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Olson, C.

Publications and source records attributed to Olson, C..

2 recordsLinked to original sources

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism

Growth rate and yield are fundamental features of micro-bial growth. However, we lack a mechanistic and quantita-tive understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Es-cherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield trade-off in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mecha-nistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

microbiology

Model-driven design and evolution of non-trivial synthetic syntrophic pairs

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.

systems biology