In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Abstract

Background

Succinic acid is a valuable product due to its wide-ranging utilities. To improve succinate production and reduce by-products formation, Escherichia coli NZN111 was constructed by insertional inactivation of lactate dehydrogenase (LDH) and pyruvate formate lyase (PFL) encoded by the genes ldhA and pflB, respectively. However, this double-deletion mutant is incapable of anaerobically growing on glucose in rich or minimal medium even with acetate supplementation. A widespread hold view is that the inactivation of NADH-dependent LDH limits the regeneration of NAD⁺ and consequently disables proper growth under anaerobic conditions.

Results

In this study, genome-scale metabolic core model of E. coli was reconstructed and employed to perform all simulations in silico according to the reconstruction of engineered strain E. coli NZN111. Non-optimized artificial centering hit-and-run (ACHR) method and metabolite flux-sum analysis were utilized to evaluate metabolic characteristics of strains. Thus, metabolic characteristics of the strains wild-type E. coli, ldhA mutant, pflB mutant, and NZN111 under anaerobic conditions were successfully unraveled.

Conclusions

We found a viewpoint contrary to the widespread realization that an NADH/NAD⁺ in NZN111 mainly resulted from the inactivation of PFL rather than the inactivation of LDH. In addition, the two alternative anaerobic fermentation pathways, lactate and ethanol production pathways, were blocked owing to the disruption of ldhA and pflB, resulting in insufficient NAD⁺ regeneration to oxidize or metabolize glucose for cell growth. Furthermore, we speculated reaction NADH16, the conversion of ubiquinone-8 (q8) to ubiquinol-8 (q8h2), as a potential amplification target for anaerobically improving cell growth and succinate production in NZN111.

Keywords

NZN111 / Succinate / NADH/NAD⁺ / Artificial centering hit-and-run (ACHR) / Sample solution space / Metabolite flux-sum analysis

References

[1] Ahn JH, Jang YS, Lee SY. Production of succinic acid by metabolically engineered microorganisms. Curr Opin Biotechnol, 2016, 42: 54-66.

[2] Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabasi AL. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature, 2004, 427(6977): 839-843.

[3] Berrios-Rivera SJ, Bennett GN, San KY. The effect of increasing NADH availability on the redistribution of metabolic fluxes in Escherichia coli chemostat cultures. Metab Eng, 2002, 4(3): 230-237.

[4] Berrios-Rivera SJ, Bennett GN, San KY. Metabolic engineering of Escherichia coli: increase of NADH availability by overexpressing an NAD(+)-dependent formate dehydrogenase. Metab Eng, 2002, 4(3): 217-229.

[5] Bunch PK, Matjan F, Lee N, Clark DP. The ldhA gene encoding the fermentative lactate dehydrogenase of Escherichia coli. Microbiol, 1997, 143(1): 187-195.

[6] Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng, 2003, 84(6): 647-657.

[7] Chatterjee R, Millard CS, Champion K, Clark DP, Donnelly MI. Mutation of the ptsG gene results in increased production of succinate in fermentation of glucose by Escherichia coli. Appl Environ Microbiol, 2001, 67: 148-154.

[8] Chung BKS, Lee DY. Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network. BMC Syst Biol, 2009, 3(1): 1-10.

[9] Famili I, Forster J, Nielsen J, Palsson BØ. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci USA, 2003, 100(23): 13134-13139.

[10] Graef MRD, Alexeeva S, Snoep JL, Mattos MJTD. The steady-state internal redox state (NADH/NAD) reflects the external redox state and is correlated with catabolic adaptation in Escherichia coli. J Bacteriol, 1999, 181(8): 2351-2357.

[11] Gu D, Zhang C, Zhou S, Wei L, Hua Q. IdealKnock: a framework for efficiently identifying knockout strategies leading to targeted overproduction. Comput Biol Chem, 2016, 61: 229-237.

[12] Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A, Ho PY, Kakazu Y, Sugawara K, Igarashi S, Harada S, Masuda T, Sugiyama N, Togashi T, Hasegawa M, Takai Y, Yugi K, Arakawa K, Iwata N, Toya Y, Nakayama Y, Nishioka T, Shimizu K, Mori H, Tomita M. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science, 2007, 316(5824): 593-597.

[13] Jamshidi N, Palsson BØ. Systems biology of SNPs. Mol Syst Biol, 2006, 2(1): 38-38.

[14] Jian X, Zhou S, Zhang C, Hua Q. In silico identification of gene amplification targets based on analysis of production and growth coupling. Biosystems, 2016, 145: 1-8.

[15] Jiang M, Liu SW, Ma JF, Chen KQ, Yu L, Yue FF, Xu B, Wei P. Effect of growth phase feeding strategies on succinate production by metabolically engineered Escherichia coli. Appl Environ Microbiol, 2010, 76(4): 1298-1300.

