Microbial Translocation in Chronic Fatigue Syndrome

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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.