Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of diseaseData Availability Statement: The Gilbert et al. L4 metagenomes and metatranscriptomes are available from the MG-RAST database (project number 109,  http://metagenomics.anl.gov/metagenomics.cgi?page=MetagenomeProject&project=109), the Qin et al. MetaHIT inflammatory bowel disease metagenomes are available in the EBI (accession ERA000116), and the Nielsen et al. MGS inflammatory bowel disease metagenomes are available in the EBI (accession ERP002061)

Species-level functional profiling of metagenomes and metatranscriptomes.

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types

Decoding DOM Degradation with Metatranscriptomics : How Do Sunlight and Microbial Communities Interact to Degrade Dissolved Organic Matter in Arctic Freshwaters?

Arctic soils are warming, making vast stores of organic carbon available for conversion to CO₂. This could create a positive feedback loop and accelerate global warming, but the processes that convert this carbon into CO₂ are not well understood. We investigated how the combined activities of sunlight and microbes degrade soil dissolved organic matter (DOM), an important component of the carbon processed in arctic freshwaters. DOM leached from the organic layer of moist acidic tundra was exposed to natural sunlight (24 h) or kept in the dark, inoculated and incubated with a soil microbial community, and analyzed for DOM composition (FT-ICR MS) and microbial gene expression (metatranscriptomics). We found that DOM degraded by sunlight was similar in composition to DOM degraded by microbes, and consequently, microbial activity was lower when incubated with sunlight-exposed DOM. We also found sunlight-exposed DOM caused global shifts in both microbial gene expression and the taxonomic groups conducting this expression. Greater expression of transcription and translation genes suggested growth, while lower expression of metabolism, motility, and transport genes suggested reduced investment in scavenging. Photo-exposure of DOM also caused reduced expression of enzymes involved in aromatic degradation, oxygenases, and decarboxylases, suggesting sunlight degraded aromatics, oxidized DOM, and decarboxylated DOM. Shifts in expression of transporters for small, labile compounds and nutrient-containing compounds suggested photo-exposure may have altered bioavailability of these compounds in the DOM pool. These findings demonstrate that even small amounts of sunlight can alter DOM in ways that evoke profound changes in microbial functioning, supporting the idea that sunlight plays a key role in determining the microbial processing of DOM in arctic freshwaters

Tailoring bioinformatics strategies for the characterization of the human microbiome in health and disease

The human microbiome is a very active area of research due to its potential to explain health and disease. Advances in high throughput DNA sequencing in the last decade have catalyzed the growth of microbiome research; DNA sequencing allows for a cost-effective method to characterize entire microbial communities directly, including unculturable microbes which were previously difficult to study. 16S rRNA sequencing and shotgun metagenomics, coupled with bioinformatics methods have powered the characterization of the human microbiome in different parts of the body. This has led to the discovery of novel links between the microbiome and diseases such as allergies, cancer, and autoimmune diseases. This thesis focuses on the application of both 16S rRNA sequencing and shotgun metagenomics for the characterization of the human microbiome and its relationship with health and disease. We established two methodologies to address these questions. The first methodology is a bench-to-bioinformatics pipeline to discover putative viral pathogens involved in disease using shotgun metagenomics technology. In paper I, we apply the proposed pipeline to explore the hypothesis of viral infection as a putative cause of childhood Acute Lymphoblastic Leukemia. In paper II, we propose a complementary method to the pipeline to improve the detection of unknown viruses, especially those with little or no homology to currently known viruses. We applied this method on a collection of viral-enriched libraries which resulted in the characterization of a new viral-like genome. The second methodology was developed to explore and generate hypothesis from a human skin microbiome dataset of Psoriasis and Atopic Dermatitis patients. The results of the analysis are presented in Paper III and Paper IV. Paper III is a pure data-driven exploration of the dataset to discover different aspects on how the microbiome is linked to both diseases. Paper IV follows up from the results of paper III but focuses on characterizing the skin site microbiome variability in Atopic Dermatitis

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

<div><p>Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.</p></div

Comparative Metagenomic Investigations Link the Functional Capacity of the Gut Microbiome to Vertebrate Physiology

Microscopic organisms inhabit virtually every niche on this planet, where they perform functions vital to all life on earth. Accordingly, humans host a complex community of microorganisms (i.e. the gut microbiome) that inhabit the gastrointestinal tract and modulate host physiology. Insight into the specific mechanisms through which gut microbes influence physiology remains limited. Metagenomic clinical studies can reveal gut microbial functions that stratify healthy and diseased individuals. However, the typical single-disease focus of microbiome studies limits insight into which microbiome features robustly associate with health, indicate general deviations from health, or predict specific diseases. Additionally, the focus on taxonomy may limit our understanding of how the microbiome relates to health given observations that different taxonomic members can fulfill similar functional roles. To improve our understanding of the association between the functional capacity of the gut microbiome and health, we integrated about 2,000 gut metagenomes obtained from eight clinical studies in a statistical meta-analysis. We identified characteristics of the gut microbiome that associated generally with disease and resolved microbiome functions that stratified diseased individuals from healthy controls in a manner independent of study-specific effects. While additional analysis is needed to verify these findings, this work identified putative microbiome disease markers and clarified potential mechanisms through which the microbiome modulates human health. One way to assess the importance of putative disease markers is through studies in animal models. Animal models have proven to be critical research tools in efforts to uncover and validate specific mechanisms of interaction between gut microbes and physiology. However, it is largely unknown whether the biochemical pathways encoded in the gut microbiome of these animal models are consistent with those encoded in the human gut microbiome, which complicates the translational application of discoveries made using these models. To advance the translational utility of animal model research, we conducted a comprehensive comparative metagenomic analysis across 135 individuals spanning 10 vertebrate species, including commonly used animal models. Functional metagenomic profiling identified a core set of 3,368 orthologous protein families that are common to the gut microbiomes of all examined animals, many of which are enriched in guts relative to non-host-associated (i.e. free-living) communities. Variance-weighted linear models also found that vertebrates generally represent the functional diversity and inter-individual variation encoded in the human gut. However, at the level of specific microbiome functional modules, host species inconsistently reflected human gut microbiome relative abundance and dispersion. These results support the use of animal models to study the mechanisms through which gut microbes impact human health, but suggest that researchers should cautiously consider which model will optimally represent a specific mechanism of interest. However, these results could be confounded by incomplete inferences of the diversity within the gut microbiomes of these animals. For example, less than 50% of the sequence data generated from the 135 vertebrate samples was classified by the protein family database used. To enable more robust comparisons across vertebrates, we constructed a catalogue of the genomic diversity of the gut microbiome across vertebrates and clustered microbial proteins into protein families. This protein family database recruited substantially more sequence data from samples and revealed unequal recruitment of proteins to existing databases. Additionally, we found that while there are high levels of homology between hosts, there is a subset of proteins that are only found in a single host lineage. This resource provides a valuable tool to future research examining the genetic diversity of the gut microbiome in vertebrates and to identify which biomedical models have homologs for microbial proteins of interest. Collectively, these results advance our understanding of how the functional composition of the gut microbiome relates to host physiology by 1) identifying putatively robust microbiome-based signatures of disease, 2) quantifying the translational relevance of common biomedical animal models and 3) developing a database that profiles the genomic diversity of the gut microbiome across vertebrates