bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction and evaluation of a bioluminescent Pseudomonas aeruginosa reporter for use in preservative efficacy testing
Authors: Rushton Laura , Donoghue Denise , Bull Matthew , Jay Peter , Mahenthiralingam Eshwar
Published: August 2021

Preservative efficacy testing (PET) is a fundamental practice in industrial microbiology used to ensure product shelf-life and quality. To improve on current growth-based PET, bioluminescence was evaluated as a real-time bacterial viability indicator using

Pseudomonas aeruginosa

. Random mutagenesis of an industrial

P. aeruginosa

strain with a promoter-less luxCDABE mini-Tn5 was used to select a stable reporter (LUX12H5) with an un-altered growth and preservative susceptibility phenotype. Bioluminescence and viability were measured with and without preservatives (isothiazolinones, phenoxyethanol, and dimethyl dimethylol hydantoin) and an antibiotic comparator (ciprofloxacin). In the absence of antimicrobials, a good correlation between bioluminescence and viability (r2=0.92) was established. However, metabolic inhibition by isothiazolinone preservatives caused a rapid decline in light output that did not correlate to a reduced viability. Conversely, after ciprofloxacin exposure, the decline in viability was greater than that of bioluminescence. A positive attribute of the bioluminescence was the early detection of metabolic recovery and re-growth of preservative injured bacteria. Overall, while initial bioluminescence read-outs were less suited to current PET requirements, it shows promise as an early, direct indicator of bacterial regrowth in the context of long-term evaluation of preservative efficacy.
Salivary bacterial signatures in depression-obesity comorbidity are associated with neurotransmitters and neuroactive dipeptides
Authors: Aleti Gajender , Kohn Jordan N. , Troyer Emily A. , Weldon Kelly , Huang Shi , Tripathi Anupriya , Dorrestein Pieter C. , Swafford Austin D. , Knight Rob , Hong Suzi
Published: August 2021

Background

Depression and obesity, both of which are highly prevalent and inflammation underlies, often co- occur. Microbiome perturbations are implicated in obesity-inflammation-depression interrelationships, but how microbiome alterations contribute to underlying pathologic processes remains unclear. Metabolomic investigations to uncover microbial neuroactive metabolites may offer mechanistic insights into host-microbe interactions.

Methods

Using 16S sequencing and untargeted mass spectrometry of saliva, and blood monocyte inflammation regulation assays, we determined key microbes, metabolites and host inflammation in association with depressive symptomatology, obesity, and depressive symptomatology-obesity comorbidity.

Results

Gram-negative bacteria with inflammation potential were enriched relative to Gram-positive bacteria in comorbid obesity-depression, supporting the inflammation-oral microbiome link in obesity-depression interrelationships. Oral microbiome was highly predictive of depressive symptomatology-obesity co-occurrences than obesity and depressive symptomatology independently, suggesting specific microbial signatures associated with obesity-depression co- occurrences. Mass spectrometry analysis revealed significant changes in levels of signaling molecules of microbiota, microbial or dietary derived signaling peptides and aromatic amino acids among host phenotypes. Furthermore, integration of the microbiome and metabolomics data revealed that key oral microbes, many previously shown to have neuroactive potential, co- occurred with potential neuropeptides and biosynthetic precursors of the neurotransmitters dopamine, epinephrine and serotonin.

Conclusions

Together, our findings offer novel insights into oral microbial-brain connection and potential neuroactive metabolites involved.
A randomised controlled trial to test the effects of fish aggregating devices and SBC activities promoting fish consumption in Timor-Leste: A study protocol
Authors: Tilley Alexander , Byrd Kendra A , Pincus Lauren , Klumpyan Katherine , Dobson Katherine , do Reis Lopes Joctan , Shikuku Kelvin Mashisia
Published: August 2021

Timor-Leste is one of the world’s most malnourished nations where micronutrient-deficient diets are a contributing factor to the prevalence of child stunting, currently estimated to be 45.6% of children under five. Fish are an important source of micronutrients and one that may assist the country’s predominantly rural population of agriculturalists to exit poverty and malnutrition. However, a small national fishing fleet producing low catch volumes place fish out of reach of most inland and upland populations where it is needed most. Fish consumption is very low in rural areas compared to coastal, regional, and global averages. This trial is a one-year, cluster-randomized, partially masked, controlled trial among families living in rural, inland Timor-Leste. This trial aims to test and compare the effects of two treatments, alone and in combination, on the frequency and volume of household fish consumption in upland areas as a proxy for improved dietary diversity and micronutrient intake. Treatment 1 is the installation of nearshore, moored fish aggregating devices (FADs) to improve catch rates with existing fishing gears. Treatment 2 is social and behaviour change (SBC) activities to promote fish consumption. Villages in inland communities will be randomized to receive treatment 1, treatment 2, both treatments, or neither treatment. Some households with one child under five will be recruited, and data will be collected at baseline (prior to the rollout of the treatments) and endline. Our study will determine the impact of an improved supply of fish, along with nutrition-oriented SBC activities, on the fish purchasing and consumption practices of rural, inland households. Findings from this study are urgently needed by small island developing states in order to make policy and investment decisions on how best to improve households’ diets using locally available, nutrient-dense foods such as fish. Investments such as these are needed to break the cycle of malnutrition.
Substance Use Onset in High-Risk 9-13 Year-Olds in the ABCD Study
Authors: Wade Natasha E. , Tapert Susan F. , Lisdahl Krista M. , Huestis Marilyn A. , Haist Frank
Published: August 2021

