Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning
| Autor: | Luke W. Koblan, Britt Adamson, Albert Xu, Mandana Arbab, Tyler A Sisley, Jordan L. Doman, Dian Yang, Joseph M. Replogle, David R. Liu, Andrew V. Anzalone, Jeffrey A. Hussmann, Max W. Shen, Gregory A. Newby, Beverly Mok, Jonathan S. Weissman |
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| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
CRISPR interference business.industry Computer science Biomedical Engineering Bioengineering Machine learning computer.software_genre Base (topology) Applied Microbiology and Biotechnology 03 medical and health sciences 0302 clinical medicine Molecular Medicine Coding region Artificial intelligence business Transversion computer 030217 neurology & neurosurgery 030304 developmental biology Biotechnology |
| Zdroj: | Nature Biotechnology. 39:1414-1425 |
| ISSN: | 1546-1696 1087-0156 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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