[16] Kaufman DE, Smith RL. Direction choice for accelerated convergence in hit-and-run sampling. Oper Res, 1998, 46(1): 84-95.

[17] Lakshmanan M, Kim TY, Chung BKS, Lee SY, Lee DY. Flux-sum analysis identifies metabolite targets for strain improvement. BMC Syst Biol, 2015, 9(1): 1-11.

[18] Lakshmanan M, Yu K, Koduru L, Lee DY. In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories. J Ind Microbiol Biotechnol, 2015, 42(10): 1-14.

[19] Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol, 2012, 10(4): 291-305.

[20] Lucy Stols MID. Production of succinic acid through overexpression of NAD-dependent malic enzyme in an Escherichia coli Mutant. Appl Environ Microbiol, 1997, 63(7): 2695-2701.

[21] Matjan F, Alam KY, Clark DP. Mutants of Escherichia coli deficient in the fermentative lactate dehydrogenase. J Bacteriol, 1989, 171(1): 342-348.

[22] Matsuoka Y, Shimizu K. Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. Bioresour Bioprocess., 2015, 2(1): 1-19.

[23] Millard CS, Chao YP, Liao JC, Donnelly MI. Enhanced production of succinic acid by overexpression of phosphoenolpyruvate carboxylase in Escherichia coli. Appl Environ Microbiol, 1996, 62(5): 1808-1810.

[24] Occhipinti R, Puchowicz MA, LaManna JC, Somersalo E, Calvetti D. Statistical analysis of metabolic pathways of brain metabolism at steady state. Annu Biomed Eng, 2007, 35(6): 886-902.

[25] Orth JD, Thiele I, Palsson BØ. What is flux balance analysis?. Nat Biotechnol, 2010, 28(3): 245-248.

[26] Papin JA, Reed JL, Palsson BØ. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem Sci, 2004, 29(12): 641-647.

[27] Price ND, Schellenberger J, Palsson BØ. Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J, 2004, 87: 2172-2186.

[28] Schellenberger J, Palsson BØ. Use of randomized sampling for analysis of metabolic networks. J Biol Chem, 2009, 284(9): 5457-5461.

[29] Schellenberger J, Park JO, Conrad TM, Palsson BØ. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinform, 2010, 11(1): 174-178.

[30] Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc, 2011, 6(9): 1290-1307.

[31] Singh A, Lynch MD, Gill RT. Genes restoring redox balance in fermentation-deficient E. coli NZN111. Metab Eng, 2009, 11(6): 347-354.

[32] Singh A, Soh KC, Hatzimanikatis V, Gill RT. Manipulating redox and ATP balancing for improved production of succinate in E. coli. Metab Eng, 2011, 13(1): 76-81.

[33] Thiele I, Price ND, Vo TD, Palsson BØ. Candidate metabolic network states in human mitochondria. J Biol Chem, 2005, 280(12): 11683-11695.

[34] Wang W, Li Z, Xie J, Ye Q. Production of succinate by a pflB ldhA double mutant of Escherichia coli overexpressing malate dehydrogenase. Bioprocess Biosyst Eng, 2009, 32(6): 737-745.

[35] Werpy T, Petersen G, Added TV, Werpy T, Petersen G, Added TV. Top value added chemicals from biomass. Nato Adv Sci Inst, 2004, 1(12): 263-275.

[36] Wu H, Li ZM, Zhou L, Ye Q. Improved succinic acid production in the anaerobic culture of an Escherichia coli pflB ldhA double mutant as a result of enhanced anaplerotic activities in the preceding aerobic culture. Appl Environ Microbiol, 2007, 73(73): 7837-7843.

[37] Yang YT, Aristidou AA, San KY, Bennett GN. Metabolic flux analysis of Escherichia coli deficient in the acetate production pathway and expressing the Bacillus subtilis acetolactate synthase. Metab Eng, 1999, 1(1): 26-34.

[38] Yang YT, Bennett GN, San KY. Effect of inactivation of nuo and ackA-pta on redistribution of metabolic fluxes in Escherichia coli. Biotechnol Bioeng, 1999, 65(3): 291-297.

[39] Yun NR, San KY, Bennett GN. Enhancement of lactate and succinate formation in adhE or pta-ackA mutants of NADH dehydrogenase-deficient Escherichia coli. J Appl Microbiol, 2005, 99(6): 1404-1412.

[40] Zhu J, Shimizu K. The effect of pfl gene knockout on the metabolism for optically pure D-lactate production by Escherichia coli. Appl Microbiol Biotechnol, 2004, 64(3): 367-375.

Funding

National Basic Research Program of China(2012CB721101)

National Natural Science Foundation of China (CN)(21576089)