4.5 Article

Metabolic model reconstruction and analysis of an artificial microbial ecosystem for vitamin C production

期刊

JOURNAL OF BIOTECHNOLOGY
卷 182, 期 -, 页码 61-67

出版社

ELSEVIER
DOI: 10.1016/j.jbiotec.2014.04.027

关键词

Artificial microbial ecosystem; Ketogulonicigenium vulgare; Bacillus megaterium; Genome-scale metabolic models; Vitamin C

资金

  1. Provincial Outstanding Youth Foundation of Jiangsu Province [BK2012002]
  2. Program for New Century Excellent Talents in University [NCET-10-0456]
  3. Major State Basic Research Development Program of China (973 Program) [2013CB733602]
  4. Program for Innovative Research Team in University [IRT1249]

向作者/读者索取更多资源

An artificial microbial ecosystem (AME) consisting of Ketogulonicigenium vulgare and Bacillus megaterium is currently used in a two-step fermentation process for vitamin C production. In order to obtain a comprehensive understanding of the metabolic interactions between the two bacteria, a two-species stoichiometric metabolic model (iWZ-KV-663-BM-1055) consisting of 1718 genes, 1573 metabolites, and 1891 reactions (excluding exchange reactions) was constructed based on separate genome-scale metabolic models (GSMMs) of K. vulgare and B. megaterium. These two compartments (k and b) of iWZ-KV-663-BM-1055 shared 453 reactions and 548 metabolites. Compartment b was richer in subsystems than compartment k. In minimal media with glucose (MG), metabolite exchange between compartments was assessed by constraint-based analysis. Compartment b secreted essential amino acids, nucleic acids, vitamins and cofactors important for K. vulgare growth and biosynthesis of 2-keto-L-gulonic acid (2-KLG). Further research showed that when co-cultured with B. megaterium in L-sorbose-CSLP medium, the growth rate of K. vulgare and 2-KLG production were increased by 111.9% and 29.42%, respectively, under the constraints employed. Our study demonstrated that GSMMs and constraint-based methods can be used to decode the physiological features and inter-species interactions of AMEs used in industrial biotechnology, which will be of benefit for improving regulation and refinement in future industrial processes. (C) 2014 Elsevier B.V. All rights reserved.

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