Aim

A key aim of the Adolescent Brain Cognitive Development□ (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting can obscure accurate drug use characterization. Hair toxicology provides objective historical substance use data but is rarely used in studies of youth. Here, we compare objective hair toxicology results with self-reported substance use in high-risk youth.

Methods

A literature-based substance use risk algorithm prioritized 696 ABCD Study® participants for hair sample analysis at baseline, and 1 and 2-year follow-ups (spanning ages 9-13). Chi-square and t-tests assessed differences between participants’ demographics, positive and negative hair tests, risk-for-use algorithm scores, and self-reported substance use.

Results

Hair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=97), two (n=14), three (n=2), or four (n=2) drug classes. After considering prescribed medication use, 10.3% had a positive test incongruent with self-report. No participant with a positive result self-reported recreational substance use that was consistent with their toxicology results. They also reported less sipping of alcohol (p < 0.001) and scored higher on the risk-for-use algorithm (p < 0.001) than those with negative toxicology results.

Conclusions

10% of at-risk 9-13 year-olds tested positive for at least one unreported substance, suggesting underreporting in this age range when participating in a research study. As hair testing prioritized youth with risk characteristics, the overall extent of underreporting will be calculated in future studies. Nonetheless, hair toxicology was key to characterizing substance use in high-risk youth.
An individual-level socioeconomic measure for assessing algorithmic bias in health care settings: A case for HOUSES index
Authors: Juhn Young J. , Ryu Euijung , Wi Chung-Il , King Katherine S. , Brufau Santiago Romero , Weng Chunhua , Sohn Sunghwan , Sharp Richard , Halamka John D.
Published: August 2021

While artificial intelligence (AI) algorithms hold great potential for improving health and reducing health disparities, biased AI algorithms have a potential to negatively impact the health of under-resourced communities or racial/ethnic minority populations. Our study highlights the major role of socioeconomic status (SES) in AI algorithm bias and (in)completeness of electronic health records (EHRs) data, which is commonly used for algorithm development. Understanding the extent to which SES impacts algorithmic bias and its pathways through which SES operates its impact on algorithmic bias such as differential (in)completeness of EHRs will be important for assessing and mitigating algorithmic bias. Despite its importance, the role of SES in the AI fairness science literature is currently under-recognized and under-studied, largely because objective and scalable individual-level SES measures are frequently unavailable in commonly used data sources such as EHRs. We addressed this challenge by applying a validated individual-level socioeconomic measure that we call the HOUSES index. This tool allows AI researchers to assess algorithmic bias due to SES. Although our study used a cohort with a relatively small sample size, these study results highlight a novel conceptual strategy for quantifying AI bias by SES.
Size dependent regeneration capacity of functionalized Capra ear derived micro-tissue scaffolds for treatment of cartilage defects
Authors: Maity Pritiprasanna , Mukherjee Sumanta , Das Subahayan , Mandal Mahitosh , Dhara Santanu
Published: August 2021

Cartilage regeneration remains a great challenge in orthopedic treatment owing to their avascular in nature and lack of self-healing ability. Various clinical treatment options are widely used including osteochondral graft transplantation, micro-fracture, blood clot formation and tissue debridement to recover the damaged cartilage. In this circumstance, cartilage defect recovery via tissue engineered functional micro tissue delivery is becoming an emerging trend in musculoskeletal therapeutics. In this study, functional micro-scaffolds (MS) were generated from Capra ear cartilage, and were separated into size-wise groups. The scaffolds were decellularized via NaOH treatment. The cell adhesion study indicated that Capra adipose tissue derived mesenchymal stem cells (ADMSCs) adhesion is more in ~100 μm MSs in comparison to 150-300 μm MSs. It may be assumed that the cells are compatible to grow on fibrous surface area (100 μm) in comparison to dense surface (150-300 μm). Further, 100 μm MSs were transformed into functional micro tissues (FMTs) in presence of high density ADMSCs in a hanging droplet culture system. The FMTs was transferred to F127 block polymer hydrogel for 3D culture. After 21 d cultures, the FMT clusters were evaluated for quantitative gene expression. To assess the in vivo cartilage defect regeneration potential, FMTs were delivered to rabbit auricular cartilage defect for 15, 30 and 60 d studies. The H&E-stained histological analysis showed that the cartilage defect is almost healed in 60 d study in comparison to 15 and 30 d study.
The D614G virus mutation enhances anosmia in COVID-19 patients: Evidence from a systematic review and meta-analysis of studies from South Asia
Authors: von Bartheld Christopher S. , Hagen Molly M. , Butowt Rafal
Published: August 2021

