclusion In this study, we present a novel virtual approach of determining optimal tDCS intervention setups to counteract network disruptions in AD. With this approach, we identified a montage using anodal stimulation of the right parietal lobe in combination with a contralateral supraor- bital cathode as significantly producing the most improvement in network activity. We aim to validate our model findings in an upcoming clinical tDCS-MEG intervention study, in which the model-predicted directions of change (and effect size) in outcome measures in response to tDCS will be compared those seen in empirical data. Materials and methods Study design To predict the performance of a set of tDCS montages on the evolution of spectral power and functional connectivity of a brain subjected to Alzheimer’s disease, a study consisting of four phases was developed. The first phase was to determine a relevant set of stimulation positions to test with the aim to cover all major brain regions, guided by but not limited to current litera- ture. The second phase involved the use of current flow modeling (CFM) to determine which regions of the model are influenced in each specific setup (Fig 4C and 4D). In the third phase, simulations were run with appropriate model adjustments for three conditions; a virtual healthy control without AD-related damage, one with AD damage but without intervention (Fig 4A and 4B) as well as each of the conditions in which the AD damage was counteracted by a specific intervention (Fig 4D and 4E). Finally, the fourth phase consisted quantitative analysis of the resulting performance of all intervention conditions in comparison to each other as well as the healthy control and the AD condition without intervention. Neural mass model and network embedding The model employed in the current study consists of interconnected neural masses, which in turn correspond to interconnected populations of excitatory and inhibitory neurons. This neu- ral mass model is an adjusted version based on the original by Lopes da Silva et al. [46,47], which has been optimized for reproducing the human alpha rhythm [48,61,62]. While the model parameters, such the ones for postsynaptic potentials and threshold functions, can be adjusted to reproduce a more typical 1/f power spectrum, we chose to keep the model settings focused on the alpha band in order to study the decline of the dominant alpha rhythm in AD. The model has been used extensively in related network studies on Alzheimer’s disease, owing to the relevance of the alpha band in assessing oscillatory slowing in the disease [49,50,63,64]. A key feature of the model is that the excitatory and inhibitory populations making up the neu- ral masses generate EEG/MEG-like output, relating neuronal circuit characteristics to cortical oscillatory activity. Instead of focusing on absolute values, which can be influenced by numer- ous choices in model settings and can lead to overfitting and over interpretation, we primarily used the model to assess directions of change in response to AD-damage and virtual tDCS, which it is well suited for. Here, we give a short description, please refer to previous studies for more detailed information. While the model does not take spatial effects into account, the 78 neural masses of the model, corresponding to the cortical regions of the automated anatomical labeling (AAL) PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 11 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 4. Study workflow overview. (A) In a schematic network diagram of the AD brain model, neural masses consisting of excitatory and inhibitory neuronal populations are connected to each other based on human topology. AD is simulated in the model using an activity-dependent damage algorithm. According to this algorithm, connections of relatively active regions are damaged (dashed lines) with priority compared to the less active (solid lines). (B) Due to the damaged local and network activity, the MEG-like output of neural masses in the model becomes slower and less synchronized. (C) The (virtual) tDCS intervention is carried out by placing a positive anode (red) and negative cathode PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 12 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease (blue) on the scalp, between which a low current is passed. (D) Current flow modeling can be used to predict the current spread in the brain, determining the regions in which excitability is modulated by tDCS. (E) In the model, the excitability of the corresponding neural masses is increased for the anodal stimulation and decreased for the cathodal stimulation. (F) With an optimized stimulation setup, local and network activity is restored towards normal levels. https://doi.org/10.1371/journal.pcbi.1011164.g004 atlas, are coupled to one another in order to introduce brain topology to the model [65]. The connectivity of the neural masses of the model is based on diffusion tensor imaging (DTI) results by Gong et al. [66], which assessed the large-scale structural connectivity of the human cortex. Coupling between neural masses was always excitatory and reciprocal. Therefore, changes in neural mass activity arose from both external influence (tDCS) as well as their con- nectivity to other neural masses. For a more detailed description of the neural mass model, please refer to S1 Fig and S3 Table and de Haan et al., 2012 [49]. AD damage simulation: the activity-dependent degeneration (ADD) algorithm In order to simulate the damaging effects of Alzheimer’s disease pathology at the network level, an algorithm of activity-dependent degeneration (ADD) was introduced to the model [49]. The ADD algorithm damages the network by lowering the ‘synaptic’ coupling strength within and between neural masses as a function of spike density of the main excitatory popula- tion. In essence, the higher the recent activity level in a neural mass, the more the structural connectivity of that neural mass is damaged. tDCS electrode montages Transcranial direct current stimulation (tDCS) is carried out between positive (anode) and negative (cathode) electrodes placed on the scalp. Current entering the cortex from the anode depolarizes the membrane potential of the perpendicularly aligned pyramidal neurons, there- fore increasing their excitability [17,19]. The opposite effect is seen for the cathode. Therefore, the relative positioning of the electrodes determines the current spread and effects on the excit- ability of targeted neuronal populations. When systematically searching for the optimal tDCS electrode position setup, including every conceivable combination of anode and cathode placements would quickly lead to a very large number of experiments (±202 options in the normal 10–20 EEG layout alone). While a vir- tual approach has the potential advantage of extensive setup comparisons that can be done much faster than actual patient studies, it is not necessary to include all options. Since the field generated by traditional, non-high-density tDCS usually reaches across multiple cerebral regions, many slightly altered electrode positions lead to similar current flow results, constrain- ing the variability. Therefore, for this tDCS pilot study in AD, we pragmatically limited the elec- trode placement variation to 20 different setups, jointly encompassing all brain regions. All unique electrode positions were varied to reflect either a change in polarity (switching the positions of the anode and cathode), contralateral hemisphere, or both. This resulted in four variations for each unique electrode position, except for the two bilaterally symmetrical montages, for which there were two variations each. These montages, with their electrode placements as well as the rationale behind each choice are described in Figs 5 and 6. Current flow modeling In order to simulate the effects of tDCS, we first identified which cortical regions (neural mas- ses) should be modulated in the intervention conditions. Various studies have demonstrated PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 13 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease that the notion of anodal excitation and cathodal inhibition underneath the scalp electrodes is out- dated and simplistic [18]. Instead, the relative positions of the electrodes to the cortical gyri as well as one another have a large impact on the spread of the current through the cortex [19]. CFM pro- vides a software solution to estimating the spread of the tDCS current for any given electrode montage [67]. CFM uses a structural magnetic resonance imaging (MRI) scan of the head as the basis for estimating current flow. This MRI scan is segmented by the software into tissue types such as scalp, skull and white matter, each of which are assigned conductive properties. By com- bining this information with an input of the stimulation intensity and electrode shapes, sizes and positions, CFM software can predict and visualize the current flow through the cortex. In the current study we used the free open-source software, SimNIBS, to perform the CFM [67]. We used a standard template of the healthy young adult male brain (‘Ernie’) provided with the software as the MRI input, along with specifications for 5x5 cm electrodes with impedance gel. We then performed the CFM using a 2mA stimulation intensity with six differ- ent electrode positions based previous literature on tDCS in AD (Figs 5 and 6). These electrode positions were defined on the 10–20 system for scalp electrode placement. The electric fields were considered strong enough to influence associated AAL regions if the field strength exceeded 0.6 of the maximum field strength in at least 50% of the area of the region. The elec- tric fields were assigned to either the anode or the cathode based on proximity, and AAL regions falling within the current flow area closer to the anode were considered to be excited, while to falling within the current flow area closer to the cathode were considered to be inhib- ited (See S2 Fig for reference). Simulating tDCS The effect of each tDCS intervention on the model network was simulated by modulating the likelihood of action potential firing, i.e. the excitability, for the excitatory neuronal populations of affected regions (neural masses). This rationale was based on the presumed tDCS mecha- nism: briefly, current entering the cortex depolarizes the membrane potential of affected neu- ronal compartments, making them more likely to fire, while current exiting the cortex has the opposite, hyperpolarizing effect [17,75,76]. Furthermore, the orientation of the current is pro- posed to be important due to its relationship to the orientation of the dendritic trees of aligned pyramidal neurons [19]. As the current is entering the cortex, the apical dendrites of the excit- atory neurons are hyperpolarized, while the soma and basal dendrites become depolarized, facilitating the firing of action potentials and vice versa. The neural mass model includes parameters that describe the neuronal excitability of the dif- ferent neuronal groups (excitatory and inhibitory). Altering these parameters is a straightfor- ward and flexible way to simulate tDCS effects. Fig 7 demonstrates how the excitability of the targeted pyramidal neurons was manipulated in the model: the threshold potential of the excit- atory populations (Vd1) was altered in all neural masses that were determined to be involved in a specific electrode setup by the current flow modelling (CFM). Lowering the threshold poten- tial Vd1 leads to depolarization, and therefore easier action potential generation and a more excited, disinhibited state. As such, if an excitatory (anodal) current was considered to influence a specific neural mass, its Vd1 was lowered from the default 7 to 5 in order to simulate an increase in excitability. In contrast, neural masses where current was considered to be exiting the cortex, Vd1 was instead raised from 7 to 9, making it less likely that action potentials will be fired. These values were chosen due to previous experimentation on the model, which has shown that these are sufficiently large changes to result in clear shifts in output, but not so large that they cause the activity to become physiologically implausible [50,77]. Model settings are described in S1 Fig and S3 Table, and were kept at default unless otherwise noted. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 14 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 5. Electrode montages. The left column displays each unique electrode position used, and the middle column displays the associated current flow modeling done in SimNIBS [67]. Red denotes anode, blue denotes cathode. The right column describes the intended target of interest, and the motivating literature [23,24,26,27,31–33,38,68–70]. https://doi.org/10.1371/journal.pcbi.1011164.g005 Simulating tDCS effects over time The progress of the simulations over time was introduced by advancing the simulation in steps of 1 unit of virtual time, with the activity-dependent degeneration (ADD) algorithm set to active. This process was repeated for a total length of 40 virtual time points and each time PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 15 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 6. Electrode montages continued. The left column displays each unique electrode position used, and the middle column displays the associated current flow modeling done in SimNIBS [67]. Red denotes anode, blue denotes cathode. The right column describes the intended target of interest, and the motivating literature [9,22,52,71–74]. https://doi.org/10.1371/journal.pcbi.1011164.g006 point was repeated for 100 runs. Naturally, the healthy control simulations were performed the same, without the ADD algorithm. The tDCS interventions were introduced into the model at virtual time point t = 10. This intervention onset point was chosen to give the ADD disease algorithm enough time to have an effect on the simulation prior to the tDCS PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 16 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Fig 7. Effects of the virtual tDCS in the neural masses. (A) In the model, brain networks are divided into regions according to the automated anatomical labeling (AAL) atlas. These regions are represented by homogenic neural masses (circles and squares in 1A) that are connected to each other according to a human DTI-derived connectome. (B) A schematic representation of a neural mass consists of a population of excitatory pyramidal neurons and a population of inhibitory interneurons, which are interconnected. Inhibitory drive from the interneurons and external excitatory drive from other neural masses determines the membrane potential of the excitatory population. Membrane and threshold potential values taken together determine action potential likelihood. The density of spikes determines the excitatory output to connected neural masses as well as to the inhibitory population. (C) To simulate the tDCS effects on the neural masses, the threshold potential of the pyramidal neurons (Vd1) is manipulated, by changing it from the default value of 7. For excitatory anodal stimulation, Vd1 is lowered to 5, increasing the spike density, and icreased to 9 for the inhibitory anodal stimulation. https://doi.org/10.1371/journal.pcbi.1011164.g007 intervention. However, relating this virtual time trajectory to specific stages in the AD disease course is speculative. After the introduction of the virtual tDCS, the change in the Vd1 was kept constant for the remainder of the simulation. Outcome measures The outcome measures based on spectral power density were posterior peak frequency, total power, as well as relative power in traditional frequency bands. The frequency bands of interest were the theta (4–8 Hz), lower alpha (8–10 Hz), higher alpha (10–13 Hz) and beta (13–30 Hz) bands. As the model is mainly built around the alpha (8–13 Hz) band, it was the main fre- quency range of interest, while the adjacent frequency bands were investigated to assess the direction of any possible shift from alpha towards the theta or beta range. The delta and gamma bands are more artefact-prone and therefore less used as quantitative markers for AD diagnosis and effect monitoring in the clinic [78]. They are also less well represented in our neural mass model, which is why they were not included in our analysis. For the functional connectivity (FC) analysis, two complementary measures were chosen; the amplitude envelope correlation (AEC) and the phase-lag index (PLI). Both measures were calculated for the main frequency band of interest, the lower alpha band (8–10 Hz). AEC mea- sures correlations between amplitude envelopes of specific frequency, and is therefore does not depend on phase coherence to detect signal coupling [79]. Due to the absence of volume conduction in the model, the corrected version of the measure (AECc) was not used [80,81]. The PLI was used as a phase-based FC measure [82]. PLI measures asymmetry in the distribu- tion of phase differences of time series detect phase synchronization. Statistical analysis The analysis of the results was based on a quantification of the different outcome measures per condition over virtual time. Each test condition was re-iterated 100 times for consistency. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 17 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease Every separate strategy was given a composite score summarizing whether it resulted in a sig- nificant shift of the outcome measures towards healthy control values. This composite score denotes whether a strategy resulted in a shift towards healthy values (1), no change (0), or shift away from healthy values (-1) in terms of peak frequency, total power, relative power in the lower alpha band and the upper alpha band, PLI and AEC. This resulted in composite scores ranging between -6 and 6. For simplicity, all outcome measure scores were weighed equally. Relatively successful stimulation setups with a composite score of 6, as well as their corre- sponding stimulation setups with reversed polarity, hemisphere or both, were subjected to fur- ther statistical analysis. To this end, an independent t-test comparing each selected condition to the ADD condition was calculated for each outcome measure at virtual time points 10, 15 and 20. These time points were chosen to gain a more detailed look at the stimulation onset, a point where the contrast of the intervention and ADD conditions was generally at its highest, and a point where the effects of the intervention compared to the ADD condition was gener- ally waning, respectively. Similar independent t-tests were then made to compare the best per- forming interventions to each other. In this case, the t-tests were carried using the mean differences from the ADD condition, in which positive values signified a shift towards healthy values, with negative values denoting further deterioration. Relation between strategy success and network profile Post hoc Pearson’s correlations were calculated to assess the relations between intervention success and network profile of stimulated regions. For a given region, we obtained its AEC and DTI node degree, as well as the averaged composite score of performance of all stimulation set- ups in which the region was anodally stimulated. Only anodal stimulation was considered in this investigation for simplicity, as reversing the polarity can reverse the stimulation effects. Pearson’s correlations were then calculated between average composite score and AEC to account for functional connectivity, and between average composite score and DTI to account for structural connectivity. Supporting information S1 Table. Best performing setups versus ADD. The results of independent t-tests comparing outcome measures in virtual stimulation setups to those in the ADD condition. Positive values indicate a shift towards healthy control values (bold if significant), while negative values indi- cate a shift further away from healthy control values (in italics if significant). ** p < 0.001. (DOCX) S2 Table. Comparison of best performing setups based on difference from the ADD condi- tion. The results of independent t-tests comparing setups based on their difference in outcome measures from those in the ADD condition. Positive values indicate a shift towards healthy control values (bold if significant), while negative values indicate a shift further away from healthy control values (italic if significant). * p < 0.5, ** p < 0.001. (DOCX) S3 Table. Overview of model parameters, from de Haan et al., 2012 [49]. The final model consisted of 78 of the NMMs as described above, which were coupled together based on the structural DTI network results from Gong et al. (2009). Coupling between two NMMs, if pres- ent, was always reciprocal, and excitatory. The output E(t) of the main excitatory neurons of one NMM was used as the input for the impulse response he(t) of the excitatory neurons of the second NMM; the output E(t) of the second module was coupled to the impulse response he(t) of the excitatory neurons of the first NMM. Coupling strength between neural masses PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011164 January 17, 2024 18 / 24 PLOS COMPUTATIONAL BIOLOGYModel-guided optimization of non-invasive brain stimulation in Alzheimer’s disease was set at S = 1. A schematic illustration of the coupling between two NMMs is shown in S1 Fig. For the present study the model was extended in order to be able to deal with activity dependent degeneration of connection strength between multiple NMMs coupled according to human DTI connectivity. The effects of tDCS were introduced into the model by increasing (for cathodal stimulation) or decreasing (for anodal stimulation) Vd1 to 5 or 9 from the base- line value 7. This change in threshold potential has the opposite effect on the excitability of the pyramidal excitatory neuronal population of the affected neural mass. (DOCX) S1 Fig. Specifications of the neural mass model, from de Haan et al., 2012 [49]. A) Sche- matic presentation of single neural mass model. Abbreviations are described in S2 Table. The upper rectangle represents a mass of excitatory neurons, the lower rectangle a mass of inhibi- tory neurons. The state of each mass is modeled by an average membrane potential [Ve(t) and Vi(t)] and a pulse density [E(t) and I(t)]. Membrane potentials are converted to pulse densities by sigmoid functions S1[x] and S2[x]. Pulse densities are converted to membrane potentials by impulse responses he(t) and hi(t). C1 and C2 are coupling strengths between the two popula- tions. P(t) and Ej(t) are pulse densities coming from thalamic sources or other cortical areas respectively. B) Coupling of two neural mass models. Two masses are coupled via excitatory connections. C) Essential functions of the model. The upper left panel shows the excitatory [he (t)] and inhibitory [hi(t)] impulse responses. The upper right shows the sigmoid function relat- ing average membrane potential to spike density. (DOCX) S2 Fig. Example of translating current flow modeling to AAL regions in the neural mass model. A) Current flow modeling for the F7a-F4c setup at 2mA, with 5cm x 5cm electrodes using gel, carried out in the free SimNIBS software (Thielscher et al., 2015 [67]). The red square shows the anode position, while the blue square shows the cathode. B) Delineation (in red) of the triangular part of the left frontal inferior gyrus, corresponding to AAL region/neu- ral mass 10. As more than 50% of AAL region 10 is contained in the electric field adjacent to the anode that exceeds 0.6 of total field strength (in this case 0.322), neural mass 10 in the model is considered anodally stimulated, and will have its excitability increased by lowering the threshold potential of the its excitatory pyramidal cell population Vd1 from default value 7 to 5. (DOCX) Acknowledgments Research of Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzhei- mer Nederland and Stichting Steun Alzheimercentrum Amsterdam. The clinical database structure was developed with funding from Stichting Dioraphte. Author Contributions Conceptualization: Janne J. Luppi, Cornelis J. Stam, Philip Scheltens, Willem de Haan. Formal analysis: Janne J. Luppi. Funding acquisition: Willem de Haan. Investigation: Janne J. Luppi. Methodology: Janne J. Luppi, Willem de Haan. 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10.1371_journal.pclm.0000290	RESEARCH ARTICLE Sea otter recovery buffers century-scale declines in California kelp forests Teri E. Nicholson1, Loren McClenachan2, Kisei R. Tanaka1, Kyle S. Van HoutanID 1,3 1 Monterey Bay Aquarium, Monterey, California, United States of America, 2 Department of History & School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada, 3 Duke University, Nicholas School of the Environment, Durham, North Carolina, United States of America * tnicholson@mbayaq.org (TEN); kyle.vanhoutan@gmail.com (KSVH) Abstract The status of kelp forests and their vulnerability to climate change are of global significance. As the foundation for productive and extensive ecosystems, understanding long-term kelp forest trends is critical to coastal ecosystem management, climate resiliency, and restora- tion programs. In this study, we curate historical US government kelp canopy inventories, develop methods to compare them with contemporary surveys, and use a machine learning framework to evaluate and rank the drivers of change for California kelp forests over the last century. Historical surveys documented Macrocystis and Nereocystis kelp forests covered approximately 120.4 km2 in 1910–1912, which is only slightly above surveys in 2014–2016 (112.0 km2). These statewide comparisons, however, mask dramatic regional changes with increases in Central California (+57.6%, +19.7 km2) and losses along the Northern (-63.0%, -8.1 km2), and Southern (-52.1%, -18.3 km2) mainland coastlines. Random Forest models rank sea otter (Enhydra lutris nereis) population density as the primary driver of kelp changes, with benthic substrate, extreme heat, and high annual variation in primary produc- tivity also significant. This century-scale perspective identifies dramatically different out- comes for California’s kelp forests, providing a blueprint for nature-based solutions that enhance coastal resilience to climate change. Introduction Kelp forest ecosystems, and the essential services they provide, are under threat worldwide [1, 2]. Located in every ocean basin, and spanning 25% of the planet’s temperate and arctic coast- lines, canopy-forming kelps are the foundational basis of unique marine ecosystems [3, 4]. These ecosystems supply critical services including refuge habitat for commercially important fisheries, nutrient recycling and carbon storage, protection from seabed erosion, and highly productive assemblages of biodiversity [5–7]. Though they are considered important for global carbon budgets [1, 8], kelp forests are not currently included in blue carbon initiatives [9]. Understanding the magnitude and drivers of kelp declines is therefore key to developing inte- grated conservation plans to promote the persistence of these ecosystems, their services, and coastal resilience regionally and globally. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Nicholson TE, McClenachan L, Tanaka KR, Van Houtan KS (2024) Sea otter recovery buffers century-scale declines in California kelp forests. PLOS Clim 3(1): e0000290. https://doi.org/ 10.1371/journal.pclm.0000290 Editor: Katharina C. Wollenberg Valero, University College Dublin College of Science, IRELAND Received: March 13, 2023 Accepted: October 30, 2023 Published: January 18, 2024 Copyright: © 2024 Nicholson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All datasets and code used here are available at the third-party repositories GitHub (https://bit.ly/3pvUkQI) and Open Science Framework (https://osf.io/gsjex/). Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 1 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Kelp forests are vulnerable to multiple threats across a range of temporal and spatial scales. In the last decade, marine heatwaves have become intense, persistent [10, 11], and globally common, with particular severity in historically cool, largely temperate latitudes [12] that con- tain the major kelp ecoregions [13]. At the organismal scale, prolonged heat stress intensifies nutritional depletion, directly damages tissue, diminishes reproduction, accelerates senes- cence, and increases kelp mortality [2]. At the population scale, persistent extreme heat reduces kelp recruitment, and can ultimately convert kelp forest ecosystems to communities dominated by benthic turf algae [14, 15]. Over decadal time scales, regional threats like water quality and substrate loss have impacted kelp survival, especially where coastal development has increased sedimentation, turbidity, and harmful algal blooms [2, 16]. In extreme cases, sed- iment accumulation may smother the native benthos, prevent kelp resettlement, and perma- nently transform bedrock to soft-sediment [17, 18]. Finally, trophic disruptions, such as overhunting of a keystone predator, the southern sea otter (Enhydra lutris nereis), have occurred over century-long time scales, corresponding with losses of kelp forests [19]. These impacts often act synergistically, so as environmental conditions deteriorate, diminishing can- opy litter can create sea urchin swarms on the remnant kelp stands [20], especially where dis- ease or overharvesting of invertebrate predators [21, 22] exacerbates an already-poor ecosystem state. This combination of important services and significant threats prioritizes a need to develop informed benchmarks for kelp forest restoration. Historical ecology has been particularly effective at interpreting data sources from the past to identify important sources and scale of human impacts to nature [23, 24]. Early nautical charts, expedition narratives, consumption records, ethnographic accounts, and museum collections—for example—can be used to dem- onstrate broad trends and have uncovered massive megafauna declines and ecosystem trans- formations during the last century [25–30]. Despite the inherent differences in contemporary and historical survey methods, thoughtful analyses may provide comparisons necessary for set- ting conservation or management goals. To date most kelp forest assessments rely on in situ or remote sensing datasets from the last 50 years [1, 2], which may downgrade important ecologi- cal relationships and underestimate restoration potential, particularly given the long time scale of decline and potential interactions among drivers of change. Extending the period of record for kelp forest ecosystems may therefore be vital to better understand sources and impacts of the full suite of anthropogenic stressors, predict future trends, inform conservation efforts, and design effective restoration [31]. The California coast presents a unique opportunity to develop an historically informed assessment of kelp forests. The state’s marine geography extends nearly 10˚ of latitude, encom- passes more than 1,600 km of linear coastline, and hosts two major canopy-forming kelps (Macrocystis pyrifera, Nereocystis luetkeana) that occur along a gradient of human impacts. Surrounding these kelp forests is a cascade of climatic influences [32], characterized in large part by the productive upwelling of the California Current system. Onshore lies a mosaic of intensely modified regions (urbanization, agriculture) and well-managed terrestrial and marine protected areas. In central California, the southern sea otter population is gradually recovering from a persistent ecological extinction and resuming its keystone function [22, 33]. Within this complex setting of environmental factors, comparison of historical and contempo- rary canopy cover surveys may yield novel insights into kelp forest dynamics through time. Here, we generate spatially explicit historical reference points of California kelp forest cover and assess the dominant drivers of change over the last century. We digitize, georeference, and quantify historical kelp surveys, compare them to modern aerial survey data, assess carbon storage, and generate a 100-year record across several spatial scales. Importantly, this timescale captures the major human drivers of change in this system, including recent warming, coastal PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 2 / 18 PLOS CLIMATEOtters buffer climate losses of kelp development, and the near absence and initial recovery of sea otters following protection in 1911. To accompany these kelp data, we curate a suite of environmental driver datasets and use Random Forest (RF) modeling to rank their influence on changes in canopy cover. This provides a more informed account of the long-term status of California kelp forest ecosystems and identifies natural strategies for climate resilience and ecosystem restoration. Methods Kelp cover time series To assess century-scale changes in kelp forests throughout California, we analyzed an histori- cal data source from early 20th century U.S. government ship-based surveys of its Pacific coast commercial resources, led by three scientists, George B. Rigg, Frank M. McFarland and Wesley C. Crandall [34]. Their data have been foundational to understanding kelp forest dynamics in Washington [35, 36] and Alaska [37] and provide a similar opportunity for examining change throughout California. While invaluable as a source of long-term information, several factors suggest these historical surveys may represent a conservative baseline. As an inventory of com- mercially harvestable kelp, scientists only mapped large beds measuring > 2.5 ha. Additionally, the government scientists who performed the surveys observed that kelp coverage was “unusu- ally low” [34]. Though historical assessments of the distribution of California kelp forests are regrettably few—a main impetus for this present study—the surveyors’ anecdotal observation is in agreement with historical assessments from Washington state that describe 1911 as a 50-year (1880–1930) kelp canopy minimum [35]. Nonetheless, considering the highly dynamic nature of kelp cover in space and time [38] and the additional need for historical reference points to assess long-term ecological change [29, 31], here we develop a cautious methodology to compare historical and contemporary kelp survey data. The historical dataset is contained in 26 map sections of the California coastline, represent- ing ship-based surveys from 1911–1912 with additional beds reported from 1910. To extract individual kelp beds, we georeferenced each map to fixed reference points from the California state shapefile [39, 40], confirming alignment by matching survey depths with modern bathymetry data. Within each survey map, we then digitized canopy cover by tracing each des- ignated kelp bed. This resulted in 187 polygons described as Macrocystis, Nereocystis or mixed kelp species along the California mainland with an additional 56 patches in the Channel Islands. For each harvestable bed, historical surveyors attributed 6 kelp densities–from “very thin” to “very-heavy”–that they originally derived empirically and quantitatively in meticulous detail and subsequently binned into categories [34]. We explored using these quantitative den- sities [34] as a correction factor (see S1 Text, S1 Table) to discount the area of the smoothed historical kelp bed polygons (Fig 1A), but developed a stepwise routine to facilitate comparing historical and contemporary kelp data (see below). We obtained contemporary kelp canopy estimates from CDFW aerial surveys (https://bit. ly/3bI1D4l) [41] for 2014–2016, encompassing a similar 3-year period. These surveys captured high-resolution multispectral imagery that were later downsampled to 2m resolution and gen- erated into shapefiles of kelp polygons. This procedure has become an established method for coastal monitoring and ground-truthing coarser (30m) LANDSAT imagery [36, 38], especially when kelp cover is sparse [42–44] or fringes rugose coastlines [45]. Monitoring was standard- ized to occur during the fall season of peak kelp abundance and when tidal currents, fog and glare are at their minima [41]. To build a contemporary dataset comparable with the three- year historical survey, we used ArcGIS tools [39] to overlay the 2014, 2015, and 2016 shapefiles (Fig 1B), then created a novel layer by outlining the union of kelp polygons. The resulting out- lined shapefile (Fig 1C) mimicked the resolution and form of the historical “harvestable” kelp PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 3 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Fig 1. Regional discount rates for comparing historical and contemporary kelp canopy surveys. Regional mainland examples of (A) historical maps and noted harvestable beds (map images provided from [34], and in the public domain), (B) composite of contemporary (2014–16) CDFW aerial surveys, (C) their reframing at comparable scale (or as harvestable beds), and (D) proportional canopy cover distributions derived from the intersections of (B) and (C) throughout California. The 1911–12 kelp survey represents an effort by the US Department of Agriculture to assess potash resources from California’s summer to fall seaweed canopy. Similarly, during the mid-summer to fall seasonal peak, CDFW periodically conducted annual statewide aerial surveys of kelp canopy from 1989 through 2016. Map base layer provided by ArcGIS Hub (https://hub.arcgis.com/datasets/1612d351695b467eba75fdf82c10884f/explore) with U.S. Census data and licensed as public domain. https://doi.org/10.1371/journal.pclm.0000290.g001 bed output by further smoothing pixelated vector data that originated as high-resolution raster imagery, and by excluding all polygons < 2.5 ha. Next, we used the “intersect” function to cal- culate regional mean polygon overlap values between the unioned and outlined contemporary kelp shapefiles (Fig 1D), to be used as correction factors for estimating kelp canopy area from the historical maps. To create a comparably scaled statewide historical benchmark, we dis- counted the area of each harvestable kelp bed from the 1910–1912 surveys by applying the cor- rection factors for northern, central, and southern California. These regional boundaries (marked at Pigeon Point and Point Conception) are widely recognized in marine ecology and specifically relevant here due to kelp composition; northern California is dominated by Nereo- cystis, southern is exclusively Macrocystis, and the central region is a mixture of the two. To compare the historical vector and contemporary raster datasets, we overlayed both geor- eferenced surveys with a 500 m linear coastal transect, extending from shoreline to the 30 m isobath. This linearized binning of the California coastline, from the Mexico (0 km) to Oregon border (1,620 km), is our geospatial framework for all datasets and analyses. We then charac- terized century-scale changes in kelp forests along California’s mainland coast by calculating the difference between recent (2014–2016) and historical (1910–1912) canopy area within each 500 m unit. To contextualize and visualize local trends, we then fit a uniform-span locally weighted regression (“LOESS”, α = 0.075) to these data [46]. For the historical kelp surveys, we calculated the standing biomass of kelp carbon from bed areas, derived densities [34] and PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 4 / 18 PLOS CLIMATEOtters buffer climate losses of kelp species-specific wet kelp to dry carbon biomass ratios [47, 48]. For recent surveys, we used a similar procedure but applied area-weighted averages for bed density and wet-to-dry biomass conversions derived for each region using the historical surveys. For all surveys, we express kelp carbon storage in CO2 equivalents and calculate its social cost—the estimated costs of eco- nomic damages from CO2 emissions or benefits from CO2 removal [49]. While international carbon frameworks typically conduct CO2 accounting in terms of C sequestration, these rela- tionships for kelps are currently unresolved at scale. Until such empirically verified sequestra- tion rates exist, here we report kelp CO2 equivalents in terms of standing biomass—a metric which is of value. Driver datasets and analytical models Next, we identified and curated spatially resolved environmental features that represent likely drivers of regional kelp ecosystem changes over the last century. To assess the potential effects of long-term oceanographic warming events (e.g., ENSO, marine heatwaves), we examined two gridded, monthly 1˚ × 1˚ SST products (HadlSSTv1.1, COBESSTv2) and one 0.25˚ × 0.25˚ product (NOAA OISST) [50–52]. Similar to previous work [15], we defined extreme heat as exceeding the 95th percentile of SST observed during the first 50 years of record (1870–1919) for each calendar month within each coastal grid cell, averaged from the HadlSSTv1.1 and COBESSTv2 data series. With these historical benchmarks, we quantified extreme heat over the contemporary period (1983–2016) with the finer scale NOAA OISST product. For the same contemporary period, we calculated the months with mean NOAA OISST values � 20˚C, rep- resenting a maximum physiological tolerance for Macrocystis recruitment [53, 54]. In addition to climate, we characterized contemporary coastal benthic habitat by proportion of hard sub- strate, using data derived from the California Seafloor and Coastal Mapping program [55]. To incorporate trophic dynamics, we calculated 2014–2016 mean sea otter population density from annual USGS range-wide spring surveys throughout central California [56]. We also integrated an approximate measure of human-related stressors by obtaining 30 arc-second gridded (~1 km2) coastal (within 2.5 km of shore) population data [49]. To explore effects of net primary productivity (NPP) variability on changes in kelp canopy, we acquired available (2003–2016) monthly estimates from the Vertically Generalized Production Model (VGPM; https://bit.ly/ 3kQBgO8). From these data, we estimated both annual and monthly mean measures of variabil- ity along the California coastline at a spatial resolution of 0.083˚ × 0.083˚. To standardize all datasets and match with kelp cover, we assigned all variables to the closest 500 m coastal seg- ment, applying a uniform-span locally weighted regression (“LOESS”, α = 0.075) to factors where data are not static (sea otters, humans) or derived from coarser scale models (SST, NPP). Finally, we modeled the relationships between environmental features and kelp cover changes using RF [57]. RF is a type of machine learning algorithm that generates random sub- sets of model inputs to predict the response variable, through bootstrapping a set of training data (sampled with replacement) and growing a “forest” of diverse and uncorrelated “trees” [58, 59]. Here the RF framework is appealing as it capably describes non-linear and non- parametric relationships, provides robust model predictions with an unbiased assessment of the generalized error, and offers unique insight into variable interactions (partial dependency visualizations). More generally, machine learning is becoming critical in conservation science to manage large, sensor-based data streams into efficient analytical workflows and system learning [60]. Previously, we applied RF [26, 61–64] in a similar manner to understand long- term changes in marine ecosystems. Within our RF model, we used raw (or non-transformed) data series for the output variable (kelp differences) and resolved, static input variables (hard substrate), but smoothed (LOESS, PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 5 / 18 PLOS CLIMATEOtters buffer climate losses of kelp α = 0.075) input factors where data are not static (sea otter and human population densities) or derived from coarser scale models (SST heat extremes, and NPP variances). The model excludes coastal transect bins where kelp was not detected during any surveys, so that a zero result singularly refers to a lack of change in kelp forested areas, not the absence of this ecosys- tem. To ensure sampling independence, we tested for spatial autocorrelation among model residuals (Moran’s I = -0.01) [65]. We then improved model performance by eliminating highly correlated variables [66], and tuning model parameters (‘mtry’ and ‘ntree’) using a sim- ple grid search routine. We also assessed model robustness by randomly generating 100 itera- tions of training and validation datasets, then summarized results to characterize model performance and rank variable importance [58]. Finally, to examine interactive effects between factors influencing kelp changes, we created partial dependency plots, pairing key environ- mental drivers from the final model output. All scripts are available at the third-party repositories GitHub (https://bit.ly/3pvUkQI) and Open Science Framework (https://osf.io/gsjex/), or in the Supplemental files (S1 and S2 Data in S1 File). All analyses and figures were conducted in version 4.0.3 of the R statistical environ- ment [67] and using ArcGIS desktop 10.8.1 software, with base map layers under license by Esri [39, 40, 68]. Results California’s overall kelp canopy area declined slightly (-6.9%, -8.4 km2) between historical (1910–1912) and contemporary (2014–2016) time periods, but differences among regional trends were dramatic along the mainland (Table 1). Gains in central California (+57.6%, +19.7 km2) nearly compensated for losses in the northern (-63.0%, -8.1 km2) and southern (-52.1%, -18.3 km2) regions. By comparison, kelp in the offshore Channel Islands declined slightly (-4.5%, -1.7 km2) in part from significant increases at San Miguel (32%) and San Nicolas (68%) Islands, which balanced losses from all other islands. Fig 2 plots century kelp area differ- ences along a continual mainland transect from south to north California. The 3 most extreme kelp declines occur at both margins of the southern California Bight (e.g., from Santa Barbara to San Diego) and near Cape Mendocino in the north where there was a near total loss (Fig 2). By contrast, kelp canopy increased nearly everywhere throughout the central coast. The estimated historical standing biomass of carbon in California kelp amounted to 556.5 kt CO2, with 444.6 kt CO2 on the mainland, and 111.9 kt CO2 in the Channel Islands during the 1910–1912 survey composite (Table 1). Though kelp canopy declined over the last century, we estimate carbon biomass may have increased by 5.3% to 586.0 kt CO2 in the 2014–2016 Table 1. Statewide and regional changes in California kelp over the last century. At the state level, the total area (-6.9%), carbon biomass (+5.3%), and social costs (+5.3%) of harvestable kelp beds (see Methods) were not considerably different from 1910–1912 to 2014–2016 surveys. These trends, however, obscure stark regional dif- ferences that encompass a dramatic shift of California kelp over this period. In central California, kelp increased 57.6%, growing 19.7 km2 and adding an estimated 145.6 kt CO2. In all other regions kelp declined. Most notably, northern California saw 63% declines in kelp amounting to an estimated 8.1 km2 and 63.2 kt CO2 lost. The overall decline in kelp canopy area with a simultaneously estimated increase in kelp carbon biomass over time highlights regional differences in species composition and associ- ated bed density and carbon content. The estimated social cost of kelp carbon follows the biomass trends, and in both periods exceeds $US 100M. Harvestable Kelp Beds (km2) 1910–12 2014–16 Δ (km2) Kelp C storage (kt CO2 equivalents) 2014–16 1910–12 Δ (kt CO2) Δ (%) Social Cost of C ($1M) Δ (%) 1910–12 2014–16 Δ ($1M) Region Northern Central Southern Channel Islands Mainland total California total 12.8 34.2 35.0 38.3 82.1 4.7 54.0 16.8 36.5 75.5 120.4 112.0 -8.1 19.7 -18.3 -1.7 -6.6 -8.4 -63.0 57.6 -52.1 -4.5 -8.1 -6.9 100.3 252.7 91.7 111.9 444.6 556.5 37.1 398.3 43.9 106.8 479.2 586.0 -63.2 145.6 -47.8 -5.1 34.6 29.5 -63.0 57.6 -52.1 -4.5 7.8 5.3 18.6 46.7 17.0 20.7 82.2 6.9 73.7 8.1 19.8 88.7 103.0 108.4 https://doi.org/10.1371/journal.pclm.0000290.t001 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 -11.7 26.9 -8.8 -0.9 6.4 5.5 6 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Fig 2. Century-scale, mainland kelp canopy losses throughout northern and southern regions of California slightly surpassed increases along the central coastline. Mainland kelp canopy resources depicted by (A) total area (ha), and (B) changes within nearshore habitat (� 30m depth) during 1911–12 and 2014–2016 (composite) from (C) the Mexico to Oregon state border (0 to 1620 km) [68]. Canopy area gains along central California nearly offset losses within northern and southern coastal regions (see Table 1). To better visualize broad regional trends, we fit a locally weighted regression (LOESS, span 0.075) to these kelp features. Kelp canopy changes between contemporary and historical surveys are indicated by circles, with gains in blue and losses in red. All measurements reflect peak seasonal abundance in kelp from mid-summer through fall. Southern- central and central-northern region dividing landmarks are Point Conception and Pigeon Point, respectively, with San Francisco Bay, Monterey Bay, Santa Barbara Channel, Los Angeles Basin, and San Diego Bay noted as geographic features. Map base layer provided by ArcGIS Hub (https:// hub.arcgis.com/datasets/1612d351695b467eba75fdf82c10884f/explore) with U.S. Census data and licensed as public domain. https://doi.org/10.1371/journal.pclm.0000290.g002 survey. This is the result of regional differences in species composition, their associated impli- cations for the density of kelp beds, and the consequent carbon composition of kelp tissues (see S1 Text). We estimate increases of 57.6% in the total standing biomass of kelp in the cen- tral California (252.7 to 398.3 kt CO2), steep declines in the northern (-63.2 kt CO2, -63.0%) and southern (-47.8 kt CO2, -52.1%) regions, and a modest decline in the Channel Islands (-5.1 kt CO2, -4.5%). These reginal trends represent a dramatic spatial realignment of Califor- nia kelp. In 1910–1912, 45.4% of California’s kelp carbon biomass was in central California, which jumped to 68.0% in 2014–2016. Changes in the estimated social cost of carbon kelp fol- low biomass proportionally, with a slight increase from $US 103.0M in 1910–1912 to $US 108.4M in 2014–2016 (Table 1), with the same regional realignment. Sources of influence on kelp canopy cover (sea otters, substrate, climate, NPP variability, and humans) varied along the transect revealing areas of higher and lower potential resilience and impact (Fig 3). Kelp canopy gains throughout central California indicate a confluence of optimal conditions, where sea otters are recovering (Fig 3E), extreme heat and annual NPP variability are low (Fig 3A, 3B, 3F and 3G) hard substrate is abundant (Fig 3C), and human populations (and coastal development) are minimal overall (Fig 3D). In southern California, where kelp declines were greatest, the opposite conditions are true. Perhaps due to greater sea- sonal variability of NPP (Fig 3F), Northern California experienced major kelp forest declines despite several positive features–abundant hard substrates, low human population densities, and a lack of absolute extreme heat (SST � 20˚C). However, no California region is free from PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 7 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Fig 3. Potential coastal sources of influence to statewide kelp canopy area. (A) Sea surface temperature (SST) heat extremes and (B) kelp climate maximum events (� 20˚ C) occurred most frequently throughout the southern or low latitude portion of (H) California. We estimated occurrence of coastal heat extremes by calculating mean-monthly frequency of events (1983–2016) within the 95th percentile of historical SSTs recorded from 1870 to 1919. (C) Hard seafloor substrate (� 30-meter depth) is more abundant throughout northern and central coastal regions, nearly the reverse distribution of (D) human population density. (E) Sea otter population densities are greatest within the central portion of the state’s coastline, where recovery is occurring. (F) Monthly and (G, J) annual net primary productivity (NPP) variability distributions are nearly mirror opposites, corresponding with greater seasonality in northern California and longer cycles of extreme climate conditions in the southern coastline. Raw data are indicated by circles and smoothed using a uniform-span, locally weighted regression (LOESS, α = 0.075). During analysis, we used smoothed data to characterize both non static factors (i.e., sea otter, humans) and environmental data derived from coarser scale models (i.e., SST, NPP). Map base layer provided by ArcGIS Hub (https://hub.arcgis.com/datasets/1612d351695b467eba75fdf82c10884f/explore) with U.S. Census data and licensed as public domain. https://doi.org/10.1371/journal.pclm.0000290.g003 PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 8 / 18 PLOS CLIMATEOtters buffer climate losses of kelp extreme marine heat (Fig 3A and 3F), and sea otters are functionally absent outside the state’s central coastline (Fig 3E). The measured raw (Fig 4A) and modeled (Fig 4B) influences to California kelp forest changes show that kelp increased with the population density of sea otters (and their ecosystem functions), declined with the prevalence of extreme heat and NPP variability, declined where hard substrates were scarce, and was ambiguously influenced by human population density. Even though relative and absolute measures of climate change might affect kelp physiology dif- ferently, these two climate factors were highly correlated (Fig 3A and 3B). Following best prac- tices [66], we removed the less resolved absolute heat stress series from the model, improving model performance. During sensitivity analysis, the final RF model explained > 70% of the data variability, performing equally well using either training (mean R2 = 0.71, SE 0.0014) or validation (mean R2 = 0.71, SE 0.0031) datasets and indicating no overfitting. Trophic dynam- ics (i.e., sea otter functional presence or absence), hard benthic substrate, extreme marine heat represented by a fixed historical benchmark from before and during the earliest kelp survey data, and NPP variability explain most of the observed changes in California kelp (Fig 3C). Benefits to kelp occurred where sea otters are now relatively abundant, with model predictions indicating kelp stabilization or gains at population levels > 0.03 sea otters ha-1. While extreme heat was a dominant model factor explaining kelp changes (Fig 4C), its effect declined where kelp losses were highest (Fig 4A and 4B). Apart from individual variable effects, understanding their interactions to model outputs can provide greater practical insights. Fig 4D examines interactions among model features using two-way partial dependency plots (PDPs). This shows the primary effect of sea otters on kelp changes, enhancing gains across a gradient of hard substrate and buffering losses from extreme heat (Fig 4D). Sea otters exerted the greatest influence on kelp ecosystem resilience (e.g., green shaded area in Fig 3D) corresponding to regional kelp canopy expansion between 1910 and 2016. In their absence, kelp declined from every other stressor (loss of hard substrate, ocean warming, NPP variability, and humans). Discussion Assessing ecological trends over relevant temporal and spatial scales is essential to identify the full magnitude and key drivers of change, but reliable information rarely exists over this time span. Here, we extend a previously reported 35-year baseline [1] by 70 years along the full extent of California’s coastline, which spans roughly 10˚ of latitude and represents a broad range of coastal ecosystem states, from highly impacted, densely populated industrial outfalls to more remote, nearly intact marine protected areas with recovering sea urchin predators. By examining environmental factors related to century-scale, spatially resolved kelp canopy changes along California’s mainland coastline, we identify four important findings. First, although overall statewide canopy decline was low, regional changes were dramatic with cen- tral California kelp forest gains nearly offsetting losses along northern and southern mainland coastlines (Table 1, Fig 2). Second, sea otters outweighed all other environmental factors, rep- resenting a strong driver of kelp forest gains by increasing canopy resilience to impacts from more detrimental factors (Figs 2–4). Third, in the absence of sea otters, extreme heat, high var- iation in NPP, and soft benthic substrate corresponded most with declines (Figs 3 and 4). Fourth, we translate our kelp area metrics to carbon accounting and social costs to assess the importance of kelp ecosystems and their climate resiliency in global conservation and policy frameworks. Our identification of substantial regional declines in kelp canopy over the last century sug- gests staggering alterations of California’s coastline, capturing not only recent losses in PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 9 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Fig 4. Large-scale SST anomalies and net primary productivity variability corresponded most with overall