Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. Here, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library, and in doing so improve screens in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified novel mutations for metabolic engineering of high lipid accumulation.

Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays. Enhancing phenotypic drug discovery with synthetic biologyDevelopment of new drugs requires identification and optimization of functional molecules that often proceeds sequentially through high-throughput discovery screening, hit validation, hit expansion and optimization, and preclinical disease modeling (see Glossary) (Figure 1, Key figure)[1]. Target-based drug discovery (TDD) programs aim to identify drug candidates that modulate a defined biological target that is hypothesized to play a causal role in initiating or sustaining a disease. TDD programs offer precision by directly screening for drug candidate efficacy using purified molecular targets [2]. By contrast, phenotypic drug discovery (PDD) programs aim to identify drug candidates that modulate a physiologically-relevant biological system or cellular signaling pathway and screen for drug efficacy using cell-based assays [2]. Cell-based assays are target-agnostic and provide the opportunity to identify therapeutics that resolve diseaseassociated phenotypic deficits even when the disease etiology remains obscure and targets are undefined. Thus, PDD has recently received renewed interest for its potential to identify first-in-class drugs that act via a novel mechanism of action (MoA) as well as identify drugs for disease with complex or unknown etiology [1][2][3][4]. In order to identify drug candidates that translate to effective therapeutics in the clinic, PDD programs require cell-based assays that faithfully recapitulate and report on disease-relevant processes. To efficiently and effectively screen in phenotypic models, assay development for initial discovery screens must balance throughput with the ability to capture these disease processes [1]. As PDD probes a larger biological space than TDD to identify potential therapeutics, the number of hits from an initial discovery screen in a PDD program may be very large [2]. Following initial discovery screens, hit validation and triage are required to produce a small number of high-confidence candidates to proceed to the resource-intensive stages of hit expansion, compound optimization, and hig...

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Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. To address this limitation, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled highthroughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which functional determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified mutations that led to high lipid accumulation and eliminated pseudohyphal morphology.