The prevalence of chemosensory dysfunction in patients with COVID-19 varies greatly between populations. It is unclear whether such differences are due to factors at the level of the human host, or at the level of the coronavirus, or both. At the host level, the entry proteins which allow virus binding and entry have variants with distinct properties, and the frequency of such variants differs between ethnicities. At the level of the virus, the D614G mutation enhances virus entry to the host cell. Since the two virus strains (D614 and G614) co-existed in the first six months of the pandemic in most populations, it has been difficult to distinguish between contributions of the virus and contributions of the host for anosmia. To answer this question, we conducted a systematic review and meta-analysis of studies in South Asian populations when either the D614 or the G614 virus was dominant. We show that populations infected predominantly with the G614 virus had a much higher prevalence of anosmia (pooled prevalence of 31.8%) compared with the same ethnic populations infected mostly with the D614 virus strain (pooled anosmia prevalence of 5.3%). We conclude that the D614G mutation is a major contributing factor that increases the prevalence of anosmia in COVID-19, and that this enhanced effect on olfaction constitutes a previously unrecognized phenotype of the D614G mutation. The new virus strains that have additional mutations on the background of the D614G mutation can be expected to cause a similarly increased prevalence of chemosensory dysfunctions.

Graphical Abstract
Non-mycorrhizal root associated fungi of a tropical montane forest are relatively robust to the long-term addition of moderate rates of nitrogen and phosphorus
Authors: Dueñas Juan F. , Hempel Stefan , Homeier Jürgen , Suárez Juan Pablo , Rillig Matthias C. , Camenzind Tessa
Published: August 2021

Andean forests are biodiversity hotspots and globally important carbon (C) repositories. This status might be at risk due to increasing rates of atmospheric nutrient deposition. As fungal communities are key in the recirculation of soil nutrients, assessing their responses to soil eutrophication can help establish a link between microbial biodiversity and the sustainability of the C sink status of this region. Beyond mycorrhizal fungi, which have been studied more frequently, a wide range of other fungi associate with the fine root fraction of trees. Monitoring these communities can offer insights into how communities composed of both facultative and obligate root associated fungi are responding to soil eutrophication.

Here we document the response of non-mycorrhizal root associated fungal (RAF) communities to a long-term nutrient manipulation experiment. The stand level fine root fraction of an old growth tropical montane forest was sampled after seven years of nitrogen (N) and phosphorus (P) additions. RAF communities were characterized by a deep sequencing approach. As per the resource imbalance model, we expected that asymmetries in the availability of C, N and P elicited by fertilization will lead to mean richness reductions and alterations of the community structure.

We recovered moderately diverse fungal assemblages composed by sequence variants classified within a wide set of trophic guilds. While mean richness remained stable, community composition shifted, particularly among Ascomycota and after the addition of P. Fertilization factors, however, only accounted for a minor proportion of the variance in community composition. These findings suggest that, unlike mycorrhizal fungi, RAF communities are less sensitive to shifts in soil nutrient availability. A plausible explanation is that non-mycorrhizal RAF have fundamentally different nutrient acquisition and life history traits, thus allowing them greater stoichiometric plasticity and an array of functional acclimation responses that collectively express as subtle shifts in community level attributes.
Invalidity of light sensor data in field studies and a proposal of an algorithmic approach for detection and filtering of non-wear time
Authors: Hunt Larissa C. , Fritz Josef , Herf Michael , Herf Lorna , Vetter Celine
Published: August 2021

