The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease.

Altered gut bacterial and metabolic signatures and their interaction in inflammatory bowel disease

The NIH Human Microbiome Project (HMP) has been carried out over ten years and two phases to provide resources, methods, and discoveries that link interactions between humans and their microbiomes to health-related outcomes. The recently completed second phase, the Integrative Human Microbiome Project, comprised studies of dynamic changes in the microbiome and host under three conditions: pregnancy and preterm birth; inflammatory bowel diseases; and stressors that affect individuals with prediabetes. The associated research begins to elucidate mechanisms of host–microbiome interactions under these conditions, provides unique data resources (at the HMP Data Coordination Center), and represents a paradigm for future multi-omic studies of the human microbiome.

BackgroundThe essential roles of gut microbiome have been emphasized in modulating human health and disease. Fusobacterium nucleatum (F. nucleatum), an obligate Gram-negative microorganism residing in oral cavity, gastrointestinal tract and elsewhere, has been recently considered as a potential oncobacterium associated with human cancers. However, the consequence of its enrichment was not extensively explored in terms of microbial homeostasis and stability at the early stage of disease development.ResultOur analysis on longitudinal metagenomic data generated by the Integrative Human Microbiome Project (iHMP) showed that F. nucleatum was frequently found in inflammatory bowel diseases (IBD) subjects with reduced microbial diversity. Using non-parametric logarithmic linear discriminant analysis (LDA) effect size (LEfSe) algorithm, 12 IBD- and 14 non-IBD-specific bacterial species were identified in the fecal metagenome and the IBD-specific ones were over-represented in the F. nucleatum-experienced subjects during long-term surveillance. In addition, F. nucleatum experience severely abrogated intra-personal stability of microbiome in IBD patients and induced highly variable gut microbiome between subjects. From the longitudinal comparison between microbial distributions prior and posterior to F. nucleatum detection, 41 species could be proposed as indicative “classifiers” for dysbiotic gut state. By multiple logistic regression models established on these classifiers, the high probability of experiencing F. nucleatum was significantly correlated with decreased alpha-diversity and increased number of biomarker species for IBD and colorectal cancer (CRC). Finally, microbial clustering confirmed that biomarker species for IBD and non-IBD conditions as well as CRC signature markers were well distinguishable and could be utilized for explaining gut symbiosis and dysbiosis.ConclusionF. nucleatum opportunistically appeared under early dysbiotic condition in gut, and discriminative classifier species associated with F. nucleatum were successfully applied to predict microbial alterations in both IBD and non-IBD conditions. Our prediction model and microbial classifier biomarkers for estimating gut dysbiosis should provide a novel aspect of microbial homeostasis/dynamics and useful information on non-invasive biomarker screening.

ABSTRACTThe Integrative Human Microbiome Project and other cohort studies have indicated that inflammatory bowel disease is accompanied by dysbiosis of gut microbiota, decreased production of secondary bile acids, and increased levels of primary bile acids. Secondary bile acids, such as ursodeoxycholic acid (UDCA) and lithocholic acid (LCA), have been reported to be anti-inflammatory, yet it remains to be studied whether introducing selected bacteria strains to restore bile acid metabolism of the gut microbiome can alleviate intestinal inflammation. In this study, we screened human gut bacterial strains for bile acid metabolism and designed a consortium of three species, including Clostridium AP sp000509125, Bacteroides ovatus, and Eubacterium limosum, and named it BAC (bile acid consortium). We showed that the three-strain gut bacterial consortium BAC is capable of converting conjugated primary bile acids taurochenodeoxycholic acid and glycochenodeoxycholic acid to secondary bile acids UDCA and LCA in vitro. Oral gavage treatment with BAC in mice resulted in protective effects against dextran sulfate sodium (DSS)-induced colitis, including reduced weight loss and increased colon length. Furthermore, BAC treatment increased the fecal level of bile acids, including UDCA and LCA. BAC treatment enhanced intestinal barrier function, which may be attributed to the increased activation of the bile acid receptor TGR5 by secondary bile acids. Finally, we examined the remodeling of gut microbiota by BAC treatment. Taken together, the three-strain gut bacterial consortium BAC restored the dysregulated bile acid metabolism and alleviated DSS-induced colitis. Our study provides a proof-of-concept demonstration that a rationally designed bacterial consortium can reshape the metabolism of the gut microbiome to treat diseases.IMPORTANCE Secondary bile acids have been reported to be anti-inflammatory, yet it remains to be studied whether introducing selected bacteria strains to restore bile acid metabolism of the gut microbiome can alleviate intestinal inflammation. To address this gap, we designed a consortium of human gut bacterial strains based on their metabolic capacity to produce secondary bile acids UDCA and LCA, and we evaluated the efficacy of single bacterial strains and the bacterial consortium in treating the murine colitis model. We found that oral gavage of the bacterial consortium to mice restored secondary bile acid metabolism to increase levels of UDCA and LCA, which induced the activation of TGR5 to improve gut-barrier integrity and reduced the inflammation in murine colitis. Overall, our study demonstrates that rationally designed bacterial consortia can reshape the metabolism of the gut microbiome and provides novel insights into the application of live biotherapeutics for treating IBD.

