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

项目总结/摘要 帕金森病（PD）是最常见的神经退行性运动障碍， 世界上有200万人感染艾滋病毒，预计在今后几十年内流行率将翻一番。PD是一种 根据症状严重程度可识别临床病理亚型的异质性疾病， 优势准确的分子谱可以减少PD患者的临床异质性。几 研究已经将蛋白基因组学方法应用于来自PD和PD痴呆（PDD）患者的样品，以鉴定 与临床、神经影像或神经病理亚型相关的蛋白质。该提案旨在建立一个 发现蛋白质，基因，途径和潜在的生物标志物，将改善我们的框架， 了解潜在的疾病机制，预测疾病进程，并设计临床试验。我们提出 利用独特的资源，包括定量蛋白质组学分析约5,000蛋白质，从脑脊液 临床诊断的PD患者的脑脊液（CSF）和血浆与尸检证实的 例这项对约3，110个样本的大规模筛选可以鉴定出差异表达的蛋白质水平， 已知参与PD的分子途径或与PD风险有明确的遗传联系。为了实现这些目标， 我们计划进行一个三阶段的研究设计：发现，复制和荟萃分析，使用SOMA扫描， 来自健康个体、PD和阿尔茨海默病患者的血浆（n = 1，244）、CSF（n= 1，215）和脑组织（n=659） 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 （n= 8，873），脑（n=144）和CSF（n=232）。我们计划使用疾病状态、发病年龄和临床量表， 运动障碍，以找到一个蛋白质组学谱，可用于创建生物标志物驱动的临床分子 PD患者的表型（目的1A）。我们还计划发现蛋白质组学谱与认知测试的关联 评分、CSF生物标志物和神经影像学（匹兹堡化合物B），以揭示与 PD痴呆患者中的淀粉样蛋白病理学（Aim 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(Aim 1C）。最后，我们将整合蛋白质组学和GWAS数据，以确定pQTL，并应用多基因 风险评分和孟德尔随机化方法，以确定参与致病途径的蛋白质， PD，是潜在的新型PD生物标志物（目的2）。使用这种方法，我们将能够选择可靠的PD 用于验证的生物标志物候选物。我们希望揭示一个基因组-蛋白质组网络， 用于PD诊断和药理学应用的新方法。

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.