小柯机器人

爱沙尼亚塔尔图大学Leopold Parts团队近期取得重要工作进展，他们研究利用序列特征和DNA修复决定因素预测先导编辑插入效率。相关论文于2023年2月16日在线发表于《自然—生物技术》杂志上。

据介绍，大多数短序列可以使用先导编辑精确地写入选定的基因组靶标。然而，目前尚不清楚哪些因素决定了插入。

研究人员设计了一个包含3604个不同长度序列的文库，并在不同的DNA修复环境中使用不同的先导编辑系统，测量了它们插入三个人类细胞系中四个基因组位点的频率。研究人员发现，插入序列的长度、核苷酸组成和二级结构都会影响插入效率。他们还发现3′核酸酶TREX1和TREX2抑制较长序列的插入。将序列和修复特征结合到机器学习模型中，研究人员可以预测插入位点的相对频率，R = 0.70 。

最后，研究人员展示了这一准确预测和用户友好的软件如何帮助选择高效插入的常见融合标签的密码子变体，并为超过100个有用序列提供了经验确定的插入率目录。

附：英文原文

Title: Prediction of prime editing insertion efficiencies using sequence features and DNA repair determinants

Author: Koeppel, Jonas, Weller, Juliane, Peets, Elin Madli, Pallaseni, Ananth, Kuzmin, Ivan, Raudvere, Uku, Peterson, Hedi, Liberante, Fabio Giuseppe, Parts, Leopold

Issue&Volume: 2023-02-16

Abstract: Most short sequences can be precisely written into a selected genomic target using prime editing; however, it remains unclear what factors govern insertion. We design a library of 3,604 sequences of various lengths and measure the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems in varying DNA repair contexts. We find that length, nucleotide composition and secondary structure of the insertion sequence all affect insertion rates. We also discover that the 3′ flap nucleases TREX1 and TREX2 suppress the insertion of longer sequences. Combining the sequence and repair features into a machine learning model, we can predict relative frequency of insertions into a site with R=0.70. Finally, we demonstrate how our accurate prediction and user-friendly software help choose codon variants of common fusion tags that insert at high efficiency, and provide a catalog of empirically determined insertion rates for over a hundred useful sequences.

DOI: 10.1038/s41587-023-01678-y

Source: https://www.nature.com/articles/s41587-023-01678-y

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