In silico prediction of gene knockout candidates in Escherichia coli genome-scale model for enhanced succinic acid production from glycerol.

BACTERIAL genetics; ESCHERICHIA coli; GENE knockout; SUCCINIC acid; GLYCERIN; METABOLISM; FORMATE acetyltransferase

The use of genome-scale models of Escherichia coli to guide future metabolic engineering strategies for increased succinic acid production has received renewed attention in recent years. Substrate selectivity such as glycerol is of particular interest, because it is currently generated as a by-product of biodiesel industry and therefore can serve as a solitary carbon source. However, study on the prediction of gene knockout candidates for enhanced succinate production from glycerol using Minimization of Metabolic Adjustment Algorithm with the OptFlux software platform remained underexplored. Here, we show that metabolic engineering interventions by gene knockout simulation of some pyruvate dissimilating pathway enzymes (lactate dehydrogenase A and pyruvate formate lyase A) using E. coli genome-scale model can reduce acetate flux and enhance succinic acid production under anaerobic conditions. The introduced genetic perturbations led to substantial improvement in succinate flux of about 597% on glycerol and 120% on glucose than that of the wild-type control strain BSKO. We hypothesize that the deletion of pyruvate formate lyase A (pflA) in E. coli can led to no acetate production from glucose, lower acetate production from glycerol and increased succinic acid productivities on both substrates under anaerobic conditions. Our results demonstrate a predicted increase in succinate production (597% higher than the wild-type model) among others, from glycerol after deletion of pflA/b0902 gene in E. coli genome-scale model. This would open up a novel platform for model-guided experimental inquiry and/or allow a comprehensive biological discovery on the metabolic processes of pflA in E. coli for succinate production when glycerol is the substrate. [ABSTRACT FROM AUTHOR]

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