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种最常见的进行性神经退行性疾病。 老年痴呆症虽然PD与黑质和其他神经元中路易体的形成有关， PD发病和进展过程中发生的病理和代谢改变 还没有明确定义。尽管迫切需要可靠的PD诊断标志物，但目前 没有这样的生物标志物可以在临床实践中准确地用于确定PD的明确诊断。 鉴定可靠的生物标志物的困难可归因于临床样品的可变性， PD发病机制中涉及的蛋白质丰富，并且在验证中缺乏重现性 生物标记候选物。为了克服这些局限性，我们建议使用大量脑脊液（CSF） 具有更大统计功效的队列，可进行真正的发现和深度蛋白质组分析，以发现PD生物标志物 参与PD发病机制，但以低丰度存在。此外，多重样品 通过具有用于数据标准化的共同参考的同量异位素串联质量标记（TMT）的分析将确保 定量蛋白质组学数据的强大分析精度，用于从更大的样品集中发现。此外，委员会认为， 将使用黑质的额外蛋白质组学分析来选择那些显示出差异的生物标志物。 在CSF以及黑质中表达。这些发现平台将使用生物信息学方法， 选择最合理的候选人进行有针对性的验证研究，然后对 发现了生物标志物候选者。为了实现这些目标，我们提出了三个目标：具体目标1： 发现帕金森病患者中差异表达的蛋白质。我们计划进行一项 PD患者和对照组CSF和黑质样本的定量蛋白质组学分析 通过采用基于TMT的复用技术。通过这种方法，我们希望获得更多 全面覆盖分析样品中大量定量的蛋白质。具体目标 2：根据CSF和黑质改变的综合分析对候选人进行优先排序。通过 将CSF和黑质中的表达变化与网络方法相结合， 在PD中描述的已知生物学途径中，我们的方法应该能够选择 用于通过靶向PRM实验验证的可靠的PD生物标志物候选物。具体目标3：验证 使用靶向平行反应监测（PRM）质量在较大队列中的候选蛋白质生物标志物 使用来自约翰霍普金斯的PD队列的CSF样品进行光谱分析。选择的生物标志物 基于这些PRM实验的选择算法最终将用盲态PDBP CSF进行确认 样品通过上述方法，我们期望发现和验证可靠的PD生物标志物 以可再现的方式。

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.