Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum

Niu, Jinhui; Mao, Zhitao; Mao, Yufeng; Wu, Ke; Shi, Zhenkun; Yuan, Qianqian; Cai, Jingyi; Ma, Hongwu

doi:10.3390/biom12101499

Cited by 15 publications

(41 citation statements)

References 58 publications

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“…Recently, we developed the simplified Python-based workflow ECMpy, which allows the construction of an ecModel by directly adding a total enzyme amount constraint into a GEM [ 15 ]. Recently, ecModels have been constructed for several species, including Escherichia coli [ 9 , 12 , 15 ], Saccharomyces cerevisiae [ 13 ], Aspergillus niger [ 16 ], Corynebacterium glutamicum [ 17 ] and B. subtilis [ 10 ]. The first ecModel for B. subtilis (ec_iYO844) only integrated enzyme kinetic parameters for 17 reactions located in the central carbon metabolism using the GECKO method, but this model allowed more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to the GEM [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Mao

et al. 2023

Microorganisms

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Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

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Section: Introductionmentioning

confidence: 99%

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

Mao

et al. 2023

Microorganisms

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“…Accordingly, we systematically corrected the EC number and GPR relationships. We used the GPRuler tool [18] and protein homology similarity to uncover the potential GPR errors present in the reaction (see [17] for details). To meet the requirements of AutoPACMEN processes for metabolic network format input, we converted most of the KEGG IDs and ModelSEED [19] IDs (both metabolites and reactions) into BiGG [20] IDs.…”

Section: Model Updatementioning

confidence: 99%

“…Two major factors influence the final MW of the enzyme assigned to a specific reaction: whether the protein is composed of subunits (GPR relationship) and the number of each subunit. We systematically corrected the GPR relationships in the model by referring to the methods used in CGL1 (GPRuler tool and protein homology similarity) [17]. We first identified 146 reactions containing protein complex information using the GPRuler tool, 80 of which were consistent with the model.…”

Section: Gpr Correction Of Ibsu1147mentioning

confidence: 99%

“…Enzyme-constrained models have been used to simulate overflow metabolism in S. cerevisiae [13,30], C. glutamicum [17], and E. coli [15]. It has been reported in the literature that B. subtilis exhibits overflow metabolism in the presence of excess glucose [31].…”

Section: Simulation Of Overflow Metabolismmentioning

confidence: 99%

“…Recently, we developed the simplified Python-based workflow ECMpy, which allows the construction of an ecModel by directly adding a total enzyme amount constraint into a GEM [15]. Recently, ecModels have been constructed for several species, including Escherichia coli [9,12,15], Saccharomyces cerevisiae [13], Aspergillus niger [16], Corynebacterium glutamicum [17] and B. subtilis [10]. The first ecModel for B. subtilis (ec_iYO844) only integrated enzyme kinetic parameters for 17 reactions located in the central carbon metabolism using the GECKO method, but this model allowed more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to the GEM [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ecBSU1: A Genome-scale Enzyme-constrained Model of <em>Bacillus subtilis</em> based on the ECMpy Workflow

Wu¹,

Mao²,

Mao³

et al. 2022

Preprint

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Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Then, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.

show abstract

Co‐Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

Xue,

Liu,

Yang

et al. 2023

Advanced Science

Self Cite

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Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome‐scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co‐cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of “independent + global”. This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co‐culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

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Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum

Cited by 15 publications

References 58 publications

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow

ecBSU1: A Genome-scale Enzyme-constrained Model of <em>Bacillus subtilis</em> based on the ECMpy Workflow

Co‐Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction

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