Engineered S. cerevisiae construction for high-gravity ethanol production and targeted metabolomics

Abstract

Strong sugar tolerance and high bioethanol yield of yeast under high-gravity fermentation have caused great attention in the bioethanol industry. In this study, Clustered Regularly Interspaced Short Palindromic Repeats Cas9 (CRISPR-Cas9) technology was used to knock out S. cerevisiae GPD2, FPS1, ADH2, DLD3, ERG5, NTH1, and AMS1 to construct engineering strain S. cerevisiae GFADENA. Under high-gravity fermentation with 400 g/L of sucrose, S. cerevisiae GFADENA produced 135 g/L ethanol, which increased 17% compared with the wild-type strain. In addition, S. cerevisiae GFADENA produced 145 g/L of ethanol by simultaneous saccharification and fermentation (SSF) using 400 g/L of corn syrup with a sugar-ethanol conversion rate of 41.1%. Further, the targeted metabolomics involving energy, amino acid, and free fatty acid metabolisms were performed to unravel its molecular mechanisms. The deletion of seven genes in S. cerevisiae GFADENA caused a more significant effect on energy metabolism compared with amino acid and free fatty acid metabolisms based on the significantly different metabolites. Two metabolites α-ketoglutaric acid and fructose-1,6-bisphosphate were the most significantly different upregulation and downregulation metabolites, respectively (p < 0.05). Functions of metabolism, environmental information processing, and genetic information processing were related to sucrose tolerance enhancement and ethanol production increase in S. cerevisiae GFADENA by the regulation of significantly different metabolites. This study provided an effective pathway to increase ethanol yield and enhance sucrose tolerance in S. cerevisiae through bioengineering modification.

• S. cerevisiae GFADENA with gene deletion was constructed by the CRISPR-Cas9 approach

• S. cerevisiae GFADENA could produce ethanol using high-gravity fermentation condition

• The ethanol yield of 145 g/L was produced using 400 g/L corn syrup by the SSF method