Genomic structure predicts metabolite dynamics in microbial communities

ORAL

Abstract

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to 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 metabolite dynamics from genomes remains elusive. Here we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically-diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics 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.

*This work was supported by the National Science Foundation Division of Emerging Frontiers EF 2025293 and EF 2025521, the National Science Foundation Physics Frontiers Center Program PHY 0822613 and PHY 1430124, James S. McDonnell Foundation Postdoctoral Fellowship Award 220020499, and the Simons Foundation Investigator Award 597491.

Publication: https://www.biorxiv.org/content/10.1101/2020.09.29.315713v2

Presenters

  • Karna Gowda

    • University of Chicago

Authors

  • Karna Gowda

    • University of Chicago
  • Derek J Ping

    • University of Illinois at Urbana-Champaign
  • Madhav Mani

    • Northwestern University
  • Seppe Kuehn

    • University of Chicago