Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Artificial Intelligence, Sungkyunkwan University College of Computing and Informatics, Suwon, Republic of Korea.
Commun Biol. 2024 Nov 7;7(1):1461. doi: 10.1038/s42003-024-07136-y.
Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed using an allele-specific expression (ASE)-based measure to reduce noise caused by random variations. Additionally, by combining our data with the updated allele frequency database of gnomAD, we demonstrate the spectrum of NMD efficiency according to the degree of gene-level mutational constraints (degree of a gene-tolerating loss-of-function variants). The NMD prediction model, trained on TCGA data, shows that gene-level mutational constraint is an important predictor of NMD efficiency. Findings of this study suggest the potential role of NMD on shaping the landscape of mutational constraints.
尽管非编码介导的 mRNA 降解 (NMD) 对于调节基因表达非常重要，但它仍然知之甚少。在这里，我们扩展了之前一项具有里程碑意义的研究的发现，该研究使用来自癌症基因组图谱计划 (TCGA) 的匹配基因组和转录组数据，提出了与 NMD 效率相关的几个因素，并结合了包括 Genotype-Tissue Expression (GTEx)、gnomAD 和突变约束度量在内的其他数据。我们使用基于等位基因特异性表达 (ASE) 的度量来分析影响 NMD 效率的因素，以减少随机变异引起的噪声。此外，通过将我们的数据与 gnomAD 的更新等位基因频率数据库相结合，我们根据基因水平突变约束的程度（容忍功能丧失变异的程度）展示了 NMD 效率的范围。基于 TCGA 数据训练的 NMD 预测模型表明，基因水平的突变约束是 NMD 效率的重要预测因子。这项研究的结果表明，NMD 可能在塑造突变约束景观方面发挥作用。
