Driving research of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME / CFS),
Post Treatment Lyme Disease Syndrome (PTLDS), Fibromyalgia and Long Covid

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Biological Outlier and Subtyping
Software for Myalgic Encephalomyelitis

Study AIM

This project will develop a software tool to rapidly look for metabolism anomalies in an individual which might be explained by their genes. It will also look for potentially damaging genes in individuals and it will attempt to group ME/CFS patients based on their genetic and metabolic profiles.

LEAD INVESTIGATORS

  • Christopher Armstrong, PhD

  • Katherine Huang

  • Neil McGregor, PhD

Updates and Potential
  • Method optimization is underway for ongoing side projects: Systematic review compiling NMR metabolomics workflows has been completed.
  • Completed application for UK Biobank data: outcome successful.
  • Currently analyzing UKB metabolism data to identify potential biomarkers between ME/CFS and healthy and similar diseases.
  • We have identified several leads for genes that may be relevant to ME/CFS.  Regulatory regions of the genome appear to be the most interesting.
STUDY HYPOTHESIS AND DESCRIPTION

The analytical tool will be developed using pre-existing UK Biobank gene and metabolism data from over 1000 self- reported ME/CFS patients. Findings from this data will then be validated on gene and metabolism data we produce ourselves from blood received from 300 ME/CFS patients recruited by the Australian ME/CFS Biobank.

This tool will be important in understanding the complexity of the individual with ME/CFS and may provide clues to potential missed diagnoses.

OBJECTIVES

  1. Observe the relationship between genes and metabolism to identify outlier genetic anomalies and pathways in ME/CFS.
  2. Determine the relationship between metabolism and genes in ME/CFS as compared to other similar diseases.
  3. Develop an algorithm to rapidly extract outlier and pattern pathways of disease.
  4. Test and validate algorithm produced on a well-curated set of ME/CFS patients with metabolism and gene data.
  5. Eventually produce software to simplify this process.
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