bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability and heterogeneity in the anti-microbiota reactivity of human milk-derived Immunoglobulin A
Authors: Johnson-Hence Chelseá B. , Gopalakrishna Kathyayini P. , Bodkin Darren , Coffey Kara E. , Burr Ansen H.P. , Rahman Syed , Rai Ali T. , Abbott Darryl A. , Sosa Yelissa A. , Tometich Justin T. , Das Jishnu , Hand Timothy W. 
Published: March 2023

AbstractImmunoglobulin A (IgA) is secreted into breast milk and is critical to both protecting against enteric pathogens and shaping the infant intestinal microbiota. The efficacy of breast milk-derived maternal IgA (BrmIgA) is dependent upon its specificity, however heterogeneity in BrmIgA binding ability to the infant microbiota is not known. Using a flow cytometric array, we analyzed the reactivity of BrmIgA against bacteria common to the infant microbiota and discovered substantial heterogeneity between all donors, independent of preterm or term delivery. We also observed intra-donor variability in the BrmIgA response to closely related bacterial isolates. Conversely, longitudinal analysis showed that the anti-bacterial BrmIgA reactivity was relatively stable through time, even between sequential infants, indicating that mammary gland IgA responses are durable. Together, our study demonstrates that the anti-bacterial BrmIgA reactivity displays inter-individual heterogeneity but intra-individual stability. These findings have important implications for how breast milk shapes the development of the infant microbiota and protects against Necrotizing Enterocolitis.SummaryWe analyze the ability of breast milk-derived Immunoglobulin A (IgA) antibodies to bind the infant intestinal microbiota. We discover that each mother secretes into their breast milk a distinct set of IgA antibodies that are stably maintained over time.
VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language Models
Authors: Lin Weining , Wells Jude , Wang Zeyuan , Orengo Christine , Martin Andrew C.R. 
Published: March 2023

AbstractComputational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (VariantimpactPredictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. By exploiting one of the best performing protein language models (ESM-1b), we established a robust classifier, VariPred, requiring no pre-calculation of structural features or multiple sequence alignments. We compared the performance of VariPred with other representative models including 3Cnet, EVE and ‘ESM variant’. VariPred outperformed all these methods on the ClinVar dataset achieving an MCC of 0.751vs. an MCC of 0.690 for the next closest predictor.
mosnareveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer
Authors: Coullomb Alexis , Monsarrat Paul , Pancaldi Vera 
Published: March 2023

AbstractSpatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, performing a rational analysis including multiple algorithms while integrating different conditions such as clinical data is still not trivial. To make such investigations more accessible, we developedmosna, a Python package to analyze spatial omics data with respect to clinical or biological data and to gain insight on cell interaction patterns or tissue architecture of biological relevance.mosnais compatible with all spatial omics methods, it leveragestysserandto build accurate spatial networks, and is compatible with Squidpy. It proposes an analysis pipeline, in which increasingly complex features computed at each step can be explored in integration with clinical data, either with easy-to-use descriptive statistics and data visualization, or by seamlessly training machine learning models and identifying variables with the most predictive power.mosnacan take as input any dataset produced by spatial omics methods, including sub-cellular resolved transcriptomics (MERFISH, seqFISH) and proteomics (CODEX, MIBI-TOF, low-plex immuno-fluorescence), as well as spot-based spatial transcriptomics (10x Visium). Integration with experimental metadata or clinical data is adapted to binary conditions, such as biological treatments or response status of patients, and to survival data. We demonstrate the proposed analysis pipeline on two spatially resolved proteomic datasets containing either binary response to immunotherapy or survival data. mosna identifies features describing cellular composition and spatial distribution that can provide biological insight regarding factors that affect response to immunotherapies or survival.Availability and implementationmosnais made publicly available to the community, together with relevant documentation and tutorials implemented as Jupyter notebooks to reproduce the result athttps://github.com/AlexCoul/mosna
Cell-Cell Separation Device: measurement of intercellular detachment forces
Authors: Eckert Julia , Matylitskaya Volha , Kasemann Stephan , Partel Stefan , Schmidt Thomas 
Published: March 2023

