A new genome-scale metabolic model of and its application.

BACKGROUND

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 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 ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering.

RESULTS

Here, we constructed a new GEM for ATCC13032, CW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, CW773 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 cultivated on seven different carbon sources using CW773 were consistent with experimental values. Pearson's correlation coefficient between the CW773-simulated and experimental fluxes was 0.99, suggesting that CW773 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 OptForce were in accordance with reported experimental results, indicating the practicability of CW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of CW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered 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.

CONCLUSIONS

Our developed CW773 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.