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通过机器学习预测SNP对mRNA翻译的功能影响

Zheyu Li¹, Liang Chen²

¹Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, CA 90089, USA.

Nucleic acids research

|July 10, 2023

概括

引起疾病的单核酸多态 (SNP) 可以通过影响核糖体占用来改变基因翻译. 这些核糖体占用SNP (RibOc-SNP) 突出了翻译规范.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
计算生物学计算生物学

背景情况:

单核酸多态 (SNP) 在全基因组关联研究 (GWAS) 中对于识别疾病联系至关重要.
在疾病优先排序中,SNP对mRNA翻译和核糖体占用的影响仍未得到充分研究.
了解转化调节为遗传疾病机制提供了新的见解.

研究的目的:

通过分析核糖体碰撞来研究SNP对翻译的功能影响.
识别显著改变核糖体占用率 (RibOc-SNPs) 的SNP及其与疾病的关联.
探索转化调节在SNP驱动的致病性中的作用.

主要方法:

机器学习模型应用于全基因组的核糖体分析数据.
基于预测mRNA转化过程中的核糖体碰撞,预测SNP功能.
在RibOc-SNPs中分析核酸和氨基酸转化模式.

主要成果:

与疾病相关的SNP经常会导致核糖体占用率的显著变化,称为RibOc-SNP.
特定的核酸转换 ('G → T', 'T → G', 'C → A') 在RibOc-SNP中得到丰富.
RibOc-SNP集中在5'-编码区域,这表明它们在翻译启动调节中的作用.
显著的RibOc-SNP百分比对其他转录异型的翻译效率产生相反的影响.

结论:

翻译调节是SNP相关疾病发病的一个关键因素.
RibOc-SNPs提供了一个新的层,用于优先考虑从GWAS引起疾病的变体.
通过改变翻译效率,SNP可以调节基因表达,从而可能加剧异形特异性影响.

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