Chronic fatigue syndrome is presently diagnosed by a constellation of symptoms, which limit its identification until a minimum of 6 months after disease onset. Furthermore, the lack of an objective molecular or biochemical test that can be ordered by medical personnel unfamiliar with the illness often results in even further delay and misdiagnosis with other fatiguing illnesses. Treatment options are limited. Development of new therapies is hindered by several obstacles: (1) the absence of simple objective monitoring tools to determine whether a therapy is efficacious (2) difficulty in distinguishing possible sub-groups of patients and (3) lack of information about the pathophysiology of the disease that could suggest development of novel medications. These problems will be attacked through identification of gene expression differences between CFS patients and healthy controls using state-of-the-art deep sequencing methods and bioinformatic analysis. Past studies implicate infectious agents, autoimmunity, or immune system disturbances that affect the activity of circulating leukocytes. Messenger RNAs and microRNAs from whole blood and B, T, and NK cells purified by flow cytometry will be characterized through Illumina sequencing of barcoded cDNA libraries. The expression of a subset of candidate dysregulated mRNA-encoding genes will be assayed at the protein level. Markers will be confirmed in cohorts independent of those where they have been discovered. Multivariate biomarker models will be developed and validated in order to predict diagnosis as well as disease severity. Integrative analyses of immune cell gene expression will be carried out to help further our understanding of CFS pathogenesis.