Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity

Journal Article (2022)
Author(s)

Rongjie Fu (The University of Texas MD Anderson Cancer Center)

Wei He (The University of Texas MD Anderson Cancer Center)

Jinzhuang Dou (The University of Texas MD Anderson Cancer Center)

Oscar D. Villarreal (The University of Texas MD Anderson Cancer Center)

Ella Bedford (The University of Texas MD Anderson Cancer Center)

Helen Wang (The University of Texas MD Anderson Cancer Center)

Connie Hou (The University of Texas MD Anderson Cancer Center)

Liang Zhang (The University of Texas MD Anderson Cancer Center)

Martin Depken (Kavli institute of nanoscience Delft, TU Delft - BN/Bionanoscience)

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DOI related publication
https://doi.org/10.1038/s41467-022-28028-x Final published version
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Publication Year
2022
Language
English
Journal title
Nature Communications
Issue number
1
Volume number
13
Article number
474
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366
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Abstract

The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. To systematically explore the sequence determinants governing CRISPR/Cas9 specificity, here we describe a dual-target system to measure the relative cleavage rate between off- and on-target sequences (off-on ratios) of 1902 gRNAs on 13,314 synthetic target sequences, and reveal a set of sequence rules involving 2 factors in off-targeting: 1) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context; 2) an “epistasis-like” combinatorial effect of multiple mismatches, which are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. These sequence rules lead to the development of MOFF, a model-based predictor of Cas9-mediated off-target effects. Moreover, the “epistasis-like” combinatorial effect suggests a strategy of allele-specific genome editing using mismatched guides. With the aid of MOFF prediction, this strategy significantly improves the selectivity and expands the application domain of Cas9-based allele-specific editing, as tested in a high-throughput allele-editing screen on 18 cancer hotspot mutations.