Metabolic Network Tuning to Increasing Malonyl-CoA Biosynthesis In Escherichia Coli and Its Application to Flavonoid Production
Zachary Fowler and Mattheos Koffas. Chemical and Biological Engineering, State University of New York at Buffalo, 914 Furnas Hall, Buffalo, NY 14260
Constraint-based modeling is an emerging tool for analyzing and understanding biological systems, specifically the connectivity of the metabolic network. As such, we have developed a Cipher of Evolutionary Design (CiED) to investigate the impact of gene deletions and other network modifications on the metabolite profile of microorganisms. By incorporating constraint-based modeling into an evolutionary algorithm we have created a computational platform where, like other evolutionary algorithms, the process of natural selection is used to find optimal phenotypes for the production of end products, such as recombinant natural products, while maintaining high levels of cellular biomass. Here we report the use of CiED to investigate the metabolic potential of Escherichia coli to channel carbon towards malonyl-CoA in an effort to generate recombinant strains with elevated flavonoid production capacity. Flavonoids, along with several of their substituted unnatural analogues, have potential therapeutic value in the treatment of various chronic diseases such as cancer, obesity, type II diabetes, and heart disease. Our engineered E. coli strains were first modified by the targeted deletion of native genes controlling competing enzymatic processes predicted by CiED and secondly through the incorporation of selected enzyme overexpressions. These included the coexpression of the plant-derived flavanone genes as well as acetate assimilation, acetyl-CoA carboxylase and the biosynthesis of Coenzyme A. As a result, the specific flavanone production from our optimally engineered strains was increased by over 660% for naringenin (15 to 100 mg/L/OD) and by over 420% for eriodictyol (13 to 55 mg/L/OD). These efforts demonstrate the utility of an evolutionary model based solely on stoichiometry in predicting improved microbial phenotypes for natural product production.