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Genome Biol. 2016 Mar 8;17:45. doi: 10.1186/s13059-016-0900-9.

Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design.

Author information

1
Stanford Genome Technology Center, Department of Biochemistry, Stanford University, 3165 Porter Drive, Palo Alto, CA, 94304, USA.
2
Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
3
Department of Anesthesia, Stanford University School of Medicine, Stanford University, Stanford, California, 94305, USA.
4
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Genome Campus, Hinxton, CB101SD, UK.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117, Heidelberg, Germany.
6
Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. leopold.parts@sanger.ac.uk.
7
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117, Heidelberg, Germany. leopold.parts@sanger.ac.uk.
8
Current address: Wellcome Trust Sanger Institute, Hinxton, CB101SA, UK. leopold.parts@sanger.ac.uk.
9
Stanford Genome Technology Center, Department of Biochemistry, Stanford University, 3165 Porter Drive, Palo Alto, CA, 94304, USA. bstonge@stanford.edu.

Abstract

BACKGROUND:

Genome-scale CRISPR interference (CRISPRi) has been used in human cell lines; however, the features of effective guide RNAs (gRNAs) in different organisms have not been well characterized. Here, we define rules that determine gRNA effectiveness for transcriptional repression in Saccharomyces cerevisiae.

RESULTS:

We create an inducible single plasmid CRISPRi system for gene repression in yeast, and use it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identifies previously described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments using gRNA libraries allows us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility are clearly more effective. We also find that the best region to target gRNAs is between the transcription start site (TSS) and 200 bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, the specificity of truncated gRNAs (18 nt of complementarity to the target) is not clearly superior to full-length gRNAs (20 nt of complementarity), as truncated gRNAs are generally less potent against both mismatched and perfectly matched targets.

CONCLUSIONS:

Our results establish a powerful functional and chemical genomics screening method and provide guidelines for designing effective gRNAs, which consider chromatin state and position relative to the target gene TSS. These findings will enable effective library design and genome-wide programmable gene repression in many genetic backgrounds.

PMID:
26956608
PMCID:
PMC4784398
DOI:
10.1186/s13059-016-0900-9
[Indexed for MEDLINE]
Free PMC Article
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