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疾病谱中的中枢致病机制。脑脊液提供了一个“窗口”，以了解潜在的功能失调的调节，先天免疫和神经通路。神经元、胶质细胞、脉络丛上皮细胞和软脑膜细胞可能是CFS相关蛋白的来源。尽管临床症状多样，但CFS相关蛋白质组是相同的，提示统一的发病机制。资料：我们对CFS和健康对照组的脑脊液进行了串联质谱（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（生物特征）预测CFS的显著优势比为34.5，一致性为80%。淀粉样蛋白，抗蛋白酶，IG λ，血红素和铁清除剂，调节激素原与CFS有关。这是第一个完全从客观数据定义的CFS预测模型。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)进行定性MS-MS，以鉴定队列4所有样品中的蛋白质。用队列4训练SVM算法，然后在一组独立的42个样本（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CFS和合并对照样品进行定量MS-MS，其中一份用O 16标记，另一份用O 18标记。混合样品并鉴定CFS中O 16/O 18比值显著高于或低于对照组的肽（及其亲本蛋白）。(C)这些CFS相关蛋白将使用新的、高灵敏度的荧光素酶-融合蛋白竞争免疫测定来测量。CFS和对照之间以及2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灵敏度、特异性和预测准确性。(D)主观心理测量和其他输入变量将通过SVM学习进行测试，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疾病谱的其他组分高度相关。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.