4.7 Article

Metabolic engineering of Escherichia coli for poly(3-hydroxybutyrate) production via threonine bypass

期刊

MICROBIAL CELL FACTORIES
卷 14, 期 -, 页码 -

出版社

BIOMED CENTRAL LTD
DOI: 10.1186/s12934-015-0369-3

关键词

Genome-scale metabolic network; Threonine bypass; Strain optimization; Poly(3-hydroxybutyrate)

资金

  1. National 973 Project [2012CB725203, 2011CBA00804]
  2. National Natural Science Foundation of China [NSFC-21176182, NSFC-21206112, NSFC-21390201]
  3. National High-tech R&D Program of China [2012AA02A702, 2012AA022103]

向作者/读者索取更多资源

Background: Poly(3-hydroxybutyrate) (PHB), have been considered to be good candidates for completely biodegradable polymers due to their similar mechanical properties to petroleum-derived polymers and complete biodegradability. Escherichia coli has been used to simulate the distribution of metabolic fluxes in recombinant E. coli producing poly(3-hydroxybutyrate) (PHB). Genome-scale metabolic network analysis can reveal unexpected metabolic engineering strategies to improve the production of biochemicals and biofuels. Results: In this study, we reported the discovery of a new pathway called threonine bypass by flux balance analysis of the genome-scale metabolic model of E. coli. This pathway, mainly containing the reactions for threonine synthesis and degradation, can potentially increase the yield of PHB and other acetyl-CoA derived products by reutilizing the CO2 released at the pyruvate dehydrogenase step. To implement the threonine bypass for PHB production in E. coli, we deregulated the threonine and serine degradation pathway and enhanced the threonine synthesis, resulting in 2.23-fold improvement of PHB titer. Then, we overexpressed glyA to enhance the conversion of glycine to serine and activated transhydrogenase to generate NADPH required in the threonine bypass. Conclusions: The result strain TB17 (pBHR68) produced 6.82 g/L PHB with the yield of 0.36 g/g glucose in the shake flask fermentation and 35.92 g/L PHB with the yield of 0.23 g/g glucose in the fed-batch fermentation, which was almost 3.3-fold higher than the parent strain. The work outlined here shows that genome-scale metabolic network analysis can reveal novel metabolic engineering strategies for developing efficient microbial cell factories.

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