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High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells
Nature Biomedical Engineering ( IF 28.1 ) Pub Date : 2020-01-14 , DOI: 10.1038/s41551-019-0505-1
Hui Kwon Kim 1, 2, 3, 4 , Sungtae Lee 1 , Younggwang Kim 1, 2 , Jinman Park 1, 2 , Seonwoo Min 5 , Jae Woo Choi 1, 6 , Tony P Huang 7, 8, 9 , Sungroh Yoon 5, 10 , David R Liu 7, 8, 9 , Hyongbum Henry Kim 1, 2, 3, 4, 6
Affiliation  

The applications of clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing can be limited by a lack of compatible protospacer adjacent motifs (PAMs), insufficient on-target activity and off-target effects. Here, we report an extensive comparison of the PAM-sequence compatibilities and the on-target and off-target activities of Cas9 from Streptococcus pyogenes (SpCas9) and the SpCas9 variants xCas9 and SpCas9-NG (which are known to have broader PAM compatibility than SpCas9) at 26,478 lentivirally integrated target sequences and 78 endogenous target sites in human cells. We found that xCas9 has the lowest tolerance for mismatched target sequences and that SpCas9-NG has the broadest PAM compatibility. We also show, on the basis of newly identified non-NGG PAM sequences, that SpCas9-NG and SpCas9 can edit six previously unedited endogenous sites associated with genetic diseases. Moreover, we provide deep-learning models that predict the activities of xCas9 and SpCas9-NG at the target sequences. The resulting deeper understanding of the activities of xCas9, SpCas9-NG and SpCas9 in human cells should facilitate their use.



中文翻译:

高通量分析 xCas9、SpCas9-NG 和 SpCas9 在人类细胞中匹配和不匹配的靶序列上的活性

基于成簇的规则间隔短回文重复序列 (CRISPR) 的基因组编辑的应用可能会受到缺乏兼容的 protospacer 相邻基序 (PAM)、靶向活性不足和脱靶效应的限制。在这里,我们报告了 PAM 序列兼容性以及化脓性链球菌中 Cas9 的靶向和脱靶活性的广泛比较(SpCas9) 和 SpCas9 变体 xCas9 和 SpCas9-NG(已知其具有比 SpCas9 更广泛的 PAM 兼容性)在人体细胞中的 26,478 个慢病毒整合靶序列和 78 个内源靶位点。我们发现 xCas9 对错配目标序列的容忍度最低,而 SpCas9-NG 具有最广泛的 PAM 兼容性。我们还表明,基于新发现的非 NGG PAM 序列,SpCas9-NG 和 SpCas9 可以编辑六个以前未编辑的与遗传疾病相关的内源性位点。此外,我们提供了预测 xCas9 和 SpCas9-NG 在目标序列上的活动的深度学习模型。由此产生的对人类细胞中 xCas9、SpCas9-NG 和 SpCas9 活动的更深入了解应该有助于它们的使用。

更新日期:2020-01-14
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