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.

A compendium of multi-omics data illuminating host responses to lethal human virus infections.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

The Effect of Bifidobacterium animalis subsp. lactis Bl-04 on Influenza A Virus Infection in Mice.

Influenza A virus infection is a major global disease requiring annual vaccination. Clinical studies indicate that certain probiotics may support immune function against influenza and other respiratory viruses, but direct molecular evidence is scarce. Here, mice were treated with a placebo or Bifidobacterium animalis subsp. lactis Bl-04 (Bl-04) orally via food (cereal) and also by gavage and exposed to Influenza A virus H1N1 (H1N1). The symptoms of the infection were observed, and tissues and digesta were collected for viral load RT-qPCR, transcriptomics, and microbiomics. The treatment decreased the viral load by 48% at day 3 post-infection in lungs and symptoms of infection at day 4 compared to placebo. Tissue transcriptomics showed differences between the Bl-04 and placebo groups in the genes in the Influenza A pathway in the intestine, blood, and lungs prior to and post-infection, but the results were inconclusive. Moreover, 16S rRNA gene profiling and qPCR showed the presence of Bl-04 in the intestine, but without major shifts in the microbiome. In conclusion, Bl-04 treatment may influence the host response against H1N1 in a murine challenge model; however, further studies are required to elucidate the mechanism of action.

A metagenome-level analysis of a microbial community fermenting ultra-filtered milk permeate.

Fermentative microbial communities have the potential to serve as biocatalysts for the conversion of low-value dairy coproducts into renewable chemicals, contributing to a more sustainable global economy. To develop predictive tools for the design and operation of industrially relevant strategies that utilize fermentative microbial communities, there is a need to determine the genomic features of community members that are characteristic to the accumulation of different products. To address this knowledge gap, we performed a 282-day bioreactor experiment with a microbial community that was fed ultra-filtered milk permeate, a low-value coproduct from the dairy industry. The bioreactor was inoculated with a microbial community from an acid-phase digester. A metagenomic analysis was used to assess microbial community dynamics, construct metagenome-assembled genomes (MAGs), and evaluate the potential for lactose utilization and fermentation product synthesis of community members represented by the assembled MAGs. This analysis led us to propose that, in this reactor, members of the Actinobacteriota phylum are important in the degradation of lactose, via the Leloir pathway and the bifid shunt, and the production of acetic, lactic, and succinic acids. In addition, members of the Firmicutes phylum contribute to the chain-elongation-mediated production of butyric, hexanoic, and octanoic acids, with different microbes using either lactose, ethanol, or lactic acid as the growth substrate. We conclude that genes encoding carbohydrate utilization pathways, and genes encoding lactic acid transport into the cell, electron confurcating lactate dehydrogenase, and its associated electron transfer flavoproteins, are genomic features whose presence in Firmicutes needs to be established to infer the growth substrate used for chain elongation.

Comparison of metagenomes from fermentation of various agroindustrial residues suggests a common model of community organization.

The liquid residue resulting from various agroindustrial processes is both rich in organic material and an attractive source to produce a variety of chemicals. Using microbial communities to produce chemicals from these liquid residues is an active area of research, but it is unclear how to deploy microbial communities to produce specific products from the different agroindustrial residues. To address this, we fed anaerobic bioreactors one of several agroindustrial residues (carbohydrate-rich lignocellulosic fermentation conversion residue, xylose, dairy manure hydrolysate, ultra-filtered milk permeate, and thin stillage from a starch bioethanol plant) and inoculated them with a microbial community from an acid-phase digester operated at the wastewater treatment plant in Madison, WI, United States. The bioreactors were monitored over a period of months and sampled to assess microbial community composition and extracellular fermentation products. We obtained metagenome assembled genomes (MAGs) from the microbial communities in each bioreactor and performed comparative genomic analyses to identify common microorganisms, as well as any community members that were unique to each reactor. Collectively, we obtained a dataset of 217 non-redundant MAGs from these bioreactors. This metagenome assembled genome dataset was used to evaluate whether a specific microbial ecology model in which medium chain fatty acids (MCFAs) are simultaneously produced from intermediate products (e.g., lactic acid) and carbohydrates could be applicable to all fermentation systems, regardless of the feedstock. MAGs were classified using a multiclass classification machine learning algorithm into three groups, organisms fermenting the carbohydrates to intermediate products, organisms utilizing the intermediate products to produce MCFAs, and organisms producing MCFAs directly from carbohydrates. This analysis revealed common biological functions among the microbial communities in different bioreactors, and although different microorganisms were enriched depending on the agroindustrial residue tested, the results supported the conclusion that the microbial ecology model tested was appropriate to explain the MCFA production potential from all agricultural residues.

