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Appl Environ Microbiol. 2005 December; 71(12): 7880–7887.
PMCID: PMC1317394

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


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 formed. Amplification of CO2-fixing anaplerotic pathways catalyzed by phosphoenolpyruvate (PEP) carboxylase (18), malic enzyme (12, 27, 28), and pyruvate carboxylase (9, 31) have resulted in the enhancement of succinic acid production in E. coli. Recently, Kim et al. (14) reported that amplification of the Actinobacillus succinogenes PEP carboxykinase in a PEP carboxylase-negative E. coli strain increased succinic acid production. Lin et al. (17) showed that succinic acid could be aerobically produced by utilizing the glyoxylate cycle in E. coli. Also, Sanchez et al. (23) reported impressive results for anaerobic production of succinic acid with a cumulative yield of 160 mM of succinic acid from 100 mM glucose in 24 h by repeated feeding fermentation of recombinant E. coli.

We recently reported the complete genome sequence of M. succiniciproducens MBEL55E, a gram-negative capnophilic bacterium, which was isolated from bovine rumen (16), and explored the genome-scale metabolic characteristics leading to high-level succinic acid production (11). It was reasoned that efficient production of succinic acid by E. coli would be possible by engineering its metabolic pathways to mimic those of M. succiniciproducens.

In this paper, we report comparative analysis of the central metabolic pathways of M. succiniciproducens and E. coli to select important metabolic pathways with respect to the formation of fermentation products. Subsequent in silico analysis was performed to predict the target genes among the candidates to be removed from E. coli for the efficient production of succinic acid. Finally, these predictions were validated by actual gene knockout experiments. In addition, the reasons for different fermentation characteristics between the rumen bacterium and E. coli are discussed.


Strains and plasmids.

All strains and plasmids used in this study are listed in Table Table1.1. All DNA manipulations were carried out by following standard protocols (22). Plasmid pKD46 (5), harboring the bacteriophage λ red operon, which contains the exo, beta, and gam genes, was used for disrupting the genes of interest in the chromosome of E. coli W3110 with appropriate antibiotic markers (Table (Table2).2). Recombinant E. coli W3110 harboring pKD46 was cultivated at 30°C, and λ recombinases were induced by adding l-arabinose (1 mM) at an optical density at 600 nm (OD600) of 0.4. Then electrocompetent cells were prepared by standard protocol (22). Two-step PCRs were performed using the antibiotic resistance genes as templates and primers listed in Table Table2.2. The PCR products were transformed into the electrocompetent E. coli W3110 harboring pKD46. Colonies were selected on Luria-Bertani (LB; 10 g tryptone liter−1, 5 g yeast extract liter−1, 10 g NaCl liter−1) agar plates containing the appropriate antibiotics at the following concentrations (μg ml−1): chloramphenicol, 34; kanamycin, 25; phleomycin, 5; tetracycline, 15; spectinomycin, 50. Successful gene replacement with the antibiotic marker was confirmed by PCR.

Bacterial strains and plasmids used in this study
Oligonucleotides used for gene replacement experiments

Comparative genome analysis.

The central metabolic pathways of E. coli K-12 were reconstructed using the information extracted from the metabolic database BioSilico (13). The fermentation pathways of M. succiniciproducens MBEL55E were reconstructed based on the recent genome annotation ( Two reconstructed metabolic pathways were compared to select the target genes in E. coli to be manipulated for mimicking efficient succinic acid production in M. succiniciproducens.

Glucose phosphorylation assay.

To elucidate whether M. succiniciproducens uses a PEP-sugar phosphotransferase system (PTS) to actively uptake glucose, 14C-labeled glucose phosphorylation assay was carried out as described previously (25). Cell extracts of E. coli W3110 and M. succiniciproducens MBEL55E were employed for the assays using 14C-labeled glucose as a substrate and PEP or ATP as a phosphate donor. The amounts of phosphorylated glucose passed through a polystyrene column were analyzed by a scintillation counter.

In silico analysis.

A genome-scale in silico E. coli model (8, 21) was used with slight modifications. The specific growth rate can be calculated by a biomass equation derived from the drain of biosynthetic precursors into E. coli biomass with their appropriate ratios (19). Under the pseudo-steady-state assumption, the unknown internal fluxes within the underdetermined metabolic reaction network can be evaluated by means of linear programming subject to the constraints provided by mass conservation, thermodynamics, and reaction stoichiometry (8, 24, 30). The in silico analysis was carried out using the MetaFluxNet program package, version 1.69 (15).

Culture conditions.

