Biosensor-guided genome-wide CRISPR screens enable cis,cis-muconic acid overproduction in yeast

Abstract

Classical pathway metabolic engineering relies on enhancing strain productivity through the introduction of rationally identified genetic perturbations, which while useful, are time-consuming and miss unintuitive targets. Genome-wide CRISPR screens offer an alternative by efficiently sampling genomes, enabling the engineering of novel phenotypes, and are further enhanced when combined with high-throughput strain interrogation methods. Here, using Saccharomyces cerevisiae, we engineered small molecule biosensors into population enrichment tools using zeocin resistance and fluorescence as signal outputs. We then introduced the multi-functional genome-wide CRISPR (MAGIC) library that targets every gene for activation, interference, or deletion, thus creating biosensor-aided MAGIC, or senMAGIC. As a case study, we utilized senMAGIC to identify single genetic determinants that improve productivity of the nylon precursors protocatechuic and cis,cis-muconic acids (PCA and ccM). These were then randomly assembled as triple gRNA-tRNA arrays for multiplexed gene perturbations to identify combinatorial effects. Top gRNA hits were then confirmed using direct genomic integrations in senMAGIC-free production strains, resulting in identifying known as well as novel targets that increase PCA and ccM titres. Our results demonstrate senMAGIC as a robust automation-friendly whole-genome evolution tool and highlight limitations of the method, providing a roadmap for future multifunctional CRISPR screens to optimize host metabolism for efficient bioprocesses.