Parallel Mutations Result in a Wide Range of Cooperation and Community Consequences in a Two-Species Bacterial Consortium.
Multi-species microbial communities play a critical role in human health, industry, and waste remediation. Recently, the evolution of synthetic consortia in the laboratory has enabled adaptation to be addressed in the context of interacting species. Using an engineered bacterial consortium, we repeatedly evolved cooperative genotypes and examined both the predictability of evolution and the phenotypes that determine community dynamics. Eight Salmonella enterica serovar Typhimurium strains evolved methionine excretion sufficient to support growth of an Escherichia coli methionine auxotroph, from whom they required excreted growth substrates. Non-synonymous mutations in metA, encoding homoserine trans-succinylase (HTS), were detected in each evolved S. enterica methionine cooperator and were shown to be necessary for cooperative consortia growth. Molecular modeling was used to predict that most of the non-synonymous mutations slightly increase the binding affinity for HTS homodimer formation. Despite this genetic parallelism and trend of increasing protein binding stability, these metA alleles gave rise to a wide range of phenotypic diversity in terms of individual versus group benefit. The cooperators with the highest methionine excretion permitted nearly two-fold faster consortia growth and supported the highest fraction of E. coli, yet also had the slowest individual growth rates compared to less cooperative strains. Thus, although the genetic basis of adaptation was quite similar across independent origins of cooperative phenotypes, quantitative measurements of metabolite production were required to predict either the individual-level growth consequences or how these propagate to community-level behavior.
Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America.|Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Sarah M
SM
Douglas
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Lon M
LM
Chubiz
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
William R
WR
Harcombe
Department of Physics, University of Idaho, Moscow, Idaho, United States of America.|Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America.|Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America.
F Marty
FM
Ytreberg
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.|Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America.|Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America.|Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America.|Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America.
Christopher J
CJ
Marx
D001426:Bacterial Proteins / Q000737:chemistry; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D005798:Genes, Bacterial; D008715:Methionine / Q000378:metabolism; D008958:Models, Molecular; D009154:Mutation; D053038:Quorum Sensing; D019779:Salmonella enterica / Q000235:genetics* / Q000378:metabolism
D001426:Bacterial Proteins; D008715:Methionine