In silico target-based strain engineering of Saccharomyces cerevisiae for terpene precursor improvement

Area of Science:

• Metabolic Engineering
• Synthetic Biology
• Biotechnology

Background:

• Systems-based metabolic engineering utilizes computational tools to predict gene targets for enhancing cellular product formation.
• Terpenoid production in Saccharomyces cerevisiae is a key target for biotechnological applications.
• Genome-scale models provide a framework for understanding and manipulating cellular metabolism.

Purpose of the Study:

• To predict gene knockout and overexpression targets for improving flux in the terpenoid pathway of Saccharomyces cerevisiae using the iMM904 model.
• To validate the predicted targets through in vitro studies and assess their impact on squalene synthesis.

Main Methods:

• Utilized FOCuS, a metaheuristic tool, to predict flux improvement targets for the terpenoid pathway.
• Employed three established gene amplification algorithms to identify overexpression targets.
• Performed in vitro experiments overexpressing ALD6 and TPI1 to validate computational predictions.

Main Results:

• Key predicted knockout targets included LYS1, GAP1, AAT1, AAT2, TH17, KGD-m, MET14, PDC1, and ACO1, involved in amino acid, fatty acid, and nucleotide biosynthesis.
• Identified overexpression targets such as PFK1, FBA1, ZWF1, TDH1, PYC1, ALD6, TPI1, PDX1, and ENO1, primarily in glycolytic and pentose phosphate pathways.
• In vitro overexpression of ALD6 and TPI1 resulted in 2.23- and 4.24-fold increases in squalene synthesis, respectively.

Conclusions:

• The predicted gene targets, through knockout or overexpression, plausibly enhance sterol pathway flux by increasing acetyl-CoA precursor pools or regenerating NADPH.
• Computational predictions for metabolic engineering targets can be effectively validated through experimental studies.
• This study demonstrates a systems-based approach for optimizing microbial cell factories for valuable compound production.