AbstractWhether at the intramolecular or cellular scale in organisms, cell-cell adhesion adapt to external mechanical cues arising from the static environment of cells and from dynamic interactions between neighboring cells. Cell-cell adhesions need to resist detachment forces to secure the integrity and internal organization of organisms. In the past, various techniques have been developed to characterize adhesion properties of molecules and cellsin vitro,and to understand how cells sense and probe their environment. Atomic force microscopy and dual-pipette aspiration, where cells are mainly present in suspension, are common methods for studying detachment forces of cell-cell adhesions. How cell-cell adhesion forces are developed for adherent and environment-adapted cells, however, is less clear. Here, we designed the Cell-Cell Separation Device (CC-SD), a microstructured substrate that measures both intercellular forces and external stresses of cells towards the matrix. The design is based on micropillar arrays originally designed for cell traction-force measurements. We designed PDMS micropillar-blocks, to which cells could adhere and be able to connect to each other across the gap. Controlled stretching of the whole substrate changed the distance between blocks and increased gap size. That allowed us to apply strains to cell-cell contacts, eventually leading to cell-cell adhesion detachment, which was measured by pillar deflections. The CC-SD provided an increase of the gap between the blocks of up to 2.4-fold, which was sufficient to separate substrate-attached cells with fully developed F-actin network. Simultaneously measured pillar deflections allowed us to address cellular response to the intercellular strain applied. The CC-SD thus opens up possibilities for the analysis of intercellular force detachments and sheds light on the robustness of cell-cell adhesions in dynamic processes in tissue development.
Reconstructing the first COVID-19 pandemic wave with minimal data in the UK
Authors: Chen Siyu , Flegg Jennifer A , Lythgoe Katrina A , White Lisa J 
Published: March 2023

AbstractAccurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it is particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. In our previous publication[1], we developed an early pandemic diagnostic tool that can link minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys. Here, we re-examined and evaluated the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation from ONS Coronavirus Infection Survey, relationship between model performance and data abundance and time-varying case detection rate. We found that our model can recover the first but unobserved epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as model inputs additionally, with the second wave during winter of 2020 validated by the estimates from ONS Coronavirus Infection Survey. Moreover, the model estimated that by the end of October in 2020 the UK government’s official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1% (95%CrI (8.7%,9.8%)) of cumulative exposure, dramatically varying across two epidemic waves in England in 2020 (4.3% (95%CrI (4.1%, 4.6%)) vs 43.7% (95%CrI (40.7%, 47.3%))).
Novel Tools for Lassa Virus Surveillance in Peri-domestic Rodents
Authors: Smither Allison R. , Koninga James , Kanneh Franklyn B. , Foday Momoh , Boisen Matthew L. , Bond Nell G. , Momoh Mambu , Demby Sandi John , Kanneh Lansana , Alhasan Foday , Mustapha Kanneh Ibrahim , Yillah Mohamed S. , Grant Donald S. , Bush Duane J. , Nelson Diana K. S. , Cruz Kaitlin M. , Klitting Raphaëlle , Pauthner Matthias , Andersen Kristian G. , Shaffer Jeffrey G. , Cross Robert W. , Schieffelin John S. , Garry Robert F. 
Published: March 2023

AbstractBackgroundLassa fever (LF) is a rodent-borne disease endemic to West Africa. In the absence of licensed therapeutics or vaccines, rodent exclusion from living spaces remains the primary method of preventing LF. Zoonotic surveillance of Lassa virus (LASV), the etiologic agent of LF, can assess the burden of LASV in a region and guide public health measures against LF.MethodsIn this study, we adapted commercially available LASV human diagnostics to assess the prevalence of LASV in peri-domestic rodents in Eastern Sierra Leone. Small mammal trapping was conducted in Kenema district, Sierra Leone between November 2018-July 2019. LASV antigen was detected using a commercially available LASV NP antigen rapid diagnostic test. LASV IgG antibodies against LASV nucleoprotein (NP) and glycoprotein (GP) were tested by adapting a commercially available semi-quantitative enzyme linked immunosorbent assay (ELISA) for detection of mouse-related and rat-related species IgG.FindingsOf the 373 tested specimens, 74 (20%) tested positive for LASV antigen. 40 (11%) specimens tested positive for LASV NP IgG, while an additional 12 (3%) specimens only tested positive for LASV GP IgG. Simultaneous antigen presence and IgG antibody presence was linked inMastomys sp. specimens (p< 0.01), but notRattus sp. specimens (p= 1). Despite the link between antigen presence and IgG antibody presence inMastomys sp., the strength of antigen response did not correlate with the strength of IgG response to either GP IgG or NP IgG.InterpretationThe tools developed in this study can aid in the generation of valuable public health data for rapid field assessment of LASV burden during outbreak investigations and general LASV surveillance.FundingFunding for this work was supported by the National Institute of Allergy and Infectious Diseases National Institute of Health, Department of Health and Human Services under the following grants: International Collaboration in Infectious Disease Research on Lassa fever and Ebola - ICIDR - U19 AI115589, Consortium for Viral Systems Biology - CViSB - 5U19AI135995, West African Emerging Infectious Disease Research Center - WARN-ID - U01AI151812, West African Center for Emerging Infectious Diseases: U01AI151801.
Developing a blood cell-based diagnostic test for myalgic encephalomyelitis/chronic fatigue syndrome using peripheral blood mononuclear cells
Authors: Xu Jiabao , Lodge Tiffany , Kingdon Caroline , Strong James W L , Maclennan John , Lacerda Eliana , Kujawski Slawomir , Zalewski Pawel , Huang Wei E. , Morten Karl J. 
Published: March 2023

AbstractMyalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients’ lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, we utilized a single-cell Raman platform and artificial intelligence to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. Our results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, we identified specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS, and could be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.
Costs of digital adherence technologies for tuberculosis treatment support
Authors: Nsengiyumva Ntwali Placide , Khan Amera , Gler Maricelle Ma. Tarcela S. , Lopez Mariceif , Marcelo Danaida , Andrews Mark C. , Duverger Karine , Ahmed Shahriar , Ibrahim Tasmia , Morales Mona Lisa , Villanueva Andre , Efo Egwumo , Onajare Baraka , Celan Cristina , Schwartzman Kevin 
Published: March 2023

AbstractDigital adherence technologies (DATs) are increasingly used for tuberculosis (TB) adherence support, but information about their costs remains limited. We used microcosting to estimate health system costs (in 2022 US dollars) of 99DOTS pill sleeves and video-observed treatment (VOT), implemented in demonstration projects during 2018-21. Local cost estimates for standard directly observed treatment (DOT) were also obtained. The estimated per-person costs of 99DOTS for drug-sensitive (DS-) TB were $98, $106, and $174 in Bangladesh (n=719), the Philippines (n=396), and Tanzania (n=976) respectively. The estimated per-person costs of VOT were $1 154, $304, $457, and $661 in Haiti (n=87 DS-TB), Moldova (n=173 DS-TB), Moldova (n=135 drug-resistant [DR]-TB) and the Philippines (n=110 DR-TB) respectively. Health system costs of 99DOTS may be similar to or cheaper than standard DOT. VOT is considerably more expensive; labor cost offsets and/or economies of scale may yield savings relative to standard DOT in some settings.SummaryIn diverse settings, health system costs of 99DOTS pill sleeves may be similar to or cheaper than standard directly observed treatment for TB; video-observed treatment is considerably more expensive, but labor cost offsets and/or economies of scale may yield savings.
Schwann cells modulate nociception in neurofibromatosis 1
Authors: Raut Namrata G.R. , Maile Laura A. , Oswalt Leila M. , Mitxelena Irati , Adlakha Aaditya , Sprague Kourtney L. , Rupert Ashley R. , Bokros Lane , Hofmann Megan C. , Patritti-Cram Jennifer , Rizvi Tilat A. , Queme Luis F. , Choi Kwangmin , Ratner Nancy , Jankowski Michael P. 
Published: March 2023

SummaryPain of unknown etiology is frequent in individuals with the tumor predisposition syndrome Neurofibromatosis 1 (NF1), even when tumors are absent. Schwann cells (SC) were recently shown to play roles in nociceptive processing, and we find that chemogenetic activation of SCs is sufficient to induce afferent and behavioral mechanical hypersensitivity in mice. In mouse models, animals show afferent and behavioral hypersensitivity when SC, but not neurons, lackNf1. Importantly, hypersensitivity corresponds with SC-specific upregulation of mRNA encoding glial cell line derived neurotrophic factor (GDNF), independent of the presence of tumors. Neuropathic pain-like behaviors in the NF1 mice were inhibited by either chemogenetic silencing of SC calcium or by systemic delivery of GDNF targeting antibodies. Together, these findings suggest that Nf1 loss in SCs causes mechanical pain by influencing adjacent neurons and, data may identify cell-specific treatment strategies to ameliorate pain in individuals with NF1.Graphical AbstractGDNF released from Schwann cells acts on sensory neurons leading to mechanical hypersensitivity and pain-like behaviors in preclinical models of NF1.
Protein-intrinsic properties and context-dependent effects regulate pioneer-factor binding and function
Authors: Gibson Tyler J. , Harrison Melissa M. 
Published: March 2023

AbstractChromatin is a barrier to the binding of many transcription factors. By contrast, pioneer factors access nucleosomal targets and promote chromatin opening. Despite binding to target motifs in closed chromatin, many pioneer factors display cell-type specific binding and activity. The mechanisms governing pioneer-factor occupancy and the relationship between chromatin occupancy and opening remain unclear. We studied threeDrosophilatranscription factors with distinct DNA-binding domains and biological functions: Zelda, Grainy head, and Twist. We demonstrated that the level of chromatin occupancy is a key determinant of pioneering activity. Multiple factors regulate occupancy, including motif content, local chromatin, and protein concentration. Regions outside the DNA-binding domain are required for binding and chromatin opening. Our results show that pioneering activity is not a binary feature intrinsic to a protein but occurs on a spectrum and is regulated by a variety of protein-intrinsic and cell-type-specific features.
Eliciting and prioritising determinants of improved care in Multiple Long Term Health Conditions (MLTC): A modified online Delphi study
Authors: Simpson Glenn , Stuart Beth , Hijryana Marisza , Akyea Ralph Kwame , Stokes Jonathan , Gibson Jon , Jones Karen , Morrison Leanne , Santer Miriam , Boniface Michael , Zlatev Zlatko , Farmer Andrew , Dambha-Miller Hajira 
Published: March 2023

