美国哈佛大学刘如谦(David R. Liu)课题组利用靶标文库分析与机器学习发现影响碱基编辑结果的决定因素。相关论文于2020年6月12日在线发表在《细胞》杂志。
Title: Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
Author: Mandana Arbab, Max W. Shen, Beverly Mok, Christopher Wilson, aneta Matuszek, Christopher A. Cassa, David R. Liu
Issue&Volume: 2020-06-12
Abstract: Although base editors are widely used to install targeted point mutations, the factorsthat determine base editing outcomes are not well understood. We characterized sequence-activityrelationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomicallyintegrated targets in mammalian cells and used the resulting outcomes to train BE-Hive,a machine learning model that accurately predicts base editing genotypic outcomes(R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including675 alleles with bystander nucleotides that BE-Hive correctly predicted would notbe edited. We discovered determinants of previously unpredictable C-to-G, or C-to-Aediting and used these discoveries to correct coding sequences of 174 pathogenic transversionSNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novelCBE variants that modulate editing outcomes. These discoveries illuminate base editing,enable editing at previously intractable targets, and provide new base editors withimproved editing capabilities.
DOI: 10.1016/j.cell.2020.05.037
Source: https://www.cell.com/cell/fulltext/S0092-8674(20)30632-2
本期文章:《细胞》:Online/在线发表