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Nature. 2018 Nov;563(7733):646-651. doi: 10.1038/s41586-018-0686-x. Epub 2018 Nov 7.

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

Shen MW1,2, Arbab M3,4,5, Hsu JY6,7, Worstell D8, Culbertson SJ8, Krabbe O8,9, Cassa CA8,10, Liu DR11,12,13, Gifford DK14,15,16,17, Sherwood RI18,19.

Author information

1
Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.
2
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
3
Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
4
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
5
Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
6
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
7
Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA.
8
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
9
Hubrecht Institute for Developmental Biology and Stem Cell Research, Royal Netherlands Academy of Arts and Sciences (KNAW), Utrecht, The Netherlands.
10
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
11
Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA. drliu@fas.harvard.edu.
12
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA. drliu@fas.harvard.edu.
13
Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA. drliu@fas.harvard.edu.
14
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. gifford@mit.edu.
15
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. gifford@mit.edu.
16
Broad Institute of MIT and Harvard, Cambridge, MA, USA. gifford@mit.edu.
17
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. gifford@mit.edu.
18
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. rsherwood@rics.bwh.harvard.edu.
19
Hubrecht Institute for Developmental Biology and Stem Cell Research, Royal Netherlands Academy of Arts and Sciences (KNAW), Utrecht, The Netherlands. rsherwood@rics.bwh.harvard.edu.

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.

PMID:
30405244
PMCID:
PMC6517069
DOI:
10.1038/s41586-018-0686-x
[Indexed for MEDLINE]
Free PMC Article

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