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Uncovering the dynamics of precise repair at CRISPR/Cas9-induced double-strand breaks

Author

Listed:
  • Daniela Ben-Tov

    (Weizmann Institute of Science)

  • Fabrizio Mafessoni

    (Weizmann Institute of Science)

  • Amit Cucuy

    (Weizmann Institute of Science)

  • Arik Honig

    (Weizmann Institute of Science)

  • Cathy Melamed-Bessudo

    (Weizmann Institute of Science)

  • Avraham A. Levy

    (Weizmann Institute of Science)

Abstract

CRISPR/Cas9 is widely used for precise mutagenesis through targeted DNA double-strand breaks (DSBs) induction followed by error-prone repair. A better understanding of this process requires measuring the rates of cutting, error-prone, and precise repair, which have remained elusive so far. Here, we present a molecular and computational toolkit for multiplexed quantification of DSB intermediates and repair products by single-molecule sequencing. Using this approach, we characterize the dynamics of DSB induction, processing and repair at endogenous loci along a 72 h time-course in tomato protoplasts. Combining this data with kinetic modeling reveals that indel accumulation is determined by the combined effect of the rates of DSB induction processing of broken ends, and precise versus error repair. In this study, 64–88% of the molecules were cleaved in the three targets analyzed, while indels ranged between 15–41%. Precise repair accounts for most of the gap between cleavage and error repair, representing up to 70% of all repair events. Altogether, this system exposes flux in the DSB repair process, decoupling induction and repair dynamics, and suggesting an essential role of high-fidelity repair in limiting the efficiency of CRISPR-mediated mutagenesis.

Suggested Citation

  • Daniela Ben-Tov & Fabrizio Mafessoni & Amit Cucuy & Arik Honig & Cathy Melamed-Bessudo & Avraham A. Levy, 2024. "Uncovering the dynamics of precise repair at CRISPR/Cas9-induced double-strand breaks," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49410-x
    DOI: 10.1038/s41467-024-49410-x
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    References listed on IDEAS

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    1. Xi Xiang & Giulia I. Corsi & Christian Anthon & Kunli Qu & Xiaoguang Pan & Xue Liang & Peng Han & Zhanying Dong & Lijun Liu & Jiayan Zhong & Tao Ma & Jinbao Wang & Xiuqing Zhang & Hui Jiang & Fengping, 2021. "Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    2. Shiran Abadi & Winston X Yan & David Amar & Itay Mayrose, 2017. "A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-24, October.
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