Wearable light sensors are increasingly used in intervention and population-based studies investigating the consequences of environmental light exposure on human physiology. An important step in such analyses is the reliable detection of non-wear time. We observed in light data that days with less wear-time also have lower variability in the light signal, and we sought to test if the standard deviation of the change between subsequent samples can detect this condition. In this study, we propose and validate an easy-to-implement algorithm designed to discriminate between days with a non-wear time >4h (“invalid days”) vs. ≤4h (“valid days”) and investigate to which extent values of commonly used physiologically meaningful light variables differ between invalid days, valid days, and algorithm-selected non-wear days. We used 83 days of light data from a field study with high participant compliance, complemented by 47 days of light data where free-living individuals were instructed not to wear the sensor for varying amounts of time. Light data were recorded every two minutes using the pendant-worn f.luxometer light sensor; validity was derived from daily logs where participants recorded all non-wear time. The algorithm-derived score discriminated well between valid and invalid days (area under the curve (AUC): 0.77, 95% CI: 0.67-0.87). The best cut-off value (i.e., highest Youden index) correctly recognized valid days with a probability of 87% (“sensitivity”), and invalid days with a probability of 63% (“specificity”). Values of various light variables derived from algorithm-selected days only (median: 264.3 (Q1: 153.6, Q3: 420.0) for 24h light intensity (in lux); 496.0 (404.0, 582.0) for time spent above 50-lux) gave values close to those derived from self-reported valid days only. However, these values did not significantly differ when derived across all days compared to self-reported valid days. Our results suggest that our proposed algorithm discriminates well between valid and invalid days. However, in high compliance cohorts, distortions in aggregated light measures of individual-level environmental light recordings across days appear to be small, making the application of our algorithm optional, but not necessary.
Whole genome analysis for 163 guide RNAs in Cas9 edited mice reveals minimal off-target activity
Authors: Peterson Kevin A. , Khalouei Sam , Wood Joshua A. , Lanza Denise G. , Lintott Lauri G. , Willis Brandon J. , Seavitt John R. , Hanafi Nour , Braun Robert E. , Dickinson Mary E. , White Jacqueline K. , Lloyd K.C. Kent , Heaney Jason D. , Murray Stephen A. , Ramani Arun , Nutter Lauryl M.J.
Published: August 2021

Introductory Paragraph

The Knockout Mouse Phenotyping Program (KOMP2) uses CRISRPR/Cas9 for high-throughput mouse line production to generate null alleles in the inbred C57BL/6N strain for broad-based in vivo phenotyping. In order to assess the risk of spurious S. pyogenes Cas9-induced off-target mutagenesis, we applied whole genome sequencing to compare the genomes of 50 Cas9-derived founder mice representing 163 different gRNAs to 28 untreated inbred control mice. Our analysis pipeline detected 28 off-target sequence variants associated with 21 guides. These potential off-targets were identified in 18/50 (36%) founders with 9/28 (32%) independently validated corresponding to 8 founder animals. In total, only 4.9% (8/163) of all guides exhibited off-target activity resulting in a rate of 0.16 Cas9 off-target mutations per founder analyzed. In comparison, we observed ~1225 unique variants in each mouse regardless of whether or not it was exposed to Cas9. These findings indicate that Cas9-mediated off-target mutagenesis is rare in founder knockout mice generated using guide RNAs designed to minimize off-target risk. Overall, bona fide off-target variants comprise a small fraction of the genetic heterogeneity found in carefully maintained colonies of inbred strains.
Site Pattern Probabilities Under the Multispecies Coalescent and a Relaxed Molecular Clock: Theory and Applications
Authors: Richards A. , Kubatko L.
Published: August 2021

1

The first step in statistical inference of the evolutionary histories of species is developing a probability model that describes the mutation process as accurately and realistically as possible. A major complication of this inference is that different loci on the genome can have histories that diverge from the common species history and each other. The multispecies coalescent process is commonly used to model one source of this divergence, incomplete lineage sorting, or ILS. Chifman and Kubatko (2015) computed the site pattern probabilities for four taxa under a full probability model based on the Jukes-Cantor substitution model when the molecular clock holds. This paper generalizes that work to a relaxed clock model, allowing for mutation rates to differ among species. This will enable better phylogentic inference in cases where the molecular clock does not hold.
Cross-sectional genomic perspective of epidemic waves of SARS-CoV-2: a pan India study
Authors: Kumar Sanjeet , Bansal Kanika
Published: August 2021

COVID-19 has posed unforeseen circumstances and throttled major economies worldwide. India has witnessed two waves affecting around 31 million people representing 16% of the cases globally. To date, the epidemic waves have not been comprehensively investigated to understand pandemic progress in India. In the present study, we aim for a cross-sectional analysis since its first incidence up to 26th July 2021. We have performed the pan Indian evolutionary study using 20,086 high-quality complete genomes of SARS-CoV-2. Based on the number of cases reported and mutation rates, we could divide the Indian epidemic into seven different phases. First, three phases constituting the pre-first wave had a very less average mutation rate (<11), which increased in the first wave to 17 and then doubled in the second wave (~34). In accordance with the mutation rate, variants of concern (alpha, beta, gamma and delta) and interest (eta and kappa) also started appearing in the first wave (1.5% of the genomes), which dominated the second (~96% of genomes) and post-second wave (100% of genomes) phases. Whole genome-based phylogeny could demarcate the post-first wave isolates from previous ones by the point of diversification leading to incidences of VOCs and VOIs in India. Nation-wide mutational analysis depicted more than 0.5 million events with four major mutations in ~97% of the total 20,086 genomes in the study. These included two mutations in coding (spike (D614G) and NSP 12b (P314L) of RNA dependent RNA polymerase), one silent mutation (NSP3 F106F) and one extragenic mutation (5’ UTR 241). Large scale genome-wide mutational analysis is crucial in expanding knowledge on evolution of deadly variants of SARS-CoV-2 and timely management of the pandemic.
Investigate Inhibitory Effects of Ginger polyphenols compare to Simvastatin towards HMG-CoA reductase: An Integrated Molecular Docking and Molecular dynamic simulation
Authors: Belachew Aweke Mulu , Feyisa Asheber , Ufgaa Mulugeta Gajaa , Yesuf Teslim Yimama
Published: August 2021