Inflammatory bowel disease (IBD) is a group of chronic diseases of the digestive tract affecting millions of people worldwide. Genetic, environmental and microbial factors have been implicated in onset and exacerbation of IBD. However, the mechanisms associating gut microbial dysbioses and aberrant immune responses remain largely unknown. The integrative Human Microbiome Project (iHMP) seeks to close these gaps by examining the dynamics of microbiome functionality in disease by profiling the gut microbiomes of more than 100 individuals sampled over a one year period. Here, we present the first results based on 78 paired fecal metagenomes/metatranscriptomes and 222 additional metagenomes from 59 Crohn’s disease (CD), 34 ulcerative colitis (UC), 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, while 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 microbes per host (e.g. by Faecalibacterium prausnitzii). Therefore, a loss of these organisms in disease may have more far-reaching consequences than suggested by their genomic abundances. Further, we identified organisms that were metagenomically abundant but inactive or dormant in the gut with little or no expression (e.g. Dialister invisus). Lastly, certain disease-specific microbial characteristics were more pronounced or only detectable at the transcript level, such as pathways predominantly expressed by different organisms in IBD patients (e.g. Bacteroides vulgatus and Alistipes putredinis). This provides potential insights into gut microbial pathway transcription that can vary over time, inducing phenotypic changes complementary to those linked to metagenomic abundances. The study’s results highlight the strength of analyzing both the activity and presence of gut microbes to provide insight into the role of the microbiome in IBD.

All our genes together are called genomes. The Human Genome Project (HGP), an international collaborative research program, was conducted to fully map and understand the human genome. The sum of the genes of all microorganisms closely related to our genome is called microbiome. Following the completion of the Human Genome Project (HGP), the Human Microbiome Project (HMP) was conducted in which all microorganisms in different anatomical regions of our body were investigated. In this review, microbiome characteristics of oral cavity, skin, nasal cavity, digestive system and urogenital regions of donors in HMP studies were discussed. Special attention was paid to current microbiome research on women and child health. In the Integrative Human Microbiome Project (iHMP), which is considered as the continuation of HMP, pregnancy and preterm birth constitute one of the three main topics, which shows the importance given to women and child health. It was reported in the findings of HMP that there was no core microbiome in the urogenital microbiota, but the dysbiosis that occurred had a negative effect on the health of women and children. More and more evidence shows that human microbiome plays important role in human health and disease. In this context, it is provided that human microbiome research will be transformed into individual diagnostic, therapeutic and preventive applications in the field of health. Keywords: Microbiota, Urogenital microbiota, Pregnancy, Neonatal DOI: 10.7176/JSTR/6-01-04

ObjectivesA number of converging strands of research suggest that the intestinal Enterobacteriaceae plays a crucial role in the development and progression of inflammatory bowel disease (IBD), however, the changes in the abundance of Enterobacteriaceae species and their related metabolic pathways in Crohn’s disease (CD) and ulcerative colitis (UC) compared to healthy people are not fully explained by comprehensive comparative metagenomics analysis. In the current study, we investigated the alternations of the Enterobacterales population in the gut microbiome of patients with CD and UC compared to healthy subjects.MethodsMetagenomic datasets were selected from the Integrative Human Microbiome Project (HMP2) through the Inflammatory Bowel Disease Multi’omics Database (IBDMDB). We performed metagenome-wide association studies on fecal samples from 191 CD patients, 132 UC patients, and 125 healthy controls (HCs). We used the metagenomics dataset to study bacterial community structure, relative abundance, differentially abundant bacteria, functional analysis, and Enterobacteriaceae-related biosynthetic pathways.ResultsCompared to the gut microbiome of HCs, six Enterobacteriaceae species were significantly elevated in both CD and UC patients, including Escherichia coli, Klebsiella variicola, Klebsiella quasipneumoniae, Klebsiella pneumoniae, Proteus mirabilis, Citrobacter freundii, and Citrobacter youngae, while Klebsiella oxytoca, Morganella morganii, and Citrobacter amalonaticus were uniquely differentially abundant and enriched in the CD cohort. Four species were uniquely differentially abundant and enriched in the UC cohort, including Citrobacter portucalensis, Citrobacter pasteurii, Citrobacter werkmanii, and Proteus hauseri. Our analysis also showed a dramatically increased abundance of E. coli in their intestinal bacterial community. Biosynthetic pathways of aerobactin siderophore, LPS, enterobacterial common antigen, nitrogen metabolism, and sulfur relay systems encoded by E. coli were significantly elevated in the CD samples compared to the HCs. Menaquinol biosynthetic pathways were associated with UC that belonged to K. pneumoniae strains.ConclusionsIn conclusion, compared with healthy people, the taxonomic and functional composition of intestinal bacteria in CD and UC patients was significantly shifted to Enterobacteriaceae species, mainly E. coli and Klebsiella species.

