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

项目概要/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。尽管 高通量基因组学和蛋白质组学的改进，大大促进了 在其他神经退行性疾病中发现的生物标志物中，没有可靠的PD生物标志物。目前， PD诊断几乎完全依赖于临床检查。有几个原因缺乏可靠的 包括大多数研究在内的PD生物标志物都集中在一个组织中的单个分子，小样本 规模和缺乏独立的复制队列。为了克服这些限制，我们建议利用 独特的资源，包括定量蛋白质组学分析的约1,300蛋白质从CSF和血浆， 临床诊断的PD患者，结合来自尸检确认的PD病例的脑样品中的验证。我们 将蛋白质组数据与新的和强大的无偏见（无假设）的基因组方法配对， 最有可能用于靶向复制研究的候选者。此次大规模筛查约3,110个样本 可以识别涉及PD或与PD有明确遗传联系的已知分子途径的生物标志物 风险为了实现这些目标，我们提出了三个目标：具体目标1：鉴定差异表达的蛋白质 在PD患者的血浆、CSF或脑组织中。我们计划进行定量蛋白质组学分析， 来自临床诊断的PD患者的血浆（n=600）和CSF（n=200）的Somalogic SOMAscan®测定，以及 尸检证实的PD患者的脑组织（n=200）。我们还将评价CSF（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CSF（n=275）、血浆（n=234） 和脑组织（n=345）。我们希望获得大量的精确和准确的水平 分析样本中多种组织中的蛋白质。具体目标2：优先考虑候选生物标志物 基于蛋白质组学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数据来确定蛋白质定量位点（pQTL） 并应用MR方法来确定参与PD致病途径的蛋白质。使用这种方法，我们 将能够选择可靠的PD生物标志物候选物进行验证。具体目标3：确定 遗传和蛋白质组学数据提高了生物标志物的特异性。我们将确定是否结合蛋白质组学和 基因组数据可以提高生物标志物的准确性。我们希望发现一个基因组-蛋白质组网络， 为PD的诊断和药理学应用提供了新方法的基础。

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.