Genome-scale analysis of Mannheimia succiniciproducens metabolism

Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Sŏul, Seoul, South Korea
Biotechnology and Bioengineering (Impact Factor: 4.13). 07/2007; 97(4):657-71. DOI: 10.1002/bit.21433
Source: PubMed


Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.


Available from: Hyohak Song, Oct 16, 2014
    • "Updating the metabolic model is a crucial process for performing systems biology studies, even though it is often laborious and as difficult as reconstructing a new metabolic model [5] [6] [18]. Updated metabolic models of various microbes have been used to provide more accurate predictions of cellular phenotypes [18] [19] [20], to identify antibacterial drug targets [21], to investigate production capability of cell factories [22] [23], and to suggest new metabolic engineering strategies [24]. The metabolic model of S. coelicolor, iIB711 was published almost a decade ago. "
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    ABSTRACT: Streptomycetes are industrially and pharmaceutically important bacteria that produce a variety of secondary metabolites including antibiotics. Streptomycetes have a complex metabolic network responsible for producing secondary metabolites and utilizing organic residues existing in soil. In this study, we reconstructed a high-quality metabolic model for Streptomyces coelicolor A3(2), iMK1208, to understand and engineer the metabolism of this model species. Compared to iIB711, the previous metabolic model for S. coelicolor, iMK1208 was updated on biomass equation, stoichiometric matrix, and energetic parameters which could enhance the predictive power of the model. We validated iMK1208 by making predictions for growth capability in various growth media, and comparing the results with experimental data. Furthermore, we applied a strain design algorithm, flux scanning based on enforced objective flux (FSEOF), to iMK1208 for actinorhodin overproductions. FSEOF results identified not only the previously known gene overexpression targets such as actII-ORF4 and acetyl-CoA carboxylase, but also novel targets such as branched-chain a-keto acid dehydrogenase (BCDH). We constructed and evaluated the BCDH overexpression mutant, which showed 52-fold increase in actinorhodin production, thereby validated the prediction power of iMK1208. iMK1208 becomes a useful and valuable framework for studying this biotechnologically important genus with principles of systems biology and metabolic engineering.
    Biotechnology Journal 09/2014; 9(9). DOI:10.1002/biot.201300539 · 3.49 Impact Factor
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    • "Alternatively, global data sets, most commonly transcriptomics data, can be used to identify highly regulated parts of the metabolic network [19], even if the metabolic reconstruction does not contain representations of regulatory mechanisms per se. This, in combination with computer simulations of gene knock-outs or knock-ins in the stoichiometric model, can identify attractive targets for metabolic engineering in the organism [20]. "
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    BMC Systems Biology 03/2013; 7(1):19. DOI:10.1186/1752-0509-7-19 · 2.44 Impact Factor
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    • "Linear programming (LP), subject to the constraints pertaining to mass conservation, reaction thermodynamics, and capacity, was carried out to determine the fluxes [35]. These constraints were presented in the forms of upper and lower bounds for the fluxes (vj,min ≤ vj ≤ vj,max) for each reaction j, and used together with an objective function Z, usually the growth rate [14,36]. "
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    BMC Systems Biology 05/2012; 6(1):49. DOI:10.1186/1752-0509-6-49 · 2.44 Impact Factor
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