Details

Autor(en) / Beteiligte

• Ye, Chao
• Zou, Wei
• Xu, Nan
• Liu, Liming

Titel

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

Ist Teil von

• Journal of biotechnology, 2014-07, Vol.182-183, p.61-67

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2014

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •GSMMs of two industrial strains in vitamin C production were reconciled and compared.•An integrated two-species metabolic model of Ketogulonicigenium vulgare and Bacillus megaterium was constructed and analyzed.•Relationship between K. vulgare and B. megaterium for 2-KLG were investigated based on two-species metabolic model.•A normal pipeline for analysis of the metabolic interactions in industrial microbial ecosystems was represented. 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.