Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy.

We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Gut microbiota in multiple sclerosis: possible influence of immunomodulators.

OBJECTIVES: Differences in gut bacteria have been described in several autoimmune disorders. In this exploratory pilot study, we compared gut bacteria in patients with multiple sclerosis and healthy controls and evaluated the influence of glatiramer acetate and vitamin D treatment on the microbiota. METHODS: Subjects were otherwise healthy white women with or without relapsing-remitting multiple sclerosis who were vitamin D insufficient. Patients with multiple sclerosis were untreated or were receiving glatiramer acetate. Subjects collected stool at baseline and after 90 days of vitamin D3 (5000 IU/d) supplementation. The abundance of operational taxonomic units was evaluated by hybridization of 16S rRNA to a DNA microarray. RESULTS: While there was overlap of gut bacterial communities, the abundance of some operational taxonomic units, including Faecalibacterium, was lower in patients with multiple sclerosis. Glatiramer acetate-treated patients with multiple sclerosis showed differences in community composition compared with untreated subjects, including Bacteroidaceae, Faecalibacterium, Ruminococcus, Lactobacillaceae, Clostridium, and other Clostridiales. Compared with the other groups, untreated patients with multiple sclerosis had an increase in the Akkermansia, Faecalibacterium, and Coprococcus genera after vitamin D supplementation. CONCLUSIONS: While overall bacterial communities were similar, specific operational taxonomic units differed between healthy controls and patients with multiple sclerosis. Glatiramer acetate and vitamin D supplementation were associated with differences or changes in the microbiota. This study was exploratory, and larger studies are needed to confirm these preliminary results.

Identifying qualitative effects of different grazing types on below-ground communities and function in a long-term field experiment.

Herbivory is an important modulator of plant biodiversity and productivity in grasslands, but our understanding of herbivore-induced changes on below-ground processes and communities is limited. Using a long-term (17 years) experimental site, we evaluated impacts of rabbit and invertebrate grazers on some soil functions involved in carbon cycling, microbial diversity, structure and functional composition. Both rabbit and invertebrate grazing impacted soil functions and microbial community structure. All functional community measures (functions, biogeochemical cycling genes, network association between different taxa) were more strongly affected by invertebrate grazers than rabbits. Furthermore, our results suggest that exclusion of invertebrate grazers decreases both microbial biomass and abundance of genes associated with key biogeochemical cycles, and could thus have long-term consequences for ecosystem functions. The mechanism behind these impacts are likely to be driven by both direct effects of grazing altering the pattern of nutrient inputs and by indirect effects through changes in plant species composition. However, we could not entirely discount that the pesticide used to exclude invertebrates may have affected some microbial community measures. Nevertheless, our work illustrates that human activity that affects grazing intensity may affect ecosystem functioning and sustainability, as regulated by multi-trophic interactions between above- and below-ground communities.

An in vitro culture model to study the dynamics of colonic microbiota in Syrian golden hamsters and their susceptibility to infection with Clostridium difficile.

Clostridium difficile infections (CDI) are caused by colonization and growth of toxigenic strains of C. difficile in individuals whose intestinal microbiota has been perturbed, in most cases following antimicrobial therapy. Determination of the protective commensal gut community members could inform the development of treatments for CDI. Here, we utilized the lethal enterocolitis model in Syrian golden hamsters to analyze the microbiota disruption and recovery along a 20-day period following a single dose of clindamycin on day 0, inducing in vivo susceptibility to C. difficile infection. To determine susceptibility in vitro, spores of strain VPI 10463 were cultured with and without soluble hamster fecal filtrates and growth was quantified by quantitative PCR and toxin immunoassay. Fecal microbial population changes over time were tracked by 16S ribosomal RNA gene analysis via V4 sequencing and the PhyloChip assay. C. difficile culture growth and toxin production were inhibited by the presence of fecal extracts from untreated hamsters but not extracts collected 5 days post-administration of clindamycin. In vitro inhibition was re-established by day 15, which correlated with resistance of animals to lethal challenge. A substantial fecal microbiota shift in hamsters treated with antibiotics was observed, marked by significant changes across multiple phyla including Bacteroidetes and Proteobacteria. An incomplete return towards the baseline microbiome occurred by day 15 correlating with the inhibition of C. difficile growth in vitro and in vivo. These data suggest that soluble factors produced by the gut microbiota may be responsible for the suppression of C. difficile growth and toxin production.

Changes in abundance of oral microbiota associated with oral cancer.

