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

TitleEfficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning.
Publication TypeJournal Article
Year of Publication2021
AuthorsKoblan, LW, Arbab, M, Shen, MW, Hussmann, JA, Anzalone, AV, Doman, JL, Newby, GA, Yang, D, Mok, B, Replogle, JM, Xu, A, Sisley, TA, Weissman, JS, Adamson, B, Liu, DR
JournalNat Biotechnol
Volume39
Issue11
Pagination1414-1425
Date Published2021 11
ISSN1546-1696
KeywordsAnimals, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems, Gene Editing, Machine Learning, Mammals, RNA, Guide
Abstract

<p>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.</p>

DOI10.1038/s41587-021-00938-z
Alternate JournalNat Biotechnol
PubMed ID34183861
PubMed Central IDPMC8985520
Grant ListUG3 AI150551 / AI / NIAID NIH HHS / United States
R35 GM138167 / GM / NIGMS NIH HHS / United States
P30 CA072720 / CA / NCI NIH HHS / United States
RM1 HG009490 / HG / NHGRI NIH HHS / United States
/ HHMI / Howard Hughes Medical Institute / United States
T32 GM087237 / GM / NIGMS NIH HHS / United States
R35 GM118062 / GM / NIGMS NIH HHS / United States
U01 AI142756 / AI / NIAID NIH HHS / United States
F31 NS115380 / NS / NINDS NIH HHS / United States