Genome-scale analysis of Mannheimia succiniciproducens metabolism
Article first published online: 2 APR 2007
Copyright © 2007 Wiley Periodicals, Inc.
Biotechnology and Bioengineering
Volume 97, Issue 4, pages 657–671, 1 July 2007
How to Cite
Kim, T. Y., Kim, H. U., Park, J. M., Song, H., Kim, J. S. and Lee, S. Y. (2007), Genome-scale analysis of Mannheimia succiniciproducens metabolism. Biotechnol. Bioeng., 97: 657–671. doi: 10.1002/bit.21433
- Issue published online: 24 MAY 2007
- Article first published online: 2 APR 2007
- Manuscript Accepted: 8 MAR 2007
- Manuscript Received: 12 JAN 2007
- Mannheimia succiniciproducens;
- genome-scale metabolic model;
- model validation;
- constraints-based flux analysis
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. Biotechnol. Bioeng. 2007;97: 657–671. © 2007 Wiley Periodicals, Inc.