Diabetes is an increasing problem in Ethiopia, affecting up to 6.5% of Ethiopian adults. There are serious complications associated with diabetes including macrovascular and microvascular. Controlling Lipid profiles and blood glucose significantly reduces the risk of complications. Statins are the only current treatment for both type 1 diabetes and Type 2 diabetes dyslipidemia. HMG-CoA reductase plays a central role in the production of cholesterol which, associated with cardiovascular disease (CVD). Statins have been found to reduce cardiovascular disease and mortality in those who are at high risk. Nonetheless, it has adverse effect, such as drug-related hypoglycemia and high cost. These situations lead to develop suitable phytotherapeutic agents with less frequent side effects. Ginger (Zingiber officinale) is widely consumed as a spice, and numerous studies suggest that ginger may have beneficial effects for diabetes and dyslipidemia. But, further studies are needed to investigate effects of binding affinity and binding site residues for major ginger extract polyphenols towards target HMG-CoA reductase. In this study, ADMET web server, Auto-Dock 5.4 and Gromacs 2020 were used. Out of eleven major gingers polyphenols screened three selected based on docking energy compare to Simvastatin for MD simulation. The predicted binding affinity for 6-paradol, 6-shogaol and gingerdione were −8.51, −6.93, −9.24 kcal/mol, respectively. The results of molecular dynamic simulation are consistence with docking. The predicted ligand binding site residues are Arg641, Gly808, Arg641, Met781, Ser794 and Arg595. In conclusion, 6-paradol, 6-shogaol and gingerdione could be possible therapy because, of interactions with target HMG-CoA reductase. Therefore, further wet lab study will be needed, for the better understanding of the mechanism of action of ginger extract by which it modulates liver and kidney vivo condition.
Compendium of secondary metabolite biosynthetic diversity encoded in bacterial genomes
Authors: Gavriilidou Athina , Kautsar Satria A , Zaburannyi Nestor , Krug Daniel , Mueller Rolf , Medema Marnix H , Ziemert Nadine
Published: August 2021

Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~ 170,000 bacterial genomes and ~ 47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We found that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation of secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on Relative Evolutionary Distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation.
Reference genome and resequencing of 305 accessions provide insights into spinach evolution, domestication and genetic basis of agronomic traits
Authors: Cai Xiaofeng , Sun Xuepeng , Xu Chenxi , Sun Honghe , Wang Xiaoli , Ge Chenhui , Zhang Zhonghua , Wang Quanxi , Fei Zhangjun , Jiao Chen , Wang Quanhua
Published: August 2021

Spinach is a nutritious leafy vegetable belonging to the family Chenopodiaceae. Here we report a chromosome-scale reference genome assembly of spinach, which has a total size of 894.3 Mb and an N50 contig size of 23.8 Mb, with 98.3% anchored and ordered on the six chromosomes. Reconstruction of ancestral Chenopodiaceae karyotype indicates substantial genome rearrangements in spinach after its divergence from ancestral Chenopodiaceae, coinciding with high repeat content in the spinach genome. Resequencing the genomes of 305 cultivated and wild spinach accessions provides insights into spinach genetic diversity and population differentiation. Genome-wide association studies of 20 agronomical traits identify numerous significantly associated regions and candidate genes for these traits. Domestication sweeps in the spinach genome are identified, some of which are associated with important traits (e.g., leaf phenotype, bolting and flowering), demonstrating the role of artificial selection in shaping spinach phenotypic evolution. This study provides not only insights into the spinach evolution and domestication but also valuable resources for facilitating spinach breeding.
The Proteomic Architecture of Schizophrenia Cerebral Organoids Reveals Alterations in GWAS and Neuronal Development Factors
Authors: Notaras Michael , Lodhi Aiman , Fang Haoyun , Greening David , Colak Dilek
Published: August 2021

