Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation

Author:

Lee Sang Jun12,Lee Dong-Yup13,Kim Tae Yong1,Kim Byung Hun1,Lee Jinwon4,Lee Sang Yup123

Affiliation:

1. Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering

2. Center for Ultramicrochemical Process Systems

3. Department of BioSystems, BioProcess Engineering Research Center and Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea

4. Department of Chemical and Biomolecular Engineering, Sogang University, Seoul 121-742, Republic of Korea

Abstract

ABSTRACT Comparative analysis of the genomes of mixed-acid-fermenting Escherichia coli and succinic acid-overproducing Mannheimia succiniciproducens was carried out to identify candidate genes to be manipulated for overproducing succinic acid in E. coli . This resulted in the identification of five genes or operons, including ptsG , pykF , sdhA , mqo , and aceBA , which may drive metabolic fluxes away from succinic acid formation in the central metabolic pathway of E. coli . However, combinatorial disruption of these rationally selected genes did not allow enhanced succinic acid production in E. coli . Therefore, in silico metabolic analysis based on linear programming was carried out to evaluate the correlation between the maximum biomass and succinic acid production for various combinatorial knockout strains. This in silico analysis predicted that disrupting the genes for three pyruvate forming enzymes, ptsG , pykF , and pykA , allows enhanced succinic acid production. Indeed, this triple mutation increased the succinic acid production by more than sevenfold and the ratio of succinic acid to fermentation products by ninefold. It could be concluded that reducing the metabolic flux to pyruvate is crucial to achieve efficient succinic acid production in E. coli. These results suggest that the comparative genome analysis combined with in silico metabolic analysis can be an efficient way of developing strategies for strain improvement.

Publisher

American Society for Microbiology

Subject

Ecology,Applied Microbiology and Biotechnology,Food Science,Biotechnology

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