Dataset Information

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

ABSTRACT: The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

SUBMITTER: Niu J

PROVIDER: S-EPMC9599660 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Construction and Analysis of an Enzyme-Constrained Metabolic Model of <i>Corynebacterium glutamicum</i>.

Niu Jinhui J Mao Zhitao Z Mao Yufeng Y Wu Ke K Shi Zhenkun Z Yuan Qianqian Q Cai Jingyi J Ma Hongwu H

Biomolecules 20221017 10

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to b ...[more]

PMID: 36291707

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Project description:BackgroundCorynebacterium glutamicum is an important platform organism for industrial biotechnology to produce amino acids, organic acids, bioplastic monomers, and biofuels. The metabolic flexibility, broad substrate spectrum, and fermentative robustness of C. glutamicum make this organism an ideal cell factory to manufacture desired products. With increases in gene function, transport system, and metabolic profile information under certain conditions, developing a comprehensive genome-scale metabolic model (GEM) of C. glutamicum ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering.ResultsHere, we constructed a new GEM for ATCC13032, iCW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, iCW773 supplemented 496 gene-protein-reaction associations, refined five lumped reactions, balanced the mass and charge, and constrained the directionality of reactions. The simulated growth rates of C. glutamicum cultivated on seven different carbon sources using iCW773 were consistent with experimental values. Pearson's correlation coefficient between the iCW773-simulated and experimental fluxes was 0.99, suggesting that iCW773 provided an accurate intracellular flux distribution of the wild-type strain growing on glucose. Furthermore, genetic interventions for overproducing l-lysine, 1,2-propanediol and isobutanol simulated using OptForceMUST were in accordance with reported experimental results, indicating the practicability of iCW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of iCW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered C. glutamicum strain produced 66.43 g/L l-proline in 60 h with a yield of 0.26 g/g (l-proline/glucose) and a productivity of 1.11 g/L/h. To our knowledge, this is the highest titer and productivity reported for l-proline production using glucose as the carbon resource in a minimal medium.ConclusionsOur developed iCW773 serves as a high-quality platform for model-guided strain design to produce industrial bioproducts of interest. This new GEM will be a successful multidisciplinary tool and will make valuable contributions to metabolic engineering in academia and industry.

Project description:Simple Summary The fermentative production of desired chemicals from renewable resources is one of the promising biosynthetic routes to replace the petrochemical-based process. Gamma-aminobutyric acid (GABA) can be synthesized from l-glutamic acid and used as a building block for the synthesis of butyrolactam and polyamide 4 (nylon 4). The genome-scale metabolic model can predict the growth ability and metabolic flux distribution by genetic disturbances, which provides a strategy to construct a microbial cell factory for GABA and butyrolactam biosynthesis. Here, we performed model-guided metabolic engineering of Corynebacterium glutamicum ATCC13032 for GABA and butyrolactam fermentation from glucose. The biosynthetic pathways of GABA and butyrolactam were constructed by overexpressing the heterologous genes using a bi-cistronic expression cassette. The genetic modifications of the metabolic network cooperatively forced the carbon flux toward GABA and butyrolactam synthesis. This study provides new insights into engineering industrial microorganisms to produce target chemicals from renewable carbon sources. Abstract Gamma-aminobutyric acid (GABA) can be used as a bioactive component in the pharmaceutical industry and a precursor for the synthesis of butyrolactam, which functions as a monomer for the synthesis of polyamide 4 (nylon 4) with improved thermal stability and high biodegradability. The bio-based fermentation production of chemicals using microbes as a cell factory provides an alternative to replace petrochemical-based processes. Here, we performed model-guided metabolic engineering of Corynebacterium glutamicum for GABA and butyrolactam fermentation. A GABA biosynthetic pathway was constructed using a bi-cistronic expression cassette containing mutant glutamate decarboxylase. An in silico simulation showed that the increase in the flux from acetyl-CoA to α-ketoglutarate and the decrease in the flux from α-ketoglutarate to succinate drove more flux toward GABA biosynthesis. The TCA cycle was reconstructed by increasing the expression of acn and icd genes and deleting the sucCD gene. Blocking GABA catabolism and rewiring the transport system of GABA further improved GABA production. An acetyl-CoA-dependent pathway for in vivo butyrolactam biosynthesis was constructed by overexpressing act-encoding ß-alanine CoA transferase. In fed-batch fermentation, the engineered strains produced 23.07 g/L of GABA with a yield of 0.52 mol/mol from glucose and 4.58 g/L of butyrolactam. The metabolic engineering strategies can be used for genetic modification of industrial strains to produce target chemicals from α-ketoglutarate as a precursor, and the engineered strains will be useful to synthesize the bio-based monomer of polyamide 4 from renewable resources.

Dataset Information

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

Publications

Construction and Analysis of an Enzyme-Constrained Metabolic Model of <i>Corynebacterium glutamicum</i>.

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