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.

"It's so Natural It Goes Hand in Hand": A Qualitative Study of Maternal Perceptions of Cannabis Use during Pregnancy.

As the market continues to embrace marijuana as a holistic product, perceptions about its uses are increasingly contradictory to public health recommendations. The purpose of this research was to qualitatively analyze the perceived risks and benefits of cannabis use during pregnancy via in-depth semi-structured interviews conducted with nine women. This research is intended to inform patient-provider interactions regarding cannabis use in prenatal clinical settings.

Metagenomes and Metagenome-Assembled Genomes from Microbiomes Metabolizing Thin Stillage from an Ethanol Biorefinery.

Here, we report the metagenomes from five anaerobic bioreactors, operated under different conditions, that were fed carbohydrate-rich thin stillage from a corn starch ethanol plant. The putative functions of the abundant taxa identified here will inform future studies of microbial communities involved in valorizing this and other low-value agroindustrial residues.

Metagenome-Assembled Genomes from a Microbiome Grown in Dairy Manure Hydrolysate.

Anaerobic microbiomes can be used to recover the chemical energy in agroindustrial and municipal wastes as useful products. Here, we report a total of 109 draft metagenome-assembled genomes from a bioreactor-fed carbohydrate-rich dairy manure hydrolysate. Studying these genomes will aid us in deciphering the metabolic networks in anaerobic microbiomes.

Metagenomes and Metagenome-Assembled Genomes from Microbial Communities Fermenting Ultrafiltered Milk Permeate.

Fermentative microbial communities can be utilized for the conversion of various agroindustrial residues into valuable chemicals. Here, we report 34 metagenomes from anaerobic bioreactors fed lactose-rich ultrafiltered milk permeate and 278 metagenome-assembled genomes (MAGs). These MAGs can inform future studies aimed at generating renewable chemicals from dairy and other agroindustrial residues.

Mixed Acid Fermentation of Carbohydrate-Rich Dairy Manure Hydrolysate.

Dairy manure (DM) is an abundant agricultural residue that is largely composed of lignocellulosic biomass. The aim of this study was to investigate if carbon derived from DM fibers can be recovered as medium-chain fatty acids (MCFAs), which are mixed culture fermentation products of economic interest. DM fibers were subjected to combinations of physical, enzymatic, chemical, and thermochemical pretreatments to evaluate the possibility of producing carbohydrate-rich hydrolysates suitable for microbial fermentation by mixed cultures. Among the pretreatments tested, decrystalization dilute acid pretreatment (DCDA) produced the highest concentrations of glucose and xylose, and was selected for further experiments. Bioreactors fed DCDA hydrolysate were operated. Acetic acid and butyric acid comprised the majority of end products during operation of the bioreactors. MCFAs were transiently produced at a maximum concentration of 0.17 mg CODMCFAs/mg CODTotal. Analyses of the microbial communities in the bioreactors suggest that lactic acid bacteria, Megasphaera, and Caproiciproducens were involved in MCFA and C4 production during DCDA hydrolysate metabolism.

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.

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.

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.

The vaccinia virus based Sementis Copenhagen Vector vaccine against Zika and chikungunya is immunogenic in non-human primates.

The Sementis Copenhagen Vector (SCV) is a new vaccinia virus-derived, multiplication-defective, vaccine technology assessed herein in non-human primates. Indian rhesus macaques (Macaca mulatta) were vaccinated with a multi-pathogen recombinant SCV vaccine encoding the structural polyproteins of both Zika virus (ZIKV) and chikungunya virus (CHIKV). After one vaccination, neutralising antibody responses to ZIKV and four strains of CHIKV, representative of distinct viral genotypes, were generated. A second vaccination resulted in significant boosting of neutralising antibody responses to ZIKV and CHIKV. Following challenge with ZIKV, SCV-ZIKA/CHIK-vaccinated animals showed significant reductions in viremias compared with animals that had received a control SCV vaccine. Two SCV vaccinations also generated neutralising and IgG ELISA antibody responses to vaccinia virus. These results demonstrate effective induction of immunity in non-human primates by a recombinant SCV vaccine and illustrates the utility of SCV as a multi-disease vaccine platform capable of delivering multiple large immunogens.

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.

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.

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.