F23.00008. Predicting microbial community metabolic function from genomic structure

Presented by: Karna Gowda


Abstract

The genes and organisms present in microbial communities determine metabolic flows that drive global nutrient cycles. A primary objective of microbial ecology is therefore to predict the metabolic function of a community from its genomic structure. We approach this prediction problem using denitrification as a model metabolic process. Denitrification is mediated by bacterial consortia that convert nitrate to dinitrogen gas. Using metabolite measurements and sequencing of denitrifying bacteria isolated from local soils, we develop a statistical-empirical approach to predicting community function from genomic structure. We show that for each strain in monoculture, the dynamics of denitrification are parameterized by a consumer-resource model. With some well-defined exceptions, we then find that the dynamics of simple communities are predictable from monoculture dynamics. This means we need only predict single-strain metabolite flows from genomes to successfully predict community-level metabolism. We solve this problem by predicting consumer-resource model parameters via regression onto the presence/absence of each strain's denitrification genes, thereby providing a complete map from community genomic structure to metabolic function.

Authors

  • Karna Gowda
  • Derek Ping
  • Laura Troyer
  • Madhav Mani
  • Seppe Kuehn


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