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Intracellular Flux Prediction of Recombinant Escherichia coli Producing Gamma-Aminobutyric Acid

  • Sung Han Bae (Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology(KAIST)) ;
  • Myung Sub Sim (Department of Biotechnology and Bioengineering, Chonnam National University) ;
  • Ki Jun Jeong (Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology(KAIST)) ;
  • Dan He (College of Life Science and Agriculture Forestry, Qiqihar University) ;
  • Inchan Kwon (School of Materials Science and Engineering, Gwangju Institute of Science and Technology) ;
  • Tae Wan Kim (Department of Biotechnology and Bioengineering, Chonnam National University) ;
  • Hyun Uk Kim (Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology(KAIST)) ;
  • Jong-il Choi (Department of Biotechnology and Bioengineering, Chonnam National University)
  • Received : 2023.12.13
  • Accepted : 2024.01.10
  • Published : 2024.04.28

Abstract

Genome-scale metabolic model (GEM) can be used to simulate cellular metabolic phenotypes under various environmental or genetic conditions. This study utilized the GEM to observe the internal metabolic fluxes of recombinant Escherichia coli producing gamma-aminobutyric acid (GABA). Recombinant E. coli was cultivated in a fermenter under three conditions: pH 7, pH 5, and additional succinic acids. External fluxes were calculated from cultivation results, and internal fluxes were calculated through flux optimization. Based on the internal flux analysis, glycolysis and pentose phosphate pathways were repressed under cultivation at pH 5, even though glutamate dehydrogenase increased GABA production. Notably, this repression was halted by adding succinic acid. Furthermore, proper sucA repression is a promising target for developing strains more capable of producing GABA.

Keywords

Acknowledgement

This work was supported by the ERC Center funded by the National Research Foundation of Korea (NRF-2022R1A5A1033719), and the 2023 Research Supporting Program (2023-1315-01) by Chonnam National University.

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