Journal Article
mSystems, vol. 1, iss. 1, 2016
Authors
Cecilia Noecker, Alexander Eng, Sujatha Srinivasan, Casey M. Theriot, Vincent B. Young, Janet K. Jansson, David N. Fredricks, Elhanan Borenstein, Laura M. Sanchez
Abstract
Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our understanding of microbial community metabolism.
