Quantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait

Engineering
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 of Saccharomyces cerevisiae during fermentation of a Miscanthus x giganteus plant 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 multikilobase locus replacements, we
engineered an S288C-derived strain with superior hydrolysate tolerance
than JAY291. Our methods should be generalizable to engineering any
complex trait in S. cerevisiae, as well as other
organisms.