Researchers Identify Shared Molecular Networks Behind Fatigue-Related Illnesses

Persistent, disabling fatigue complicates diagnosis and care across myalgic encephalomyelitis/chronic fatigue syndrome, long COVID, post-traumatic stress disorder, rheumatoid arthritis, and multiple sclerosis. Patients often experience brain fog, poor concentration, disturbed sleep, and autonomic dysfunction, yet objective laboratory markers remain limited, particularly for ME/CFS and long COVID. Clarifying shared mechanisms could support assay development and more consistent case definitions. New evidence now suggests that common biological regulatory networks may underlie fatigue across these disorders.

The University of East Anglia, working with Oxford BioDynamics, identified convergent biology across the five conditions using the EpiSwitch Orion platform. The technology interrogates three‑dimensional genome architecture, examining how DNA folds and forms regulatory contact points inside living cells. In this study, investigators conducted a computational analysis that merged published genomic data from long COVID, post‑traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) with three‑dimensional genomic data from an earlier myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patient study, without collecting new samples.

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Across disorders that originate from distinct triggers—viral infection, autoimmune activity, or psychological trauma—the analysis found minimal overlap at the level of individual genes but strong convergence within higher‑order regulatory networks. Disease‑linked genes mapped to shared systems that included immune and inflammatory signaling, mitochondrial energy production, metabolic regulation, stress‑response pathways, and neuroendocrine signaling. The pattern provides a mechanistic framework for why patients report similarly profound fatigue and cognitive symptoms across diagnoses.

Network analysis highlighted several “hub genes” positioned at key regulatory junctions. Among ME/CFS‑specific findings, the analysis flagged LAG3, a molecule associated with T‑cell exhaustion, as a candidate for further investigation. The authors emphasized that these genes are candidates requiring confirmation and that persistent immune dysfunction may have a larger role in fatigue‑related illnesses than previously recognized.

The work also references earlier efforts on the EpiSwitch platform that produced a blood‑based ME/CFS test showing high diagnostic accuracy and readiness for further clinical validation. The new findings raise the prospect of blood‑based signatures shared across multiple conditions and are published in the Journal of Translational Medicine on August 22, 2026. Collaborators included the London School of Hygiene and Tropical Medicine and Cornwall Partnership NHS Foundation Trust.

“We expected to find at least some overlap in genes across the conditions. But we actually found the opposite. At an individual gene level, there was surprisingly little direct overlap between long COVID, ME/CFS, PTSD, multiple sclerosis and rheumatoid arthritis. But when we analyzed how those genes interact in complex biological networks, a completely different picture emerged. Suddenly, the diseases appeared deeply connected. This is not something you can see by reading the genetic sequence alone, which is why these conditions may have looked unrelated for so long. Although these conditions are triggered by completely different events, they may ultimately disrupt the same fundamental biological systems and produce the similarly devastating exhaustion experienced by millions worldwide,” said Dmitry Pshezhetskiy, Professor at UEA’s Norwich Medical School.

"Perhaps the most significant implication is what this could mean for diagnosis. ME/CFS and long COVID are currently diagnosed largely through symptoms, with no universally accepted laboratory test available. That has left many patients facing years of uncertainty. We hope our work could pave the way for objective blood tests capable of identifying underlying biological signatures rather than relying solely on patient-reported symptoms," said Pshezhetskiy.

Related Links
UEA Norwich Medical School
Oxford BioDynamics