Proteomics of Cerebrospinal Fluid in Chronic Fatigue Syndrome

慢性疲劳综合征脑脊液蛋白质组学

• 批准号: 7447344
• 负责人: JAMES N BARANIUK
• 金额: $ 36.66万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-14 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7447344
• 关键词: Affective; Age; Albumins; Algorithms; Ally; Biological Markers; Blood - brain barrier anatomy; Cells; Cerebrospinal Fluid; Cerebrospinal Fluid Proteins; Chimeric Proteins; Chronic Disease; Chronic Fatigue Syndrome; Class; Clinical; Control Groups; Creatinine; Data; Diagnostic; Epithelial; Fatigue; Functional disorder; Gender; Gulf War; Heme; Hyperalgesia; Immune; Immune system; Immunoassay; Immunoglobulin G; Label; Learning; Luciferases; Machine Learning; Measures; Methods; Neuraxis; Neuroglia; Neurohormones; Neurologic; Neurologic Dysfunctions; Neurons; Odds Ratio; Output; Parents; Pathogenesis; Pathway interactions; Pattern; Peptides; Persian Gulf; Phenotype; Plasma; Protease Inhibitor; Protein Secretion; Proteins; Proteome; Proteomics; Psychometrics; Recruitment Activity; Relative (related person); Research Design; Sampling; Sensitivity and Specificity; Serum; Severities; Severity of illness; Source; Stable Isotope Labeling; Statistical Models; Structure of choroid plexus; Symptoms; Syndrome; Testing; Training; Training Support; Urea; cohort; healthy aging; novel; predictive modeling; prognostic; prohormone; response; tandem mass spectrometry; treatment effect

DESCRIPTION (provided by applicant): HYPOTHESIS: Central nervous system dysfunction is a central pathogenic mechanism in the CFS spectrum of illnesses. Cerebrospinal fluid provides a "window" into potential dysfunctional regulatory, innate immune, and neurological pathways. Neurons, glial cells, epithelial choroid plexus and leptomeningeal cells may be sources of CFS-related proteins. Despite the diverse clinical syndromes, the CFS-related proteome is the same, suggesting a unified pathogenesis. DATA: We have performed tandem mass spectrometry (MS-MS) on cerebrospinal fluid from CFS and healthy control subjects. Traditional and support vector machine (SVM) learning statistical analyses identified nearly identical CFS-related proteomes. A specific pattern of proteins (biosignature) predicted CFS with a significant odds ratio of 34.5 and concordance of 80%. Amyloidogenic proteins, antiproteases, Ig lambda, heme and Fe scavengers, and regulatory prohormones were associated with CFS. This is the first predictive model of CFS to be defined solely from objective data. PLAN: Recruit a new set of CFS and HC subjects (n=50 per group, "cohort 4") to a cross-sectional "training-test" study design. (A) Perform qualitative MS-MS to identify proteins in all samples of cohort 4. Train the SVM algorithm with cohort 4, then test the output "classifier" on an independent set of 42 samples (cohort 3). Determine the prediction accuracy, sensitivity and specificity of the SVM classifier. (B) Perform quantitative MS-MS on pooled CFS and pooled control samples by labeling one with O16 and the other with O18. Mix the samples and identify peptides (and their parent proteins) with O16/O18 ratios that are significantly higher or lower in CFS than controls. (C) These CFS-related proteins will be measured using novel, high sensitivity, luciferase- fusion protein competition immunoassays. Significant concentrations differences between CFS and control and between the 2 cohorts will define protein biomarkers and their sensitivity, specificity and predictive accuracy. (D) Subjective psychometric and other input variables will be tested by SVM learning to define a highly predictive model of CFS. The subjective results and objective proteomic results will also be analyzed to determine if the biomarkers are highly correlated with fatigue, systemic hyperalgesia, or other components of the CFS spectrum of illness. These methods and biomarkers may be of diagnostic value. They will be useful for assessing longitudinal changes in disease severity, phenotype, or the effects of treatment.

描述（由申请人提供）：假设：中枢神经系统功能障碍是CFS疾病频谱中的一种中心致病机制。脑脊液为潜在的功能失调的调节，先天免疫和神经系统途径提供了“窗口”。神经元，神经胶质细胞，上皮脉络丛和瘦脑膜细胞可能是CFS相关蛋白的来源。尽管有多种临床综合征，但与CFS相关的蛋白质组是相同的，表明统一的发病机理。数据：我们在CFS和健康对照受试者的脑脊液上进行了串联质谱（MS-MS）。传统和支持向量机（SVM）学习统计分析确定了几乎相同的CFS相关蛋白质组。一种特定的蛋白质模式（生物签名）预测了34.5的优势比，一致性为80％。淀粉样蛋白，抗蛋白酶，IG Lambda，血红素和Fe探测器以及调节性激素与CFS相关。这是仅根据客观数据定义的CFS的第一个预测模型。计划：招募一组新的CFS和HC受试者（每组n = 50，“队列4”）到横截面“训练测试”研究设计。 （a）执行定性MS-MS以识别队列4的所有样品中的蛋白质。训练与同类4的SVM算法，然后在独立的42个样品集（同类群3）上测试输出“分类器”。确定SVM分类器的预测准确性，灵敏度和特异性。 （b）通过将O16标记为O16，对合并的CFS和合并对照样品进行定量MS-MS。混合样品并用CFS明显更高或更低的O16/O18比鉴定肽（及其母体蛋白质）。 （c）这些与CFS相关的蛋白质将使用新型，高灵敏度，荧光素酶融合蛋白竞争免疫测定法进行测量。 CFS和对照之间以及两个队列之间的显着浓度差异将定义蛋白质生物标志物及其敏感性，特异性和预测精度。 （d）将通过SVM学习来测试主观心理测量和其他输入变量，以定义CFS的高度预测模型。还将分析主观结果和客观蛋白质组学结果，以确定生物标志物是否与疲劳，全身性痛觉过敏或CFS疾病频谱的其他成分高度相关。这些方法和生物标志物可能具有诊断价值。它们将有助于评估疾病严重程度，表型或治疗作用的纵向变化。