Dataset Information

Multicenter integrated analysis of noncoding CRISPRi screens.

ABSTRACT: The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.

SUBMITTER: Yao D

PROVIDER: S-EPMC11009116 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Multicenter integrated analysis of noncoding CRISPRi screens.

Yao David D Tycko Josh J Oh Jin Woo JW Bounds Lexi R LR Gosai Sager J SJ Lataniotis Lazaros L Mackay-Smith Ava A Doughty Benjamin R BR Gabdank Idan I Schmidt Henri H Guerrero-Altamirano Tania T Siklenka Keith K Guo Katherine K White Alexander D AD Youngworth Ingrid I Andreeva Kalina K Ren Xingjie X Barrera Alejandro A Luo Yunhai Y Yardımcı Galip Gürkan GG Tewhey Ryan R Kundaje Anshul A Greenleaf William J WJ Sabeti Pardis C PC Leslie Christina C Pritykin Yuri Y Moore Jill E JE Beer Michael A MA Gersbach Charles A CA Reddy Timothy E TE Shen Yin Y Engreitz Jesse M JM Bassik Michael C MC Reilly Steven K SK

Nature methods 20240319 4

The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelin ...[more]

PMID: 38504114

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Project description:An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data.

Dataset Information

Multicenter integrated analysis of noncoding CRISPRi screens.

Publications

Multicenter integrated analysis of noncoding CRISPRi screens.

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