Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning

Luke W Koblan; Mandana Arbab; Max W Shen; Jeffrey A Hussmann; Andrew V Anzalone; Jordan L Doman; Gregory A Newby; Dian Yang; Beverly Mok; Joseph M Replogle; Albert Xu; Tyler A Sisley; Jonathan S Weissman; Britt Adamson; David R Liu

doi:10.1038/s41587-021-00938-z

Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning

Nat Biotechnol. 2021 Nov;39(11):1414-1425. doi: 10.1038/s41587-021-00938-z. Epub 2021 Jun 28.

Authors

Luke W Koblan^#^{1

2

3}, Mandana Arbab^#^{1

2

3}, Max W Shen^#^{1

2

3

4}, Jeffrey A Hussmann^{5

6

7

8

9}, Andrew V Anzalone^{1

2

3}, Jordan L Doman^{1

2

3}, Gregory A Newby^{1

2

3}, Dian Yang^{5

7

8

9}, Beverly Mok^{1

2

3}, Joseph M Replogle^{5

7

10

11

8

9}, Albert Xu^{5

6

10

12}, Tyler A Sisley², Jonathan S Weissman^{13

14

15

16

17}, Britt Adamson^{18

19

20

21}, David R Liu^{22

23

24}

Affiliations

¹ Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
² Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
³ Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
⁴ Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁵ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
⁶ Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA.
⁷ Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.
⁸ Whitehead Institute for Biomedical Research, Cambridge, MA, USA.
⁹ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁰ Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA.
¹¹ Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
¹² Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
¹³ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. weissman@wi.mit.edu.
¹⁴ Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA. weissman@wi.mit.edu.
¹⁵ Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA. weissman@wi.mit.edu.
¹⁶ Whitehead Institute for Biomedical Research, Cambridge, MA, USA. weissman@wi.mit.edu.
¹⁷ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. weissman@wi.mit.edu.
¹⁸ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. badamson@princeton.edu.
¹⁹ Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA. badamson@princeton.edu.
²⁰ Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. badamson@princeton.edu.
²¹ Department of Molecular Biology, Princeton University, Princeton, NJ, USA. badamson@princeton.edu.
²² Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA, USA. drliu@fas.harvard.edu.
²³ Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA. drliu@fas.harvard.edu.
²⁴ Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA. drliu@fas.harvard.edu.

^# Contributed equally.

Abstract

Programmable C•G-to-G•C base editors (CGBEs) have broad scientific and therapeutic potential, but their editing outcomes have proved difficult to predict and their editing efficiency and product purity are often low. We describe a suite of engineered CGBEs paired with machine learning models to enable efficient, high-purity C•G-to-G•C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C•G-to-G•C editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638 genomically integrated target sites in mammalian cells and trained machine learning models that accurately predict the purity and yield of editing outcomes (R = 0.90) using these data. These CGBEs enable correction to the wild-type coding sequence of 546 disease-related transversion single-nucleotide variants (SNVs) with >90% precision (mean 96%) and up to 70% efficiency (mean 14%). Computational prediction of optimal CGBE-single-guide RNA pairs enables high-purity transversion base editing at over fourfold more target sites than achieved using any single CGBE variant.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
CRISPR-Cas Systems / genetics
Clustered Regularly Interspaced Short Palindromic Repeats*
Gene Editing*
Machine Learning
Mammals / genetics
RNA, Guide, CRISPR-Cas Systems / genetics

Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning

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