Genomic structure predicts metabolite dynamics in microbial communities

Abstract

The metabolic function of microbial communities has played a defining role in the evolution and persistence of life on Earth, driving redox reactions that form the basis of global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolic dynamics from genomic structure remains elusive. Here we show, for the process of denitrification, that community metabolism is predictable from the genes each member of the community possesses. Machine learning reveals a sparse and generalizable mapping from gene content to metabolite dynamics across a genomically-diverse library of bacteria. A consumer-resource model correctly predicts community metabolism from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community function, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.