PNNL

Abstract

Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date analyses of such multi-meta-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites' abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributors. Focusing on two independent vaginal microbiome datasets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that significantly contribute to these predictions. Interestingly, our analysis also detects metabolites for which predicted variation negatively correlates with measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome datasets reveals similar trends,including prediction of bile acid metabolite shifts. This framework is an important first step towards a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease.