Fig. 1

Integrated metagenomics–metabolomics approach for dissecting the strain-level contribution of the gut microbiome to human metabolic disease. Longitudinal, interventional experiments are accompanied by time-series and multisite sampling for capturing strain-level changes in the gut microbiota, and variations of host disease phenotypes and metabotypes. From blood samples, bioclinical parameters are obtained as measurements of changes in disease phenotypes. From the fecal samples, total DNA is extracted and shotgun sequenced. Genes assembled and identified in individual samples are then integrated to form a cross-sample, non-redundant gene catalog. The abundance profile of each gene in the catalog is assessed by counting the matching sequence reads in each sample. A canopy-based algorithm is used to cluster the large number of genes in the catalog into co-abundance gene groups (CAGs). Sequence reads from individual samples that map to the CAGs and their contigs are then extracted and used to assemble high-quality draft genomes, each of which is a strain or a group of highly similar strains. For the urine, plasma, or fecal water samples, metabolomic approaches such as nuclear magnetic resonance (NMR)-based metabolite profiling is used to capture variations in metabolites or host–bacteria co-metabolites. Variations in specific metabolites during the interventions or correlated with disease phenotypes are identified via multivariate statistics. Correlation analysis between these specific metabolites and prevalent genomes may lead to the identification of specific strains that harbor the genes needed to produce precursors of the disease-relevant metabolites or host–bacteria co-metabolites. These strains can be isolated based on their genomic information. Gnotobiotic animal models can be established by colonization with individual or combinations of these strains for mechanistic studies to validate and understand their causative roles in the development of metabolic disease phenotypes. Eventually, we may answer questions such as “Who?” does “What?” and “How?” regarding the role of the gut microbiome in human metabolic diseases. FBI fasting blood insulin, FBS fasting blood sugar, GC–MS gas chromatography–mass spectrometry, HDL high-density lipoprotein, IL interleukin, ITT insulin tolerance test, LC liquid chromatography, LC–MS liquid chromatography–mass spectrometry, LDL low-density lipoprotein, OGTT oral glucose tolerance test, TC total cholesterol, TE triglycerides, TNF tumor necrosis factor