PT  - JOURNAL ARTICLE
AU  - Colton J. Lloyd
AU  - Zachary A. King
AU  - Troy E. Sandberg
AU  - Ying Hefner
AU  - Connor A. Olson
AU  - Patrick V. Phaneuf
AU  - Edward J. O’brien
AU  - Adam M. Feist
TI  - Model-driven design and evolution of non-trivial synthetic syntrophic pairs
AID  - 10.1101/327270
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 327270
4099  - http://biorxiv.org/content/early/2018/05/21/327270.short
4100  - http://biorxiv.org/content/early/2018/05/21/327270.full
AB  - Synthetic microbial communities are attractive for applied biotechnology and healthcare applications through their ability to efficiently partition complex metabolic functions. By pairing auxotrophic mutants in co-culture, nascent E. coli communities can be established where strain pairs are metabolically coupled. Intuitive synthetic communities have been demonstrated, but the full space of cross-feeding metabolites has yet to be explored. A novel algorithm, OptAux, was constructed to design 66 multi-knockout E. coli auxotrophic strains that require significant metabolite cross-feeding when paired in co-culture. Three OptAux predicted auxotrophic strains were co-cultured with an L-histidine auxotroph and validated via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community fitness and provided insights on mechanisms for sharing and 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 a novel computational method to elucidate metabolic changes that empower community formation and thus guide the optimization of co-cultures for a desired application.