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

被引：172

作者：

Koblan, Luke W. ^{[1
,2
,3
]}

Arbab, Mandana ^{[1
,2
,3
]}

Shen, Max W. ^{[1
,2
,3
,4
]}

Hussmann, Jeffrey A. ^{[5
,6
,7
,13
,14
]}

Anzalone, Andrew V. ^{[1
,2
,3
]}

Doman, Jordan L. ^{[1
,2
,3
]}

Newby, Gregory A. ^{[1
,2
,3
]}

Yang, Dian ^{[5
,7
,13
,14
]}

Mok, Beverly ^{[1
,2
,3
]}

Replogle, Joseph M. ^{[5
,7
,8
,9
]}

Xu, Albert ^{[5
,6
,8
,10
]}

Sisley, Tyler A. ^{[2
]}

Weissman, Jonathan S. ^{[5
,7
,8
,13
,14
]}

Adamson, Britt ^{[5
,7
,11
,12
]}

Liu, David R. ^{[1
,2
,3
]}

机构：

[1] Broad Inst MIT & Harvard, Merkin Inst Transformat Technol Healthcare, Cambridge, MA 02142 USA

[2] Harvard Univ, Dept Chem & Chem Biol, Cambridge, MA 02138 USA

[3] Harvard Univ, Howard Hughes Med Inst, Cambridge, MA 02138 USA

[4] MIT, Computat & Syst Biol Program, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[5] Univ Calif San Francisco, Dept Cellular & Mol Pharmacol, San Francisco, CA 94143 USA

[6] Univ Calif San Francisco, Dept Microbiol & Immunol, San Francisco, CA 94143 USA

[7] Univ Calif San Francisco, Howard Hughes Med Inst, San Francisco, CA 94143 USA

[8] Univ Calif San Francisco, Med Scientist Training Program, San Francisco, CA 94143 USA

[9] Univ Calif San Francisco, Tetrad Grad Program, San Francisco, CA 94143 USA

[10] Univ Calif San Francisco, Biomed Sci Grad Program, San Francisco, CA 94143 USA

[11] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 USA

[12] Princeton Univ, Dept Mol Biol, Princeton, NJ 08544 USA

[13] Whitehead Inst Biomed Res, 9 Cambridge Ctr, Cambridge, MA 02142 USA

[14] MIT, Dept Biol, Cambridge, MA 02139 USA

来源：

NATURE BIOTECHNOLOGY | 2021年 / 39卷 / 11期

关键词：

DNA GLYCOSYLASE UDGX; GENOMIC DNA; TRANSCRIPTION; BINDING;

D O I：

10.1038/s41587-021-00938-z

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Programmable C center dot G-to-G center dot 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 center dot G-to-G center dot C base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect C center dot G-to-G center dot 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. Efficiency of transversion base editing is increased by matching a set of base editors to target sequences.

引用

页码：1414 / +

页数：18

共 43 条

[1] Covalent binding of uracil DNA glycosylase UdgX to abasic DNA upon uracil excision [J].