Predictable and precise template-free CRISPR editing of pathogenic variants.

Journal: Nature
PMID: 30405244

Abstract

Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.

Authors

  • Max W Shen
    Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Mandana Arbab
    Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Jonathan Y Hsu
    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Daniel Worstell
    Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Sannie J Culbertson
    Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Olga Krabbe
    Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Christopher A Cassa
    Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • David R Liu
    Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA. drliu@fas.harvard.edu.
  • David K Gifford
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
  • Richard I Sherwood
    Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. rsherwood@rics.bwh.harvard.edu.

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