Biomarker Discovery and Validation in Parkinson's Disease

摘要 帕金森病(PD)是仅次于帕金森病的第二大常见的进行性神经退行性疾病 阿尔茨海默氏症。尽管帕金森病与黑质和其他脑区路易体的形成有关 帕金森病发生和发展过程中的病理和代谢变化 都没有明确的定义。尽管迫切需要一种可靠的PD诊断标记物，但目前有 没有这样的生物标志物可以在临床实践中准确地用于建立明确的帕金森病诊断。 确定可靠的生物标志物的困难可以归因于临床样本的变异性， 参与帕金森病发病机制的蛋白质的丰度和验证的重复性缺乏 生物标记物候选者。为了克服这些局限性，我们建议使用大容量脑脊液(Csf)。 具有更大统计能力的队列真正发现和深入的蛋白质组分析，以发现PD生物标记物 参与帕金森病发病机制，但存在于低丰度。此外，多路复用的样本 通过等压串联质量标记(TMT)进行分析，并提供数据归一化的通用参考，可确保 从更大的样本集合中发现定量蛋白质组数据的强大分析精度。此外， 对黑质的额外蛋白质组学分析将被用于选择那些显示差异的生物标记物。 在脑脊液和黑质均有表达。这些发现平台将使用生物信息学方法来 选择最可信的候选对象进行有针对性的验证研究，然后对 发现了候选生物标记物。为了实现这些目标，我们提出了三个目标：具体目标1： 发现帕金森氏症患者差异表达的蛋白质。我们计划开展一项 帕金森病患者和对照组脑脊液和黑质的定量蛋白质组学分析 通过采用基于TMT的多路复用技术。通过这种方法，我们希望获得更多 全面覆盖了分析样本中量化的大量蛋白质。特定目标 2：根据脑脊液和黑质改变的综合分析，确定候选人的优先顺序。通过 利用网络方法整合脑脊液和黑质的表达变化 在PD中描述的已知生物通路中，我们的方法应该能够选择 可靠的PD生物标志物候选，可通过目标PRM实验进行验证。具体目标3：验证 利用靶向平行反应监测(PRM)质量在较大队列中寻找候选蛋白质生物标记物 使用约翰·霍普金斯大学帕金森病队列的脑脊液样本。由以下人员选择的生物标志物 基于这些PRM实验的选择算法最终将被盲化的PDBP-CSF所证实 样本。通过以上概述的方法，我们期望发现和验证可靠的钯生物标志物 以一种可复制的方式。

ABSTRACT Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder after Alzheimer's disease. Although PD is associated with Lewy body formation in the substantia nigra and other regions of the brain, the pathologic and metabolic alterations occurring during the onset and progression of PD have not been clearly defined. Despite a critical need for a reliable diagnostic marker for PD, there is currently no such biomarker that can be used accurately in clinical practice for establishing a definitive diagnosis of PD. The difficulty of identifying reliable biomarkers can be attributed to the variability of clinical samples, low abundance of proteins that are involved in PD pathogenesis and the lack of reproducibility in validating biomarker candidates. To overcome these limitations, we propose use of a large cerebrospinal fluid (CSF) cohort with greater statistical power for true discovery and deep proteome analysis to discover PD biomarkers that are involved in PD pathogenesis, but are present at low abundance. In addition, multiplexed sample analysis by isobaric tandem mass tagging (TMT) with a common reference for data normalization will ensure robust analytical precision of quantitative proteomic data for discovery from a larger set of samples. Moreover, additional proteomic analysis of substantia nigra will be used to select those biomarkers that show differential expression in CSF as well as substantia nigra. These discovery platforms will use a bioinformatics approach to select the most plausible candidates for targeted validation studies followed by an intensive validation of the discovered biomarker candidates. To achieve these goals, we propose three aims: Specific Aim 1: To discover proteins that are differentially expressed in patients with Parkinson's disease. We plan to carry out a quantitative proteomic analysis of CSF and substantia nigra samples from patients with PD and from controls by employing TMT-based multiplexing technology. With this approach, we expect to obtain a more comprehensive coverage of a larger number of proteins quantified across the analyzed samples. Specific Aim 2: To prioritize candidates based on an integrative analysis of alterations in CSF and substantia nigra. By integrating the expression changes in CSF and substantia nigra with a network approach that takes advantage of the known biological pathways that have been described in PD, our approach should be able to select reliable PD biomarker candidates for validation by targeted PRM experiments. Specific Aim 3: To validate candidate protein biomarkers in a larger cohort using targeted parallel reaction monitoring (PRM) mass spectrometry using CSF samples from a PD cohort at Johns Hopkins. Biomarkers that are selected by selection algorithms based on these PRM experiments will finally be confirmed with blinded PDBP CSF samples. Through the approaches outlined above, we expect to discover and validate reliable PD biomarkers in a reproducible fashion.