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Dynamic Modeling of Lactic Acid Fermentation Metabolism with Lactococcus lactis

  • Oh, Euh-Lim (Department of Chemical and Biomolecular Engineering, Sogang University) ;
  • Lu, Mingshou (Department of Chemical and Biomolecular Engineering, Sogang University) ;
  • Choi, Woo-Joo (Department of Chemical and Biomolecular Engineering, Sogang University) ;
  • Park, Chang-Hun (Department of Chemistry, Sogang University) ;
  • Oh, Han-Bin (Department of Chemistry, Sogang University) ;
  • Lee, Sang-Yup (Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Jin-Won (Department of Chemical and Biomolecular Engineering, Sogang University)
  • Received : 2010.07.30
  • Accepted : 2010.11.01
  • Published : 2011.02.28

Abstract

A dynamic model of lactic acid fermentation using Lactococcus lactis was constructed, and a metabolic flux analysis (MFA) and metabolic control analysis (MCA) were performed to reveal an intensive metabolic understanding of lactic acid bacteria (LAB). The parameter estimation was conducted with COPASI software to construct a more accurate metabolic model. The experimental data used in the parameter estimation were obtained from an LC-MS/MS analysis and time-course simulation study. The MFA results were a reasonable explanation of the experimental data. Through the parameter estimation, the metabolic system of lactic acid bacteria can be thoroughly understood through comparisons with the original parameters. The coefficients derived from the MCA indicated that the reaction rate of L-lactate dehydrogenase was activated by fructose 1,6-bisphosphate and pyruvate, and pyruvate appeared to be a stronger activator of L-lactate dehydrogenase than fructose 1,6-bisphosphate. Additionally, pyruvate acted as an inhibitor to pyruvate kinase and the phosphotransferase system. Glucose 6-phosphate and phosphoenolpyruvate showed activation effects on pyruvate kinase. Hexose transporter was the strongest effector on the flux through L-lactate dehydrogenase. The concentration control coefficient (CCC) showed similar results to the flux control coefficient (FCC).

