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Title: Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Abstract

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Moreover, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Boston Univ., Boston, MA (United States)
Publication Date:
Research Org.:
Boston Univ., MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1526296
Grant/Contract Number:  
SC0004962; SC0012627
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Zomorrodi, Ali R., and Segrè, Daniel. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. United States: N. p., 2017. Web. doi:10.1038/s41467-017-01407-5.
Zomorrodi, Ali R., & Segrè, Daniel. Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. United States. doi:10.1038/s41467-017-01407-5.
Zomorrodi, Ali R., and Segrè, Daniel. Thu . "Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities". United States. doi:10.1038/s41467-017-01407-5. https://www.osti.gov/servlets/purl/1526296.
@article{osti_1526296,
title = {Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities},
author = {Zomorrodi, Ali R. and Segrè, Daniel},
abstractNote = {Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Moreover, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.},
doi = {10.1038/s41467-017-01407-5},
journal = {Nature Communications},
number = 1,
volume = 8,
place = {United States},
year = {2017},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
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Cited by: 19 works
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Figures / Tables:

Fig. 1 Fig. 1: General scheme for the proposed genome-driven evolutionary game theory approach. Annotated genomes of community members are used to construct genome-scale metabolic models. For each possible pair of genotypes in the community, constraint-based analysis tools for metabolic models, such as flux balance analysis, are used to estimate the fitnessmore » (or “payoff”) of each genotype as they engage in a specific metabolitemediated interaction. These payoffs form the payoff matrix of the game. Based on this payoff matrix, we identify all pure strategy Nash equilibria of the game, using an automated pipeline (NashEq Finder, see “Methods”). The payoff matrix also allows one to model evolutionary dynamics (i.e., how genotype frequencies change over time) and to determine which of the identified Nash equilibria are evolutionarily stable (see “Methods”). Supplementary Figs. 11 and 12 provide a more specific representation of this scheme for the presented case studies in this paper« less

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    Works referencing / citing this record:

    A multidimensional perspective on microbial interactions
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