The genetic basis for adaptation of model-designed syntrophic co-cultures

Colton J. Lloyd, Zachary A. King, Troy E. Sandberg, Ying Hefner, Connor A. Olson, Patrick V. Phaneuf, Edward J. O'Brien, Jon G. Sanders, Rodolfo A. Salido, Karenina Sanders, Caitriona Brennan, Gregory Humphrey, Rob Knight, Adam M. Feist*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities.

Original languageEnglish
Article numbere1006213
JournalPLOS Computational Biology
Volume15
Issue number3
Number of pages28
ISSN1553-7358
DOIs
Publication statusPublished - 2019

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