We plan to integrate datasets from different types of assays to yield additional insights into the pathophysiology of ME/CFS and, in particular, to identify molecular mechanisms of post-exertional malaise (PEM).

Integrating multiomic data with the clinical and exercise-physiology data from our patient cohort may allow us to identify unique molecular signatures in different subsets of people with ME/CFS. These molecular signatures may provide a basis for tailored diagnostics and treatments for clinical and demographic subsets of ME/CFS patients in the future.

Predictive modeling using a combination of molecules from different classes that have significantly different levels in ME/CFS patients and controls at baseline may enable more accurate classification of ME/CFS patients and healthy controls than an assay utilizing biomarkers from only a single class.

Researchers from all projects will be involved in data comparisons. This work is led by Dr. Katherine Glass in the Hanson lab; Dr. Dawei Li of Texas Tech University, a collaborator with Dr. Andrew Grimson; and Jen Grenier’s group in the Genomics Innovation Hub, especially Dr. Paul Munn and Dr. Adrian McNairn.

This work is funded by the National Institutes of Health (U54AI178855).

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