西班牙巴塞罗那科学技术学院Ben Lehner等研究人员合作完成基因组范围内致病性终止密码子的量化和预测。该项研究成果于2024年8月22日在线发表在《自然—遗传学》杂志上。

研究人员通过量化大约5800个人类致病性终止密码子在八种药物作用下的通读，直接应对定义药物疗效的挑战。研究人员发现，不同的药物促进了由局部序列背景定义的互补过早终止密码子（PTC）子集的通读。

这使研究人员能够构建可解释的模型，准确预测全基因组范围内药物引发的通读，并通过量化内源性终止密码子的通读验证这些模型。准确的通读量化和预测将有助于临床试验设计以及个性化无义抑制疗法的开发。

据介绍，PTC导致约10-20%的遗传性疾病，并且是癌症中抑癌基因失活的主要机制之一。缓解PTC影响的一种普遍策略是促进翻译通读。小分子引发的无义抑制在各种疾病模型中已被证明有效，但由于许多PTC的通读效果不佳，其在临床中的转化受到阻碍。

附：英文原文

Title: Genome-scale quantification and prediction of pathogenic stop codon readthrough by small molecules

Author: Toledano, Ignasi, Supek, Fran, Lehner, Ben

Issue&Volume: 2024-08-22

Abstract: Premature termination codons (PTCs) cause ~10–20% of inherited diseases and are a major mechanism of tumor suppressor gene inactivation in cancer. A general strategy to alleviate the effects of PTCs would be to promote translational readthrough. Nonsense suppression by small molecules has proven effective in diverse disease models, but translation into the clinic is hampered by ineffective readthrough of many PTCs. Here we directly tackle the challenge of defining drug efficacy by quantifying the readthrough of ~5,800 human pathogenic stop codons by eight drugs. We find that different drugs promote the readthrough of complementary subsets of PTCs defined by local sequence context. This allows us to build interpretable models that accurately predict drug-induced readthrough genome-wide, and we validate these models by quantifying endogenous stop codon readthrough. Accurate readthrough quantification and prediction will empower clinical trial design and the development of personalized nonsense suppression therapies.

DOI: 10.1038/s41588-024-01878-5

Source: https://www.nature.com/articles/s41588-024-01878-5