High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeast
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Abstract Yarrowia lipolytica and acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest. -
Abstract Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in
E. coli , use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeastYarrowia lipolytica . A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide’s ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms. -
Abstract In many organisms of biotechnological importance precise genome editing is limited by inherently low homologous recombination (HR) efficiencies. A number of strategies exist to increase the effectiveness of this native DNA repair pathway; however, most strategies rely on permanently disabling competing repair pathways, thus reducing an organism's capacity to repair naturally occurring double strand breaks. Here, we describe a CRISPR interference (CRISPRi) system for gene repression in the oleochemical‐producing yeast
Yarrowia lipolytica . By using a multiplexed sgRNA targeting strategy, we demonstrate efficient repression of eight out of nine targeted genes to enhance HR. Strains with nonhomologous end‐joining repressed were shown to have increased rates of HR when transformed with a linear DNA fragment with homology to a genomic locus. With multiplexed targeting ofKU70 andKU80 , and enhanced repression with Mxi1 fused to deactivated Cas9 (dCas9), rates of HR as high as 90% were achieved. The developed CRISPRi system enables enhanced HR inY. lipolytica without permanent genetic knockouts and promises to be a potent tool for other metabolic engineering, synthetic biology, and functional genomics studies.