Microbial Translocation in Chronic Fatigue Syndrome

描述（由申请人提供）：迈阿密CFS/ME研究小组与布罗德里克计算实验室（阿尔伯塔）合作，正在开发一个有趣的数据集，使用两个模型，每个模型在两个非常不同的时间尺度上探索这种疾病的机制。在一个模型中，在运动挑战之前、期间和之后抽取样品。使用基因表达数据，蛋白质组学和临床数据相结合的运动挑战范例，我们已经显示了重要的异常，在监管网络是相当独特的，并有助于解释如何快速反应的机制，压力可能会受到损害的持续性疾病。在这个应用程序中，我们建议利用我们的第二个模型，重点是了解驱动疾病自然进展缓慢得多的过程，以及症状严重程度恶化的自发发作。为此，我们使用另一组受试者：“好日子坏日子”纵向方案，在18个月内抽取4个样本，其中一个样本在功能相对良好的时期（“好日子”），一个样本在复发期间（“坏日子”）。我们将使用这个大型独特的纵向队列的数据库和生物库，初步研究肠道微生物易位（MT）作为慢性免疫激活和随后疾病进展和持续的机制的可能作用。我们将通过测定血浆脂多糖（LPS）和可溶性CD 14（sCD 14）水平来调查队列中的MT。我们将使用系统生物学框架将MT的这些标志物与免疫、自主神经和神经内分泌标志物以及临床数据的数据库联系起来。我们的目的是探索如何周期的减轻或增加的严重程度的症状复合体及其演变随着时间的推移与MT生物标志物。通过这种方式，我们将实现几个目标：开发疾病持续性模型;识别有助于识别亚组或疾病严重程度的生物标志物，这将支持潜在的靶向干预措施的发展。

DESCRIPTION (provided by applicant): The Miami CFS/ME research group in collaboration with the Broderick computational laboratory (University of Alberta) is developing a fascinating data set using two models that each explores the mechanisms of this illness over two very different time scales. In one model samples are drawn before, during and after exercise challenge. Using a combination of gene expression data, proteomics and clinical data in the exercise challenge paradigm, we have shown important abnormalities in the regulatory networks that are quite distinct and help to explain how the mechanisms of rapid response to stress may be compromised by persistent illness. In this application we propose to exploit our second model focused on understanding the processes that drive the much slower natural progression of the illness as well as the spontaneous episodes of worsening symptom severity. To do this we use another set of subjects: the "good day bad day" longitudinal protocol with 4 samples drawn over 18 months include one during a relatively good period of function ("good day") and one during a relapse ("bad day"). We will use the database and the bio-bank of this large, unique longitudinal cohort for a preliminary study of the possible role of microbial translocation (MT) from the gut as a mechanism of chronic immune activation and subsequent disease progression and persistence. We will survey MT in the cohort by determining plasma levels of lipopolysaccharide (LPS) and soluble CD14 (sCD14). We will use a systems biology framework to link these markers of MT to the database of immune, autonomic and neuroendocrine markers and clinical data. We aim to explore how cycles of abatement or increasing severity of symptom complexes and their evolution over time are related to MT biological markers. In this way we will accomplish several goals: developing disease persistence models; identification of biomarkers useful to identify subgroups or disease severity that will support the development of potential targeted interventions.