Proteomics of Cerebrospinal Fluid in Chronic Fatigue Syndrome

描述(由申请人提供)：假设：中枢神经系统功能障碍是CFS疾病谱中的一个中心致病机制。脑脊液为了解潜在的功能失调的调节、先天免疫和神经通路提供了一个“窗口”。神经元、神经胶质细胞、上皮性脉络丛和软脑膜细胞可能是CFS相关蛋白的来源。尽管临床症状不同，但CFS相关蛋白质组是相同的，提示发病机制是统一的。资料：我们对CFS和健康对照组的脑脊液进行了串联质谱仪(MS-MS)。传统的和支持向量机(SVM)学习的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(生物特征)预测CFS的显着优势比为34.5，符合率为80%。淀粉样蛋白、抗蛋白水解酶、Ig lambda、血红素和铁清除剂以及调节性前激素与CFS相关。这是第一个仅根据客观数据定义的CFS预测模型。计划：招募一组新的CFS和HC受试者(每组50人，“队列4”)参加横断面“培训-测试”研究设计。(A)进行定性MS-MS以识别队列4的所有样本中的蛋白质。用队列4训练支持向量机算法，然后在独立的42个样本集(队列3)上测试输出的分类器。确定支持向量机分类器的预测精度、灵敏度和特异度。(B)对混合的CFS和混合的对照样品进行定量的MS-MS，其中一个标有O16，另一个标有O18。将样本混合，并识别具有O16/O18比率的多肽(及其母蛋白)，这些多肽在CFS中显著高于或低于对照组。(C)将使用新的、高灵敏度的荧光素酶融合蛋白竞争免疫分析来测量这些与CFS相关的蛋白质。CFS和对照组之间以及两个队列之间的显著浓度差异将定义蛋白质生物标记物及其敏感性、特异性和预测准确性。(D)将通过支持向量机学习测试主观心理测量学和其他输入变量，以确定CFS的高度预测性模型。主观结果和客观蛋白质组结果也将被分析，以确定生物标记物是否与疲劳、全身性痛觉过敏或CFS疾病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、表型或治疗效果的纵向变化。

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.