Back to Search Start Over

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

Authors :
Lloyd CJ
King ZA
Sandberg TE
Hefner Y
Olson CA
Phaneuf PV
O'Brien EJ
Sanders JG
Salido RA
Sanders K
Brennan C
Humphrey G
Knight R
Feist AM
Source :
PLoS computational biology [PLoS Comput Biol] 2019 Mar 01; Vol. 15 (3), pp. e1006213. Date of Electronic Publication: 2019 Mar 01 (Print Publication: 2019).
Publication Year :
2019

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.<br />Competing Interests: The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
1553-7358
Volume :
15
Issue :
3
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
30822347
Full Text :
https://doi.org/10.1371/journal.pcbi.1006213