基于Prime编辑器的高通量筛选揭示了人类细胞中的功能同义突变，这一成果由北京大学魏文胜研究小组经过不懈努力而取得。该研究于2025年6月24日发表于国际一流学术期刊《自然—生物技术》杂志上。

在这里，课题组人员利用PEmax系统创建了一个包含297900个工程化引物编辑指导RNA的文库，并进行了广泛的筛选，以确定影响细胞适应性的同义突变。与最近在酵母中的发现不同，群体水平的分析表明，同义突变与非同义突变在适应度效应上存在差异，但相对于阴性对照，它们表现出相似的表型分布。在严格的质量控制之后，只有一小部分显示出可测量的效果。对于这些功能性突变，课题组开发了一个专门的机器学习工具，并揭示了它们对各种生物过程的影响，如信使RNA剪接和转录，并得到多方面实验证据的支持。课题组发现同义突变可以改变RNA折叠并影响翻译，正如PLK1_S2所证明的那样。通过整合筛选数据与他们的模型，研究团队预测临床删除他们的同义突变。这项研究加深了他们对同义突变的理解，为临床疾病研究提供了见解。

据了解，同义突变通常被认为是中性的，而它们在人类基因组中的作用在很大程度上仍未被探索。

附：英文原文

Title: Prime editor-based high-throughput screening reveals functional synonymous mutations in human cells

Author: Niu, Xuran, Tang, Wei, Liu, Yongshuo, Mo, Binrui, Yu, Ying, Liu, Ying, Wei, Wensheng

Issue&Volume: 2025-06-24

Abstract: Synonymous mutations are generally considered neutral, while their roles in the human genome remain largely unexplored. Here we use the PEmax system to create a library of 297,900 engineered prime-editing guide RNAs and perform extensive screening to identify synonymous mutations affecting cell fitness. Unlike recent findings in yeast, group-level analyses show that synonymous mutations diverge from nonsynonymous mutations in fitness effects yet exhibit similar phenotypic distributions relative to negative controls. Following rigorous quality control, only a small subset demonstrated measurable effects. For these functional mutations, we develop a specialized machine learning tool and uncover their impact on various biological processes such as messenger RNA splicing and transcription, supported by multifaceted experimental evidence. We find that synonymous mutations can alter RNA folding and affect translation, as demonstrated by PLK1_S2. By integrating screening data with our model, we predict clinically deleterious synonymous mutations. This research deepens our understanding of synonymous mutations, providing insights for clinical disease studies.

DOI: 10.1038/s41587-025-02710-z

Source: https://www.nature.com/articles/s41587-025-02710-z