Model-guided chemical environment and metabolic network design to couple pathways with cell fitness

Abstract

Heterologous compound production is a complex trait since the native metabolic fluxes supplying the precursors, redox power, and energy are under multilevel cellular regulation. Improving complex traits using targeted engineering needs combinatorially charting the complex genetic underpinnings. While this is laborious, adaptive laboratory evolution (ALE) has been used to improve many traits of microbial strains that are of application relevance such as tolerance of harsh conditions and nutrient utilization. However, in contrast to such traits, heterologous production can seldom be intuitively coupled with cellular fitness.

Here, a novel method EvolveXGA was developed for genome-scale metabolic model guided design of strategies combining chemical environments and genetic engineering of the metabolic network to allow ALE of desired traits. Adaptive evolution of traits occurs when the co-variance between the traits and fitness involves a genetic dependency like a flux coupling would indicate. Thus, combinations of chemical environments and metabolic network structures were searched using a genetic algorithm to identify those that render desired traits (i.e., sets of metabolic fluxes) flux-coupled with fitness. The search was performed for the production of 29 heterologous compounds in yeast Saccharomyces cerevisiae. Strategies for coupling the production routes of 13 compounds with fitness were found with four metabolic reaction knock outs and three components in the chemical environment. In addition, strategies for fitness-coupling native fluxes involved in the production was found for the remaining compounds. In addition, a model-guided strategy was implemented for fitness-coupling of heterologous glycolic acid (GA) synthesis in S. cerevisiae via oxaloacetase, oxalyl-CoA synthetase, and oxalyl-CoA reductase (i.e., oxalate pathway). ALE was performed and evolved populations and isolated clones were characterized using whole-genome sequencing and quantitative metabolite analysis. Three out of six isolates had better GA yield from glucose than a non-optimized control strain expressing the oxalate pathway and glyoxylate reductase.

EvolveXGA generalizes metabolic model-guided design of strategies to couple production routes with cell fitness. The strategies bring optimizing heterologous production in engineered microbial cells in the realm of ALE. Slow and expensive strain optimization is a major hinder of novel processes using engineered microbial cells reaching industrial realization. Thus, EvolveXGA contributes to biotechnological solutions for the brighter future.