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Model-driven in Silico glpC Gene Knockout Predicts Increased Succinate Production from Glycerol in Escherichia Coli

Bioinformatics Research Group (BIRG), Department of Biosciences and Health Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor Bahru. Malaysia

Metabolic engineered targeting for increased succinate production in Escherichia coli using glycerol as a low cost carbon source has attracted global attention in recent years. Succinate production in engineered E. coli has progressed significantly using an experimental trial and error approach. The use of a model-guided, targeted metabolic gene knockout prediction for increased succinate production from glycerol under anaerobic conditions in E. coli still remains largely underexplored. In this study, we applied a model-driven, targeted glpC/b2243 in silico metabolic gene knockout using E. coli genome scale model iJO1366 under the OptFlux software platform with the aim of predicting high succinate flux. The results indicated that the mutant model lacking the glpC/b2243 gene will demonstrate increased succinate flux that is 30% higher than its wild-type control model. We can hypothesize that an additional NADH molecule was generated following the deletion of the gene and/or the alternatively preferred GldA-DhaKLM fermentative route for glycerol metabolism in E. coli may have been activated. Although the exact metabolic mechanism involved in increasing the succinate flux still remains obscure; the current study informs other studies that a model-driven, metabolic glpC/b2243 gene knockout could be applicable in filling our knowledge gap using a comprehensive experimental inquiry in the future; leading to a better understanding of the underlying metabolic function of this gene in relation to succinate production in E. coli from glycerol.
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References

1. Blankschien MD, Clomburg JM, Gonzalez R (2010) Metabolic engineering of Escherichia coli for the production of succinate from glycerol. Metab Eng 12: 409-419.    

2. Thakker C, Martinez I, San KY, et al. (2012) Succinate production in Escherichia coli. Biotechnol J 7: 213-224.    

3. McKinlay JB, Vieille C, Zeikus JG (2007) Prospects for a bio-based succinate industry. Appl Microbiol Biotechnol 76: 727-740.    

4. Clomburg JM, Gonzalez R (2013) Anaerobic fermentation of glycerol: a platform for renewable fuels and chemicals. Trends Biotechnol 31: 20-28.    

5. Dharmadi Y, Murarka A, Gonzalez R (2006) Anaerobic fermentation of glycerol by Escherichia coli: a new platform for metabolic engineering. Biotechnol Bioeng 94: 821-829.    

6. Durnin G, Clomburg J, Yeates Z, et al. (2009) Understanding and harnessing the microaerobic metabolism of glycerol in Escherichia coli. Biotechnol Bioeng 103: 148-161.    

7. Mattam AJ, Clomburg JM, Gonzalez R, et al. (2013) Fermentation of glycerol and production of valuable chemical and biofuel molecules. Biotechnol Lett 35: 831-842.    

8. Shams Yazdani S, Gonzalez R (2008) Engineering Escherichia coli for the efficient conversion of glycerol to ethanol and co-products. Metab Eng 10: 340-351.    

9. Yazdani SS, Gonzalez R (2007) Anaerobic fermentation of glycerol: a path to economic viability for the biofuels industry. Curr Opin Biotechnol 18: 213-219.    

10. Monk J, Palsson BO (2014) Genetics. Predicting microbial growth. Science 344: 1448-1449.

11. Varma A, Bernhard BW, Palsson OB (1993) Stoichiometric Interpretation of Escherichia coli Glucose Catabolism under Various Oxygenation Rates. Appl Environ Microbiol 59: 2465-2473.

12. Feist AM, Henry CS, Reed JL, et al. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3: 121.

13. Orth JD, Conrad TM, Na J, et al. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011. Mol Syst Biol 7: 535.

14. Oberhardt MA, Palsson BO, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5: 320.

15. Feist AM, Zielinski DC, Orth JD, et al. (2010) Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab Eng 12: 173-186.    

16. Hara KY, Shimodate N, Ito M, et al. (2009) Systematic genome-wide scanning for genes involved in ATP generation in Escherichia coli. Metab Eng 11: 1-7.    

17. McCloskey D, Palsson BO, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 9: 661.

18. Rocha I, Maia P, Evangelista P, et al. (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4: 45.    

19. Schellenberger J, Que R, Fleming RM, et al. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6: 1290-1307.    

20. Guzman GI, Utrilla J, Nurk S, et al. (2015) Model-driven discovery of underground metabolic functions in Escherichia coli. Proc Natl Acad Sci U S A 112: 929-934.    

21. Lee SJ, Lee DY, Kim TY, et al. (2005) Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 71: 7880-7887.    

22. Chen Z, Liu H, Zhang J, et al. (2010) Elementary mode analysis for the rational design of efficient succinate conversion from glycerol by Escherichia coli. J Biomed Biotechnol 2010: 518743.

23. von Kamp A, Schuster S (2006) Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 22: 1930-1931.

24. Kim P, Laivenieks M, Vieille C, et al. (2004) Effect of Overexpression of Actinobacillus succinogenes Phosphoenolpyruvate Carboxykinase on Succinate Production in Escherichia coli. Appl Environ Microb 70: 1238-1241.    

25. Gokarn RR, Evans JD, Walker JR, et al. (2001) The physiological effects and metabolic alterations caused by the expression of Rhizobium etli pyruvate carboxylase in Escherichia coli. Appl Microbiol Biot 56: 188-195.    

26. Sanchez AM, Bennett GN, San KY (2005) Efficient Succinic Acid Production from Glucose through Overexpression of Pyruvate Carboxylase in an Escherichia coli Alcohol Dehydrogenase and Lactate Dehydrogenase Mutant. Biotechnol Prog 21: 358-365.

27. Wang Q, Wu C, Chen T, et al. (2006) Expression of galactose permease and pyruvate carboxylase in Escherichia coli ptsG mutant increases the growth rate and succinate yield under anaerobic conditions. Biotechnol Lett 28: 89-93.    

28. Vemuri GN, Eiteman MA, Altman E (2002) Effects of Growth Mode and Pyruvate Carboxylase on Succinic Acid Production by Metabolically Engineered Strains of Escherichia coli. Appl Environ Microbiol 68: 1715-1727.    

29. Zhang X, Jantama K, Moore JC, et al. (2009) Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli. Proc Natl Acad Sci U S A 106: 20180-20185.    

30. Jantama K, Haupt MJ, Svoronos SA, et al. (2008) Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol Bioeng 99: 1140-1153.    

31. Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99: 15112-15117.    

32. Varma A, Palsson BO (1994) Stoichiometric Flux Balance Models Quantitatively Predict Growth and Metabolic By-Product Secretion in Wild-Type Escherichia coli W3110. Appl Environ Microbiol 60: 3724-3731.

33. Cheng K-K, Lee B-S, Masuda T, et al. (2014) Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol. Nat Commun 5.

34. Cole ST, Eiglmeier K, Ahmed S, et al. (1988) Nucleotide Sequence and Gene-Polypeptide Relationships of the glpABC Operon Encoding the Anaerobic sn-Glycerol-3-Phosphate Dehydrogenase of Escherichia coli K-12. J Bacteriol 170: 2448-2456.

35. Sauer U, Eikmanns BJ (2005) The PEP-pyruvate-oxaloacetate node as the switch point for carbon flux distribution in bacteria. FEMS Microbiol Rev 29: 765-794.    

Copyright Info: © 2015, Bashir Sajo Mienda, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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