Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

Metagenome-assembled genomes and gene catalog from the chicken gut microbiome aid in deciphering antibiotic resistomes

Gut microbial reference genomes and gene catalogs are necessary for understanding the chicken gut microbiome. Here, we assembled 12,339 microbial genomes and constructed a gene catalog consisting of ~16.6 million genes by integrating 799 public chicken gut microbiome samples from ten countries. We found that 893 and 38 metagenome-assembled genomes (MAGs) in our dataset were putative novel species and genera, respectively. In the chicken gut, Lactobacillus aviarius and Lactobacillus crispatus were the most common lactic acid bacteria, and glycoside hydrolases were the most abundant carbohydrate-active enzymes (CAZymes). Antibiotic resistome profiling results indicated that Chinese chicken samples harbored a higher relative abundance but less diversity of antimicrobial resistance genes (ARGs) than European samples. We also proposed the effects of geography and host species on the gut resistome. Our study provides the largest integrated metagenomic dataset from the chicken gut to date and demonstrates its value in exploring chicken gut microbial genes.

Metagenomic Analysis Identifies Sex-Related Cecal Microbial Gene Functions and Bacterial Taxa in the Quail

Background: Japanese quail (Coturnix japonica) are important and widely distributed poultry in China. Researchers continue to pursue genetic selection for heavier quail. The intestinal microbiota plays a substantial role in growth promotion; however, the mechanisms involved in growth promotion remain unclear.Results: We generated 107.3 Gb of cecal microbiome data from ten Japanese quail, providing a series of quail gut microbial gene catalogs (1.25 million genes). We identified a total of 606 main microbial species from 1,033,311 annotated genes distributed among the ten quail. Seventeen microbial species from the genera Anaerobiospirillum, Alistipes, Barnesiella, and Butyricimonas differed significantly in their abundances between the female and male gut microbiotas. Most of the functional gut microbial genes were involved in metabolism, primarily in carbohydrate transport and metabolism, as well as some active carbohydrate-degrading enzymes. We also identified 308 antibiotic-resistance genes (ARGs) from the phyla Bacteroidetes, Firmicutes and Euryarchaeota. Studies of the differential gene functions between sexes indicated that abundances of the gut microbes that produce carbohydrate-active enzymes varied between female and male quail. Bacteroidetes was the predominant ARG-containing phylum in female quail; Euryarchaeota was the predominant ARG-containing phylum in male quail.Conclusion: This article provides the first description of the gene catalog of the cecal bacteria in Japanese quail as well as insights into the bacterial taxa and predictive metagenomic functions between male and female quail to provide a better understanding of the microbial genes in the quail ceca.

Increasing the power of interpretation for soil metaproteomics data

Background Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples. Results The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases. Conclusions A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.

AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence

Antimicrobial resistance (AMR) is a significant public health threat. With the rise of affordable whole genome sequencing, in silico approaches to assessing AMR gene content can be used to detect known resistance mechanisms and potentially identify novel mechanisms. To enable accurate assessment of AMR gene content, as part of a multi-agency collaboration, NCBI developed a comprehensive AMR gene database, the Bacterial Antimicrobial Resistance Reference Gene Database and the AMR gene detection tool AMRFinder. Here, we describe the expansion of the Reference Gene Database, now called the Reference Gene Catalog, to include putative acid, biocide, metal, stress resistance genes, in addition to virulence genes and species-specific point mutations. Genes and point mutations are classified by broad functions, as well as more detailed functions. As we have expanded both the functional repertoire of identified genes and functionality, NCBI released a new version of AMRFinder, known as AMRFinderPlus. This new tool allows users the option to utilize only the core set of AMR elements, or include stress response and virulence genes, too. AMRFinderPlus can detect acquired genes and point mutations in both protein and nucleotide sequence. In addition, the evidence used to identify the gene has been expanded to include whether nucleotide or protein sequence was used, its location in the contig, and presence of an internal stop codon. These database improvements and functional expansions will enable increased precision in identifying AMR genes, linking AMR genotypes and phenotypes, and determining possible relationships between AMR, virulence, and stress response.

An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants

Background Gastrointestinal tract (GIT) microbiomes in ruminants play major roles in host health and thus animal production. However, we lack an integrated understanding of microbial community structure and function as prior studies. are predominantly biased towards the rumen. Therefore, to acquire a microbiota inventory of the discrete GIT compartments, In this study, we used shotgun metagenomics to profile the microbiota of 370 samples that represent 10 GIT regions of seven ruminant species. Results Our analyses reconstructed a GIT microbial reference catalog with > 154 million nonredundant genes and identified 8745 uncultured candidate species from over 10,000 metagenome-assembled genomes. The integrated gene catalog across the GIT regions demonstrates spatial associations between the microbiome and physiological adaptations, and 8745 newly characterized genomes substantially expand the genomic landscape of ruminant microbiota, particularly those from the lower gut. This substantially expands the previously known set of endogenous microbial diversity and the taxonomic classification rate of the GIT microbiome. These candidate species encode hundreds of enzymes and novel biosynthetic gene clusters that improve our understanding concerning methane production and feed efficiency in ruminants. Overall, this study expands the characterization of the ruminant GIT microbiota at unprecedented spatial resolution and offers clues for improving ruminant livestock production in the future. Conclusions Having access to a comprehensive gene catalog and collections of microbial genomes provides the ability to perform efficiently genome-based analysis to achieve a detailed classification of GIT microbial ecosystem composition. Our study will bring unprecedented power in future association studies to investigate the impact of the GIT microbiota in ruminant health and production.

