How to Improve Target Discovery with Genome-Scale Engineering

Identifying and validating key variants associated with desired traits is hard work. Really hard work.

Recently, the use of pooled cultivation methods — including adaptive laboratory evolution, chemogenomic profiling, and evolutionary engineering — has been growing. This is a proven approach for understanding complex biological systems by studying large and diverse populations of genetic variants.

But the success of these techniques depends heavily on access to genetic diversity in the organism of interest. That’s why it is of great interest to the team at Inscripta, developer of the world’s first fully automated benchtop instrument for CRISPR-based genome-scale engineering. We believe that massively parallel genome engineering will be fundamental to making significant improvements in target discovery for a broad range of applications.

We completed a study of targeted strain engineering using Inscripta’s Onyx platform, which enables genome-wide CRISPR editing at scale. We used E. coli, performing genome-wide engineering with knockouts and with promoter ladders of varying strengths. We retained only high-quality designs, leading to a population of 3,676 genes with five promoter designs and 3,966 knockout designs. The resulting six engineered cell libraries were pooled and grown in the presence of four compounds known to inhibit E. coli growth to understand variant function in the face of selective pressure.

We found hundreds to thousands of edits that were significantly enriched or depleted in response to each inhibitory compound. The availability of a ladder of gene expression variants paired with the concomitant knockout strain for nearly every gene makes it possible to obtain a deeply nuanced view into the mechanisms of inhibitor tolerance in E. coli.

This high-throughput approach to genome engineering will be incredibly important for target discovery in academia and industry alike. It should be particularly useful for poorly characterized genes and for industrial production conditions that diverge from standard experimental conditions.