kelp canopy declines, but sea otter density mitigated statewide losses. (A) raw, pair-wise comparisons of kelp changes to model factors, and (B) modeled relationships of individual conditional expectations (ICE) from the Random Forest (RF) model outputs for the highest ranked variables (please note the varying y-axis scaling). Predominantly soft seafloor substrate, moderately high temperature heat extreme frequency and NPP variabilities, and densely populated coastlines PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 10 / 18 PLOS CLIMATEOtters buffer climate losses of kelp related most strongly with canopy kelp losses. By contrast, sea otters corresponded with minimal to low declines, or even kelp gains at higher population densities (> 0.03 ha -1). We assigned (C) variable importance rankings from comparative increases in model MSE when each factor was removed. Overall, this six factor RF model explains 71% of variability related to century-scale kelp canopy area changes. (D) Two-way partial dependency plots describe the predicted interactions between impact of selected factors on kelp canopy changes. Here kelp increases with y^, symbolized with cool colors. Among all environmental factors, only sea otters consistently correspond with predicted gains in kelp canopy area. https://doi.org/10.1371/journal.pclm.0000290.g004 northern California [22] but mid-century decreases along the southern transect [17, 69]. How- ever, this may reflect a fraction of true losses incurred during the last two centuries when con- sidering effects of nineteenth-century, grassland erosion from cattle grazing and crop cultivation along southern California coastal watersheds [28]. By the early 1900s, rapid, unmanaged agricultural development yielded an estimated 10-fold increase in sediment depo- sition from the Los Angeles and Orange county alluvial plain, smothering historically abun- dant marine granite substrate and a complex benthos formed by millennia of shelled invertebrates and gravel, which may have provided suitable substrate to support extensive off- shore kelp forests [28]. After 1900, port excavations, inadequate wastewater management, and shallow sewage outfalls degraded nearshore kelp beds off the southern California coastline [17, 18, 70, 71] during dramatic, mid-twentieth century human population growth [72]. Where kelp forests remained, anchoring to softer sediments increased their vulnerability to cata- strophic removal from more severe and frequent seasonal storms in a warming ocean [73]. Such patterns are like effects seen in other nearshore ecosystems (e.g., coral reefs) where impacts from early agricultural development and land use resulted in sedimentation and loss prior to the onset of acute global climate change [25, 27]. Our findings here suggest that man- aging terrestrial land use is an important component of maintaining and restoring the health of marine and coastal ecosystems, alongside managing contemporary impacts from warming oceans. Future research that reconstructs benthic substrate dynamics over a similar 100-year time may provide greater insights into long-term drivers and resiliency planning for kelp ecosystems. Perhaps most notably, we found that kelp canopy declines along northern and southern mainland regions of the state were offset by gains within the central coast, corresponding with the presence of sea otters. Absent from our model, we found similar trends among the Channel Islands with kelp canopy gains along islands where sea otters are observed or recovering (San Miguel and San Nicolas Islands) balancing dramatic losses among all others, where sea otters are absent (Santa Rosa, Santa Cruz, Anacapa, Santa Barbara, Santa Catalina, and San Cle- mente, also see [74]). Sea otter recovery is currently limited to central California and San Nico- las Island, where protections and active reintroductions have been most effective [62, 75, 76]. Although sea otters are recognized as integral to healthy kelp forests throughout the North Pacific [77–79], their role in California, where trophic cascades and species assemblages are complex [32, 80, 81], is more difficult to measure. Similar to recent long-term kelp assessments in Alaska [37], our results suggest that otters are critical to maintaining kelp forest health throughout their range, buffering long-term kelp loss where their population densities are highest in central California (Figs 2–4). Sea otter populations may contribute to increased climate resilience by providing for a mul- titude of kelp ecosystem services, perhaps including carbon storage. However, recent research from a spatially constrained section of the central California coast [81] suggests otters may be limited in recovering kelp ecosystems from a barren state where conditions are already degraded by coastal development. The role of otters in increasing kelp forest canopy therefore underscores the potential for trophic rewilding—the reintroduction of herbivores and PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 11 / 18 PLOS CLIMATEOtters buffer climate losses of kelp carnivores to systems where they have been lost—to support natural climate resistance and resilience. Research from terrestrial ecosystems suggest that carbon cycling may benefit from such trophic rewilding [82]. Higher elephant densities in central African rainforests, for exam- ple, led to shifts toward larger trees with higher wood density, enhancing carbon storage [83]. Across ecosystems, this role of animals in carbon storage has been underappreciated [84]. Given that marine megafauna population declines across the globe approach 90% [85], the co- benefits of restoring marine animal populations to enhance biodiversity, build natural climate resilience, and store carbon must be given serious consideration. Our results demonstrate the damaging effects of warming temperatures on kelp [14, 15, 22, 86, 87], especially within ecosystems already subjected to trophic downgrading. The large spa- tial scale of our analysis also allows insight into pockets of resilience and vulnerability. For example, our finding that the effect of extreme marine heat declined when kelp losses were highest is consistent with previous research, suggesting local adaptation and heat tolerance in southern California [88]. Single species Macrocystis stands are dominant in this region, and this species occurs on 4 continents and in 4 ocean sub-basins (real-time crowdsourced data at https://bit.ly/3QNEgoI), likely indicating significant genetic diversity and phenotypic plasticity [89, 90]. Northern California, by contrast, saw more moderate extreme heat and human popu- lations, yet had similar extreme declines in kelp cover by comparison to southern California. Unlike southern California, northern California is more dominated by Nereocystis stands. N. luetkeana has a limited distribution in the North Pacific and an annual life cycle, perhaps con- ferring less phenotypic diversity and greater susceptibility to extreme heat [91]. Collectively, our findings provide valuable information about the importance of restoring trophic relationships and minimizing stressors from coastal development to increase kelp for- est resilience within a warming and more variable climate. Although kelp enhancements have been successful at the small scale [8], California lacks coordinated, broad scale activities, and these are also rare globally. Large-scale kelp forest restoration programs might benefit from recognition and support from international blue carbon initiatives. Blue carbon initiatives cur- rently focus on mangroves, sea grass meadows, and salt marsh ecosystems [92]. The omission of kelp forests may underestimate the carbon storage potential from coastal ecosystems [5, 9] while also reducing programmatic resources and strategic capacity for nearshore ecosystem restoration. The addition of macroalgae into carbon crediting initiatives may provide funding for restoration and gardening initiatives that offer potential solutions to rebuilding marine resources and their economic, cultural, and life-supporting value in a world where climate change continues to alter and threaten coastal communities. As an example, our historical esti- mate of 556.5 kt CO2 equivalents stored in California kelp has a monetized value of $ 103.0M, as determined by the most recent “social cost of CO2” (mean projection, 2020 USD) [49]. This historical value of CO2 equivalents in California kelp standing biomass is $ 5.4M less than the value ($ 108.4M) estimated from the 586.0 kt CO2 in the 2014–2016 survey average, but again this masks dramatic regional differences. Although interpreting historical data is imperfect and not without limitations, long-term ecological records are essential for understanding ecosystem dynamics, climate resiliency, and effective restoration [24, 31]. Because kelp is highly variable across seasons and individual years [38], we focused on comparing kelp maximums (or spatial unions) observed across two multi-year time periods, separated by a century. To resolve differences between ship-based and aerial survey methods, we created less granular, blocky patches from aerial surveys, mim- icking the historical data, then calculated regional canopy area discount rates based on con- temporary values. While the corrected historical kelp area may underestimate canopy cover in the early 1900s, it provides a conservative record to compare with contemporary data. Given the meticulous and extensive nature of the early U.S. government inventories [34], and the PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 12 / 18 PLOS CLIMATEOtters buffer climate losses of kelp global significance of kelp ecosystems, these historical data presented an important opportunity. This century-long evaluation of trends in California highlights dramatic regional declines, resulting from anthropogenic effects of climate warming, coastal development, and trophic disruptions. This magnitude of California kelp deforestation is greater than other reported assessments [1, 8] perhaps from a finer geographic scale and longer baseline reference, which may still underrepresent true losses when considering human impacts before 1900. Our study also indicates that among stressors, a warming climate has a profound single influence, but this factor may be enhanced by the sedimentation and smothering of nearshore benthic sub- strates during rapid coastal development. Where coastal development is managed (or miti- gated), recovery of sea otters and their trophic relationships may increase kelp forest resilience to climate change, especially when warming temperatures intensify sea urchin recruitment and herbivory. Restoration of California’s coastline resources requires the rapid implementa- tion of innovative, collaborative, and sustainable ocean gardening strategies to address climate change and prevent further decline in kelp forest ecosystems. Supporting information S1 Text. This document provides additional details on the derived quantitative densities and ordinal descriptions of historical kelp beds and calculating carbon stores from kelp biomass. (DOCX) S1 Table. Ordinal and quantitative kelp bed densities obtained during 1911–1912 histori- cal surveys from Cameron et al 1915. 1 Densities provided in the original (lbs yard-2) and converted (kg m-2) units. “Proportion total” is the value of each density category relative to the maximum density category (“very heavy”) for each kelp bed type. Here “KELP BED TYPE” is the dominant kelp species and “DENSITY ORDINAL” is the narrative density characteriza- tion. “DENS_MIN lb y-2” and “DENS_MAX lb y-2” are the minimum and maximum derived densities, respectively, and “DENS_MIN kg m-2” and “DENS_MAX kg m-2” are those densities converted to metric units. “PROPORTION OF TOTAL” is the quantitative density relative to each kelp bed type ‘s maximum value. (XLSX) S1 File. S1 Data and S2 Data: For both data files, the column headers are defined as follows. “OBJECTID” is a unique polygon identifier associated with the ArcGIS database. For the his- torical data, “densitycode” refers to the ordinal density categories, where the lowest number, 1, equals the lowest density “very thin” and so forth. “ChartNum” refers to the original chart number listed in Cameron et al 1915. For both files, “kelptype” refers to the dominant species composition, “kelpdensity” is kelp bed density in kg m-2 (see above), “aream2” is the polygon area in m2, “aream2_corr” is the discounted polygon area in m2 (according to density, see Sec- tion 1 above), and “location” is either California mainland or Channel Islands. “kelpwetmass”, “mt_C”, and “mt_CO2” are all calculated columns of each polygon’s total wet mass, dry C bio- mass, and CO2 equivalents; respectively, where the unit is mt = 1000 kg. For the contemporary file, “year” is the calendar year of the survey, “kelpbed” is the assigned bed number, and “class_name” is whether the surveyed kelp bed canopy was at the surface or just below (subsur- face). (ZIP) PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000290 January 18, 2024 13 / 18 PLOS CLIMATEOtters buffer climate losses of kelp Acknowledgments E. Mapstone, W. Rex, T. Wang, C. Ross aided in the georeferencing and curation of historical data. J. Fujii improved earlier versions of this manuscript. C. Pfister and K. Miranda provided data and advice on kelp carbon storage. C. Colgan advised on carbon pricing schemes. Author Contributions Conceptualization: Teri E. Nicholson, Loren McClenachan, Kyle S. Van Houtan. Data curation: Teri E. Nicholson, Loren McClenachan, Kisei R. Tanaka. Formal analysis: Teri E. Nicholson, Kisei R. Tanaka, Kyle S. Van Houtan. Funding acquisition: Kyle S. Van Houtan. Investigation: Teri E. Nicholson, Loren McClenachan, Kisei R. 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10.1371_journal.pdig.0000447	RESEARCH ARTICLE Identification of integrated proteomics and transcriptomics signature of alcohol- associated liver disease using machine learning Stanislav Listopad1¤, Christophe Magnan1, Le Z. Day2, Aliya Asghar3, Andrew Stolz4, John A. Tayek5, Zhang-Xu Liu4, Jon M. Jacobs2, Timothy R. Morgan3, Trina M. Norden- KrichmarID 1,6 1 Department of Computer Science, University of California, Irvine, California, United States of America, 2 Biological Sciences Division and Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America, 3 Medical and Research Services, VA Long Beach Healthcare System, Long Beach, California, United States of America, 4 Division of Gastrointestinal & Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 5 Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Department of Internal Medicine, David Geffen School of Medicine, University of California Los Angeles, Torrance, California, United States of America, 6 Department of Epidemiology and Biostatistics, University of California, Irvine, California, United States of America ¤ Current address: Department of Neuroscience, Scripps Research, La Jolla, California, United States of America * slistopa@uci.edu (SL); tnordenk@uci.edu (TMN-K) Abstract Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcrip- tomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and prote- omics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our data- set and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accu- racy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classifi- cation accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Listopad S, Magnan C, Day LZ, Asghar A, Stolz A, Tayek JA, et al. (2024) Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning. PLOS Digit Health 3(2): e0000447. https://doi.org/10.1371/journal. pdig.0000447 Editor: Nicole Yee-Key Li-Jessen, McGill University, CANADA Received: September 8, 2023 Accepted: January 9, 2024 Published: February 9, 2024 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: The human RNA raw sequencing data in this study requires deposit into the Database of Genotypes and Phenotypes (dbGAP) of the National Center for Biotechnology Information (United States National Library of Medicine) with controlled access. The data will be available through dbGaP (https://www.ncbi.nlm. nih.gov/gap/) under accession number: phs003112.v1.p1. The public RNA data used for validation in this study is available in the GEO database under accession number GSE142530 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 1 / 16 PLOS DIGITAL HEALTH(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE142530). Proteomic data can be found in the MassIVE repository under accession number MSV000089168. Funding: Funding for this study was provided to the researchers in the Southern California Alcoholic Hepatitis Consortium (SCAHC) by the National Institute on Alcohol Abuse and Alcoholism (NIAAA, https://www.niaaa.nih.gov/) award numbers: U01AA021838 (TMNK), U01AA021886 (TRM), U01AA021884 (TRM), U01AA021918 (JMJ), and U01AA021857 (ZXL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Integrating liver disease protein and gene data using machine learning improvement in classification accuracy for the PBMC data. The set of integrated gene-pro- tein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol- associated liver disease. Author summary Alcohol-associated cirrhosis and alcohol-associated hepatitis can be difficult to classify clinically. Previously, we established that these two diseases can be differentiated using RNA sequencing gene expression data collected from either liver tissue biopsies or from peripheral blood mononuclear cells (PBMCs), which are extracted from blood samples. In the current study, we investigated whether using protein expression data, in addition to gene expression data, would improve our machine learning models’ ability to distinguish between the two alcohol-associated liver diseases and enable identification of gene and protein biomarkers. We found that our models accurately classified alcohol-associated liver diseases with each data type. We were also able to identify promising tissue and blood-based diagnostic gene and protein biomarkers. Additionally, we have demonstrated that challenges present in analyzing small sample size, high dimensional genomic data can be addressed through careful application of appropriate software, bioinformatics, and machine learning methods. By applying these computational approaches to this liver dis- ease genomics data set, we have identified blood-based diagnostic biomarkers of liver dis- ease that will potentially contribute to the development of highly accurate blood tests that will replace invasive liver biopsies. Introduction In this study, we focused on alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) because these are deadly liver conditions with similar clinical presentation. In 2019 there were 23,780 deaths from alcohol-associated cirrhosis (AC) in United States [1]. This is more than triple the number of deaths from alcohol-associated cirrhosis in 1999. The patients with alcohol-associated liver disease (ALD) account for 18% of liver transplants [2]. However, attaining a liver transplant as an ALD patient is difficult, since donor livers are scarce and there are concerns about allocation to individuals with alcohol addiction [2]. Typically, a 6-month abstinence from alcohol is required to be a candidate for liver transplant [2]. Many of ALD patients have alcohol-associated hepatitis (AH) a condition which carries mortality as high as 50% at 3 months [3]. For the severe AH patients, the 6-month abstinence requirement can be tantamount to a death sentence [2]. When carefully selected, ALD patients can benefit from liver transplantation [4,5,6,7]. Currently, establishing AH diagnosis can require liver biopsy, typically done using a transjugular route [3]. Liver biopsy has several limitations, such as proce- dural risk of internal bleeding, high cost, and patient dissatisfaction. Thus, development of a non-invasive test that can reliably distinguish between AH and AC would be beneficial. Cur- rently, there are a large number of imaging and blood tests for diagnosis of liver cirrhosis [8]. However, liver biopsy remains the current standard for diagnosis [9]. Further improvement in accuracy of non-invasive tests is necessary to reduce the need for liver biopsy [10]. In a previous study, we established that gene expression biomarkers from liver tissue and peripheral mononuclear blood cells (PBMCs) can be used with a multiclass machine learning approach to successfully distinguish between multiple liver diseases [11]. In the present study, PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 2 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning in addition to transcriptomic data, we also obtained proteomic data for participants from the same cohort [12]. Addition of proteomic data presented new opportunities, but it also further increased the ratio of feature size to sample size. This made overfitting a greater challenge than when we only used the gene expression data. First, we compared how well gene and protein biomarkers could be used to classify these conditions separately. Then we examined whether further improvement in classification accuracy could be obtained by combining transcrip- tomic and proteomic data. As part of the classification process, we have identified the most effective gene and protein biomarkers of alcohol-associated liver disease. We also examined the degree of concordance between top differentially expressed proteins and genes for the three conditions. The gene and protein biomarkers identified in this study, with further valida- tion, could be used to develop new highly accurate blood tests to distinguish between various types of ALD. Materials and methods Study population This study was primarily conducted using biospecimens collected from participants enrolled by the Southern California Alcoholic Hepatitis Consortium (SCAHC). The protocol was approved by the IRB, and informed written consent was obtained from all participants. The liver tissue from participants with AC and healthy controls were obtained from the liver tissue cell distribution system (LTCDS) at University of Minnesota. The study population demo- graphics for liver tissue and PBMC samples for transcriptomic and proteomic analyses can be found in Tables 1 and 2. The biospecimens consisted of 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver tissue RNAseq samples, and 53 liver tissue proteomic samples. The liver diseases Table 1. Study population demographics (liver) for proteomic and RNAseq analysis. Liver tissue samples (proteomics) Liver tissue samples (transcriptomics) Age: mean ± std MELD: mean ± std Maddrey’s DF: mean BMI: mean ± std Gender: N (percent) Female Male Ethnicity: N (percent) Hispanic NHW Black Other Source AH n = 33 42.7 ± 11.4 25.2 ± 5.7 53.3 ± 22.2 29 ± 5.3 CT n = 10 56 ± 8.6 NA NA NA 3(9.1%) 30(90.9%) 0(0.0%) 10(100%) 25(75.8%) 5(15.1%) 2(6.1%) 1(3.0%) SCAHC NA NA NA NA LTCDS AC n = 10 51.9 ± 13.1 32 ± 6.1* NA 25.6 ± 8.4* 0(0.0%) 9(90%) 0(0.0%) 5(50%) 0(0.0%) 0(0.0%) LTCDS AH n = 32 43.3 ± 11.3 25.1 ± 5.7 52.3 ± 22.1 29.4 ± 5.9 CT n = 8 55.4 ± 4.3 NA NA NA AC n = 8 54.2 ± 6.9* NA NA NA 3(9.4%) 29(90.6%) 0(0.0%) 7(87.5%) 0(0.0%) 5(62.5%) 25 (78.1%) 5 (15.6%) 1 (3.1%) 1 (3.1%) SCAHC NA NA NA NA LTCDS 0 (0.0%) 4 (50.0%) 0 (0.0%) 0 (0.0%) LTCDS Abbreviations: AC, alcohol-associated cirrhosis; AH, alcohol-associated hepatitis; CT, healthy controls; MELD, model for end-stage liver disease; NHW, non-Hispanic White; NA, not available; SCAHC, Southern California Alcoholic Hepatitis Consortium. Missing MELD scores for 7 proteomic AC samples, BMI for 8 proteomic AC samples, and age for 3 transcriptomic AC samples. https://doi.org/10.1371/journal.pdig.0000447.t001 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 3 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Table 2. Study population demographics (PBMCs) for proteomic and RNAseq analysis. Age: mean ± std MELD: mean ± std Maddrey’s DF: mean BMI: mean ± std Gender: N (percent) Female Male Ethnicity: N (percent) Hispanic NHW Black Other Source AH n = 20 48.7 ± 11.6 24.5 ± 3.6 49.3 ± 17.3 29.6 ± 5.5 1(5%) 19(95%) 12(60%) 5(25%) 2(10%) 1(5%) SCAHC PBMC samples (proteomics) PBMC samples (transcriptomics) CT n = 22 34.8 ± 15.1 7.5 ± 2.5 2.5 ± 7.8 27.1 ± 4 AC n = 13 54.2 ± 11.2 13.6 ± 6.7 22.1 ± 23.3 30 ± 4.8 AH n = 38 47.3 ± 11.5 25 ± 3.8 52.6 ± 20.7 30 ± 6.2 CT n = 20 35.9 ± 15.6 7.3 ± 2.6 2.4 ± 8.1 27 ± 3.5 AC n = 40 54.5 ± 9.7 13.4 ± 5.8 21.1 ± 19.1 30.4 ± 5.1 10(45.4%) 12(54.6%) 0(0.0%) 13(100%) 1 (2.6%) 37 (97.4%) 8 (40.0%) 12 (60.0%) 0 (0.0%) 40(100.0%) 12(54.5%) 0(0.0%) 1(4.5%) 12(54.5%) SCAHC 10(76.9%) 2(15.4%) 0(0.0%) 1(7.7%) SCAHC 25 (65.8%) 10 (26.3%) 2 (5.3%) 1 (2.6%) SCAHC 8 (40.0%) 0 (0.0%) 2 (10.0%) 10 (50.0%) SCAHC 25 (62.5%) 13 (32.5%) 1 (2.5%) 1 (2.5%) SCAHC The ethnicity and sex percentages may not add up to 100% due to missing data. Abbreviations: AC, alcohol-associated cirrhosis; AH, alcohol-associated hepatitis; CT, healthy controls; LTCDS, Liver Tissue Cell Distribution System; MELD, model for end-stage liver disease; NHW, non-Hispanic White; NA, not available; SCAHC, Southern California Alcoholic Hepatitis Consortium. https://doi.org/10.1371/journal.pdig.0000447.t002 represented were encoded with two letter symbols as follows: alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC). Most of the AC participants within the SCAHC study were expected to be in-patients with decompensated cirrhosis. Best efforts were made during recruitment of the AH and AC groups within SCAHC study to match based on age, gender, and ethnicity. Severity-based matching was not possible due to small sample size. One of the main reasons for small sample size in our study and in publicly available data sets, is dif- ficulty in recruiting patients with AH. AH has a low incidence rate of an estimated 4.5 hospital- izations per 100,000 person per year [13]. Additional information about the inclusion and exclusion criteria, sample collection, sample processing, and preliminary data processing can be found in S1 Text. Partitioning samples into datasets Because some proteomic and transcriptomic samples came from the same participants, while others did not, we implemented a strategy to partition and balance the samples in the datasets into matched and unmatched sets. Table 3 summarizes the degree of matching between prote- omic and transcriptomic samples in liver tissue and PBMC. For several algorithms in the pipe- line, some of the unmatched subsets were too small. Therefore, we moved some matched samples into unmatched sample categories, and we will refer to these new categories as “bal- anced matched” and “balanced unmatched” subsets. We divided our data into the following dataset categories described below. Full datasets. These datasets are composed of all available samples for the given tissue and genomic datatype: PBMC 3-Way Full proteomics, PBMC 3-Way Full RNAseq, Liver 3-Way Full proteomics, and Liver 3-Way Full RNAseq. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 4 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Table 3. The degree of matching between proteomic and transcriptomic samples for PBMC and liver tissue. The numbers in parenthesis denote the number of sam- ples that were moved from matched category into matched balanced and unmatched balanced categories. PBMC (proteomics) PBMC (transcriptomics) Full Matched Unmatched Matched Balanced Unmatched Balanced Full Matched Unmatched Matched Balanced Unmatched Balanced AH 20 18 2 9(-9) 11(+9) AH 33 29 4 24(-5) 9(+5) CT 22 19 3 12(-7) 10(+7) Liver (proteomics) CT 10 3 7 3 7 AC 13 13 0 6(-7) 7(+7) AC 10 5 5 3(-2) 7(+2) AH 38 18 20 9(-9) 29(+9) AH 32 29 3 24(-5) 8(+5) CT 20 19 1 12(-7) 8(+7) Liver (transcriptomics) CT 8 3 5 3 5 AC 40 13 27 6(-7) 34(+7) AC 8 5 3 3(-2) 5(+2) https://doi.org/10.1371/journal.pdig.0000447.t003 Unmatched balanced datasets. These datasets consist of a mixture of matched and unmatched samples: PBMC 3-Way Unmatched Balanced proteomics, PBMC 3-Way Unmatched Balanced RNAseq, Liver 3-Way Unmatched Balanced proteomics, and Liver 3-Way Unmatched Balanced RNAseq. Matched balanced datasets. These datasets consist of only matched samples, such that for each RNAseq sample there is also a proteomic sample obtained from the same individual: PBMC 3-Way Matched Balanced proteomics, PBMC 3-Way Matched Balanced RNAseq, Liver 3-Way Matched Balanced proteomics, and Liver 3-Way Matched Balanced RNAseq. Matched balanced integrated datasets. These datasets were formed by merging the proteomic and RNAseq data from Matched Balanced datasets: PBMC 3-Way Matched Bal- anced Integrated and Liver 3-Way Matched Balanced Integrated. Validation dataset We validated our proteomic liver tissue machine learning (ML) models using data obtained from MassIVE repository (accession number MSV000089168) [12]. This dataset contained liver tissue proteomic data from participants with AH (n = 6) and healthy controls (n = 12). Notably, the healthy controls came from two different sources, 7 from University of Louisville and 5 from John Hopkins University. Publicly available proteomic data from PBMCs was not available for the conditions in our study, and therefore, only the liver tissue datasets were vali- dated using independent data. Information regarding the RNAseq liver tissue validation data- set can be found in our previous publication [11]. RNAseq Classification and Feature Selection Pipeline The detailed methods used to classify RNAseq counts and identify best genes are described in [11]. Briefly, the classification was performed using nested cross-validation with feature selec- tion. Features were selected using either differential expression software or information gain algorithm. Additionally, outlier features were removed prior to feature selection. Domain expertise was incorporated into the pipeline via enrichment analysis. For each dataset, multiple pipeline configurations were executed, resulting in multiple, promising, candidate gene sets. For each dataset, we then selected a single best gene set that maximized classification perfor- mance and in-silico biological relevancy (attained via enrichment analysis), while minimizing PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 5 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning the gene set size. The methods used throughout were focused on minimizing the possibility of overfitting. Note that for any given pipeline configuration, there is a resultant set of genes (can- didate gene set). Subsequently, when referring to candidate or best gene sets, we are also refer- ring to the pipeline configurations that resulted in those gene sets. Proteomic Classification and Feature Selection Pipeline Methods used to classify proteomic counts and identify best proteins were similar to the meth- ods used for analysis of RNAseq data with the following exceptions. Feature sizes. The feature sizes for proteomic data were largely based on our findings when dealing with RNAseq data. Due to smaller number of proteomic samples the maximum number of features used was reduced from 500 to 200. The following feature sizes were selected: 15, 25, 35, 50, 60, 70, 80, 90, 100, 150, and 200. Imputation. Unlike the RNAseq data, the proteomic data contained missing values. We used median and replacement with zero imputation strategies to address this. Median imputa- tion replaces missing values using the median along each column (feature, in this case protein). Zero imputation replaces all missing values with zeros. Imputed values were used for proteins that were missing data for a small number of sam- ples. The following imputation thresholds were used 0%, 5%, and 10%. That is, values for a given protein were only imputed if less than the threshold % of total samples were missing data. Threshold of 0% means no imputation took place and all proteins with missing values were removed. Differential expression feature selection. Cuffdiff [14] was used for the differential expression analysis of the RNAseq data, while we used INFERNORDN to perform differential expression analysis with proteomic counts [15]. Proteins were filtered by q-value � 0.05. After- ward, any proteins that had too much missing data (above imputation threshold) were removed. In silico biological validation and best protein set selection. Enrichr [16], which was used for RNAseq data analysis, was replaced with AGOTOOL [17] for enrichment analysis of proteins. When selecting the best protein set, an identical algorithm was used for both tran- scriptomic and proteomic data, with one exception. That is, for proteomic data, protein sets produced by configurations with the least imputation were preferred for selection. Analysis outline The analysis pipeline was divided into the 3 stages, which are shown in Fig 1. Stage 1 (No Integration). In the first stage, we used machine learning approaches with nested cross-validation to separately classify the Full datasets (Liver 3-Way RNAseq Full, Liver 3-Way Proteomics Full, PBMC 3-Way RNAseq Full, and PBMC 3-Way Proteomics Full). This enabled us to identify the best genes and proteins, independently of each other, for both sam- ple types using our RNAseq and proteomic pipelines. Refer to Fig 2 for the classification per- formance for Stage 1. Stage 2 (Integration). Part A: We performed the same type of analyses as in Stage 1, i.e. nested cross-validation, to clas- sify the Liver 3-Way Unmatched Balanced and PBMC 3-Way Unmatched Balanced gene and protein datasets. Each pipeline configuration produced a unique candidate gene/pro- tein set. We noted several best performing candidate gene and protein sets for later use in parts B and C. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 6 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Fig 1. Flowchart of the 3 stages of the analysis. Stage 1: Separate analyses of full RNAseq and proteomics datasets (Liver 3-Way RNAseq Full, Liver 3-Way Proteomics Full, PBMC 3-Way RNAseq Full, and PBMC 3-Way Proteomics Full). To simplify the flowchart, we are only showing one representative dataset, which we will refer to as “3-Way Full Datasets”. Stage 2: Training ML models in unmatched balanced data with subsequent testing and integration in matched balanced data. Part A: Identification of top transcriptomic and proteomic pipeline configurations along with their corresponding gene and protein sets for unmatched balanced datasets. Part B: Evaluation of top performing models with their corresponding gene and protein sets from part A in matched balanced data. Part C: Integration of paired sets of the top performing gene and proteomics models with their corresponding gene and protein sets, in matched balanced data. Stage 3: Intersection analysis of the combined best gene-protein sets for liver samples and for PBMCs. https://doi.org/10.1371/journal.pdig.0000447.g001 Part B: We trained classifiers, corresponding to the best performing RNAseq and proteomic ML pipeline configurations from part A, on the entirety of unmatched balanced data. The resulting ML models were then tested in matched balanced data. This would serve as a reference, to which we could later compare the integrated model, as shown in Fig 3. Part C: Pairings of the best performing RNAseq and proteomic ML models for each sample type from part B (using their corresponding gene/protein sets) were integrated and evaluated in matched balanced data using cross-validation (Table AA in S1 Text for models tested for the liver samples, and Table AD in S1 Text for the PBMC models tested). The integration was per- formed by supplying the output prediction probabilities from each pair of RNAseq and proteo- mic models as input into an integrated model. The pair of candidate gene and candidate protein sets that attained the best classification accuracy was reported as the best combined gene and protein panel. The performance of integrated model in matched balanced data was compared to the performance of separate (RNAseq and proteomic) models in matched bal- anced data (from part B) as shown in Fig 3. Stage 3 (Intersection). In the third stage, we examined which genes and proteins matched within the best gene and protein panel. That is, we can consider a protein and a gene that codes for it, as a match. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 7 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Fig 2. Confusion matrices corresponding to the best gene and protein sets of the full datasets and the liver tissue validation datasets. The Liver 3-way Full best gene and protein sets contained 33 genes and 27 proteins, respectively. The PBMC 3-Way Full best gene and protein sets contained 16 genes and 28 proteins, respectively. (A) Confusion matrix for classification of Liver 3-Way Full RNAseq dataset using best gene set identified by filter feature selection. The diagonal contains the number and percentage of the correctly predicted samples. (B) Confusion matrix for classification of AH, AC, and healthy control (CT) samples within independent validation RNAseq dataset. (C) Confusion matrix for classification of PBMC 3-Way Full RNAseq dataset using best gene set identified by filter feature selection. (D) Confusion matrix for classification of Liver 3-Way Full proteomic dataset using best protein set identified by filter feature selection. (E) Confusion matrix for classification of AH, AC, and CT samples within independent validation proteomic dataset. (F) Confusion matrix for classification of PBMC 3-Way Full proteomic dataset using best protein set identified by filter feature selection. https://doi.org/10.1371/journal.pdig.0000447.g002 Validation in independent liver tissue data All liver tissue ML models (RNAseq and proteomic) were validated in independent liver tissue validation data. Briefly, the ML model that performed best during nested cross-validation was trained on entirety of our liver tissue data. This trained classifier was then evaluated in inde- pendent liver tissue validation data. The methods for independent validation were identical for both RNAseq and proteomic datatypes. The further description of these methods can be found in our previous publication [11] methods. Machine learning classifiers The classifiers used in the individual analysis of the transcriptomic and proteomic data were: k nearest neighbors (kNN), logistic regression (LR), and support vector machine (SVM). For the integrated transcriptomic and proteomic analysis, we used only logistic regression and linear kernel SVM classifiers, due to ease of interpretation. Within the integrated model, the models that directly utilized the RNAseq and proteomic counts were either LR or linear kernel SVM. The classifier that used the prediction probabilities supplied via the RNAseq and proteomic models was LR with default hyperparameters. The LR model has been shown to be well suited for small sample size proteomic data previously [18]. Both LR and SVM classifiers were regularized. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 8 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Fig 3. Confusion matrices corresponding to the best gene and protein sets in the matched balanced data set tested separately, and tested with the integrated gene/protein set. Confusion matrices corresponding to the best gene and protein sets (59 genes and 19 proteins, respectively) evaluated within Liver 3-Way Matched Balanced data and within PBMC 3-Way Matched Balanced data (16 genes and 33 proteins, respectively). (A) Confusion matrix for classification of Liver 3-Way Matched Balanced RNAseq dataset using best gene set identified by filter feature selection. (B) Confusion matrix for classification of Liver 3-Way Matched Balanced proteomic dataset using best protein set identified by filter feature selection. (C) Confusion matrix for classification of Liver 3-Way Matched Balanced dataset using a combination of best gene and protein sets. (D) Confusion matrix for classification of PBMC 3-Way Matched Balanced RNAseq dataset using best gene set identified by filter feature selection. (E) Confusion matrix for classification of PBMC 3-Way Matched Balanced proteomic dataset using best protein set identified by filter feature selection. (F) Confusion matrix for classification of PBMC 3-Way Matched Balanced dataset using a combination of best gene and protein sets. https://doi.org/10.1371/journal.pdig.0000447.g003 Feature importance The combined gene-protein panels for integrated Liver 3-Way and integrated PBMC 3-Way datasets were evaluated for feature importance. Feature importance was evaluated separately for genes and proteins due to the nature of machine learning architecture. The feature impor- tance was evaluated using trained model coefficients. Visualizations of feature importance for integrated Liver 3-Way and integrated PBMC 3-Way datasets can be found in S1 Text. Summary of computational methods Table 4 contains a summary of the computational methods used in the final configurations of the ML models for the RNAseq and Proteomics datasets. Further details can be found in S1 Text. Results Classification of Liver 3-Way Full (AH vs Healthy vs AC) The gene and protein sets produced via various methods were compared according to classifi- cation performance and biological validation scores in order to select the best gene and protein PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 9 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Table 4. Summary of methods used with transcriptomic and proteomic data types. Data Type Feature Selection Transcriptomic Filter (DE, IG) Feature Sizes Imputation 10, 25, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500 None Proteomic Filter (DE) 15, 25, 35, 50, 60, 70, 80, 90, 100, 150, 200 Median and Zero (Thresholds: 0%, 5%, and 10%) https://doi.org/10.1371/journal.pdig.0000447.t004 ML Classifiers In-silico Biological Validation LR, kNN, SVM LR, kNN, SVM Enrichr AGOTOOL sets. The best gene set contained 33 genes, attained 90% accuracy in main data and 82% accu- racy in validation data (Fig 2A and 2B). The best protein set contained 27 proteins, and attained 100% accuracy in main data and 61% accuracy in validation data (Fig 2D and 2E). RNAseq and proteomic data proved similarly effective at classifying our Liver 3-Way samples. However, the best gene set derived from RNAseq data achieved better performance in RNAseq validation data than the best protein set derived from proteomic data achieved in proteomic validation data. The heatmaps of RNAseq and proteomic counts can be found in Figures A-H in S1 Text. The enriched pathways, tissues, and diseases for best gene and protein sets can be found in the Tables E and H in S1 Text. The best gene and protein sets for each dataset are shown in Table 5. Table 5. Best genes and proteins for each dataset. For the integrated datasets, the matching genes and proteins are bolded. Dataset Genes Proteins Liver 3-Way Full PBMC 3-Way Full Liver 3-Way Matched Balanced Integrated PBMC 3-Way Matched Balanced Integrated AKR1B10, C15orf52, CFTR, CREB3L3, CXCL6, CYP2A7, CYP2B6, DBNDD1, EEF1A2, EPS8L1, FAM198A, FCGR3B, FCN3, FITM1, GPC3, GPNMB, HAMP, HAO2, IGSF9, KRT23, LCN2, LYZ, MMP7, MT1G, PLA2G2A, PPP1R1A, RGS1, S100A8, SCTR, STAG3, TMEM132A, TREM2, VCAN. ETS2, FLVCR2, FPR1, GRB10, IMPA2, ITGAM, ITGB2, LILRA5, MYO7A, PTGR1, RAB31, RNASE2, SERPINB1, SLC36A1, ST14, TLR4. ACKR1, AKR1B10, BBOX1, C15orf52, CFTR, CLEC4M, CREB3L3, CSF3R, CXCL1, CXCL6, DCDC2, DHODH, DHRS2, F3, FABP4, FAM118A, FCGR3B, FCN3, GADD45B, GADD45G, GPC3, GSTA2, HAMP, HAO2, ID4, IGSF9, IL7R, KRT23, LBP, LCN2, LRG1, MARCO, MMP7, MT1A, MT1G, MT1H, MT1M, MT1X, MUC13, MUC6, NRTN, PAPLN, PID1, PLA2G2A, PLCB1, PPP1R1A, S100A12, S100A8, S100A9, SLC13A5, SLC22A1, SOCS1, SPINK1, STAG3, STMN2, TREM2, TRIB3, VSIG2, VTCN1. AHSP, ALAS2, CA1, CD177, CDK10, EHMT1, HBD, HBM, IFI27, IL1R2, MECP2, MMP8, MMP9, SELENBP1, SLC4A1, TANGO2. https://doi.org/10.1371/journal.pdig.0000447.t005 ACBP, ADH1A, ADH1B, ADH4, ADH6, ALBU, ARF3, CD34, CO1A2, CP1A2, CP3A4, CP3A7, CRP, DDTL, ERI3, FABPL, GSTA1, GSTA2, GSTM4, H2B1C, K2C79, K2C80, LDH6A, MFAP4, PAL4C, SAA1, UDB17. APOA1, BLVRB, CATS, CSRP1, EST1, FIBA, FIBB, FIBG, G6B, GP1BB, GPIX, HBD, ILK, ITA2B, ITB3, LTBP1, MYL9, PMGE, RAP1A, RSU1, SDPR, SEP11, SRC, TBA4A, TOR4A, TSP1, URP2, VINC. ACBP, ADH1A, ADH1B, ADH4, ADH6, ALBU, ASSY, CD34, CLC4M, CO1A2, CP1A2 CRP, CYB5, ERI3, GSTA1, HBAZ, LDH6A, SAA1, UDB17. ACTN1, ALBU, CCL5, CXCL7, FHL1, FIBA, FIBB, FIBG, FRIL, FSTL1, GP1BB, ILK, ITA2B, ITB1, ITB3, LIMS1, LYSC, MYL9, PP14A, RAP1A, RS4Y1, SBP1, SDPR, TBA4A, TBA8, TBB1, TPM2, TRML1, TSN15, TSP1, URP2, VINC, VTDB. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 10 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning Classification of PBMC 3-Way Full (AH vs Healthy vs AC) The best gene set contained 16 genes and attained 83% accuracy in main data (Fig 2C). The best protein set contained 28 proteins and attained 89% accuracy in main data (Fig 2F). RNA- seq and proteomic data proved equally effective at classifying our PBMC 3-Way samples. The heatmaps of RNAseq and proteomic counts can be found in Figures I-L in S1 Text. The enriched pathways, tissues, and diseases for best gene and protein sets can be found in the Tables K and N in S1 Text. The best gene and protein sets for each dataset are shown in Table 5. Classification of Liver 3-Way Matched Balanced (AH vs Healthy vs AC) Integration of genes and proteins. The best gene set and protein set derived from Liver 3-Way Unmatched Balanced datasets were evaluated in Liver 3-Way Matched Balanced data- sets separately and in combination. Using the best gene set of 59 genes we attained 83% classi- fication accuracy within matched balanced RNAseq data (Fig 3A). Using the best protein set of 19 proteins we attained 100% classification accuracy within matched balanced proteomic data (Fig 3B). Using a combination of best gene and protein sets, we attained 96% accuracy in matched balanced integrated data (Fig 3C). Additionally, we generated a one-vs-rest micro- averaged receiver operating characteristic (ROC) curve for the integrated Liver 3-Way model, which resulted in AUC of 1.0 (Figure AE in S1 Text). The constituent transcriptomic (59 genes) and proteomic (19 proteins) models resulted in AUCs of 0.94 and 1.0 respectively (Fig- ures AF and AG in S1 Text). Intersection. Additionally, we examined which biomarkers were shared between the best gene and protein sets of the integrated model with liver tissue. The CLEC4M-CLC4M, GSTA1-GSTA2 were found in common. The CLEC4M-CLC4M was a direct match, while the GSTA1 (protein) was a familial match with GSTA2 (gene). If the genes and proteins had been selected randomly from among significantly differentially expressed genes and proteins, an expected 0.12 would be shared. Calculation of expected value can be found in S1 Text. There- fore, we have identified more biomarkers in common than expected. Best gene and protein sets were commonly enriched for several different inflammation pathways. The best protein set was more strongly enriched for metabolism pathways than the best gene set (Tables Q and T in S1 Text). Classification of PBMC 3-Way Matched Balanced (AH vs Healthy vs AC) Integration of genes and proteins. The best gene and protein sets derived from PBMC 3-Way Unmatched Balanced datasets were evaluated in PBMC 3-Way Matched Balanced data- sets separately and in combination. Using the best gene set of 16 genes we attained 74% classi- fication accuracy within matched balanced RNAseq data (Fig 3D). Using the best protein set of 33 proteins we attained 77% classification accuracy within matched balanced proteomic data (Fig 3E). Using a combination of best gene and protein sets, we attained 81% accuracy in matched balanced integrated data (Fig 3F). We also generated a one-vs-rest micro-averaged ROC curve for the integrated PBMC 3-Way model, which resulted in AUC of 0.96 (Figure AK in S1 Text). The constituent transcriptomic (16 genes) and proteomic (33 proteins) models resulted in identical AUCs of 0.89 (Figures AL and AM in S1 Text). Intersection. With the integrated model for PBMCs, the SELENBP1-SBP1 gene-protein was found in common between the best gene and protein sets. For a random selection from the significantly differentially expressed genes and proteins, we calculated that an expected 0.05 would be shared. Thus, more biomarkers were found to be shared than expected. The best PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 11 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning gene and protein sets for PBMCs were mainly enriched for several different inflammation and cancer related pathways (Tables W and Z in S1 Text). Discussion In this study, we used machine learning approaches with transcriptomics and proteomics data from liver tissue and PBMCs to effectively classify samples from participants with alcohol- associated hepatitis (AH), alcohol-associated cirrhosis (AC), and healthy controls. Liver tissue models outperformed PBMC models by a small margin in our data. Both transcriptomic and proteomic liver tissue ML models generalized relatively well in the independent validation data. Overall, the transcriptomic and proteomic models performed similarly well in each sam- ple type. The integration of proteomic and transcriptomic data did not increase classification accu- racy with liver tissue, mainly because the classification accuracy was already high in both data types separately. For PBMCs, on the other hand, the integration improved classification accu- racy slightly. While the performance of PBMC biomarkers is less than that of liver tissue bio- markers for classification of ALDs, the integration of multiple -omics data types could help close the gap in the future. To our knowledge, this is the first study in which a combined PBMC gene-protein expression biomarker panel has been identified for distinguishing AH, AC, and healthy controls. Of special interest are the gene-protein matches present in the combined gene-protein sets identified for Liver 3-Way and PBMC 3-Way Matched Balanced Integrated datasets. All the matched liver tissue genes have been established as relevant biomarkers of liver disease in prior literature. CLEC4M has been identified as prognostic liver tissue biomarker of hepatocel- lular carcinoma [19]. GSTA1 and GSTA2 have been previously identified as biomarkers of liver injury (including ethanol injury) and hepatocellular carcinoma respectively [20,21]. Less is known about the role of the matched PBMC genes in liver disease. Differential expression of SELENBP1 in PBMCs of hepatocellular carcinoma patients has been established previously [22]. The differential expressions of these biomarkers in both transcriptomic and proteomic data increases our confidence in their significance. The gene-protein panels for Liver 3-Way and PBMC 3-Way integrated datasets were exam- ined using enrichment analysis. The genes and proteins were examined separately. For Liver 3-Way the proteins were overwhelmingly enriched for metabolic pathways, including ethanol metabolism (Table AB in S1 Text). Notably, many of the key liver proteins are alcohol dehy- drogenases, some of which have been implicated in alcohol and liver disorders [23,24]. Other notable proteins include CRP, SAA1, ALBU. All of these have been previously established as diagnostic biomarkers of inflammatory liver diseases [25,26,27]. The genes were enriched for homeostasis, metabolism, and inflammatory pathways (Table AC in S1 Text). For PBMC 3-Way both the genes and proteins were enriched for blood processes, immune system func- tions, and cellular movement (Tables AE and AF in S1 Text). Some of the PBMC proteins have been previously connected to liver disease including FSTL1, TSP1, CCL5, and TPM2 [28,29,30,31]. Overall, the identified genes and proteins are consistent with previous findings. We have discussed the importance of using appropriate ML methods for analysis of small sample size RNAseq data [11] previously. Our recommendations for analysis of small sample size proteomic data are largely similar. In addition to the importance of filter feature selection we would like to highlight the importance of nested cross-validation (NCV) and performing feature selection within both inner and outer loops of NCV. The use of nested cross validation is necessary to separate model selection and evaluation if hyperparameter tuning is being done. Meanwhile, it is necessary to perform feature selection within nested cross validation to PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 12 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning avoid data leakage and the resulting bias [32]. The use of in-silico biological relevancy (via enrichment analysis) in our pipeline was also important as it decreased overfitting by favoring feature sets that corresponded to existing literature. The liver tissue proteomics model’s performance in independent validation data was lower than expected. The healthy control samples in independent validation proteomic dataset were collected from two different clinical sources. Most misclassified healthy controls were from one of the two sources. The heterogeneity in healthy samples may explain their unexpectedly poor classification performance. The PBMC models could not be independently validated due to lack of relevant public data. However, the methods used to derive the best biomarkers were identical in both tissues. The integrated models also could not be validated due to lack of appropriate publicly available genomic data in which both RNAseq and proteomics were avail- able for the same individuals. A larger sample size and an independent integrated validation cohort are needed to further investigate these biomarkers. Integrating two -omics datatypes further amplified the challenges we encountered in our earlier work [11]. The number of genes and proteins for each sample is much larger than the number of samples in our dataset. This makes data prone to overfitting, since a complex model can perfectly separate a small number of samples. Some of the other challenges were ensuring that the integrated model did not have a bias toward transcriptomic or proteomic features, performing feature selection with integrated gene and protein expression data, and addressing partial matching between our transcriptomic and proteomic samples (most were obtained from the same individuals, but some were not). Overall, the integration of proteomic and transcriptomic data from liver tissue and PBMCs for ALD proved promising in two aspects. In the case of PBMCs in our study, combining tran- scriptomic and proteomic biomarkers was more effective than using either type of biomarkers alone for classification. Additionally, by examining both transcriptomic and proteomic data, we were able to identify gene-protein pairs that were significantly differentially expressed in both domains and were thus more likely to be relevant to the liver disease conditions in ques- tion. The possibility of using PBMCs to distinguish among alcohol-associated liver diseases is encouraging, and the relevant biomarkers warrant further examination. Supporting information S1 Text. Supplemental methods and supplemental results for this study. (PDF) Acknowledgments The authors would like to thank and acknowledge that the participant recruitment and sample collection for the PBMCs and the AH liver tissue biopsies were performed by the SCAHC at the following locations: Long Beach Veterans Healthcare System (VALB), Long Beach, CA [Jessica Clare Gozum, Sheena Cruz, Hema Buddha, Yuxin Ouyang, Gregory Botwin, Lauren MacHarg, Monique French]; Harbor-UCLA Medical Center, Torrance, CA [Lavanya Cheru- kuri, Sajad Hamal, Wayne Fleischman, Divya Birudaraju]; University of Southern California (USC), Los Angeles, CA [Christy Rico, Susan Milstein, Carol Jones, John Donovan, Neil Kaplowitz]; VA Loma Linda, CA [Daniel Chen-Kang Chao]; and VA Albuquerque [Joseph Alcorn]. The authors would also like to thank and acknowledge the members of the UC Irvine Genomics High-Throughput Facility (GHTF) for their role in the RNA extraction and sequencing of the samples. The liver tissue from participants with AC and healthy control were obtained from the LTCDS at University of Minnesota. (https://med.umn.edu/pathology/ PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000447 February 9, 2024 13 / 16 PLOS DIGITAL HEALTHIntegrating liver disease protein and gene data using machine learning research/liver-tissue-system). Portions of this manuscript were submitted as a thesis in partial fulfillment of the requirements for the degree of Doctor of Philosophy (S.L.). Author Contributions Conceptualization: Stanislav Listopad, Timothy R. Morgan, Trina M. Norden-Krichmar. Data curation: Stanislav Listopad, Christophe Magnan, Le Z. Day, Aliya Asghar, Zhang-Xu Liu, Jon M. Jacobs, Trina M. Norden-Krichmar. Formal analysis: Stanislav Listopad, Christophe Magnan, Le Z. Day, Zhang-Xu Liu, Jon M. Jacobs, Trina M. Norden-Krichmar. Funding acquisition: Zhang-Xu Liu, Jon M. Jacobs, Timothy R. Morgan, Trina M. Norden- Krichmar. Investigation: Le Z. Day, Aliya Asghar, Andrew Stolz, John A. Tayek, Zhang-Xu Liu, Jon M. Jacobs, Timothy R. Morgan. Methodology: Stanislav Listopad. Project administration: Zhang-Xu Liu, Jon M. Jacobs, Timothy R. Morgan, Trina M. Nor- den-Krichmar. Resources: Aliya Asghar, Andrew Stolz, John A. Tayek, Zhang-Xu Liu, Jon M. Jacobs, Timo- thy R. Morgan, Trina M. Norden-Krichmar. Software: Stanislav Listopad, Christophe Magnan. Supervision: Zhang-Xu Liu, Jon M. Jacobs, Timothy R. Morgan, Trina M. Norden-Krichmar. Validation: Stanislav Listopad, Trina M. Norden-Krichmar. Visualization: Stanislav Listopad. Writing – original draft: Stanislav Listopad. Writing – review & editing: Stanislav Listopad, Christophe Magnan, Le Z. Day, Aliya Asghar, Andrew Stolz, John A. 