Profile of circulating microRNAs in myalgic encephalomyelitis and their relation to symptom severity, and disease pathophysiology, by Evguenia Nepotchatykh, Wesam Elremaly, Iurie Caraus, Christian Godbout, Corinne Leveau, Lynda Chalder, Catherine Beaudin, Emi Kanamaru, Renata Kosovskaia, Shawn Lauzon, Yanick Maillet, Anita Franco, Viorica Lascau-Coman, Saadallah Bouhanik, Yaned Patricia Gaitan, Dawei Li & Alain Moreau in Scientific Reports volume 10, no. 19620 (2020) 12 Nov 2020
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex chronic disease, rooted in multi-system dysfunctions characterized by unexplained debilitating fatigue. Post-exertional malaise (PEM), defined as the exacerbation of the patient’s symptoms following minimal physical or mental stress, is a hallmark of ME/CFS. While multiple case definitions exist, there is currently no well-established biomarkers or laboratory tests to diagnose ME/CFS.
Our study aimed to investigate circulating microRNA expression in severely ill ME/CFS patients before and after an innovative stress challenge that stimulates PEM.
Our findings highlight the differential expression of eleven microRNAs associated with a physiological response to PEM. The present study uncovers specific microRNA expression signatures associated with ME/CFS in response to PEM induction and reports microRNA expression patterns associated to specific symptom severities.
The identification of distinctive microRNA expression signatures for ME/CFS through a provocation challenge is essential for the elucidation of the ME/CFS pathophysiology, and lead to accurate diagnoses, prevention measures, and effective treatment options.
“We based our test on the cardinal symptom of the disease, which is discomfort after exertion,” said Moreau. “This is really what is most specific to myalgic encephalomyelitis.”
The test could also allow patients to be grouped into subgroups, in order not only to better understand the molecular mechanisms involved in certain symptoms but also to better select patients who could benefit from certain treatments.
“It becomes more interesting for both trying to work with more homogeneous subgroups to understand the disease and see what explains the severity or all of the symptoms,” said Moreau. “It may speed up research to better understand the disease.”
Finally, the test could allow early detection of patients in whom ME is developing, so that they can start treating them as quickly as possible.
MicroRNAs can represent potential indicators for diseases such as ME/CFS, and changes in microRNA expression could indicate cellular dysfunction and degeneration. Using a test to induce mild-but-reproducible Post Exertional Malaise (PEM), our team has so far uncovered and validated 11 different microRNAs associated with ME/CFS that are capable of differentiating ME/CFS patients from healthy patients — with 90 percent accuracy!
This post-exertional stress challenge provoking PEM in ME/CFS patients has helped us gain unprecedented insight into the pathophysiology of ME/CFS. Based on the 11 different microRNA signatures discovered in ME/CFS, machine learning algorithms have also led to the classification of ME/CFS patients into four clusters associated with symptom severity.
These exciting results could lead to the development of a new, non-invasive diagnostic test for ME/CFS, a prognostic tool used to predict future cases, and identification of effective treatment options.