Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes

Abstract

In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets. Using iBridge, overexpression and downregulation targets are suggested for the production of 298 chemicals and validated for 36 chemicals experimentally demonstrated in previous studies. Finally, iBridge is employed to engineer Escherichia coli strains capable of producing 10.3 g/L of D-panthenol, a compound not previously produced, as well as putrescine and 4-hydroxyphenyllactate at enhanced titers, 63.7 and 8.3 g/L, respectively.

Keywords: 4-hydroxyphenyllactate; D-panthenol; Escherichia coli; bridge reaction; downregulation targets; genome-scale metabolic model; overexpression targets; putrescine; sum of covariances; systems metabolic engineering.