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A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease 1-9 . Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units 10 ), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches 11-13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry 14 . These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.

Inflammatory bowel diseases (IBD) are associated with alterations in gut microbial abundances and lumenal metabolite concentrations, but the effects of specific metabolites on the gut microbiota in health and disease remain largely unknown. Here, we analysed the influences of metabolites that are differentially abundant in IBD on the growth and physiology of gut bacteria that are also differentially abundant in IBD. We found that N-acylethanolamines (NAEs), a class of endogenously produced signalling lipids elevated in the stool of IBD patients and a T-cell transfer model of colitis, stimulated growth of species over-represented in IBD and inhibited that of species depleted in IBD in vitro. Using metagenomic sequencing, we recapitulated the effects of NAEs in complex microbial communities ex vivo, with Proteobacteria blooming and Bacteroidetes declining in the presence of NAEs. Metatranscriptomic analysis of the same communities identified components of the respiratory chain as important for the metabolism of NAEs, and this was verified using a mutant deficient for respiratory complex I. In this study, we identified NAEs as a class of metabolites that are elevated in IBD and have the potential to shift gut microbiota towards an IBD-like composition.

Obesity and type 2 diabetes (T2D) are metabolic disorders that are linked to microbiome alterations. However, their co-occurrence poses challenges in disentangling microbial features unique to each condition. We analyzed gut microbiomes of lean non-diabetic (n = 633), obese non-diabetic (n = 494), and obese individuals with T2D (n = 153) from German population and metabolic disease cohorts. Microbial taxonomic and functional profiles were analyzed along with medical histories, serum metabolomics, biometrics, and dietary data. Obesity was associated with alterations in microbiome composition, individual taxa, and functions with notable changes in Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes, as well as in serum metabolites that correlated with gut microbial patterns. However, microbiome associations were modest for T2D, with nominal increases in Escherichia/Shigella. Medications, including antihypertensives and antidiabetics, along with dietary supplements including iron, were significantly associated with microbiome variation. These results differentiate microbial components of these interrelated metabolic diseases and identify dietary and medication exposures to consider in future studies., (Copyright © 2019. Published by Elsevier Inc.)

Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

Inflammatory bowel diseases, which include Crohn's disease and ulcerative colitis, affect several million individuals worldwide. Crohn's disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study's infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi'omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases.

The inflammatory bowel diseases (IBDs), which include Crohn's disease (CD) and ulcerative colitis (UC), are multifactorial chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome-the molecular interface between host and microbiota-are less well understood. To address this gap, we performed untargeted metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n = 155) and validation (n = 65) cohorts of CD, UC and non-IBD control patients. Metabolomic and metagenomic profiles were broadly correlated with faecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While > 50% of differentially abundant metabolite features were uncharacterized, many could be assigned putative roles through metabolomic 'guilt by association' (covariation with known metabolites). Differentially abundant species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between differentially abundant species and well-characterized differentially abundant metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome-metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.

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.

Evaluating progression risk and determining optimal therapy for ulcerative colitis (UC) is challenging as many patients exhibit incomplete responses to treatment. As part of the PROTECT (Predicting Response to Standardized Colitis Therapy) Study, we evaluated the role of the gut microbiome in disease course for 405 pediatric, new-onset, treatment-naive UC patients. Patients were monitored for 1 year upon treatment initiation, and microbial taxonomic composition was analyzed from fecal samples and rectal biopsies. Depletion of core gut microbes and expansion of bacteria typical of the oral cavity were associated with baseline disease severity. Remission and refractory disease were linked to species-specific temporal changes that may be implicative of therapy efficacy, and a pronounced increase in microbiome variability was observed prior to colectomy. Finally, microbial associations with disease-associated serological markers suggest host-microbial interactions in UC. These insights will help improve existing treatments and develop therapeutic approaches guiding optimal medical care., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Type 1 diabetes (T1D) is an autoimmune disease that targets pancreatic islet beta cells and incorporates genetic and environmental factors 1 , including complex genetic elements 2 , patient exposures 3 and the gut microbiome 4 . Viral infections 5 and broader gut dysbioses 6 have been identified as potential causes or contributing factors; however, human studies have not yet identified microbial compositional or functional triggers that are predictive of islet autoimmunity or T1D. Here we analyse 10,913 metagenomes in stool samples from 783 mostly white, non-Hispanic children. The samples were collected monthly from three months of age until the clinical end point (islet autoimmunity or T1D) in the The Environmental Determinants of Diabetes in the Young (TEDDY) study, to characterize the natural history of the early gut microbiome in connection to islet autoimmunity, T1D diagnosis, and other common early life events such as antibiotic treatments and probiotics. The microbiomes of control children contained more genes that were related to fermentation and the biosynthesis of short-chain fatty acids, but these were not consistently associated with particular taxa across geographically diverse clinical centres, suggesting that microbial factors associated with T1D are taxonomically diffuse but functionally more coherent. When we investigated the broader establishment and development of the infant microbiome, both taxonomic and functional profiles were dynamic and highly individualized, and dominated in the first year of life by one of three largely exclusive Bifidobacterium species (B. bifidum, B. breve or B. longum) or by the phylum Proteobacteria. In particular, the strain-specific carriage of genes for the utilization of human milk oligosaccharide within a subset of B. longum was present specifically in breast-fed infants. These analyses of TEDDY gut metagenomes provide, to our knowledge, the largest and most detailed longitudinal functional profile of the developing gut microbiome in relation to islet autoimmunity, T1D and other early childhood events. Together with existing evidence from human cohorts 7,8 and a T1D mouse model 9 , these data support the protective effects of short-chain fatty acids in early-onset human T1D.

