Multi-tissue high-throughput proteomic and genomic study in Parkinson's Disease

项目摘要/摘要 帕金森氏病(PD)是最常见的神经退行性运动障碍，影响超过6 全世界有100万人，预计在未来几十年内患病率将翻一番。帕金森是一名 具有基于症状严重程度和可识别临床病理亚型的异质性疾病 占优势。准确的分子图谱可以减少帕金森病患者的临床异质性。一些 研究应用蛋白质组学方法对帕金森病和帕金森病(PDD)患者的样本进行鉴定 与临床、神经影像或神经病理亚型相关的蛋白质。这项提案旨在创建一个 揭示蛋白质、基因、途径和潜在生物标记物的框架，将改善我们的 了解潜在的疾病机制，预测疾病进程，并设计临床试验。我们建议 利用独特的资源，包括对脑脊液中约5,000种蛋白质的定量蛋白质组学分析 临床诊断为帕金森病患者的脑脊液(CSF)和血浆与尸检的脑样本相结合-确认 案子。这项对~3,110个样本的大规模筛选可以识别出差异表达的蛋白质水平 已知的参与帕金森病的分子途径或与帕金森病风险有明确遗传联系的分子途径。为了实现这些目标， 我们计划实施一项分三个阶段的研究设计：发现、复制和使用SOMAscan的元分析 血浆(n=1,244)、脑脊液(n=1,215)和脑组织(n=659)来自健康人、帕金森病和阿尔茨海默病患者 疾病患者(目标1)。对于复制研究，我们访问并处理了来自血浆研究的数据 其中脑损伤8,873例，脑损伤144例，脑脊液损伤232例。我们计划使用疾病状态、发病年龄和临床量表 运动障碍寻找可用于创建生物标记物驱动的临床分子的蛋白质组图谱 帕金森病患者的表型(目标1A)。我们还计划发现蛋白质组学特征与认知测试的关联 分数、脑脊液生物标记物和神经成像(匹兹堡化合物B)以发现与 活体帕金森病患者的淀粉样病理学(目标1B)。我们将整合神经病理学和蛋白质组学数据 在PD、PDD和阿尔茨海默病中识别不同的分子特征或共享相似的异常通路 疾病。(目标1C)。最后，我们将整合蛋白质组学和GWAS数据来识别pQTL并应用多基因 风险评分和孟德尔随机化方法确定与致病途径有关的蛋白质 Pd，这是潜在的新型Pd生物标志物(目标2)。使用这种方法，我们将能够选择可靠的PD 需要验证的生物标志物候选。我们希望发现一个基因组-蛋白质组网络，这将为 用于帕金森病的诊断和药物治疗的新方法。

Project Summary/Abstract Parkinson's disease (PD) is the most common neurodegenerative movement disorder, affecting more than 6 million people worldwide, with the prevalence projected to double in the next few decades. PD is a heterogeneous disorder with identifiable clinical-pathological subtypes based on symptom severity and predominance. An accurate molecular profile could reduce clinical heterogeneity among PD patients. Few studies have applied a proteogenomic approach to samples from PD and PD dementia (PDD) patients to identify proteins associated with clinical, neuroimaging, or neuropathological subtypes. This proposal aims to create a framework for uncovering proteins, genes, pathways, and potential biomarkers that will improve our understanding of underlying disease mechanisms, predict disease course, and design clinical trials. We propose leveraging a unique resource that includes quantitative proteomic analysis of ~5,000 proteins from cerebrospinal fluid (CSF) and plasma of clinically diagnosed PD patients coupled with brain samples from autopsy-confirmed cases. This large-scale screening of ~3,110 samples could identify differentially expressed protein levels of known molecular pathways involved in PD or with a clear genetic connection to PD risk. To achieve these goals, we plan to carry out a three-stage study design: discovery, replication, and meta-analyses using SOMAscan of plasma (n=1,244), CSF (n=1,215), and brain tissue (n=659) from healthy individuals, PD, and Alzheimer's disease patients (Aim 1). For replication studies, we have accessed and processed data from studies in plasma (n=8,873), brain (n=144), and CSF (n=232). We plan to use disease status, age-at-onset, and clinical scales of motor impairment to find a proteomic profile that could be used to create a biomarker-driven clinical-molecular phenotype in PD patients (Aim 1A). We also plan to find associations of proteomic profiles with cognitive test scores, CSF biomarkers, and neuroimaging (Pittsburgh compound B) to uncover proteins associated with amyloid pathology in living PD dementia patients (Aim 1B). We will integrate neuropathology and proteomic data to identify a divergent molecular signature or share similar aberrant pathways in PD, PDD, and Alzheimer's disease. (Aim 1C). Finally, we will integrate proteomic and GWAS data to identify pQTLs and apply polygenic risk scores and Mendelian Randomization approaches to determine proteins involved in the causal pathway of PD, which are potential novel PD biomarkers (Aim 2). Using this approach, we will be able to select reliable PD biomarker candidates for validation. We expect to uncover a genome-proteome network that will provide a basis for novel approaches to diagnostic and pharmacotherapeutic applications in PD.