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。尽管 高通量基因组学和蛋白质组学方面的改进，大大促进了这一进展 在其他神经退行性疾病中发现的生物标记物，目前还没有可靠的帕金森病生物标记物。目前， 帕金森病的诊断几乎完全依靠临床检查。缺乏可靠性的原因有几个 帕金森病的生物标志物，包括大多数研究，一直集中在一个组织中的单个分子，小样本 大小和缺乏独立的复制队列。为了克服这些限制，我们建议利用 独一无二的资源，包括从脑脊液和血浆中定量分析~1,300种蛋白质 临床诊断的帕金森病患者与尸检确认的帕金森病患者的脑样本的验证相结合。我们 将蛋白质组数据与新的、强大的、无偏倚(无假设)的基因组方法配对以选择 最有可能进行靶向复制研究的候选者。这项对~3110个样本的大规模筛选 可以识别与帕金森病有关或与帕金森病有明确遗传联系的已知分子途径的生物标记物 风险。为了实现这些目标，我们提出了三个目标：具体目标1：识别差异表达的蛋白质 帕金森病患者血浆、脑脊液或脑组织中。我们计划进行一项定量的蛋白质组学分析 临床诊断的PD患者的血浆(n=600)和脑脊液(n=200)的SomaLogic SOMAscan®分析 取自尸检证实的帕金森病患者的脑组织标本(n=200)。我们还将评估脑脊液(n=740)、血浆(n=410)。 和来自健康个体的独立队列的脑组织(n=114)和脑脊液(n=275)、血浆(n=234) AD患者脑组织(n=345)。我们期望获得大量的精确和准确的水平 在分析的样本中跨多个组织的蛋白质。具体目标2：确定候选生物标志物的优先顺序 基于使用孟德尔的蛋白质组和全基因组基因分型数据的综合分析 随机化(MR)。我们计划整合蛋白质组学和GWAS数据以确定蛋白质数量基因座(PQTL) 并应用磁共振方法来确定参与帕金森病病因途径的蛋白质。使用这种方法，我们 将能够选择可靠的PD生物标记物候选进行验证。具体目标3：确定是否 遗传和蛋白质组学数据提高了生物标记物的特异性。我们将确定将蛋白质组学和 基因组数据可以提高生物标记物的准确性。我们希望发现一个基因组-蛋白质组网络，它可能 为帕金森病的诊断和药物治疗应用提供新的方法基础。

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. Despite the improvements in high-throughput genomics and proteomics that have significantly facilitated the advancement of biomarker discovery in other neurodegenerative diseases, there are no reliable biomarkers for PD. Currently, the PD diagnosis relies almost entirely on clinical examination. There are several reasons for the lack of reliable biomarkers in PD including most studies have been focused on single molecules in one tissue, small samples sizes and a lack of independent replication cohorts. To overcome these limitations, we propose leveraging a unique resource that includes quantitative proteomic analysis of ~1,300 proteins from CSF and plasma of clinically diagnosed PD patients coupled with validation in brain samples from autopsy-confirmed PD cases. We will pair the proteomic data with novel and powerful unbiased (hypothesis-free) genomic approaches to select the most plausible candidates for targeted replication studies. This large-scale screening of ~3,110 samples could identify biomarkers of known molecular pathways involving PD or with a clear genetic connection to PD risk. To achieve these goals, we propose three aims: Specific Aim 1: To identify proteins differentially expressed in PD patients in plasma, CSF or brain tissue. We plan to carry out a quantitative proteomic analysis using Somalogic SOMAscan® assay of plasma (n=600) and CSF (n=200) from clinically diagnosed PD patients and of brain tissue (n=200) from autopsy-confirmed PD patients. We will also evaluate CSF (n=740), plasma (n=410) and brain tissue (n=114) from an independent cohort of healthy individuals and CSF (n=275), plasma (n=234) and brain tissue (n=345) from AD patients. We expect to obtain precise and accurate levels of a large number of proteins across multiple tissues in the analyzed samples. Specific Aim 2: To prioritize candidate biomarkers based on an integrative analysis of proteomic and genome-wide genotyping data using Mendelian Randomization (MR). We plan to integrate proteomic and GWAS data to identify protein quantitative loci (pQTLs) and apply MR approaches to determine proteins involved in the causal pathway of PD. Using this approach, we will be able to select reliable PD biomarker candidates for validation. Specific Aim 3: To determine whether genetic and proteomic data improves biomarker specificity. We will ascertain whether combining proteomic and genomic data could increase biomarker accuracy. We expect to uncover a genome-proteome network that may provide a basis for novel approaches to diagnostic and pharmacotherapeutic applications in PD.