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来自目标库分析和机器学习的基础编辑结果的决定因素

Mandana Arbab¹, Max W Shen², Beverly Mok¹

¹Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA.

|June 14, 2020

概括

这项研究介绍了BE-Hive,一种预测基准编辑结果和效率的机器学习模型. 它可以精确地纠正与疾病相关的突变,并开发改进的基准编辑器.

科学领域:

遗传学和基因组学
生物技术
生物信息学

背景情况:

基准编辑器对于目标点突变至关重要,但它们的编辑决定因素仍然不清楚.
了解序列-活动关系是优化基础编辑工具的关键.
哺乳动物细胞系统为基因编辑结果提供了一个强大的平台.

研究的目的:

描述细胞因子和腺因基编辑器 (CBEs和ABEs) 的序列活性关系.
开发一个可预测的机器学习模型 (BE-Hive) 来实现基础编辑结果和效率.
设计具有增强功能的新型基础编辑器.

主要方法:

在哺乳动物细胞中对38,538个集成点进行11个基因编辑器的全基因组表征.
使用实验基础编辑数据训练机器学习模型 (BE-Hive).
在 silico 预测和编辑结果的实验验证,包括旁观者编辑.

主要成果:

BE-Hive准确地预测基因编辑的基因型结果 (R ≈ 0.9) 和效率 (R ≈ 0.7).
成功纠正了3388个与疾病相关的单核酸变体 (SNVs).
鉴定了C-to-G和C-to-A编辑的新决定因素,使174种致病转变SNV的纠正成为可能.
设计了具有调节编辑能力的新CBE变种.

结论:

BE-Hive显著提升了基础编辑的可预测性和精度.
这项研究将基础编辑的范围扩展到以前难以解决的目标,包括转换.
新的基础编辑器提供了更好的编辑性能和控制,为治疗应用铺平了道路.

关键词:

基础编辑疾病纠正机器学习精确基因组编辑翻转基数编辑

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