Bayesian multivariant fine mapping using the Laplace prior.

Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the corresponding Gaussian prior and FINEMAP. Our results indicate that, for the simulation scenarios we consider here, the Laplace prior can lead to higher PIPs than either the Gaussian prior or FINEMAP, particularly for moderately sized fine-mapping studies. The Laplace prior also appears to have better worst-case scenario properties. We reanalyse the iCOGS case-control data from the CASP8 region on Chromosome 2. Even though this study has a total sample size of nearly 90,000 individuals, there are still some differences in the top few ranked SNPs if the Laplace prior is used rather than the Gaussian prior. R code to implement the Laplace (and Gaussian) prior is available at https://github.com/Kevin-walters/lapmapr.

The utility of the Laplace effect size prior distribution in Bayesian fine-mapping studies.

The Gaussian distribution is usually the default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian population-based fine-mapping association studies, but a recent study showed that the heavier-tailed Laplace prior distribution provided a better fit to breast cancer top hits identified in genome-wide association studies. We investigate the utility of the Laplace prior as an effect size prior in univariate fine-mapping studies. We consider ranking SNPs using Bayes factors and other summaries of the effect size posterior distribution, the effect of prior choice on credible set size based on the posterior probability of causality, and on the noteworthiness of SNPs in univariate analyses. Across a wide range of fine-mapping scenarios the Laplace prior generally leads to larger 90% credible sets than the Gaussian prior. These larger credible sets for the Laplace prior are due to relatively high prior mass around zero which can yield many noncausal SNPs with relatively large Bayes factors. If using conventional credible sets, the Gaussian prior generally yields a better trade off between including the causal SNP with high probability and keeping the set size reasonable. Interestingly when using the less well utilised measure of noteworthiness, the Laplace prior performs well, leading to causal SNPs being declared noteworthy with high probability, whilst generally declaring fewer than 5% of noncausal SNPs as being noteworthy. In contrast, the Gaussian prior leads to the causal SNP being declared noteworthy with very low probability.

Probabilistic identification of bacterial essential genes via insertion density using TraDIS data with Tn5 libraries.

MOTIVATION: Probabilistic Identification of bacterial essential genes using transposon-directed insertion-site sequencing (TraDIS) data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We assess the effectiveness of our approach via analysis of both simulated data and three previously published Escherichia coli, Salmonella Typhimurium and Staphylococcus aureus datasets. These three bacteria have relatively well characterized essential genes which allows us to test our classification procedure using receiver operating characteristic curves and area under the curves. We compare the classification performance with that of Bio-Tradis, a standard tool for bacterial gene classification. RESULTS: Our method is able to classify genes in the three datasets with areas under the curves between 0.967 and 0.983. Our simulated synthetic datasets show that both the number of insertions and the extent to which insertions are tolerated in the distal regions of essential genes are both important in determining classification accuracy. Importantly our method gives the user the option of classifying essential genes based on the user-supplied costs of false discovery and false non-discovery. AVAILABILITY AND IMPLEMENTATION: An R package that implements the method presented in this paper is available for download from https://github.com/Kevin-walters/insdens. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hypergraph models of biological networks to identify genes critical to pathogenic viral response.

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Mutations in the Neuraminidase-Like Protein of Bat Influenza H18N11 Virus Enhance Virus Replication in Mammalian Cells, Mice, and Ferrets.

