Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. .
Microbial Cell Factories (Impact Factor: 4.25). 09/2009; 8:43. DOI: 10.1186/1475-2859-8-43
Source: PubMed

ABSTRACT In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.
The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production.
The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Synthetic bioengineering is a strategy for developing useful microbial strains with innovative biological functions. Novel functions are designed and synthesized in host microbes with the aid of advanced technologies for computer simulations of cellular processes and the system-wide manipulation of host genomes. Here, we review the current status and future prospects of synthetic bioengineering in the yeast Saccharomyces cerevisiae for bio-refinery processes to produce various commodity chemicals from lignocellulosic biomass. Previous studies to improve assimilation of xylose and production of glutathione and butanol suggest a fixed pattern of problems that need to be solved, and as a crucial step, we now need to identify promising targets for further engineering of yeast metabolism. Metabolic simulation, transcriptomics, and metabolomics are useful emerging technologies for achieving this goal, making it possible to optimize metabolic pathways. Furthermore, novel genes responsible for target production can be found by analyzing large-scale data. Fine-tuning of enzyme activities is essential in the latter stage of strain development, but it requires detailed modeling of yeast metabolic functions. Recombinant technologies and genetic engineering are crucial for implementing metabolic designs into microbes. In addition to conventional gene manipulation techniques, advanced methods, such as multicistronic expression systems, marker-recycle gene deletion, protein engineering, cell surface display, genome editing, and synthesis of very long DNA fragments, will facilitate advances in synthetic bioengineering.
    Journal of Biotechnology 06/2012; 163(2). DOI:10.1016/j.jbiotec.2012.05.021 · 2.88 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of actinomycetes. Based on in silico knockouts we generated topological and genomic maps for each organism. Combining the collection of genome-wide models, we constructed a global enzyme association network to identify both a conserved "core network" and an "essential core network" of the entire group. As has been reported for low-degree metabolites in several organisms, low-degree enzymes (in linear pathways) turn out to be generally more essential than high-degree enzymes (in metabolic hubs).
    FEBS letters 06/2011; 585(14):2389-94. DOI:10.1016/j.febslet.2011.06.014 · 3.34 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Here, we describe the development of a genetically defined strain of l-lysine hyperproducing Corynebacterium glutamicum by systems metabolic engineering of the wild type. Implementation of only 12 defined genome-based changes in genes encoding central metabolic enzymes redirected major carbon fluxes as desired towards the optimal pathway usage predicted by in silico modeling. The final engineered C. glutamicum strain was able to produce lysine with a high yield of 0.55 g per gram of glucose, a titer of 120 g L(-1) lysine and a productivity of 4.0 g L(-1) h(-1) in fed-batch culture. The specific glucose uptake rate of the wild type could be completely maintained during the engineering process, providing a highly viable producer. For these key criteria, the genetically defined strain created in this study lies at the maximum limit of classically derived producers developed over the last fifty years. This is the first report of a rationally derived lysine production strain that may be competitive with industrial applications. The design-based strategy for metabolic engineering reported here could serve as general concept for the rational development of microorganisms as efficient cellular factories for bio-production.
    Metabolic Engineering 03/2011; 13(2):159-68. DOI:10.1016/j.ymben.2011.01.003 · 8.26 Impact Factor

Preview (3 Sources)

Available from