Schizophrenia (Scz) is a brain disorder that has a typical onset in early adulthood but otherwise maintains unknown disease origins. Unfortunately, little progress has been made in understanding the molecular mechanisms underlying neurodevelopment of Scz due to ethical and technical limitations in accessing developing human brain tissue. To overcome this challenge, we have previously utilized patient-derived Induced Pluripotent Stem Cells (iPSCs) to generate self-developing, self-maturating, and self-organizing 3D brain-like tissue known as cerebral organoids. As a continuation of this prior work [1], here we provide a molecular architectural map of the developing Scz organoid proteome. Utilizing iPSCs fromn= 25 human donors (n= 8 healthy Ctrl donors, andn= 17 Scz patients), we generated 3D human cerebral organoids, employed 16-plex isobaric sample-barcoding chemistry, and simultaneously subjected samples to comprehensive high-throughput liquid-chromatography/mass-spectrometry (LC/MS) quantitative proteomics. Of 3,705 proteins identified by high-throughput proteomic profiling, we identified that just ~2.62% of the organoid global proteomic landscape was differentially regulated in Scz organoids. In sum, just 43 proteins were up-regulated and 54 were down-regulated in Scz patient-derived organoids. Notably, a range of neuronal factors were depleted in Scz organoids (e.g., MAP2, TUBB3, SV2A, GAP43, CRABP1, NCAM1 etc.). Based on global enrichment analysis, alterations in key pathways that regulate nervous system development (e.g., axonogenesis, axon development, axon guidance, morphogenesis pathways regulating neuronal differentiation, as well as substantia nigra development) were perturbed in Scz patient-derived organoids. We also identified prominent alterations in two novel GWAS factors, Pleiotrophin (PTN) and Podocalyxin (PODXL), in Scz organoids. In sum, this work serves as both a report and a resource whereby researchers can leverage human-derived neurodevelopmental data from Scz patients, which can be used to mine, compare, contrast, or orthogonally validate novel factors and pathways related to Scz risk identified in datasets from observational clinical studies and other model systems.
CsrA-controlled proteins reveal new dimensions of Acinetobacter baumannii desiccation tolerance
Authors: Oda Yasuhiro , Shapiro Madelyn M. , Lewis Nathan M. , Zhong Xuefei , Huse Holly K. , Zhong Weizhi , Bruce James E. , Manoil Colin , Harwood Caroline S.
Published: August 2021

Hospital environments are excellent reservoirs for the opportunistic pathogen Acinetobacter baumannii in part because it is exceptionally tolerant to desiccation. We found that relative to other A. baumannii strains, the virulent strain AB5075 was strikingly desiccation resistant at 2% relative humidity (RH), suggesting that it’s a good model for studies of the functional basis of this trait. Consistent with results from other A. baumannii strains at 30% RH, we found the global post-transcriptional regulator CsrA to be critically important for desiccation tolerance of AB5075 at 2% RH. To identify CsrA-controlled proteins that may contribute to desiccation tolerance we used proteomics to identify proteins that were differentially present in wild type and csrA mutant cells. Subsequent mutant analysis revealed nine genes that were required for wild type levels of desiccation tolerance, five of which had modest phenotypes. Catalase and a universal stress protein gene were moderately important for desiccation tolerance and two genes of unknown function had very strong desiccation phenotypes. The predicted amino acid sequence of one of these genes predicts an intrinsically disordered protein. This category of proteins is widespread in eukaryotes but less so in prokaryotes. Our results suggest there may be mechanisms responsible for desiccation tolerance that have not previously been explored in bacteria.

IMPORTANCE

Acinetobacter baumannii is commonly found in terrestrial environments but can cause nosocomial infections in very sick patients. A factor that contributes to the prevalence of A. baumannii in hospital settings is that it is intrinsically resistant to dry conditions. Here, we established the virulent strain A. baumannii AB5075 as a model for studies of desiccation tolerance at very low relative humidity. Our results show that this trait depends on two proteins of unknown function, one of which is predicted to be an intrinsically disordered protein. This category of protein is critical for the small animals named tardigrades to survive desiccation. Our results suggest that A. baumannii may have novel strategies to survive desiccation that have not previously been seen in bacteria.
Refining Bulk Segregant Analyses: Ontology-Mediated Discovery of Flowering Time Genes in Brassica oleracea
Authors: Vos Rutger A. , van der Veen-van Wijk Catharina A. M. , Schranz M. Eric , Vrieling Klaas , Klinkhamer Peter G. L. , Lens Frederic
Published: August 2021

Abstract

Background

Bulk segregant analysis (BSA) can help identify quantitative trait loci (QTLs), but this may result in substantial bycatch of functionally irrelevant genes. Here we develop a Gene Ontology-mediated approach to zoom in on specific markers implicated in flowering time from among QTLs identified by BSA of the giant woody Jersey kale phenotyped in four bulks of flowering onset.

Results

Our BSA yielded tens of thousands of candidate genes. We reduced this by two orders of magnitude by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. A further enrichment test led to the pathway for circadian rhythm in plants. The genes that enriched this pathway are attested from previous research as regulating flowering time. Some of these genes were also identified as having functionally significant variation compared to Arabidopsis

Conclusions

We validated and confirmed our ontology-mediated results through a more targeted, homology-based approach. However, our ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery. We view our method as potentially applicable to the study of other complex traits and therefore make our workflows available as open-source code and a reusable Docker container.
Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction
Authors: Hostallero David Earl , Li Yihui , Emad Amin
Published: August 2021

Motivation

The increasing number of publicly available databases containing drugs’ chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy.

