acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens

Abstract

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 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.

Optimized genome-wide CRISPR screening enables rapid engineering of growth-based phenotypes inYarrowia lipolytica

“…FS is computed as the log 2 -ratio of sgRNA abundance in the PO1f Cas9 strain to that in the PO1f control strain. We used acCRISPR, an analysis pipeline for CRISPR screens that correct screening outcomes based on the activity of each guide used in the screen (Ramesh et al, 2023) to identify essential genes in glucose, and high and low FS value genes in the two acetate conditions. The analysis pipeline identified 1580 essential genes for growth with glucose as the sole carbon source, as well as 868 and 901 genes that reduce or improve growth on acetate, respectively ( Fig.…”

“…Read counts from the glucose screen in SD-leu media in strains containing KU70 gene knockout and Cas9 were used to calculate cutting score (CS) for each sgRNA by computing log2 ratio of the total normalized abundances in control and treatment samples, as described in (Ramesh et al, 2023). Similarly, read counts from glucose and acetate screens in Turki media were used to determine sgRNA fitness scores (FS) as log2 ratio of total normalized guide abundance in Cas9-containing treatment sample to that in the wildtype control.…”

“…The result is a convolutional neural network model that accurately predicts high activity CRISPR-Cas9 sgRNAs (Baisya et al, 2022). In order to pick sgRNAs for library v2, library v1 was first filtered to only retain sgRNAs having a CS greater than 4.0 (a value close to the optimum CS threshold for high-activity sgRNAs previously determined by (Ramesh et al, 2023). Library v2 was thus designed to contain the top two guides with CS > 4.0 for each gene from the filtered library v1, and a guide with the highest DeepGuide-predicted CS for that gene.…”

“…The pooled mutant library cells may then undergo phenotypic divergence when grown under a selection pressure (Smith et al, 2016). While individual genotypes cannot be directly observed or isolated in the pooled culture, deep sequencing can be used to find sgRNA prevalences and by extension, essential genes or genes involved in a phenotype of interest (Ramesh et al, 2023). Pooled genome-wide screens are an ideal high-throughput method for studying unknown relationships between genes, how those relationships affect desired phenotypes, and how those interactions affect metabolite production (Li et al, 2020).…”

“…Functional genome-wide screens have been especially effective tools for engineering industrially beneficial traits into yeast (Patterson et al, 2018). Yeasts such as Saccharomyces cerevisiae (Chen et al, 2016; Fujita et al, 2006), Yarrowia lipolytica (Jagtap et al, 2021; Lupish et al, 2022; Patterson et al, 2018; Ramesh et al, 2023), Candida albicans (Adames et al, 2019; Gervais et al, 2021; Wang et al, 2020), and Komagataella phaffii (Alva et al, 2021; Nishi et al, 2022; Tkachenko et al, 2023) have all undergone genome-wide screens to better understand industrially significant metabolic pathways. The outputs have led to promising improvements, such as increased stress tolerance (Crook et al, 2016; Fujita et al, 2006), improved protein production and secretion (Huang et al, 2015; Wang et al, 2019), and alternative nutrient utilization (Xu et al, 2019).…”