小柯机器人

近日，美国哈佛大学刘如谦等研究人员合作开发出高效的C•G到G•C碱基编辑器。相关论文于2021年6月28日在线发表在《自然—生物技术》杂志上。

据研究人员介绍，可编程C•G到G•C碱基编辑器（CGBE）具有广泛的科学和治疗潜力，但其编辑结果已被证明难以预测，并且其编辑效率和产品纯度往往较低。

研究人员报道了一套与机器学习模型配对的工程化CGBE，其能够实现高效、高纯度的C•G到G•C碱基编辑。研究人员进行了针对DNA修复基因的CRISPR干扰（CRISPRi）筛选，以确定影响C•G到G•C编辑结果的因素，并利用这些信息开发了具有不同编辑信息的CGBE。研究人员在哺乳动物细胞中10,638个基因组整合目标位点的库中表征了10个有前途的CGBE，并训练了机器学习模型，使用这些数据能够准确预测编辑结果的纯度和产量（R=0.90）。

这些CGBE能够以>90%的精度（平均 96%）和高达70%的效率（平均14%）校正546个与疾病相关的颠换单核苷酸变体（SNV）的野生型编码序列。最佳CGBE-单向导RNA的计算预测能够在比使用任何单个CGBE变体多四倍的目标位点上进行高纯度颠换碱基编辑。

附：英文原文

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

Author: Luke W. Koblan, Mandana Arbab, Max W. Shen, Jeffrey A. Hussmann, Andrew V. Anzalone, Jordan L. Doman, Gregory A. Newby, Dian Yang, Beverly Mok, Joseph M. Replogle, Albert Xu, Tyler A. Sisley, Jonathan S. Weissman, Britt Adamson, David R. Liu

Issue&Volume: 2021-06-28

Abstract: Programmable CG-to-GC 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 CG-to-GC base editing. We performed a CRISPR interference (CRISPRi) screen targeting DNA repair genes to identify factors that affect CG-to-GC editing outcomes and used these insights to develop CGBEs with diverse editing profiles. We characterized ten promising CGBEs on a library of 10,638genomically 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 546disease-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.

DOI: 10.1038/s41587-021-00938-z

Source: https://www.nature.com/articles/s41587-021-00938-z

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex