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ACS Synth Biol. 2017 Mar 17;6(3):566-581. doi: 10.1021/acssynbio.6b00264. Epub 2016 Dec 28.

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

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Energy Biosciences Institute and ‡Department of Bioengineering, University of California , Berkeley, California 94720, United States.
Biological Systems and Engineering Division, and ∥Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States.


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.


CRISPR-Cas9; biofuel; genetic engineering; hydrolysate; quantitative trait loci; strain development

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