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10.1371_journal.pdig.0000181	RESEARCH ARTICLE Exploring the use of social media and online methods to engage persons with lived experience and healthcare professionals in creating research agendas: Lessons from a pediatric cancer research priority-setting partnership Kyobin HwangID A. JibbID 1,2* 1‡, Surabhi Sivaratnam1,2‡, Rita Azeredo1, Elham Hashemi1, Lindsay 1 Hospital for Sick Children, Toronto, Canada, 2 University of Toronto, Toronto, Canada ‡ These authors share first authorship on this work. * lindsay.jibb@utoronto.ca Abstract Social media is increasingly used to engage persons with lived experience and healthcare professionals in research, however, there remains sparse guidance on how to effectively use social media to engage these groups in research agenda-setting. Here we report our process and experience utilizing a social media campaign to engage Canadians within the pediatric cancer community in a research priority-setting exercise. Following the James Lind Alliance method, we launched a priority-setting partnership (PSP) to develop a child with cancer-, survivor-, family member-, and healthcare professional-based Canadian pediatric cancer research agenda. Social media-based strategies were implemented to recruit partici- pants for two PSP surveys, including preparatory activities, developing a website, launching graphics and advertisements, and engaging internal and external networks. Descriptive sta- tistics of our data and analytics provided by the platforms are used presently to report our process. The framework we implemented involved preparing for social media use, identify- ing a target audience, developing campaign content, conducting the campaign, refining the campaign as needed, and evaluating its success. Our process resulted in a substantial social media-based reach, good survey completion rates, and a successfully developed pediatric cancer community-specified research agenda. Social media may represent a use- ful approach to engage persons with lived experience and healthcare professionals in research agenda development. Based on our experience, we present strategies to increase social media campaign engagement that may be useful to those seeking to conduct health research priority-setting exercises. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Hwang K, Sivaratnam S, Azeredo R, Hashemi E, Jibb LA (2024) Exploring the use of social media and online methods to engage persons with lived experience and healthcare professionals in creating research agendas: Lessons from a pediatric cancer research priority- setting partnership. PLOS Digit Health 3(1): e0000181. https://doi.org/10.1371/journal. pdig.0000181 Editor: Yuan Lai, Tsinghua University, CHINA Received: December 12, 2022 Accepted: December 6, 2023 Published: January 8, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pdig.0000181 Copyright: © 2024 Hwang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 1 / 20 PLOS DIGITAL HEALTHFunding: The study was supported by the CIHR Catalyst Grant in Patient-Oriented Research (#PAO- 169422 to LJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Social media to engage people with lived experience in research priority-setting Author summary Little is known about how best to use social media to engage people with lived experience and healthcare professionals in research agenda-setting. We present our process and expe- rience using a social media campaign to engage Canadians within the pediatric cancer community in a building such an agenda. We used social media and a network of partners to recruit participant into two research agenda building surveys. The framework we implemented involved preparing for social media use, identifying a target audience, devel- oping campaign content, conducting the campaign, refining the campaign as needed, and evaluating its success. Our process resulted in a substantial social media-based reach, good survey completion rates, and a successfully developed pediatric cancer community- specified research agenda. Based on our experience, we present strategies to increase social media campaign engagement that may be useful to those seeking to conduct health research priority-setting exercises. Background In recent years social media has gained an important role in healthcare, including engaging per- sons with lived experience and healthcare professionals in research [1,2]. Researchers are increasingly utilizing platforms including Facebook, Twitter, and YouTube to enable research participant recruitment [3–5] and to disseminate study findings [6]. Social media-based meth- ods have also been used to enable the engagement of individuals with lived healthcare experi- ences (i.e., patients, family members, clinicians, and other advocates) in setting research priority agendas, though there remains sparse methodological guidance on how to do so [7]. The devel- opment of such agendas is critically important to direct clinical practice and policy decisions but, to date, those with lived experience have not been routinely involved in the process [8–10]. Considering the imperative to engage these individuals in research agenda building and the potential opportunities to support such work through online methods, our research team utilized social media, amongst other online tools, to build our pan-Canadian pediatric cancer research pri- ority setting partnership (PSP). In brief, through our PSP, we surveyed the Canadian childhood cancer community to elicit their research questions, and subsequently engaged the group in a pri- ority setting activity to develop a childhood cancer research agenda. To create an inclusive agenda, we aimed to engage a bilingual (English and French) and diverse group of children with cancer, pediatric cancer survivors, as well as their family members and healthcare professionals. Here we review a specific case where social media and other online modalities were lever- aged to engage Canadians within the pediatric cancer community in a research priority-setting exercise. As a resource for other research teams, we offer descriptions of our social media- based recruitment strategy, engagement with our social media campaign, and factors associ- ated with increased involvement in our PSP. We also discuss the limitations of our efforts and make specific recommendations on how other research teams might use social media to involve persons with lived experience and healthcare professionals in research agenda-setting. Methods Overview of pediatric priority setting partnership We followed the James Lind Alliance process to develop a person with lived experience and healthcare professional-engaged research agenda [11]. At the onset of our PSP, a steering group, comprised of childhood cancer survivors (n = 5), family caregivers (n = 4), and health- care professionals involved in pediatric oncology (n = 6). The steering group guided all PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 2 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting methodological and operational decisions for our PSP. We launched a national bilingual (French and English) online survey in Winter 2020 to collect research questions from the Canadian pediatric cancer community. Collected questions were collapsed and collated into summary questions, which were checked against the extant scientific literature. Adequately addressed questions were removed from the question roster and a second national, bilingual online survey was launched in Winter 2021 requesting children with cancer, survivors, family members, and health professionals prioritize the unanswered research questions. A shortlist of these questions was then taken to a pan-Canadian consensus-building workshop conducted in March 2022, where the top ten research priorities in Canadian pediatric oncology were estab- lished. Social media and other online tools were utilized to promote both of our online surveys. Ethics approval for this study was received from the SickKids Research Ethics Board (REB). Consent for publication was not applicable Social media campaign overview The social media campaign used to promote participation in both online PSP surveys was spearheaded by personnel on our team with experience in digital marketing and graphic design (SS). A trained graphic designer with assisted with designing the graphics for our posts, while another member of the team, who is also a childhood cancer survivor (RA), received training and supported the process by maintaining an active presence across our platforms during the campaigning period (e.g., regularly posting, engaging with comments on posts, etc.). A multi-phase social media campaigning approach was implemented, closely mirroring the framework developed by Lang et al. for recruiting participants in pediatric research through social media [12]. Specifically, we: (i) planned for social media use as an engagement strategy, (ii) identified and attempted to understand the different target audiences and then developed a strategy accordingly, (iii) developed and designed campaign content, (iv) imple- mented, monitored and iteratively refined campaign strategies, and (v) evaluated campaign success. Establishing analytic tools Dedicated social media accounts were created on Facebook (www.facebook.com), Twitter (www.twitter.com), Instagram (www.instagram.com), LinkedIn (www.linkedin.com), You- Tube (www.youtube.com), and TikTok (www.tiktok.com). Wherever applicable, we registered our “business/professional” account types. This registration type enabled viewing of social media analytics, which included overviews of the demographic characteristics of those engag- ing with our social media content (i.e., age, gender, education levels, job titles, location, lan- guage) and page insights (i.e., likes, comments, shares, page views, page traffic and activities- including the length of time of individual sessions). Among the various utilized platforms, our social media campaigning efforts concentrated on Facebook, Twitter, and Instagram, as well as our study website as our PSP steering group felt our target participants were mostly likely to use these platforms. We also created a Hootsuite account (Hootsuite Inc), which provides access to aggregated analytics of all user profiles across networks, including the most common times of day during which followers interact with social media accounts/pages. The Hootsuite platform also enabled the research team to schedule posts and provided a single point to launch posts across various social media platforms. Social media engagement Paid advertisements. During the social media campaign, we periodically implemented paid advertisements on Facebook, Instagram, and Twitter. Our team closely monitored ad- PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 3 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting associated analytics to identify ways to augment the campaign as needed. For example, we identified that video posts produced more social media engagement than image-based posts, and all future advertisements were then solely accompanied with videos. Each social media platform required specifications on ad delivery, ad content, design language, targeted audi- ence, including location, and dates of deployment. Website. Prior to launching our social media accounts, a website for the research project was created (www.pedcancerpsp.ca). This website was created using Wix (Wix.com Ltd). Website design considered accessibility needs by ensuring alternative text was available for all graphics and the website optimized for desktop and mobile viewing to support ease of access over a variety of devices. We used the Wix search engine optimization (SEO) checklist to ensure our site could be found via search phrases on various search engines. The website pro- vided a centralized location for prospective participants to learn more about the project, the James Lind Alliance methodology in general, the research team, and registration to our mail- ing list. Engagement supported by internal and external networks. Throughout our social media campaign, we contacted external networks, including Canadian childhood cancer orga- nizations, advocacy groups with prominent social media accounts, and non-governmental organizations affiliated with pediatric cancer, and subsequently requested their assistance in promoting the project. Contact methods included email correspondence, as well as direct mes- saging via social media platforms. When individuals and organizations agreed to help with sur- vey promotion, we provided them with a template package, which included tailored graphics, pre-written messaging, sample newsletter graphics, and draft emails that they could send to their contact lists. A similar methodology was implemented to support dissemination by our internal network (i.e., members of the research team and members of the PSP steering group). Data analysis Facebook, Instagram, and Twitter provide descriptive data analytics to page administrators, allowing for monitoring and assessment of the usage of a page; a similar tool is available on Wix. These analytics allowed us to measure user engagement within each online platform. For Instagram, Facebook, and Twitter, we monitored reach, impressions, and clicks. For our web- site, we monitored unique visitors, site sessions, and page views. We also collected demo- graphic information from PSP survey participants. We used descriptive statistics to analyze all data types. Results (I) Preparatory activities for social medial campaign Scoping review. Prior to launching our social media campaign, a scoping review was con- ducted to describe: (i) the existing literature on social media–based strategies used to enhance participation of persons with lived experience and healthcare professionals in health research priority-setting, (ii) recommendations for social media–based research priority-setting cam- paigns, (iii) the benefits and limitations of the method, and (iv) recommendations for future campaigns [7]. Our review identified a total of 23 papers reporting on 22 unique studies. The central findings of our review allowed us to identify potentially useful social media platforms and other online tools to leverage and social media strategies that might enhance our engage- ment with people with lived experience of childhood cancer and healthcare professionals. The results of this review were presented to our PSP steering group and guided the social media strategies implemented in this study, including the process of creating our accounts on Face- book, LinkedIn, Instagram, Twitter, and TikTok. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 4 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Table 1. Time spent preparing for the social media campaign. Task Description of Task Time (hours) Literature review • Review of existing literature, including searching scientific databases and grey literature to consolidate known social media campaign strategies 100 Social media platform creation • Creation of accounts on Facebook, LinkedIn, Instagram, Twitter, and Tiktok • Creation of brand unified profile picture and profile description 10–20 200–250 20–40 20–40 • Brainstorming with research team and steering group on website layout • Creation of website • Compiling of graphics and pictures for the website • Liaising with communications and public affairs department to ensure website meets organization requirements • Development of a repository of external Canadian childhood cancer organizations to contact for support in promoting PSP surveys • Contacting and liaising with external organizations with requests for survey promotion • Generation of branding package, including standardized fonts, colours, graphics, and design elements, as well as centralized messaging and key phrases • Brainstorming with research team and steering group on the style and content of two videos designed to explain and promote PSP surveys • Generation of video storyboard and script and creation of videos in both English and French 49 • Creation of Instagram, Facebook, Twitter, Tiktok posts, including static graphics and videos/gifs • Creation of email flyers for external and internal network • Translation of all graphics and other post content from English to French for bilingual posting • Creation of individualized promotional packages for internal and external organizations/individuals to support them in PSP survey promotion. Each promotional package contained sample graphics, as well as associated text that organizations/individuals could use to post on their respective platforms 100–150 40–50 • Person with lived experience and healthcare professional continued engagement through routine steering group meetings where social media campaign plans and progress were discussed 5–10 595–709 Website creation Researching external organization Brand template Promotional videos Sample graphic generation Creating promotional package Steering committee meetings TOTAL: https://doi.org/10.1371/journal.pdig.0000181.t001 Building platform accounts and branding. Once we created accounts on Facebook, Lin- kedIn, Instagram, Twitter, and TikTok, a unified brand identity was developed in coordination with our steering group. The brand identity involved employing consistent fonts, colors, graphics, design elements, and messaging. We then began engagement with our external net- work of Canadian cancer organizations, including by creating the customized promotional packages for these groups. Cumulative the preparatory phase of the campaign required 525– 710 hours of research support time. Table 1 summarizes additional undertakings during this phase of the social media campaign and associated time allocations for each task. (II) Identifying and understanding target audience To gain a deeper understanding of the interest and online behaviours of children with cancer, childhood cancer survivors, family members of patients and survivors, and the healthcare pro- fessionals caring for these groups, we regularly consulted with an expert steering group. Co- design workshops were regularly held to build an understanding of each audience and support the development of tailored graphics. The steering group also assisted in recruiting external partner organizations to our network who assisted in disseminating our social media content. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 5 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting (III) Developing and designing campaign content Creation of social media content. An initial branding package was created in Adobe Photoshop (CC 2017; Adobe, San Jose, CA). During the development of these graphics, the designers utilized WebAim (https://webaim.org/resources/contrastchecker/) to ensure all graphics fulfilled the minimum colour contrast requirements for online and mobile accessibil- ity. This package was again reviewed by the PSP steering group to help ensure the content and design was appropriate for the targeted audience. Steering group suggestions were used to modify and finalize the branding package. This branding scheme was used as the basis for a hub of static graphics and videos that would be utilized in the social media campaign. Social media campaign content. The content of our social media posts included reviews of PSP goals, the key personnel involved, calls for those with lived experience of childhood can- cer and healthcare professionals to complete each survey and, intermittently, general posts considered relevant to the childhood cancer community (e.g., recognizing childhood cancer awareness month). Posts typically included graphics in the form of photos, videos, and graph- ics interchange format (GIFs), and associated text included hashtags, links to the project web- site or the survey, tags, and various relevant emojis. Sample graphics are shown in Figs 1 and 2. Throughout the social media campaign, we monitored: (i) social media analytics to identify gaps in interaction with the campaign and (ii) demographics PSP self-reported by survey par- ticipants to identify gaps in communities of individuals completing each survey. The identifi- cation of these gaps subsequently informed the content of future social media posts. For example, during the campaign, these metrics identified a lower-than-expected response rate from individuals from the Canadian province of Quebec and efforts were made to target graphics towards this group. Sample targeted graphics are shown in Fig 3. We also monitored social media analytics, which provided data on the optimal timing for posts, including the times at which our audience was generally online and interacting with our posts. Our posting schedule was continuously adjusted to reflect these optimal times. Fig 1. Example of social media promotional graphic in English. https://doi.org/10.1371/journal.pdig.0000181.g001 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 6 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 2. Example of social media promotional graphic in French. https://doi.org/10.1371/journal.pdig.0000181.g002 Fig 3. Example of graphics targeting a specific group. https://doi.org/10.1371/journal.pdig.0000181.g003 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 7 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting (IV) Implementing, monitoring and iteratively refining campaign strategies Initial launch of social media campaign. A series of introduction posts were generated with the intention of establishing credibility related to our campaign. The first posts identified the research team, and the steering group and subsequent posts introduced the concept of a PSP. At the initial launch of our social media accounts, we contacted established childhood cancer organizations with social media presence through direct messaging to introduce the PSP. We then followed the accounts of these organizations to increase the number of people interacting with our social media content. PSP survey participation. The first survey was available for completion from December 7, 2020, to March 3, 2021. During this period, a total of 330 individuals participated, of which 80 (23.9%) were childhood cancer patients and survivors, 179 (53.4%) were family members of children who have or had cancer, and 76 (22.7%) were healthcare professionals. There was representation from all of Canada’s provinces, though there were no response from territory residents and most participants lived in the province of Ontario (n = 160, 47.8%), with the sec- ond largest group living in British Columbia (n = 54;16.1%). The second survey, focused on research question prioritization, was available for comple- tion from January 10, 2022, to February 19, 2022. A total of 197 individuals participated and 49 (24.5%) were children or survivors of childhood cancer, 102 (51.0%) were family members, and 49 (24.5%) were healthcare professionals. Similar to the initial survey, there was represen- tation from all the provinces, though there were no respondents from the territories. Most sur- vey respondents were once again Ontario residents (n = 84, 42.0%), followed by Quebec (n = 59, 29.5%) and Alberta (n = 16, 8%). Campaign maintenance. To support the maintenance and coherence of our social media campaign, a structured set of strategies was implemented. We initiated a graphic generation process that created foundational but modifiable templates for our campaign’s visual content informed by our branding scheme. Weekly analysis of platform analytics was pivotal to modi- fying these templates and reviews of engagement metrics and performance data informed revi- sions to our content (e.g., if certain content was popular it would direct our next template modifications). The goal of this is iterative process was to maximize campaign engagement. To ease the process of regularly posting across our platforms, graphics were systematically scheduled for posting on designated days at the start of each week based on or analytics data, ensuring a consistent and predictable content flow. Paid ads were strategically incorporated during select campaign weeks. This necessitated careful analysis of platforms and coordination with internal financial management to optimize the impact of these promotions. The total cost of social media advertisement was $389.91 for both the first and second survey. Table 2 Table 2. Costs of social media advertisements. Platform Facebook and Instagram* Twitter Twitter Twitter Facebook and Instagram* Twitter Twitter TOTAL: Date Money Spent 02/03/2021–03/04/2021 Feb 27, 2021 Feb 25, 2021 Mar 2, 2021 Jan 27, 2022 Jan 19, 2022 Jan 28, 2022 170.00 54.10 20.00 39.31 56.50 19.19 30.81 $389.91 The ad costs for Facebook and Instagram are grouped as they are linked under the same company. https://doi.org/10.1371/journal.pdig.0000181.t002 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 8 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Table 3. Time spent maintaining the social media campaign on a weekly basis. Platform Description of Task Social media graphics generation Creation of a series of base graphics that served as a template for all future graphics. Emails to external organizations Social media post text generation Posting graphics to platforms Searches for and liaising with new possible external organizations/ individuals to disseminate social media content and promote PSP surveys Generation of text for post descriptions to be reviewed, vetted and then approved for posting by steering committtee Preparation of weekly posting schedule. Translation Translation of post graphic and text content from English to French. Paid advertisements Creation of and fee payment processing for paid ads Interacting with users Personalized engagement with target audience Reviewing analytics from the campaign Weekly review of analytics from each social media platform to optimize posts for subsequent week Brainstorming TOTAL: Weekly reflection on our campaign and review of posts from other users/organizations to develop ideas to modify posts for subsequent week https://doi.org/10.1371/journal.pdig.0000181.t003 # of Hours/ week 4–10 2–4 2 2 2 0–2 0–1 2–5 1 15–30 summarizes cost information. Social media ads were primarily utilized in early campaign peri- ods, but after establishing a substantial and self-sustaining following base, we relied more on sharing content with our existing audience. Interaction with users was a dynamic aspect of our campaign. We actively engaged with our target audience, addressing questions and responding to comments on posts. This person- alized engagement was intended to help foster a sense of community and further amplified our campaign reach. Table 3 illustrates the hour breakdown and required tasks for the week. Strategy modification. During the campaign, certain approaches were modified or phased out due to practical or strategic considerations. TikTok and LinkedIn platform layouts and post norms created challenges to delivering content that resonated with our particular audience. Thus, these components of our strategies were gradually phased out to more effec- tively allocate our campaign resources. We also discontinued our initial "following spree" where we proactively followed of pediatric oncology-related accounts across platforms after achieving a substantial and self-sustaining following base, which facilitated our growth. We also phased out the resharing of posts from other users related to pediatric cancer due to the increasing effort required to ensure the credibility of the content shared and associated users. Further, overtime, we shifted from posting separate but identical French and English content to posting bilingual posts that consolidated the messaging for both linguistic communities. Similar considerations were taken into account for video content, where the creation of sepa- rate videos for different platforms was streamlined into a unified approach, maximizing our efficiency in content distribution. (V) Evaluating campaign success Social media campaign summary. Over the course of our entire PSP—from April 2020 to September 2022—we garnered 870 Instagram followers, 450 Twitter followers, 69 Facebook page likes, 27 TikTok followers, and 20 LinkedIn followers. Figs 4 and 5 provide an overview of how survey response rates correspond to key timepoints and efforts during the social media campaign period. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 9 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 4. Impact of engagement strategies on survey response rates during initial survey period. https://doi.org/10.1371/journal.pdig.0000181.g004 Fig 5. Impact of engagement strategies on survey response rates during interim survey period. https://doi.org/10.1371/journal.pdig.0000181.g005 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 10 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 6. Site session trend over social media campaign period. https://doi.org/10.1371/journal.pdig.0000181.g006 Study website engagement. The study website received 1029 site sessions (visits ended after 30 minutes of inactivity), with 53% of users (n = 547) accessing the site via desktop, 46% (n = 470) via a mobile phone and 1% (n = 12) via tablet. Fig 6 depicts the site session trend over time. Peak site sessions occurred between December 2020 to March 2021, as well as Janu- ary 2022 to March 2022—time periods that corresponded to the interval during which each survey was open. The website garnered a total of 789 unique visitors, with most users accessing the website from Canada (n = 622, 78.8%), followed by the United States (n = 73, 9.3%). Fig 7 depicts a map with the traffic by location. Most visitors were routed to the site from social media (n = 148, 32.7%), followed by direct referrals—or visitors typing our site address into their browser—(n = 146, 32.2%), organic searches—or visitors clinking on a search engine result— (n = 105, 23.1%), and other referrals—visitors clicking a link placed on another website that is not a search engine or social media platform (n = 54, 11.9%). Table 4 provides more informa- tion on the specifics of the traffic source. Facebook, instagram and twitter-focused campaign engagement. Over the course of the project, we made 152 posts on Facebook and 148 posts on Instagram. Our Facebook page was visited 426 times, while our Instagram profile had 1,893 visits. The reach of our Facebook page, or the number of individuals seeing any content from or about the page (including from others who interact with the page), was 28,641 people and our Instagram profile reach was 2,954 people (Fig 8). Considering activity related to our paid ads over Instagram and Face- book, our reach was 24,137 people and paid impressions, or number of times a post is viewed including by the same individual, was 40,721. Paid reach compared to campaign cost can be viewed in Fig 9. Our Twitter page garnered 452 followers. The greatest number of impressions during a 90-day period occurred in January to March 2021—a period within which both Fig 7. Geographic distribution of website users (https://www.openstreetmap.org/#map=3/38.69/-72.42). https://doi.org/10.1371/journal.pdig.0000181.g007 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 11 / 20 PLOS DIGITAL HEALTHTable 4. Overview of website traffic sources. Social media to engage people with lived experience in research priority-setting Traffic source Site sessions Page views Unique visitors Traffic category Direct Organic search Social Social Social Referral Referral Email Organic search Social Referral Organic search Referral Referral Organic search Social Organic search Referral Referral Referral N/A Google Instagram Twitter Facebook sickkids.ca redcapexternal.research.sickkids.ca Email DuckDuckGo LinkedIn Linktree Bing pogon.convio.net veeva.io Yahoo YouTube ecosia.org outlook.live.com us13.admin.mailchimp.com com.linkedin.android 186 161 59 56 47 26 19 15 5 9 5 4 2 2 2 1 1 1 1 1 203 199 66 66 58 26 22 16 10 10 5 4 3 2 2 1 1 1 1 1 146 94 51 42 46 25 17 11 4 8 5 4 2 2 2 1 1 1 1 1 https://doi.org/10.1371/journal.pdig.0000181.t004 organic posts and paid advertisements were implemented. Fig 10 provides the number of impressions during 90-day time periods, the number of daily impressions, and details of the trend of impressions over time. Content and impact of individual social media posts. The top reaching post over the course of our PSP was a French-language ad (Fig 11). This post explicitly asked persons with lived experience and healthcare professionals to participate in priority-setting, included related hashtags, and had an embedded link to the French language research prioritization survey. This advertised post reached 13,393 users, resulting in 200 clicks on the survey link, with approximately a cost of 0.69 Canadian dollars per click. The top unpaid post was an English- language Facebook post that reached 5,415 users and resulted in 47 link clicks. The majority of the top 10 posts across Facebook and Instagram were video-based (n = 9, 90%) as opposed to Fig 8. Facebook and Instagram reach. https://doi.org/10.1371/journal.pdig.0000181.g008 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 12 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 9. Paid advertisement trends. https://doi.org/10.1371/journal.pdig.0000181.g009 including only static images. Table 5 provides an overview of the top five posts, from both Facebook and Instagram, including both paid ads and unpaid posts. Discussion Principal findings Social media represents a promising means to support health research, including the engage- ment in research priority-setting. Our research team used a multi-pronged strategy that lever- aged social media and other online modalities to engage individuals with lived experience of childhood cancer and healthcare professionals in setting a Canadian research agenda Overall, our results demonstrate the utility of doing so and suggest such means may be useful to health researchers interested in engaging communities in PSP-style work. Our campaigns on Facebook and Instagram had substantial success in reaching the Cana- dian pediatric cancer community with reaches of 28,641 and 2,954 people respectively, across both surveys. Further, our study website, which amassed over a thousand visits, saw most visi- tors being directed from our social media platforms. A previous pan-Canadian PSP integrated an in-person component where participants were informed of the study then provided with a device to record their responses with social media campaigning found most participants were recruited through social media (n = 337, 70.2%) [13]. Though this in-person recruitment approach was not available to our PSP due to the restrictions posed by the COVID-19 pan- demic, this suggests that social media may be an effective and possibly resource lean means to build a large sample of participants. Targeted paid advertising proved effective in increasing the reach of our social media cam- paign. Our posts with paid ad boosts reached more users than unpaid posts and each paid advertisement period was accompanied by a peak in the number of PSP surveys completed. The apparent success in engaging persons with lived experience and healthcare professionals by using paid ads reflect with previous research showing ads to reach many more users [14– 16]. Based on our social media engagement analytics, video posts also tended to reach a broader audience than image-based posts [17]. Given this insight, our paid ads were mainly used with video content to further boost engagement. In consultation with our steering group, we created social media content specifically target- ing groups underrepresented in our PSP surveys to encourage their participation. These efforts PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 13 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 10. Twitter analytics over 90-day time period. https://doi.org/10.1371/journal.pdig.0000181.g010 PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 14 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Fig 11. Top reaching post during social media campaign duration. https://doi.org/10.1371/journal.pdig.0000181.g011 correlated temporally with an increased number of survey submissions from these groups. Additionally, to encourage pan-Canadian participation and representation from Francophone Canadians, our entire social media campaign was bilingual with both French and English lan- guage posts made. In these ways, we employed a tailored approach to reach specific popula- tions on social media. In similar studies assessing the application of social media in health research, tailoring content to specific populations has been shown to enhance the engagement and user experience of these groups [18]. Reflecting the findings of our study, targeting content to particular groups has also been identified as an effective means to fill gaps in the reach of priority setting partnership survey promotions [19,20]. Our development of an external network of collaborative childhood cancer organizations and individuals was key to the success of our social media campaign. This strategy is embed- ded in the James Lind Alliance methodology and was an identified factor for success in PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 15 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Table 5. Top five posts from Facebook and Instagram. Caption Nous voulons entendre les patients, les survivants, les soignants, les parents endeuille´s et les cliniciens! Quelles questions avez-vous sur le #cancerpe´diatrique? Assurez vous que votre voix est entendue! L’enquête cutt.ly/pedcancerpspfrench bit.ly/pedcancerpspfrancais L’enquête de suivi tant attendue sur le #cancerpe´diatrique est de´sormais LIVE! Selon vous, sur quoi la future #recherche sur le cancer pe´diatrique devrait-elle se concentrer? L’enquête Plus d’informations #childhoodcancer #pediatriccancer #pediatriccancerresearch #cancerresearch #childhoodcancerresearch #canceradvocate #kidsgetcancertoo #kidscancer #pediatriccancer #pediatriccancerawareness #smallbutmighty #research #pediatriccancerfoundation #teamworkmakesthedreamwork #joinus #helpushelpthem #makeadifference #Canada #nonprofit pedcancerpsp.ca pedcancerpsp.ca Are you a patient, survivor, caregiver, or clinician of #childhoodcancer? Help us make sure that future research focuses on what matters most to children with cancer by completing this survey: cutt.ly/pedcancerpsp CA More info Eˆtes-vous un patient, un survivant, un soignant ou un clinicien de #childhoodcancer? Aidez-nous à nous assurer que les recherches futures se concentrent sur ce qui compte le plus pour les enfants atteints de cancer en re´pondant à cette enquête: cutt.ly/pedcancerpsp CA Plus d’infos #childhoodcancer #pediatriccancer #pediatriccancerresearch #cancerresearch #childhoodcancerresearch #canceradvocate #kidsgetcancertoo #kidscancer #pediatriccancer #pediatriccancerawareness #smallbutmighty #pediatriccancerfoundation #teamworkmakesthedreamwork #joinus #helpushelpthem #makeadifference #follow #share #nonprofit pedcancerpsp.ca Survey is live! Content type Paid ad Reach 13393 French Facebook video post 5415 English Facebook video post 2101 The long-awaited follow-up #childhoodcancer survey is now LIVE! What do you think future #research about childhood cancer should focus on? Survey http://bit.ly/pedcancerpsp English Facebook video post 1184 http://pedcancerpsp.ca More info We want to hear from all of Canada! Your voice matters CA #childhoodcancer #pediatriccancer #pediatriccancerresearch #cancerresearch #childhoodcancerresearch #canceradvocate #kidsgetcancertoo #kidscancer #pediatriccancer #pediatriccancerawareness #smallbutmighty #research #pediatriccancerfoundation #teamworkmakesthedreamwork #joinus #helpushelpthem #makeadifference #Canada #nonprofit Are you a patient, survivor, caregiver, bereaved parent or clinician impacted by #childhoodcancer? Help us make sure that future research focuses on what matters most to children with cancer by taking 10 minutes this holiday season to fill out this survey: cutt.ly/pedcancerpsp Eˆtes-vous un patient, un survivant, un soignant, un parent endeuille´ ou un clinicien touche´ par un cancer pe´diatrique? Aidez-nous à nous assurer que les recherches futures se concentrent sur ce qui compte le plus pour les enfants atteints de cancer en prenant 10 minutes cette saison des fêtes pour re´pondre à ce sondage: cutt.ly/pedcancerpspfrench #childhoodcancer #pediatriccancer #pediatriccancerresearch #cancerresearch #childhoodcancerresearch #canceradvocate #kidsgetcancertoo #kidscancer #pediatriccancer #pediatriccancerawareness #smallbutmighty #research #pediatriccancerfoundation #teamworkmakesthedreamwork #joinus #helpushelpthem #makeadifference #Canada #nonprofit https://doi.org/10.1371/journal.pdig.0000181.t005 Bilingual Facebook picture post 657 research priority-setting in our previous scoping review [7]. Known challenges in approaching members of the public to participate in health research complicate the recruitment process and are due in part to the inherent power dynamic that exists with healthcare providers [21]. Partnering with external organizations and communities that are known and trusted by a social media target audience can help to build campaign credibility and overcome historical challenges to PSP recruitment [22]. Although the possibility of spreading posted content widely and organically via social media exists, this is difficult without an established and well- connected social media presence [23]. Given that our project involved building and utilizing new social media channels with no prior following, we found partnerships with our external network and leveraging their established networks to be particularly valuable. Our partner- ships with external organizations were bolstered by early contact, the provision of a template package of tailored graphics and messaging, brief reminders throughout the study period, and providing some degree of mutual benefit (e.g., study progress updates, providing a summary of study results). Table 6 provides an overview of the specific recommendations derived from our social media campaign. Strengths and limitations. We observed a substantial gap between the number of visitors to our social media platforms and those that proceeded to open and complete the survey. This PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 16 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Table 6. Recommendations for future research-based social media campaign. Recommendation Category Unifying Branding Specific Instructions Applicable Platforms Prior to the launch of social media campaigns, we recommend creating a branding package (i.e., standard fonts, colour palettes, and illustrations), as this facilitates unification of social media campaigns across various platforms All (i.e., Facebook, Instagram, Twitter, LinkedIn, TikTok, Website, YouTube) Accessibility Graphics should meet minimum colour contrast requirements for web accessibility (https:// webaim.org/resources/contrastchecker/) All (i.e., Facebook, Instagram, Twitter, LinkedIn, TikTok, Website, YouTube) Alternative text should be provided for all images and graphics Navigation should be simple and accessible on mobile and desktop devices Website Website Reflecting Target Audience Text in posts should reflect target audience’s language needs (e.g., all text was available in French and English, reflective of Canada’s two official languages) All (i.e., Facebook, Instagram, Twitter, LinkedIn, TikTok, Website, YouTube) Stakeholders from the social media campaign’s target audience should review the social media campaign prior to launch to ensure values of the target community are reflected in the campaign’s content All (i.e., Facebook, Instagram, Twitter, LinkedIn, TikTok, Website, YouTube) Multiple platforms should be utilized, as different demographics may be associated with more frequent use of preferred social media platforms (e.g., researchers may frequently access LinkedIn, while pediatric patients may frequently access TikTok). All (i.e., Facebook, Instagram, Twitter, LinkedIn, TikTok, Website, YouTube) Optimizing Campaign Reach Texts and graphics should be intermittently tailored throughout the campaign to target specific under-represented groups (e.g., we intermittently posted targeted posts that included graphics with fathers taking care of a child with cancer, and text stating “Fathers of children with cancer, we want your voice heard in our survey”) Complete website platform’s requirements for SEO. The following checklist was provided by Wix: 1. Set the homepage title for search results 2. Add the homepage description for search results 3. Update the text on your homepage 4. Make your homepage visible in search results 5. Optimize your site for mobile devices 6. Connect your site to a custom domain 7. Connect your site to Google Search Console Facebook, Instagram, Twitter, LinkedIn, TikTok, YouTube Website Implement paid advertisements via social media platforms to increase reach, and fill in gaps of under-represented populations Facebook, Instagram, TikTok, LinkedIn, Twitter Expand possible audience reach by collaborating with internal networks and external organizations by requesting they share social media campaign content; we recommend facilitating this collaboration by providing template packages. Facebook, Instagram, TikTok, LinkedIn, Twitter, Email Ensure hashtags are included in posts, as this assigns the designated post to the search results of the respective hashtag. Facebook, Instagram, Twitter, LinkedIn, TikTok Monitor analytics throughout the social media campaign to identify under-represented audiences and optimal posting times; adjust social media campaign accordingly. Facebook, Instagram, Twitter, LinkedIn, TikTok, YouTube https://doi.org/10.1371/journal.pdig.0000181.t006 is a commonly cited limitation in PSPs, where the social media campaign reach is dispropor- tionately greater than the actuate survey response rate [23]. Although we provided clear instructions on accessing and completing the survey in each social media post, elaborating on these details and improving capacity to navigate to the survey may have yielded a greater response rate. Nevertheless, despite these gaps between campaign engagement and our survey response rate, our PSP succeeded in securing similar numbers of participants to other pediatric illness PSPs and was successful in communicating and raising awareness about our project and its results within the pediatric cancer community and beyond. Another limitation of social media usage in priority-setting research is the uncertainty of who is being engaged with through the posts [24,25]. Social media–based methods may unexpectedly include or exclude the research priority perspectives of certain groups [7]. In our case, the team and steering com- mittee ensured that social media posts were accompanied with a clear definition of the survey respondent eligibility criteria. Additionally, the survey was prefaced by a screening question PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 17 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting intended to filter out ineligible respondents. Despite these efforts, individuals that did not fit our PSP eligibility criteria were likely reached by our social media campaign and were still able to complete the survey. We also experienced initial difficulty engaging individuals from certain geographic regions. Particularly, it was challenging during the first to reach persons situated in Quebec. However, our use of posts and paid ads targeted towards this group and connections with Quebec-based external partners supported greater participation in the second survey. This reinforces the importance of supplementing social media posts with additional engage- ment network-based strategies. Finally, we recruited many more family members and health- care professionals than children with cancer or young cancer survivors. To engage with youths, we attempted to use TikTok, one of the most utilized social media platforms among young people [26,27]. Still, this strategy had limited impact in terms of engagement. Despite our efforts to attract the younger age groups, we had limited data on the impact of these strate- gies. Recognizing that social media preferences among young people are constantly evolving, a continued understanding of forthcoming platforms among the younger population is war- ranted [28]. Future research. We recognize that little is known on the operation of social media algo- rithms. Further research is needed to understand how social media algorithms can influence recruitment to capture representative samples. More research is also needed to understand which social media strategies and dissemination techniques are likely to be successful for research prioritization efforts, with the understanding that these strategies and techniques are likely to change over time as new social media platforms and features become available. The ethics requirements of traditional recruitment techniques are difficult to translate to research using social media given its potentially vast reach [23] and research in this area is also needed. Privacy risks also exist when social media-based recruitment methods are utilized. While our PSP only used social media to advertise, and recruitment and data collection occurred via a separate survey with data stored securely at our institution, further parameters to enhance participant privacy have been suggested [29]. Particular strategies include develop- ing privacy notices for social media campaigns, creating disclaimers on the privacy risks of social media platforms, and disabling the comment feature, though consideration of the effec- tiveness of such strategies is needed [30]. Conclusion The engagement of people with lived healthcare experiences in research priority-setting is crit- ical. We have presented our experience using social media to engage children with cancer, sur- vivors, their family members, and healthcare providers in the development of a Canadian research agenda. Diversifying recruitment across multiple platforms increased response rates and improved the reach in an efficient manner. Utilizing paid ads, tailoring social media con- tent to specific groups, and circulating promotional material to partnering external organiza- tions were key strategies used to engage those with lived experience and healthcare professionals our PSP. Further investigation of social media algorithms and dissemination techniques is needed to understand how to increase representation among survey respondents. Consideration of the privacy implications of social media use for research is also needed. Ulti- mately, continued evaluation of novel tools to enable inclusive priority-setting may amplify the voices of those with lived experience as it pertains to the next scientific efforts. Author Contributions Conceptualization: Kyobin Hwang, Surabhi Sivaratnam, Lindsay A. Jibb. Data curation: Kyobin Hwang, Surabhi Sivaratnam. PLOS Digital Health \| https://doi.org/10.1371/journal.pdig.0000181 January 8, 2024 18 / 20 PLOS DIGITAL HEALTHSocial media to engage people with lived experience in research priority-setting Formal analysis: Kyobin Hwang, Surabhi Sivaratnam. Funding acquisition: Lindsay A. Jibb. Investigation: Kyobin Hwang, Surabhi Sivaratnam, Lindsay A. Jibb. Methodology: Kyobin Hwang, Surabhi Sivaratnam, Lindsay A. Jibb. Project administration: Elham Hashemi, Lindsay A. Jibb. Resources: Lindsay A. Jibb. Supervision: Elham Hashemi, Lindsay A. Jibb. Visualization: Surabhi Sivaratnam. Writing – original draft: Kyobin Hwang, Surabhi Sivaratnam. Writing – review & editing: Kyobin Hwang, Surabhi Sivaratnam, Rita Azeredo, Elham Hashemi, Lindsay A. Jibb. References 1. Jibb LA, Stacey D, Carley M, Davis A, Graham ID, Green E, Jolicoeur L, Kuziemsky C, Ludwig C, Tru- ant T. 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10.1371_journal.pbio.3002375	RESEARCH ARTICLE Human subcortical pathways automatically detect collision trajectory without attention and awareness Fanhua Guo1,2☯, Jinyou Zou1,2,3☯, Ye Wang1,2☯, Boyan Fang4, Huanfen Zhou5, Dajiang WangID 5, Sheng He1,2,6, Peng ZhangID 1,2,6* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, 2 University of Chinese Academy of Sciences, Beijing, China, 3 Aier Institute of Optometry and Vision Science, Aier Eye Hospital Group, Changsha, China, 4 Neurological Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China, 5 Division of Ophthalmology, The Third Medical Center of PLA General Hospital, Beijing, China, 6 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China ☯ These authors contributed equally to this work. * wangdajiang301@163.com (DW); hes@ibp.ac.cn (SH); zhangpeng@ibp.ac.cn (PZ) OPEN ACCESS Citation: Guo F, Zou J, Wang Y, Fang B, Zhou H, Wang D, et al. (2024) Human subcortical pathways automatically detect collision trajectory without attention and awareness. PLoS Biol 22(1): e3002375. https://doi.org/10.1371/journal. pbio.3002375 Academic Editor: Christopher Pack, McGill University, CANADA Received: October 3, 2023 Accepted: December 14, 2023 Published: January 18, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pbio.3002375 Copyright: © 2024 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data and code to reproduce the main findings of this study can be downloaded from Open Science Framework (OSF, https://doi.org/10.17605/OSF.IO/GDJWH). Abstract AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: Detecting imminent collisions is essential for survival. Here, we used high-resolution fMRI at 7 Tesla to investigate the role of attention and consciousness for detecting collision trajec- tory in human subcortical pathways. Healthy participants can precisely discriminate collision from near-miss trajectory of an approaching object, with pupil size change reflecting collision sensitivity. Subcortical pathways from the superior colliculus (SC) to the ventromedial pulvi- nar (vmPul) and ventral tegmental area (VTA) exhibited collision-sensitive responses even when participantsAU : PleasenotethatasperPLOSstyle; donotusethewordsubjectsforhumans:Hence; }subjects}hasbeenchangedto}participants}throughoutthetext: were not paying attention to the looming stimuli. For hemianopic patients with unilateral lesions of the geniculostriate pathway, the ipsilesional SC and VTA showed significant activation to collision stimuli in their scotoma. Furthermore, stronger SC responses predicted better behavioral performance in collision detection even in the absence of awareness. Therefore, human tectofugal pathways could automatically detect collision trajectories without the observers’ attention to and awareness of looming stimuli, supporting “blindsight” detection of impending visual threats. Introduction Detecting objects approaching on a collision course is critical for the survival of animals in the environment. Specialized neurons or brain circuits highly sensitive to looming stimuli have been identified in many species, including insects [1], fish [2], pigeons [3], mice [4,5], and oth- ers. In primates, both adult monkeys and human infants elicit avoidance behaviors to symmet- rically looming stimuli [6,7], imaging studies mainly revealed looming sensitive responses in the cortical brain regions [8–12]. These previous studies mostly compared a large looming stimulus versus translating or receding stimuli, whose motion directions greatly deviate from the collision course. The neural mechanism for computing the time-to-contact (TTC) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 1 / 28 Funding: This study was supported by Ministry of Science and Technology of China (https://en.most. gov.cn/) STI2030-Major Projects (2022ZD0211900 to P.Z., 2021ZD0204200 to S.H.), National Natural Science Foundation of China (https://www.nsfc. gov.cn/english/site_1/index.html, 31871107 and 31930053 to P.Z., 82101110 to H.Z.), Chinese Academy of Sciences (https://english.cas.cn/, XDB32020200, KJZD-SW-L08, YSBR-071) to S.H., and Beijing Natural Science Foundation (https:// mis.kw.beijing.gov.cn/, 7212092) and the Capital’s Funds for Health Improvement and Research (https://wjw.beijing.gov.cn/, 2022-2-5041) to D.W.. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Collision detection in human subcortex information has been extensively studied and was suggested to provide warning detection for large approaching objects. However, humans are not only sensitive to looming versus non- looming stimuli but also efficient in detecting collision course from near-miss trajectories (i.e., a few centimeters away from the head at the pass point). For example, compared with a near- miss trajectory, an approaching object on a collision course with the observer could automati- cally capture visual attention, increase perceived object size and evoke greater pupil constric- tions, even without conscious awareness of the motion direction [13,14]. These behavioral findings suggest early subcortical mechanisms for collision detection, through precise and sen- sitive measures of motion trajectories. However, little is known about the neural mechanism in the human brain for detection of collision trajectories. AGFI, adjusted goodness of fit The superior colliculus (SC) is a phylogenetically old visual nucleus lying on the roof of the mammalian brainstem. It plays important roles in visual perception and visually guided reori- enting functions, such as attention, eye and head movements [15]. As a key retino-recipient region, the SC and its homologous structure in nonmammals, optic tectum, were found prefer- entially sensitive to looming stimuli in a number of species [4,5,8]. In rodents, specific types of neurons in the SC have been identified as the key components of several subcortical circuits to detect looming objects and trigger defensive responses, including the SC-PBGN (parabigem- inal nucleus)-Amygdala [4,16], SC-LP (lateral posterior thalamic nucleus, homolog of primate pulvinar)-Amygdala [5,16] and SC-VTA (ventral tegmental area)-Amygdala [17], and LC (locus coeruleus)-SC [18] connections. Given that the subcortex is the primitive brain and that subcortical functions might be relatively conserved in mammals, the subcortical pathways found in the rodent brain might also play important roles for collision detection in the human brain. Alternatively, with the expansion of the neocortex and reduced retinal projection to the SC in higher mammals [15], it could be possible that the cerebral cortex plays a more promi- nent role in collision detection in the human brain. An early 3T fMRI study at a relatively low resolution suggests that the human SC, pulvinar, exhibited stronger responses to looming compared to receding stimuli [8]. However, it is unclear whether these subcortical responses were a result of cortical influence. Most importantly, the underlying neural circuitry for detect- ing collision trajectories remains unknown. Abbreviations: AU : Anabbreviationlisthasbeencompiledforthoseusedthroughoutthetext:Pleaseverifythatallentriesarecorrectlyabbreviated: index; AttNet, attention network; BVF, blind visual field; CDF, cumulative distribution function; CFI, comparative fit index; DA, dopaminergic; FDR, false discovery rate; FEF, frontal eye field; FWE, family- wise error; FWHM, full-width half-maximum; GFI, goodness of fit index; GLM, general linear model; IFG, inferior frontal gyrus; INS, insular; ISI, interstimulus interval; LC, locus coeruleus; LGN, lateral geniculate nucleus; LME, linear mixed effect; LORO, leave-one-run-out; LOSO, leave-one- subject-out; LP, lateral posterior; MTG, middle temporal gyrus; NVF, normal visual field; PBGN, parabigeminal nucleus; PGFI, parsimony goodness of fit index; PV, parvalbumin; RMR, root mean square residual; RMSEA, root mean square error of approximation; SC, superior colliculus; SEM, structural equation modeling; TPJ, temporal parietal junction; TTC, time-to-contact; VC, visual cortex; vlPul, ventrolateral pulvinar; vmPul, ventromedial pulvinar; VTA, ventral tegmental area; 2-IFC, 2-interval forced choice. matically even without the observer’s attention to and awareness of the looming stimuli, as suggested by previous behavioral studies in humans [13,14]. Some patients with cortical blind- ness can detect or even discriminate visual stimuli presented to their blind visual field, despite denial of seeing the stimuli. This phenomenon, called blindsight, attracts broad interests and has been hotly debated for almost half a century [19]. Although lots of studies have been done, the neural pathways involved and their specific functional roles in blindsight remain highly controversial. Looming-evoked avoidance behavior was observed in monkeys with V1 lesions [20], suggesting a critical role of subcortical pathways in automatic “blindsight” detection of impending visual threats. However, direct evidence for this hypothesis is still lacking. Moreover, it is unknown whether these collision detection mechanisms could work auto- To answer these questions, the current study used high-resolution fMRI to investigate the neural pathways involved in detecting collision trajectory in both healthy human participants and hemianopic patients. The motion trajectory of an incoming object was varied slightly to be either on a collision course or on a near-miss trajectory regarding the head of observers. For healthy participants, we first measured their visual ability to discriminate collision course from near-miss trajectories, and the associated changes in pupillary reflex (experiment 1). In a 7T fMRI experiment (experiment 2), we further investigated the neural circuitries for detecting collision trajectories and examined whether these mechanisms require top-down attention or could operate automatically without paying attention to the stimuli. In a group of hemianopic patients with unilateral lesions of the geniculostriate pathway, experiment 3 studied whether PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 2 / 28 PLOS BIOLOGYCollision detection in human subcortex the tectofugal pathways are sensitive to collision trajectories even without awareness of stimuli presented in their blind visual field. Results Behavioral and pupil size sensitivity to collision trajectory In a behavioral experiment (experiment 1, n = 15), we measured the ability of healthy human observers to discriminate hit from near-miss trajectories. Participants responded whether an incoming object would hit or miss their head (Figs 1A and S1A). Pupil size and eye movements were also recorded. In Fig 1B, the percentage of hit responses was plotted as a function of the extrapolated impact point of the approaching objects. The psychometric functions were fur- ther fitted with cumulative normal distributions. The steep slope of the psychometric function around the edge of the head (about 6 cm from nasion) indicates that participants could pre- cisely discriminate the incoming objects on a collision course from those with near-miss 1). (a) Visual stimuli depicting an incoming ball from one Fig 1. Schematic stimulus diagram and results of the behavioral experiment (experimentAU : Pleasenotethat}exp:1; }exp:2; and}exp:3}havebeenchangedto}experiment1; }experiment2; and}experiment3; }respectively; toenforceconsistencythroughoutthetext:Pleaseconfirmthatthischangeisvalid: of the 4 quadrants of the VF were presented with a 3D LCD monitor in the behavioral experiment. The trajectory of the looming object varied slightly to either hit (hit nasion, hit eye) or miss (near miss, far miss) the head of observers. (b) The percentage of hit responses at different impact points was fitted with a normal CDF. Black circles indicate the extrapolated impact points of different trajectories. (c) The discrimination sensitivity, calculated as (cid:0) � ln 1 in which σ is the standard deviation of the fitted normal CDF, is significantly higher in the upper VF than in the lower VF. Error bars indicate SE. s *p < 0.01. (d) The change of pupil size with respect to baseline (−200~0 ms) was normalized by standard deviations and then plotted for hit (purple) and miss (dark gray) trajectories, respectively (data from the upper and lower VFs were combined here). Dark gray bars indicate the time points when the pupil size was significantly smaller (permutation test p < 0.05 using cluster-size based adjustment) in the hit condition. The vertical gray bar indicates the time interval of visual stimulus presentation. (e, f) The averaged change of pupil size from 1,000 to1,364 ms after stimulus onset was plotted for different looming stimuli from the upper (e) and lower (f) VFs. Bonferroni corrected p < 0.05, + uncorrected p < 0.05. Shaded areas in (b, d) and vertical lines in (c, e, f) indicate SEM. Data underlying (c, e, f) can be found at https://osf.io/gdjwh/. CDFAU : AbbreviationlistshavebeencompiledforthoseusedinFigs1 (cid:0) 6:Pleaseverifythatallentriesarecorrectlyabbreviated: SEM, standard error of the mean; VF, visual field. , cumulative distribution function; https://doi.org/10.1371/journal.pbio.3002375.g001 PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 3 / 28 PLOS BIOLOGYCollision detection in human subcortex trajectories, consistent with the prediction from previous studies [13,14]. S4A Fig shows the results for all individuals. The discrimination sensitivity, calculated based on the slope of the psychometric curve, was slightly but significantly higher for objects approaching from the upper visual field than from the lower visual field (permutation test p < 0.001; Fig 1C), indicating that observers could more precisely discriminate hit from near-miss trajectories for objects coming from the upper visual field. Pupil size was smaller around 1,100 ms after stimulus onset in the hit than miss conditions (Fig 1D–1F), consistent with previous findings [13]. Similar results were observed when testing with a much brighter background or in an irrelevant task condition (S5 and S6 Figs). Fixational eye movements showed no significant difference between the hit and miss events (S4B Fig). These behavioral results show that observers and their pupillary responses can precisely discriminate collision from near-miss trajectories of an incoming object. Enhanced SC responses to looming objects on a collision course To investigate potential subcortical pathways for the detection of collision trajectory in the human brain, we performed a 7T fMRI experiment (1.5-mm isotropic resolution) in a group of healthy human participants in experiment 2 (n = 20). In a long event-related design (Fig 2A), participants were asked to discriminate whether an incoming object would hit or miss their head in an attended condition or to detect occasionally and randomly presented fixation- color changes in an unattended condition. Participants’ performance was 97.2% ± 3.6% (mean ± STD) in the attended condition and 95.6% ± 4.7% in the unattended condition, indi- cating that they followed the task instructions very well. Previous studies suggest that the SC, a phylogenetically old midbrain nucleus, might play important roles for collision detection in the human brain [8]. Thus, in the current study, we carefully investigated the response of the SC to looming stimuli with direct-hit or near-miss trajectories. From the group-averaged activation maps (Fig 2C), bilateral SCs showed robust responses to the looming stimuli in both attended and unattended conditions. This confirmed that the human SC was indeed highly sensitive to looming stimuli. We further investigated the collision sensitivityAU : PleasenotethatasperPLOSstyle; italicsshouldnotbeusedforemphasis:Hence; pleaseconfirmthat}collisionsensitivity}inthesentence}Wefurtherinvestigatedthecollisionsensitivityineachvoxeldefined:::}canbechangedtoregulartext: in each voxel defined as the response difference between the direct-hit and near-miss trajectories (Fig 2C, “Hit-Miss”). In the attended condition, no significant cluster of voxels with collision sensitivity was found in the SC. However, significant or marginally signif- icant clusters can be found in the unattended condition (permutation test with small volume correction: cluster p = 0.053 for the contralateral SC, and p < 0.001 for the ipsilateral SC. The cluster defining threshold is voxel p = 0.05). These collision-sensitive clusters located more ros- tral (or anterior) (Fig 2B, foveal SC, “Hit-Miss,” unattended), corresponding to the foveal part of the SC [21,22]. More caudal (or posterior) part of the SC showed strong responses to both looming stimuli presented in the contralateral visual field (Fig 2C, extrafoveal SC, “Hit” and “Miss”), but no significant collision-sensitive clusters. Based on the normalized depth map of the SC (S5 Fig in our previous study [23]), we divided the SC into 3 compartments with equal thickness, roughly correspond to the superficial (depth from 0 to 1/3), intermediate (1/3 to 2/ 3), and deep layers (2/3 to 1). The center-of-mass location of the contralateral (depth = 0.2) and ipsilateral (depth = 0.36) clusters locate in the superficial and intermediate layers, respectively. The ROI analysis focused on the responses to stimuli from the contralateral visual field. We first investigated the responses of the whole SC (Fig 2C). Two-way repeated measures (rm) ANOVA revealed a significant main effect of trajectory (Hit/Miss, F(1,19) = 8.67, p = 0.008, Z2 p = 0.313) and attention (Attended/Unattended, F(1,19) = 49.27, p < 0.001, Z2 p = 0.722). Post hoc paired t tests showed a significant collision sensitivity in the unattended condition (t(19) = PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 4 / 28 PLOS BIOLOGYCollision detection in human subcortex Fig 2. Schematic diagram of stimuli and procedure and looming-evoked responses in the SC of healthy participants (experiment 2). (a) Looming objects with hit and near-miss trajectories were presented in 2D images with an MRI-compatible projector for 330 ms, with 7.67~11.67 s of intertrial intervals. The stimulus was presented in one of the 4 quadrants of the VF in each trial. The right panel shows the size and position of the stimulus at the last frame in each quadrant. (b) SC activation maps. Red and green squares on the coronal and sagittal slices indicate the locations of the SC. From the zoomed-in sagittal view, blue dashed lines outline the ROIs of the rostral/anterior and caudal/posterior SCs representing the foveal and extrafoveal visual fields, respectively. The ROIs was determined by the retinotopic activations (contra-ipsi; S7 Fig) shown here as the green overlay. Activation maps were thresholded at p < 0.05 (uncorrected). Color bars indicate percent signal change. Left and right SCs were mapped with responses to the contralateral and ipsilateral stimuli, respectively. Activation maps to stimuli from the left visual field were horizontally flipped and averaged with those to stimuli from the right VF. C, I, D, and V in the compass abbreviate contralateral, ipsilateral, dorsal, and ventral, respectively. Dotted lines indicate an approximate boundary between the superficial and deeper layers of the SC. (c and d) ROI-averaged BOLD responses of the whole SC to looming stimuli from the contralateral visual field. (e and f) The responses in the foveal (c) and extrafoveal (f) parts of the SC. Red/blue horizontal bars, black vertical bars, and red/ blue circles denote mean, SE, and individual data, respectively. * above a long black line indicates ANOVA p < 0.05 for the interaction between attention and trajectory. * and ** above a short black line indicate paired t test p < 0.05 and 0.01, respectively. * In between the red and blue lines in (f) indicates p < 0.05 for the main effect of trajectory. The original fMRI resolution is 1.5-mm isotropic; here, the functional maps were up-sampled at 0.6-mm isotropic to match the resolution of MNI template (see Methods for details). Data underlying (c, d, e, f) can be found at https://osf.io/gdjwh/. SC, superior colliculus; SE, standard error; VF, visual field. https://doi.org/10.1371/journal.pbio.3002375.g002 2.363, p = 0.029, Cohen’s d = 0.528), but not in the attended condition (p = 0.247). We then investigated SC responses to looming stimuli in the upper and lower visual fields in the unat- tended condition (Fig 2D). Although collision sensitivity in the upper and lower visual fields showed no significant difference (trajectory by visual field interaction: p = 0.124), the SC PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 5 / 28 PLOS BIOLOGYCollision detection in human subcortex responses to looming stimuli on a near-miss trajectory were significantly stronger in the upper visual field compared to those in the lower visual field (t(19) = 2.798, p = 0.011, Cohen’s d = 0.626), suggesting higher looming sensitivity in the SC to approaching objects from the upper visual field. This finding is consistent with the behavioral results of slightly better loom- ing-trajectory discrimination and stronger pupillary reflex in the upper visual field (Figs 1, S5, and S6). We further divided the SC into a rostral (foveal) part and a caudal (extrafoveal) part p = 0.298). Post hoc t tests showed significant collision sensitivity in the foveal SC (enclosed by blue dotted lines in Fig 2B) based on the significant contralateral-ipsilateral acti- vations (green voxels in the upper middle panel of Fig 2B; see also S7 Fig). In the foveal SC (Fig 2E), there was a significant interaction between trajectory and attention (F(1,19) = 8.06, p = 0.010, Z2 in the unattended condition (t(19) = 3.015, p = 0.007, Cohen’s d = 0.674), but not in the attended condition (p = 0.600). To further validate the collision sensitivity found in the foveal SC in the unattended condition, we performed a leave-one-subject-out (LOSO) cross-valida- tion analysis. For each participant, the ROI to calculate the collision-sensitive response was defined by voxels with significant group-level collision sensitivity from the remaining partici- pants. The LOSO results revealed a significant collision sensitivity in the foveal SC in the unat- tended condition (t(19) = 2.761, p = 0.012, Cohen’s d = 0.617). In the extrafoveal SC (Fig 2F), there was a significant main effect of trajectory (F(1,19) = 6.75, p = 0.018, Z2 p = 0.262), but no interaction between attention and trajectory (p = 0.943). This finding suggests collision sensi- tivity in the extrafoveal SC independent with the attentional state of observers. To evaluate whether looming stimuli lead to any measurable head movement, we investi- gated head movements after the onset of the looming stimuli using the motion parameters esti- mated with EPI volumes. Results showed negligible head motion to looming stimuli (less than 0.01 mm on average). We further confirmed that the observed collision sensitivity in the SC was unlikely influenced by sparsely and randomly presented fixation changes or due to retino- topic difference in luminance changes and optical flows between hit and near-miss trajectories (S2 and S3 Figs, S1 Text). Collision sensitivities in the ventral pulvinar and VTA We further investigated collision sensitivity in other subcortical regions, including the pulvi- nar, VTA, PBGN, amygdala, and LC, which form looming sensitive circuits with the SC as demonstrated by previous rodent studies [4,5,16–18]. The lateral geniculate nucleus (LGN) was also included as a potential control area. S8 Fig shows the anatomical masks for these sub- cortical regions in MNI space. For each ROI, we performed similar analyses as for the SC to check its activation map and ROI-averaged responses. Collision-sensitive responses were found in the pulvinar and VTA (Fig 3), but not in other subcortical nuclei (S9 Fig). As shown by Fig 3A, looming stimuli activated both lateral and medial portions of the ven- tral pulvinar. The ventrolateral pulvinar (vlPul) mainly connects with the early visual cortex but also with the parietal cortex [24] and may also receive sparse input from the SC [25,26]. As shown by Fig 3A, a collision-sensitive cluster was found in the vlPul in the attended condition (cluster p = 0.091). In Fig 3B (left panel), the ROI-averaged responses of vlPul to stimuli pre- sented to the contralateral visual field showed significant interaction of attention and trajectory (F(1,19) = 4.62, p = 0.045, Z2 p = 0.196). Post hoc paired t tests revealed a significant collision sensitivity in the attended condition (t(19) = 2.750, p = 0.013, Cohen’s d = 0.615), but not in the unattended condition (p = 0.347). The LOSO cross-validation analysis revealed a signifi- cant collision sensitivity in the vlPul in the attended condition (t(19) = 2.646, p = 0.016, Cohen’s d = 0.592). PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 6 / 28 PLOS BIOLOGYCollision detection in human subcortex Fig 3. BOLD responses to looming stimuli in the pulvinar and VTA (experiment 2). (a, c) Activation maps to looming stimuli in coronal views of the pulvinar (a) and VTA (c). Maps were thresholded at p < 0.05 uncorrected. Dotted lines mark the anatomical boundaries of the vlPul, vmPul, and VTA. Activation maps of the whole pulvinar were smoothed with a 2.8-mm FWHM Gaussian filter for display purpose (1.4 mm FWHM for other nuclei). (b) ROI-averaged responses of the vlPul and vmPul. (d) Left panel shows the ROI-averaged responses of the whole VTA. Right panel shows the averaged responses of the collision-sensitive cluster in (c). * or *** above a long line indicate ANOVA p < 0.05 or p < 0.001. , , or ** above a short line for t test p < 0.05 or p < 0.001. Other conventions as in Fig 2. Data underlying (b, d) can be found at https://osf.io/gdjwh/. FWHM, full-width half-maximum; vlPul, ventrolateral pulvinar; vmPul, ventromedial pulvinar; VTA, ventral tegmental area. https://doi.org/10.1371/journal.pbio.3002375.g003 The ventromedial pulvinar (vmPul) receives strong inputs from the SC and possibly sparse reciprocally connects with visual areas in the dorsal visual stream input from the retina andAU : Pleasecheckandconfirmthattheeditto}TheventromedialpulvinarðvmPulÞreceivesstronginputsfromtheSC:::}didnotaltertheintendedmeaningofthesentence: [26–28]. The dorsal part of vmPul may also connect with the amygdala and frontoparietal cor- tex [29,30]. In the group-averaged activation maps in the unattended condition (Fig 3A), there was a small cluster of collision sensitivity (Fig 3A, bottom right). The ROI-averaged responses of vmPul (Fig 3B, right panel) showed a significant interaction between trajectory and atten- tion (F(1,19) = 7.06, p = 0.016, Z2 tivity in the unattended condition (t(19) = 3.613, p = 0.002, Cohen’s d = 0.808), but not in the attended condition (p = 0.321). LOSO cross-participants validation did not find significant effect of collision sensitivity, likely due to a relatively large interindividual variability of colli- sion-sensitive response in the vmPul. No significant collision-sensitive response was found in other subnuclei of pulvinar. p = 0.271). Post hoc t tests revealed a significant collision sensi- The VTA receives direct input from the SC [17,31,32] and projects to many brain areas including the amygdala [17] and frontal lobe [33]. In the attended condition (Fig 3C, left), the VTA showed significant activations to looming stimuli but without collision sensitivity. In the unattended condition, there was a significant collision-sensitive cluster (cluster p = 0.004). The averaged responses of the collision-sensitive cluster were plotted in the right panel of Fig 3D. For the ROI-averaged response of the whole VTA (Fig 3D, left panel), we found a marginally PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 7 / 28 PLOS BIOLOGYCollision detection in human subcortex significant effect of collision sensitivity in the unattended condition (t(19) = 1.970, Cohen’s d = 0.441, p = 0.064). LOSO analysis further revealed a significant collision sensitivity in the unattended condition (t(19) = 3.234, Cohen’s d = 0.723, p = 0.004). As multiple tests of collision sensitivity were performed in several subcortical ROIs, we fur- ther controlled family-wise errors with a sequential Holm–Bonferroni approach (see statistical analysis in Methods for details). After correction, the foveal SC, vmPul, and VTA still shows significant collision sensitivity in the unattended condition (all p < 0.05). Collision sensitivities in the visual and frontoparietal cortices We further investigated whether collision sensitivity can also be found in the cortical brain regions. ROI-averaged retinotopic responses in a visual cortical area were determined by a leave-one-run-out (LORO) cross-validation approach at the individual level (see Methods for details). As shown by Fig 4A, significant collision sensitivity can be found in the early visual cortex in both attended and unattended conditions (attended condition: false discovery rate (FDR) p = 0.030 in V3, p = 0.030 in V3b, and p = 0.030 in V4 after BH-FDR correction; uncor- rected p = 0.027 in V1; unattended condition: uncorrected p = 0.044 in V4, uncorrected p = 0.031 in TO2). Fig 4B shows the group-averaged collision-sensitive (Hit-Miss) activations on the cortical surface. A few clusters with collision sensitivity (p < 0.01, uncorrected) can be found from bilateral middle temporal gyrus (MTG) in the attended condition and from bilat- eral temporal parietal junctions (TPJ), and the frontal eye field (FEF), inferior frontal gyrus (IFG), and insular (INS) in the right hemisphere in the unattended condition. However, no significant cluster can be found after family-wise error correction. These results revealed colli- sion-sensitive responses in the visual cortex and possibly in the frontoparietal areas of atten- tion network. SC-vmPul and SC-VTA pathways detect collision without attention To further investigate the subcortical circuits involved in collision detection and the potential influence from cortical brain areas, we calculated across-participant correlations between Fig 4. Collision sensitivity in the cortical areas (experiment 2). (a) BOLD responses in the visual cortex. , , or ** denote p < 0.05, p < 0.01, or p < 0.001 after FDR correction, and + for p < 0.05 (uncorrected). (b) Collision-sensitive activations in the high order visual cortex and the frontoparietal areas. The color bar indicates the percent signal change of Hit-Miss. Maps were thresholded at uncorrected p < 0.01. No significant clusters can be found after FWE correction. Data underlying (a) can be found at https://osf.io/gdjwh/. FEF, frontal eye field; FWE, family-wise error; IFG, inferior frontal gyrus; INS, insular; LH, left hemisphere; MTG, middle temporal gyrus; RH, right hemisphere; TO, temporal occipital; TPJ, temporal parietal junction. https://doi.org/10.1371/journal.pbio.3002375.g004 PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 8 / 28 PLOS BIOLOGYCollision detection in human subcortex collision-sensitive responses in the subcortical nuclei (SC, vmPul, and VTA) and cortical areas including the visual cortex (VC) and frontoparietal attention network (AttNet), followed by a path analysis with structural equation modeling (SEM) on the beta series of looming-evoked responses to infer their effective connectivity. We selected these ROIs because significant colli- sion-sensitive responses were found in these regions. The vlPul was not included in this analy- sis because the SC’s projection to this area is weak and controversial [25,26], and the collision- sensitive responses in the SC and vlPul showed no significant correlation (uncorrected p > 0.4 in both attended and unattended conditions). In the attended condition (Fig 5A, left panel), we found significant correlations of collision- sensitive responses between the SC and its downstream subcortical target vmPul (r = 0.556, uncorrected p = 0.014), between VC and VTA (r = 0.478, uncorrected p = 0.033), and between ) correction of the correla- VC and AttNet (r = 0.651, p = 0.021, after family-wise error (FWEAU : Pleasenotethat}family (cid:0) wiseerror}hasbeenaddedasfullspellingfor}FWE}atitsfirstmentioninthesentence}IntheattendedconditionðFig5A; leftpanelÞ; wefoundsignificant:::}Pleaseconfirmthatthisiscorrect: tion matrix). In the unattended condition (Fig 5A, right panel, and 5b), there were significant correlations between the SC and both downstream subcortical targets (SC-vmPul: r = 0.593, Fig 5. Correlation of collision sensitivity and path analysis of beta series between cortical and subcortical regions (experiment 2). (a) Correlation matrix of collision sensitivity. Each grid in the matrix shows the Pearson’s correlation coefficient between the collision sensitivities of 2 ROIs, in the attended (left) and unattended (right) conditions. + indicates uncorrected p < 0.05 and * for p < 0.05 after FWE correction by permutation test. Color bars indicate the size of correlation coefficient. (b) Correlations between the collision sensitivity in the SC, and those in subcortical (vmPul/VTA) and cortical (VC/AttNet) areas. Each dot represents 1 participant. (c) Candidate SEM models for the effective connectivity of beta series between ROIs. Gray solid single-headed arrows represent fixed one-way connection. Red solid double-headed arrows indicate connections with 2 alternative directions. Dotted double-headed arrows indicate that the connection may follow one of 2 alternative directions or may not exist. All combinations of alternative connections yield 216 candidate models. (d, e) Best fitted models in the attended (d) and unattended (e) conditions. Arrows with solid lines indicate significant connections. There was no insignificant connection. Data underlying (b) can be found at https://osf.io/gdjwh/. AttNet, frontoparietal attention network; FWE, family-wise error; SC, superior colliculus; VC, visual cortex; vmPul, ventromedial pulvinar; VTA, ventral tegmental area. https://doi.org/10.1371/journal.pbio.3002375.g005 PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 9 / 28 PLOS BIOLOGYCollision detection in human subcortex uncorrected p = 0.007; SC-VTA: r = 0.564, uncorrected p = 0.012), but not between cortical and subcortical regions (all uncorrected p > 0.1). Using a beta-series analysis [34] of the looming-evoked responses, we found significant functional connectivity between SC and vmPul and between SC and VTA in both attended and unattended conditions (all p < 0.001, Holm corrected). To identify the subcortical path- ways from the SC and the information flow between cortical and subcortical areas, we per- formed a SEM path analysis with the beta series from these ROIs. A totalAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 216candidatemodelswereconstructedwithdifferent:::}iscorrect; andamendifnecessary: of 216 candidate models were constructed with different combinations of 6 sets of alternative connections (Fig 5C), based on the known anatomical connections between these areas: SC-cortex [15], SC- vmPul [35], SC-VTA [17], vmPul-cortex [24], VTA-cortex [33]. Candidate models were fitted with the observed data and compared by the goodness of fit (see Methods for model compari- sons). The best-fitted models for both attended (fit index: χ2 = 18.481, df = 2, CFI = 0.975, GFI = 0.997, AGFI = 0.978, PGFI = 0.133, RMSEA = 0.057, RMR = 0.019) unattended (fit index: χ2 = 21.370, df = 2, CFI = 0.968, GFI = 0.997, AGFI = 0.975, PGFI = 0.133, RMSEA = 0.062, RMR = 0.022) conditions revealed highly significant SC-vmPul and SC-VTA connections (all p < 0.001). Importantly, the model shows no cortical influence to the SC in the unattended condition. These findings support a pivotal role of the SC-vmPul and SC-VTA pathways in imminent collision detection even without attention. The collision sensitivity found in the SC was unlikely a result of cortical influence. Collision sensitivity in the ipsilesional SC of hemianopic patients To further investigate whether the tectofugal pathways can detect collision trajectories even without awareness of visual stimuli and a functional geniculostriate pathway, we scanned a group of homonymous hemianopic patients with unilateral lesions of the geniculostriate path- way (experiment 3, n = 12). Patients lost their conscious vision of both eyes in one side of the visual field (see S1 Table for clinical characteristics and S11 Fig for visual field loss and lesioned locations). During fMRI scans, patients performed a central fixation task while stimuli with hit, near-miss, or receding trajectories were presented to their normal visual field (NVF) or blind visual field (BVF). Four patients (P09 to P12) also participated in a behavioral visibility test, in which they reported clear perception of stimuli presented to their NVF but denied see- ing stimuli from the BVF. For most patients (8 of 12), there was no significant V1 activation to stimuli presented in their BVF (S11 Fig). Four patients showed weak uncorrected activations in their lesioned hemisphere, but no collision sensitivity. Based on the findings of healthy participants, we focused our analysis on the SC, vmPul, and VTA in hemianopic patients. From the group-averaged activation maps in Fig 6A, signifi- cant clusters of activation to hit trajectories were found in the contralateral SCs regardless of whether the stimuli were presented in the NVF (cluster p = 0.023) or in the BVF (cluster p = 0.031). In the contralateral vmPul (Fig 6B) and VTA (Fig 6C), collision-sensitive clusters to stimuli presented to the BVF (voxel p < 0.05, uncorrected) were also found in similar locations as the results of heathy participants (Fig 3). To increase statistical power given the small num- ber of hemianopic patients, we performed a linear mixed effect (LME) analysis with ROI-aver- aged responses of visually responsive voxels significantly activated by the receding stimuli (group-level p < 0.05 uncorrected). In the LME model, trajectory (Hit/Miss), visual field (NVF/BVF), and ROI (contralateral SC/vmPul/VTA) were defined as the fixed effects and par- ticipants as the random effect. The analysis revealed a significant hit response averaged across all 3 ROIs in the BVF (Fig 6E, z = 2.59, p = 0.038, Holm corrected across visual fields and tra- jectories), post hoc tests revealed significant hit responses in the SC (z = 2.054, p = 0.04 uncor- rected) and VTA (z = 2.768, p = 0.017, Holm corrected across ROIs). A significant hit response PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 10 / 28 PLOS BIOLOGYCollision detection in human subcortex Fig 6. fMRI results and behavioral performance of hemianopic patients (experiment 3). (a) Group-averaged activation maps in the SC to stimuli presented to the NVF (left column) and BVF (right column). Maps were thresholded at p < 0.05 uncorrected. Color bars represent percent BOLD signal change. The left and right SCs were mapped contralateral to the NVF and BVF, respectively. (b, c) Activation maps in the vmPul (b) and VTA (c). Conventions are the same as in (a). Only activations within the ROI were shown. Red arrows indicate the location of collision-sensitive clusters from the Hit-Miss map. (d) Stimulus activations mapped to the cortical surface (thresholded at p < 0.01 uncorrected). Left columns show contra- and ipsilateral responses to stimuli presented to the NVF. Right columns show ipsi- and contralateral responses to stimuli presented to the BVF. (e) BOLD responses in the contralateral SC, vmPul, and VTA to looming stimuli presented to the NVF (left panel) and BVF (right panel). Each dot represents data from 1 patient. + and * represent p < 0.05 before and after Holm corrections, respectively. Error bars represent the 95% confidence intervals estimated in a LME model. (f) Schematic diagram and procedure for the 2-IFC detection task. (g) The detection permanence fitted with a binomial generalized linear mixed model. Error bars represent the 95% confidence intervals. * Post hoc test p < 0.05 for comparing marginal means between conditions. *** p < 0.001 for comparing marginal means to the chance level (50%). (h) The detection performance to stimuli in the BVF showed a linear relationship with the response of ipsilesional SC (in 10% most responsive voxels, chosen separately for each stimulus type). The black solid line denotes the fixed effect (SC response) of the linear mixed model. For each participant, SC responses were normalized by dividing the mean across 3 stimulus conditions and then multiplying the mean across all 4 participants. Data underlying (e, g, h) can be found at https://osf.io/gdjwh/. BVF, blind visual field; LME, linear mixed effect; NVF, normal visual field; Rec, receding stimulus; SC, superior colliculus; vmPul, ventromedial pulvinar; VTA, ventral tegmental area; 2-IFC, 2-interval forced choice. https://doi.org/10.1371/journal.pbio.3002375.g006 was found in the contralateral SC to stimuli presented in the NVF (z = 3.468, p = 0.012, Holm corrected across all 12 conditions). The LGN was also investigated since previous studies suggest that it plays a critical role in blindsight [36]. However, no collision-sensitive response was found in the LGN (S12 Fig). In the cortical regions, V1 (S11 Fig) and frontoparietal areas (Fig 6D) showed strong activations to stimuli presented in the NVF but not in the BVF. These findings demonstrate that the tecto- fugal pathways can automatically detect collision trajectories even without awareness of loom- ing stimuli and a functional geniculostriate pathway. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 11 / 28 PLOS BIOLOGYCollision detection in human subcortex SC responses predict “blindsight” detection of collision To check potential “blindsight” to approaching objects on a collision course, 4 patients (P09 to P12) also performed a 2-interval forced choice (2-IFC) detection task. The stimulus was pre- sented in one of two 330-ms intervals with a 330-ms gap in between, accompanied by a low- pitch tone and a high-pitch tone in the first and the second intervals, respectively (Fig 6F). Patients had to determine in which interval the stimulus was presented. The detection perfor- mance was fitted with a binomial generalized linear mixed model with a random effect of par- ticipant (Fig 6G). Results showed a significant main effect of trajectory (χ2(2) = 6.418, p = 0.04). Following tests showed that the accuracy for the object on a collision course was sig- nificantly higher than that on a receding trajectory (contrast = 10.6%, z = 2.529, p = 0.023 using Holm adjustment) and then 50% chance level (estimated marginal mean = 61.1%, z = 3.541, p < 0.001). Importantly, the detection performance showed a significant linear rela- tionship with the response of ipsilesional SC (main effect of SC response on detection perfor- mance in a linear mixed model with participant as the random effect: F(1, 3.05) = 14.616, p = 0.031, Fig 6H). No significant linear relationship was found between behavioral perfor- mance and the responses of vmPul or VTA. Altogether, the behavioral and fMRI results of hemianopic patients provide strong evidence in humans that the tectofugal pathways support blindsight to impending collisions. Discussion Detecting imminent collision is crucial for our survival. In experiment 1, we found that human observers can precisely discriminate whether an approaching object was on a collision course or a near-miss trajectory with their head. Collision events also induced significant changes in pupillary reflex. In experiment 2, high-resolution 7T fMRI revealed collision-sensi- tive responses in several subcortical nuclei, including the SC, ventral pulvinar and VTA. Corre- lation and path analyses further demonstrated collision sensitivities in the SC-vmPul, and SC-VTA pathways without attention and cortical influence. In experiment 3, for hemianopic patients with unilateral lesions of the geniculostriate pathway, the ipsilesional SC showed colli- sion sensitivity to stimuli presented to their BVF. Finally, stronger response in the SC was asso- ciated with better detection performance of the collision event. These findings clearly demonstrate a critical role of the human tectofugal pathways in automatic detection of colli- sion trajectories without attention and awareness, supporting “blindsight” to threating visual information. In the optic tectum and the downstream nucleus rotundus (homologues of the SC and pul- vinar in mammals) of pigeons, different types of neurons encode several optical variables of a looming stimulus, including the time-to-collision, absolute rate of expansion, and object size [3]. In the mouse SC, Shang and colleagues identified parvalbumin-positive (PV+) excitatory projection neurons in the superficial layers encoding the optical parameters of a looming stim- ulus in their receptive fields [4,16]. The response onset depended on the stimulus size and moving velocity, and the response peaked at the time of collision. Here, we show that the human SC was highly sensitive to slight trajectory differences of looming stimuli with similar times to contact, rates of expansion, and object sizes, suggesting that the primate SC contains neurons sharply tuned to looming trajectories for accurate collision detection. Interestingly, although the looming stimuli were presented in the parafovea, the strongest collision-sensitive response was observed in the anterior part of the SCs corresponding to the central visual field (Fig 2C). One possibility is that SC neurons are sensitive to the would-be point of collision, making predictions about the impact point in the immediate future. Consistent with this explanation, predictive remapping activity has been observed for SC neurons before saccades PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 12 / 28 PLOS BIOLOGYCollision detection in human subcortex [37]. Collision-sensitive activations in the foveal SCs cannot be explained by retinotopic differ- ence in optical flow or luminance, because the hit stimulus shows no overall retinotopic bias in the central visual field and the collision-sensitive clusters is clearly located outside the retinoto- pic region (S2B Fig). It was also unlikely due to fixational eye movements, because observers can maintain stable fixations that showed no significant difference in fixational eye movements between the hit and miss conditions (S4B). The lack of collision sensitivity in the attended con- dition was likely due to the saturation of SC responses. Our results consistently show that the behavioral performance and pupillary reflex can bet- ter discriminate looming trajectories from the upper visual field (Figs 1, S5, and S6). The SC response was also stronger to looming (near-miss) stimuli in the upper visual field (Fig 2D). This upper visual field advantage of looming sensitivity suggests that the phylogenetically con- served tectal pathways are also ecologically adaptive, as threatening looming objects such as diving predators or falling stones appear more often in the upper than in the lower visual field due to gravity. A recent study showed that the primate SC, like that of rodents, also overrepre- sents the upper visual field, with sharper, stronger, and faster visual representations than in the lower visual field [22]. Together, these findings support Previc’s ecological perspective of the primate visual system, suggesting that the primate SC may be optimized and specialized for detection of transient and biological salient information from extrapersonal space in the upper visual field [38]. This upper visual field advantage forms an interesting contrast with previous findings that sustained visual attention resolves and tracks objects better in the lower visual field [39]. The results of experiment 2 clearly show that compared with the looming-evoked responses during a central fixation task, paying attention to and judging the trajectory of looming stimuli strongly enhanced the responses in both superficial and deeper layers of the SC (Fig 2B). How- ever, in experiment 3, the SC’s responses to collision trajectories were comparable when the stimulus was presented to the NVF or to the BVF of hemianopic patients (Fig 6A). Thus, loom- ing-sensitive responses in the SC were strongly modulated by top-down attention but may not be sensitive to the awareness of visual stimuli. These findings demonstrate the critical role of the SC in both top-down attention and subconscious processing of threatening visual information. Our data clearly demonstrate that the SC-vmPul pathway in the human brain automatically detects collision trajectories without attention to the looming stimuli (Fig 5). Results from the hemianopic patients further suggest collision sensitivity in the ipsilesional SC and vmPul in the absence of visual awareness (Fig 6). These findings are consistent with recent rodent stud- ies that the SC-LP (or pulvinar) pathway processes looming information in an anesthetized state [16] and triggers defensive freezing behavior [5,16]. In Wei and colleagues’ study, optoge- netic mapping and electrophysiology also revealed a disynaptic circuit from the SC through LP to the lateral amygdala, which directly mediated the innate fear-related defensive response. In our study, although looming objects on a collision course changed pupillary reflex, participants did not report fear to these stimuli, and no significant response was found in the amygdala (S10 Fig). One possible explanation is that human observers quickly adapted to repetitive pre- sentations of the virtual looming objects, which may not be effective to trigger fear response or evoke strong activation in the amygdala. In contrast, collision-sensitive responses can be reli- ably observed in the SC and vmPul. We speculate that the role of the SC-vmPul pathway might be automatically processing collision-related visual information, such as the looming trajectory or time-to-collision, which could be used by the downstream brain areas (e.g., the dorsal stream) to guide quick and subconscious actions to avoid the impending threats [40]. Collision-sensitive activity was also found in the VTA without attention (Fig 3C and 3D) and awareness (Fig 6C and 6E), showing correlation with and effective connectivity from the PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 13 / 28 PLOS BIOLOGYCollision detection in human subcortex SC (Fig 5B and 5E). These findings are consistent with the rodent studies that VTA neurons responded in short latency to biologically salient or aversive stimuli [31] and that VTA neu- rons receiving direct input from the SC mediated defensive flight behaviors to large overhead looming stimuli [17]. Populated mainly with dopaminergic (DA) neurons, the VTA plays important roles in reward, motivation, and attention [41]. The role of SC-VTA pathway might be modulating the level of arousal [42] or changing the attentional state [43] of the observer to mediate rapid defensive responses [44]. Although rodent studies also suggest the PBGN and LC in processing looming information and mediating looming-evoked defensive behaviors [4,18], we did not find significant collision-sensitive response from these areas in the human brain. Given their small sizes and deep locations, the negative finding from these small subcor- tical nuclei could be due to the low SNR and partial volume effect of BOLD signals. The tectopulvinar pathway was originally proposed to support “blindsight” [19]. A critical role of the SC in visuomotor functions has been repeatedly confirmed by studies of visually guided saccades in V1-lesioned monkeys [45,46]. FMRI studies of blindsight patients also indi- cated visually evoked activation from the SC [47,48]. However, given the small sample sizes and the low-resolution fMRI approaches in these studies, the involvement of the SC in human blindsight still lacks conclusive evidence. Using high-resolution fMRI, here we showed clear evidence from 12 hemianopic patients that visually evoked response in the ipsilesional SC was highly sensitive to looming objects on a collision course (Fig 6A and 6E), which also predicted the above-chance detection performance of impending collisions (Fig 6G and 6H). These find- ings provide strong evidence for the critical role of the SC in detecting impending visual threats in human blindsight. Compared with the SC, the role of pulvinar in blindsight is more controversial. Human fMRI studies suggest that the tectopulvinar-amygdala pathway may underlie blindsight of emotionally and socially salient face stimuli [49,50]. A recent study in V1-lesioned monkeys also revealed a critical role of the tectopulvinar pathway in visually guided saccades [51]. How- ever, there is accumulating evidence that the geniculo-extrastriate pathway plays a major role in blindsight in both Monkey [36] and Human [52]. These discrepancies might depend on the scope of lesion, time of lesion, or distinct roles of the pulvinar and LGN pathways in blindsight [53,54]. Our results may suggest collision sensitivity in the ipsilesional ventromedial pulvinar to looming stimuli presented to the BVF (Fig 6B). Collision-sensitive response was also found in the VTA (Fig 6C and 6E). While visually evoked responses can be found in the LGNs of both healthy participants (S10 Fig) and hemianopic patients (S12 Fig), there was no significant activation of collision sensitivity. These findings support distinct roles of the subcortical path- ways in blindsight: The tectofugal pathways are specialized to detect impending visual threats, while the geniculo-extrastriate pathway processes basic visual information (e.g., contrast and motion). Human fMRI studies reported cortical representations of the TTC and possibly trajectories of approaching objects [55]. While the cortical responses may indicate a complex and relatively slow neural calculation for 3D motion perception, the subcortical pathway found in our study suggests an efficient and rapid computational strategy for collision trajectory detection. In sup- port of this, studies have demonstrated that simpler equations for calculating looming trajecto- ries could better predict human performance, especially the systematic errors made by observers. For example, Duke and Rushton [56] suggested that the perceived trajectory is based on the ratio of lateral angular speed to the sum of looming and changing disparity sig- nals. Our findings revealed the neural substrate for an efficient and rapid neural computation of collision trajectory in the human brain, which may inspire the optimization of computer vision algorithms for collision detection [57]. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 14 / 28 PLOS BIOLOGYCollision detection in human subcortex Methods Experiment 1: Behavior and eye tracking in healthy participants Participants. AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 15healthyparticipantsð20to30years:::}iscorrect; andamendifnecessary: total of 15 healthy participants (20 to 30 years old, mean age = 24.6 years, SD = 2.16 years, 10 females) participated in experiment 1. They had normal or corrected-to- normal vision without neurological or psychiatric disorders. All participants (including those for experiments 2 and 3) gave written informed consent in accordance with procedures and protocols approved by the Institutional Review Board of the Institute of Biophysics, Chinese Academy of Sciences (2012-IRB-011). Stimuli and procedures. Visual stimuli were generated with Psychtoolbox 3.0 [58] in MATLAB (2017a). 3D-rendered spheres depicted from slightly different perspectives were projected into 2 eyes to produce a 3D effect. Stimuli were stereoscopically presented with shut- ter glasses (NVIDIA 3D VISION) and a compatible LCD display with 120 Hz refresh rate (60 Hz for each eye). Eye positions and pupil size of the left eye were recorded at 1,000 Hz with an Eyelink1000Plus system. A standard 9-point calibration was performed at the beginning of each session. A low-pass filter with cutoff frequency of 40 Hz was performed to the eye-track- ing data to reduce the 60 Hz artifacts from shutter glasses. After interpolating the missing data due to eye blinks, pupil size and eye position time series were epoched and averaged across tri- als per condition and participant. As shown in S1A Fig, the 3D display presented a baseball-sized sphere in 6-cm diameter moving at a speed of 24 m/s from 11.3 meters to 3.3 meters in front of the observer. The trajec- tory started from one of the 4 quadrants at a 0.65-meter horizontal offset and a 0.38-meter ver- tical offset and ended in the same quadrant. The would-be point of collision varied from the middle of the face (0 cm, hit nasion) to 3 cm (hit eye), 6 cm (near miss), or 12 cm (far miss) of horizontal offset. To match their retinotopic locations, the on-screen projections of looming trajectories were aligned based on the center of mass of projected images. The on-screen dis- play was a sphere with a texture of black-and-yellow checkerboard expanding from 0.3 to 1 degree of visual angle in 330 ms, at an eccentricity of 3.8 degrees (angle with vertical meridian is 60˚; see S1 Fig for more details on screen size, starting and ending eccentricity, etc., for all 3 experiments). Participants were instructed to keep fixation and press buttons to report whether the looming object would hit or miss their head. ForAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Foreachparticipant; 400trialswerecollected:}iscorrect; andamendifnecessary: collected. each participant, 400 trials were Experiment 2: fMRI study with healthy participants Participants. AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 20healthyparticipantsð22to42years:::}iscorrect; andamendifnecessary: total of 20 healthy participants (22 to 42 years old, mean age = 26.3 years, SD = 4.0 years, 11 females) with normal or corrected-to-normal vision and no neurological or psychiatric conditions participated in experiment 2. Stimuli and procedures. Visual stimuli were rendered in 3D but presented with an MRI- safe 2D projector on a translucent screen behind the head coil. Participants viewed the stimuli through a mirror mounted inside the head coil. Looming objects (S1B Fig) were simulated to approach from 8.75 m from the observer and vanish at 0.75 m, at a speed of 24 m/s. The ball would either hit the eye of the observer to cause a collision (hit) or slightly miss the head (near miss) with a 6-cm horizontal offset. Same as in experiment 1, the on-screen projections of the 2 trajectories in the same quadrant were aligned based on the center of mass of projected images. The luminance change and overall magnitude of optical flows (S2 Fig) were similar between the 2 trajectory conditions. The stimulus on the screen expanded from 0.4 to 4.5 degrees of visual angle in 330 ms, at an eccentricity of about 5 degrees (angle with vertical meridian is 60˚). PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 15 / 28 PLOS BIOLOGYCollision detection in human subcortex In the attended condition, participants were instructed to keep fixation and respond whether the incoming object was on or off a collision course with their heads. In the unat- tended condition, they were instructed to keep fixation and detect occasional color changes of the fixation point. Participants performed 4 runs each for the attended and unattended condi- tions. The attended and unattended runs were scanned in alternation. Each run comprised 32 trials with an interstimulus interval (ISI) randomly chosen from 8, 10, and 12 seconds. Thus, 16 trials were collected for each combination of attention, trajectory, and quadrant conditions. Participant S19 lost one trial of data in some conditions due to a technical problem. MRI data acquisition. MRI data were acquired with a 7T scanner (Siemens MAGNE- TOM, Erlangen, Germany) with a 32-channel receive 1-channel transmit head coil (Nova Medical, Cambridge, MA, USA), at Beijing MRI center for Brain Research (BMCBR). The gra- dient coil has a maximum amplitude of 70 mT/m, 200 us minimum gradient rise time, and 200 T/m/s maximum slew rate. Functional images were acquired with a T2-weighted 2D GE-EPI sequence (1.5-mm in-plane resolution, 1.5-mm slice thickness without gap, 68 axial slices, TR = 2,000 ms, TE = 21.6 ms, flip angle = 80˚, image matrix = 122 × 122, FOV = 183 × 183 mm, partial Fourier factor = 6/8, bandwidth = 1,576 Hz/Px, GRAPPA accel- eration factor = 2, phase encoding direction from A to P). A few EPI images with reversed phase encoding direction (P to A) were also acquired to correct image distortions in the phase encoding direction. Anatomical images were acquired with a T1-weighted MP2RAGE sequence (0.7-mm isotropic voxels, 256 sagittal slices, FOV = 224 × 224 mm, TR = 4,000 ms, TE = 3.05 ms, TI1 = 750 ms, flip angle = 4˚, TI2 = 2,500 ms, flip angle = 5˚, bandwidth = 240 Hz/Px, phase partial Fourier = 7/8, slice partial Fourier = 7/8, GRAPPA = 3). To improve data quality, a bite-bar was used to restrict head motion. MRI data preprocessing. MRI data were analyzed using AFNI [59], FreeSurfer [60] (ver- sion 6.0), ANTs [61], and the lab-developed mripy package (https://github.com/herrlich10/ mripy). The preprocessing of volumetric data includes slice timing correction, EPI image non- linear distortion correction with reversed-blip method, linear motion correction, T1w ana- tomical image registration to EPI volumes, spatial normalization to MNI space, and per-run scaling to percent signal change before general linear model (GLM) analysis. For spatial nor- malization of subcortical areas, we estimated a 12-parameter linear transformation from the anatomical volume to a high-resolution MNI template (ICBM 152 2009c symmetric T1w tem- plate), followed by nonlinear transformation with ANTs (v2.1.0) using a subcortical mask focused on the areas of interest. All spatial transformations (including motion correction, ana- tomical to functional volume registration, and spatial normalizations) were combined alto- gether and applied to the functional volumes in one interpolation step (cubic method) at 0.6 mm isotropic resolution. A surface-based approach was used for cortical data analysis. The T1w MP2RAGE anatomi- cal volume was segmented into white matter, gray matter, and cerebrospinal fluid using Free- Surfer’s automated procedure, using its high-resolution option. The preprocessed volumetric data before spatial normalization were mapped to the inflated cortical surface. Surface data were then normalized to a standard surface (std141) with surface-based warping, followed by surface-based smoothing with a 4.5-mm FWHM (full-width half-maximum) Gaussian kernel [62]. Surface data were then normalized to percent signal change before GLM analysis. General linear model (GLM) analysis. BOLD signal changesAU : Pleasecheckandconfirmthattheeditto}BOLDsignalchangesfrombaselineforeachstimulusconditionwere:::}didnotaltertheintendedmeaningofthesentence: from baseline for each stim- ulus condition were estimated using a GLM with fixed HRFs. For cortical data, we used a canonical HRF (BLOCK4 in AFNI). For subcortical data, we used a faster and narrower HRF that is more appropriate for subcortical regions [63]. Motion parameters, their derivatives and square of derivatives, were included as regressors of no interest. In addition, we found no covariation of motion parameters with different trial conditions. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 16 / 28 PLOS BIOLOGYCollision detection in human subcortex ROI definition and group-level statistical maps. As in S8 Fig, ROIs for the SC, PBGN, LC, VTA, and LGN were carefully drawn on the MNI template according to their anatomical landmarks and atlases [64,65]. ROIs for the pulvinar subdivisions were obtained from a parcel- lation based on task-coactivation profiles [66]. The ROI of amygdala was defined based on a manually delineated high-resolution atlas of subcortical nuclei [67]. Before group-level analy- sis, spatial smoothing was performed on individual beta maps of GLM results withinAU : PleasenotethatasperPLOSstyle; italicsshouldnotbeusedforemphasis:Hence; pleaseconfirmthat}within}inthesentence}Beforegroup (cid:0) the ROI with a 1.4-mm FWHM Gaussian filter. Group-level statistical maps were then generated using standard random-effects analysis (t test) on the beta maps across participants. Cortical ROIs were defined on the cortical surface. ROIs of visual cortical areas were defined with a retinoto- pic atlas generated with the Human Connectome Project retinotopy dataset [68]. The anatomi- cal masks for frontoparietal ROIs were defined on the standard surface by the HCP-MMP1 atlas [69] (S9 Fig). levelanalysis; spatialsmoothingwasperformedonindividual:::}canbechangedtoregulartext: ROI-averaged responses. For the subcortical ROI, we obtained the averaged response of all voxels in the anatomical ROI as defined by S8 Fig. To validate the collision sensitivity in a subcortical area with independent voxel selection, a LOSO cross-validation approach was used. For each participant, the ROI to calculate the collision-sensitive response was defined by voxels with group-level collision sensitivity (hit-miss, p < 0.05 uncorrected) from the remain- ing participants. For the visual cortical areas, we utilized an intraparticipant LORO 4-fold cross-validation approach to define the retinotopic ROIs. For each quadrant, we selected the visually responsive voxels with 3 runs of data (hit + miss p < 0.05 uncorrected) within the Ben- son14 retinotopic atlas [68]. Subsequently, BOLD responses from the left-out run from the retinotopic ROI were obtained. The procedure was repeated 4 times, and the results from all iterations were averaged for this quadrant. Finally, the results from all 4 quadrants were aver- aged as the ROI-averaged responses for the visual area. Correlation analysis of collision-sensitive responses. To investigate the relationship of collision sensitivity between different brain areas, we calculated Pearson’s correlations between the hit-miss responses in subcortical and cortical areas across participants. To avoid the risk of circular testing, collision-sensitive responses were determined by the LORO 4-fold cross-vali- dation approach. In the attended or unattended condition, collision-sensitive voxels were selected by 3 runs sorted by the hit-miss T statistics, and the ROI averaged hit-miss responses from these voxels were extracted from the remaining run. The procedure was repeated for each run, and the results were averaged as the final estimate. Considering that the average size of collision-sensitive clusters in subcortical nuclei (SC, vmPul, and VTA) is about 30 μl, 10 voxels (1.5 mm isotropic) with the strongest collision sensitivity were selected for each subcor- tical ROI. For the visual cortex, 50 voxels with the strongest collision sensitivity were selected for each quadrant (200 voxels in total). For the attentional network, 200 voxels with the stron- gest collision sensitivity were selected. Beta-series functional connectivity. A beta-series method was adopted to test whether functional connectivity existed between the SC and vmPul [34]. The response for each trial was estimated with a separate regressor in a GLM analysis of the ROI-averaged BOLD time series from collision sensitive voxels, generating a series of beta values for each ROI. The colli- sion sensitive voxels for each ROI were defined by the LORO approach described from above. The beta series for each trajectory condition were Z-scored to avoid the influence of mean amplitude differences. A second-level linear regression was conducted with any 2 series of these beta-values from 2 different ROIs, andAU : Pleasecheckandconfirmthattheeditto}Asecond (cid:0) obtained as the functional connectivity. the second-level regression coefficient was Path analysis by structural equation modeling. Path analysis under SEM framework was performed to infer the causal influence of beta series between subcortical regions (SC, vmPul, and VTA), visual cortex (including V1, V2, V3, V3a, V3b, and TO1/TO2) and frontoparietal PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 17 / 28 levellinearregressionwasconductedwithany:::}didnotaltertheintendedmeaningofthesentence: PLOS BIOLOGYCollision detection in human subcortex attention networks (S8 Fig). The collision-sensitive responses across all participants were concatenated as the model input. AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 216possiblemodelswereconstructedbyall:::}iscorrect; andamendifnecessary: total of 216 possible models were constructed by all combi- nations of 6 groups of possible alternative connections. By doing so, we could directly compare the strength and possibility of our alternative hypotheses. In addition, several common con- nections were added in all models based on the prior knowledge. All path coefficients in each model were freely determined by maximum likelihood estimate in the SEM with lavaan soft- ware 0.6–3 [70]. The model selection was based on several indices. We first excluded models with a parsimony goodness of fit index (PGFI) larger than 0.1, then ranked the remaining models by their adjusted goodness of fit index (AGFI), and, finally, picked the best fitted mod- els for the attended and unattended conditions [71]. Other goodness of fit indices including χ2, comparative fit indices (CFIs), goodness of fit index (GFI), root mean square residual (RMR), and root mean square error of approximation (RMSEA) were also calculated and ranked to ensure the best model was not sensitive to the ranking methods. Different ranking methods led to the same results. Experiment 3: fMRI study with hemianopic patients Participants. AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 12hemianopicpatientsð23to64yearsold:::}iscorrect; andamendifnecessary: total of 12 hemianopic patients (23 to 64 years old, mean age = 45.5 years, SD = 14.1 years, 1 female) with unilateral lesions of the geniculostriate pathway and no other neurological or psychiatric conditions were enrolled from General Hospital of People’s Libera- tion Army, Beijing, China. Patients lost their conscious vision in one-half or a quadrant of the visual field from both eyes. They had normal or corrected-to-normal vision outside the BVF. Clinical characteristics of all patients were shown in S1 Table. Their Humphrey perimetry and lesioned locations were shown in S11 Fig. The scotoma was defined as the visual field with a relative sensitivity less than −20 dB and p < 0.5% compared with normal population in both eyes. Stimuli and procedures. Visual stimuli and procedures were similar as those in experi- ment 2. Looming trajectories were slightly changed (S1C Fig), moving from 10.75 meters to 2.75 meters in front of the observers. In addition to the “hit” trajectory (hit the eye) and “near- miss” trajectory (passing at 5 cm of horizontal shift from the eye), a “receding” trajectory was also included with the reverse trajectory as in the “hit” condition. For the 7T experiment, the on-screen size of the object changed between 0.32 and 1.25 degrees of visual angle, at about 3.99 degrees of eccentricity (angle with vertical meridian is 60˚). For the 3T experiment, the on-screen size of the object changed between 0.45 and 1.75 degrees of visual angle, at about 5.58 degrees of eccentricity (angle with vertical meridian is 60˚). Compared with the stimulus in experiment 2, the looming stimulus was smaller and disappeared earlier (time-to-collision at the vanishing point was 115 ms). Note that we adjusted the brightness of the looming object and the background between the three experiments. In experiments 1 and 3, the object was brighter than the background, while in experiment 2, it was made darker. Stimuli were pre- sented either in the NVF or in the BVF of hemianopic patients. In principle, the stimulus loca- tion was selected based on the scotoma of each patient. Stimuli were presented at the lower visual field for P02 and P08 and at the upper visual field for other patients. For P11, stimuli were presented in the upper quadrant of the BVF, while in the lower quadrant of the NVF because he could see better than in the upper quadrant. For P12, visual stimuli were presented at an eccentricity of 7.84 degrees and much closer to the vertical meridian (angle with vertical meridian is 30˚). Since the stimulus location of P12 was very different from those of other par- ticipants, we did not use his data to generate the group-averaged results in Fig 6. During fMRI scans, patients were asked to keep fixation and to detect occasional color changes of the fixation point. ForAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Foreachpatient; 6runswerecollected; except5runs:::}iscorrect; andamendifnecessary: each patient, 6 runs were collected, except 5 runs for P01 and PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 18 / 28 PLOS BIOLOGYCollision detection in human subcortex 4 runs for P03. Each run consisted of 24 trials in which each trajectory was repeated 4 times in the NVF and 4 times in the BVF. The ISI was randomly selected from 8, 10, 12, and 14 seconds. Four patients (P08 to P12) also performed a behavioral test of stimulus visibility in their BVF. In the subjective visibility test, they were asked to keep fixation and report the visibility to stimuli presented to the NVF or BVF. In the 2-IFC test, the stimulus was presented in one of two 330-ms intervals, separated by an 833-ms gap in-between. The first interval was accompa- nied by a low-pitch tone at 300 Hz and the second by a high-pitch tone at 700 Hz. Patients were required to keep fixation and to report in which interval the object was presented. Audi- tory feedback was given after incorrect answers. ForAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Foreachpatient; 240trialswerecollected; including24trials:::}iscorrect; andamendifnecessary: including 24 trials in the NVF (8 trials for each trajectory) and 216 trials in the BVF (72 trials for each trajectory). In the behavioral test, the looming stimulus was very close to that in the 7T experiment, with an on-screen size expanding approximately from 0.31 to 1.21 degrees at 3.95 degrees of eccentricity. For P12, the stimulus was at 7.84 degrees of eccentricity. each patient, 240 trials were collected, MRI data acquisition and analysis. MRI data for P01, P04, P05, P07, P10, and P11 were acquired with the same 7T scanner, head coil, and pulse sequences as in experiment 2. For P01, functional images were acquired with GE-EPI at a higher spatial resolution (1.2-mm iso- tropic voxels, 62 axial slices of 1.2-mm thickness, 150 × 150 matrix, TR/TE = 2,000/22 ms, nominal flip angle = 78˚, partial Fourier factor = 6/8, GRAPPA acceleration factor = 2, multi- band factor = 2, bandwidth = 1587 Hz/Px, phase encoding direction from A to P). P02, P03, P06, P08, P09, and P12 had nonmagnetic metal implants. Due to safety issues of overheating at ultrahigh magnetic fields, their data were acquired with a 3T scanner (Siemens Prisma, Erlangen, Germany) using a 20-channel phased array coil (Nova Medical, Cambridge, MA, USA). High-resolution anatomical images were acquired using a T1-weighted MPRAGE sequence (1-mm isotropic voxels, 192 sagittal slices at 1-mm thickness, image matrix = 256 × 224, TR/TE = 2,600/3.02 ms, inversion time = 900 ms, flip angle = 8˚, band- width = 130 Hz/Px, phase partial Fourier = 6/8, slice partial Fourier = 7/8, no in-plane acceler- ation). Functional images were acquired with a GE-EPI sequence (2-mm isotropic voxels, 52 or 54 axial slices of 2-mm thickness, 96 × 96 matrix, FOV = 192 × 192 mm, TR = 2,000 ms, TE = 30 or 31.4 ms, flip angle = 80˚, multiband or SMS factor = 2, partial Fourier factor = 7/8 or none, bandwidth = 1,860 or 2,170 Hz/Px, phase encoding direction from A to P, no in- plane acceleration). A few EPI images with reversed phase encoding direction (P to A) were acquired to correct image distortions in the phase encoding direction. Data analysis procedures were the same as those in experiment 2, except for cortical data analysis. A 6-mm FWHM spatial smoothing was performed on functional volumes after motion correction, followed by per-run scaling and GLM analysis. Statistical volumes were spatially normalized to the MNI space with ICBM 152 symmetric MNI template (2009c) with a combination of linear and nonlinear transformations. Group-level statistics were generated in the MNI space and then projected to the standard cortical surface (std141) as shown in Fig 6B. Since the stimulus location of P12 was very different from those of other participants, we did not use his data to generate the group-averaged results for the subcortical area with strong retinotopic representations, including the SC, pulvinar, and LGN. But P12 was included in the individual results in Fig 6I and 6J. P03 had severe damage and distortion in his ipsilesional visual thalamus, thus was not included in the results for ipsilesional pulvinar and LGN. Due to a similar reason, P09 was not included in the analysis of ipsilesional LGN. Therefore, for the group-level analysis, there were 11 contra- and ipsilesional SCs, 11 contralesional pulvinars and 10 ipsilesional pulvinars, 11 contralesional LGNs and 9 ipsilesional LGNs, and 12 contra- and ipsilesional VTAs. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 19 / 28 PLOS BIOLOGYCollision detection in human subcortex Statistical analysis p, and Pearson’s r were computed via JASP (0.16, The JASP Team, We adopted repeated measurements and two-sided design for all statistical tests in the study. Effect sizes including Cohen’s d, Z2 https://jasp-stats.org). Cohen’s d for t tests was computed as M_diff / SD_diff, where M_diff denotes the mean of paired differences and SD_diff denotes the standard deviation of paired differ- ences. Z2 p for ANOVA was calculated as SS_effect / (SS_effect + SS_error), where SS_effect denotes the sum of squares for the within-participants effect and SS_error denotes the sum of squares for the error term. Other details about the statistical analysis were described as below. Behavioral statistics A permutation test (exact test, in which all possible permutations were considered) was used to infer the significance of difference between discrimination sensitivities in the upper and lower visual field in Fig 1C. A cluster-based permutation test (10,000 times of permutations) was used to calculate the significant time periods in Fig 1D. The length of continuous periods (i.e., clusters) of time points showing significant difference in each permutation was used to control the FWE. Paired t tests were used to calculate the difference in pupil diameters between different trajectories in Fig 1E and 1F. The difference of behavioral performance of hemiano- pic patients against chance-level and between trajectories were assessed using a binomial gen- eralized linear mixed model with a logit link function. In the model, we tested a fixed effect of trajectory while controlling for a random effect of participants, with likelihood ratio tests method. The linear relationship of SC responses with the behavioral performance was exam- ined via a linear mixed model analysis, with random effect of participants. Statistical maps of fMRI Standard random-effects analysis was used to generate the statistical maps of subcortical and cortical areas. To control the FWE of statistical maps within each subcortical ROI (small vol- ume correction), a cluster mass–based permutation test was used to calculate the p-value of significant clusters [72]. The cluster mass was defined as the absolute sum of T values in a clus- ter. The cluster defining threshold is voxel’s p < 0.05. In each permutation, the hit and miss conditions were randomly switched for each participant, followed by a group-level t test across participants to generate the statistical map. The largest cluster mass was then recorded for this permutation. The permutation procedure was repeated 10,000 times to get the null distribu- tion for the largest cluster mass. A cluster p-value was derived as the proportion of the null dis- tribution larger than the actual cluster mass. To obtain an accurate null distribution, we subtracted the group-averaged map from each individual map (demean) and removed permu- tations less than 15% or 85% of data exchanges. FWEs of surface clusters (at p < 0.05 for indi- vidual vertex) were determined by Monte Carlo simulation in AFNI. Statistical analysis of ROI-averaged responses. Statistical analysis of ROI-averaged responses in experiment 2 was performed with two-way repeated measures ANOVA. Spheric- ity was validated by Mauchly’s test, and normality by Shapiro–Wilk test. To control FWEs, we followed the Fisher logic [73] and only performed post hoc t tests when there was a significant main effect or interaction of the two-way ANOVA. Associations between variables were assessed with Pearson correlations after removing outliers outside 1.5 times interquartile ranges of robust Mahalanobis distances of all samples (calculated using MATLAB function robustcov based on the FAST-MCD method). The FDRs of collision sensitivity across ROIs in Fig 4A were calculated by the Benjamini–Hochberg method. The FWEs of correlations in Fig 5A were calculated by a permutation test. The correspondences of data pairs were randomly PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 20 / 28 PLOS BIOLOGYCollision detection in human subcortex shuffled in each permutation. The largest absolute value of correlation coefficients was then recorded to compose the null distribution. The corrected p-values were derived from the null distribution for the largest correlation coefficient. An LME model was used to analyze ROI- averaged responses from visually responsive voxels in experiment 3. Trajectory, visual field, and ROI were defined as the fixed effect, and participant as the random effect. Multiple tests correction across subcortical ROIs. As we performed multiple tests of col- lision sensitivity in several subcortical ROIs in experiment 2, a sequential Holm–Bonferroni approach was used to correct FWEs. The p-values of collision sensitivity in the foveal SC, vmPul, VTA, PBGN, LC, and amygdala were included for correction. Holm–Bonferroni cor- rection was first performed within each ROI to adjust the p-values for the collision sensitivity of ROI-averaged responses, the collision-sensitive cluster, and the LOSO result. For the SC, the ROI-averaged response and LOSO result in the unattended condition still show significant col- lision sensitivity after Holm correction (p = 0.021 and 0.024, respectively). Since we have a well-founded hypothesis to test the collision sensitivity in the SC, it was not included in subse- quent multiple-test correction across ROIs. For the other downstream subcortical ROIs, the lowest p-value after correction within each ROI was selected for correction. After Holm cor- rection, the vmPul and VTA still showed a significant collision sensitivity in the unattended condition (p = 0.03 and 0.032, respectively). Supporting information S1 Fig. Top view of stimulus trajectories. (a) Experiment 1. A baseball-sized sphere launched 11.3 meters away from the observer, moving at a speed of 24 m/s. It disappeared at 3.3 meters from the observers at 138 ms of time-to-collision, as indicated by the location of the arrows. Yellow, red, blue, and green arrows indicate the hit-nasion, hit-eye, near-miss, and far-miss trajectories. Stimuli were presented on a 3D monitor (width = 0.51 m) 1.3 meters in front of the observers. The on-screen display was a sphere expanding from 0.3 to 1 degree of visual angle in 330 ms. While the vertical offset of the stimulus from the center of screen was 1.90˚, the horizonal offset for the starting (hit nasion: 3.14˚, hit eye: 2.79˚, near miss: 2.43˚, far miss: 2.13˚) and ending (hit nasion: 2.77˚, hit eye: 2.79˚, near miss: 2.80˚, far miss: 2.86˚) position of the stimulus varied between different trajectory conditions. (b) Experiment 2. Stimuli were presented on a translucent screen with a 2D projector. The sphere moved from 8.75 m away and disappeared on the screen (width = 0.35 m) 0.75 m in front of the observer at 33 ms of time-to-collision. Red and blue arrows indicate the hit and near-miss trajectories. The stimulus on the screen expanded from 0.4 to 4.5 degrees of visual angle in 330 ms. While the vertical off- set of the stimulus from the center of screen was 2.49˚, the horizonal offset for the starting (hit: 4.31˚, near miss: 1.70˚) and ending (hit: 4.31˚, near miss: 5.87˚) position of the stimulus varied between different trajectory conditions. (c) Experiment 3. In hit and near-miss conditions, the sphere moved from 10.75 m to 2.75 m in front of the observer. The time-to-collision at disap- pearance was 115 ms. The receding trajectory was the reverse of the hit trajectory. Red, blue, and green arrows indicate the hit, near-miss, and receding trajectories. The eccentricity and the size of the sphere (7T: 6 cm, screen width = 0.35 m; 3T: 8.4 cm, screen width = 0.51 m) was slightly different in the 7T and 3T scanning. For the 7T (or 3T) experiment, the on-screen size of the object changed between 0.32 and 1.25 (3T: 0.45 and 1.75) degrees of visual angle. While the vertical offset of the stimulus from the center of screen was (7T: 2˚; 3T: 2.79˚), the hori- zonal offset for the starting (7T: hit: 3.46˚, near miss: 2.82˚; 3T: hit: 4.83˚, near miss: 3.95˚) and ending (7T: hit: 3.46˚, near miss: 3.60˚; 3T: hit: 4.83, near miss: 5.04) position of the stimulus varied between different trajectory conditions. (PDF) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 21 / 28 PLOS BIOLOGYCollision detection in human subcortex S2 Fig. Overall magnitude of optical flows and luminance change in hit and near-miss looming stimuli (experiment 2). We used the Horn–Schunck method [74] to compute the frame-by-frame optical flow of our stimuli. The Horn–Schunck method is utilized to deter- mine the optical flow by analyzing the displacement of pixels between frames, assuming that the brightness of a pixel remains constant during its motion. In our analysis, we captured the image in each frame during the visual stimulation. Subsequently, we employed the MATLAB Optical Flow block (the mathematical algorithm [74] is given in S1 Text) from the Computer Vision Toolbox to estimate the optical flow vector for each pixel between 2 consecutive frames. The direction of pixel motion was determined by the horizontal and vertical components of the vector, while the speed or magnitude was calculated as the square of the vector’s modulus. Finally, we computed the overall flow magnitude by summing the magnitudes of all the pixels. The scripts for this analysis have been uploaded to https://doi.org/10.5281/zenodo.8251435. (a) The original stimulus images were overlaid with red lines depicting the resulting optical flow. Three example frames were displayed for each condition. (b) Upon comparing the mag- nitudes of the optical flows, it was observed that the near-miss stimulus exhibited a slightly larger overall optical flow compared to the hit stimulus. (c) To further analyze the data, we plotted the change in optical flow magnitude at various eccentricities, each shown in different panels. (d) Additionally, we calculated the time-integrated optical flow at different eccentrici- ties. The resulting figure illustrated that the near-miss stimulus generated a greater optical flow in the central visual field when compared to the hit stimulus. Therefore, the collision-sensitive activations in the foveal SC cannot be accounted by a stronger optical flow in the fovea. (e) Similarly, we also obtained the time-integrated luminance change (from the background) for both the hit and near-miss stimuli at different eccentricities. This was accomplished by calcu- lating the light intensity of each pixel in each frame using spectroradiometer data. It was observed that the near-miss stimulus produced a greater decrease in luminance at lower eccen- tricities. Thus, the difference in luminance changes cannot account for the collision-sensitive activations in the foveal SCAU : PleasenotethatthereferenceHornandSchunckð1981ÞinS2Figcaptionhasbeenincludedinthemainreferenceslistandisformattedandnumberedasreference74:Pleaseconfirmthatthischangeisvalid: (PDF) . S3 Fig. V1 response as a function of eccentricity (experiment 2). To further support that the stimuli have no systematic bias in retinotopic location, we plotted V1 response profiles as a function of eccentricity. V1 vertices corresponding to the polar angle of the visual stimuli from 0 to 10 degrees of eccentricity were selected based on the HCP retinotopic atlas [68,75]. As shown in the figure below, there was no difference in foveal activations in V1 between the hit and miss stimulus conditions. Therefore, our findings of collision-sensitive activations in the SC cannot be explained by a foveal retinotopic bias to the hit stimulus. (PDF) S4 Fig. (a) Individual data for the behavioral experiment (experiment 1). The percentage of hit responses to looming stimuli as a function of impact points was fitted with a normal CDF. In experiment 1, the horizontal offset of the would-be impact point for the near miss and far miss conditions was individually adjusted according to the performance of each participant in a preliminary session. The would-be impact point for the near miss and far miss conditions were chosen at approximately 50% and 2% of hit response, respectively. (b) Distribution of eye positions for hit and miss looming stimuli from four quadrants of the visual field. The eye gaze positions from stimulus onset to 1,000 ms after were analyzed. Error bars indicate 30 standard deviation of the distribution. No significant difference was found between the eye positions to the hit and miss stimuli. (PDF) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 22 / 28 PLOS BIOLOGYCollision detection in human subcortex S5 Fig. Pupil size changes to looming stimuli in a bright background. (a) Stimulus diagram was the same as in experiment 1, but with a brighter background than looming stimuli. AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 10participantsð7femalesand3malesÞ:::}iscorrect; andamendifnecessary: total of 10 participants (7 females and 3 males) participated in this experiment. Participants per- formed a collision detection task, i.e., paying attention to the approaching object. In addition, there was a task-irrelevant rapid presentation of letter streams in the fixation, similar to the fix- ation change in the unattended condition in experiments 2 and 3 (note in these experiments the fixation change was task relevant). (b, c, d) Time courses of the pupil size change for upper +lower visual field (VF) data, upper VF data only, and lower VF data only, respectively. The light green bar at the bottom indicates the uncorrected significant difference between hit and miss conditions. No significant difference was found after multiple comparison corrections via permutation. (e, f) Bar plots of the pupil size during the looming component (see Fig 1D) for the upper VF only data and lower VF only data, respectively. No significant difference was found between the bars. (PDF) total of 17 participants (8 females and 9 males) participated in S6 Fig. Pupil size changes to looming stimuli in the unattended condition with a bright background. (a) Stimulus diagram was the same as in experiment 2, but with a slightly longer viewing distance (0.85 m). AAU : PleasenotethatasperPLOSstyle; numeralsarenotallowedatthebeginningofasentence:Pleasecheckandconfirmthattheeditto}Atotalof 17participantsð8femalesand9malesÞ:::}iscorrect; andamendifnecessary: this experiment. Participants were instructed to count the number of color changes of central fixation point. (b, c) The time courses of changes in pupil size for stimuli in the upper (b) and lower (c) visual field are presented. The light green bar at the bottom indicates the uncorrected significant difference between hit and miss conditions. No significant difference was found after multiple comparison corrections via permutation. (PDF) S7 Fig. Looming-evoked responses across the SC (experiment 2). The first row of each panel shows the retinotopic activations with significantly stronger responses to contralateral than to ipsilateral stimuli. Red lines on the sagittal view indicate the location of the coronal slices. The second to the fourth rows show the activation maps for the hit, miss, and hit-miss responses. Maps were thresholded at voxel p < 0.05 uncorrected. (PDF) S8 Fig. Anatomical ROIs of subcortical nuclei (experiment 2 and experiment 3). From left to right are the superior colliculus (SC, 261 μl), ventral tegmental area (VTA, 708 μl), parabi- geminal nucleus (PBGN, 90 μl), locus coeruleus (LC, 86 μl), amygdala (1883 μl), lateral genicu- late nucleus (LGN, 252 μl), and pulvinar. The pulvinar was parcellated into 5 subdivisions based on the task-coactivation patterns [66], including the ventromedial pulvinar (vmPul, red, 284 μl), ventrolateral pulvinar (vlPul, orange, 359 μl), dorsolateral pulvinar (dlPul, yellow, 246 μl), dorsomedial pulvinar (dmPul, green, 224 μl), and anterior pulvinar (aPul, blue, 215 μl). The pulvinar ROIs used in this study were defined as the intersections of the original ROIs in the left and right hemispheres. (PDF) S9 Fig. Anatomical ROIs for frontoparietal attention networks (experiment 2). ROIs for dorsal (dAN) and ventral (vAN) attention networks were defined based on anatomical land- marks and HCP-MMP1 atlas. Areas of dAN include IPS/SPL (IPS1, MIP, VIP, LIPv, LIPd, IP1, and 7PL) and SFC (6a and FEF). Areas of vAN include TPJ (TPOJ1, STV, PSL, and PF) and IFC (PEF, IFJb, IFJa, IFSp, and 6r). Yellow annotations indicate selected ROIs in HCP-MMP1 atlas. (PDF) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 23 / 28 PLOS BIOLOGYCollision detection in human subcortex S10 Fig. Activation maps and ROI-averaged responses to looming stimuli in other subcor- tical nuclei (experiment 2). (a, c) LGN. (b, d) Amygdala. (e, g) LC. (f, h) PBGN. Statistical maps were thresholded at p < 0.05 uncorrected. No significant collision-sensitive cluster or ROI-averaged response can be found from these areas. LOSO analysis revealed a significant collision sensitivity in the PBGN in the unattended condition (p < 0.025), but it cannot survive the correction across multiple tests. (PDF) S11 Fig. Clinical perimetry, lesioned locations, and stimulus-evoked occipital activations for hemianopic patients (experiment 3). For each patient, the left panels show the Humphrey perimetry of visual field test. In the structural image below, relevant lesioned locations were indicated by red dashed ovals. For P17, both T1w and diffusion tensor images were shown to indicate the lesion of right optic radiation. In the middle panels, the scotoma was depicted schematically within 10 degrees of eccentricity in black color (i.e., relative sensitivity <−20 dB and p < 0.5% compared with normal population), with the yellow sphere indicating the stimu- lus in the fMRI experiment. The right panels show the occipital activations to stimuli presented to the NVF and BVF (indicated by red arrows). Although clear contralateral V1 activations can be observed to stimuli presented to the NVF, most patients (8/12) showed no significant V1 activation in the lesioned hemisphere to stimulus presented to the BVF (the left bar graph below: dashed line for individual data, ** for p < 0.001). For the 4 patients (P02, P04, P06, and P08) showing weak uncorrected activations in the occipital lobe of the lesioned hemi- sphere, no significant difference was found in the responses of these voxels to the hit and miss stimuli (the right bar graph below), which cannot explain the collision-sensitive responses in the SC (Fig 6A). (PDF) S12 Fig. Looming-evoked responses in the LGN of hemianopic patients (experiment 3). Group-averaged activation maps were thresholded at p < 0.05 uncorrected. Red arrows indi- cate the location of visually evoked response to receding stimuli in the LGN. Blue dotted lines denote the anatomical boundary of the LGN. No collision sensitivity was found from the ROI- averaged response of the whole LGN, nor from the LOSO analysis. (PDF) S1 Table. Clinical characteristics of hemianopic patients. (PDF) S1 Text. Equations to calculate optical flow. (PDF) Author Contributions Conceptualization: Jinyou Zou, Sheng He, Peng Zhang. Data curation: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Formal analysis: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Funding acquisition: Peng Zhang. Investigation: Jinyou Zou, Sheng He, Peng Zhang. Methodology: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Project administration: Peng Zhang. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3002375 January 18, 2024 24 / 28 PLOS BIOLOGYCollision detection in human subcortex Resources: Boyan Fang, Huanfen Zhou, Dajiang Wang, Peng Zhang. Software: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Supervision: Jinyou Zou, Dajiang Wang, Sheng He, Peng Zhang. Validation: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Visualization: Fanhua Guo, Jinyou Zou, Ye Wang, Peng Zhang. Writing – original draft: Fanhua Guo, Jinyou Zou, Peng Zhang. Writing – review & editing: Fanhua Guo, Jinyou Zou, Ye Wang, Sheng He, Peng Zhang. References 1. Peron S, Gabbiani F. 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10.1371_journal.pgen.1011134	RESEARCH ARTICLE Genome-wide analyses reveal the contribution of somatic variants to the immune landscape of multiple cancer types Wenjian BiID Wenge Zhong4, Peipei ZhangID 5,6, Xing Tang4 1,2,3☯, Zhiyu Xu4☯, Feng Liu4, Zhi Xie4, Hao Liu4, Xiaotian Zhu4, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, People’s Republic of China, 2 Center for Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, People’s Republic of China, 3 Medicine Innovation Center for Fundamental Research on Major Immunology-related Diseases, Peking University, Beijing, People’s Republic of China, 4 Regor Pharmaceuticals Inc., Cambridge, Massachusetts, United States of America, 5 Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, People’s Republic of China, 6 Key Laboratory for Neuroscience, Ministry of Education/National Health and Family Planning Commission, Peking University, Beijing, People’s Republic of China ☯ These authors contributed equally to this work. * peipei.zhang@pku.edu.cn(PZ); tangx1986@gmail.com (XT) OPEN ACCESS Abstract Citation: Bi W, Xu Z, Liu F, Xie Z, Liu H, Zhu X, et al. (2024) Genome-wide analyses reveal the contribution of somatic variants to the immune landscape of multiple cancer types. PLoS Genet 20(1): e1011134. https://doi.org/10.1371/journal. pgen.1011134 Editor: Peter Hammerman, MOMA Therapeutics, UNITED STATES Received: September 6, 2023 Accepted: January 9, 2024 Published: January 19, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pgen.1011134 Copyright: © 2024 Bi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. It has been well established that cancer cells can evade immune surveillance by mutating themselves. Understanding genetic alterations in cancer cells that contribute to immune reg- ulation could lead to better immunotherapy patient stratification and identification of novel immune-oncology (IO) targets. In this report, we describe our effort of genome-wide associ- ation analyses across 22 TCGA cancer types to explore the associations between genetic alterations in cancer cells and 74 immune traits. Results showed that the tumor microenvi- ronment (TME) is shaped by different gene mutations in different cancer types. Out of the key genes that drive multiple immune traits, top hit KEAP1 in lung adenocarcinoma (LUAD) was selected for validation. It was found that KEAP1 mutations can explain more than 10% of the variance for multiple immune traits in LUAD. Using public scRNA-seq data, further analysis confirmed that KEAP1 mutations activate the NRF2 pathway and promote a sup- pressive TME. The activation of the NRF2 pathway is negatively correlated with lower T cell infiltration and higher T cell exhaustion. Meanwhile, several immune check point genes, such as CD274 (PD-L1), are highly expressed in NRF2-activated cancer cells. By integrat- ing multiple RNA-seq data, a NRF2 gene signature was curated, which predicts anti-PD1 therapy response better than CD274 gene alone in a mixed cohort of different subtypes of non-small cell lung cancer (NSCLC) including LUAD, highlighting the important role of KEAP1-NRF2 axis in shaping the TME in NSCLC. Finally, a list of overexpressed ligands in NRF2 pathway activated cancer cells were identified and could potentially be targeted for TME remodeling in LUAD. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 1 / 21 PLOS GENETICSFunding: This research was supported by National Natural Science Foundation of China (62273010, W. B.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Zhiyu Xu., F.L., Zhi Xie, H.L., X.Z., W.Z., and X.T. are employees of Regor Pharmaceuticals Inc., Cambridge, Massachusetts, USA. The contribution of somatic variants to the immune landscape of multiple cancer types Author summary Recent studies have found that some genetic changes help cancer cells to evade the immune surveillance. To systematically understand the impact of cancer cell genetic alter- ations to immune regulation, we examined 74 immune traits across 22 cancer types. The tumor microenvironment (TME), crucial for cancer development, varies based on gene mutations in different cancers. Notably, the KEAP1 gene in lung adenocarcinoma (LUAD) emerged as a key player, explaining over 10% of immune trait variations. KEAP1 mutations activate the NRF2 pathway, creating a suppressive TME in LUAD with lower T cell infiltration and heightened T cell exhaustion. Additionally, genes such as CD274 (PD- L1), associated with immune checkpoints, exhibit high expression in NRF2-activated can- cer cells. By developing a NRF2 gene signature, we found that it more effectively predicts anti-PD1 therapy responses than CD274 alone in non-small cell lung cancer. Lastly, we identified ligands overexpressed in NRF2-activated cancer cells, suggesting potential tar- gets for reshaping the LUAD microenvironment. In essence, understanding these genetic interactions helps improve lung cancer treatment and enhance the efficacy of immunotherapy. Introduction Over the last decades, the discovery of immune checkpoints and their applications in cancer therapy have revolutionized the treatment of various cancer types. [1] Immune checkpoint inhibition (ICI) therapies have been utilized as single agents or in combination with chemo- therapies to treat over 50 types of cancer. Despite of these tremendous success, however, only a limited percentage of patients have achieved long-lasting benefits. [2,3] The ineffectiveness of immune-oncology (IO) therapies could be at least partially attributed to the imprecise selec- tion of patients resulted from limited understanding of tumor microenvironment (TME). In the past, the study of cancer TME was largely restricted by the technology available for TME traits retrieval. Only a small number of TME traits can be derived from expensive and laborious experiments, such as flow cytometry [4] and immunohistochemistry [5]. Nowadays, with the advancements in omics technology and bioinformatics tools, various TME traits can be derived from RNA-seq data through de-convolution methods or gene signature enrichment analysis. [6,7] These bulk RNA-seq-derived traits have been shown to be highly consistent with immune traits obtained using cell flow cytometry or scRNA-seq. In a recent study by Sayaman et al., 139 TME traits were collected from multiple studies, mostly from bulk RNA- seq data. [8] Their results showed that germline variants account for no more than 20% of the variation in TME traits, which leaves a significant proportion of variance unexplained. [8]. Recently, the regulation of TME related to somatic alternations in intrinsic pathways in cancer cell has received increasing attention. [3,9] A large number of studies have shown that previously known oncogenes or tumor suppressors regulate the cancer TME by altering the activities of cancer intrinsic pathways. [10–12] These recurrently mutated genes in cancer cells activate or inhibit various chemokines and cytokines, resulting in different TME subtypes. Characterizing the associations between these cancer cell genetic alterations and TME traits can help to better stratify patients by TME subtypes, which could be crucial for IO therapy development. In this study, we analyzed 74 TME traits and 22 cancer types in The Cancer Genome Atlas (TCGA), using genome-wide gene-level association analyses, to identify genes in which somatic variants significantly alter TME traits. Totally, 451 significant gene-trait associations PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 2 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types were reported across different cancer types. Among these associations, 14 genes were found to regulate 3 or more TME traits, which suggests their important roles in shaping multiple aspects of the TME. Of these, KEAP1 was identified as the top hit in lung adenocarcinoma (LUAD) and explained a large proportion (>10%) of variance for multiple immune traits, including interferon pathway, MHC class II expression, and the NK cell signature score. Other important gene-trait associations included TP53 mutations associated with B cell receptor (BCR) func- tion in breast cancer (BRCA), PBRM1 mutations associated with neutrophil in kidney renal clear cell carcinoma, IDH1 mutations associated with lower lymph vessel signature in Brain Lower Grade Glioma (LGG) and BRAF mutation associated with NK cell and macrophages in Thyroid Carcinoma (THCA). To validate our findings and further characterize the underlying mechanism, we collected and analyzed three scRNA-seq datasets of LUAD and confirmed the immunosuppressive role of KEAP1 mutations. [13–15] We found a NRF2 gene signature activated in KEAP1-mutated samples. This signature was significantly correlated to lower T cell infiltration, higher macro- phage and monocyte, higher Treg percentage, and higher T cell exhaustion, indicating an inflamed but immune suppressive TME subtype. Interactions of several ligand-receptor pairs between cancer cells and immune cells (CD274:PDCD1, FAM3C:PDCD1, PVR:TIGIT) are predicted to be enhanced in NRF2 signature high samples which could be potential targets to inhibit to remodel the TME. Furthermore, using clinical trial data [16], we found that non- small cell lung cancer (NSCLC) patients with an activated NRF2 signature are more likely to experience a durable response compared to other patients (p-value: 0.018). By contrast, CD274, a biomarker that has already been adopted for use in lung cancer clinical traits (NCT04294810) [17,18], failed to separate responsive patients from others (p-value: 0.51). Comprehensive genomic analyses have identified somatic mutations and other alterations in the KEAP1 or NRF2 genes in various types of cancer, and Nrf2 mutations occur less fre- quently than Keap1 mutations. [19] Disruptions in the Keap1-Nrf2 pathway is frequently asso- ciated with poor prognosis and chemotherapeutic resistance in NSCLC. [20] Since the Keap1-Nrf2 pathway plays as primary regulator of key cellular processes that contribute to resistance against chemotherapy drugs, NRF2 has been studied as a potential therapeutic target molecule in NSCLC and some other cancers. [21] Our findings explore the molecular features and the impact of KEAP1-NRF2 to TME, which will be beneficial for novel treatment approaches in NSCLC in the near future. Results Overview of genetic test to associate somatic variants with TME traits We conducted genome-wide gene-level association analyses to identify genes in which somatic variants alter TME traits significantly. Of the TME traits Sayaman et al. analyzed [8], we selected 74 traits (S1 Table), most of which were derived from bulk RNA-seq data of TCGA tumor samples by scoring different gene signatures using ssGSEA or by deconvoluting bulk RNA-seq using CIBERSORT. [8,22] These immune traits were selected to represent propor- tion of different immune cell types, activity of immune pathways, and states of different immune cells. To ensure data processing workflow consistency across different cancer types, we used somatic mutations, log2 copy number alterations, and clinical data from TCGA pan- Cancer project. [23] In total, 22 cancer types with > 100 samples were selected for analysis (S2 Table). Somatic mutations in non-coding regions (UTR or intron) except splicing changing variants were excluded from analysis. For each cancer, genes mutated in � 5 tumor samples were fed into association test pipeline. Immune traits were transformed to either quantitative or binary values depending on their distributions, and then passed to linear or logistic PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 3 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 1. Overview of genetic association test to identify immune regulators in different cancer types using TCGA data. Somatic mutations and copy number variations from 22 cancer types are downloaded from cbioportal to associate with 74 immune traits collected from Sayaman et al. (2021) [8]. https://doi.org/10.1371/journal.pgen.1011134.g001 regressions (see Methods). In addition to the confounding covariates adjusted by Sayaman et al. (including gender, days to birth, and age at initial pathologic diagnosis), radioactive ther- apy status and chemotherapy status were also incorporated in the regression model [8]. Both somatic mutations and copy number alterations (CNA) can impact cancer via TME regulation. However, CNA usually spans multiple gene regions and thus it is challenging to distinguish a driver gene from other passengers in the same CNA region without prior knowl- edge or additional experiments. Hence, we mainly focused on associations between mutations and TME traits, and included CNA as an additional confounder. Although both quantitative log2CNA value and categorized CNA value are available in cbioportal [24], we incorporated log2CNA as a covariate since it has higher correlation with gene expression. The log2CNA val- ues were adjusted by tumor purity prior to analysis (see Methods). In the germline variants association study, Sayaman et al. combined multiple cancer types together for analysis. [8] In this study, we analyzed 22 cancer types separately due to a signifi- cant diversity of mutations in cancer cells. A total of 1,628 (74×22) genome-wide analyses were conducted for 22 cancers and 74 TME traits (Fig 1). To avoid a large number of spurious posi- tives, analyses p-values were adjusted using genome control lambda (gc lambda, see Methods). For each genome-wide analysis, we calculated false discovery rate (FDR) adjusted p-values as q-values and selected genes whose q-values < 0.05 as top genes that are significantly associated with TME traits. Contribution of somatic variants to TME traits varies across cancer types We identified 451 significant gene-trait associations with q-values < 0.05 across 22 cancer types (S3 Table). For cancer types of brain Lower Grade Glioma (LGG), Thyroid Carcinoma (THCA), and Breast Cancer (BRCA), 99, 81, and 62 significant gene-trait associations were identified, respectively. Fig 2A demonstrated the 14 genes significantly associated with 3 or more TME traits, of which each of the 13 genes except TP53 was identified in only one corre- sponding cancer type, suggesting the importance of tissue context on TME regulation. Based on the identified gene-trait associations, we calculated the proportion of trait variance that is explained by somatic variants, including both somatic mutations and CNA, for each cancer type (see Methods). Totally, 32 TME traits from 9 cancer types have >20% variance explained by somatic variants, suggesting a non-negligible contribution of somatic variants to TME (Fig 2B and S4 Table). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 4 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 2. Different TME characteristics of different cancers are shaped by differentially mutated genes. (a). Top genes that are significantly associated with > = 3 TME traits. Of the 451 significant gene-trait associations, 14 genes were highlighted. The y-axis is the gene name and the x-axis is the number of the traits that are significantly associatedwith the gene. Different colors are for different cancer types. (b). Immune traits with > 20% explained variance by somatic mutations. The y-axis is the name of TME trait and the x-axis is the proportion of the variance explained by somatic mutation. Different colors are for different cancer types. https://doi.org/10.1371/journal.pgen.1011134.g002 TP53 regulates TME traits for multiple cancer types As aforementioned, TP53 is the only gene associated with multiple TME traits (> = 3) in mul- tiple cancer types (Fig 2A). In BRCA, TP53 mutations are correlated to higher tumor-infiltrat- ing lymphocyte (TIL) fraction, higher interferon gamma, higher macrophage, and higher B cell receptor (BCR) richness and diversity (S5 Table), which is consistent with previous results that TP53 mutations may promote immunogenic activity in BRCA. [25] On the contrary, in head and neck squamous cell carcinoma (HNSC), TP53 mutations are associated with inhibi- tory immune features such as lower lymphocyte infiltration signature score and lower CD8 cell signature score, which was also reported in previous studies. [26] The example of TP53 highlights the importance of tissue context for TME regulation, emphasizing the need of can- cer-type-specific TME stratification for targeted immunotherapy. Kidney Renal Clear cell carcinoma (KIRC) In KIRC, we identified neutrophils signature, for which, 28% trait variance are explained by somatic variants (Fig 2B). Neutrophils were known as the first line of defense against microbial infection. They circulate in the blood and are recruited rapidly to the site of tissue injury. Recent studies showed that neutrophils have pro-tumoral or anti-tumoral functions under dif- ferent contexts. For KIRC specifically, previous studies suggested that tumor-infiltrating neu- trophils act as an independent adverse prognostic feature. [27] Higher tumor-infiltrating neutrophils (TINs) were significantly associated with worse overall survival and higher metas- tasis rate.