Individual bacteria and shifts in the composition of the microbiome have been associated with human diseases including cancer. To investigate changes in the microbiome associated with oral cancers, we profiled cancers and anatomically matched contralateral normal tissue from the same patient by sequencing 16S rDNA hypervariable region amplicons. In cancer samples from both a discovery and a subsequent confirmation cohort, abundance of Firmicutes (especially Streptococcus) and Actinobacteria (especially Rothia) was significantly decreased relative to contralateral normal samples from the same patient. Significant decreases in abundance of these phyla were observed for pre-cancers, but not when comparing samples from contralateral sites (tongue and floor of mouth) from healthy individuals. Weighted UniFrac principal coordinates analysis based on 12 taxa separated most cancers from other samples with greatest separation of node positive cases. These studies begin to develop a framework for exploiting the oral microbiome for monitoring oral cancer development, progression and recurrence.

Comparative genomics of the Bifidobacterium breve taxon.

BACKGROUND: Bifidobacteria are commonly found as part of the microbiota of the gastrointestinal tract (GIT) of a broad range of hosts, where their presence is positively correlated with the host's health status. In this study, we assessed the genomes of thirteen representatives of Bifidobacterium breve, which is not only a frequently encountered component of the (adult and infant) human gut microbiota, but can also be isolated from human milk and vagina. RESULTS: In silico analysis of genome sequences from thirteen B. breve strains isolated from different environments (infant and adult faeces, human milk, human vagina) shows that the genetic variability of this species principally consists of hypothetical genes and mobile elements, but, interestingly, also genes correlated with the adaptation to host environment and gut colonization. These latter genes specify the biosynthetic machinery for sortase-dependent pili and exopolysaccharide production, as well as genes that provide protection against invasion of foreign DNA (i.e. CRISPR loci and restriction/modification systems), and genes that encode enzymes responsible for carbohydrate fermentation. Gene-trait matching analysis showed clear correlations between known metabolic capabilities and characterized genes, and it also allowed the identification of a gene cluster involved in the utilization of the alcohol-sugar sorbitol. CONCLUSIONS: Genome analysis of thirteen representatives of the B. breve species revealed that the deduced pan-genome exhibits an essentially close trend. For this reason our analyses suggest that this number of B. breve representatives is sufficient to fully describe the pan-genome of this species. Comparative genomics also facilitated the genetic explanation for differential carbon source utilization phenotypes previously observed in different strains of B. breve.

A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis.

BACKGROUND: Commensal microbiota play a critical role in maintaining oral tolerance. The effect of food allergy on the gut microbial ecology remains unknown. OBJECTIVE: We sought to establish the composition of the gut microbiota in experimental food allergy and its role in disease pathogenesis. METHODS: Food allergy-prone mice with a gain-of-function mutation in the IL-4 receptor α chain (Il4raF709) and wild-type (WT) control animals were subjected to oral sensitization with chicken egg ovalbumin (OVA). Enforced tolerance was achieved by using allergen-specific regulatory T (Treg) cells. Community structure analysis of gut microbiota was performed by using a high-density 16S rDNA oligonucleotide microarrays (PhyloChip) and massively parallel pyrosequencing of 16S rDNA amplicons. RESULTS: OVA-sensitized Il4raF709 mice exhibited a specific microbiota signature characterized by coordinate changes in the abundance of taxa of several bacterial families, including the Lachnospiraceae, Lactobacillaceae, Rikenellaceae, and Porphyromonadaceae. This signature was not shared by similarly sensitized WT mice, which did not exhibit an OVA-induced allergic response. Treatment of OVA-sensitized Il4raF709 mice with OVA-specific Treg cells led to a distinct tolerance-associated signature coincident with the suppression of the allergic response. The microbiota of allergen-sensitized Il4raF709 mice differentially promoted OVA-specific IgE responses and anaphylaxis when reconstituted in WT germ-free mice. CONCLUSION: Mice with food allergy exhibit a specific gut microbiota signature capable of transmitting disease susceptibility and subject to reprogramming by enforced tolerance. Disease-associated microbiota may thus play a pathogenic role in food allergy.

Murine norovirus infection does not cause major disruptions in the murine intestinal microbiota.