Results

We present two deep learning approaches, BiG-DRP and BiG-DRP+, for drug response prediction. Our models take advantage of the drugs’ chemical structure and the underlying relationships of drugs and cell lines through a bipartite graph and a heterogenous graph convolutional network that incorporate sensitive and resistant cell line information in forming drug representations. Evaluation of our methods and other state-of-the-art models in different scenarios shows that incorporating this bipartite graph significantly improves the prediction performance. Additionally, genes that contribute significantly to the performance of our models also point to important biological processes and signaling pathways. Analysis of predicted drug response of patients’ tumors using our model revealed important associations between mutations and drug sensitivity, illustrating the utility of our model in pharmacogenomics studies.

Availability and Implementation

An implementation of the algorithms in Python is provided in github.com/ddhostallero/BiG-DRP.

Contact

amin.emad@mcgill.ca

Supplementary Information

Online-only supplementary data is available at the journal’s website.
Exosomal miR-106b-5p promotes Mtb survival via targeting CREB5 followed by SOAT1-CIDEC and CASP9-CASP3 pathway
Authors: Chen Haotian , Li Chonghui , Song Taohua , Gao Jiao , Li Wenjing , Fu Yurong , Yi Zhengjun
Published: August 2021

Tuberculosis(TB) is one of the top ten fatal diseases, but the research on the mechanism of TB is still not perfect. Exosome, as an important intercellular signal transmission signal vehicle and the mechanism of exosomes in the interaction between macrophages andMycobacterium tuberculosis(Mtb), is crucial for TB treatment. In the study, we found that exosomes, derived from Mtb-infected macrophage, exhibited differential enrichment in different organs in mice, causing inflammatory cell infiltration in lungs. Further experimentsin vitroshowed that exosomes resulted in increased lipid synthesis and inhibition of apoptosis in normal macrophages. In order to further explore its molecular mechanism, bioinformatics analysis showed that miR-106b-5p was up-regulated in exosomes. Subsequently, we verified miR-106b-5p was increased through a large number of blood samples from TB patients. In addition, we demonstrated that miR-106b-5p was upregulated in exosomes from Mtb-infected macrophages, which can be engulfed by uninfected macrophages and further result in miR-106b-5p increase. We next found that miR-106b-5p mediated the same effect as the exosomes derived from infected macrophage. Through further research, we indicated that miR-106b-5p promoted lipid droplet accumulation through regulation of Creb5-SOAT1-CIDEC and suppressed macrophage apoptosis via Creb5-CASP9-CASP3 pathway, which ultimately led to Mtb survival. These findings provide a certain theoretical basis and ideas for the diagnosis and treatment of TB as well as the selection of biomarkers.
Molecular dynamics and theratyping in airway and gut organoids reveal R352Q-CFTR conductance defect
Authors: Wong Sharon L. , Awatade Nikhil T. , Astore Miro A. , Allan Katelin M. , Carnell Michael J. , Slapetova Iveta , Chen Po-chia , Setiadi Jeffry , Pandzic Elvis , Fawcett Laura K. , Widger John R. , Whan Renee M. , Griffith Renate , Ooi Chee Y. , Kuyucak Serdar , Jaffe Adam , Waters Shafagh A.
Published: August 2021

A significant challenge to making targeted CFTR modulator therapies accessible to all individuals with cystic fibrosis (CF) are many mutations in the CFTR gene that can cause CF, most of which remain uncharacterized. Here, we characterized the structural and functional defects of the rareCFTRmutation R352Q – with potential role contributing to intrapore chloride ion permeation – in patient-derived cell models of the airway and gut. CFTR function in differentiated nasal epithelial cultures and matched intestinal organoids was assessed using ion transport assay and forskolin-induced swelling (FIS) assay respectively. Two CFTR potentiators (VX-770, GLPG1837) and a corrector (VX-809) were tested. Data from R352Q-CFTR were compared to that of participants with mutations with known impact on CFTR function. R352Q-CFTR has residual CFTR function which was restored to functional CFTR activity by CFTR potentiators but not the corrector. Molecular dynamics (MD) simulations of R352Q-CFTR were carried out which indicated the presence of a chloride conductance defect, with little evidence supporting a gating defect. The combination approach ofin vitropatient-derived cell models andin silicoMD simulations to characterize rareCFTRmutations can improve the specificity and sensitivity of modulator response predictions and aid in their translational use for CF precision medicine.
Selecting Clustering Algorithms for IBD Mapping
Authors: Shemirani Ruhollah , Belbin Gillian M , Burghardt Keith , Lerman Kristina , Avery Christy L , Kenny Eimear E , Gignoux Christopher R , Ambite José Luis
Published: August 2021

Background

Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power.