AbstractIntroductionMultiple Long Term Conditions (MLTC) are a major challenge to health and social care systems around the world. There is limited research exploring the wider contextual determinants that are important to improving care for people living with MLTC. In this study, we aimed to elicit and prioritise determinants of improved care in people with MLTC.MethodsA three round online Delphi study was conducted in England with health and social care professionals, data scientists, researchers, people living with MLTC and their carers.ResultsOur findings suggest a care system which is still predominantly single condition focused. ‘Person-centred and holistic care’ and ‘coordinated and joined up care’, were highly rated determinants in relation to improved care for MLTC. We further identified a number of non-medical determinants that are important to providing holistic care for MLTC.ConclusionsFurther progress towards a holistic and patient centred model is needed to ensure that care more effectively addresses the complex range of medical and non-medical needs of people living with MLTC. This requires a move from a single condition focused biomedical model to a person-based biopsychosocial approach, which has yet to be achieved.
Risk-sensitive learning is a winning strategy for leading an urban invasion
Authors: Breen Alexis J , Deffner Dominik 
Published: March 2023

AbstractIn the unpredictable Anthropocene, a particularly pressing open question is how certain species invade urban environments. Sex-biased dispersal and learning arguably influence movement ecology, but their joint influence remains unexplored empirically, and might vary by space and time. We assayed reinforcement learning in wild-caught, temporarily-captive core-, middle- or edge-range great-tailed grackles—a bird species undergoing urban-tracking rapid range expansion, led by dispersing males. We show: across populations, both sexes initially perform similarly when learning stimulus-reward pairings, but, when reward contingencies reverse, male—versus female—grackles finish ‘relearning’ faster, making fewer choice-option switches. How do male grackles do this? Bayesian cognitive modelling revealed male grackles’ choice behaviour is governed more strongly by the ‘weight’ of relative differences in recent foraging payoffs—i.e., they show more pronounced risk-sensitive learning. Confirming this mechanism, agent-based forward simulations of reinforcement learning—where we simulate ‘birds’ based on empirical estimates of our grackles’ reinforcement learning—replicate our sex-difference behavioural data. Finally, evolutionary modelling revealed natural selection should favour risk-sensitive learning in hypothesised urban-like environments: stable but stochastic settings. Together, these results imply risk-sensitive learning is a winning strategy for urban-invasion leaders, underscoring the potential for life historyandcognition to shape invasion success in human-modified environments.
Comparative landscape of genetic dependencies in human and chimpanzee stem cells
Authors: She Richard , Fair Tyler , Schaefer Nathan K. , Saunders Reuben A. , Pavlovic Bryan J. , Weissman Jonathan S. , Pollen Alex A. 
Published: March 2023

AbstractComparative studies of great apes provide a window into our evolutionary past, but the extent and identity of cellular differences that emerged during hominin evolution remain largely unexplored. We established a comparative loss-of-function approach to evaluate whether changes in human cells alter requirements for essential genes. By performing genome-wide CRISPR interference screens in human and chimpanzee pluripotent stem cells, we identified 75 genes with species-specific effects on cellular proliferation. These genes comprised coherent processes, including cell cycle progression and lysosomal signaling, which we determined to be human-derived by comparison with orangutan cells. Human-specific robustness toCDK2andCCNE1depletion persisted in neural progenitor cells, providing support for the G1-phase length hypothesis as a potential evolutionary mechanism in human brain expansion. Our findings demonstrate that evolutionary changes in human cells can reshape the landscape of essential genes and establish a platform for systematically uncovering latent cellular and molecular differences between species.
PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing
Authors: Kurosawa Ryo , Iida Kei , Ajiro Masahiko , Awaya Tomonari , Yamada Mamiko , Kosaki Kenjiro , Hagiwara Masatoshi 
Published: March 2023

AbstractDeep-intronic variants often cause genetic diseases by altering RNA splicing. However, these pathogenic variants are overlooked in whole-genome sequencing analyses, because they are quite difficult to segregate from a vast number of benign variants (approximately 1,500,000 deep-intronic variants per individual). Therefore, we developed the Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing (PDIVAS), an ensemble machine-learning model combining multiple splicing features and regional splicing constraint metrics. Using PDIVAS, around 27 pathogenic candidates were identified per individual with 95% sensitivity, and causative variants were more efficiently prioritized than previous predictors in simulated patient genome sequences. PDIVAS is available athttps://github.com/shiro-kur/PDIVAS.