Literature DB >> 24815194

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

Chao Ye1, Wei Zou1, Nan Xu1, Liming Liu2.   

Abstract

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.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

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

Mesh:

Substances:

Year:  2014        PMID: 24815194     DOI: 10.1016/j.jbiotec.2014.04.027

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  11 in total

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2.  Chaos in synthetic microbial communities.

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Review 3.  From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality.

Authors:  Jan-Ulrich Kreft; Caroline M Plugge; Clara Prats; Johan H J Leveau; Weiwen Zhang; Ferdi L Hellweger
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4.  Analysis of Microbial Functions in the Rhizosphere Using a Metabolic-Network Based Framework for Metagenomics Interpretation.

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Journal:  Front Microbiol       Date:  2017-08-23       Impact factor: 5.640

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Journal:  Front Genet       Date:  2017-06-21       Impact factor: 4.599

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Authors:  Ming-Zhu Ding; Hao Song; En-Xu Wang; Yue Liu; Ying-Jin Yuan
Journal:  Synth Syst Biotechnol       Date:  2016-09-09

Review 7.  Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications.

Authors:  Kok Siong Ang; Meiyappan Lakshmanan; Na-Rae Lee; Dong-Yup Lee
Journal:  Curr Genomics       Date:  2018-12       Impact factor: 2.236

8.  Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments.

Authors:  Aarthi Ravikrishnan; Lars M Blank; Smita Srivastava; Karthik Raman
Journal:  Comput Struct Biotechnol J       Date:  2020-03-30       Impact factor: 7.271

9.  High-Throughput Screening of a 2-Keto-L-Gulonic Acid-Producing Gluconobacter oxydans Strain Based on Related Dehydrogenases.

Authors:  Yue Chen; Li Liu; Xiaoyu Shan; Guocheng Du; Jingwen Zhou; Jian Chen
Journal:  Front Bioeng Biotechnol       Date:  2019-12-13

10.  Genome Sequence of Bacillus endophyticus and Analysis of Its Companion Mechanism in the Ketogulonigenium vulgare-Bacillus Strain Consortium.

Authors:  Nan Jia; Jin Du; Ming-Zhu Ding; Feng Gao; Ying-Jin Yuan
Journal:  PLoS One       Date:  2015-08-06       Impact factor: 3.240

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