Modeling Splicing in normal tissues and neurodegenerative disease

描述（由申请人提供）： 该项目的长期目标是绘制人类转录组的决定因素及其在神经系统疾病中的作用。超过90%的人类基因是选择性剪接的，在许多组织如大脑和肌肉中观察到外显子包含的严格调控变化。剪接与许多疾病有关，估计15%至50%的人类疾病突变会影响剪接位点选择。通常，突变发生在非编码区，但疾病研究无法指定这些功能。目前，单独的研究测量外显子包含水平，剪接调节剂的结合位点和基因组变异。为了充分利用这些数据，需要（a）整合这些数据以鉴定潜在的调控机制，和（B）预测序列变化的后果，特别是在非编码区的调控元件中。为了满足这些需求，在本项目的第一阶段，我们将建立一个人类和小鼠模型的组织依赖性剪接模型，重点是中枢神经系统（CNS）。该模型将联合收割机结合上述数据来源，预测基因组序列的剪接结果，并通过计算机模拟评估对小核苷酸变异（SNV）剪接的影响。在该项目的第二阶段，我们将与Kristen Lynch博士合作，进行精心设计的生化实验，以验证第一阶段模型所确定的新的调控机制，重点关注与年龄相关的神经系统疾病有关的CNS和基因。具体来说，我们将预测和验证两个关键的RNA结合蛋白与CNS/疾病功能，TDP-43和QKI疾病相关的目标。在第三阶段，我们将与Alice Chen-Plotkin博士合作，将第一阶段的剪接模型和第二阶段的实验验证应用于额颞叶隆巴变性（FTLD-TDP）的研究，其中TDP-43起着关键作用。首先，我们将使用现有的不相交TDP-43基因组数据集来产生一个“以TDP-43为中心”的剪接代码模型，该模型解决了有关TDP-43在疾病和正常组织中功能的关键问题。将使用第2阶段方法检验模型中的监管假设。接下来，我们将应用以TDP-43为中心的编码来评估对512名FTLD-TPD患者队列中发现的遗传变异剪接的影响。预测影响剪接的遗传变异，与超过1000名对照组相比，FTLD-TDP患者中富集的遗传变异将使用来自Chen-Plotkin博士实验室中匹配患者大脑样本的微型基因报告基因测定和/或RNA进行验证。总的来说，这项资助中提出的研究将创建一个必要的和独特的框架，以阐明FTLD-TDP和人类转录组复杂性的决定因素。

DESCRIPTION (provided by applicant): The long-term goal of this project is to map the determinants of the human transcriptome and their effect in neurological disease. Over 90% of human genes are alternatively spliced, with tightly regulated changes in exon inclusion observed across many tissues such as brain and muscle. Splicing is associated with numerous diseases with an estimated 15 to 50 percent of human disease mutations affecting splice-site selection. Commonly, mutations occur in non-coding regions but disease studies cannot assign these function. Currently separate studies measure exon inclusion levels, binding sites of splicing regulators, and genomic variations. To fully exploit these data there is a need for methods that (a) integrate these data to identify underlying regulatory mechanisms, and (b) predict the consequences of sequence change, especially in regulatory elements in non- coding regions. To address these needs, in Phase 1 of this project we will create a human and mouse model for tissue-dependent splicing model, focusing on the central nervous system (CNS). The model will combine the abovementioned data sources to predict splicing outcome from genomic sequence and assess in silico the effect on splicing of small nucleotide variations (SNV). In Phase 2 of the project, we will collaborate with Dr. Kristen Lynch and perform elaborate biochemical experiments to validate novel regulatory mechanisms identified by the model of Phase 1, focusing on the CNS and genes involved in age-related neurological disease. Specifically, we will predict and validate disease-associated targets of two key RNA-binding proteins with CNS/disease function, TDP-43 and QKI. In Phase 3 we will collaborate with Dr. Alice Chen-Plotkin and apply Phase 1 splicing model and Phase 2 experimental validation to the study of frontaltemporal lombar degeneration (FTLD-TDP) where TDP-43 plays a key role. First, we will use the disjoint TDP-43 genomic datasets already available to produce a "TDP-43 centered" splicing code model that addresses key questions about TDP-43's function in disease and normal tissues. Regulatory hypotheses from the model will be tested using Phase 2 methods. Next, we will apply the TDP-43 centered code to assess the effect on splicing of genetic variations found in a cohort of 512 FTLD-TPD patients. Genetic variations predicted to effect splicing, and enriched in FTLD-TDP patients compared to a cohort of over 1000 controls will be verified using mini-gene reporter assays and/or RNA from matching patients' brain samples available in Dr. Chen-Plotkin's lab. Overall, the research proposed in this grant will create a necessary and unique framework to elucidate the determinants of FTLD-TDP and human transcriptome complexity.