BACKGROUND: Murine norovirus (MNV) is the most common gastrointestinal pathogen of research mice and can alter research outcomes in biomedical mouse models of inflammatory bowel disease (IBD). Despite indications that an altered microbiota is a risk factor for IBD, the response of the murine intestinal microbiota to MNV infection has not been examined. Microbiota disruption caused by MNV infection could introduce the confounding effects observed in research experiments. Therefore, this study investigated the effects of MNV infection on the intestinal microbiota of wild-type mice. RESULTS: The composition of the intestinal microbiota was assessed over time in both outbred Swiss Webster and inbred C57BL/6 mice following MNV infection. Mice were infected with both persistent and non-persistent MNV strains and tissue-associated or fecal-associated microbiota was analyzed by 16S rRNA-encoding gene pyrosequencing. Analysis of intestinal bacterial communities in infected mice at the phylum and family level showed no major differences to uninfected controls, both in tissue-associated samples and feces, and also over time following infection, demonstrating that the intestinal microbiota of wild-type mice is highly resistant to disruption following MNV infection. CONCLUSIONS: This is the first study to describe the intestinal microbiota following MNV infection and demonstrates that acute or persistent MNV infection is not associated with major disruptions of microbial communities in Swiss Webster and C57BL/6 mice.

Using QIIME to analyze 16S rRNA gene sequences from microbial communities.

QIIME (canonically pronounced "chime") is a software application that performs microbial community analysis. It is an acronym for Quantitative Insights Into Microbial Ecology, and has been used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities. The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities.

Human gut microbiome viewed across age and geography.

Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.

BACKGROUND: We present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. FINDINGS: The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. CONCLUSIONS: The BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.

Experimental and analytical tools for studying the human microbiome.

The human microbiome substantially affects many aspects of human physiology, including metabolism, drug interactions and numerous diseases. This realization, coupled with ever-improving nucleotide sequencing technology, has precipitated the collection of diverse data sets that profile the microbiome. In the past 2 years, studies have begun to include sufficient numbers of subjects to provide the power to associate these microbiome features with clinical states using advanced algorithms, increasing the use of microbiome studies both individually and collectively. Here we discuss tools and strategies for microbiome studies, from primer selection to bioinformatics analysis.

Using QIIME to analyze 16S rRNA gene sequences from microbial communities.

QIIME (canonically pronounced "chime") is a software application that performs microbial community analysis. It is an acronym for Quantitative Insights Into Microbial Ecology, and has been used to analyze and interpret nucleic acid sequence data from fungal, viral, bacterial, and archaeal communities. The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities.

Combined phylogenetic and genomic approaches for the high-throughput study of microbial habitat adaptation.

High-throughput sequencing technologies provide new opportunities to address longstanding questions about habitat adaptation in microbial organisms. How have microbes managed to adapt to such a wide range of environments, and what genomic features allow for such adaptation? We review recent large-scale studies of habitat adaptation, with emphasis on those that utilize phylogenetic techniques. On the basis of current trends, we summarize methodological challenges faced by investigators, and the tools, techniques and analytical approaches available to overcome them. Phylogenetic approaches and detailed information about each environmental sample will be crucial as the ability to collect genome sequences continues to expand.

Bayesian community-wide culture-independent microbial source tracking.

Contamination is a critical issue in high-throughput metagenomic studies, yet progress toward a comprehensive solution has been limited. We present SourceTracker, a Bayesian approach to estimate the proportion of contaminants in a given community that come from possible source environments. We applied SourceTracker to microbial surveys from neonatal intensive care units (NICUs), offices and molecular biology laboratories, and provide a database of known contaminants for future testing.

Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans.

Coevolution of mammals and their gut microbiota has profoundly affected their radiation into myriad habitats. We used shotgun sequencing of microbial community DNA and targeted sequencing of bacterial 16S ribosomal RNA genes to gain an understanding of how microbial communities adapt to extremes of diet. We sampled fecal DNA from 33 mammalian species and 18 humans who kept detailed diet records, and we found that the adaptation of the microbiota to diet is similar across different mammalian lineages. Functional repertoires of microbiome genes, such as those encoding carbohydrate-active enzymes and proteases, can be predicted from bacterial species assemblages. These results illustrate the value of characterizing vertebrate gut microbiomes to understand host evolutionary histories at a supraorganismal level.

Microbial community resemblance methods differ in their ability to detect biologically relevant patterns.

High-throughput sequencing methods enable characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We found that many diversity patterns were evident with severely undersampled communities and that methods varied widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances performed especially well for detecting gradients, whereas Gower and Canberra distances performed especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs that are important to consider when designing studies to characterize microbial communities.

Direct sequencing of the human microbiome readily reveals community differences.

Culture-independent studies of human microbiota by direct genomic sequencing reveal quite distinct differences among communities, indicating that improved sequencing capacity can be most wisely utilized to study more samples, rather than more sequences per sample.