QTL-guided metabolic engineering of a complex trait

Abstract

AbstractEngineering complex phenotypes for industrial and synthetic biology applications is difficult and often confounds rational design. Bioethanol production from lignocellulosic feedstocks is a complex trait that requires multiple host systems to utilize, detoxify, and metabolize a mixture of sugars and inhibitors present in plant hydrolysates. Here, we demonstrate an integrated approach to discovering and optimizing host factors that impact fitness ofSaccharomyces cerevisiaeduring fermentation of aMiscanthus x giganteusplant hydrolysate. We first used high-resolution Quantitative Trait Loci (QTL) mapping and systematic Bulk Reciprocal Hemizygosity analysis (bRHA) to discover 17 loci that differentiate hydrolysate tolerance between an industrially related (JAY291) and a laboratory (S288C) strain. We then used this data to identify a subset of favorable allelic loci that were most amenable for strain engineering. Guided by this “genetic blueprint”, and using a dual-guide Cas9-based method to efficiently perform multi-kilobase locus replacements, we engineered an S288C strain with superior hydrolysate tolerance than JAY291. Our methods should be generalizable to engineering any complex trait inS. cerevisiae, as well as other organisms.