Results

We simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with 51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison.

Conclusions

Markov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.
Robust and Functional Immunity up to 9 months after SARS-CoV-2 infection: a Southeast Asian longitudinal cohort
Authors: Thi My Vo Hoa , Alvino Maestri , Heidi Auerswald , Sopheak Sorn , Sokchea Lay , Seng Heng , Sotheary Sann , Nisa Ya , Polidy Pean , Philippe Dussart , Olivier Schwartz , Sovann Ly , Timothee Bruel , Sowath Ly , Veasna Duong , Erik A Karlsson , Tineke Cantaert
Published: August 2021

Assessing the duration of humoral and cellular immunity remains key to overcome the current SARS-CoV-2 pandemic, especially in understudied populations in least developed countries. Sixty-four Cambodian individuals with laboratory-confirmed infection with asymptomatic or mild/moderate clinical presentation were evaluated for humoral immune response to the viral spike protein and antibody effector functions during acute phase of infection and at 6-9 months follow-up. Antigen-specific B cells, CD4+and CD8+T cells were characterized, and T cells were interrogated for functionality at late convalescence. Anti-spike (S) antibody titers decreased over time, but effector functions mediated by S-specific antibodies remained stable. S- and nucleocapsid (N)-specific B cells could be detected in late convalescence in the activated memory B cell compartment and are mostly IgG+. CD4+and CD8+T cell immunity was maintained to S and membrane (M) protein. Asymptomatic infection resulted in decreased ADCC and frequency of SARS-CoV-2-specific CD4+T cells at late convalescence. Whereas anti-S antibodies correlated with S-specific B cells, there was no correlation between T cell response and humoral immunity. Hence, all aspects of a protective immune response are maintained up to nine months after SARS-CoV-2 infection in the absence of re-infection.

One sentence summary

Functional immune memory to SARS-CoV-2, consisting of polyfunctional antibodies, memory B cells and memory T cells are maintained up to nine months in a South-East Asian cohort in the absence of re-infection.
Tracking dynamic structural changes in catalysis by rapid 2D-XANES microscopy
Authors: Alizadehfanaloo Saba , Garrevoet Jan , Seyrich Martin , Murzin Vadim , Becher Johannes , Doronkin Dmitry E. , Sheppard Thomas L. , Grunwaldt Jan-Dierk , Schroer Christian G. , Schropp Andreas
Published: August 2021

Many processes and materials in heterogeneous catalysis undergo dynamic structural changes depending on their chemical environment. Monitoring such dynamic changes can be challenging using conventional spectroscopic characterization tools, due to the high time resolution required. Here, a high-resolution 2D X-ray camera operating at 50 Hz full-frame rate was synchronized with a QEXAFS monochromator, enabling rapid spectro-microscopic imaging with chemical contrast over individual pixels. This was used to monitor chemical gradients within a model Pt/Al2O3 catalyst during catalytic partial oxidation of methane to synthesis gas. The transition from methane combustion (partly oxidized Pt) to combustion-reforming and partial oxidation (fully reduced Pt) was observed by a characteristic reduction front, which progressed from the end of the catalyst bed towards its beginning on the second time scale. The full-field QEXAFS imaging method applied here allows acquisition of entire XANES spectra `on the fly' in a rapid and spatially resolved manner. The combination of high spatial and temporal resolution with spectroscopic data offers new opportunities for observing dynamic processes in catalysts and other functional materials at work. The methodology is flexible and can be applied at beamlines equipped with a QEXAFS or other fast-scanning monochromators and a suitable sample environment for gas phase analytics to allow for catalytic studies at the same time.
Improving reproducibility in synchrotron tomography using implementation-adapted filters
Authors: Ganguly Poulami Somanya , Pelt Daniël M. , Gürsoy Doga , de Carlo Francesco , Batenburg K. Joost
Published: August 2021

For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These robust and computationally efficient algorithms have been integrated in a broad range of software packages. The continuous mathematical formulas used for image reconstruction in such algorithms are unambiguous. However, variations in discretization and interpolation result in quantitative differences between reconstructed images, and corresponding segmentations, obtained from different software. This hinders reproducibility of experimental results, making it difficult to ensure that results and conclusions from experiments can be reproduced at different facilities or using different software. In this paper, a way to reduce such differences by optimizing the filter used in analytical algorithms is proposed. These filters can be computed using a wrapper routine around a black-box implementation of a reconstruction algorithm, and lead to quantitatively similar reconstructions. Use cases for this approach are demonstrated by computing implementation-adapted filters for several open-source implementations and applying them to simulated phantoms and real-world data acquired at the synchrotron. Our contribution to a reproducible reconstruction step forms a building block towards a fully reproducible synchrotron tomography data processing pipeline.