[27] PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 5 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Of the genes constituting the trait of neutrophils signature in KIRC, we found PBRM1 con- tributes mostly to the trait variance (~22% variance explained, S5 Table) and mutations of PBRM1 are associated with higher neutrophils (p value: 1.95×10−9). This perhaps is not sur- prising as PBRM1 encodes a protein that is involved in the regulation of chromatin remodel- ing and inflammation-related genes are highly regulated by chromatin remodeling genes in KIRC. [28] PBRM1 is highly mutated (~30%) in KIRC samples in which more than 85% of the genetic alterations lead to loss of function (deletion, truncating mutation, and splice muta- tion). PBRM1 mutation cause activation of inflammation-related genes, which can trigger neu- trophil-dependent lung metastasis in advanced KIRC. [28] Generally speaking, experiment and analyses in both mice and human validate that PBRM1 loss of function defines a nonim- munogenic tumor phenotype associated with checkpoint inhibitor resistance in renal carci- noma. [28,29] Brain Lower Grade Glioma (LGG) In LGG, we identified lymph vessels, for which, 38% trait variance are explained by somatic variants. Of the genes significantly associated with the lymph vessels trait, IDH1 gene contrib- uted mostly to the trait variance (33% variance explained). IDH1 is highly mutated in LGG tumor samples (77%) in which almost half of the mutations are annotated as truncating muta- tions. IDH1 mutations are associated with lower lymph vessel signature (p-value: 7.97×10−40). IDH1 is reported to regulate podoplanin (PDPN) expression in glioma by regulating its pro- moter methylation status. [30] And PDPN is strongly expressed in higher-grade IDH1-wild- type glioma but almost undetectable in IDH1-mutated glioma. Consistent with our analysis that IDH1 loss of function mutations is associated with lower lymph vessel signature, upregu- lation of PDPN induces lymphangiogenesis and metastasis in tumor. [31] Thyroid Carcinoma (THCA) For THCA, somatic variants account for 42.8% variance of NK cells, 28.8% variance of TGFB score, 28.6% variance of macrophages, and 22.9% variance of dendritic cells (DC). They are mainly driven by BRAF gene mutations which account for 17.8% - 39.5% of variance for the four traits. In THCA, 56% of patients are carrying the same mutation BRAF V600E mutation which is almost the only mutation found in BRAF. Based on our analysis, BRAF V600E muta- tion is significantly associated with high macrophages (p-value: 2.42e-15). In addition, patients carrying the BRAF mutation are having lower CD8 T cells % (p-value: 4.00e-7) and higher Treg cell % (p-value: 6.64e-12), which is consistent with another study of human thyroid cancer. Breast Cancer (BRCA) Besides TP53, another major influencer to TME traits in BRCA is CDH1. Most of CDH1 mutations in TCGA-BRCA cohort are loss of function mutations, of which > 80% are from LumA subtype. Deficiency of CDH1 protein is associated with higher NK cell signature score and lower macrophage cell population. This is in line with its function of encoding a ligand for interacting with killer cell lectin-like receptor G1 (KLRG1) on NK cells and memory T cells to trigger inhibitory signals. [32] It is interesting that CDH1 loss of function (LOF) mutations are highly frequent in LumA breast cancer, suggesting that high expression of LOF CDH1 mutants in LumA cancer indicates worse prognosis. Furthermore, deficiency of CDH1 maybe onco- genic for cancer initiation and over expression of CDH1 mutants could advance cancer pro- gression by inhibiting NK cells and T cells. Several studies have shown synergistic effect by blocking KLRG1 and PD-1 together in mouse models for multiple cancer types. [33, 34] Our PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 6 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types analysis supports the notion that CDH1-high BRCA patients can be potentially treated by a combination therapy of KLRG1 inhibitor and PD-1/PD-L1 inhibitor. Lung Adenocarcinoma (LUAD) In LUAD, KEAP1 stands out as the top hit associated with multiple TME traits, explaining 5% to 15% trait variance (Fig 3A). Boxplot of trait value distribution shows that many immune traits are downregulated in KEAP1 mutated patients, such as TGFβ signaling, different T cell subtypes gene signatures, NK cell gene signature, MHC expression, macrophage gene signa- ture, and interferon pathways (Fig 3B). Per our interest of LUAD, we used scRNA-seq data to further confirm these associations and characterize the molecular mechanisms behind the associations. NRF2 pathway activation in cancer cell shapes suppressive TME in LUAD KEAP1 is an adaptor protein connecting target protein to CUL3 (Culin 3)/RBX1 (Ring box 1) E3 ubiquitin ligase complex for protein degradation. NRF2 is known as a transcription factor driving expression of antioxidant genes. KEAP1 constitutively targets NRF2 for proteasomal degradation, thereby prevents nuclear accumulation of NRF2 and the downstream activation of the antioxidant gene expression. [35] It is known that most somatic mutations in KEAP1 are missense or truncating events that can generate dominant negative forms of KEAP1. [36] Somatic mutation of KEAP1 causes deficiency of KEAP1 function, which in turn reduces deg- radation of NRF2 protein, resulting in activation of NRF2 downstream genes. To validate KEAP1’s regulation of NRF2 pathway in LUAD tumor, we curated a NRF2 target gene list by Fig 3. KEAP1 is the gene shaping multiple TMEs in LUAD. (a). Immune traits that are associated with KEAP1 mutations in LUAD. The y-axis is -log10(p values) in which the p value correspond to the tests associating somatic mutation in KEAP1 with TME traits. The x- axis is the proportion of trait variance been explained by KEAP1 mutations. (b). Immune traits value distribution across KEAP1 mutated group (red) and KEAP1 wild type group (blue) in LUAD. The x-axis is for different TME traits and the y-axis are for TME traits after transformation. The p-values in the x-axis are calculated using two-sample students t-test. https://doi.org/10.1371/journal.pgen.1011134.g003 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 7 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 4. Identification of NRF2 gene signature and its association with KEAP1 mutation status in both bulk tumor RNA-seq setting and scRNA-seq setting. (a). Venn diagram for gene lists collected from three independent studies and their associated information [13–15]. (b). Distribution of NRF2 gene signature score for tumor samples from TCGA LUAD cohort colored by KEAP1 mutation status. One-sided Wilcoxon test p value < 2.2e-16. (c). Collected single cell RNA-seq datasets for validating findings from TCGA data and characterizing mechanism of immune regulation. (d). UMAP plot with annotated cell types for Maynard et al., 2020 study [13]. (e). Distribution of NRF2 gene signature score for cancer cells of tumor samples from Maynard et al. scRNA-seq study colored by KEAP1 mutation status. Fisher exact test p value = 0.00604 [13]. https://doi.org/10.1371/journal.pgen.1011134.g004 integrating results from 3 independent studies. [37–39] Each of the studies has reported a NRF2 downstream gene list by NRF2 knock down or overexpression experiment in lung can- cer cell lines. To minimize the impact of noise from each single study, we included only target genes that are supported by at least 2 studies into the NRF2 gene signature (Fig 4A). NRF2 gene signature score was then calculated for each sample in TCGA lung adenocarcinoma cohort by ssGSEA. We found a good separation of NRF2 gene signature score distribution between KEAP1 mutated group and KEAP1 wild type group (wilcox single sided test pvalue < 0.05), suggesting that KEAP1 mutation drives activation of NRF2 pathway in LUAD tumor (Fig 4B). To further validate mutation-trait associations, we did a systematic search for whole tumor scRNA-seq data for lung adenocarcinoma from public domain and got 3 high quality datasets (Fig 4C). [13–15] Raw counts of corresponding studies were downloaded and processed using the recommended parameters by each study. We accepted the annotation of cancer cells from each study but re-annotated immune cells with a standard pipeline. Given that the Maynard et al study (n = 44) (Fig 4D) has the largest sample size and also has mutation status of KEAP1, we used this dataset for primary analysis and used the other two study datasets for validation [13]. NRF2 gene signature score calculated from cancer cell pseudo-bulk expression separates KEAP1 mutated samples from non-mutated samples in Maynard et al study, suggesting that NRF2 gene signature is a good indicator of KEAP1 mutation status (Fig 4E, fisher exact test p PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 8 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 5. Associations between NRF2 signature score and different immune traits in Maynard et al. scRNA-seq study [13]. (a). Correlation between NRF2 signature score and various immune cell types percentage within Maynard et al. scRNA-seq study (upper panel) [13]. Red indicates positive correlation while blue indicates negative correlation. Forest plot of meta analysis for correlation of NRF2 signature score with T cell proportion across 3 independent scRNA-seq datasets(lower panel). The fixed effects model was applied. The grey squares represent the correlation coefficient of each dataset; the different size of the squares reflect the weight of each study in the meta-analysis. The horizontal lines indicate the 95% confidence intervals of each study. The diamond represents the effect size (pooled correlation coefficient). (b). CellphoneDB predicted interactions score between FCGR family (FCGR1A and FCGR1B) from macrophage/ monocyte and PD1 from T cell. Tumor samples are classified by by NRF2 signature score (NRF2high, NRF2low). (c). Comparison of Treg population across samples classified by NRF2 signature score (NRF2high, NRF2low). (d). Comparison of T cell exhausion across samples classified by NRF2 signature score (NRF2high, NRF2low). (e). Gene expression heatmap of the top 50 ligands that are positively correlated with NRF2 gene signature in malignant cells. For each sample, pseudo bulk is aggregated from all malignant cells and calculate the averaged expression of ligands and NRF2 gene signature from pseudo bulk data. To study ligands that are highly associated with NRF2, we performed correlation analysis for each ligand with NRF2 gene signature. In the plot, row represents ligands ranked by its correlation with NRF2 signature (top to bottom: highest to lowest). Column represents tumor samples that are ranked by NRF2 gene signature (left to right: highest to lowest). Colors in the heatmap represent the averaged expression of each ligand in pseudo bulk data that are normalized across different samples. https://doi.org/10.1371/journal.pgen.1011134.g005 value < 0.05) and we could use NRF2 gene signature to infer the molecular impact from KEAP1 mutations in other scRNA-seq studies for which no mutation information is available [13]. By correlating NRF2 signature score with various immune cell types, we found that whereas it is negatively correlated with T cell population from all three datasets (Fig 5A), it is positively correlated with macrophage and monocytes population. Although previous studies described that solid tumors contain a significant population of tumor-infiltrating myeloid cells, which promote tumor growth by suppressing the immune system, the contribution of myeloid cell to cancer progression is quite complicated. [40] Using in vivo imaging, Arlauckas et al. showed that the spatial association between macrophages and CD8 T-cell was responsible for resistance to anti-PD1 therapies. [41] Consistent with the report, we also found enhanced PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 9 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types interactions between FcγR genes from macrophage/monocytes and PD1 from T cells in NRF2 Signature high samples predicted by CellPhoneDB [42] from Maynard dataset (Fig 5B). Although NRF2 signature is negatively correlated with T cell population, it is positively cor- related with Treg cell population, which suggests that NRF2 activation in cancer cells may pro- mote Treg differentiation and maturation (Fig 5C). The p-value 0.06 is quite marginal possibly due to small sample size (n = 44). Since Treg cells are involved in inducing T cell exhaustion, we further associated NRF2 signature with T cell exhaustion markers derived from Caushi’s study. [43] NRF2 signature high samples correspond to higher T cell exhaustion score (Fig 5D, p-value: 0.03). To better characterize cell-cell communication changes due to NRF2 pathway activation, we plotted out the expression profile of top 50 ligands positively correlated with NRF2 signature in cancer cells (Fig 5E), which indicated that three genes (CD274, PVR, FAM3C) are ligands for well-known inhibitory immune check point receptors PDCD1 and TIGIT on T cells. CellPhoneDB predicted interactions between these immune checkpoint ligand receptor pairs are enhanced in NRF2 signature high samples (Fig 6A). While CD274 expression is highly correlated with NRF2 signature score in LUAD cancer cells (Fig 6B), chro- matin immunoprecipitation (ChIP) experiment in NRF2 activated human primary melano- cytes confirmed direct binding of NRF2 to CD274 promoter, [44] suggesting that NRF2 may activate CD274 expression directly at transcription level. For other inhibitory ligands whose expression is highly associated with NRF2 signature, we found that PVR expression is associ- ated with worse survival in LUAD patient cohort significantly (Fig 6D, p.value < 0.01). Expres- sion of FAM3C also shows trend of association with worse survival in LUAD patients after 50 months although the p value is not significant due to the fact that only small number of patients live longer than 50 months (Fig 6C, p.value = 0.16). We report these findings to the field to support new immunotherapy target identification for treating these NRF2 pathway activated LUAD patients. Our analysis confirmed the immune suppressive role of NRF2 pathway activation in LUAD and identified potential signaling transduction molecules from cancer cells to immune cells for conducting the suppressive regulatory function. To gain a more comprehensive view about intrinsic pathway changes within KEAP1 mutated cancer cells, we performed differential gene expression analysis with pseudo bulk gene expression profile of aggregated cancer cells and fol- lowed by GSEA analysis using the Maynard dataset. We found out that NRF2 activated genes are enriched in well-known immune regulation pathways such as interferon pathways, TGFβ pathway, TNFα pathway and complement pathway (Fig 6E). These pathways mainly mediate inflammatory cytokine synthesis and secretion and establish a cancer-prone inflammatory microenvironment to promote lung cancer progression. NRF2 signature indicates better prognosis for anti-PD1 or anti-PD-L1 therapies We proposed a model to summarize our findings for KEAP1-NRF2 regulation of TME (Fig 7A). Mutation of KEAP1 causes loss of function of KEAP1, which prevents NRF2 protein from degradation. Accumulation of NRF2 in cancer cells may activate expression of inhibitory cytokines and ligands directly by transcriptional regulation or by activating inflammation pathways such as interferon pathways, TGFβ pathway, TNFα pathway, and complement path- way. These inhibitory ligands or cytokines expressed or secreted from cancer cells inhibit cyto- toxic T cell infiltration directly or induce T cell exhaustion which facilitates formation of a suppressive cancer TME type. This TME type defines a unique LUAD patient cohort with worse survival status (Fig 7B). The upregulation of CD274 (PD-L1), FAM3C, and other T cell exhaustion markers in NRF2 activated LUAD cancer suggest that they could be the population who can benefit the most from anti-PD1 or anti-PD-L1 therapy. To test this hypothesis, we PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 10 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 6. Mechanisms of KEAP1-NRF2 pathway in promoting suppressive TME. (a). Interactions predicted by CellphoneDB of immune checkpoint ligand-receptor pairs between cancer cell (ligand: CD274, FAM3C and PVR) and multiple immune cell types including CD4+ T cell, CD8+ T cell and NK cells) (receptor: PD1 and TIGIT). (b). Correlation of CD274(PD-L1) gene expression and NRF2 signature score within malignant cells (patient samples are classified by NRF2 signature). (c). Survival analysis of FAM3C in TCGA LUAD patient cohorts. Patients are stratified based on gene expression. We observe a few individuals with a long follow-up record. The examples include sample of TCGA-49-AARQ censored at around 224 months, sample of TCGA-78-7163 censored at around 242 months, and sample of TCGA-78-8640 censored at around 235 months. (d). Survival analysis of PVR in TCGA LUAD patient cohorts. Patients are stratified based on gene expression. (e). Enriched pathways for differential expressed genes between malignant cells of NRF2 high samples and NRF2 low samples. https://doi.org/10.1371/journal.pgen.1011134.g006 PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 11 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Fig 7. Summary of KEAP1-NRF2 regulation to LUAD TME and its clinical application. (a). Proposed model for KEAP1-NRF2 regulation to TME in LUAD. (b). Survival curve of TCGA LUAD cohort grouped by NRF2 signature score (log-rank p value: 0.012). For TCGA datasets, 1.25 is selected as the cutoff to stratify patient samples into NRF2high and NRF2low. (c). Boxplot of NRF2 signature score of tumor samples before anti-PD1 treatment from durable benefited patient group and non-durable benefited group. Wilcox test shows significantly higher NFR2 signature score in tumor samples collected from patients with durable benefits for anti-PD1 treatment (p value = 0.018). (d). Boxplot of CD274 gene expression of tumor samples before anti-PD1 treatment from durable benefited patient group and non-durable benefited group. https://doi.org/10.1371/journal.pgen.1011134.g007 analyzed public anti-PD1 clinical trial data. [16] The patients with durable benefits are having higher NRF2 signature score comparing to non-durable benefits group (Fig 7C, wilcox test p- value: 0.018). Surprisingly, there is no significant difference in CD274 (PD-L1) expression between the two patient groups (Fig 7D), suggesting that NFR2 gene signature potentially can be used as a superior predictive biomarker over CD274. However, sequencing a single gene is much easier than doing expression quantification of a panel genes. Since KEAP1 mutations cause upregulation of NFR2 and its downstream signature genes which is confirmed in our analysis (Fig 4B), KEAP1 gene sequencing can be a more applicable test to be used in clinical setting than NFR2 gene signature quantification by either RNA-seq or microarray. Discussion In this study, we systematically investigated the contribution of somatic variants to TME traits using TCGA cancer data. The results demonstrated that somatic variants play an important role in shaping cancer TME in tissue-specific and cancer-type-specific manner. A total of 451 significant gene-trait associations were identified across 22 cancer types, with 14 genes signifi- cantly associated with three or more TME traits, including IDH1 in KIRC, BRAF in THCA, CDH1 in BRCA, and TP53 in multiple cancer types. We select KEAP1-NRF2 in LUAD as an example to highlight the value of our study in patient stratification for IO therapies. Our results showed that KEAP1 mutated or NRF2 acti- vated LUAD samples share unique TME characteristics, such as lower T cell infiltration, higher T cell exhaustion level, and higher expression of immune checkpoint ligands that are targeted PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 12 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types by existing therapies. Real data analysis revealed that the NRF2 gene signature curated in our study could serve as a better biomarker than the currently used biomarker CD274 for selecting patients for anti-PD1/PD-L1 therapies. Moreover, we observed upregulation of the PVR gene (ligand for TIGIT) in NRF2 signature high LUAD tumors, which suggests that targeting NRF2 signature high population with anti-PD1 (or anti-PD-L1) and anti-TIGIT combo therapy may achieve synergistic effect. By May of 2022, the phase 3 SKYSCRAPER-01 study (NCT04294810) evaluating the addition of tiragolumab (anti-TIGIT) to atezolizumab (anti- PD-L1) as first-line treatment for people with PD-L1-high, locally advanced or metastatic non- small cell lung cancer (NSCLC) did not meet its co-primary end point of progression-free sur- vival (PFS), according to Roche (https://bit.ly/37EbDJX). We propose that NRF2 signature can be used to identify patients that may benefit from tiragolumab and atezolizumab combination therapy more precisely than CD274 gene only. The NRF2 gene signature reported in our study could serve as a biomarker to define a patient group with specific TME subtype which may benefit the most from immune-therapy. In addition to KEAP1-NRF2, other key genes reported in our list (Fig 2) may also be involved in shaping specific TME type. TP53 mutation defines an immunogenic and lympho- cytes infiltrated TME type in breast cancer and defines a low cytolytic T cell infiltration TME type in head and neck squamous cell carcinoma. Loss of function mutation in chromatin remodeling gene PBRM1 activates inflammation gene expression and defines a nonimmuno- genic and neutrophil-rich TME type in KIRC which is metastatic and irresponsive to immune check point therapy. IDH1 mutation in brain lower grade glioma associates with lower lym- phangiogenesis and metastasis possibly by down regulating PDPN gene expression. CDH1 mutation in breast cancer defines a NK cell infiltrated/activated TME type by releasing the inhi- bition from NK cell inhibitory receptor KLRG1. We noticed that gene mutations in cancer cells not always promote immune evasion. To the opposite, it’s quite often that cancer cell mutations such as in IDH1 and CDH1 enhance immunity. Mutations in these genes have oncogenic effect in cancer cells but can also promote immunity at the same time. Cancer cell mutations play dynamic and multi-layer regulatory roles for cancer development and progression. The somatic mutations can result in TME traits variation and be used to define TME subtypes. The associa- tions between cancer cell genetic alterations and TME traits reported from our study provide unbiased evaluations of TME contributions from cancer cell genetic alterations, which can help stratify patients and allow researchers to develop specific targeted IO therapies. In the main text, the genome-wide analyses mainly focused on coding regions and treated the somatic mutations equally. We noticed the research studies that highlighted the important role of noncoding RNAs in remodeling TME. [45,46] Hence, we also conducted multiple sen- sitivity analyses in which 1) somatic mutations in non-coding regions were included; 2) only somatic mutations classified as missense or nonsense were included; and 3) driver somatic mutations were double-weighted. The definition of the driver somatic mutation is from OncoVar [47], an integrated database and analysis platform for oncogenic driver variants in cancers. The detailed results can be found in S1 Fig and S6–S9 Tables. In general, upweighting driver mutations does not significantly help find more findings. If the analysis targeted Mis- sense Mutation only, a significant majority of the initially identified signals were successfully replicated. That is because Missense Mutation takes more than 52.9% of the somatic mutations (S10 Table). Meanwhile, when the analysis confined to Nonsense Mutations exclusively, only a limited number of the initially identified signals were reproduced because Nonsense Mutation only takes ~ 4% of the somatic mutations (S10 Table). And both analyses do not exclusively identify many signals missed by the initial analyses. Incorporating the somatic mutations in non-coding regions revealed a slightly broader spectrum of findings, of which potentially sig- nificant discoveries include UBTF in Ovarian Cancer (OV) and ACTBL2 in HNSC (S6 Table). PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 13 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Please note that all conclusions have been drawn from bioinformatic analyses. We fully acknowledge the importance of experimental validation as a necessary step for future research. Materials and Methods TCGA source and data transformation Genetic alteration data was downloaded from https://www.cbioportal.org/ for each cancer type. Clinical data was downloaded from https://portal.gdc.cancer.gov/. Immune traits were downloaded from Supplementary Tables 2–3 of Sayaman et al.[8] We first transform the TME traits to a quantitative or binary value depending on its distri- bution (S2 Table). For a TME trait, 1) if the raw trait values of more than 50% subjects are 0, then we dichotomize the trait to 0 and 1, depending on if the raw value is 0 or not; 2) if the raw trait values of more than 10% subjects are 0, we dichotomize the TME traits to 0 and 1, depending of if the raw value is less than median or not; 3) otherwise, we use inverse normali- zation transformation to calculate a quantitative value. Based on Variant Classification from cbioportal, we excluded somatic mutations annotated as 3’Flank, 3’UTR, 5’Flank, 5’UTR, Intron, and RNA. For each gene, if the sample is a somatic mutation carrier, the genotype was coded as 1, otherwise, the genotype was codes as 0. Genes with fewer than 5 somatic mutation carriers were excluded from analysis. The basic idea of the transformation follows previous studies of Sayaman et al, [8] where parts of the TME traits were also dichotomized and treated as binary variables in case of a large number of 0 values. For example, for ~ 50% of the participants, the corresponding TME traits of B Cells Memory (%) are 0. In this case, the trait cannot be normalized, as requested by linear regression. The data transformation process is completely data-driven and remains the same for all TME traits and cancer types. Genome-wide gene-level association testing We use linear and logistic regression to associate the transformed TME traits and gene-level somatic mutations. Potential confounders of gender, days to birth, age at initial pathologic diagnosis, radiation therapy, and chemotherapy history were incorporated if applicable (S1 Table). For each gene, we also incorporated the corresponding log2CNA value in the model after adjusting for tumor purity as fomula 3 which is derived from fomula 1 and 2 by assuming 2 copy of genes in normal cells. Log2CNVratio ¼ log2CNVtumor (cid:0) log2CNVnormal Formula 1 CNVtumor ¼ CNVCancerCell∗TumorPurity þ 2∗ð1 (cid:0) TumorPurityÞ Formula 2 adjusted log2CNA ¼ log2ð2a (cid:0) 2∗ð1 (cid:0) purityÞ=purityÞ (cid:0) 1 Formula 3 where α = raw log2CNA + 1 TME traits are complicated, and the confounders mentioned above can only cover a limited proportion. For example, infection disease status, autoimmune disease, and immune defi- ciency disease history are important confounding factors and are not provided from TCGA. To avoid the misunderstanding due to the model misspecification, analyses whose gc lambda > 5 or < 0.3 were removed. If gc lambda is > 1, we update gene-level chi-square statis- tics by dividing it over gc lambda and then calculate p-values. Then, false discovery rate (FDR) q-values were used to correct for multiple testing. In this paper, the gene-trait association with FDR q-values < 0.05 were identified as a significant finding. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 14 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Estimation for proportion of traits variance explained by significant genes We fit an analysis of variance from linear and logistic model to estimate the proportion of trait variants explained by top genes whose FDR q-values < 0.05. For each cancer type and TME trait, in addition to the confounders, we incorporated the somatic mutations and CNA of all genes identified in genome-wide association testing. The variance proportions explained by all top genes, including both somatic mutations and CNA, were summed up as an overall vari- ance explained by somatic variants. Since we incorporated genotypes of all top genes in the model, the collinearity between genes can cause inconsistency of p-values and effect sizes compared to a univariate analysis. Although the gene-level variance proportion should be interpreted carefully, the overall vari- ance explained by somatic variants is still accurate. NRF2 gene signature curation Three sets of NRF2 downstream genes are collected from 3 independent literatures. For Oka- zaki et al. 2020, 77 canonical NRF2 target genes identified by monitoring epigenetic profiling changes in NRF2 knockdown experiments from lung cancer cell line A549 are collected [37]. For Qian et al. 2015 and Namani et al. 2017, 593 genes and 477 genes that are downregulated by NRF2 knock down experiment from A549 cell line are collected respectively from associ- ated literatures.[38,39] NRF2 signature score calculation The gene signature score was calculated by using ssGSEA method from GSVA R package for each sample based on gene expression profile. For scRNA-seq specifically, pseudo bulk data aggregated from all cancer (malignant) cells are used. The median NRF2 signature score of all samples is used as cutoff to separate all samples into two groups NRF2high and NRF2low with each group has 22 samples. Fisher exact test is used to test enrichment of KEAP1 mutation within NRF2high samples(p.value = 0.00604). For TCGA bulk RNA-seq a different cutoff was used based on the score distribution of KEAP1 mutated vs wild type to best separate the two populations. Based on Fig 4B, samples with NRF2 signature score > 1.25 are considered as NRF2high while others are considered as NRF2low. Single cell RNA-seq data processing Raw counts are downloaded for public datasets (GSE123902, GSE131907, and PRJNA591860) following standard Seurat procedure including data normalization, variable gene selection (Ngene = 2000), scaling, dimensionality reduction, and clustering using Seurat v4.1.0. All three datasets provide cell type annotation information which were used to identify malignant cells and immune cells separately. To unify cell types across different studies, we re-annotated immune cells from each study. First, we extract immune cells from each dataset and used Harmony to remove potential batch effect by individual patient/sample. After integration, cells were plotted into a Uniform Mani- fold Approximation and Projection (UMAP) dimension based on reduction matrix by Har- mony. Neighbor analysis was performed by invoking FindNeighbors() using Harmony reduction and the first 30 principle components. Clustering was then performed with FindClusters() at resolution 0.3. For cell type annotation, we applied three different strategies: reference-mapping approach by Seurat [48], reference-based annotating by SingleR [49] and cell marker-based annotation PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 15 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types by SCINA [50]. For Seurat reference-mapping, we used the coupled PBMC dataset as the refer- ence dataset following a standard procedure. Annotation results were then summarized at the level of cluster given by the dominant cell type. For SingleR annotation, we specifically used pure immune cells expression profile from Blueprint/ENCODE projects from celldex R pack- age[49]. In SingleR, annotation was performed on the aggregated clusters. Cell markers that were used for SCINA annotation were provided. SCINA were invoked with parameter max_i- ter = 100, convergence_n = 10, convergence_rate = 0.999, sensitivity_cutoff = 0.8. SCINA anno- tation were also summarized at the level of cluster as mentioned above. Results given by three methods were combined and fed into a voting system. Considering all these three methods use different methodology for cell annotation, final cell cluster annotation was defined if two or more methods is able to predict the same annotation. If not, manually curation were per- formed to assign the cell type for that cluster. CellPhoneDB analysis We used the CellphoneDB (CPDB) database to investigate difference in cell-to-cell communi- cation in lung cancer. Ligand-receptor interaction scores were computed and tested using cell- phonedb method statistical_analysis, executed on the tumor samples of either NRF2 Sig high or NRF2 Sig low samples. Differential ligand-receptor interaction was compared by calculating log2FC of ligand-receptor intensity between NRF2 Sig high and NRF2 Sig low samples. To study the interaction between myeloid cells and T cells, a user-specific custom database was gener- ated by adding FCGR family and PD1 interaction. Differential gene expression testing We used Seurat function FindMarkers() to call differential expression between NRF2 Sig high and NRF2 Sig low samples. Gene Set Enrichment Analysis (GSEA) was performed to study pathway activity in NRF2 Sig high samples. Survival analysis We used “survival” package from R to do survival analysis with clinical data download from TCGA data portal. For single gene expression-based analysis, patients are separated into two groups by median expression. For NRF2 signature survival analysis, patients are separated as described previously. Clinical trial data re-analysis We downloaded gene expression profile data (TPM) from GEO: GSE136961 and anti-PD-1 response data from the original paper [16]. Patients are separated into two groups as defined in the paper, group with durable benefits and group with non-durable benefits. Wilcoxon test was performed in R to compare the difference of NRF2 signature score between the two groups. Supporting information S1 Table. Summary of 74 TME traits in genome-wide association analyses. (CSV) S2 Table. Summary of 22 cancer types in genome-wide association analyses. (CSV) PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 16 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types S3 Table. 451 significant gene-trait associations with q-values < 0.05 across 22 cancer types and 74 TME traits. The analyses only include somatic mutations in coding region. (CSV) S4 Table. Overall proportion of variance explained by somatic variants to TME traits. (CSV) S5 Table. Gene-level proportion of variance explained by somatic variants to TME traits. (CSV) S6 Table. Significant gene-trait associations with q-values < 0.05 across 22 cancer types and 74 TME traits. The analyses include somatic mutations in both coding and non-coding regions. (CSV) S7 Table. Significant gene-trait associations with q-values < 0.05 across 22 cancer types and 74 TME traits. The analyses only include missense mutations. (CSV) S8 Table. Significant gene-trait associations with q-values < 0.05 across 22 cancer types and 74 TME traits. The analyses only include nonsense mutations. (CSV) S9 Table. Significant gene-trait associations with q-values < 0.05 across 22 cancer types and 74 TME traits. The analyses double weight driver mutations. (CSV) S10 Table. Counts of somatic variants in different variant classes across 22 cancer types. (CSV) S1 Fig. Sensitivity analyses results of the significant associations between genes and TME traits. In all the four panels, the x-axis corresponds to the analysis including only coding regions (denoted as Coding). The y-axis corresponds to the alternative analyses as below. (a) the somatic mutations in non-coding regions were additionally included (denoted as NonCod- ing); (b) driver somatic mutations were double-weighted (denoted as Driver); (c) only somatic mutations classified as Nonsense Mutation were included (denoted as Nonsense); (d) only somatic mutations classified as Missense Mutation were included (denoted as Missense). In our analysis, genes with fewer than 5 somatic mutation carriers were excluded for both coding and alternative analyses. Consequently, the sets of genes included in different analyses were not identical. For genes not included in either coding or alternative analyses, we set the corre- sponding p-value to 1. (JPEG) Acknowledgments This research is supported by High-performance Computing Platform of Peking University. Jun Li gives great suggestions for scRNA-seq data analysis. Author Contributions Conceptualization: Xing Tang. Data curation: Wenjian Bi, Zhiyu Xu, Xing Tang. Formal analysis: Wenjian Bi, Zhiyu Xu, Peipei Zhang, Xing Tang. PLOS Genetics \| https://doi.org/10.1371/journal.pgen.1011134 January 19, 2024 17 / 21 PLOS GENETICSThe contribution of somatic variants to the immune landscape of multiple cancer types Funding acquisition: Wenjian Bi. Investigation: Wenjian Bi, Feng Liu, Zhi Xie, Hao Liu, Xiaotian Zhu, Wenge Zhong, Peipei Zhang, Xing Tang. Methodology: Wenjian Bi, Zhiyu Xu. Project administration: Wenjian Bi, Wenge Zhong, Peipei Zhang. Supervision: Wenjian Bi, Wenge Zhong, Xing Tang. Validation: Zhiyu Xu, Feng Liu, Zhi Xie, Hao Liu, Xiaotian Zhu, Wenge Zhong, Peipei Zhang, Xing Tang. Visualization: Wenjian Bi, Zhiyu Xu, Peipei Zhang. Writing – original draft: Wenjian Bi, Zhiyu Xu, Peipei Zhang, Xing Tang. Writing – review & editing: Feng Liu, Zhi Xie, Hao Liu, Xiaotian Zhu, Wenge Zhong, Peipei Zhang, Xing Tang. References 1. Robert C. A decade of immune-checkpoint inhibitors in cancer therapy. Nature Communications. 2020; 11(1):1–3. 2. 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10.1371_journal.pmed.1004337	RESEARCH ARTICLE Association of a healthy beverage score with total mortality in the adult population of Spain: A nationwide cohort study Montserrat Rodrı´guez-AyalaID Diana Marı´a Me´ ridaID Pilar Guallar-Castillo´ nID 1,7* 1,2, Carolina Donat-VargasID 1,3,4, Bele´ n Moreno-FrancoID 5,6, 1, Jose´ Ramo´ n BanegasID 1, Fernando Rodrı´guez-ArtalejoID 1,7, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Preventive Medicine and Public Health, School of Medicine, Universidad Auto´ noma de Madrid and CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain, 2 Department of Microbiology and Parasitology, Hospital Universitario La Paz, Madrid, Spain, 3 ISGlobal, Campus Mar., Barcelona, Spain, 4 Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden, 5 Instituto de Investigacio´n Sanitaria (IIS) Arago´ n, Hospital Universitario Miguel Servet, Zaragoza, Spain, 6 CIBERCV (CIBER of Cardiovascular), Instituto de Salud Carlos III, Madrid, Spain, 7 IMDEA-Food Institute, CEI UAM+CSIC., Madrid, Spain * mpilar.guallar@uam.es OPEN ACCESS Abstract Citation: Rodrı´guez-Ayala M, Donat-Vargas C, Moreno-Franco B, Me´rida DM, Ramo´n Banegas J, Rodrı´guez-Artalejo F, et al. (2024) Association of a healthy beverage score with total mortality in the adult population of Spain: A nationwide cohort study. PLoS Med 21(1): e1004337. https://doi.org/ 10.1371/journal.pmed.1004337 Received: July 12, 2023 Accepted: December 21, 2023 Published: January 23, 2024 Background Despite the substantial evidence of the relationship between diet and mortality, the role of beverage consumption patterns is not well known. The aim of this study was to assess the association of the adherence to a Healthy Beverage Score (HBS) and all-cause mortality in a representative sample of the Spanish adult population. Methods and findings Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pmed.1004337 Copyright: © 2024 Rodrı´guez-Ayala et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data are freely available upon request by contacting Esther Lo´pez- Garcı´a at the Department of Preventive Medicine and Public Health, Faculty of Medicine, Universidad Auto´noma de Madrid (UAM)-IdiPaz, CIBERESP (CIBER of Epidemiology and Public Health), 28029, We conducted an observational cohort study using data from the Study on Nutrition and Cardiovascular Risk in Spain (ENRICA), which included 12,161 community-dwelling individ- uals aged �18 years recruited in 2008 to 2010 and followed until January 2022. At baseline, food consumption was collected using a validated diet history. The HBS consists of 7 items, each of which is scored from 1 to 4 (highest adherence). The HBS ranges from 7 to 28 points with a higher score representing a healthier pattern. Adherence was assigned as a higher consumption of low-fat milk, and coffee and tea, a lower consumption of whole-fat milk, no consumption of fruit juice, artificially sweetened beverages, or sugar-sweetened beverages, and no or moderate consumption of alcohol. Total mortality was ascertained by linkage to the Spanish National Death Index. Statistical analyses were performed with Cox models and adjusted for the main confounders, including sociodemographic, lifestyle, dietary vari- ables, and morbidity. After a mean follow-up of 12.5 years (SD: 1.7; range: 0.5 to 12.9), a total of 967 deaths occurred. For all-cause mortality, the fully adjusted hazard ratio (HR) for the highest versus lowest sex-specific quartiles of HBS was 0.72 (95% confidence interval [0.57, 0.91], p lin- ear-trend = 0.015), corresponding to an 8.3% reduction in the absolute risk of death. A linear PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 1 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Madrid, Spain. E-mail address: esther.lopez@uam. es. UAM website: https://www.uam.es/ss/Satellite/ Medicina/es/1242658444664/subhome/ Departamento_de_Medicina_Preventiva_y_Salud_ Publica_y_Microbiologia.htm. relationship between the risk of death and the adherence to the HBS was observed using restricted cubic splines. The results were robust to sensitivity analyses. The main limitation was that repeated measurements on beverage consumption were not available and bever- age consumption could have changed during follow-up. Funding: This work was supported by FIS grants 17/1709, and 20/144 from the Carlos III Health Institute, the Secretary of R+D+I, and the European Regional Development Fund/European Social Fund (to P.G-C); by the National Plan on Drugs grant 2020/17, Spanish Ministry of Health, Spain (to F.R- A); by the FACINGLCOVID-CM project, Comunidad de Madrid and European Regional Development Fund (ERDF), European Union (to F.R-A); and by the REACT EU Program, Comunidad de Madrid and European Regional Development Fund (ERDF), European Union (to F.R-A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: BMI, body mass index; CI, confidence interval; ENRICA, Study on Nutrition and Cardiovascular Risk in Spain; HBS, Healthy Beverage Score; HR, hazard ratio; METs-hour/ week, metabolic equivalents in hours per week; SD, standard deviation. Conclusions In this study, we observed that higher adherence to the HBS was associated with lower total mortality. Adherence to a healthy beverage pattern could play a role in the prevention of pre- mature mortality. Author summary Why was this study done? • Most dietary patterns focus solely on solid foods, and the role of beverages as a whole has received little attention. • Our aim was to assess the relationship between a Healthy Beverage Score (HBS) and mortality in a representative sample of community-dwelling individuals from Spain. • Our hypothesis was that high adherence to the HBS would be associated with lower mortality. What did the researchers do and find? • We included a representative sample of 12,161 adults (18 years and older) from Spain who were recruited in 2008 to 2010 and followed up until 2022. A total of 967 deaths occurred. • Participants were categorized according to their adherence to the HBS. • A higher total score was achieved with a higher consumption of low-fat milk, and coffee and tea, no consumption of whole-fat milk, fruit juice, artificially sweetened beverages, sugar-sweetened beverages, and no consumption or moderate consumption of alcohol. • Each HBS item scored from 1 (minimum adherence) to 4 points (maximum adherence) and the HBS ranged from 7 to 28 points. The higher the HBS, the healthier. • When comparing extreme categories, higher adherence to the HBS was associated with lower all-cause mortality in the Spanish adult population, with an 8.3% reduction in the absolute risk of death. What do these findings mean? • The adherence to the HBS could serve as a potential diet-based strategy to prevent pre- mature mortality. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 2 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality • The quality of beverage patterns could influence health outcomes in the general population. Introduction The influence of unhealthy dietary factors on adverse outcomes, including premature mortal- ity, is a public health concern [1]. Thus, the association between diet and mortality has been examined by using several approaches such as analyzing individual nutrients, food and, more recently, assessing dietary patterns and indexes [2–4]. Most of the indexes include mainly solid food (e.g., meat, poultry, fish, as well as fruit and vegetables) [5], although some of them also comprise beverages [6]. The mechanisms by which beverages influence health are complex and are not only based on the nutritional quality of their components (such as energy provided, macronutrients, fiber, min- erals, and vitamins) [7], but also rely on other factors such as satiety mechanisms and factors that affect the assimilation of beverages such as rapid gastric emptying and intestinal absorption [8]. Moreover, the addition of artificial sweeteners influences mortality [9]. On the other hand, some beverages are also an important source of other additives (e.g., phosphates) as well as contami- nants from packaging or processing (e.g., organophosphate esters, phthalates) [10]. In 2015, a healthy beverage index, based on commonly consumed drinks, was developed to evaluate the role of beverage quality on cardiometabolic risk in adult Americans. A low adher- ence to this index was associated with several detrimental cardiometabolic markers [11]. Con- sistent results were obtained in another US study where the association between the adherence to a healthy beverage pattern and total mortality was evaluated. Data were obtained from a cohort of 2,283 adults, aged �21 years, with a previous diagnosis of mild to moderate chronic renal insufficiency. A healthier beverage index was inversely associated with the progression of chronic kidney disease and all-cause mortality [12]. Although the association of specific beverages has been studied previously, the role of a healthy beverage index and its association with mortality has not been assessed in the general population yet. We hypothesized that higher adherence to a 7-item Healthy Beverage Score (HBS), previously proposed by Hu and colleagues [12] and adapted to the Spanish beverage consumption, could be associated with lower mortality. Therefore, the aim of this study is to assess the association between the HBS and all-cause mortality in a representative cohort of Spanish adults. Methods Study design and participants Data were obtained from the Study on Nutrition and Cardiovascular Risk in Spain (ENRICA) whose methods have been reported elsewhere [13]. In brief, 13,105 individuals aged �18 years were recruited from 2008 to 2010. A stratified cluster sampling based on the census sections of Spain was performed to guarantee the representativeness of the sample. Sample weights were based on the size of municipalities, sex, and age. Three sequential stages were followed for data collection. First, sociodemographic, lifestyle characteristics, and morbidity information was obtained through a telephone interview. Second, blood and urine samples were collected on a first home visit. Third, a physical examination and a face-to-face dietary history (DH-EN- RICA) were completed during a second home visit. The response rate was 51% and the main reasons for non-participation were refusal to provide a blood sample (51.7%) and not being interested in the study (37.8%). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 3 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality From the initial sample (13,105 individuals), 944 participants were excluded: 60 (0.5%) without information on diet and 884 (6.8%) with implausible values for total energy intake (<800 kcal/day or >5,000 kcal/day in males; <500 kcal/day or >4,000 kcal/day in females). Therefore, a total of 12,161 participants were included in the analysis (S1 Fig). The Clinical Research Ethics Committee of La Paz University Hospital in Madrid provided ethical approval. All participants from the ENRICA Study gave written informed consent after explaining the details of the study. Study variables Dietary history. Information on diet was obtained through a computerized dietary his- tory (DH-ENRICA), conducted by trained and certified nonmedical interviewers. The DH-ENRICA collected information on 861 items of food, with 82 beverages included. Par- ticipants informed about all items of food and beverages consumed at least once every 2 weeks in the previous year. Food consumed during weekdays and weekends were consid- ered. A total of 127 sets of digitalized photos, household measurements, as well as the usual proportion sizes of food from typical Spanish recipes were used to estimate portion sizes in grams per day. Regarding beverages, a total of 14 digitized photos and 23 household mea- surements were used to later estimate beverage consumption in milliliters per day. In addi- tion, for alcoholic beverages, the consumption of ethanol in grams per day was calculated using Spanish food composition tables [14]. The validity correlation coefficients in HD-EN- RICA for beverages were: 0.71 for coffee, 0.69 for milk, 0.40 for soft drinks, and 0.64 for alcoholic beverages [15]. The Healthy Beverage Score (HBS). A Healthy Beverage Score (HBS) was previously described by Hu and colleagues [12]. Based on the HBS, we built a 7-item HBS modifying its cut-off points to fit with the beverage consumption of a representative sample of the Spanish adult population. Each item of the HBS scored from 1 (minimal adherence) to 4 points (maxi- mal adherence) based on sex-specific categories of consumption. Thus, the HBS ranged from 7 (low adherence) to 28 points (high adherence). The higher the HBS, the healthier the pattern. Items were grouped in 2 main components: adequacy and moderation (Table 1). Two bever- ages were considered as adequacy components: low-fat milk as well as coffee and tea consump- tion. For these 2 components, the higher the score, the healthier the pattern. No low-fat milk consumption scored 1, while the remaining sample was divided into tertiles among consum- ers; coffee and tea consumption was grouped into quartiles. Five items were included as moderation components: whole-fat milk, fruit juice, artificially sweetened beverages, sugar- sweetened beverages, and alcohol. For these 5 items, the higher the consumption, the lower the score, with a specific classification for alcohol consumption. Whole-fat milk and sugar-sweet- ened beverages scored 4 for no consumption and the remaining sample was divided into ter- tiles among consumers. Fruit juice and artificially sweetened beverages consumption scored 4 for no consumption and 1 for any consumption. The scoring of fruit juices and artificially sweetened beverages was decided on the basis of the lack of a wide range of consumption, and to maintain the relative weight of these items in the score, as previously described by Hu and colleagues [12]. Finally, for alcohol consumption, participants with no consumption or moder- ate drinkers (<40 g/day for males and <24 g/day for females) scored 4, and heavy drinkers (�40 g/day for males and �24 g/day for females) scored 1. Mortality assessment. All-cause mortality was ascertained through a computerized link- age with the Spanish National Death Index. Participants were followed from baseline in 2008 to 2010 to January 31, 2022. Follow-up was censored at the date of death or at the end of fol- low-up, whichever occurred first. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 4 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Table 1. Scoring criteria for the HBS in the ENRICA Study (2008–2010). Components Adequacy Low-fat milk Coffee and tea Moderation Whole-fat milk Fruit juice Artificially sweetened beverages Sugar-sweetened beverages Alcohola Minimum score 1 (No consumption) 1 (Quartile 1) 2 (Tertile 1 among consumers) 2 (Quartile 2) 3 (Tertile 2 among consumers) 3 (Quartile 3) 1 (Tertile 3 among consumers) 1 (Any consumption) 1 (Any consumption) 2 (Tertile 2 among consumers) 3 (Tertile 1 among consumers) – – – – 1 (Tertile 3 among consumers) 1 (Heavy drinkers) 2 (Tertile 2 among consumers) 3 (Tertile 1 among consumers) – – Range 7 Maximum score 4 (Tertile 3 among consumers) 4 (Quartile 4) 4 (No consumption) 4 (No consumption) 4 (No consumption) 4 (No consumption) 4 (No consumption or moderate drinkers) 28 a Heavy drinkers were defined as consumption �40 g/day for males and �24 g/day for females. Among drinkers, a moderate alcohol consumption was defined as <40 g/day for males and <24 g/day for females. https://doi.org/10.1371/journal.pmed.1004337.t001 Confounders. Participants provided information regarding age, sex, educational level, and smoking status which were obtained through the computer-assisted telephone interview performed at baseline. On the second home visit and following standardized procedures, blood pressure, and weight and height were measured. Body mass index (BMI) was calculated as weight divided by the square of the height in meters (kg/m2). Leisure time and household physical activity were evaluated with the EPIC short questionnaire, collecting information on 17 activities. Then, each activity was multiplied by their respective energy expenditure rate in metabolic equivalents in hours per week (METs-hour/week) [16] and total energy expenditure was obtained by summing up all activities. Hours spent watching television were used to account for sedentary activities. In order to control for good dietary quality, analyses were adjusted for total energy intake, fiber, fruit and vegetable consumption, as well as the Mediter- ranean Diet Adherence defined by Trichopoulou and colleagues [17] without including alco- hol. Blood samples collected on the first home visit were centrally analyzed in the CORE laboratory of La Paz University Hospital in Madrid. A colorimetric enzymatic method with lipase and glycerol kinase (for triglycerides) and a colorimetric enzymatic method with choles- terol-oxidase, esterase, and peroxidase (for cholesterol) were used. To define hypertriglyceride- mia, we used a threshold of �150 mg/d in fasting plasma triglycerides levels, and for hypercholesterolemia, a fasting plasma total cholesterol level of �200 mg/dL or prescribed lipid-lowering medications. Hypertension was defined as �140/90 mmHg or taking antihyper- tensive medication. Analyses were also controlled for the number of chronic conditions (chronic obstructive pulmonary disease, coronary heart disease, stroke, heart failure, osteoar- thritis, cancer, depression diagnosed by a physician, and diabetes), as well as the number of prescribed medications to consider prevalent morbidity. Independent variables with missing values were imputed by using multiple imputation [18]: educational level (<1%), smoking status (<1%), BMI (1.5%), number of television hours (<1%), hypertriglyceridemia (<1%), hypercholesterolemia (<1%), and high blood pressure (1.1%). The validity of imputed data was examined against analyses performed with variables that contained full information. PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 5 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Statistical analysis Across sex-specific quartiles of adherence to the HBS, age-adjusted baseline characteristics of participants were computed using marginals. Age-adjusted means for continuous variables and age-adjusted proportions (%) for categorical variables were provided. To estimate hazard ratios (HR) and their 95% confidence intervals (CIs), Cox proportional hazards regression models were built and age was considered as the underlying time metric. The survey command was applied to account for the complex sampling design. The lowest category of adherence to the HBS was used as reference. Three sequential Cox models were used. Model 1 was an unadjusted model. Model 2 was additionally adjusted for age, sex, educational level, smoking status, ex-drinker status, BMI, physical activity in leisure time, total energy intake, fruit and vegetable consumption, total fiber intake, hypertriglyceridemia, hypercholesterolemia, hypertension, number of self- reported chronic conditions, and number of medications. Finally, Model 3 was adjusted for the Mediterranean index by Trichopoulou excluding alcohol (maximum score = 8), but excluding fruit, vegetable, and fiber consumption. Schoenfeld residuals were plotted against time to assess proportional hazards assumptions and visually no violations were found. To test for linear trend, categories of the HBS were modeled as a continuous variable. The dose– response relationship was assessed by using restricted cubic splines with 3 knots (at the 10th, 50th, and 90th percentiles). Interactions between the HBS and age (<65 years versus �65 years), sex (male versus female), BMI (<30 kg/m2 versus �30 kg/m2), physical activity (�median 61.5 METs-hour/week versus >median 61.5 METs-hour/week), vegetable con- sumption (�median 183.5 g/d versus >median 183.5 g/d), adherence to the Mediterranean diet without including alcohol (�median 4 versus >median 4), and the prevalence of chronic conditions (yes/no) were tested by including multiplicative terms in Model 3. Sensitivity analy- ses were conducted excluding deaths in the first 3 years of follow-up to account for the effect of subclinical conditions at baseline. Also, individual items of the HBS were assessed according to Model 3 plus adjustment for the remaining items that are part of the score. This was a preplanned study and data analysis was conducted according to a prespecified plan (S1 Text). The HBS was previously used to assess the association between adherence to HBS and age-related frailty in a sample of Spanish older adults [19]. A minor modification was introduced in alcohol consumption classification. For older adults, moderate alcohol con- sumption was considered the healthy option due to their high cardiovascular risk [19]. How- ever, in the current analysis, which involves the adult general population (aged �18 years) with lower cardiovascular risk, the category of alcohol considered healthy was “no consump- tion or moderate consumption.” This study was reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). A two-tailed p value less than 0.05 was considered statistically significant. All analyses were performed with Stata, version 17.0 (StataCorp, College Station, Texas, United States of America). Results The median age of participants (N = 12,161) was 46 years old (interquartile range 35 to 61) and 52.6% were females. Compared with those in quartile 1 (less healthy) of adherence to the HBS, participants in quartile 4 (healthier) were older, more frequently females, with a higher level of education and with a less sedentary lifestyle, and were more physically active. Also, those in quartile 4 had lower energy intake, consumed more fiber, fruit and vegetables, showed PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 6 / 20 PLOS MEDICINETable 2. Age-adjusted baseline characteristics of participants in the ENRICA Study (2008–2010) by quartiles of the HBS (N = 12,161). A healthy beverage score is associated with lower total mortality Characteristics Quartile 1 (Less healthy) n = 2,813 38.9 Age, mean, y Female, % Educational level, % Primary or less Secondary University Smoking, % Non-smoker Former smoker Current smoker Ex-drinker, % BMI, % <25 kg/m2 25-<30 kg/m2 � 30 kg/m2 Time watching TV, mean, h Physical activity, mean, METs-hour/week Energy consumption, mean, Kcal/d Fiber consumption, mean, g/d Fruit consumption, mean, g/d Vegetable consumption, mean, g/d Mediterranean diet score (without alcohol), mean Hypertriglyceridemia, % Hypercholesterolemia, % Hypertension, % Number of chronic conditionsa, % None One Two or more Number of medications, % None One to 3 More than 3 Quartiles of adherence to the HBS Quartile 2 n = 2,985 Quartile 3 n = 2,745 45.3 39.3 24.3 46.1 29.6 45.6 25.8 28.6 50.0 33.2 43.3 23.6 1.9 65.4 2,261.9 22.9 233.7 197.2 3.9 19.8 50.5 32.2 74.8 20.5 4.6 69.4 27.6 3.0 48.5 50.7 23.8 43.2 32.9 47.3 25.6 27.1 52.1 35.6 42.9 21.5 1.9 68.3 2,135.5 23.2 245.5 211.6 4.1 18.3 52.7 27.6 71.7 22.2 6.1 71.4 24.5 4.1 p for trend <0.001 <0.001 <0.001 <0.001 0.320 <0.001 0.005 <0.001 <0.001 0.035 <0.001 <0.001 <0.001 0.127 <0.001 0.109 <0.001 <0.001 <0.001 Quartile 4 (Healthier) n = 3,618 53.9 57.9 27.2 40.0 32.8 49.6 26.5 23.9 48.3 38.0 39.5 22.5 1.9 72.1 1,992.8 23.0 247.5 209.6 4.0 16.8 52.8 28.3 69.0 24.7 6.3 71.0 25.5 3.5 53.4 29.5 43.8 26.7 53.4 20.1 26.5 56.1 39.9 38.6 21.6 2.1 67.0 2,331.9 22.5 222.8 184.0 3.7 17.8 46.9 29.2 71.9 22.3 5.8 72.0 25.4 2.6 a Chronic conditions included: chronic obstructive pulmonary disease, coronary heart disease, stroke, heart failure, osteoarthritis, cancer, depression diagnosed by a physician, and diabetes. HBS, Healthy Beverage Score; BMI, body mass index; METs-hour/week, metabolic equivalents in hours per week. https://doi.org/10.1371/journal.pmed.1004337.t002 a higher adherence to the Mediterranean diet, and had more frequently hypercholesterolemia (Table 2). Sex-specific cut-off points for individual items of the HBS are shown in S1 Table. In accordance with the rules for the construction of the HBS, compared with those in quar- tile 1 (less healthy), participants in quartile 4 (healthier) consumed more low-fat milk, coffee and tea, and alcohol, but consumed less whole-fat milk, fruit juice, artificially sweetened bever- ages, and sugar-sweetened beverages (Table 3). PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 7 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Table 3. Beverage consumption by quartiles of the HBS in the ENRICA Study (2008–2010) (N = 12,161). HBS components Adequacy Low-fat milk, mean (SD), mL/d Coffee and tea, mean (SD), mL/d Moderation Whole-fat milk, mean (SD), mL/d Fruit juice, mean (SD), mL/d Artificially sweetened beverages, mean (SD), mL/d Sugar-sweetened beverages, mean (SD), mL/d Alcohol, mean (SD), g/db Quartile 1 (Less healthy) n = 2,813 Quartiles of adherence to the HBSa Quartile 3 n = 2,745 Quartile 2 n = 2,985 Quartile 4 (Healthier) n = 3,618 50.0 (92.7) 64.3 (88.2) 100.3 (128.2) 103.1 (121.8) 149.3 (155.1) 127.5 (138.8) 215.5 (148.3) 161.5 (154.6) 139.3 (159.3) 100.4 (139.9) 48.9 (156.2) 162.0 (249.4) 11.4 (19.2) 82.8 (121.6) 66.1 (124.2) 34.5 (219.7) 71.0 (168.2) 10.7 (17.8) 38.2 (76.4) 36.9 (87.7) 17.7 (115.0) 43.2 (125.7) 7.9 (14.0) 8.8 (23.7) 7.6 (42.6) 3.5 (51.3) 10.2 (68.7) 5.8 (10.4) p value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 a Cut-off points for the HBS = for males: Q1 10–18; Q2 19–21; Q3 22–23; Q4 24–28; for females: Q1 10–19; Q2 20–21; Q3 22–23; Q4 24–28. b Alcohol was defined as the consumption of ethanol in grams per day. HBS, Healthy Beverage Score; SD, standard deviation. https://doi.org/10.1371/journal.pmed.1004337.t003 After a mean follow-up of 12.5 years (SD: 1.7; range: 0.5 to 12.9) and 151,459 person-years of follow-up, a