Cells were routinely cultured in LB medium at 37°C. Antibiotics were added at the concentrations shown above depending on the knockout markers used. For the fermentation experiments, cells were first grown in 10 ml LB medium at 37°C with shaking. One milliliter of seed culture was used to inoculate a 125-ml butyl rubber-stoppered serum vial containing 100 ml of fermentation medium, which contains (per liter): glucose, 9 g (50 mM); yeast extract, 5 g; NaHCO3,10 g; NaH2PO4 · H2O, 8.5 g; K2HPO4,15.5 g (pH 7.0). The vial headspace was filled with CO2, and Na2S · 9H2O was added to a final concentration of 1 mg liter−1 to ensure anaerobic conditions.

Analytical procedure.

For the measurement of the concentrations of glucose and organic acids, culture supernatant was passed through a syringe filter (pore size of 0.2 μm) after centrifugation at 10,000 × g for 10 min. The glucose concentration was determined by using a glucose analyzer (model 2300 STAT; Yellow Springs Instrument Co., Yellow Springs, Ohio). The concentrations of organic acids were determined by high-performance liquid chromatography (L-3300 RI monitor, L-4200 UV-VIS detector, D2500 chromato-integrator; Hitachi, Tokyo, Japan) equipped with an Aminex HPX-87H column (300 by 7.8 mm, Hercules, Calif.). The column was eluted isocratically at 50°C at a flow rate of 0.6 ml/min with 0.01 N H2SO4. Cell growth was monitored by measuring the absorbance at 600 nm (OD600) using an Ultrospec3000 spectrophotometer (Pharmacia Biotech, Uppsala, Sweden).


Comparison of central metabolic pathways of E. coli and M. succiniciproducens.

M. succiniciproducens has been reported to produce succinic acid very efficiently (11). On the other hand, succinic acid is a minor product of mixed-acid fermentation in E. coli. It was reasoned that production of succinic acid in E. coli would be possibly enhanced by mimicking the metabolism of M. succiniciproducens. Toward this goal, we compared the central metabolic pathways of E. coli and M. succiniciproducens, aiming to identify candidate metabolic genes that possibly determine the different fermentation patterns in the two bacteria. Considering that the genome size of E. coli is about twice as large as that of M. succiniciproducens, we focused on metabolic pathways that are found in E. coli but not in M. succiniciproducens, since they were thought to drive metabolic flux away from succinic acid formation in E. coli. Compared in Fig. Fig.1,1, succinate dehydrogenase (sdhABCD), malate dehydrogenase (mqo), glyoxylate shunt (aceBA), and ptsG homologue genes were found in E. coli but not in M. succiniciproducens. While E. coli has two pyruvate kinases, A and F, encoded by the pykA and pykF genes, M. succiniciproducens has pyruvate kinase A only.

FIG. 1.
Comparison of the central metabolic pathways related to succinic acid formation in M. succiniciproducens (A) and E. coli (B). Gene names are shown in italics. Dashed arrows in panel B represent those metabolic pathways only existing in E. coli. Acetyl-coenzyme ...

Even though the sfcA, fumAB, poxB, and acs genes were only present in E. coli, they were not considered as knockout candidates for the following reasons. The malic enzyme encoded by the sfcA gene has been reported to be beneficial for succinic acid production (12, 27). The fumarases encoded by the fumAB genes do not seem to negatively affect succinic acid production because fumA expression is strongly repressed under anaerobic conditions (32), while FumB generates fumarate for use as an anaerobic electron acceptor (33). The products of the poxB and acs genes play important roles in efficient aerobic growth (1) and growth on acetic acid (20), respectively, and thus do not seem to affect succinic acid production under anaerobic conditions. Therefore, we selected five genes or operons, ptsG, pykF, mqo, sdhABCD, and aceBA, as the initial targets to be inactivated to make E. coli mimic M. succiniciproducens.

Comparative metabolic engineering.

Comparative engineering of E. coli metabolic pathways was performed based on the above findings. The ptsG, pykF, mqo, sdhABCD, and aceBA genes in E. coli, which were not found in the genome of M. succiniciproducens, were sequentially disrupted to examine their effects on fermentation profiles. The mutant strains constructed are listed in Table Table1.1. First, the ptsG and pykF genes involved in pyruvate formation were inactivated, hoping that more PEP will be available for the carboxylation reaction. Then, the mqo and sdhA genes were sequentially deleted to prevent the reverse flux from succinic acid. Finally, the aceBA genes responsible for the glyoxylate shunt were inactivated.

Anaerobic fermentation profiles of these mutant strains are summarized in Table Table3.3. Interestingly, mutants rationally constructed by comparative metabolic engineering did not change the fermentation profiles much compared with the wild-type strain. Even the most heavily engineered W3110GFOHE strain, which was designed to be the strain most similar to M. succiniciproducens, did not show a significant difference in the growth rate andfermentation patterns compared with the wild-type strain (Table (Table3).3). Metabolic engineering based only on genome and pathway comparison was not successful, probably because we did not examine all possible combinational mutations of the candidate reactions. Since it is practically impossible to construct all these possible combinatorial mutant strains, in silico knockout experiments were carried out.

