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基因组变异破坏miRNA-mRNA调控。

Genomic Variants Disrupt miRNA-mRNA Regulation.

作者信息

Xi Ellie, Bai Judy, Zhang Klaira, Yu Hui, Guo Yan

机构信息

Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA.

出版信息

Chem Biodivers. 2022 Oct;19(10):e202200623. doi: 10.1002/cbdv.202200623. Epub 2022 Sep 29.

Abstract

Micro RNA (miRNA) and its regulatory effect on messenger RNA (mRNA) gene expression are a major focus in cancer research. Disruption in the normal miRNA-mRNA regulation network can result in serious cascading biological repercussions. In this study, we curated miRNA-related variants from major genomic consortiums and thoroughly evaluated how these variants could exert their effects by cross-validating with independent functional knowledge bases. Nearly all known variants (more than 664 million) categorized by type (germline, somatic, epigenetic) were mapped to the genomic regions involved in miRNA-mRNA binding (miRNA seeds and miRNA-mRNA 3'-UTR binding sequence). Subsets of miRNA-related variants supported by additional functional evidence, such as expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS), were identified and scrutinized. Our results show that variants in miRNA seeds can substantially alter the composition of a miRNA's target mRNA set. Various functional analyses converged to reveal a post-transcriptional complex regulatory network where miRNA, eQTL, and RNA-binding protein intertwined to disseminate the impact of genomic variants. These results may potentially explain how certain variants affect disease/trait risks in genome wide association studies.

摘要

微小RNA(miRNA)及其对信使RNA(mRNA)基因表达的调控作用是癌症研究的主要焦点。正常的miRNA-mRNA调控网络的破坏会导致严重的级联生物反应。在本研究中,我们从主要基因组联盟中筛选出与miRNA相关的变异,并通过与独立的功能知识库进行交叉验证,全面评估了这些变异如何发挥其作用。几乎所有按类型(种系、体细胞、表观遗传)分类的已知变异(超过6.64亿个)都被映射到参与miRNA-mRNA结合的基因组区域(miRNA种子和miRNA-mRNA 3'-UTR结合序列)。我们鉴定并审查了由额外功能证据支持的miRNA相关变异子集,如表达数量性状位点(eQTL)和全基因组关联研究(GWAS)。我们的结果表明,miRNA种子中的变异可显著改变miRNA靶mRNA集的组成。各种功能分析汇聚在一起,揭示了一个转录后复杂调控网络,其中miRNA、eQTL和RNA结合蛋白相互交织,以传播基因组变异的影响。这些结果可能潜在地解释了某些变异在全基因组关联研究中如何影响疾病/性状风险。

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