Fecal microbiome variation in the average, healthy population has remained under-investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the Belgian Flemish Gut Flora Project (FGFP; discovery cohort; N = 1106) and the Dutch LifeLines-DEEP study (LLDeep; replication; N = 1135). Integration with global data sets (N combined = 3948) revealed a 14-genera core microbiota, but the 664 identified genera still underexplore total gut diversity. Sixty-nine clinical and questionnaire-based covariates were found associated to microbiota compositional variation with a 92% replication rate. Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations. Early-life events such as birth mode were not reflected in adult microbiota composition. Finally, we found that proposed disease marker genera associated to host covariates, urging inclusion of the latter in study design.

No abstract

Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis. Although these relative approaches permit detection of disease-associated microbiome variation, they are limited in their ability to reveal the interplay between microbiota and host health. Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential. If microbial load varies substantially between samples, relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations. Saliently, relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration. To enable genuine characterization of host-microbiota interactions, microbiome research must exchange ratios for counts. Here we build a workflow for the quantitative microbiome profiling of faecal material, through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells. We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation. We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype. Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses. Finally, we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohn's disease, here associated with a low-cell-count Bacteroides enterotype (as defined through relative profiling).

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

The gut microbiome is shaped by diet and influences host metabolism, but these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of >1,100 gut microbiomes from individuals with detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements. We found strong associations between microbes and specific nutrients, foods, food groups, and general dietary indices, driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across cohorts, and blood markers of cardiovascular disease and impaired glucose tolerance were more strongly associated with microbiome structure. While some microbes such as Prevotella copri and Blastocystis spp., were indicators of reduced postprandial glucose metabolism, several species were more directly predictive for postprandial triglycerides and C-peptide. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favourable cardiometabolic and postprandial markers, indicating our large-scale resource can potentially stratify the gut microbiome into generalizable health levels among individuals without clinically manifest disease. Fig. 1: The PREDICT 1 study associates gut microbiome structure with habitual diet and blood cardiometabolic markers. (A)The PREDICT 1 study assessed the gut microbiome of 1,098 volunteers from the UK and US via metagenomic sequencing of stool samples. Phenotypic data obtained through in-person assessment, blood/biospecimen collection, and the return of validated study questionnaires queried a range of relevant host/environmental factors including (1) personal characteristics, such as age, BMI, and estimated visceral fat; (2) habitual dietary intake using semi-quantitative food frequency questionnaires (FFQs);(3) fasting; and (4) postprandial cardiometabolic blood and inflammatory markers, total lipid and lipoprotein concentrations, lipoprotein particle sizes, apolipoproteins, derived metabolic risk scores, glycaemic-mediated metabolites, and metabolites related to fatty acid metabolism. (B) Overall microbiome alpha diversity, estimated as the total number of confidently identified microbial species in a given sample (richness), was correlated with HDL-D (positive) and estimated hepatic steatosis (negative). Up to ten strongest absolute Spearman correlations are reported for each category with q<0.05. Top species based on Shannon diversity are reported in Supplementary Fig. 1A and all correlations are in Supplementary Table 1. Microbial diversity and composition are linked with diet and fasting and postprandial biomarkersWe first leveraged a unique subpopulation of our study comprised of 480 twins to disentangle the confounding effects of shared genetics from other factors on microbiome composition. Our data confirmed that host genetics influences microbiome composition only to a small extent 18 , as intra-twin pair microbiome ...

Culture-independent analyses of microbial communities have advanced dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1,262 metagenomes) and IBD (1,635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4,077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

No abstract

Despite recent progress, the organization and ecological properties of the intestinal microbial ecosystem remain under-investigated. Here, using a manually curated metabolic module framework for (meta-)genomic data analysis, we studied species-function relationships in gut microbial genomes and microbiomes. Half of gut-associated species were found to be generalists regarding overall substrate preference, but we observed significant genus-level metabolic diversification linked to bacterial life strategies. Within each genus, metabolic consistency varied significantly, being low in Firmicutes genera and higher in Bacteroides. Differentiation of fermentable substrate degradation potential contributed to metagenomic functional repertoire variation between individuals, with different enterotypes showing distinct saccharolytic/proteolytic/lipolytic profiles. Finally, we found that module-derived functional redundancy was reduced in the low-richness Bacteroides enterotype, potentially indicating a decreased resilience to perturbation, in line with its frequent association to dysbiosis. These results provide insights into the complex structure of gut microbiome-encoded metabolic properties and emphasize the importance of functional and ecological assessment of gut microbiome variation in clinical studies.