Results of anaerobic fermentation of E. coli W3110 and its various mutant strains

In silico prediction of gene candidates for enhanced succinic acid production.

The effects of combinatorial gene knockouts on succinic acid formation were examined in silico using the genome-scale E. coli metabolic model (21). The maximum biomass formation rate was used as an objective function during the optimization. When the enzymes (genes) were knocked out, the fluxes of the corresponding metabolic reactions were set to zero. It should also be noted that the metabolic reactions catalyzed by the pyruvate kinases A and F cannot be distinguished in the in silico E. coli metabolic model because they catalyze the same reaction. The results of in silico experiments for the representative mutants are shown in Table Table44.

Prediction of maximum biomass formation rate, succinic acid production rate, and succinic acid flux ratio in various in silico E. coli mutant strains

It was found that single-gene knockouts and many multiple-gene knockouts did not significantly change the succinic acid production. The succinic acid production rate was increased by 100-fold when the ptsG and pykFA genes were knocked out in silico. Further knockout of the sdhA, mqo, and aceBA genes or their combinations did not increase the succinic acid production rate. However, inactivation of the pfl gene further increased the succinic acid production rate (Table (Table4).4). In conclusion, the in silico simulation results suggested that three pyruvate-forming enzymes encoded by the ptsG and pykFA genes need to be inactivated for the enhanced production of succinic acid in E. coli, which can be further increased by inactivating the pfl gene.

Validation of in silico prediction.

According to the in silico prediction, E. coli W3110GFA was constructed by knocking out the ptsG and pykFA genes. Anaerobic fermentation of W3110GFA for 24 h resulted in an about 3.4-fold increase in succinic acid formation (from less glucose consumed) and a significant reduction of other fermentation products compared with the wild-type strain (Table (Table3).3). The succinic acid ratio increased by more than eightfold using the mutant strain. In 80 h, E. coli W3110GFA was able to convert 50 mmol of glucose to 17.4 mmol of succinic acid, which is more than 7 times higher than that produced by wild-type and other mutant strains constructed based on pathway comparison. It can also be seen that the formation of other fermentation products was considerably reduced in W3110GFA, resulting in the 9.23-fold increase in succinic acid ratio compared with the wild-type strain (Table (Table3).3). These results suggest that the anaerobic fermentation pattern of E. coli could be dramatically affected by simultaneous disruption of strong pyruvate-forming enzymes present in E. coli as predicted by in silico analysis. It also indicates that blocking the major pathways converting PEP to pyruvate is beneficial in redirecting the metabolic flux to succinic acid in E. coli.

Further disruption of the pfl and ldhA genes in a W3110GFA strain.

With an aim to further reduce other fermentation products, the pfl and ldhA genes were inactivated in a W3110GFA strain. An E. coli W3110GFAP strain lacking the pfl gene showed slower growth than its parental W3110GFA strain. Formic acid was not detected, and acetic acid formation was dramatically decreased. However, a twofold increase of lactic acid formation was observed. Even though the final succinic acid concentration obtained with W3110GFAP was less than half of that obtained with W3110GFA, the succinic acid molar ratio was increased by 12.6-fold (Table (Table3),3), which is consistent with the in silico prediction results (Table (Table4).4). The W3110GFAPL strain, additionally lacking the ldh gene, showed normal growth under aerobic conditions but only marginal growth under anaerobic conditions. It could utilize only a little glucose under anaerobic conditions because of the lack of major anaerobic respiratory pathways (Table (Table3).3). In the absence of Na2S, slightly enhanced succinic acid formation (<2 mM) was observed in the anaerobic culture of W3110GFAPL. These results suggest that pyruvate-forming enzymes (encoded by the ptsG, pykF, and pykA genes) and pyruvate-dissimilating enzymes (encoded by the ldhA and pfl genes) play essential roles in succinic acid production by E. coli.

Pyruvate supply affects the fermentation profiles of a W3110GFA strain.

To examine the effects of pyruvate on the anaerobic metabolism of E. coli, pyruvate was supplied externally during the fermentation of E. coli W3110GFA. It was found that the addition of glucose plus pyruvate in the medium changed the active succinic acid fermentation pattern of W3110GFA to the mixed-acid fermentation, as in the wild-type strain (Table (Table5).5). When pyruvate was added as a sole carbon source, significant amounts of formic and acetic acids were formed. Notably, the succinic acid ratio also returned to that of the wild-type strain by the addition of pyruvate. These results suggest that pyruvate is mainly converted to formic and acetic acids rather than succinic acid in the anaerobic fermentation of E. coli.

