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谱系疾病的主要致病机制。脑脊液为研究潜在功能失调的调节、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、上皮脉络膜丛和轻脑膜细胞可能是cfs相关蛋白的来源。尽管临床综合征多种多样，但cfs相关蛋白质组是相同的，提示其发病机制是统一的。资料：我们对CFS和健康对照者的脑脊液进行了串联质谱分析（MS-MS）。传统和支持向量机（SVM）学习统计分析发现了几乎相同的cfs相关蛋白质组。一种特定的蛋白质模式（生物特征）预测CFS的优势比为34.5，一致性为80%。淀粉样蛋白、抗蛋白酶、igg、血红素和铁清除剂以及调节性原激素与CFS有关。这是第一个完全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n=50，“队列4”）进行横断面“训练-测试”研究设计。(A)对队列4所有样品进行质谱联用鉴定。用队列4训练SVM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SVM分类器的预测精度、灵敏度和特异性。(B)对合并的CFS和合并的对照样品进行定量MS-MS，一个用O16标记，另一个用O18标记。混合样品，并鉴定与CFS中O16/O18比率显著高于或低于对照组的肽（及其亲本蛋白）。(C)这些cfs相关蛋白将使用新型的、高灵敏度的荧光素酶融合蛋白竞争免疫分析法进行测定。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志物及其敏感性、特异性和预测准确性。(D)通过SVM学习对主观心理测量和其他输入变量进行检验，定义CFS的高预测模型。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。这些方法和生物标记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.