Genome-scale reconstruction and in silico analysis of Klebsiella oxytoca for 2,3-butanediol production.

Klebsiella oxytoca, a Gram-negative, rod-shaped, and facultative anaerobic bacterium, is one of the most promising 2,3-butanediol (2,3-BD) producers. In order to improve the metabolic performance of K. oxytoca as an efficient biofactory, it is necessary to assess its metabolic characteristics with a system-wide scope, and to optimize the metabolic pathways at a systems level. Provision of the complete genome sequence of K. oxytoca enabled the construction of genome-scale metabolic model of K. oxytoca and its in silico analyses. The genome-scale metabolic model of K. oxytoca was constructed using the annotated genome with biochemical and physiological information. The stoichiometric model, KoxGSC1457, is composed of 1,457 reactions and 1,099 metabolites. The model was further refined by applying biomass composition equations and comparing in silico results with experimental data based on constraints-based flux analyses. Then, the model was applied to in silico analyses to understand the properties of K. oxytoca and also to improve its capabilities for 2,3-BD production according to genetic and environmental perturbations. Firstly, in silico analysis, which tested the effect of augmenting the metabolic flux pool of 2,3-BD precursors, elucidated that increasing the pyruvate pool is primarily important for 2,3-BD synthesis. Secondly, we performed in silico single gene knockout simulation for 2,3-BD overproduction, and investigated the changes of the in silico flux solution space of a ldhA gene knockout mutant in comparison with that of the wild-type strain. Finally, the KoxGSC1457 model was used to optimize the oxygen levels during fermentation for 2,3-BD production. The genome-scale metabolic model, KoxGSC1457, constructed in this study successfully investigated metabolic characteristics of K. oxytoca at systems level. The KoxGSC1457 model could be employed as an useful tool to analyze its metabolic capabilities, to predict its physiological responses according to environmental and genetic perturbations, and to design metabolic engineering strategies to improve its metabolic performance.