Biomarker Discovery and Validation in Parkinson's Disease

帕金森病生物标志物的发现和验证

• 批准号: 9269667
• 负责人: Ted M. Dawson
• 金额: $ 66.04万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9269667
• 关键词: Affect; Algorithms; Alzheimer' s Disease; American; Area; Biochemical Process; Bioinformatics; Biological; Biological Assay; Biological Markers; Blinded; Brain; Brain region; Cell Death; Cells; Cerebrospinal Fluid; Cerebrospinal Fluid Proteins; Clinical; Complex; Data; Data Discovery; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Progression; Ensure; Evaluation; Exhibits; Fourier Transform; Functional disorder; Goals; Individual; Label; Lewy Bodies; Mass Spectrum Analysis; Mediating; Medical History; Metabolic; Methods; Monitor; Motor; Neurodegenerative Disorders; Neurons; Onset of illness; Parkinson Disease; Parkinsonian Disorders; Pathogenesis; Pathologic; Pathway interactions; Patients; Performance; Pharmacotherapy; Proteins; Proteome; Proteomics; Reaction; Reproducibility; Resolution; Resources; Sampling; Set protein; Substantia nigra structure; Techniques; Technology; Testing; Universities; Validation; base; biomarker discovery; candidate marker; candidate validation; clinical practice; cohort; cost; diagnostic biomarker; differential expression; disorder control; experimental study; nervous system disorder; neuroimaging; programs; protein biomarkers; proteomic signature; response; validation studies; Affect; Algorithms; Alzheimer' s Disease; American; Area; Biochemical Process; Bioinformatics; Biological; Biological Assay; Biological Markers; Blinded; Brain; Brain region; Cell Death; Cells; Cerebrospinal Fluid; Cerebrospinal Fluid Proteins; Clinical; Complex; Data; Data Discovery; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease Progression; Ensure; Evaluation; Exhibits; Fourier Transform; Functional disorder; Goals; Individual; Label; Lewy Bodies; Mass Spectrum Analysis; Mediating; Medical History; Metabolic; Methods; Monitor; Motor; Neurodegenerative Disorders; Neurons; Onset of illness; Parkinson Disease; Parkinsonian Disorders; Pathogenesis; Pathologic; Pathway interactions; Patients; Performance; Pharmacotherapy; Proteins; Proteome; Proteomics; Reaction; Reproducibility; Resolution; Resources; Sampling; Set protein; Substantia nigra structure; Techniques; Technology; Testing; Universities; Validation; base; biomarker discovery; candidate marker; candidate validation; clinical practice; cohort; cost; diagnostic biomarker; differential expression; disorder control; experimental study; nervous system disorder; neuroimaging; programs; protein biomarkers; proteomic signature; response; validation studies

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.

抽象的 帕金森氏病（PD）是第二大最常见的进行性神经退行性疾病 阿尔茨海默氏病。尽管PD与黑质和其他底层中的Lewy身体形成有关 大脑的区域，PD发作和进展过程中发生的病理和代谢改变 尚未明确定义。尽管对PD的可靠诊断标记至关重要，但目前仍有 没有这种生物标志物可以在临床实践中准确使用，以确定对PD的明确诊断。 识别可靠生物标志物的困难可能归因于临床样本的变异性，低 涉及PD发病机理的蛋白质丰富和验证缺乏可重复性 生物标志物候选人。为了克服这些局限性，我们建议使用大型脑脊液（CSF） 具有更大的统计能力，可用于真正发现和深度蛋白质组分析，以发现PD生物标志物 与PD发病机理有关的，但存在于低丰度。另外，多重样品 通过等速器串联质量标记（TMT）的分析，具有通用数据归一化的参考将确保 从较大样本中发现的定量蛋白质组学数据的可靠分析精度。而且， 黑质的其他蛋白质组学分析将用于选择那些显示差异的生物标志物 CSF和底层中的表达。这些发现平台将使用生物信息学方法来 选择针对目标验证研究的最合理的候选人，然后对 发现了生物标志物候选人。为了实现这些目标，我们提出了三个目标：特定目标1： 发现在帕金森氏病患者中差异表达的蛋白质。我们计划执行 来自PD患者的CSF和质体NIGRA样品的定量蛋白质组学分析以及对照组 通过采用基于TMT的多重技术。通过这种方法，我们希望获得更多 在分析样品中量化了更多蛋白质的全面覆盖范围。具体目标 2：基于CSF和Passia Nigra的变化的综合分析来优先考虑候选人。经过 通过利用优势的网络方法整合CSF和Sucdia Nigra中的表达变化 在PD中描述的已知生物途径中，我们的方法应该能够选择 可靠的PD生物标志物候选者通过有针对性的PRM实验验证。特定目标3：验证 使用靶向平行反应监测（PRM）质量的候选蛋白生物标志物在较大的队列中 使用约翰·霍普金斯（Johns Hopkins）的PD队列中的CSF样品的光谱法。由 基于这些PRM实验的选择算法最终将通过盲目的PDBP CSF确认 样品。通过上面概述的方法，我们希望发现并验证可靠的PD生物标志物 以可重复的方式。