Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Hum Mutat. 2022 Nov;43(11):1629-1641. doi: 10.1002/humu.24379. Epub 2022 May 10.
Alternative RNA splicing is an important means of genetic control and transcriptome diversity. However, when alternative splicing events are studied independently, coordinated splicing modulated by common factors is often not recognized. As a result, the molecular mechanisms of how splicing regulators promote or repress splice site recognition in a context-dependent manner are not well understood. The functional coupling between multiple gene regulatory layers suggests that splicing is modulated by additional genetic or epigenetic components. Here, we developed a bioinformatics approach to identify causal modulators of splicing activity based on the variation of gene expression in large RNA sequencing datasets. We applied this approach in a neurological context with hundreds of dorsolateral prefrontal cortex samples. Our model is strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. We identified novel modulators of the splicing factor SRSF1, including UIMC1 and the long noncoding RNA CBR3-AS1, that function over dozens of SRSF1 intron retention splicing targets. This strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.
可变剪接是遗传调控和转录组多样性的重要手段。然而，当独立研究可变剪接事件时，通常不会识别出由共同因素调节的协调剪接。因此，拼接调节剂如何以依赖于上下文的方式促进或抑制剪接位点识别的分子机制尚不清楚。多个基因调控层之间的功能偶联表明，剪接受到其他遗传或表观遗传成分的调节。在这里，我们开发了一种基于大规模 RNA 测序数据集基因表达变化来识别剪接活性因果调节剂的生物信息学方法。我们在数百个背外侧前额叶皮质样本的神经学背景下应用了这种方法。我们的模型通过结合遗传变异来在基于孟德尔随机化的方法中推断基因表达得到了加强。我们确定了剪接因子 SRSF1 的新型调节剂，包括 UIMC1 和长非编码 RNA CBR3-AS1，它们在数十个 SRSF1 内含子保留剪接靶标中起作用。这种策略可以广泛用于鉴定涉及组织特异性可变剪接的 RNA 结合蛋白的调节剂。
