Alternative splicing of CD19 mRNA in pediatric leukemia and CART-19 therapy resistance - Regulators, genetic variants and cryptic isoforms

选择性剪接增加了真核细胞中的蛋白质多样性，在发育和组织特性中起着重要作用，但在癌症等疾病中也起着重要作用。剪接反应由剪接体催化，并由辅助RNA结合蛋白（rbp）调节，rbp识别附近的RNA序列并指导剪接体的活性。多个蛋白复合物如何在mrna前体序列上动态相互作用以控制剪接（剪接代码）的规则仍然知之甚少。在这个项目中，我们采用系统方法来更好地理解拼接代码。作为一个典型的例子，我们研究了MSTR1基因的选择性剪接，该基因在癌症中经常改变并促进肿瘤侵袭。开发的工具将扩展到其他有趣的拼接场景，例如Alu exonisation。为了研究剪接的决定因素，我们建立了一个随机突变筛选，其中我们生成了一个超过5,500个MST1R迷你基因变体的文库，每个变体平均包含三个点突变。然后，我们将文库转染到HEK293T细胞中，并使用靶向RNA-seq评估剪接变化。生物信息学分析将为每个突变体提供5个典型剪接异构体的频率，并将额外检测从隐剪接位点产生的新剪接事件。这些复杂的剪接模式将使用数学模型来解释，以推断个体剪接率的动力学变化，并确定致病突变。综上所述，Aim 1将产生MSTR1中顺式调控元件的概要。在Aim 2中，我们计划描述辅助rbp和核心剪接因子如何解释前mrna序列以建立上下文特异性剪接模式。为此，我们将进行敲低筛选，在该筛选中，我们剔除44个已知在MSTR1剪接中起作用的rbp，并评估5500个迷你基因变体中的每一个的剪接模式。对这一综合数据集的建模将揭示RBP如何影响特定的剪接率，它们与哪些顺式调节元件结合，以及它们如何组装成更大的RBP复合物。生成的网络将进行生物信息学分析，以整合先前的知识，并使用下拉实验验证选定的相互作用。在Aim 3中，将结合生物信息学和全基因组实验方法（RNA-seq, iCLIP）来评估MSTR1剪接的决定因素是否可以转移到全球范围，从而提供剪接规则的一般见解。一个MSTR1剪接的动态数学模型将被导出来描述RBP表达模式如何影响剪接决策。总之，我们提出了一种系统的方法来研究原型剪接事件，并有可能将知识转移到全基因组范围。我们的研究提供了通过单核苷酸多态性（snp）和细胞RBP含量调节疾病相关剪接事件的见解。

Alternative splicing increases protein diversity in eukaryotic cells, and plays an important role in development and tissue identity, but also in diseases such as cancer. Splicing reactions are catalysed by the spliceosome and modulated by auxiliary RNA-binding proteins (RBPs) which recognise nearby RNA sequences and guide spliceosome activity. The rules how multiple protein complexes dynamically interact on pre-mRNA sequence to control splicing (splice code) remain poorly understood.In this project, we employ a systems approach to better understand the splice code. As a prototypical example, we study the alternative splicing of the MSTR1 gene which is frequently altered in cancer and promotes tumour invasiveness. The developed tools will be extended to other interesting splicing scenarios, e.g. Alu exonisation.To study the determinants of splicing, we established a random mutagenesis screen in which we generate a library of more than 5,500 MST1R minigene variants, each containing on average three point mutations. We then transfect the library into HEK293T cells and assess splicing changes using targeted RNA-seq. Bioinformatics analyses will yield the frequency of five canonical splice isoforms for each of the mutants and will additionally detect novel splicing events arising from cryptic splice sites. These complex splicing patterns will be interpreted using mathematical models to infer changes in the kinetics of individual splice rates and to identify causative mutations. Taken together, Aim 1 will yield a compendium of cis-regulatory elements in MSTR1.In Aim 2, we plan to characterise how auxiliary RBPs and core splicing factors interpret the pre-mRNA sequence to establish context-specific splicing patterns. To this end, we will perform a knockdown screen in which we deplete 44 RBPs with a known role in MSTR1 splicing, and assess splicing patterns in each of the 5,500 minigene variants. Modelling of this comprehensive data set will reveal how RBPs impact on specific splice rates, which cis-regulatory elements they bind to, and how they assemble into larger RBP complexes. The resulting network will be subjected to a bioinformatics analysis to integrate prior knowledge, and selected interactions will be validated using pull-down experiments.In Aim 3, bioinformatics and genome-wide experimental approaches (RNA-seq, iCLIP) will be combined to assess whether determinants of MSTR1 splicing can be transferred to a global scale, thus providing general insights into the rules of splicing. A dynamic mathematical model of MSTR1 splicing will be derived to describe how RBP expression patterns influence splicing decisions. In conclusion, we propose a systematic approach to study prototypical splicing events with the potential to transfer the knowledge to a genome-wide scale. Our research provides insights into the modulation of disease-relevant splicing events by single-nucleotide polymorphisms (SNPs) and by the RBP content of the cell.