To characterize bat influenza H18N11 virus, we propagated a reverse genetics-generated H18N11 virus in Madin-Darby canine kidney subclone II cells and detected two mammal-adapting mutations in the neuraminidase (NA)-like protein (NA-F144C and NA-T342A, N2 numbering) that increased the virus titers in three mammalian cell lines (i.e., Madin-Darby canine kidney, Madin-Darby canine kidney subclone II, and human lung adenocarcinoma [Calu-3] cells). In mice, wild-type H18N11 virus replicated only in the lungs of the infected animals, whereas the NA-T342A and NA-F144C/T342A mutant viruses were detected in the nasal turbinates, in addition to the lungs. Bat influenza viruses have not been tested for their virulence or organ tropism in ferrets. We detected wild-type and single mutant viruses each possessing NA-F144C or NA-T342A in the nasal turbinates of one or several infected ferrets, respectively. A mutant virus possessing both the NA-F144C and NA-T342A mutations was isolated from both the lung and the trachea, suggesting that it has a broader organ tropism than the wild-type virus. However, none of the H18N11 viruses caused symptoms in mice or ferrets. The NA-F144C/T342A double mutation did not substantially affect virion morphology or the release of virions from cells. Collectively, our data demonstrate that the propagation of bat influenza H18N11 virus in mammalian cells can result in mammal-adapting mutations that may increase the replicative ability and/or organ tropism of the virus; overall, however, these viruses did not replicate to high titers throughout the respiratory tract of mice and ferrets.IMPORTANCE Bats are reservoirs for several severe zoonotic pathogens. The genomes of influenza A viruses of the H17N10 and H18N11 subtypes have been identified in bats, but no live virus has been isolated. The characterization of artificially generated bat influenza H18N11 virus in mammalian cell lines and animal models revealed that this virus can acquire mammal-adapting mutations that may increase its zoonotic potential; however, the wild-type and mutant viruses did not replicate to high titers in all infected animals.

Changes in the gut microbiome community of nonhuman primates following radiation injury.

BACKGROUND: Composition and maintenance of the microbiome is vital to gut homeostasis. However, there is limited knowledge regarding the impact of high doses of radiation, which can occur as a result of cancer radiation therapy, nuclear accidents or intentional release of a nuclear or radioactive weapon, on the composition of the gut microbiome. Therefore, we sought to analyze alterations to the gut microbiome of nonhuman primates (NHPs) exposed to high doses of radiation. Fecal samples were collected from 19 NHPs (Chinese rhesus macaques, Macaca mulatta) 1 day prior and 1 and 4 days after exposure to 7.4 Gy cobalt-60 gamma-radiation (LD70-80/60). The 16S V4 rRNA sequences were extracted from each sample, followed by bioinformatics analysis using the QIIME platform. RESULTS: Alpha Diversity (Shannon Diversity Index), revealed no major difference between pre- and post-irradiation, whereas Beta diversity analysis showed significant differences in the microbiome after irradiation (day + 4) compared to baseline (pre-irradiation). The Firmicutes/Bacteriodetes ratio, a factor known to be associated with disruption of metabolic homeostasis, decreased from 1.2 to less than 1 post-radiation exposure. Actinobacillus, Bacteroides, Prevotella (Paraprevotellaceae family) and Veillonella genera were significantly increased by more than 2-fold and Acinetobacter and Aerococcus genus were decreased by more than 10-fold post-irradiation. Fifty-two percent (10/19) of animals exposed to radiation demonstrated diarrhea at day 4 post-irradiation. Comparison of microbiome composition of feces from animals with and without diarrhea at day 4 post-irradiation revealed an increase in Lactobacillus reuteri associated with diarrhea and a decrease of Lentisphaerae and Verrucomicrobioa phyla and Bacteroides in animals exhibiting diarrhea. Animals with diarrhea at day 4 post-irradiation, had significantly lower levels of Lentisphaere and Verrucomicrobia phyla and Bacteroides genus at baseline before irradiation, suggesting a potential association between the prevalence of microbiomes and differential susceptibility to radiation-induced diarrhea. CONCLUSIONS: Our findings demonstrate that substantial alterations in the microbiome composition of NHPs occur following radiation injury and provide insight into early changes with high-dose, whole-body radiation exposure. Future studies will help identify microbiome biomarkers of radiation exposure and develop effective therapeutic intervention to mitigate the radiation injury.

MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape.

Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ-dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response.

Using GWAS top hits to inform priors in Bayesian fine-mapping association studies.

The default causal single-nucleotide polymorphism (SNP) effect size prior in Bayesian fine-mapping studies is usually the Normal distribution. This choice is often based on computational convenience, rather than evidence that it is the most suitable prior distribution. The choice of prior is important because previous studies have shown considerable sensitivity of causal SNP Bayes factors to the form of the prior. In some well-studied diseases there are now considerable numbers of genome-wide association study (GWAS) top hits along with estimates of the number of yet-to-be-discovered causal SNPs. We show how the effect sizes of the top hits and estimates of the number of yet-to-be-discovered causal SNPs can be used to choose between the Laplace and Normal priors, to estimate the prior parameters and to quantify the uncertainty in this estimation. The methodology can readily be applied to other priors. We show that the top hits available from breast cancer GWAS provide overwhelming support for the Laplace over the Normal prior, which has important consequences for variant prioritisation. This work in this paper enables practitioners to derive more objective priors than are currently being used and could lead to prioritisation of different variants.

Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies.

Several methods have been proposed to allow functional genomic information to inform prior distributions in Bayesian fine-mapping case-control association studies. None of these methods allow the inclusion of partially observed functional genomic information. We use functional significance (FS) scores that combine information across multiple bioinformatics sources to inform our effect size prior distributions. These scores are not available for all single-nucleotide polymorphisms (SNPs) but by partitioning SNPs into naturally occurring FS score groups, we show how missing FS scores can easily be accommodated via finite mixtures of elicited priors. Most current approaches adopt a formal Bayesian variable selection approach and either limit the number of causal SNPs allowed or use approximations to avoid the need to explore the vast parameter space. We focus instead on achieving differential shrinkage of the effect sizes through prior scale mixtures of normals and use marginal posterior probability intervals to select candidate causal SNPs. We show via a simulation study how this approach can improve localisation of the causal SNPs compared to existing mutli-SNP fine-mapping methods. We also apply our approach to fine-mapping a region around the CASP8 gene using the iCOGS consortium breast cancer SNP data.

A heterodimeric glutathione S-transferase that stereospecifically breaks lignin's ß(R)-aryl ether bond reveals the diversity of bacterial ß-etherases.

Lignin is a heterogeneous polymer of aromatic subunits that is a major component of lignocellulosic plant biomass. Understanding how microorganisms deconstruct lignin is important for understanding the global carbon cycle and could aid in developing systems for processing plant biomass into valuable commodities. Sphingomonad bacteria use stereospecific glutathione S-transferases (GSTs) called ß-etherases to cleave the ß-aryl ether (ß-O-4) bond, the most common bond between aromatic subunits in lignin. Previously characterized bacterial ß-etherases are homodimers that fall into two distinct GST subclasses: LigE homologues, which cleave the ß(R) stereoisomer of the bond, and LigF homologues, which cleave the ß(S) stereoisomer. Here, we report on a heterodimeric ß-etherase (BaeAB) from the sphingomonad Novosphingobium aromaticivorans that stereospecifically cleaves the ß(R)-aryl ether bond of the di-aromatic compound ß-(2-methoxyphenoxy)-γ-hydroxypropiovanillone (MPHPV). BaeAB's subunits are phylogenetically distinct from each other and from other ß-etherases, although they are evolutionarily related to LigF, despite the fact that BaeAB and LigF cleave different ß-aryl ether bond stereoisomers. We identify amino acid residues in BaeAB's BaeA subunit important for substrate binding and catalysis, including an asparagine that is proposed to activate the GSH cofactor. We also show that BaeAB homologues from other sphingomonads can cleave ß(R)-MPHPV and that they may be as common in bacteria as LigE homologues. Our results suggest that the ability to cleave the ß-aryl ether bond arose independently at least twice in GSTs and that BaeAB homologues may be important for cleaving the ß(R)-aryl ether bonds of lignin-derived oligomers in nature.

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks.

BACKGROUND: Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs. RESULTS: Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFß signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype. CONCLUSIONS: Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

An overview of health and safety in the Colorado cannabis industry.

