Dataset Information

Metabolic engineering of Bacillus subtilis based on genome-scale metabolic model to promote fengycin production.

ABSTRACT: Fengycin is an important lipopeptide antibiotic that can be produced by Bacillus subtilis. However, the production capacity of the unmodified wild strain is very low. Therefore, a computationally guided engineering method was proposed to improve the fengycin production capacity. First, based on the annotated genome and biochemical information, a genome-scale metabolic model of Bacillus subtilis 168 was constructed. Subsequently, several potential genetic targets were identified through the flux balance analysis and minimization of metabolic adjustment algorithm that can ensure an increase in the production of fengycin. In addition, according to the results predicted by the model, the target genes accA (encoding acetyl-CoA carboxylase), cypC (encoding fatty acid beta-hydroxylating cytochrome P450) and gapA (encoding glyceraldehyde-3-phosphate dehydrogenase) were overexpressed in the parent strain Bacillus subtilis 168. The yield of fengycin was increased by 56.4, 46.6, and 20.5% by means of the overexpression of accA, cypC, and gapA, respectively, compared with the yield from the parent strain. The relationship between the model prediction and experimental results proves the effectiveness and rationality of this method for target recognition and improving fengycin production.

Supplementary information

The online version contains supplementary material available at 10.1007/s13205-021-02990-7.

SUBMITTER: He M

PROVIDER: S-EPMC8463648 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Metabolic engineering of Bacillus subtilis based on genome-scale metabolic model to promote fengycin production.

He Mingliang M Wen Jianping J Yin Ying Y Wang Pan P

3 Biotech 20210924 10

Fengycin is an important lipopeptide antibiotic that can be produced by Bacillus subtilis. However, the production capacity of the unmodified wild strain is very low. Therefore, a computationally guided engineering method was proposed to improve the fengycin production capacity. First, based on the annotated genome and biochemical information, a genome-scale metabolic model of Bacillus subtilis 168 was constructed. Subsequently, several potential genetic targets were identified thr ...[more]

PMID: 34631349

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Project description:Terpenoids are natural products known for their medicinal and commercial applications. Metabolic engineering of microbial hosts for the production of valuable compounds, such as artemisinin and Taxol, has gained vast interest in the last few decades. The Generally Regarded As Safe (GRAS) Bacillus subtilis 168 with its broad metabolic potential is considered one of these interesting microbial hosts. In the effort toward engineering B. subtilis as a cell factory for the production of the chemotherapeutic Taxol, we expressed the plant-derived taxadiene synthase (TXS) enzyme. TXS is responsible for the conversion of the precursor geranylgeranyl pyrophosphate (GGPP) to taxa-4,11-diene, which is the first committed intermediate in Taxol biosynthesis. Furthermore, overexpression of eight enzymes in the biosynthesis pathway was performed to increase the flux of the GGPP precursor. This was achieved by creating a synthetic operon harboring the B. subtilis genes encoding the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway (dxs, ispD, ispF, ispH, ispC, ispE, ispG) together with ispA (encoding geranyl and farnesyl pyrophosphate synthases) responsible for providing farnesyl pyrophosphate (FPP). In addition, a vector harboring the crtE gene (encoding geranylgeranyl pyrophosphate synthase, GGPPS, of Pantoea ananatis) to increase the supply of GGPP was introduced. The overexpression of the MEP pathway enzymes along with IspA and GGPPS caused an 83-fold increase in the amount of taxadiene produced compared to the strain only expressing TXS and relying on the innate pathway of B. subtilis. The total amount of taxadiene produced by that strain was 17.8 mg/l. This is the first account of the successful expression of taxadiene synthase in B. subtilis. We determined that the expression of GGPPS through the crtE gene is essential for the formation of sufficient precursor, GGPP, in B. subtilis as its innate metabolism is not efficient in producing it. Finally, the extracellular localization of taxadiene production by overexpressing the complete MEP pathway along with IspA and GGPPS presents the prospect for further engineering aiming for semisynthesis of Taxol.

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

Metabolic engineering of Bacillus subtilis based on genome-scale metabolic model to promote fengycin production.

Supplementary information

Publications

Metabolic engineering of <i>Bacillus subtilis</i> based on genome-scale metabolic model to promote fengycin production.

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