CAREER: Uncovering the regulation of low fidelity mRNA splicing

分子生物学的中心法则涉及从脱氧核糖核酸（DNA）分子中定义的基因合成核糖核酸（RNA）。其中一些RNA分子充当信使，传送指导蓝图，使细胞正常运作。RNA编码的信息内容通过剪接过程被放大，这使得从一个基因序列中产生几十个不同的RNA分子成为可能。剪接过程非常复杂，需要一个最大的分子复合体和许多辅助因子共同协调信使rna的精确水平和组成。这种复杂性为剪接中的噪音和错误打开了大门，这些噪音和错误可能构成细胞中RNA输出的很大一部分。虽然在过去十年中已经确定了检查和纠正这些错误的质量控制机制，但对于这些错误发生的频率以及为什么某些错误rna逃避检测和去除的原因知之甚少。该项目将使用新的基因组学和计算工具来直接识别和量化错误剪接的RNA，从而更好地了解细胞功能中正确RNA合成的调控。更广泛地说，该项目旨在通过以下方式向未来的研究人员和教育工作者传授生物数据分析的跨领域技能：(1)接待伍斯特地区的本科生进行计算生物学的联合暑期研究和拓展经验；(2)促进马萨诸塞大学陈医学院为伍斯特公立学校科学教师提供的丰富项目。几十年来，mRNA剪接过程被认为是高效的，几乎没有机会偏离受调节的可选剪接决策。这一观点导致了一种广泛的观点，即所有可选择的mRNA亚型都必须受到调节，并且可能具有生物学功能。然而，最近研究人员发现了普遍存在的非规范剪接位点选择，包括那些低保真剪接体结合序列。这些发现表明，mRNA剪接机制存在广泛的错误和隐式剪接位点使用，而随机生物噪声在塑造后生动物转录组多样性中的作用尚未被探索。该项目将使用新生RNA的高通量测序，通过细胞区室跟踪剪接中间体，创建隐剪接位点目录，并检查与这些事件相关的遗传和生化特征。这些研究将深入了解不良剪接产物如何以及何时通过mRNA质量控制机制逃逸或被修剪。利用此目录，将建立一个统计模型，以实现对隐剪接位点使用的预测性理解，并使用基因编辑工具来测试该模型的预测。该项目的长期研究目标是定义mRNA剪接中的噪声构成，并利用这些研究重新定义mRNA剪接代码。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The central dogma of molecular biology involves the synthesis of ribonucleic acid (RNA) from the genes defined in deoxyribonucleic acid (DNA) molecules. Some of these RNA molecules then act as messengers that shuttle instructional blueprints to enable proper cellular function. The informational content encoded by RNA is amplified by the process of splicing, which enables the creation of dozens of different RNA molecules from a single gene sequence. The process of splicing is very complex, with one of the largest molecular complexes and many auxiliary factors working together to coordinate the precise levels and compositions of messenger RNAs. This intricacy opens the door for noise and errors in splicing that may comprise a substantial fraction of RNA output in a cell. While quality control mechanisms to check for and correct such errors have been identified over the last decade, less is known about how often these errors occur and why certain erroneous RNAs escape detection and removal. This project will use novel genomics and computational tools to identify directly and quantify erroneously spliced RNAs, gaining a better understanding of the regulation of proper RNA synthesis for cellular function. More broadly, the project aims to teach the cross-cutting skills of biological data analysis to up and coming researchers and educators by (1) hosting Worcester area undergraduates for a combined summer research and outreach experience in computational biology and (2) facilitating an enrichment program at UMass Chan Medical School for Worcester Public School science teachers.For decades, the process of mRNA splicing was thought to be highly efficient, with little opportunity for deviation outside of regulated alternative splicing decisions. This idea led to a widespread perspective that all alternative mRNA isoforms must be regulated and, potentially, have biological function. Recently, however, researchers have identified pervasive, non-canonical splice site choice, including those at low-fidelity spliceosome binding sequences. These discoveries suggest that there is widespread error and cryptic splice site usage by mRNA splicing mechanisms, with unexplored roles for stochastic, biological noise in shaping metazoan transcriptome diversity. This project will use high-throughput sequencing of nascent RNA to track splicing intermediates through cellular compartments, creating a catalog of cryptic splice sites and examining genetic and biochemical features associated with these events. These studies will provide insight into how and when undesirable splicing products escape or are pruned by mRNA quality control mechanisms. Leveraging this catalog, a statistical model will be built to enable a predictive understanding of cryptic splice site usage and employ gene editing tools to test predictions from this model. The long-term research goal of this project is to define what constitutes noise in mRNA splicing and use these studies to redefine the mRNA splicing code.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.