BACKGROUND: Understanding worker health and safety in the rapidly growing legal U.S. cannabis industry is important. Although little published research exists, workers may be exposed to biological, chemical, and physical hazards. This study investigated the Colorado cannabis industry workforce and both physical and psychosocial hazards to worker health and safety. METHODS: Two hundred and fourteen Colorado cannabis workers completed an online survey after in-person and online recruitment. Participants answered questions about their occupation, job tasks, general well-being, occupational health and safety, cannabis use, and tobacco use. RESULTS: Colorado cannabis workers were generally job secure and valued safety. However, they regularly consumed cannabis, expressed low concerns about workplace hazards, reported some occupational injuries and exposures, and reported inconsistent training practices. CONCLUSIONS: Working in the cannabis industry is associated with positive outcomes for workers and their organizations, but there is an imminent need to establish formal health and safety training to implement best practices.

Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling.

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.

Novel bayes factors that capture expert uncertainty in prior density specification in genetic association studies.

Bayes factors (BFs) are becoming increasingly important tools in genetic association studies, partly because they provide a natural framework for including prior information. The Wakefield BF (WBF) approximation is easy to calculate and assumes a normal prior on the log odds ratio (logOR) with a mean of zero. However, the prior variance (W) must be specified. Because of the potentially high sensitivity of the WBF to the choice of W, we propose several new BF approximations with logOR ∼N(0,W), but allow W to take a probability distribution rather than a fixed value. We provide several prior distributions for W which lead to BFs that can be calculated easily in freely available software packages. These priors allow a wide range of densities for W and provide considerable flexibility. We examine some properties of the priors and BFs and show how to determine the most appropriate prior based on elicited quantiles of the prior odds ratio (OR). We show by simulation that our novel BFs have superior true-positive rates at low false-positive rates compared to those from both P-value and WBF analyses across a range of sample sizes and ORs. We give an example of utilizing our BFs to fine-map the CASP8 region using genotype data on approximately 46,000 breast cancer case and 43,000 healthy control samples from the Collaborative Oncological Gene-environment Study (COGS) Consortium, and compare the single-nucleotide polymorphism ranks to those obtained using WBFs and P-values from univariate logistic regression.

Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region.

Genome-wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p-values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease-associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000.

COVID-19 preparedness and response in the Pitcairn Islands: keeping one of the world's smallest and most isolated populations safe in a pandemic.

Problem: While the COVID-19 pandemic threatened the entire world, the extremely remote Pitcairn Islands faced unique vulnerabilities. With only a physician and a nurse to care for an ageing population of fewer than 40 residents, and with very limited referral pathways, Pitcairn encountered distinct challenges in preparing for and responding to the COVID-19 pandemic. Context: The Pitcairn Islands is an overseas territory of United Kingdom of Great Britain and Northern Ireland consisting of four islands in the South Pacific: Pitcairn, Henderson, Ducie and Oeno. Pitcairn is the only inhabited island with a local resident population of approximately 31 people, around half of whom were over 60 years old in 2023. The islands are only accessible by sea and are located more than 2000 km from the nearest referral hospital in French Polynesia. Actions: Pitcairn's Island Council took aggressive action to delay the importation of SARS-CoV-2, vaccinate its small population and prepare for the potential arrival of the virus. Outcomes: As of May 2024, Pitcairn was one of the only jurisdictions in the world not to have had a single COVID-19 hospitalization or death. Nevertheless, the pandemic presented the islands' population with many economic, social and health challenges. Discussion: Pitcairn's population avoided COVID-19-related hospitalizations and deaths despite its elderly population's vulnerability to COVID-19, a significant level of comorbidities, and limited clinical management capabilities and options for emergency referrals. The pandemic highlighted some of the population's health vulnerabilities while also underscoring some of their innate strengths.

Metagenome-assembled genomes from microbiomes fermenting dairy coproducts.

To advance knowledge of microbial communities capable of fermenting agro-industrial residues into value-added products, we report metagenomes of microbial communities from six anaerobic bioreactors that were fed a mixture of ultra-filtered milk permeate and cottage cheese acid whey. These metagenomes produced 122 metagenome-assembled genomes that represent 34 distinct taxa.