Title | Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Koblan, 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 |
Journal | Nat Biotechnol |
Volume | 39 |
Issue | 11 |
Pagination | 1414-1425 |
Date Published | 2021 Nov |
ISSN | 1546-1696 |
Keywords | Animals, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems, Gene Editing, Machine Learning, Mammals, RNA, Guide, Kinetoplastida |
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> |
DOI | 10.1038/s41587-021-00938-z |
Alternate Journal | Nat Biotechnol |
PubMed ID | 34183861 |
PubMed Central ID | PMC8985520 |
Grant List | UG3 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 |