Inflammatory bowel disease (IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. Genetic, environmental and microbial factors have been implicated in the onset and exacerbation of IBD. However, the mechanisms associating gut microbial dysbioses and aberrant immune responses remain largely unknown. The integrative Human Microbiome Project seeks to close these gaps by examining the dynamics of microbiome functionality in disease by profiling the gut microbiomes of >100 individuals sampled over a 1-year period. Here, we present the first results based on 78 paired faecal metagenomes and metatranscriptomes, and 222 additional metagenomes from 59 patients with Crohn's disease, 34 with ulcerative colitis and 24 non-IBD control patients. We demonstrate several cases in which measures of microbial gene expression in the inflamed gut can be informative relative to metagenomic profiles of functional potential. First, although many microbial organisms exhibited concordant DNA and RNA abundances, we also detected species-specific biases in transcriptional activity, revealing predominant transcription of pathways by individual microorganisms per host (for example, by Faecalibacterium prausnitzii). Thus, a loss of these organisms in disease may have more far-reaching consequences than suggested by their genomic abundances. Furthermore, we identified organisms that were metagenomically abundant but inactive or dormant in the gut with little or no expression (for example, Dialister invisus). Last, certain disease-specific microbial characteristics were more pronounced or only detectable at the transcript level, such as pathways that were predominantly expressed by different organisms in patients with IBD (for example, Bacteroides vulgatus and Alistipes putredinis). This provides potential insights into gut microbial pathway transcription that can vary over time, inducing phenotypical changes that are complementary to those linked to metagenomic abundances. The study's results highlight the strength of analysing both the activity and the presence of gut microorganisms to provide insight into the role of the microbiome in IBD.

This corrects the article DOI: 10.1038/nature23889.

The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, diversity, biogeography, and molecular function. The National Institutes of Health Human Microbiome Project has provided one of the broadest such characterizations so far. Here we introduce a second wave of data from the study, comprising 1,631 new metagenomes (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to provide new characterizations of microbiome personalization. Strain identification revealed subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity and enables an understanding of personalized microbiome function and dynamics.

Community composition within the human microbiome varies across individuals, but it remains unknown if this variation is sufficient to uniquely identify individuals within large populations or stable enough to identify them over time. We investigated this by developing a hitting set-based coding algorithm and applying it to the Human Microbiome Project population. Our approach defined body site-specific metagenomic codes: sets of microbial taxa or genes prioritized to uniquely and stably identify individuals. Codes capturing strain variation in clade-specific marker genes were able to distinguish among 100s of individuals at an initial sampling time point. In comparisons with follow-up samples collected 30-300 d later, ∼30% of individuals could still be uniquely pinpointed using metagenomic codes from a typical body site; coincidental (false positive) matches were rare. Codes based on the gut microbiome were exceptionally stable and pinpointed >80% of individuals. The failure of a code to match its owner at a later time point was largely explained by the loss of specific microbial strains (at current limits of detection) and was only weakly associated with the length of the sampling interval. In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability-a result with important ethical implications for microbiome study design. The datasets and code used in this work are available for download from huttenhower.sph.harvard.edu/idability.

The human microbiome has become a recognized factor in promoting and maintaining health. We outline opportunities in interdisciplinary research, analytical rigor, standardization, and policy development for this relatively new and rapidly developing field. Advances in these aspects of the research community may in turn advance our understanding of human microbiome biology., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

Recently, we applied structural systems biology to host-pathogen interaction and constructed the human-virus structural interaction network (SIN) based on a combination of solved structures and homology models. Subsequent analysis of the human-virus SIN revealed significant differences between antagonistic human-virus and cooperative within-human protein-protein interactions (PPIs). Although the SIN approach is advantageous due to the complementary nature of 3D structure and network data, integration of these data sources is associated with two potential issues: reduced coverage of the full human-virus PPI network, and the introduction of specific biases from structure determination. In this work, we evaluate the impact of these issues by comparing the growth and properties of human-virus and within-human PPI networks with and without structural models. We find that although the human-virus SIN is small in size, it is largely depleted for false positives, which are common in the full network. In addition, the SIN shows potential for major growth in the near future. Furthermore, compared to the full network, the coverage of viral species in the human-virus SIN is large relative to its size, suggesting that it may be a less biased sampling of the universe of human-virus PPIs. Next, we systematically compare structural versus full networks of human-virus and within-human PPIs in terms of functional, physicochemical, and network properties. We find that although there exist biases inherent to the structural approach, such biases tend to affect both human-virus and within-human PPIs equally. As a result, the significant differences between structured human-virus and within-human PPI networks are never contradicted by the full networks. Collectively, these results suggest that a structural approach to host-pathogen systems biology is not only justified, but also highly complementary to previous approaches. In particular, conclusions drawn from direct comparisons of host-virus and host-host PPIs within the SIN are minimally confounded by the inherent biases of the structural approach.