total of 967 deaths occurred. The HR for all-cause mortality when comparing extreme quartiles of the adherence to the HBS was 0.72 (95% CI, 0.57 to 0.91, p for linear trend = 0.015) in the fully adjusted model (Table 4). The decrease in absolute risk of death was 4.3% for quartile 2, 6.3% for quartile 3, and 8.3% for quartile 4. No significant interactions were found for age, sex, BMI, physical activity, vegetable consumption, or adherence to the Mediterranean diet (without including alcohol). However, a statistically significant interaction was found when stratifying for the presence of at least 1 chronic condition (p = 0.030). Among those with at least 1 chronic condition, higher adherence to the HBS was associated with lower mortality. No association was observed among those with no chronic conditions (Table 5). Table 4. Mortality risk according to quartiles of the adherence to the HBS in the ENRICA Study from baseline (2008–2010) to January 2022 (N = 12,161). Total mortality Deaths/n Person-years Model 1a Model 2b Model 3c Quartile 1 HR (95% CI) (Less healthy) 141/2,813 36,216 1 (ref.) 1 (ref.) 1 (ref.) Quartile 2 HR (95% CI) Quartile 3 HR (95% CI) 227/2,985 38,058 0.86 [0.68, 1.10] 0.79 [0.61, 1.02] 0.79 [0.61, 1.02] 228/2,745 32,860 0.84 [0.65, 1.08] 0.77 [0.59, 1.00] 0.78 [0.60, 1.02] Quartile 4 HR (95% CI) (Healthier) 371/3,618 44,325 0.75 [0.59, 0.94] 0.72 [0.57, 0.92] 0.72 [0.57, 0.91] p for linear trendd 0.011 0.017 0.015 aModel 1 was an unadjusted model. Age was the underlying time metric. bModel 2 was adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (<25, �25 and �30, >30 kg/m2), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), fiber intake (g/d continuous), fruit and vegetable consumption (g/d, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and �2), and number of medications (0, 1–3, >3). Age was the underlying time metric. cModel 3 was adjusted for factors in Model 2 plus adherence to the Mediterranean diet without including alcohol (maximum score = 8) and excluding fruit, vegetable, and fiber consumption. Age was the underlying time metric. dp value for quartile 4 vs. quartile 1: Model 1 p = 0.012, Model 2 p = 0.007; Model 3 p = 0.007. BMI; body mass index; CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio. https://doi.org/10.1371/journal.pmed.1004337.t004 PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 8 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Table 5. Mortality risk according to quartiles of the adherence to the HBS in the ENRICA Study from baseline (2008–2010) to January 2022 by age, sex, BMI, physi- cal activity, vegetable consumption, adherence to the Mediterranean diet without including alcohol and prevalence of chronic conditions (N = 12,161). Total mortality Quartile 1 HR (95% CI) (Less healthy) Quartile 2 HR (95% CI) Quartile 3 HR (95% CI) Quartile 4 HR (95% CI) (Healthier) p for linear trend p for interactiona Age <65 years, n = 9,774 Deaths, n Model 3b �65 years, n = 2,387 Deaths, n Model 3b Sex Male, n = 5,760 Deaths, n Model 3b Female, n = 6,401 Deaths, n Model 3b BMI <30 kg/m2, n = 9,513 Deaths, n Model 3b �30 kg/m2, n = 2,648 Deaths, n Model 3b Physical activity 39/2,514 1 (ref.) 102/299 1 (ref.) 63/1,243 1 (ref.) 78/1,570 1 (ref.) 104/2,332 1 (ref.) 37/481 1 (ref.) 89/1,451 �Median (61.5 METs-hour/week), n = 6,082 Deaths, n Model 3b >Median (61.5 METs-hour/week), n = 6,079 Deaths, n Model 3b Vegetable consumption 52/1,362 1 (ref.) 1 (ref.) 67/2,486 58/2,193 79/2,581 1.19 [0.74, 1.90] 1.11 [0.70, 1.78] 0.99 [0.64, 1.54] 0.680 160/499 170/552 292/1,037 0.71 [0.53, 0.95] 0.73 [0.53, 0.99] 0.68 [0.51, 0.89] 0.025 141/1,750 125/1,294 206/1,473 0.93 [0.65, 1.33] 0.90 [0.62, 1.31] 0.89 [0.63, 1.24] 0.482 86/1,235 103/1,451 165/2,145 0.67 [0.47, 0.97] 0.68 [0.47, 0.98] 0.58 [0.42, 0.81] 0.004 144/2,338 158/2,148 244/2,695 0.80 [0.60, 1.07] 0.82 [0.61, 1.10] 0.72 [0.55, 0.95] 0.038 83/647 70/597 127/923 0.72 [0.44, 1.18] 0.65 [0.38, 1.09] 0.65 [0.40, 1.05] 0.144 142/1,557 148/1,373 246/1,701 0.79 [0.58, 1.09] 0.80 [0.59, 1.09] 0.76 [0.58, 1.02] 0.140 85/1,428 80/1,372 125/1,917 0.84 [0.55, 1.27] 0.79 [0.50, 1.26] 0.65 [0.43, 0.99] 0.038 72/1,576 121/1,532 �Median (183.5 g/d), n = 6,083 Deaths, n Model 3b >Median (183.5 g/d), n = 6,078 Deaths, n Model 3b Adherence to the Mediterranean diet without including alcohol 0.71 [0.50, 1.02] 82 [0.58, 1.17] 106/1,453 69/1,237 1 (ref.) 1 (ref.) 125/1,287 198/1,688 0.93 [0.65, 1.32] 0.75 [0.54, 1.03] 0.113 103/1,458 173/1,930 0.58 [0.39, 0.85] 0.64 [0.45, 0.92] 0.043 �Median (4), n = 7,400 Deaths, n Model 3b >Median (4), n = 4,761 Deaths, n Model 3b 82/2,002 1 (ref.) 59/811 1 (ref.) 123/1,843 117/1,557 181/1,998 0.84 [0.59, 1.19] 0.94 [0.66, 1.35] 0.77 [0.55, 1.07] 0.175 104/1,142 111/1,188 190/1,620 0.74 [0.51, 1.08] 0.63 [0.43, 0.90] 0.66 [0.47, 0.92] 0.033 0.364 0.287 0.932 0.603 0.284 0.325 (Continued ) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 9 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Table 5. (Continued) Total mortality Quartile 1 HR (95% CI) (Less healthy) Quartile 2 HR (95% CI) Quartile 3 HR (95% CI) Quartile 4 HR (95% CI) (Healthier) p for linear trend p for interactiona Prevalence of chronic conditions No, n = 8,151 Deaths, n Model 3b Yes, n = 4,010 Deaths, n Model 3b 45/2,143 1 (ref.) 96/670 1 (ref.) 82/2,124 70/1,811 114/2,073 1.15 [0.73, 1.81] 1.18 [0.75, 1.87] 1.16 [0.74, 1.81] 0.616 0.030 145/861 158/934 257/1,545 0.67 [0.49, 0.91] 0.63 [0.46, 0.86] 0.57 [0.43, 0.76] <0.001 ap for interaction was calculated using the Wald test. bModel 3 was adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (<25, �25 and �30, >30 kg/m2), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and �2), number of medications (0, 1–3, >3), adherence to the Mediterranean diet without including alcohol (maximum score = 8) as appropriate. Age was the underlying time metric. BMI, body mass index; CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio; METs-hour/week, metabolic equivalents in hours per week. https://doi.org/10.1371/journal.pmed.1004337.t005 After excluding the first 3 years of follow-up, the inverse association between the adherence to the HBS and total mortality remained similar (S2 Table). When assessing dose–response, a linear rela- tionship was observed using restricted cubic splines (p value for non-linearity = 0.010) (Fig 1). When individual HBS items were analyzed using Model 3 for adjustment, a higher con- sumption of coffee and tea, and no consumption of fruit juices and artificially sweetened bev- erages contributed most to the association with lower all-cause mortality (Fig 2). Unadjusted results are also shown (S3 Fig). Discussion In this large population-based study of Spanish adults with a mean follow-up of 12.5 years, a higher adherence to the HBS was inversely associated with total mortality, after adjusting for potential confounders. Those with higher adherence to the HBS had an 8.3% reduction in the absolute risk of death compared to those with lower adherence. The association was linear and robust. It may also be of particular interest to people with preexisting chronic conditions, as they had lower mortality, although these findings need to be confirmed in future research. Regarding to items of the HBS, 2 recent prospective studies performed in Spain found an inverse association between coffee consumption and all-cause mortality [20,21]. Results from the EPIC study (with 500,000 participants from 10 European countries) [22] and from the UK Biobank study were also similar to the findings in this study [23]. Our results are also in line with meta-analyses comprising cross-sectional studies, longitudinal cohorts, as well as inter- ventional studies [24–26]. The beneficial effect of coffee might rely, among others, on the anti- oxidant and anti-inflammatory activity exhibited by its bioactive components, mainly melanoidins, chlorogenic acids, and caffeine [27]. These compounds reduce oxidative stress and inflammation [28], enhance endothelial function [29], and counteract carcinogenesis on in vitro studies [30]. Coffee also increases the metabolic rate [31], improves the glucose metab- olism [32], and lowers long-term blood pressure [33]. Moreover, coffee could reduce mortality even in those with impaired caffeine metabolism [34] and independently to the addition of sweeteners [35]. However, high coffee consumption has been associated with an increase in PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 10 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Fig 1. Adjusted restricted cubic splines of the association of the HBS with mortality risk in the ENRICA Study from baseline (2008–2010) to January 2022 (N = 12,161). Lines are restricted cubic splines, showing the dose–response association of the HBS with mortality. The solid line represents the HR, and the dashed lines indicate the lower and upper 95% CIs. The knots were located at the 10th, 50th, and 90th percentiles (corresponding to HBS scores 17, 22, and 25, respectively); p for non-linearity = 0.010. Adjusted as in Model 3. Data were adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (<25, �25 and �30, >30 kg/m2), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and �2), number of medications (0, 1–3, >3), and adherence to the Mediterranean diet without including alcohol (maximum score = 8). Age was the underlying time metric. https://doi.org/10.1371/journal.pmed.1004337.g001 serum levels of total cholesterol, LDL-cholesterol, and triglycerides [36]. On the other hand, evidence suggests that coffee consumption above 4 cups/day is not associated with further lower mortality [25]. Spain is included among the European countries with the lowest tea consumption [37] and we did not find studies that evaluated its relationship with mortality among Spanish adults. Therefore, it is unlikely that tea consumption accounts for our results. However, in literature, both all-cause and cardiovascular mortality were reduced among tea consumers [38]. The effect of milk on health has been widely studied due to its fatty acid composition [39]. Two recent cohort studies showed that low-fat milk consumption was associated with lower all-cause mortality when compared to whole-fat milk consumption [40,41]. Whole-fat milk has a higher content of saturated fats that has been related to an increase in LDL-cholesterol and atherosclerosis [42]. As a result, whole-fat milk consumption could be particularly harmful among individuals with known cardiovascular risk. However, in a clinical trial among normo- cholesterolemic individuals, whole-fat milk consumption showed no impairment in lipid pro- file nor in glucose-insulin metabolism when compared to low-fat milk consumption [43]. It is of note that, milk could modulate satiety mechanisms [44] and also has several components with potential beneficial effects, such as caseins with antioxidant properties [45]. Milk is also rich in minerals, vitamins, and other bioactive compounds involved in anti-inflammatory and PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 11 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality Fig 2. Adjusted mortality risk for individual items of the HBS when comparing extreme categories (quartile 4 vs. quartile 1) in the ENRICA Study from baseline (2008–2010) to January 2022 (N = 12,161). Adjusted as in Model 3. Data were adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (<25, �25 and �30, >30 kg/ m2), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/ no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and �2), number of medications (0, 1–3, >3), adherence to the Mediterranean diet without including alcohol (maximum score = 8), and for the rest of items of the HBS (as appropriate). Age was the underlying time metric. CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio. https://doi.org/10.1371/journal.pmed.1004337.g002 immune regulation [46]. A recent meta-analysis showed that whole-fat milk consumption was associated with a higher risk of all-cause, cardiovascular disease and cancer mortality; however, low-fat milk showed a protective but nonsignificant association [47]. Results on fruit juice consumption and mortality mostly depend on the distinction between processed or fresh fruit juice [48]. A recent meta-analysis showed that processed fruit juice consumption was associated with a higher risk of type 2 diabetes and total mortality [49]. Evi- dence on fresh fruit juice consumption and health, however, is insufficient to draw conclu- sions. Results of a cohort study from the US with 13,440 participants showed that a higher consumption of 100% fruit juice was associated with a higher mortality [50]. Conversely, a study with 198,285 individuals from the UK found a positive association between sugar-sweet- ened beverages and mortality, but not for 100% fruit juice consumption [51]. Similarly, a recent meta-analysis of prospective cohorts concluded that there was no association between 100% fruit juice consumption and all-cause mortality [52]. Several studies have also found PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 12 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality that, compared with 100% fruit juice, sugary or processed fruit beverages produce harmful glu- cose levels after ingestion, mainly due to the higher content of free sugars [53]. In our study, bottled, sweetened, as well as fresh fruit juices were analyzed together as a unique item because of their rapid absorption [8] and similar effect on postprandial glucose levels [54]. Fructose intake, particularly from sugar-sweetened beverages at any dose, or from fruit juice at higher doses, contributes a rapid extra dietary energy source that could explain its detrimental effect on health [55]. However, food-based dietary guidelines from various countries from Europe Union, including Spain, consent to replace occasionally 1 daily portion of fruit with fresh fruit juice [56,57]. In order to lower calorie intake and control body weight, artificially sweetened beverages could be adequate short-term substitutes [58]. However, when considering the long-term influence of artificially sweetened beverage consumption, several studies have found associa- tions with higher obesity, hypertension, type 2 diabetes, stroke, cardiovascular disease inci- dence and mortality, and all-cause mortality [59–61]. Since these beverages contain few to no calories nor sugars [51], some investigations have related them with weight gain as a result of an increased consumption of sweet food due to a greater affinity for sweet flavors or the per- ception of eating fewer calories [62]. In addition, their flavoring components have been associ- ated with the formation of advanced glycation end-products [63], which are involved in the development of metabolic diseases [64]. Moreover, some sweeteners such as sucralose and sac- charin could induce glucose intolerance and alterations in gut microbiota [65] that are linked to obesity [66]. In literature, a low to moderate alcohol consumption is related to a reduction in all-cause mortality [67]. Biological explanations for this protective role on health are based on lipid reg- ulation, insulin response, and endothelial function [68] resulting from the modulation of some anti-inflammatory biomarkers [69]. However, at high doses, alcohol is detrimental to cardio- vascular health and is related to several types of cancer [70]. A harmful alcohol consumption is associated to neurodegenerative processes [71], microbial dysbiosis [70], and an increased intestinal permeability that leads to a permanent hepatic exposure to bacterial translocation, oxidative stress, and other inflammatory components [72]. In addition, alcohol use could result in hepatic steatosis and de novo lipogenesis, and also could reduce the utilization of lip- ids [73]. Lastly, alcohol may injure myocardium with potential cardiomyopathy and heart fail- ure [74], and increase the risk of hypertension [75]. On the other hand, the beneficial association of alcohol consumption with mortality found in some studies may rely on absti- nence bias, insufficient adjustment for covariates, or consumption changes due to disease detection [76,77]. However, recent studies from an epigenetic perspective have proposed that alcohol at restricted doses could be particularly beneficial in older adults based on changes in alcohol metabolism related to age [78]. In this regard, a meta-analysis for the Global Burden of Disease Study has proposed a change from sex-specific to age-specific recommendations on alcohol consumption [79]. Then, low alcohol consumption could be beneficial among older adults, but not for younger adults. For our analyses, we considered that being a heavy drinker was harmful because of its well-established association. In a previous study, a 10-item Healthy Beverage Index was constructed using an a priori approach based on the US recommendations for beverage consumption [11]. Similar to this index, Hu and colleagues described the HBS as a more suitable score for use in large epidemio- logical studies [12]. The HBS excluded water consumption as well as 2 items on total energy from beverages and calculations of daily fluid intake. In our study, the same scoring weights (from 1 to 4) were maintained for all items in the HBS. However, in contrast to Hu and col- leagues, we considered both no alcohol consumption and moderate alcohol consumption as healthy. Additionally, we also modified the HBS cut-off points of the items to fit with the PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 13 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality beverage consumption of the Spanish adult population. As a result, the HBS used in our study retained the same items as originally described by Hu and colleagues, as well as the relative weight of the items. The use of the HBS as an overall measure of beverage consumption has several advantages. First, the use of this score overcomes the limitations of analyses of relationships between indi- vidual beverages and diseases, as beverage consumption may be correlated, and an increase in consumption of one beverage may be associated with a decrease in the others. Secondly, the HBS could be a complementary tool for assessing adherence to dietary patterns that include only solid foods, in order to assess dietary quality as a whole. Thus, the HBS could serve as a simple and rapid screener to obtain information on the quality of beverage consumption from the general population, similar to other indexes used to assess adherence to certain diets, such as the Mediterranean diet. Also, the use of this 7-item pattern may be an optimal choice when dealing with patients in time-constrained clinical settings. Finally, the HBS includes items on commonly consumed beverages and could be easily adapted to other populations with only minor modifications to account for their specific beverage consumption. We have used the HBS in the general population and caution should be exercised in deriv- ing beverage consumption recommendations from this score in specific populations, especially those with restricted fluid requirements, long-term liquid diets, and other preexisting condi- tions involving fluid consumption. Further studies are also needed in specific population sub- groups. In addition, a future study could consider intercorrelations and specific population- based patterns of beverage consumption using an a posteriori approach. This study has some limitations. First, when measuring diet, non-differential misclassifica- tion of the exposure is always possible, in general, resulting in an underestimation of the asso- ciations found. Second, no information concerning behavioral changes or repeated measurements on beverage consumption were available, and beverage consumption could have changed during follow-up. Third, water consumption was not available in this study. Water is universally recommended as a safe beverage and as the main source of hydration. As water does not provide energy, macronutrients or micronutrients, its consumption is consid- ered free for the general population. There are also some strengths. To our knowledge, this is the first examination on the rela- tion between a healthy beverage score and all-cause mortality among the Spanish adult popula- tion. In addition, we used a dietary history that allowed us to collect information on beverages with validity and reproducibility in a Spanish population. Also, the national vital statistics rec- ords, accessed through linkage to the Spanish National Death Index, ensured an extensive fol- low-up of the cohort for mortality assessment. Finally, several confounders were considered in more adjusted models. In conclusion, in this representative study of the Spanish adult population, higher adher- ence to the HBS was associated with a reduction in total mortality. As the consumption of a healthy solid diet should be encouraged, adherence to a healthy beverage consumption pattern may also play an important role in the prevention of premature mortality as part of public health nutrition prevention strategies. Supporting information S1 STROBE Checklist. STROBE Checklist. (DOCX) S1 Text. Prespecified analysis plan and modifications. (DOCX) PLOS Medicine \| https://doi.org/10.1371/journal.pmed.1004337 January 23, 2024 14 / 20 PLOS MEDICINEA healthy beverage score is associated with lower total mortality S1 Table. Sex-specific cut-off points for individual items of the Healthy Beverage Score (HBS) in the ENRICA Study (2008–2010) (N = 12,161). (DOCX) S2 Table. Mortality risk according to quartiles of the adherence to the Healthy Beverage Score (HBS) in the ENRICA Study from baseline (2008–2010) to January 2022 (N = 12,161) excluding the first 3 years of follow-up. (DOCX) S1 Fig. Flow diagram. (TIF) S2 Fig. Unadjusted restricted cubic splines of the association of the Healthy Beverage Score (HBS) with mortality risk in the ENRICA Study from baseline (2008-2010) to Janu- ary 2022 (N = 12,161). Lines are restricted cubic splines, showing the dose-response associa- tion of the Healthy Beverage Score (HBS) with mortality. The solid line represents the hazard ratio (HR), and the dashed lines indicate the lower and upper 95% confidence intervals. The knots were located at the 10th, 50th, and 90th percentiles (corresponding to HBS scores 17, 22 and 25, respectively). p for non-linearity = 0.003. (TIF) S3 Fig. Unadjusted mortality risk for individual items of the Healthy Beverage Score (HBS) when comparing extreme categories (quartile 4 vs. quartile 1) in the ENRICA Study from baseline (2008-2010) to January 2022 (N = 12,161). HBS, Healthy Beverage Score; HR, hazard ratio; CI, confidence interval. (TIF) Author Contributions Conceptualization: Pilar Guallar-Castillo´n. 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10.1371_journal.pclm.0000362	RESEARCH ARTICLE Climate factors associated with cancer incidence: An ecological study covering 33 cancers from population-based registries in 37 countries Haowen Wang1☯, Hongmei Zeng2☯, Hui Miao3, Chang ShuID 4, Yuming Guo5, John S. JiID 1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Vanke School of Public Health, Tsinghua University, Beijing, China, 2 National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 3 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America, 4 Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 5 School of Public Health and Preventive Medicine, Monash University, Victoria, Australia ☯ These authors contributed equally to this work. * johnji@tsinghua.edu.cn OPEN ACCESS Abstract Citation: Wang H, Zeng H, Miao H, Shu C, Guo Y, Ji JS (2024) Climate factors associated with cancer incidence: An ecological study covering 33 cancers from population-based registries in 37 countries. PLOS Clim 3(3): e0000362. https://doi.org/ 10.1371/journal.pclm.0000362 Editor: Noureddine Benkeblia, University of the West Indies, JAMAICA Received: October 22, 2023 Accepted: February 20, 2024 Published: March 28, 2024 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pclm.0000362 Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The cancer incidence, NDVI, precipitation, temperature, solar radiation, ozone concentration, population density, and other covariates are available online: CI5PLUS: CANCER Cancer etiology is multifactorial, with climate change and environmental factors such as extreme weather events and ozone layer destruction potentially increasing cancer risk. Investi- gating climate factors with cancer incidence can provide valuable insights for prevention and future disease burden prediction. We conducted a population-based ecological study using data from the World Health Organization’s Cancer Incidence in Five Continents (CI5plus, 89 cancer registries from 1998 to 2012) and the Surveillance, Epidemiology, and End Results (SEER, 607 US counties from 2000 to 2018) Program. We tracked climate factors through sat- ellite-based remote sensing, including green space, stratospheric ozone concentration, solar radiation, precipitation, and temperature. We performed linear panel regression models to esti- mate the effects of both long-term exposure, lag effect, and change rate of climate factors on cancer incidences. We adjusted for smoking prevalence, air pollution, and gross domestic product (GDP) per capita to account for potential confounding factors. Our study included more than 430 million underlying populations across 37 countries. Higher green space exposure (per 0.1-unit normalized difference vegetation index, NDVI) was associated with decreased inci- dence of lung cancer (up to 6.66 cases [95%CI 4.38–8.93] per 100,000) and prostate cancer (up to 10.84 cases [95% CI 7.73–13.95] per 100,000). Increased solar radiation was associ- ated with a higher incidence of melanoma, but a lower incidence of prostate cancer. No evi- dence was found to suggest associations between temperature or precipitation and cancer incidence. However, a rapid increase in temperature was linked to higher incidences of corpus uteri cancer and melanoma. Long-term exposure and rapid changes in climate factors may influence changes in cancer incidence, particularly lung and prostate cancers. While some associations were supported by existing evidence (such as solar radiation and melanoma), fur- ther research is necessary to investigate the etiology of novel cancer risk factors. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 1 / 17 PLOS CLIMATEINCIDENCE IN FIVE CONTINENTS TIME TRENDS: https://ci5.iarc.fr/CI5plus/Pages/download.aspx; The Surveillance, Epidemiology, and End Results (SEER) Program: https://seer.cancer.gov/data/; USGS Landsat 5 TM Collection 2 Tier 1 TOA Reflectance, USGS Landsat 7 Collection 2 Tier 1 TOA Reflectance: https://www.usgs.gov/landsat- missions/landsat-data-access; ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis: https:// cds.climate.copernicus.eu/cdsapp#!/search?type= dataset; TOMS and OMI Merged Ozone Data: https://developers.google.com/earth-engine/ datasets/catalog/TOMS_MERGED; GHS-POP R2023A - GHS population grid multitemporal (1975-2030): https://data.jrc.ec.europa.eu/dataset/ 2ff68a52-5b5b-4a22-8f40-c41da8332cfe; Global/ Regional estimates (V5.GL.03) of surface PM2.5: https://sites.wustl.edu/acag/datasets/surface-pm2- 5/; CEIC: Global Economic Data, Indicators, Charts & Forecasts https://www.ceicdata.com/en/ products; Global Burden of Disease Study 2019 (GBD 2019) Smoking Tobacco Use Prevalence 1990-2019: https://ghdx.healthdata.org/record/ ihme-data/gbd-2019-smoking-tobacco-use- prevalence-1990-2019; Code availability: https:// github.com/johnjiresearchlab/ ClimateChangeCancer. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Climate factors associated with cancer incidence Introduction Climate change can affect cancer incidence through direct and indirect pathways [1,2]. Direct pathways involve exposure to risk factors that are influenced by climate factors, such as increased exposure to ultraviolet radiation from the sun due to the depletion of the ozone layer. Indirect pathways involve changes in health’s social and environmental determinants that affect cancer risk, such as disrupting access to cancer healthcare services. The Intergovern- mental Panel on Climate Change (IPCC) stated in the Sixth Assessment Report, Climate Change 2021: The Physical Science Basis, that human influence has unequivocally warmed the atmosphere, ocean, and land. Downstream changes in the atmosphere, ocean, cryosphere, and biosphere can also impact cancer occurrence through the air, drinking water, food supply, or epidemic of infectious diseases. The long-term influence of climate variables on cancer rates is still not well understood, especially the impact on cancer incidence, which can take years or decades to develop. Most current studies focus on the immediate effects of climate change, such as emergency hospi- tal visits, rather than on chronic diseases like cancer [3]. These research studies have shown associations between non-optimal environmental conditions (e.g., air pollution) and cancer incidence (e.g., lung cancer) [4], the effect of climate change extends beyond direct carcino- gen changes. The ecological aspects, such as green space change, global warming, ozone depletion, rain patterns, and extreme climate events, indicate a need to systematically understand the diverse impacts of climate change on human health [5,6]. Cancer develop- ment is a long and complex process, influenced by environmental factors, lifestyle choices, and genetic predispositions. Climate change’s fluctuations and long-term effects, which may alter cancer rates, have become a significant area of interest. The impacts of these changes could potentially overshadow the efforts made in cancer prevention, screening, and treatment [7]. In this study, we leverage the comprehensive data in global cancer registries to explore the influence of climate factors on cancer incidence. These registries provide extensive, pop- ulation-based data, enabling us to examine large-scale patterns and associations with high spatial and temporal resolution. We estimated climate-related factors with long-period accessibility in satellite-based products including temperature, precipitation, solar radia- tion, total column ozone concentration, and green space, defining our exposure variables as the rate of change or long-term average to capture the prevalent climate and changes over time effectively. Our analysis incorporated multiple panel models for 33 major cancer types, a selection that covers the most common and deadliest cancers globally. We looked for pos- sible effect modification by age or gender groups. This research is a novel global analysis delving into the associations between cancer incidence, the rate of climate change, and long-term environmental exposure. Materials and methods Study design We used a global ecological study design by constructing a panel dataset that compiled data on cancer incidence, demographic information, climate factors, regional economy, and popula- tion behavior, with more than 430 million underlying populations in 37 countries. The base- line climate scenario in this study is defined as the 10-year average before the first registration year of each cancer registry. We calculated the 3-year moving average of each climate factor with 0-to-10-year lags and the growth rate of climate change using the ratio of the 3-year mov- ing average and baseline climate scenario, to assess their effect on cancer incidence. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 2 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Cancer case ascertainment This ecological study included cancer registries from two datasets: Cancer Incidence in Five Con- tinents, CI5plus: IARC CancerBase No. 9 publication, and the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute [Incidence—SEER Research Plus Data, 18 Registries, Nov 2020 Sub (2000–2018)]. The Cancer Incidence in Five Continents (CI5) series is a registry-based data source for the evolving trends in global cancer incidence, contrib- uted by the International Agency for Research on Cancer (IARC) and the International Associa- tion of Cancer Registries (IACR). The CI5 databases offer in-depth data on cancer incidences as documented by community-based cancer registries, at a national or subnational level. The CI5plus provided annual incidence rates for 124 selected populations from 108 cancer registries (from 1998 to 2012), and SEER provided cancer incidences for 33 cancer types of 612 US counties, with 19 consecutive years (from 2000 to 2018). The geographical boundary of each location was extracted from The United States Census Bureau TIGER 2018 dataset and The Global Adminis- trative Unit Layers (GAUL) 500m dataset. We excluded cancer registries that were not defined as administrative units, or not covered in the database to be matched. 33 major cancer types and one category for all cancers were included and filtered based on their ICD-10 code (Table 1). In both registry databases, we ascertained age-standardized cancer incidences (per 100,000), by dividing the number of newly reported cancer patients each year by the total pop- ulation, of each age group. The age-standardized incidence was calculated using the world standard population introduced by Segi (1960) [8]. Climate exposure assessment We used available satellite imagery and tools for analysis from Google Earth Engine, with extraction since 1985 (S1 Table). Due to uneven distribution of climatic factors at the geo- graphical level, we weighted the climatic factors according to the population distribution within the administrative boundaries to obtain regional exposure levels. Our analysis consid- ered population-density-weighted greenspace, temperature, stratospheric ozone concentra- tion, surface net solar radiation, and precipitation as climate factors. We used population density data from Global Human Settlement Layers, Settlement Grid 1975-1990-2000-2014. To quantify residential green space, we utilized the normalized difference vegetation index (NDVI) calculated based on satellite images from Landsat-5 and Landsat-7 images from the U. S. Geological Survey. The data provides information on calibrated top-of-atmosphere (TOA) reflectance, with a resolution of 30 meters. Annual stratospheric ozone concentrations were calculated using TOMS and OMI Merged Ozone Data produced by the Laboratory for Atmo- spheres at NASA’s Goddard Space Flight Center. This satellite-based observation of total col- umn ozone concentration provides level 3 gridded data (1.0˚ x 1.25˚) for regional trends in ozone concentration, spanning from 1978 to the present. Temperature, precipitation, and sur- face net solar radiation measurements were sourced from ERA5-Land Daily Aggregated— ECMWF Climate Reanalysis [9]. The data provided aggregated values of 50 land climate vari- ables from 1963 to the present. Covariate Covariates were included in the models to account for potential confounding and grouped to test for effect modification. Smoking status and particulate matter with diameter not greater than 2.5 μm (PM2.5) were important risk factors for certain cancer types, such as lung cancer. The annual and monthly ground-level concentration of fine particulate matter (PM2.5) from 1998 to 2020 was estimated by Washington University in St. Louis estimations, by merging Aerosol Optical Depth (AOD) data retrieved from NASA MODIS, MISR, and SeaWiFS PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 3 / 17 PLOS CLIMATETable 1. ICD-10 code of cancer types considered in the analysis and averaged incidence by gender (age-standardized rates per 100,000) during the research period. Cancer site All cancers except non-melanoma skin cancer ICD-10 code C00-96/C44 Incidence, male Incidence, female 309.25 244.86 Climate factors associated with cancer incidence Lip and oral cavity Salivary glands Nasopharynx Hypopharynx Esophagus Stomach Colorectal Liver Gallbladder Pancreas Larynx Lung, bronchus, and trachea Melanoma of the skin Mesothelioma Kaposi sarcoma Breast Vulva Vagina Cervix uteri Corpus uteri Ovary Penis Prostate Testis Kidney Bladder Brain and central nervous system Thyroid Hodgkin lymphoma Non-Hodgkin lymphoma Multiple myeloma Leukemia https://doi.org/10.1371/journal.pclm.0000362.t001 C00-06 C07-08 C11 C12-13 C15 C16 C09-10 C22 C23 C25 C32 C33-34 C43 C45 C46 C50 C51 C52 C53 C54 C56 C60 C61 C62 C64-65 C67 C70-72 C73 C81 C82-86, C96 C88+C90 C91-95 1.02 0.80 0.99 1.29 5.68 14.65 24.89 10.84 2.04 7.76 4.78 43.65 12.74 1.17 0.51 0.61 69.67 4.91 10.86 16.45 2.07 3.68 2.25 11.57 3.63 5.32 0.28 0.57 0.37 0.2 1.3 6.6 17.92 3.38 2.14 5.45 0.65 21.4 10.42 0.28 0.05 64.94 1.06 0.33 9.93 11.23 8.81 5.39 3.91 1.58 12.47 1.81 8.16 2.51 3.27 instruments with the GEOS-Chem chemical transport model [10]. Annual smoking preva- lence by country and gender was extracted from the Global Burden of Disease (GBD) study 2019 [11]. We also included regional gross domestic product per capita as a covariate to reflect economic status, which can influence disease occurrence through improved disease screening conditions and medical care services. These data were sourced from the World Bank and CEIC: Global Eco- nomic Data, Indicators, Charts & Forecasts. In cases where data for the corresponding adminis- trative units were missing, we utilized data from higher-level administrative units instead. Statistical analysis In the descriptive analysis, we calculate the recent trend of incidence of all the cancer types by annual incidence change, using a two-stage modeling framework. This approach has been PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 4 / 17 PLOS CLIMATEClimate factors associated with cancer incidence described previously [12]. In the first stage, the linear regression models for each location and cancer type were constructed to assess the relationship between the cancer incidence and year. The coefficients from these regression models represent the estimated annual change in cancer incidence for each specific cancer type in each specific geographical location. In the second stage, a univariate meta-regression model was built using the location-specific estimates from the first-stage analysis, weighted by the population [13]. The two-stage analysis is performed in regions and age groups. The models are as follows: First stage model : Nij � aij þ bij∗t Second stage model : bij � Nðm; tau2=P iÞ where Nij is the incidence of cancer type j in location i, t is the year, aij is the intercept, bij is the coefficient, m is the overall annual change in cancer incidence, and tau^2 is the between-study variance, representing the variability in annual changes across different locations and cancer types. P_i is the average population in location i. In the main analysis, multivariable panel linear regression models were constructed to investigate the relationships between climate factors and cancer incidence, adjusted for smok- ing prevalence, PM2.5 concentration, and Gross Domestic Product (GDP) per capita [14,15]. We considered the rate of climate change and long-term lagged effects, and all the outcomes (33 cancer types) in the models separately. First, we considered the 3-year moving average of each climate factor with 0-to-10-year lags in the panel regression models. In this model, the exposure period with the greatest impact on cancer incidence was identified. Next, we exam- ined the effect of the rate of climate change (the ratio of the 3-year moving average and base- line climate scenario), adjusted for the climate factors tested in the first model. 95% Cis were then estimated for both models. The models are as follows: 1. L_3yrn = (Lagn-1+Lagn+Lagn+1)/3, n = 0, 1, . . ., 10 2. Baseline climate scenario ¼ XT X1t=10 T(cid:0) 9 3. X1 crt ¼ L13yr0;it =Baseline climate scenario 4. - First main model : Nit ¼ b0 þ b1∗L13yrn;it þ b4∗Smokingit þ mi þ εit; n ¼ 0; 1; . . . ; 10 X10 n¼0 5. - b∗L03yr n;it þ b2∗GDPit þ b3∗PM2:5it þ Second main model : Nit ¼ b0 þ b1∗X1 crit þ b2∗X2 crit þ b3∗X3 crit þ b4∗X4 crit þ b5∗GDPit þ b6∗PM2:5it þ b7∗Smokingit þ mi þ εit Where Nit is the incidence of a certain cancer type in location i and year t, L13yr,n is the 3-year average of climate factor 1 with lag n year(s), L0 is the 3-year average of other climate factors, X1 the value of climate factor 1, T is the start registration year of the cancer registries, X1_cr is the change rate of the climate factors 1. The fixed effects for location i are represented by μi, and the error term is represented by εit. The models are adjusted for smoking prevalence, PM2.5, and GDP per capita. To account for multiple comparisons in the models, we per- formed false discovery rate (FDR) correction in R 4.2 [16] to reduce the likelihood of false-pos- itive results in the regression outcomes. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 5 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Sensitivity analysis Sensitivity analyses were done using different model designs, like the spatial panel regression model. We did not include this model in the main study design, because the spatial panel lin- ear model only applied to the balanced panel data. In the spatial panel linear model, the k- nearest neighbor spatial weights matrix is generated according to the latitude and longitude of each region. The effect modifications by gender and age were tested by performing the models in different gender or age group. Moreover, the quadratic term for climate factors was added to models to test non-linear relationships and identify the optimal range. Results Our study included 696 regions from 37 countries, covering 430 million people in the underly- ing population. Table 2 provides a detailed breakdown of the locations of country-specific can- cer registries, the annual tally of cancer cases, and the annually varying population size over the duration of the study. Our study locations are in North America, South America, Asia, Europe, and Oceania, with locations in the U.S. covering the largest population and number of sites. Temperature and solar radiation were negatively related to latitude, and stratospheric ozone concentration was higher in high-latitude regions. The distributions of NDVI and pre- cipitation reflect climate zones; for example, in the Ko¨ppen climate classification system, NDVI is generally higher in zone A (tropical or equatorial zone) and zone C (warm/mild tem- perate zone) but lower in zone B (arid or dry zone) [17] (Fig 1). During our study period (1998 to 2012/2000 to 2018), cancer incidence (per 100000) dem- onstrated diverse trends across different geographic regions (Fig 2). Notably, breast and thy- roid cancers witnessed the steepest rise globally among females. The significant rise in cancer incidence within younger age groups in Asia also demands attention. In contrast, for the male demographic, data from the American registry (primarily sourced from the United States) dis- played encouraging strides in curtailing prostate cancer incidence, while liver cancer incidence sustained an upward trend. More broadly, our observations indicated a greater array of cancer types demonstrating significant increases in incidence among females, highlighting that the cancer burden varies discernibly between genders. The associations between climate factors and the risk of cancers were described (Fig 3). Ele- vated greenness, measured by NDVI (per 0.1-unit), is linked to decreased incidence of prostate cancer (up to 10.84 cases [95% CI 7.73–13.95] per 100,000 population), lung cancer (up to 6.66 cases [95%CI 4.38–8.93]), and colorectal cancer (up to 3.60 cases [95% CI 1.67–5.53]). Consid- ering the lag period, the association between NDVI and cancer incidence typically reaches its peak effect size around 8–9 years prior, following a U-shaped pattern (Fig 4). Our model esti- mates that a 0.1-unit increase in 3-year average NDVI during a lag period of 8 years is associ- ated with a decrease of 10.84 cases of prostate cancer per 100,000 population, accounting for 12.2% of the average annual incidence (88.6 per 100,000 population). Surface net solar radia- tion is positively associated with melanoma of the skin in both males (up to 3.20 cases [95% CI 1.34–5.06]) and females (up to 8.06 cases [95% CI 1.70–14.42], in population aged 45 to 59), as expected. The intensity of solar radiation occurring 6–7 years ago has the greatest impact on the incidence of melanoma in the current year, as demonstrated by different exposure win- dows (Fig 4). The correlation between temperature and cancer incidence showed variation based on whether temperatures fell within optimal ranges. By incorporating a quadratic term for tem- perature in our model, we noted a significant correlation with prostate cancer incidence (lag = 7, coefficient of quadratic term is 0.29 [95%CI 0.10–0.48]). Within the optimal tempera- ture ranges (annual temperatures below 19.9 degrees Celsius), warmer climates were PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 6 / 17 PLOS CLIMATETable 2. Baseline characteristics of the study population. Country Locations (n) Period Average population coverage (n/year) Average total cancer cases (n/year) Climate factors associated with cancer incidence Australia Austria Brazil Bulgaria Chile China Colombia Costa Rica Croatia Czech Republic Denmark Ecuador Estonia France Germany Iceland India Israel Italy Japan Kuwait Lithuania Malta Netherlands New Zealand Norway Philippines Poland Republic of Korea Slovakia Slovenia Spain Switzerland Thailand Turkey U.K. of Great Britain and Northern Ireland 7 3 1 1 1 4 1 1 1 1 1 1 1 10 2 1 1 1 8 4 1 1 1 1 1 1 1 1 5 1 1 8 5 4 2 3 USA 608 https://doi.org/10.1371/journal.pclm.0000362.t002 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 1998–2012 2000–2018 20458788 1058615 1193254 7768022 371350 8951762 2041874 3937504 4403036 10330106 5432902 1409401 1356986 7739032 2783621 299637 4473938 6835882 5571039 13174317 978655 3373173 401720 16282261 4109706 4669549 5915876 1288473 18895163 4676249 2013277 6503994 1872646 5352543 5293040 14495579 85212605 101312 36966 2510 28770 863 28720 4074 6001 20829 51133 29291 2206 6046 38734 16015 1233 4757 23113 35405 68755 783 14438 1519 79833 18894 23312 7360 4568 55132 17695 9431 30859 9758 8431 10986 62805 408166 associated with a decline in prostate cancer incidence. In contrast, regions with higher annual temperatures (annual temperature over 19.9 degrees Celsius), which presumably experience more frequent hot days beyond the optimal temperature range, exhibited an inverse trend. Air pollution also showed a significant association with cancer incidences, a 10 μg/m3 increment in PM2.5 was associated with the increasing incidence of lung cancer (8.07 cases [95% CI 6.49–9.65), prostate cancer (9.78 cases [95% CI 7.69–11.87]), and colorectal cancer (2.36 cases [95% CI 1.10–3.64]). Associations between precipitation or ozone levels and cancer incidence did not present strong evidence in our analysis. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 7 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Fig 1. Global distribution of climate variables and their changes across cancer registries (averaged for the study periods, 1998-2012/2000-2018). The climate variables were calculated by taking the annual average of daily measurements. Source of the base layer of the map: https://www.naturalearthdata.com/ downloads/10m-cultural-vectors/10m-admin-0-countries/. A. Greenness: Normalized difference vegetation index (NDVI), gridded at a resolution of 0.05˚. B. Total ozone: Average of primary long-term, continuous record of satellite-based observations of total column ozone (DU). C. Precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth’s surface (mm). D. Surface net solar radiation: The amount of net solar radiation reaching the surface of the Earth (10^6 Jm-2). E. Skin temperature: Temperature of the surface of the Earth (K). https://doi.org/10.1371/journal.pclm.0000362.g001 Evidence also indicates that the rate of climate change impacts cancer incidence. For instance, the growth rate of NDVI was negatively correlated with the incidence of certain can- cers, such as lung and prostate cancer, although the effect was relatively small. For each 10% faster rate of NDVI growth, compared to the baseline scenario, there would be a reduction of 1.01 cases (95% CI 0.63–1.38) of excess prostate cancer incidence per 100,000 population. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 8 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Fig 2. Distribution and recent trend in cancer incidence (per 100,000) across study locations (from 1998 to 2012/ 2000 to 2018). A. Annual changes in cancer incidence (per 100,000), segregated by age groups, regions, and genders. B and C. Distribution of the annual incidence (per 100,000) of all types of cancers across the study locations. The color scheme indicates the direction of the trend: Red for an increase and blue for a decrease. Results that are not statistically significant are marked with an asterisk (). https://doi.org/10.1371/journal.pclm.0000362.g002 Faster precipitation and temperature rise rates were slightly associated with higher cancer inci- dence in specific cancer types (precipitation: prostate cancer, colorectal cancer, lung cancer; temperature: melanoma, corpus uteri cancer) (Fig 5). However, we did not find a relationship between the rate of change in solar radiation and cancer incidence. Discussion Our study, covering more than 430 million individuals across 37 countries, has identified novel and significant correlations between climate factors and cancer incidences. We found that higher exposure to green spaces and their faster growth rate contributed to a notable decrease in lung and prostate cancer incidences in males. Solar radiation demonstrated a dual role, increasing melanoma incidence while decreasing prostate cancer incidence. Interestingly, PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 9 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Fig 3. Relationships between climate factors and cancer incidence. The color of each cell represents the direction and statistical significance of the respective covariate coefficients: Red signifies positive effects, while blue indicates negative effects. The numbers shown in cells represent the coefficients of climate factors in corresponding models. NDVI: Per 0.1 unit; Precipitation: Per 0.1mm; Temperature: Per 1 K; Solar radiation: Per 10^6 Jm-2. https://doi.org/10.1371/journal.pclm.0000362.g003 a rapid temperature rise was associated with an upsurge in melanoma and corpus uteri cancer incidences. Our results underscore the protective effect of green spaces against cancer, specifi- cally prostate and lung cancers, illuminating the time-lagged effect of environmental greenness on cancer incidence. Of interest in our study is the varying estimates in different exposure periods may be attrib- utable to the period between initial exposure to climate-sensitive factors and cancer diagnosis. For instance, the NDVI lagged effect demonstrated greater significance within an 8–9 years window as opposed to more recent exposures, suggesting that prolonged exposure to high NDVI may yield more potent cancer preventive effects. An emerging number of studies detected the relationship between greenness on prostate cancer [18,19]. This protective effect may be facilitated by the enhancement of air quality, minimization of exposure to environ- mental pollutants, and promotion of physical activity associated with higher NDVI levels [20– 24]. Both prostate and lung cancer occurrence or prognosis may be influenced by these factors [25,26]. Furthermore, etiological evidence points to inflammation as a possible underlying mecha- nism. For example, increased distance-weighted vegetated land cover has been associated with improved neuroendocrine, metabolic, and immune functions, including a reduction in inter- leukin-8 (IL-8) levels. Lower IL-8 levels may impede cancer progression by uncoupling tumor growth from androgen hormone regulation. [27,28]. Despite these findings, the exact mecha- nisms and the magnitude of the NDVI effect remain uncertain. Not all studies, for instance, have consistently found an association between NDVI and lung or prostate cancer incidence, suggesting that more research is needed to understand these complex relationships fully [29]. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 10 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Fig 4. Association between cancer incidence (cases per 100,000 population) and climate factors, analyzed over 10 different lag periods. Each lagged term represents a three-year sliding average. The model accounts for GDP, smoking prevalence, PM2.5 concentration, stratospheric ozone concentration, annual average temperature, precipitation, and surface net solar radiation. A. Relationship with solar radiation (per 10^6 Jm-2). B. Relationship with the Normalized Difference Vegetation Index (NDVI, per 0.1 unit). https://doi.org/10.1371/journal.pclm.0000362.g004 Our analysis revealed significant associations between rapid precipitation growth and the incidence of colorectal cancer, lung cancer, and prostate cancer in males, although the mecha- nisms behind these associations are not fully understood. One possible explanation could be PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 11 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Fig 5. Relationship between the rate of change in climate factors and the incidence of cancer. Predicted changes in the number of cancer cases per 100,000 population, corresponding to each 10% faster increase in climate factors. The color of each cell represents the direction and statistical significance of the corresponding covariate coefficient: Positive effects are depicted in red, while negative effects are shown in blue. The intensity of the color corresponds to the magnitude of the coefficient. Only coefficients with a significance level of p�0.05 are colored. https://doi.org/10.1371/journal.pclm.0000362.g005 the increased microbial contamination in public drinking water and mildew growth on food resulting from heavy precipitation events [30,31]. These conditions could induce gastrointesti- nal diseases [32], potentially increasing cancer risk. However, this complex pathway likely involves numerous factors and warrants further investigation. In addition to environmental PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 12 / 17 PLOS CLIMATEClimate factors associated with cancer incidence factors, dietary habits may also play a role in cancer risk. For instance, previous studies have reported an increased risk of colorectal and gastric cancer associated with a high intake of refined grains [33,34]. Our analysis detected an association between solar radiation, which is also correlated with stratospheric ozone, and skin melanoma incidence—a finding that corroborates numerous research studies. Environmental science measurements in the past four decades found UV radiation, a component of solar radiation, has increased, with ozone depletion identified as a key contributor. UV radiation is a known cancer risk factor, especially skin melanoma [35,36]. Interestingly, we found that solar radiation exhibited more immediate effects on cancer risk compared to the delayed impact of greenness on cancer incidence. This confirms that solar radiation will influence cancer risk more directly, by directly impacting genetic mutations. Notably, we also observed that higher UV radiation levels could potentially reduce the risk of certain cancers, such as prostate cancer. The double-edged sword is of interest, as the protec- tive effect is likely due to the activation of the 1,25-dihydroxy vitamin D synthetic pathway triggered by UV radiation [37–39], through promotion of physical activity. Despite these find- ings, our study did not identify a relationship between the rate of solar radiation change and cancer incidence. This suggests that the relationship between solar radiation and cancer risk may adhere to a strict dose-response model, with any level of increased radiation potentially affecting health outcomes. Non-optimal temperature, often associated with the extremes of cold and heat, is perhaps the area that has the most attention in climate change research, especially on excess mortality [40,41]. Our analysis uncovered nuanced differences in temperature-related cancer incidence associations. In areas with lower average annual temperatures, increased temperature appeared to negatively correlate with cancer incidence. Conversely, in regions with higher average annual temperatures, we observed a positive correlation. We hypothesize there is a threshold in human biological or behavioral adaptation to non-optimal temperature. Further, our find- ings suggest that locations with accelerated warming rates may face elevated risks for certain cancers, such as thyroid and melanoma. This could be due to heat stress-induced production of reactive oxygen species (ROS) [42], which can lead to DNA damage and consequent cancer development. Despite these findings, comprehensive population-based evidence elucidating the link between non-optimal temperature and cancer risk remains scarce. Our research offers several strengths. First, by utilizing global time-series data, we were able to examine the long-term impact and growth rate of climate change on cancer incidence, facil- itating the identification of novel associations for future exploration. Second, our analysis encompassed various climate factors and their relationship with 33 common cancers, includ- ing those overlooked in previous studies. Third, our study furnishes population-based evi- dence linking climate-sensitive factors with cancer risks over substantial geographic regions and extensive time scales. Our study has several limitations that should be considered. Although we integrated a com- prehensive set of adjustment variables based on prior literature, other unmeasured factors that relate to climate change or potentially influence cancer occurrence still exist, like food supply, sea-level rise, and other air pollutions (e.g., Polycyclic Aromatic Hydrocarbons, PAHs). Addi- tionally, limited by the registry distribution, our results are mainly based on high-income or upper-middle-income countries, with only 2 low-income countries included, potentially low- ering the generalizability of our findings to other regions. Despite this, our study boasts diverse and sizable population coverage from different parts of the globe, bolstering the applicability of our findings. Lastly, our study does not establish the specific pathways through which envi- ronmental factors might impact different types of cancers. For example, we do not have data to determine whether a favorable environment directly influences the development of these PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 13 / 17 PLOS CLIMATEClimate factors associated with cancer incidence cancers or if it indirectly affects behaviors such as physical activity levels or smoking habits. Further research is warranted to investigate these potential mediating pathways. Conclusion Our study, encompassing more than 430 million individuals across 37 countries, has unveiled significant associations between climate factors and incidences of major types of cancer. We discovered novel associations. Increased exposure to green spaces was correlated with decreased incidences of lung and prostate cancers in males. Rapid increases in precipitation, for instance, were associated with higher incidences of prostate, colorectal, and lung cancers, while a temperature rise was linked to higher incidences of melanoma and corpus uteri can- cers. We also confirmed the association between increased solar radiation and a higher inci- dence of skin melanoma, while observing a lower incidence of prostate cancer under similar conditions. These findings underscore the importance of considering the rate of change in these climate factors for future cancer and climate change research. They also highlight the sig- nificance of ongoing research into the intricate connections between climate change and can- cer incidence. Such studies are essential in formulating effective strategies to tackle this multifaceted global health issue, particularly in the face of ongoing climate change. Our study adds an important perspective that adaptation to climate change can complement screening, prevention, and cancer treatment in improving overall population health. Supporting information S1 Table. Climate-sensitive factors considered in this analysis. (XLSX) S2 Table. Estimate of per 0.1 unit increase of NDVI on cancer incidences in different lag periods, 95% confidence interval. (XLSX) S3 Table. Estimate of per 10^6 Jm-2 increase of solar radiation on cancer incidences in different lag periods, 95% confidence interval. (XLSX) S4 Table. Estimate of per 1K increase of temperature on cancer incidences in different lag periods, 95% confidence interval. (XLSX) S5 Table. Estimate of per 1mm increase of daily precipitation on cancer incidences in dif- ferent lag periods, 95% confidence interval. (XLSX) S6 Table. Estimate of per 10 DU increase of total ozone concentration on cancer incidences in different lag periods, 95% confidence interval. (XLSX) S7 Table. Estimate of climate factors on cancer incidences in population aged 0 to 44, 95% confidence interval. (XLSX) Author Contributions Conceptualization: Hongmei Zeng, John S. Ji. PLOS Climate \| https://doi.org/10.1371/journal.pclm.0000362 March 28, 2024 14 / 17 PLOS CLIMATEClimate factors associated with cancer incidence Data curation: Hui Miao. Formal analysis: Haowen Wang. Investigation: Haowen Wang. Methodology: Haowen Wang, Hui Miao, Yuming Guo. Project administration: John S. Ji. Supervision: Hongmei Zeng, John S. Ji. Validation: Haowen Wang. Visualization: Haowen Wang. Writing – original draft: Haowen Wang. Writing – review & editing: Hongmei Zeng, Chang Shu, John S. Ji. References 1. Hiatt RA, Beyeler N. Cancer and climate change. Lancet Oncology. 