Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

Abstract

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed. In this study, a GEM of Escherichia coli NZN111 was employed by the analysis of production and growth coupling (APGC) algorithm to identify genetic strategies for the overproduction of succinate. Through in silico simulation and reaction expression analysis, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), triosephosphate isomerase (TPI), and phosphoenolpyruvate carboxylase (PPC), encoded by gapA, pgk, tpiA, and ppc, respectively, were selected for experimental overexpression. The results showed that overexpressing any of these could improve both growth and succinate production. Specifically, overexpression of GAPDH or PGK showed a significant effect with up to 24% increase in succinate production. These results indicate that the APGC algorithm can be effectively used to guide genetic manipulation for strain design by identifying genome-wide gene amplification targets.