Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
复制标题
DOI：
10.1016/j.cell.2020.05.037
复制
发表时间：
2020-07-23
期刊：
影响因子：
64.5
通讯作者：
Liu, David R.
中科院分区：
文献类型：
--
作者：
Arbab, Mandana;Shen, Max W.;Liu, David R.
虽然碱基编辑器被广泛用于安装靶向点突变，但决定碱基编辑结果的因素还没有得到很好的理解。我们表征了哺乳动物细胞中38，538个基因组整合靶点上11个胞嘧啶和腺嘌呤碱基编辑器（CBE和ABE）的序列-活性关系，并使用所得结果训练BE-Hive，这是一种机器学习模型，可准确预测碱基编辑基因型结果（R约为0.9）和效率（R约为0.7）。我们以>= 90%的精确度校正了3，388个疾病相关SNV，包括675个具有BE-Hive正确预测不会被编辑的旁观者核苷酸的等位基因。我们发现了以前不可预测的C到G或C到A编辑的决定因素，并使用这些发现来校正174个致病性颠换SNV的编码序列，精确度>= 90%。最后，我们使用BE-Hive的见解来设计调节编辑结果的新型CBE变体。这些发现阐明了基础编辑，使编辑在以前棘手的目标，并提供新的基础编辑器与改进的编辑能力。
Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated 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 approximate to 0.9) and efficiency (R approximate to 0.7). We corrected 3,388 disease-associated SNVs with >= 90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with >= 90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities.