Predictable and precise template-free CRISPR editing of pathogenic variants
Name
nihms-1014007.pdf
Description
Accepted version
Size
4.14 MB
Format
Adobe PDF
Checksum (MD5)
7b0d9b42c893f498feb6773d911f6a84
Author(s)
Shen, Max Walt
Arbab, Mandana
Hsu, Jonathan Yee-Ting
Worstell, Daniel
Culbertson, Sannie J.
Krabbe, Olga
Cassa, Christopher A.
Liu, David R.
Gifford, David K.
Sherwood, Richard I.
Date Issued
November 2018
Journal
Nature
Publisher
Springer Nature
Citation
Shen, Max W. et al "Predictable and precise template-free CRISPR editing of pathogenic variants." Nature 563, 7733 (November 2018): 646–651 ©2018, Springer Nature Limited.
Version
Author's final manuscript
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. Keywords: Functional genomics; Genome informatics
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Broad Institute of MIT and Harvard
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/s41586-018-0686-x
