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

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.Succinic acid is one of the fermentation products of anaerobic metabolism as well as an intermediate of the tricarboxylic acid cycle. It has been used as a precursor for various chemicals, a food additive, an ion chelator, and a supplement to pharmaceuticals (34). Succinic acid has mostly been produced chemically from maleic anhydride. Recently, fermentative production of succinic acid has been receiving much research attention, as several bacteria can produce succinic acid as a major fermentation product. The naturally isolated obligate anaerobe Anaerobiospirillum succiniciproducens (6) and facultative anaerobes belonging to the family Pasteurellaceae, such as Actinobacillus succinogenes (10) and Mannheimia succiniciproducens (16), have been shown to be able to produce succinic acid efficiently.Escherichia coli also produces succinic acid but as a minor fermentation product. E. coli prefers to produce much more acetic acid, formic acid, lactic acid, and ethanol rather than succinic acid during anaerobic fermentation. Thus, it is necessary to redirect metabolic fluxes for increasing succinic acid production as well as reducing formation of other metabolites. Toward this goal, an ldhA and pfl double mutant E. coli strain NZN111 was developed to block the formation of lactic, acetic, and formic acids (2). However, cell growth was relatively slow, possibly due to the inactivation of enzymes involved in pyruvate dissimilation (3). Chatterjee and coworkers (4) reported that an additional ptsG gene mutation recovered the growth of NZN111 to some extent. However, acetic acid, formic acid, and ethanol were still...

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

“…Through the application of computational methods that incorporate linear, mixed integer linear, and non-linear programming, it has been demonstrated that model-directed strain design can lead to increased metabolite production [20][21][22][23][24][25][26][27][28][29][30] . In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest.…”

“…In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest. Thus, E. coli strains have been systematically designed through in silico analysis to overproduce target metabolites such as lycopene 23,24 , lactic acid 25 , ethanol 26 , succinic acid 27,28 , Lvaline 29 , L-threonine 30 , additional amino acids 21 , as well as diverse products from hydrogen to vanillin 22 . Select exemplary metabolic engineering applications will be described in more detail.…”

“…Growth rate, uptake and secretion rate profiles were the measures by which this property was examined and thus this study demonstrated the utility of adaptive evolution as a design tool 87 . Additional noteworthy examples of GEM aided design are two studies which demonstrated 27,28 that GEM modeling using iJR904 17 was beneficial to screen genes that were deemed to be important for succinate production. Combinatorial knock-outs that were predicted to be overproducers in silico were experimentally verified to display the same overproducing phenotype in vivo.…”

“…Combinatorial knock-outs that were predicted to be overproducers in silico were experimentally verified to display the same overproducing phenotype in vivo. Furthermore, this method had an advantage over using comparative genomics for strain design, which was also performed in one of the studies 27 .…”