A Generalizable Crispr-Cas9 System for Rapid and Scalable Gene Target Discovery and Optimization of Metabolism

Metabolic engineering enabled the enhanced bio-renewable production of numerous drop-in fuel replacements and specialty chemicals. Often, these traits are achieved by rewiring the native metabolism in many hosts as well as fine-tuning the expression of local pathways. However, traditional strain engineering faces many limitations in its scope: DNA incorporation to create new strains is time-consuming and there are only a limited number of markers available to multiplex this process. The speed of genetic modification juxtaposed with a large number of required design-build-test cycles required to optimize a strain thus limits strain engineering. To bypass these limitations, we first develop a plasmid-based CRISPR-Cas9 system in yeast to specifically program gene expression in the absence of “hard-wired” genetic modification. Second, we utilize this system to rapidly prototype metabolic pathways via expression perturbation in order to identify key bottlenecks limiting flux and then multiplex these gene targets in a manner that is rapidly scalable and combinatorial. We generalize this method by demonstrating several pathway case studies of interest enabling us to screen and optimize gene targets across yeast metabolism for varied phenotypes. Finally, this method for target identification scales with transformation, thus allowing for rapid identification of gene targets using a combinatorial approach.