The gut microbiome is more than just the gastrointestinal tract and the digestive organ. It is a unique community of microbes, the particular composition of which may play an important role in the development of disease, including inflammatory bowel disease (IBD). Research to date has focused on the role that genetics, the immune system, and environmental conditions may play in IBD, but additional investigations are needed to fully understand the connection between gut microbiota and the development of the disease. To illuminate this connection, data were analyzed from the Integrative Human Microbiome Project from children with Crohn′s disease (CD), and these data were compared to comparable data from healthy children. Data samples were analyzed through statistical analyses including the Welch t ‐test and PERMANOVA test. Significant differences in the composition of the gut microbial communities were observed between individuals with IBD and the control group. Specifically, samples with IBD had an increased abundance o f Escherichia coli and Ruminococcus torques. Additionally, increased levels of Bacteroides fragilis were observed, indicating that elevated levels of Bacteroides fragilis , associated with the Bacteroides fragilis toxin, may have a potential role in the onset of IBD. Furthermore, CD samples exhibited decreased Chao1 richness when compared with healthy control samples. These findings signify a potential connection between the gut microbiome and the abundance of certain microbiota with IBD onset. This paper is aimed at identifying potential connections associated with the presence of specific microbiota in the gut microbiome and the manifestation of chronic disease, particularly IBD.

RNA editing is a post-transcriptional modification that alters single-nucleotide sites within RNA strands, thus diversifying transcriptomes and proteomes and modulating gene expression. While better characterized in eukaryotes and in a few microbes, the study of RNA editing in entire microbiomes remains unexplored. Recent studies have demonstrated that A-to-I RNA editing contributes to bacterial adaptation and pathogenicity. Previously, we developed MetaEdit, a reference-based computational pipeline to detect RNA edit sites in microbiomes. While MetaEdit successfully identified RNA edit sites in Escherichia coli within the context of the human gut microbiome, including previously reported loci, it relied primarily on aligning reads to reference genomes of target bacteria. This dependence on reference genomes introduced potential biases, as editing can only be identified in reference genomes, while editing in novel microbial strains missing from the reference databases could be overlooked. Even for reference genomes, the search for edit sites is inefficient since it would have to be conducted one reference genome at a time.Here, we introduce MEditome, employing de novo assembly to overcome these limitations. This crucial change enables the detection of RNA edit sites across all microbial organisms in the microbiome, including novel bacterial strains for which comprehensive reference genomes are unavailable. Using sequencing data from the Integrative Human Microbiome Project, MEditome identified 2,295 unique RNA editing sites across diverse bacterial taxa. Several of these overlaps with previously identified edits in E. coli detected by MetaEdit in hok/gef gene family and arginine-associated genes, providing in silico validation of accuracy. We observed taxon-specific editing patterns and gene-level differential editing associated with inflammatory bowel disease, highlighting RNA editing as a potential regulatory mechanism influencing microbial adaptation and host-microbe interactions.

After the Integrative Human Microbiome Project, what's next for the microbiome community?

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.

Machine learning is helping the interpretation of biological complexity by enabling the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e.g., from genomic, transcriptomic and metagenomic analyses. A number of available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box is sufficient, it is interesting to pursue an enhanced understanding of how system variables end up contributing to a specific output, as an avenue toward new mechanistic insight. Here we address this challenge through a suite of algorithms, named BowSaw, which takes advantage of the structure of a trained random forest algorithm to identify combinations of variables (“rules”) frequently used for classification. We first apply BowSaw to a simulated dataset and show that the algorithm can accurately recover the sets of variables used to generate the phenotypes through complex Boolean rules, even under challenging noise levels. We next apply our method to data from the integrative Human Microbiome Project and find previously unreported high-order combinations of microbial taxa putatively associated with Crohn’s disease. By leveraging the structure of trees within a random forest, BowSaw provides a new way of using decision trees to generate testable biological hypotheses.

KEYWORDS: Microbiomemicrobiotahuman microbiome projectdysbiosismetabolometherapeuticsmetabolomicsecological niche

Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. timeOmics is available on Bioconductor and github.com/abodein/timeOmics. Supplementary data are available at Bioinformatics online.

The majority of genes in microbial communities remain uncharacterized. Here we develop a method to infer putative function for microbial proteins at scale by assessing community-wide multiomics data. We predict high-confidence functions for >443,000 protein families (~82.3% previously uncharacterized), including >27,000 protein families with weak homology to known proteins and >6,000 protein families without homology. These were drawn from 1,595 gut metagenomes and 800 metatranscriptomes from the Integrative Human Microbiome Project (HMP2/iHMP). Integrating additional information such as sequence similarity, genomic proximity and domain-domain interactions improves performance of the method. Our method's implementation, FUGAsseM, is generalizable and predicts protein function in both well-studied and undercharacterized communities. FUGAsseM achieves similar levels of accuracy in the context of microbial communities when compared to state-of-the-art approaches designed for application to single organisms while simultaneously providing much greater breadth of coverage. This initial study expands the functional landscape of the human gut microbiome and allows for exploration of microbial proteins in undercharacterized communities.