Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA.
Departments of Pathology & Laboratory Medicine, Neurology, and Biological Chemistry, School of Medicine, and the UCI Institute for Neurotherapeutics, University of California Irvine, Irvine, CA, 92697, USA.
Nat Commun. 2023 Feb 3;14(1):583. doi: 10.1038/s41467-023-36311-8.
Alternative polyadenylation (APA) plays an essential role in brain development; however, current transcriptome-wide association studies (TWAS) largely overlook APA in nominating susceptibility genes. Here, we performed a 3' untranslated region (3'UTR) APA TWAS (3'aTWAS) for 11 brain disorders by combining their genome-wide association studies data with 17,300 RNA-seq samples across 2,937 individuals. We identified 354 3'aTWAS-significant genes, including known APA-linked risk genes, such as SNCA in Parkinson's disease. Among these 354 genes, ~57% are not significant in traditional expression- and splicing-TWAS studies, since APA may regulate the translation, localization and protein-protein interaction of the target genes independent of mRNA level expression or splicing. Furthermore, we discovered ATXN3 as a 3'aTWAS-significant gene for amyotrophic lateral sclerosis, and its modulation substantially impacted pathological hallmarks of amyotrophic lateral sclerosis in vitro. Together, 3'aTWAS is a powerful strategy to nominate important APA-linked brain disorder susceptibility genes, most of which are largely overlooked by conventional expression and splicing analyses.
可变聚腺苷酸化(APA)在大脑发育中起着至关重要的作用;然而,目前的全转录组关联研究(TWAS)在提名易感基因方面在很大程度上忽略了 APA。在这里,我们通过将 11 种脑部疾病的全基因组关联研究数据与 2937 个人的 17300 个 RNA-seq 样本相结合,对 11 种脑部疾病进行了 3'非翻译区(3'UTR)APA TWAS(3'aTWAS)。我们确定了 354 个 3'aTWAS 显著基因,包括已知的 APA 相关风险基因,如帕金森病中的 SNCA。在这 354 个基因中,约 57%在传统的表达和剪接 TWAS 研究中并不显著,因为 APA 可能独立于 mRNA 水平表达或剪接来调节靶基因的翻译、定位和蛋白质-蛋白质相互作用。此外,我们发现 ATXN3 是肌萎缩侧索硬化症的 3'aTWAS 显著基因,其调节对肌萎缩侧索硬化症的体外病理特征有显著影响。总之,3'aTWAS 是一种强有力的策略,可以提名重要的 APA 相关脑疾病易感基因,其中大多数在传统的表达和剪接分析中被大大忽视。