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

Journal Article · 16 November 2017 · Nature Communications

DOI: https://doi.org/10.1038/s41467-017-01407-5 · OSTI ID:1526296

Zomorrodi, Ali R.  ^[1]; Segrè, Daniel  ^[1]

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1. Boston Univ., Boston, MA (United States)

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.

• Research Organization: Boston Univ., MA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0004962; SC0012627
• OSTI ID: 1526296
• Journal Information: Nature Communications, Vol. 8, Issue 1; ISSN 2041-1723
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English