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

Autor: Koblan, Luke W., Arbab, Mandana, Shen, Max W., Hussmann, Jeffrey A., Anzalone, Andrew V., Doman, Jordan L., Newby, Gregory A., Yang, Dian, Mok, Beverly, Replogle, Joseph M., Xu, Albert, Sisley, Tyler A., Weissman, Jonathan S., Adamson, Britt, Liu, David R.
Přispěvatelé: Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Chemistry, Massachusetts Institute of Technology. Computational and Systems Biology Program
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Luke Koblan
Nat Biotechnol
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.
US NIH (Grants F31NS115380, U01AI142756, UG3AI150551, RM1HG009490, R35GM118062, R35GM138167 and P30CA072720)
Databáze: OpenAIRE
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