Predictable and precise template-free CRISPR editing of pathogenic variants

By Max W. Shen, Mandana Arbab, Jonathan Y. Hsu, D. Worstell, Sannie J. Culbertson, Olga Krabbe, C. Cassa, David R. Liu, David K. Gifford, R. Sherwood. Nature, 2018

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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. The authors use a machine-learning algorithm to predict the spectrum of CRISPR–Cas9-nuclease-mediated DNA repair outcomes at human genomic target sites.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e3b25bd5e69701eac7613e9dbfba8e074f824749

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