Keywords

References

  1. Chassagnole, C., N. Noisommit-Rizzi, J. W. Schmid, K. Mauch, and M. Reuss. 2002. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79: 53-73. https://doi.org/10.1002/bit.10288
  2. de Koning, W. and K. van Dam. 1992. A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Anal. Biochem. 204: 118-123. https://doi.org/10.1016/0003-2697(92)90149-2
  3. Doi, Y. 1990. Microbial Polyesters. VCH Publishers, NY, USA.
  4. Faijes, M., A. E Mars, and E. J. Smid. 2007. Comparision of quenching and extraction methodologies for metabolome analysis of Lactobacillus plantarum. Microb. Cell Fact. 6: 27. https://doi.org/10.1186/1475-2859-6-27
  5. Fromm, H. J. and V. Zewe. 1962. Kinetics studies of yeast hexokinase. J. Biol. Chem. 237: 3027-3032.
  6. Heinrich, R. and T. A. Rapoport. 1974. A linear steady state treatment of enzymatic chains: General properties, control and effector strength. Eur. J. Biochem. 42: 89-95. https://doi.org/10.1111/j.1432-1033.1974.tb03318.x
  7. Hoefnagel, M. H. N., M. J. C. Starrenburg, D. E. Martens, J. Hugenholtz, M. Kleerebezem, I. Van Swam, R. Bongers, H. V. Westerhoff, and J. L. Snoep. 2002. Metabolic engineering of lactic acid bacteria, the combined approach: Kinetic modelling, metabolic control and experimental analysis. Microbiology 148: 1003-1013. https://doi.org/10.1099/00221287-148-4-1003
  8. Hoefnagel, M. H. N., A. van der Burgt, D. E. Martens, J. Hugenholtz, and J. L. Snoep. 2002. Time dependent responses of glycolytic intermediates in a detailed glycolytic model of Lactococcus lactis during glucose run-out experiments. Mol. Biol. Rep. 29: 157-161. https://doi.org/10.1023/A:1020313409954
  9. Ishii, N., M. Robert, Y. Nakayama, A. Kanai, and M. Tomita. 2004. Toward large-scale modeling of the microbial cell for computer simulation. J. Biotechnol. 113: 281-294. https://doi.org/10.1016/j.jbiotec.2004.04.038
  10. Kacser, H. and J. A Burns. 1973. The control of flux. Symp. Soc. Exp. Biol. 27: 65-104.
  11. Katoh, T., D. Yuguchi, H. Yoshii, H. Shi, and K .Shimizu. 1999. Dynamics and modeling on fermentative production of poly ($\beta$-hydroxybutyric acid) from sugars via lactate by a mixed culture of Lactobacillus delbrueckii and Alcaligenes eutrophus. J. Biotechnol. 67: 113-134. https://doi.org/10.1016/S0168-1656(98)00177-1
  12. Lee, S. Y., D. Y. Lee, and T. Y. Kim. 2005. Systems biotechnology for strain improvement. Trends Biotechnol. 23: 349-358. https://doi.org/10.1016/j.tibtech.2005.05.003
  13. Luo, B., K. Groenke, R. Takors, C. Wandrey, and M. Oldiges. 2007. Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pahtway and tricarboxylic acid cycle by liquid chromatography-mass spectrometery. J. Chromatogr. A 1147: 153-164. https://doi.org/10.1016/j.chroma.2007.02.034
  14. Melchiorsen, C. R., N. B. Siemsen Jensen, B. Christensen, V. K. Jokumsen, and J. Villadsen. 2000. Dynamics of pyruvate metabolism in Lactococcus lactis. Biotechnol. Bioeng. 74: 271-279.
  15. .Neves, A. R., A. Ramos, C. M. Nunes, M. Kleerebezem, J. Hugenholtz, M. W. de Vos, J. Almeida, and H. Santos. 1999. In vivo nuclear magnetic resonance studies of glycolytic kinetics in Lactococcus lactis. Biotechnol, Bioeng. 64: 200-212. https://doi.org/10.1002/(SICI)1097-0290(19990720)64:2<200::AID-BIT9>3.0.CO;2-K
  16. Neves, A. R., W. A. Pool, J. Kok, O. P. Kuipers, and H. Santos. 2005. Overview on sugar metabolism and its control in Lactococcus lactis - The input from in vivo NMR. FEMS Microbiol. Rev. 29: 531-554.
  17. Ramos, A., A. R. Neves, and H. Santos. 2002. Metabolism of lactic acid bacteria studied by nuclear magnetic resonance. Antonie Van Leeuwenhoek 82: 249-261. https://doi.org/10.1023/A:1020664422633
  18. Richter, O., A. Betz, and C. Giersch. 1975. The response of oscillating glycolysis to perturbations in the NADH/NAD system: A comparison between experiments and a computer model. BioSystems 7: 137-146. https://doi.org/10.1016/0303-2647(75)90051-9
  19. Rizzi, M., U. Theobald, E. Querfurth, T. Rohrhirsch, M. Baltes, and M. Reuss. 1996. In vivo investigations of glucose transport in Saccharomyces cerevisiae. Biotechnol. Bioeng. 49: 316-327.
  20. Rizzi, M., M. Baltes, U. Theobald, and M. Reuss. 1997. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae; II. Mathematical model. Biotechnol. Bioeng. 55: 592-608. https://doi.org/10.1002/(SICI)1097-0290(19970820)55:4<592::AID-BIT2>3.0.CO;2-C
  21. Sjoberg, A., I. Persson, M. Quednau, and B. Hahn-Hagerdal. 1995. The influence of limiting and non-limiting growth conditions on glucose and maltose metabolism in Lactococcus lactis ssp. lactis strains. Appl. Microbiol. Biotechnol. 42: 931-938. https://doi.org/10.1007/BF00191193
  22. Stephanopoulos, G. N., A. A. Aristidou, and J. Nielsen. 1998. Metabolic Engineering. Academic Press, San Diego, USA.
  23. Theobald, U., W. Mailinger, M. Blates, M. Rizzi, and M. Reuss. 1997. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol. Bioeng. 55: 305-316. https://doi.org/10.1002/(SICI)1097-0290(19970720)55:2<305::AID-BIT8>3.0.CO;2-M
  24. Thompson, J. 1987. Regulation of sugar transport and metabolism in lactic acid bacteria. FEMS Microbiol. Lett. 46: 221-231. https://doi.org/10.1111/j.1574-6968.1987.tb02462.x
  25. van Niel, W. J., K. Hofvendahl, and B. Hahn-Hagerdal. 2002. Formation and conversion of oxygen metabolites by Lactococcus lactis subsp. lactis ATCC 19435 under different growth conditions. Appl. Environ. Microbiol. 68: 4350-4356. https://doi.org/10.1128/AEM.68.9.4350-4356.2002
  26. Vickory, B. T. 1985. Lactic Acid, pp. 761-776. Dic Pergamon, Toronto, Canada.

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