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Improvement of Bacillus subtilis for poly-?-glutamic acid production by genome shuffling.

ABSTRACT: Poly-?-glutamic acid (?-PGA) is a promising microbial polymer with potential applications in industry, agriculture and medicine. The use of high ?-PGA-producing strains is an effective approach to improve productivity of ?-PGA. In this study, we developed a mutant, F3-178, from Bacillus subtilis GXA-28 using genome shuffling. The morphological characteristics of F3-178 and GXA-28 were not identical. Compared with GXA-28 (18.4 ± 0.8 g l-1 ), the yield of ?-PGA was 1.9-fold higher in F3-178 (34.3 ± 1.2 g l-1 ). Results from batch fermentation in 3.7 l fermenter showed that F3-178 was satisfactory for industrial production of ?-PGA. Metabolic studies suggested that the higher ?-PGA yield in F3-178 could be attributed to increased intracellular flux and uptake of extracellular glutamate. Real-time PCR indicated that mRNA level of pgsB in F3-178 was 18.8-fold higher than in GXA-28, suggesting the higher yield might be related to the overexpression of genes involved in ?-PGA production. This study demonstrated that genome shuffling can be used for rapid improvement of ?-PGA strains, and the possible mechanism for the improved phenotype was also explored at the metabolic and transcriptional levels.

SUBMITTER: Zeng W

PROVIDER: S-EPMC5072198 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Improvement of Bacillus subtilis for poly-γ-glutamic acid production by genome shuffling.

Zeng Wei W Chen Guiguang G Wu Hao H Wang Jun J Liu Yanliao Y Guo Ye Y Liang Zhiqun Z

Microbial biotechnology 20160826 6

Poly-γ-glutamic acid (γ-PGA) is a promising microbial polymer with potential applications in industry, agriculture and medicine. The use of high γ-PGA-producing strains is an effective approach to improve productivity of γ-PGA. In this study, we developed a mutant, F3-178, from Bacillus subtilis GXA-28 using genome shuffling. The morphological characteristics of F3-178 and GXA-28 were not identical. Compared with GXA-28 (18.4 ± 0.8 g l<sup>-1</sup> ), the yield of γ-PGA was 1.9-fold higher in F3 ...[more]

PMID: 27562078

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Project description:BackgroundGenome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design.ResultsHere we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain.ConclusionsThis work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis.

Dataset Information

Improvement of Bacillus subtilis for poly-?-glutamic acid production by genome shuffling.

Publications

Improvement of Bacillus subtilis for poly-γ-glutamic acid production by genome shuffling.

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