Control of gene output by intron RNA structure

Intron removal through pre-mRNA splicing is a central step in gene expression across Eukarya. The process initiates with the recognition of intronic sequence elements (splice sites) by highly conserved RNA-protein components of the spliceosome. Intron sequences themselves are not generally conserved beyond the splice sites, yet intronic mutations are often associated with genetic disease. Here we systematically test if and how intron RNA structure formation modulates gene output. We generated intron variant libraries that measure the impact of base pairing at every position across a natural intron. Using a massively parallel reporter assay (MPRA), we find that base pairing involving the splice sites modulates splicing across orders of magnitude. An additional intron region upstream of the branch site was also sensitive to structure, suggesting steric hindrance. Combining thermodynamic structure prediction with libraries designed to sequester splice sites in structures of varying stability, we show that machine learning models can nearly fully explain observed gene output. Informed by this, designed alterations in intron sequence that modulate base pairing are shown to improve inefficient splicing of human β-globin IVS1. Finally, intronic mutations that alter RNA structure emerge rapidly under selection pressure, providing eukaryotes with a simple evolutionary strategy to fine-tune gene output.通过前体mRNA剪接实现内含子切除，是真核生物（Eukarya）基因表达过程中的核心步骤。该过程起始于剪接体（spliceosome）高度保守的RNA-蛋白质复合物识别内含子序列元件（如剪接位点，splice sites）。除剪接位点（splice sites）外，内含子序列本身通常不具有保守性，但内含子突变常与遗传疾病相关。本研究系统探究了内含子RNA结构形成是否以及如何调控基因表达产出。我们构建了内含子变异文库，用于检测天然内含子上每个位点的碱基配对效应。利用大规模平行报告基因检测法（massively parallel reporter assay, MPRA），我们发现涉及剪接位点（splice sites）的碱基配对可在数个数量级范围内调控剪接效率。分支位点（branch site）上游的另一内含子区域同样对结构变化敏感，这提示存在空间位阻效应（steric hindrance）。我们将热力学结构预测与设计为不同稳定性结构以遮蔽剪接位点（splice sites）的文库相结合，结果表明机器学习模型可近乎完全解释观测到的基因表达产出。基于此结果，经设计的可调控碱基配对的内含子序列变异被证实可改善人类β-珠蛋白IVS1的低效剪接。最后，在选择压力下，改变RNA结构的内含子突变会快速出现，这为真核生物（Eukarya）提供了一种微调基因表达产出的简单进化策略。