No abstract

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

Microbes in ecosystems often develop coordinated metabolic interactions. Therefore, understanding metabolic interdependencies between microbes is critical to deciphering ecosystem function. In this study, we sought to deconstruct metabolic interdependencies in organohalide-respiring consortium ACT-3 containing Dehalobacter restrictus using a combination of metabolic modeling and experimental validation. D. restrictus possesses a complete set of genes for amino acid biosynthesis yet when grown in isolation requires amino acid supplementation. We reconciled this discrepancy using flux balance analysis considering cofactor availability, enzyme promiscuity, and shared protein expression patterns for several D. restrictus strains. Experimentally, 13 C incorporation assays, growth assays, and metabolite analysis of D. restrictus strain PER-K23 cultures were performed to validate the model predictions. The model resolved that the amino acid dependency of D. restrictus resulted from restricted NADPH regeneration and predicted that malate supplementation would replenish intracellular NADPH. Interestingly, we observed unexpected export of pyruvate and glutamate in parallel to malate consumption in strain PER-K23 cultures. Further experimental analysis using the ACT-3 transfer cultures suggested the occurrence of an interspecies malate-pyruvate shuttle reconciling a redox imbalance, reminiscent of the mitochondrial malate shunt pathway in eukaryotic cells. Altogether, this study suggests that redox imbalance and metabolic complementarity are important driving forces for metabolite exchange in anaerobic microbial communities.

Microbial metabolism can be harnessed to produce a broad range of industrially important chemicals. Often, three key process variables: Titer, Rate and Yield (TRY) are the target of metabolic engineering efforts to improve microbial hosts toward industrial production. Previous research into improving the TRY metrics have examined the efficacy of having distinct growth and production stages to achieve enhanced productivity. However, these studies assumed a switch from a maximum growth to a maximum production phenotype. Hence, phenotypes with intermediate growth and chemical production for the growth and production stages of two-stage processes are yet to be explored. The impact of reduced growth rates on substrate uptake adds to the need for intelligent choice of operating points while designing two-stage processes. In this work, we develop a computational framework that scans the phenotypic space of microbial metabolism to identify ideal growth and production phenotypic targets, to achieve optimal TRY targets. Using this framework, with Escherichia coli as a model organism, we compare two-stage processes that use dynamic pathway regulation, with one-stage processes that use static intervention strategies, for different bioprocess objectives. Our results indicate that two-stage processes with intermediate growth during the production stage always result in optimal TRY values even in cases where substrate uptake is limited due to reduced growth during chemical production. By analyzing the flux distributions for the production enhancing strategies, we identify key reactions and reaction subsystems that require perturbation to achieve a production phenotype for a wide range of metabolites in E. coli. Interestingly, flux perturbations that increase phosphoenolpyruvate and NADPH availability are enriched among these production phenotypes. Furthermore, reactions in the pentose phosphate pathway emerge as key control nodes that function together to increase the availability of precursors to most products in E. coli. The inherently modular nature of microbial metabolism results in common reactions and reaction subsystems that need to be regulated to modify microbes from their target of growth to the production of a diverse range of metabolites. Due to the presence of these common patterns in the flux perturbations, we propose the possibility of a universal production strain.

Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.