PLoS Computational Biology (Jan 2021)

Bacterial fitness landscapes stratify based on proteome allocation associated with discrete aero-types.

  • Ke Chen,
  • Amitesh Anand,
  • Connor Olson,
  • Troy E Sandberg,
  • Ye Gao,
  • Nathan Mih,
  • Bernhard O Palsson

DOI
https://doi.org/10.1371/journal.pcbi.1008596
Journal volume & issue
Vol. 17, no. 1
p. e1008596

Abstract

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The fitness landscape is a concept commonly used to describe evolution towards optimal phenotypes. It can be reduced to mechanistic detail using genome-scale models (GEMs) from systems biology. We use recently developed GEMs of Metabolism and protein Expression (ME-models) to study the distribution of Escherichia coli phenotypes on the rate-yield plane. We found that the measured phenotypes distribute non-uniformly to form a highly stratified fitness landscape. Systems analysis of the ME-model simulations suggest that this stratification results from discrete ATP generation strategies. Accordingly, we define "aero-types", a phenotypic trait that characterizes how a balanced proteome can achieve a given growth rate by modulating 1) the relative utilization of oxidative phosphorylation, glycolysis, and fermentation pathways; and 2) the differential employment of electron-transport-chain enzymes. This global, quantitative, and mechanistic systems biology interpretation of fitness landscape formed upon proteome allocation offers a fundamental understanding of bacterial physiology and evolution dynamics.