Small molecule inhibition of ATM kinase increases CRISPR-Cas9 1-bp insertion frequency
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s41467-021-25415-8.pdf
Description
Published version
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1.19 MB
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Checksum (MD5)
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Author(s)
Bermudez-Cabrera, Heysol C
Culbertson, Sannie
Barkal, Sammy
Holmes, Benjamin
Shen, Max W
Zhang, Sophia
Gifford, David K
Sherwood, Richard I
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Bermudez-Cabrera, Heysol C, Culbertson, Sannie, Barkal, Sammy, Holmes, Benjamin, Shen, Max W et al. 2021. "Small molecule inhibition of ATM kinase increases CRISPR-Cas9 1-bp insertion frequency." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractMutational outcomes following CRISPR-Cas9-nuclease cutting in mammalian cells have recently been shown to be predictable and, in certain cases, skewed toward single genotypes. However, the ability to control these outcomes remains limited, especially for 1-bp insertions, a common and therapeutically relevant class of repair outcomes. Here, through a small molecule screen, we identify the ATM kinase inhibitor KU-60019 as a compound capable of reproducibly increasing the fraction of 1-bp insertions relative to other Cas9 repair outcomes. Small molecule or genetic ATM inhibition increases 1-bp insertion outcome fraction across three human and mouse cell lines, two Cas9 species, and dozens of target sites, although concomitantly reducing the fraction of edited alleles. Notably, KU-60019 increases the relative frequency of 1-bp insertions to over 80% of edited alleles at several native human genomic loci and improves the efficiency of correction for pathogenic 1-bp deletion variants. The ability to increase 1-bp insertion frequency adds another dimension to precise template-free Cas9-nuclease genome editing.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computational and Systems Biology Program
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-021-25415-8