Effects of external supply of pyruvate on the anaerobic fermentation profile of E. coli W3110GFA


E. coli carries out mixed-acid fermentation under anaerobic conditions and produces much more acetic acid, formic acid, lactic acid, and ethanol than succinic acid. In contrast, a rumen bacterium, M. succiniciproducens, can produce succinic acid as a major fermentation product. It was therefore assumed that different fermentation patterns might be caused by different fermentation pathways in operation in two bacteria.

To make E. coli mimic the fermentation pattern of M. succiniciproducens, several E. coli mutants were constructed based on the results of pathway comparison (Table (Table1).1). The ptsG, pykF, mqo, sdhABCD, and aceBA genes were selected as the target genes to be disrupted. It should be mentioned that M. succiniciproducens does not operate the PTS. We have previously reported that M. succiniciproducens transports glucose by the PTS if we accept the annotation results obtained using the clusters of orthologous group database. On the other hand, the annotation results based on the nonredundant database suggest that M. succiniciproducens does not utilize the PTS for glucose uptake. Therefore, we carried out actual experiments and found that the M. succiniciproducens does not utilize the PTS. E. coli W3110 was able to efficiently phosphorylate glucose using PEP or ATP as a phosphate donor. On the other hand, M. succiniciproducens could efficiently transfer phosphate from ATP to glucose but rather poorly from PEP. This is the reason why the E. coli ptsG gene was chosen as one of the candidate genes to be knocked out. However, even the most heavily engineered W3110GFHOE strain did not show significant changes in the fermentation profiles, suggesting that the robust metabolism aided by many shunts and isoenzymes present in E. coli hinders the flux changes. Thus, metabolic engineering based on simple yet rational comparison of the two genome-scale metabolic pathways was not successful. This was thought to be because we could not examine all the possible combinatorial knockout mutant strains. Since it is practically impossible to generate all the possible combinatorial mutant strains, we decided to carry out in silico knockout experiments. However, it requires great computational power and a long time to perform in silico simulations for all possible combinations. It was thus invaluable to have the results of rational genome comparison, as we could focus the simulations on those genes that were found to be important for succinic acid production.

The results of in silico experiments suggested that knocking out the ptsG, pykF, and pykA genes is the most beneficial for the enhanced production of succinic acid. It is interesting to note that in silico E. coli cannot distinguish the products of the pykF and pykA genes, as they catalyze the same reaction. During the comparative metabolic engineering studies, the pykA gene was not considered a candidate gene to be knocked out because M. succiniciproducens also possessed this gene. It was, however, concluded from the in silico analysis that reducing the pyruvate-forming flux is very important for enhanced succinic acid production. The actual experiments showed the predicted results. E. coli W3110GFA produced significantly more succinic acid and concomitantly less of other fermentation products, though it grew slightly slower than the wild-type strain under anaerobic conditions (Table (Table3).3). It is important to note that the metabolic fluxes could be altered to lead to enhanced succinic acid formation by manipulating the upstream pathways (e.g., the ptsG, pykF, and pykA genes) rather than the pathways directly involved in the end product formation (such as the ldhA, pfl, and pta genes). These findings are reasonable, as high-level succinic acid producers such as M. succiniciproducens and A. succinogenes efficiently convert PEP to succinic acid via oxaloacetate, malate, and fumarate (11, 29). Therefore, the metabolic flux to pyruvate is not beneficial for succinic acid production, since pyruvate is efficiently converted to other fermentation products in E. coli.

In a pyruvate complementation test, the formation of acetic and formic acids was significantly increased by the addition of pyruvate in the pyruvate-deficient W3110GFA strain (Table (Table5).5). Apparently, pyruvate is more readily converted to acetic and formic acids in E. coli. While E. coli has strong pathways for converting pyruvate ultimately to acetic acid (encoded by the poxB, pta, ackA, and acs genes) to generate ATP, M. succiniciproducens has relatively weak pyruvate-forming pathways, since it has no active PTS for glucose uptake and only one pyruvate kinase (compared with two in E. coli) as mentioned above. This is the reason why M. succiniciproducens can efficiently produce succinic acid via anaplerotic pathways while sustaining the low level of pyruvate. Therefore, pyruvate was found to be the most important central metabolite affecting, most significantly, the fermentation pattern of E. coli. This difference seems to determine the characteristic fermentation patterns of the two microorganisms.

In summary, successful metabolic engineering of E. coli for enhanced succinic acid production could be achieved by combining genome and pathway comparison, in silico metabolic characterization, and validation by knockout experiments. This strategy may be generally useful in improving microbial strains for the enhanced production of metabolites.


We thank B. L. Wanner for providing plasmid pKD46 and E. Dervyn for plasmids pIC156 and pUC19-phleo.

This work was supported by the Genome-Based Integrated Bioprocess Project of the Ministry of Science and Technology and by the BK21 project. Further support by the LG Chem Chair Professorship, IBM-SUR program, Microsoft, and the Center for Ultramicrochemical Process Systems sponsored by KOSEF is appreciated.


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