Variation of Metagenome From Feedstock to Digestate in Full-Scale Biogas Plants

Anaerobic digestion (AD) has been widely used to resolve the problem of organic wastes worldwide. Previous studies showed that the types of feedstock have a great influence on the AD microbiome, and a huge number of AD populations are migrated from upstream feedstocks. However, the changes of microbial compositions from feedstock to AD digestate are still less understood. We collected feedstock samples from 56 full-scale biogas plants, generated 1,716 Gb feedstock metagenomic data in total, and constructed the first comprehensive microbial gene catalog of feedstock containing 25.2 million genes. Our result indicated that the predominant phyla in feedstock are Firmicutes, Bacteroidetes, and Proteobacteria, which is similar to that in AD digestate, and the microbial diversity of feedstock samples is higher than that of AD digestate samples. In addition, the relative abundance of most genes involved in methanogenesis increase from feedstock to AD digestate. Besides, the amount of antibiotic resistance genes (ARGs) and pathogenic bacteria in AD are effectively reduced compared to feedstocks. This study provides a comprehensive microbial gene catalog of feedstock, and deepens the understanding of variation of microbial communities from feedstock to AD digestate of full-scale AD. The results also suggest the potential of AD to reduce the level of ARGs and pathogens in animal manure.

An Expanded Gene Catalog of Mouse Gut Metagenomes

ABSTRACT High-quality and comprehensive reference gene catalogs are essential for metagenomic research. The rather low diversity of samples used to construct existing catalogs of the mouse gut metagenome limits the numbers of identified genes in existing catalogs. We therefore established an expanded catalog of genes in the mouse gut metagenome (EMGC) containing >5.8 million genes by integrating 88 newly sequenced samples, 86 mouse gut-related bacterial genomes, and 3 existing gene catalogs. EMGC increases the number of nonredundant genes by more than 1 million genes compared to the so-far most extensive catalog. More than 60% of the genes in EMGC were assigned to Bacteria, with 54.20% being assigned to a phylum and 35.33% to a genus, while 30.39% were annotated at the KEGG orthology level. Nine hundred two metagenomic species (MGS) assigned to 122 taxa are identified based on the EMGC. The EMGC-based analysis of samples from groups of mice originating from different animal providers, housing laboratories, and genetic strains substantiated that diet is a major contributor to differences in composition and functional potential of the gut microbiota irrespective of differences in environment and genetic background. We envisage that EMGC will serve as a valuable reference data set for future metagenomic studies in mice. IMPORTANCE We established an expanded gene catalog of the mouse gut metagenome not only to increase the sample size compared to that in existing catalogs but also to provide a more comprehensive reference data set of the mouse gut microbiome for bioinformatic analysis. The expanded gene catalog comprises more than 5.8 million unique genes, as well as a wide range of taxonomic and functional information. Particularly, the analysis of metagenomic species with the expanded gene catalog reveals a great novelty of mouse gut-inhabiting microbial species. We envisage that the expanded gene catalog of the mouse gut metagenome will serve as a valuable bioinformatic resource for future gut metagenomic studies in mice.

Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome

Gut microbiota plays an important role in pig health and production. Still, availability of sequenced genomes and functional information for most pig gut microbes remains limited. Here we perform a landscape survey of the swine gut microbiome, spanning extensive sample sources by deep metagenomic sequencing resulting in an expanded gene catalog named pig integrated gene catalog (PIGC), containing 17,237,052 complete genes clustered at 90% protein identity from 787 gut metagenomes, of which 28% are unknown proteins. Using binning analysis, 6339 metagenome-assembled genomes (MAGs) were obtained, which were clustered to 2673 species-level genome bins (SGBs), among which 86% (2309) SGBs are unknown based on current databases. Using the present gene catalog and MAGs, we identified several strain-level differences between the gut microbiome of wild boars and commercial Duroc pigs. PIGC and MAGs provide expanded resources for swine gut microbiome-related research.

A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog

The human gut microbiota performs functions that are essential for the maintenance of the host physiology. However, characterizing the functioning of microbial communities in relation to the host remains challenging in reference-based metagenomic analyses. Indeed, as taxonomic and functional analyses are performed independently, the link between genes and species remains unclear. Although a first set of species-level bins was built by clustering co-abundant genes, no reference bin set is established on the most used gut microbiota catalog, the Integrated Gene Catalog (IGC). With the aim to identify the best suitable method to group the IGC genes, we benchmarked nine taxonomy-independent binners implementing abundance-based, hybrid and integrative approaches. To this purpose, we designed a simulated non-redundant gene catalog (SGC) and computed adapted assessment metrics. Overall, the best trade-off between the main metrics is reached by an integrative binner. For each approach, we then compared the results of the best-performing binner with our expected community structures and applied the method to the IGC. The three approaches are distinguished by specific advantages, and by inherent or scalability limitations. Hybrid and integrative binners show promising and potentially complementary results but require improvements to be used on the IGC to recover human gut microbial species.