刘如谦团队揭示影响碱基编辑结果的决定因素

本期文章：《细胞》：Online/在线发表

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。

研究人员在哺乳动物细胞中38,538个基因组整合靶标上表征了11个胞嘧啶和腺嘌呤碱基编辑器（CBE和ABE）的序列与活性关系，并使用所得结果来训练了BE-Hive，这是一种机器学习模型，可以准确地预测碱基编辑基因型结果（R≈0.9）和效率（R≈0.7）。

研究人员以≥90％的准确度纠正了3388个与疾病相关的SNV，其中包括675个等位基因，其旁观者核苷酸被BE-Hive正确预测，且无法编辑。研究人员发现了以前无法预测的C-to-G或C-to-A编辑的决定因素，并利用这些发现以≥90％的准确性纠正了174个病原性SNV的编码序列。

最后，研究人员利用BE-Hive的见解来设计了新颖的CBE变体，以调节编辑结果。这些发现启发了碱基编辑，实现了以前难以处理的靶标编辑，并提供了具有增强编辑功能的新碱基编辑器。

据悉，尽管碱基编辑器被广泛用于目标位点突变，但是决定碱基编辑结果的因素尚不十分清楚。

附：英文原文

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

期刊信息

Cell：《细胞》，创刊于1974年。隶属于细胞出版社，最新IF：36.216

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