Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing.
Quantitative prediction of variant effects on alternative splicing in MAPT using endogenous pre-messenger RNA structure probing.
复制标题
DOI：
10.7554/elife.73888
复制
发表时间：
2022-06-13
期刊：
影响因子：
7.7
通讯作者：
Staley, Jonathan P.
中科院分区：
文献类型：
--
作者：
Kumar, Jayashree;Lackey, Lela;Waldern, Justin M.;Dey, Abhishek;Mustoe, Anthony M.;Weeks, Kevin M.;Mathews, David H.;Laederach, Alain;Staley, Jonathan P.
关键词：
剪接是高度调节的，并受到许多因素的调节。定量预测突变将如何影响前体mRNA（pre-mRNA）结构和下游功能特别具有挑战性。在这里，我们使用一种新的化学探测策略来可视化细胞中的内源性前体和成熟MAPT mRNA结构。我们使用这些数据来估计玻尔兹曼次优结构系综，然后对其进行分析以预测突变对前体mRNA结构的影响。对剪接体在剪接周期不同阶段的最新冷冻-EM结构的进一步分析显示，具有前体mRNA的Bact复合物的足迹最好地预测了包含选择性剪接的MAPT基因的外显子10的选择性剪接结果，实现了74%的准确性。我们进一步开发了一个β-回归加权框架，该框架结合了剪接位点强度、RNA结构和外显子/内含子剪接调控元件，能够以90%的准确度预测47个已知突变和6个新发现的突变对MAPT外显子10的影响。这种结合实验和计算的框架代表了准确预测剪接相关致病变异的前进道路。
Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.