Improved Production of Taxol(R) Precursors in S. cerevisiae using Combinatorial in silico Design and Metabolic Engineering

Abstract

Integrated metabolic engineering approaches combining system and synthetic biology tools allow the efficient designing of microbial cell factories to synthesize high-value products. In the present study, in silico design algorithms were used on the latest yeast genome-scale model 8.5.0 to predict potential genomic modifications that could enhance the production of early-step Taxol(R) in previously engineered Saccharomyces cerevisiae cells. The solution set containing genomic modification candidates was narrowed down by employing the COnstraints Based Reconstruction and Analysis (COBRA) methods. 17 genomic modifications consisting of nine gene deletions and eight gene overexpression were screened using wet-lab studies to determine whether these modifications can increase the production yield of taxadiene, the first metabolite in the Taxol(R) through the mevalonate pathway. Depending on the cultivation condition, most of the single genomic modifications resulted in higher taxadiene production. The best-performing strain, named KM32, contained four overexpressed genes, ILV2, TRR1, ADE13 and ECM31, from the branched-chain amino acid biosynthesis, thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively. Using KM32, taxadiene production was increased by 50%, reaching 215 mg/L of taxadiene. The engineered strain also produced 43.65 mg/L of taxa-4(20),11-dien-5-ol (T5-ol), and 26.2 mg/L of taxa-4(20),11-dien-5--yl acetate (T5Ac) which are the highest productions of these early-step Taxol(R) metabolites reported until now in S. cerevisiae. The findings of this study highlight that the use of computational and integrated approaches can ensure determining promising modifications that are difficult to estimate intuitively to develop yeast cell factories.