2020; 21(11):E519–E27. https:// doi.org/10.1016/S1470-2045(20)30448-4 PMID: 33152311 2. Nogueira LM, Yabroff KR, Bernstein A. Climate change and cancer. 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10.1172_jci.insight.150114	Lipin 1 modulates mRNA splicing during fasting adaptation in liver Huan Wang,1 Tracey W. Chan,2 Ajay A. Vashisht,3 Brian G. Drew,4 Anna C. Calkin,4,5,6 Thurl E. Harris,7 James A. Wohlschlegel,3,8 Xinshu Xiao,2,8,9 and Karen Reue1,8 1Human Genetics, David Geffen School of Medicine, 2Bioinformatics Interdepartmental Program and 3Biological Chemistry, University of California, Los Angeles, California, USA. 4Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia. 5Central Clinical School, Monash University, Melbourne, Victoria, Australia. 6Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia. 7Pharmacology, University of Virginia, Charlottesville, Virginia, USA. 8Molecular Biology Institute and 9Integrative Biology and Physiology, University of California, Los Angeles, California, USA. Lipin 1 regulates cellular lipid homeostasis through roles in glycerolipid synthesis (through phosphatidic acid phosphatase activity) and transcriptional coactivation. Lipin 1–deficient individuals exhibit episodic disease symptoms that are triggered by metabolic stress, such as stress caused by prolonged fasting. We sought to identify critical lipin 1 activities during fasting. We determined that lipin 1 deficiency induces widespread alternative mRNA splicing in liver during fasting, much of which is normalized by refeeding. The role of lipin 1 in mRNA splicing was largely independent of its enzymatic function. We identified interactions between lipin 1 and spliceosome proteins, as well as a requirement for lipin 1 to maintain homeostatic levels of spliceosome small nuclear RNAs and specific RNA splicing factors. In fasted Lpin1–/– liver, we identified a correspondence between alternative splicing of phospholipid biosynthetic enzymes and dysregulated phospholipid levels; splicing patterns and phospholipid levels were partly normalized by feeding. Thus, lipin 1 influences hepatic lipid metabolism through mRNA splicing, as well as through enzymatic and transcriptional activities, and fasting exacerbates the deleterious effects of lipin 1 deficiency on metabolic homeostasis. Introduction The regulation of lipid storage in mammalian tissues is critical for metabolic homeostasis. Excessive or inad- equate triglyceride storage is associated with insulin resistance, fatty liver disease, and dyslipidemia (1). The lipin proteins (lipin 1, lipin 2, and lipin 3) perform a key reaction in the synthesis of triglycerides and phos- pholipids through their phosphatidate phosphatase (PAP) activity, which converts phosphatidic acid to diacyl- glycerol at the endoplasmic reticulum (ER) membrane (2, 3). In addition to lipin 1 PAP activity, lipin 1 transits to the nucleus, where it influences the activity of several metabolic transcription factors. These include key regulators of fatty acid oxidation during fasting, such as PPARα and PPARγ coactivator 1α (PGC-1α; ref. 4). Lipin 1 transcriptional coactivator activity appears not to require PAP function, but it does require a hydrophobic motif (LXXIL) located downstream of the PAP active site to mediate protein-protein interactions between lipin 1 and transcription factors (4). Lipin 1 in the nucleus also leads to reduced levels of a key lipogenic transcription factor, sterol regulatory element binding protein 1 (SREBP1) (5). Lipin 1 nuclear translocation from the cytoplasm is regulated by several factors, including interaction with 14-3-3 proteins and sumoylation (6, 7). Thus, lipin proteins modulate cellular lipid homeostasis through the enzymatic conversion of lipid intermediates, as well as through interactions with transcription factors that regulate lipogenic and fatty acid oxidation gene expression. Lipin proteins are required for human health. Lipin 1 deficiency causes recurrent episodes of rhabdo- myolysis and myoglobinuria in children, with about a 10% mortality rate, likely promoted by acute kidney failure, cardiac arrhythmia, and hyperkalemia (8–11). Rhabdomyolytic bouts can be triggered in adults with lipin 1 deficiency, as well (12–14). Disease episodes in lipin 1–deficient individuals are triggered by metabolic stressors such as fasting, extreme exercise, or fever (12–15). Notably, prolonged fasting is also a trigger for oth- er genetic deficiencies that cause rhabdomyolytic disease, such as deficiencies in fatty acid oxidation enzymes (16). There is no treatment for lipin 1 deficiency. Current interventions during acute episodes aim to alleviate symptoms through aggressive fluid and electrolyte replacement, shortened fasting periods, dietary regimens 1 Conflict of interest: The authors have declared that no conflict of interest exists. Copyright: © 2021, Wang et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License. Submitted: March 31, 2021 Accepted: July 23, 2021 Published: September 8, 2021 Reference information: JCI Insight. 2021;6(17):e150114. https://doi.org/10.1172/jci. insight.150114. RESEARCH ARTICLEthat restrict fat and/or increase carbohydrate intake, and monitoring for hyperkalemia and cardiac arrhyth- mias (12, 13, 15, 17). Current recommendations for long-term management of lipin 1 deficiency are to reduce fasting periods (including drinking high carbohydrate supplement drinks when meals are not possible), avoid excessive exercise, and prevent fever (12, 13, 15, 17). Based on the role of fasting as a trigger for disease episodes in lipin 1 deficiency, we hypothesized that lipin 1 has a critical role in metabolic adaptation to fasting. Consistent with this, studies in lipin 1–deficient mice have demonstrated that lipin 1 is required for normal metabolic fuel switching between fasting and feeding (18). Sta- ble-isotope flux analysis revealed impaired hepatic glucose production in the fasted state and increased glycogen storage and fatty acid synthesis during the fed state, likely to compensate for glucose production during the fasted state. These alterations in glucose and lipid metabolism in Lpin1–/– mice are achieved, in part, by altered gene expression of hepatic gluconeogenic and fatty acid oxidation genes in Lpin1–/– compared with WT mice (18). We sought to identify critical activities of lipin 1 function in the fasted compared with the fed state. We determined that lipin 1 is required for the maintenance of mRNA splicing fidelity during fasting adaptation. This requires the nonenzymatic function of lipin 1 and is mediated by lipin 1 interaction with spliceosome components and regulation of RNA binding proteins. Results Fasting alters mRNA levels and induces widespread alternative splicing in Lpin1–/– liver. To understand the role of lipin 1 in maintenance of metabolic homeostasis during the fasted state, we performed high-coverage RNA sequencing (RNA-Seq) in liver from Lpin1–/– and isogenic WT (Lpin1+/+) mice under fasting conditions (16 hours) or fed conditions (refeeding for 5 hours following a 16-hour fast). Visualization of RNA-Seq data sets by t-distributed stochastic neighbor embedding (t-SNE) plot showed separation based on Lpin1 genotype, as well as separation between fasted and refed Lpin1–/– liver (Figure 1A). Analysis of individual gene expression values revealed that fasting altered the expression of nearly 10-fold more genes in Lpin1–/– liver compared with Lpin1+/+ liver (Figure 1B and Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.150114DS1). Compared with the fed state, fasting in Lpin1+/+ liv- er led to altered expression of genes involved in cholesterol and amino acid metabolism, whereas in Lpin1–/– liver, fasting affected expression of mRNA processing, circadian entrainment, and various signaling pathway genes (Supplemental Figure 1A). A comparison of the 2 genotypes during fasting showed decreased fatty acid oxidation gene expression in Lpin1–/– compared with Lpin1+/+ liver, whereas during refeeding, Lpin1–/– differed from Lpin1+/+ in cholesterol biosynthesis and urea cycle gene expression (Supplemental Figure 1B). Fasting is known to influence alternative mRNA splicing in liver, which may have a role in adaptation to the fasting/feeding transition (19–21). We assessed whether fasting impacts mRNA alternative splicing differentially in Lpin1–/– compared with Lpin1+/+ liver. We evaluated 5 mRNA splicing patterns (alternative 5′ or 3′ splice sites, mutually exclusive exons (MXEs), retained introns (RIs), and skipped exons (SEs); Sup- plemental Figure 2). Consistent with previous reports (21), fasting led to alternative splicing events in liver of WT (Lpin1+/+ mice) (Figure 1C). Notably, Lpin1–/– liver exhibited higher levels of alternative splicing than Lpin1+/+ liver in response to fasting (Figure 1C). For example, in the SE category, Lpin1–/– liver had 1 × 105 events compared with 1 × 103 events in Lpin1+/+ liver in the fasted compared with fed states (Figure 1C and Supplemental Table 2). Of the differential exon skipping events induced in fasting compared with fed Lpin1–/– liver, the majority are expected to generate out-of-frame transcripts that would alter the amino acid sequence or introduce a premature stop codon in the corresponding protein product (Figure 1D). In both WT and lipin 1–deficient liver, transcripts with fasting-induced alternative splicing were enriched in metabolic processes such as fatty acid metabolism, oxidation-reduction reactions, and gene expression processes; more categories showed enrichment in lipin 1–deficient liver due to the greater number of transcripts affected (Figure 1E). To better characterize the impact of alternative mRNA splicing in fasted Lpin1–/– liver, we focused on SE events because they are the most abundant and the majority (74%) are predicted to lead to altered protein products. These were enriched for transcripts encoding RNA processing proteins (adjusted P < 1 × 10–7), circadian rhythm regulation (adjusted P < 0.02), phospholipid biosynthesis (adjusted P < 0.05), cell pluripotency/differentiation (adjusted P < 0.03), and mitogen-activated protein (MAP) kinase signaling (adjusted P < 0.03; Figure 2A and Supplemental Table 3). Remarkably, refeeding Lpin1–/– mice after fasting led to normalization of approximately 75% of transcripts with alternative exon splicing (Figure 2A). How- ever, a small set of transcripts had alternative SEs in Lpin1–/– compared with Lpin1+/+ liver in both the fasted and refed states, and these were enriched for function in the PPAR signaling pathway (adjusted P < 0.05). JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 2 RESEARCH ARTICLEFigure 1. Fasting alters mRNA levels and splicing patterns in lipin 1–deficient liver. (A) t-SNE visualization of top 1000 differentially expressed genes with highest variance in liver of fasted and refed Lpin1+/+ or Lpin1–/– mice (n = 3). Each symbol represents an individual mouse. (B) Volcano plots of differentially expressed genes in liver of fasted compared with refed Lpin1+/+ or Lpin1–/– mice. Blue dots represent differential expression with an absolute value of the Log2 fold-change > 0.05 and adjusted P < 0.05 (Benjamini-Hochberg corrected). Differentially expressed genes are listed in Supplemental Table 1. (C) The number of alternatively spliced transcripts in fasted compared with refed Lpin1+/+ or fasted compared with refed Lpin1–/– liver. Five splice patterns were assessed: A5SS, alternative 5′ splice site; A3SS, alternative 3′ splice site; MXE, mutually exclusive exons; RI, retained intron; and SE, skipped exon. (D) Classes of aberrant splicing events in fasted Lpin1–/– compared with fasted Lpin1+/+ liver. Of the SE events, 74% are predicted to result in frame shifts in protein coding sequence in Lpin1–/– liver. (E) Enrichment analysis (via GO term) of genes undergoing alternative skipped exon events in liver of fasted compared with refed Lpin1+/+ or Lpin1–/– mice. Bubble color indicates log10 (P value); bubble size indicates the frequency of GO term in the Gene Ontology database. A small number of transcripts exhibited alternative exon inclusion in Lpin1–/– liver exclusively in the refed state; these showed no functional enrichment (Figure 2A). We verified splicing patterns detected by RNA-Seq for representative transcripts using conventional reverse transcription PCR (RT-PCR) with primers that flank differentially spliced exons (Figure 2B). The splicing patterns recapitulated the RNA-Seq results. Figure 2B shows examples of mRNAs with aberrant splicing in Lpin1–/– liver exclusively under fasted conditions (e.g., the RNA binding proteins Hnrnpa2b1 and Rbm5 and the aldoketoreductase Akr7a5), or in both fasted and refed states (e.g., the RNA binding factors U2af26 and Puf60 and promoter binding factor Gpbp1). These data also illustrate that the proportion of a transcript for a particular gene that shows alternative splicing in fasted Lpin1–/– liver varies from about 10%– 20% of the total (e.g., Hnrnpa2b1) to more than 50% (e.g., Rbm5). Lipin 1 PAP-independent activity influences alternative mRNA splicing in hepatocytes. The fasting-induced alter- ations in lipin 1–deficient liver could result from a direct requirement for lipin 1 to maintain mRNA splicing fidelity, or from a secondary effect that lipin 1 deficiency elicits in liver. We sought to determine if lipin 1 plays a direct role, such that acute reduction in lipin 1 levels impacts splicing. Furthermore, we sought to establish whether the PAP enzymatic function of lipin 1 is required for its effect on mRNA splicing. We knocked down lipin 1 levels in a hepatic cell line and then complemented cells with plasmids express- ing either WT lipin 1 (with both phosphatase and coactivator function) or with mutant lipin 1 that retains only JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 3 RESEARCH ARTICLEFigure 2. Splicing abnormalities in lipin 1–deficient liver differ in fasted and refed conditions. (A) Heatmap of the alter- native skipped exon (SE) events in Lpin1+/+ and Lpin1–/– liver in fasted and refed conditions. The scale at left shows degree of exon inclusion as the SD from the mean inclusion levels, with red indicating increased exon inclusion and blue indicating reduced exon inclusion. Heatmap is divided into 3 classes of altered splicing events in Lpin1–/– liver: those occurring exclu- sively in fasted conditions, those in both fasted and refed conditions, and those exclusively in refed conditions. Functional enrichment categories of mRNAs with altered splicing in Lpin1–/– liver under each of the conditions is indicated at right. (B) Splicing patterns for selected genes from heatmap in A visualized by RT-PCR using PCR primers that span alternatively included exons (n = 3). Splicing patterns that give rise to each band on agarose gels are shown at right. All samples shown in a row were run on the same gel; samples that were on the same gel but not in adjacent lanes are indicated by vertical lines. Full, uncut gels are provided in online supplemental material. coactivator function (Lpin1D679E, which inactivates the phosphatase active site; refs. 4, 22) (Figure 3A). For these studies, we used shRNA directed against the lipin 1 mRNA 3′ untranslated region to target endogenous tran- scripts and complemented cells with lipin 1 expression vectors that lack the shRNA target sequence. Treatment with shLpin reduced lipin 1 protein levels > 20-fold without substantially altering lipin 2 protein levels (Figure 3B). Expression of WT lipin 1 and lipin 1D679E complementation vectors achieved similar levels of lipin 1 pro- tein (Figure 3C). Adenovirally mediated expression of shLpin caused alternative splicing compared with cells infected with a control viral vector expressing LacZ, as illustrated for U2af26 and Rbm5 (Figure 3, D and E; pink bars compared with white bars). Following knockdown with shLpin, complementation with WT Lpin1 expres- sion plasmid restored splice patterns to those indistinguishable from the control (Figure 3, D and E; green bars compared with white bars). Complementation with Lpin1D679E, which lacks PAP activity, normalized the levels of most splice forms (Figure 3, D and E; purple bars compared with white bars). These results indicate that PAP-independent action of lipin 1 influences alternative mRNA splicing. To rule out potential off-target effects of the shLpin that was used in the experiments described above, we performed lipin 1 knockdown using an antisense oligonucleotide (ASO) that targets the Lpin1 mRNA coding region (23). An 80% knockdown of Lpin1 mRNA levels with the ASO led to alternative splicing, as illustrated for U2af26 and Hnrnpa2b1 (Supplemental Figure 3, A–D). The effects of lipin 1 knockdown on splicing were observed at all concentrations of ASO employed (Supplemental Figure 3B). We note that the splicing patterns after adenoviral delivery of shRNA were slightly different than after transfection with JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 4 RESEARCH ARTICLEFigure 3. Lipin1 PAP-independent activity modulates mRNA splicing. (A) Experimental design to assess effect of acute lipin 1 inhibition on mRNA splic- ing fidelity, and the requirement for lipin 1 PAP or coactivator function. Cells treated with short hairpin RNA (shRNA) directed against lipin 1 mRNA 3′-UTR were subsequently complemented with either WT lipin 1 (PAP and coactivator activity) or mutant lipin 1 (coactivator activity only). RNA splicing pattern was assessed by RT-PCR. (B) Immunoblot shows that adenoviral vector expressing shLpin1 reduces lipin1 protein levels but has negligible effect on lipin 2 protein levels in Hepa1-6 cells. LacZ, adenovirus vector expressing lacZ as a negative control. (C) Comparable protein expression levels of WT lipin 1 (Lip1) and lipin 1D679E mutant protein (Lip1DE) via cDNA transfection after treatment of cells with shLpin1. (D and E) RNA splicing following acute lipin 1 knockdown and knockdown followed by complementation with Lip1 or Lip1DE. U2af26 and Rbm5 splicing was assessed by RT-PCR (D), and splice variants quantitated by densitometry (E) (n = 3). Bars in E represent mean ± SD; bars with different letters are significantly different from one another at P < 0.05 via Tukey’s HSD post hoc comparison. ASO DNA. We suspect that this is related to known effects of adenovirus infection on cellular splicing pat- terns (24–26). Our results demonstrate that 2 independent methods used to knock down lipin 1 expression induce alternative mRNA splicing. Lipin 1 interacts with components of the U2 spliceosome complex. The PAP-independent function of lipin 1 as a transcriptional coactivator/corepressor relies upon its physical interaction with transcription factors (4, 27). JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 5 RESEARCH ARTICLEWe wondered whether lipin 1 also interacts with proteins that influence mRNA splicing. To assess this, we performed a screen of lipin 1–interacting proteins using proximity labeling. Lipin 1 was fused to the BirA* biotin ligase (28) and expressed in Hepa1-6 mouse hepatoma cells. The subcellular distribution of lipin 1– BirA* was consistent with that of endogenous lipin 1, being detected in cytoplasmic, membrane, and nuclear/ chromatin compartments (Supplemental Figure 4). Biotin was introduced to cells expressing lipin 1–BirA* to allow labeling of interacting proteins and complexes, and biotinylated proteins were purified by affinity capture and identified by mass spectrometry. We filtered mass spectrometry data to remove endogenously biotinylated proteins (identified in controls that omitted exogenous biotin treatment), and we considered hits with a minimum of 2 unique peptides per protein and a peptide-level FDR of less than 5%. Lipin 1 interactions included known or suspected protein partners and many components of the spli- ceosome. Consistent with the formation of heterodimers among the lipin family members (29, 30), lipin 1 interactions were detected with lipin 2 and lipin 3 (Supplemental Table 4). Lipin 1–interacting partners also included a network of transcription machinery proteins (Supplemental Table 4 and Supplemental Figure 4C). Pathway enrichment analysis revealed that lipin 1 associates with a large network of proteins involved in mRNA processing and the spliceosome (adjusted P < 1 × 10–3; Figure 4A). The spliceosome consists of pro- tein and RNA components, and it assembles at splice donor and acceptor sites on nascent mRNA transcripts to catalyze the excision of intervening introns (31). Lipin 1–spliceosome associations clustered with the U2 and U2-related small nuclear ribonucleoprotein (snRNP) complex (Figure 4B; red stars). Associations were also detected with a few proteins of the U4/U6 snRNP. We verified interactions of lipin 1 with spliceosome components via streptavidin pull-down. As a positive control, streptavidin pull-down detected known interac- tions of lipin 1 with lipin 2 and lipin 3. Lipin 1 was also present in pull-downs with all 4 spliceosome proteins that we tested (SF3B6, SPF45, SM, and U2AF1; Figure 4C). We also confirmed interactions between endoge- nous lipin 1 and endogenous SF3B6 and SPF45 by coimmunoprecipitation from nuclear extracts (Figure 4D). We hypothesized that, if lipin 1 has a role in spliceosome structure or activity, lipin 1 deficiency may lead to aberrant levels of spliceosome proteins and/or small nuclear RNAs (snRNAs) that are associated with the spliceosome complex. We assessed protein levels of several U2 spliceosome components that interact with lipin 1 in hepatic nuclear extracts and found that SPF45 and SF3B6 show slight alterations in protein levels, but other U2 components (SM, U2AF1) appear to have normal levels, as does the U1 protein, U170K (Figure 4E). Several snRNA species showed fasting-related alterations in levels in isolated chromatin (32) from Lpin1–/– compared with Lpin1+/+ liver. Specifically, the levels of U2, U4, U5, and U6 snRNAs were altered in fasted Lpin1–/– liver but were normalized upon refeeding (Figure 4F). Our data indicate that lipin 1 interacts with spli- ceosome proteins and is required for maintenance of the appropriate levels of snRNA species during fasting. RNA binding protein expression is dysregulated in fasted Lpin1–/– liver and normalized by feeding. We next sought to understand what factors determine which mRNA species undergo alternative splicing in Lpin1–/–, specifically in the fasted state. We scrutinized hepatic gene expression patterns for transcripts that follow a similar pattern: they are dysregulated in Lpin1–/– liver during the fasted state but are restored to WT levels upon feeding. We identified several mRNA transcripts that have roles in mRNA splicing and processing that exhibit this pattern (Figure 5, A and B). These include Esrp2 (epithelial splicing regulatory protein 2), U2af2 (U2 snRNP splicing factor U2AF 65 kDa subunit), Srsf1 and Srsf10 (serine and arginine-rich splicing factors), Rbm4 and Rbm5 (RNA binding motif proteins), and Tardbp (TAR DNA-binding protein 43; Figure 5C). Gene expression values for these splicing factors were reduced in Lpin1–/– compared with Lpin1+/+ liver in the fasted state but were increased by feeding to levels similar to or higher than those in Lpin1+/+ liver (Figure 5D and Supplemental Figure 5A). An analysis of alternatively spliced mRNAs in fasted Lpin1–/– liver for common RNA binding motifs using RNA Map Analysis and Plotting Server 2 (rMAPS2) (33) identified ESRP2 as the top-ranked motif (Supple- mental Table 5). Lpin1–/– liver exhibited more than 7500 instances of alterative exon splicing near ESRP2 motifs in the fasted state, compared with only 1200 such instances in Lpin1+/+ liver (Supplemental Figure 5, B and C). This suggested that many of the mRNAs that are alternatively spliced in fasted Lpin1–/– compared with Lpin1+/+ liver may be regulated by ESRP2 binding, which occurs near exon/intron boundaries and influences intron exci- sion of transcripts that are induced during postnatal liver maturation and liver regeneration (34, 35). We assessed splicing patterns for 16 known ESRP2 targets in liver of fasted and refed mice (34, 35). Fasted Lpin1–/– mice exhibited altered exon inclusion for multiple transcripts that are involved in liver development (2 splice forms of Csnk1d, as well as Slk and Nf2; Figure 5E, left panel). Other classic ESRP2 targets with altered exon inclusion levels in fasted Lpin1–/– liver included Pdgfa, Zdhhc16, Epb41, Vegfa, Arhgef10l, Lsm14b, Phldb2, and Scrib. Consis- tent with the restoration of Esrp2 expression upon feeding (Figure 5D), refeeding also normalized the splicing of JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 6 RESEARCH ARTICLEFigure 4. Lipin 1 interacts with mRNA processing proteins. (A) Network of lipin 1 interactions with mRNA processing factors (enrichment score = adjusted P < 1 × 10–3). Network drawn in STRING (string-db.org). (B) Lipin 1 associates with many proteins that are part of the U2 snRNP complex (indicated by red stars). (C) Validation of lipin 1 protein interactions by streptavidin pulldown followed by immunoblot. These include known lipin 1–interacting proteins (lipin 2 and lipin 3), as well as several components of the U2 snRNP. The negative control reaction was performed to demonstrate no pull-down in the presence of endogenously biotinylated proteins. Antibody information provided in Supplemental Table 8. (D) Coim- munoprecipitation of endogenous lipin 1 with endogenous spliceosome proteins SF3B6 and SPF45. Immunoprecipitation was performed from hepatic nuclear extracts with antibodies against spliceosome proteins and detected on blots with lipin 1 antibody. FT, flow-through; IP, immunoprecipitate; IB, immunoblot detection. (E) Protein levels of representative U2 and U1 spliceosome proteins in fasted hepatic nuclear extracts assessed by Western blot. (F) Expression levels of snRNAs U1, U2, U4, U5, and U6 in fasted and refed Lpin1+/+ or Lpin1–/– liver. snRNA expression was normalized to 18S ribosomal RNA (n = 4). Values shown are mean ± SD; P < 0.05 via 1-way ANOVA followed by t test. P < 0.01; **P < 0.0001. JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 7 RESEARCH ARTICLEFigure 5. Lipin 1 deficiency leads to impaired fasting expression levels of RNA binding proteins such as ESRP2, as well as altered splicing of ESRP2 targets. (A) Volcano plots of differentially expressed genes in liver of refed compared with fasted Lpin1+/+ or Lpin1–/– mice. Red dots represent differentially expressed genes in the refed versus fasted state (P < 1 × 10–6, FDR = 0.1). The numbers of differentially expressed genes that are increased (Up) or decreased (Down) by refeeding are indicated. (B) Genes with increased expression in refed compared with fasted Lpin1–/– liver are functionally enriched for RNA binding and RNA processing proteins (enrichment scores indicated at top). (C) Heatmap of mRNA expression levels of RNA-binding protein (RBP) encoding genes are reduced in fasted Lpin1–/– liver and normalized to Lpin1+/+ levels by refeeding. (D) Expression of the RNA binding protein ESRP2 in fasted and fed liver. Data analyzed by 2-way ANOVA followed by t test (n = 3); P < 0.05; *P < 0.01. (E) ESRP2 target gene splicing in Lpin1–/– liver is dysregulated in fasting conditions and normalized by feeding. Experimentally determined ESRP2 exon inclusion events (34, 35) were assessed in RNA-Seq data from fasted and refed Lpin1–/– and Lpin1+/+ liver (n = 3). Data are shown as mean ± SD; P < 0.05 in Lpin1+/+ versus Lpin1–/– liver as determined by RNA-Seq analysis (FDR< 5%). (F) Verification of splicing of representative ESRP2 targets by standard PCR. Splice products were quantitated by densitometry (n = 3). Asterisks indicate significant differences analyzed by 2-way ANOVA followed by t test; P < 0.01, *P < 0.0001. ESRP2 targets in Lpin1–/– liver (Figure 5, E and F). Thus, lipin 1 is required for maintenance of hepatic splicing factor gene expression and corresponding mRNA splicing patterns during the fasted state. Fasting promotes widespread alternative splicing of phospholipid biosynthetic genes and aberrant phospholipid levels in Lpin1–/– liver. The alternative exon splicing events in fasted Lpin1–/– liver typically involve the utilization of JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 8 RESEARCH ARTICLEFigure 6. Lipin 1 deficiency promotes alternative splicing of phospholipid biosynthetic genes and dysregulated phospholipid levels in fasted liver. (A) Phospholipid biosynthetic enzymes and lipid products. Red outlines indicate enzymes with alternative splicing in Lpin1–/– compared with Lpin1+/+ liver in the fasted state (left) and fed state (right). Total levels of the lipid species shown in red differ between Lpin1–/– and Lpin1+/+ liver in the fasted (left) and fed (right) states. PA, phosphatidic acid; DG, diacylglycerol; PS phosphatidylserine; PE, phosphatidylethanolamine; PC, phospha- tidylcholine; PI, phosphatidylinositol; PG, phosphoglycerate; CL, cardiolipin; SM, sphingomyelin. (B) Volcano plots show number of PE, PC, and PI species that differ between Lpin1–/– and Lpin1+/+ liver in the fasted (left) and fed (right) states. Based on mean lipid levels from n = 5 mice/genotype. an alternative exon for a proportion (10%–20%) of all transcripts from a given gene (Figure 2B and Figure 5F). This suggests that the physiological impact of a single splice variant may be modest. Even so, we hypoth- esized that pathways in which multiple steps undergo alternative splicing in fasted Lpin1–/– liver may impact liver physiology. Within the pathways for synthesis of major phospholipid species, we detected alternative splicing for 10 of 17 enzymes in fasted Lpin1–/– liver (Figure 6A, red outlines). Notably, all except 3 of the alternative splicing events in fasted Lpin1–/– liver were normalized in the fed state (Figure 6A and Supple- mental Table 6). We also assessed hepatic phospholipid levels by mass spectrometry in fasted and fed states. Numerous phosphatidylethanolamine (PE), phosphatidylcholine (PC), and phosphatidylinositol (PI) species had altered levels in fasted Lpin1–/– compared with Lpin1+/+ liver (Figure 6B). Consistent with the known role of lipin 1 in glycerolipid synthesis, abnormalities in phospholipid levels remained in the fed state. However, fewer PE, PC, and PI species differed between Lpin1–/– and Lpin1+/+ liver in the fed state (Figure 6B, Supple- mental Figure 6, and Supplemental Table 7), in concert with normalized splicing patterns for many of the phospholipid biosynthetic enzyme transcripts in fed compared with fasted conditions. JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 9 RESEARCH ARTICLEFigure 7. Lipin 1 deficiency promotes choline kinase α (Chka) alternative splicing and altered protein levels, which are more pronounced in the fasted state. (A) Chka gene region with predicted binding sites for the ESRP2 splicing factor. (B) Chka mRNA splice variants and corresponding protein products. (C and D) Chka splice variants in fasted and refed Lpin1+/+ and Lpin1–/– liver assessed by RT-PCR and quantitated. Splice products were quantitated by densitometry (n = 3). Asterisks indicate significant differences analyzed by 2-way ANOVA followed by t test; P < 0.05, *P < 0.001, P < 0.0001. (E and F) CHKA protein levels by Western blot, with total protein levels shown via Ponceau staining. CHKA protein quantita- tion after normalization to α-tubulin. Protein levels were quantitated by densitometry and normalized by α-tubulin (n = 3). Asterisks indicate significant differences analyzed by 2-way ANOVA followed by t test; P < 0.01, ***P < 0.0001. It is unknown how the splice variants that occur in most of the enzymes in Figure 6A influence protein function. However, some information is available for Chka (encoding choline kinase α), for which 3 prominent splice variants have been previously described (36). Alternatively spliced Chka exons are flanked by ESRP2 binding motifs (Figure 7A; ref. 37). Alternative splicing results in enzymatically active α1 and α2 isoforms, as well as an inactive α3 isoform that results from inclusion of an alternative exon with a premature stop codon (Figure 7B). The α3 variant truncates the protein sequence and omits enzyme active sites (36). Chka splice variants α1 and α2 predominated in Lpin1+/+ mice in both fasted and fed states (Figure 7C). However, fasting in Lpin1–/– mice expressed large amounts of the α3 variant, which specifies an inactive enzyme (Figure 7, C and D). Analysis of full-length CHKA protein levels verified that fasted Lpin1–/– mice have reduced levels compared with Lpin1+/+, and these are partly normalized in the fed state (Figure 7, E and F). These findings provide a proof-of-principle illustration that alternative splice variants occurring in fasted Lpin1–/– liver could influence protein levels and function. Although the changes in levels of any specific protein are likely to be small, the aggregate effect on proteins from widespread alternative splicing could contribute to metabolic dysregulation that occurs in lipin 1–deficient mice and humans (3). Discussion The overarching function of lipin 1 is to regulate cellular and tissue lipid homeostasis. This occurs through the enzymatic role of lipin 1 in glycerolipid synthesis, as well as through its transcriptional coactivation of genes JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 1 0 RESEARCH ARTICLEFigure 8. Lipin 1 influences hepatic mRNA splicing fidelity in the fasted state. Lipin 1 interacts with several compo- nents of the spliceosome, particularly with proteins of the U2 snRNP. In lipin 1–deficient liver, widespread alternative mRNA splicing occurs in the fasted state. This is associated with fasting-specific alterations in levels of spliceo- some-associated snRNAs and RNA binding proteins (RBPs) with roles in mRNA splicing. Alternative splice variants in fasted Lpin1–/– liver are enriched for RBP motifs for factors such as ESRP2 and SRSFs, which exhibit impaired expres- sion during fasting. Alternatively spliced mRNAs are predicted to generate nonfunctional or altered function proteins in fasted Lpin1–/– liver. The maintenance of splicing fidelity requires the PAP-independent activity of lipin 1. involved in fatty acid metabolism. Here, we uncover an additional mechanism by which lipin 1 influences meta- bolic homeostasis — by maintaining mRNA splicing fidelity in a metabolically stressful state, fasting (Figure 8). Mechanisms for metabolic adaptation during the feeding-fasting transition include alterations in mRNA transcription and mRNA splicing, with downstream effects on protein function (19, 20). Alternative mRNA splicing is also altered in response to physiological signals, such as the circadian cycle (20), weight loss (38, 39), and metabolic pathologies such as fatty liver (40) and obesity (41). In some cases, the changes in splicing are known to be associated with altered expression levels of RNA binding proteins and splicing factors (41). We observed that WT mice adapt to fasting with alterations in both the hepatic transcriptome and mRNA splicing patterns. Our data indicate that lipin 1 normally has a role in maintaining mRNA splicing patterns, as evidenced by its interaction with splicing proteins in WT cells and the altered splicing that occurs in lipin 1–deficient liver. The exaggerated alternative splicing events that occur in fasted Lpin1–/– liver are likely a result of loss of 1 level of regulation and may contribute to disease symptoms. The dysregulated alternative mRNA splicing in Lpin1–/– liver typically affects only a portion of transcripts for a given protein product and, therefore, is expected to produce modest effects on the function of any specif- ic protein. However, the composite effect of alterations in multiple proteins that function within a particular metabolic pathway could have physiological consequences. As an illustration of this point, we detected alter- native splicing in fasted Lpin1–/– liver for the majority (10 of 17) of enzymes in the phospholipid biosynthetic pathway, and we found substantial dysregulation of numerous phospholipid species (Figure 6 and Supple- mental Figure 6). In fed Lpin1–/– liver, both mRNA splicing of these enzymes and phospholipid levels were partially normalized to more closely resemble WT. The consequence of most alternative splicing events have not been characterized for their effects on protein function. An exception is choline kinase α, for which 3 alternative splice variants have been characterized previously (36). We found that fasted Lpin1–/– liver predom- inantly expressed the α3 splice variant (Figure 7C), which specifies a truncated protein without enzymatic activity (36). The Chka splicing pattern was normalized with feeding, and levels of full-length CHKA protein also resemble those in Lpin1+/+ liver in the refed state. Although the function of the splice variants detected for JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 1 1 RESEARCH ARTICLEother phospholipid biosynthetic enzymes are not known, it is likely that the differential splicing in the fasted Lpin1–/– liver may further contribute to dysregulation of phospholipid homeostasis during fasting. The role of lipin 1 in mRNA splicing was largely independent of its PAP enzymatic function. Analogous to lipin 1 interaction with DNA-binding transcription factors, we detected lipin 1 interactions with spliceo- some proteins, particularly components of the U2 complex. We also found that lipin 1 is required for the reg- ulation of spliceosome-associated snRNA levels during fasting. Additionally, lipin 1 influences RNA binding protein gene expression, which was reduced specifically in the fasted state in lipin 1–deficient liver. Additional mechanisms may contribute to the pronounced role for lipin 1 in mRNA splicing in the fasted state. For exam- ple, lipin 1 posttranslational modifications, which are known to occur in response to insulin and other stimuli (6, 7, 42), could influence lipin 1 interactions with spliceosome components through effects on protein charge or alterations in lipin 1 subcellular localization. Further studies are warranted to investigate these possibilities. In our analysis of all transcripts with alternative splicing in fasting Lpin1–/– liver, motifs for the ESRP2 splicing factor were the most prevalent (Supplemental Figure 5). ESRP2 regulates a shift in the mRNA splic- ing program during the maturation of neonatal to adult liver in mice and humans (34, 43). Fasted Lpin1–/– liver exhibited reduced levels of Esrp2 expression and aberrant exon inclusion in numerous established ESRP2 target genes (Figure 5E). In the fed state, Esrp2 expression levels and target gene splicing patterns were normal- ized to those present in WT mice (Figure 5D). Consistent with ESRP2 dysregulation, Lpin1–/– mice exhibit impaired liver regeneration following partial hepatectomy (44). This has been attributed to reduced avail- ability in Lpin1–/– mice of adipose tissue–derived fatty acids, which are thought to be important during liver regeneration. However, our studies raise the possibility that aberrant mRNA splicing programs necessary for liver maturation may also contribute to impaired liver regeneration in lipin 1 deficiency. Several of the splicing factors that are dysregulated in fasted Lpin1–/– liver (Figure 5C and Supplemental Figure 5A) have been implicated in liver disease. Nonalcoholic fatty liver disease is characterized by dys- regulation of SRSF1, SRSF10, and ESRP2 (45). Reduced levels of RBM4, as seen in fasted Lpin1–/– liver, are associated with poor prognosis in hepatocellular carcinoma following hepatectomy (46). Fasted Lpin1–/– liver also had reduced levels of TRA2B (also known as SFRS10), which was previously found to be reduced in liver of obese subjects and to regulate splicing of the human LPIN1 gene and hepatic lipogenesis (47). In summary, our findings reveal that lipin 1 is critical for the regulation of hepatic mRNA splicing fidelity in response to metabolic stress that occurs during fasting (Figure 8). Analogous to the role of lipin 1 in tran- scriptional coactivation, this role of lipin 1 appears to involve the interaction with proteins that specialize in splicing and does not require lipin 1 enzymatic function. Our findings raise the possibility that aberrant alter- natively spliced mRNA transcripts and the corresponding impact on protein function contribute to the disease symptoms in lipin 1 deficiency. They also suggest that small molecules that have been useful to treat diseases characterized by widespread splicing defects (48, 49) could be potentially beneficial in lipin 1 deficiency. Methods Mice. Lpin1–/– (fatty liver dystrophy) mice and WT littermates (BALB/cByJ background) were from a colony maintained at UCLA that was originally established from Lpin1+/– mice obtained from The Jackson Labora- tory (001592, BALB/cByJ-Lpin1fld/J). Mice were reared in groups of 3–4/cage, at ambient temperature of 22°C–24°C with 12-hour light/12-hour dark conditions and fed a laboratory chow diet. Fasting was performed for 16 hours (1700–0900 hours) with singly caged mice at a temperature of 18°C–20°C. Refeeding was per- formed on fasted mice for 5 hours (0900–1400 hours) by providing chow pellets ad libitum. Male mice (aged 2–8 months) were used for RNA-Seq and lipidomic analyses; both male and female mice (aged 2–5 months) were used to validate splicing changes, effects on splicing factor protein, and snRNA levels. RNA-Seq and analysis. Total RNA from liver tissue was isolated using RNeasy tissue mini kit (QIAGEN). RNA quality and quantity was determined using the TapeStation 2200 system (Agilent Technologies) before library generation. Libraries were prepared by the UCLA Neuroscience Genomics Core with Illumina standard kits (TruSeq stranded RNA v2) according to standard protocols. All samples were barcoded and sequenced with 12 samples (n = 3) per lane and 6 lanes in total, with HiSeq2000 4K using a 69 bp paired-end sequencing pro- tocol to achieve approximately 150 million reads per sample. A round-robin design was implemented such that biological replicates were sequenced on different lanes, and each sample was part of more than 1 sequencing pool. The samples were demultiplexed, and forward- and reverse-read fastq files were generated for each sample. Quality of the raw RNA-Seq reads was assessed with FastQC software, and RNA-Seq reads were aligned to mouse genome and transcriptome (GRCm38 Ensembl release 84) using HISAT2. Gene-level read counts were JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 1 2 RESEARCH ARTICLEobtained using htseq-count. Gene expression level was quantified as reads per kilobase of transcript per million mapped reads (RPKM). Differential gene expression analysis was performed by DESeq with an FDR < 0.05 as previously described. Differential splicing events between groups of triplicate samples were identified using rMATS (50). Five major types of splicing events were assessed: SE, alternative 5′ splice site (A5SS), alternative 3′ splice site (A3SS), MXE, and RI. RNA-Seq data that include all global gene expression data and splicing data are available at Gene Expression Omnibus database (GEO; GSE160984). Functional enrichment analysis. Functional pathway analysis of differentially expressed genes was performed using Enrichr (51). Functional enrichment analysis of alternative SEs events was performed using Gene Ontolo- gy enrichment analysis and visualization tool (GOrilla; ref. 52). Scatter plots were generated with REViGO (53). Heatmaps and hierarchical cluster analysis. Hierarchical clustering by Euclidean distance for exon inclusion levels and differential expressed RNA-binding protein genes was performed with the Dynamic Tree Cut package using R (54). Heatmaps were generated using the heatmap.2 function from gplots (55). Motif enrichment analysis and RNA maps analysis. We used the rMAPS online platform (33) to identify differ- ential enrichment of RNA binding protein motifs in exon skipping events between fasted and refed conditions in Lpin1–/– and Lpin1+/+ liver. We applied 115 known RNA binding protein motifs corresponding to characterized splicing factors (33). Motif scores were calculated for a region that included the exon body, 250 bp of upstream and downstream intron, flanking exons, and 250 bp intronic regions of flanking exons. For each motif, the P val- ue for enrichment analysis was calculated based on the number of occurrences in the differentially spliced exons and the control exons through the Fisher’s exact test (right-sided) and using Benjamini-Hochberg FDR correc- tion to adjust for multiple testing. We considered enriched motifs with an FDR < 5% and P < 0.01. An alternative exon was classified as a control alternative exon if it did not show any splicing change (rMATS FDR > 50%, maximum percent spliced in PSI > 15%, minimum percent spliced in PSI < 85%) and if it was from a highly expressed gene (average fragments per kilobase of transcript per million mapped reads > 5.0 in at least 1 group). Cell culture and transfection. Murine Hepa1-6 hepatoma cells (American Type Culture Collection, CRL-1830) were maintained in complete DMEM with 10% FBS (Corning Inc.). All experiments were performed with cells having 2–6 passages. Cells were transfected with plasmids using BioT reagent (Bioland Scientific LLC). Subcellular fractionation and immunoprecipitation. Hepa1-6 cells were fractionated into cytoplasmic, mem- brane-bond, soluble nuclear, and chromatin-bound nuclear fractions using the Subcellular Protein Fractionation Kit for Cultured Cells (Thermo Fisher Scientific, 78840). Studies were performed with the addition of 1 mM phenylmethylsulfonyl fluoride (PMSF) and 1× protease inhibitor cocktail (MilliporeSigma). Immunoblot analy- sis was performed on Hepa1-6 cell lysates or mouse liver tissue as previously described (11). Antibodies used are presented in Supplemental Table 9. Liver nuclei were purified from freshly harvested liver. Tissue (100 mg) was minced and washed with PBS. The pellet was lysed in hypotonic buffer (10 mM Tris-HCl [pH 7.9], 1.5 mM MgCl2, 10 mM KCl, 1 mM dithiothreitol, 1 mM PMSF, and 1× protease inhibitor cocktail) in a Dounce homogenizer (40 strokes). Nuclear pellets were collected by centrifugation (20,000g for 30 minutes at 4°C), washed twice in hypotonic buffer, and resuspended in nuclear extraction buffer (50 mM Tris-HCl [pH 7.9], 1 mM MgCl2, 1 mM DTT, 0.1% Nonidet P-40 [NP-40], 250 units/mL Benzonase [MilliporeSigma], 1 mM PMSF, and 1× protease inhibitor cocktail) with sonication. The supernatant (nuclear extract) was collected after centrifugation for 10 minutes at 4°C at 17,000g. For immunoprecipitation, nuclear extracts were incubated with antibodies against spliceosome components (Supplemental Table 2) at 4°C overnight. Protein A/G-agarose beads were added for 2 hours at 4°C. Immuno- precipitates were collected by centrifugation (1500g for 1 minute at room temperature), washed 3 times with lysis buffer, and subjected to Western blot analysis with antibody against lipin 1. Lipin 1 knockdown and splicing analysis by RT-PCR. Lipin 1 knockdown was performed with shRNA or ASO. For shRNA studies, U6 promoter-driven shRNA hairpins targeting the 3′-UTR of lipin 1 (3765-3784 of Lpin1 transcript variant X1; accession XM_006514975) and the coding region of lipin 2 (7633-7651 of Lpin2 tran- script variant X1; accession XM_006524786) were subcloned into pAdTRACK and used to generate adenovirus (XM_006514975, https://www.ncbi.nlm.nih.gov/nuccore/XM_006514975) (XM_006524786, https://www. ncbi.nlm.nih.gov/nuccore/XM_006524786) (56). Adenovirus was packaged with poly-L–Lysine and delivered to Hepa1-6 cells for 24 hours. For complementation studies, cells were coinfected with shLpin1 and adenovirus vectors expressing lipin 1 cDNAs (WT or D679E mutant lipin 1) that lack the shRNA binding site. Cells were harvested 1 day later for RNA splicing analysis by RT-PCR. For ASO studies, cells were transfected with ASO targeting the lipin 1 coding region or a control nonspe- cific ASO (23) with Lipofectamine RNAiMAX (Thermo Fisher Scientific). RNA was isolated for splice variant JCI Insight 2021;6(17):e150114 https://doi.org/10.1172/jci.insight.150114 1 3 RESEARCH ARTICLEanalysis 4 days after transfection. RT-PCR was performed using PCR primers that span alternative exons (sequences in Supplemental Table 8), and products were analyzed by agarose gel electrophoresis. Proximity-dependent biotin identification (BioID). Protein-protein associations were detected by fusion of lipin 1 coding sequences (57) with the BirA biotin ligase (28) in pcDNA3.1 MCS-BirA-HA (Addgene, 36047). Hepa1- 6 cells were seeded at 200,000 cells per well in 6-well plates. The following day, cells were transfected with lipin- BirA plasmids in BioT reagent (Bioland Scientific). Twenty-four hours after transfection, cells were supplied with fresh medium containing 100 μM Biotin (MilliporeSigma), and cell protein lysates were collected 24 hours later and supplemented with protease and phosphatase inhibitors. Prior to submitting samples for mass spec- trometric analysis, successful biotinylation was confirmed by subjecting lysates to Western blot probed with streptavidin-HRP (Thermo Fisher Scientific). The level of endogenously biotinylated proteins was assessed by Western blot of lysates from untransfected cells that were treated with biotin. Antibodies used in the verification of lipin 1–interacting proteins are listed in Supplemental Table 9. Protein mass spectrometry. Purified proteins bound to streptavidin beads were reduced, alkylated, and digested by sequential addition of Lys-C and trypsin proteases (58). Samples were desalted and subjected to ultra high– pressure liquid chromatography and tandem mass spectrometry. Data analysis was performed with ProLuCID and DTASelect2 implemented in the integrated proteomics pipeline IP2 (Integrated Proteomics Applications Inc.; ref. 58). Protein and peptide data were filtered using DTASelect and required a minimum of 2 unique pep- tides per protein and a peptide-level false-positive rate of less than 5%. Chromatin-associated RNA extraction and snRNA real-time RT-PCR. To isolate chromatin-associated RNA, fresh liver tissue was homogenized and chromatin-associated RNA was isolated as described (32). Briefly, nuclei were purified and RNA isolated with TRIzol (Invitrogen). Following digestion with DNase I, RT-PCR was performed using primers complementary to mouse snRNAs (Supplemental Table 7) and normalized to 18S ribosomal RNA using SYBR Green PCR Master Mix (Bio-Rad). Absence of contaminating genomic DNA was verified by the lack of amplified products in mock reverse-transcription reactions in which no enzyme was added. Lipid analyses. Lipidomic analyses were performed on hepatic lipids extracted by a modification of the Bligh and Dyer method (59). Lipid species were quantified by electrospray ionization–tandem mass spectrometry as described previously (60). Data availability. The authors declare that the data supporting the findings of this study are available with- in the paper and its supplementary information files or are available in the public GEO database (accession GSE160984). Statistics. Statistical analyses for group comparisons in Figure 5E and Supplemental Figure 3A were by t test. Group comparisons in Figure 4F; Figure 5D; Figure 7, D and F; Supplemental Figure 3D; and Supplemental Figure 5A were by 2-way ANOVA; significant ANOVA (P < 0.05) tests followed by t tests. All t tests are 2 tailed. Statistical analyses in Figure 3E were performed with Tukey’s HSD post hoc comparison. Statistics for analysis of differential RNA splicing, motif enrichment, enrichment of protein, or gene expression functional groups were as described under methodology and in figure legends. P values presented for RNA-Seq mRNA levels and levels of splice variants, functional enrichment, and motif enrichment analyses were all adjusted P values, taking into account multiple comparisons. Study approval. Animal studies were performed after approval from the UCLA IACUC. Author contributions HW performed mouse and cell culture experiments, interpreted omics data, and wrote the manuscript; TWC analyzed RNA-Seq data; AAV and JAW designed and performed proteomics experiments; BGD and ACC performed lipidomic analyses; TEH provided shRNA vectors; XX supervised RNA-Seq analysis; and KR designed experiments, obtained funding, and wrote the manuscript. Acknowledgments We thank Qin Han, Faatima Ellahy, and Jessie Chen for technical assistance. We thank Peter J. Meikle and Natalie Mellett from the “Metabolomics Laboratory at Baker Heart & Diabetes Institute” for assistance with lipidomics determinations. We thank Brian Finck (Washington University) for the lipin 2 antibody and will- ingness to share mouse samples. We thank Carrie Wiese and Laurent Vergnes for critical reading of the manuscript. This work was supported by the NIH P01 HL028481 (KR), P01 HL090553 (KR), and American Heart Association 18POST34060200 (HW). 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