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A Hidden Genetic Thread May Link Long COVID, Chronic Fatigue, and Three Other Illnesses
In A Nutshell
- Chronic fatigue syndrome, long COVID, PTSD, multiple sclerosis, and rheumatoid arthritis share very few genes.
- When researchers mapped how those genes connect, the five illnesses linked up through shared immune, energy, and stress-response pathways.
- A gene tied to immune burnout, called LAG3, showed up in the chronic fatigue network, a possible clue to shared biology.
- Results come from computer analysis of existing data, so they are early leads, not proof of cause or a new treatment.
Chronic fatigue syndrome, long COVID, PTSD, multiple sclerosis (MS), and rheumatoid arthritis look like five unrelated problems. One follows a viral infection, one follows trauma, and two are autoimmune diseases, in which the immune system attacks the body’s own tissue. The remaining one, chronic fatigue syndrome (known as ME/CFS), is still poorly understood. Yet a new study suggests some of the same biological circuitry inside cells could be involved in all five.
Patients with these conditions often share overlapping troubles: deep exhaustion, brain fog, and an immune system that seems to misfire. ME/CFS still has no widely accepted blood test. Doctors diagnose it from a symptom checklist and by ruling out other causes, a process that often brings delays and misclassification.
Researchers from Oxford BioDynamics, a company that sells genetic tests, and several U.K. institutions reported in the Journal of Translational Medicine that the genes tied to each illness barely overlap. But when they mapped how those genes connect, the five conditions linked up through shared immune, energy, and stress-response pathways.
Genes Linked to Chronic Fatigue and Long COVID Overlap Very Little
Reading DNA letter by letter misses part of the story. Inside a cell, DNA is folded and looped, much like a folded map that puts two distant towns side by side. Those folds bring faraway stretches of DNA into contact, which helps decide which genes switch on or off. The team used a platform called EpiSwitch, developed by the company that employs many of the authors, to study this folding.
For ME/CFS, the researchers built on an earlier blood study from the same group. That work used 47 patients with severe ME/CFS and 61 healthy volunteers to create a test, then tried it on 24 new patients and 45 new healthy volunteers. It correctly sorted patients from healthy volunteers 96% of the time, though that second group was small.
For the new work, no patients were recruited. Instead, the team mined genetic data from large public studies that scanned the DNA of roughly 226,000 people with one of the five conditions, along with far larger groups of people without them. The ME/CFS data came from people of European ancestry in the U.K., the MS data from German and Sardinian participants, and the other three studies spanned multiple ancestries. Software then predicted where DNA folds in each condition, so most of the folding was estimated by computer, not measured in patients’ cells.
Each illness was tied to several hundred genes, from 324 for PTSD to 885 for rheumatoid arthritis, yet only a small share of those genes turned up in more than one illness.
Gene Networks Connect Chronic Fatigue to Four Other Illnesses
Genes work in teams, so the researchers next mapped how the proteins those genes produce are linked, using databases of known and predicted interactions. Rather than five isolated clusters, the genes formed one densely connected web. Many of the most highly connected genes came from the long COVID data and acted as bridges linking genes from different conditions. Rheumatoid arthritis and MS formed especially dense clusters, and ME/CFS and long COVID genes plugged strongly into them. PTSD-linked genes were woven into the same network.
Immune signaling, interferon responses (the body’s antiviral alarm system), energy production inside cells, and stress hormone pathways kept surfacing as shared territory.
A Gene Tied to Immune Burnout Shows Up in the Chronic Fatigue Network
One standout was LAG3, a gene that acts as a brake on immune cells and is associated with T-cell exhaustion, a state in which immune cells worn down by prolonged activation stop working properly. LAG3 was not among the genes already tied to ME/CFS, yet it emerged as a strongly connected part of the disease’s network.
Paired with other immune-related genes in the network, that raises the possibility that chronic immune activation followed by a kind of immune burnout could be part of the shared biology behind all five conditions. Another heavily connected gene helps cells build their energy-producing machinery, a possible link to the fatigue patients describe. Shared pathways like these could help explain how triggers as different as a virus and trauma end up looking alike.
Shared Networks Could Eventually Guide Blood Tests for Long COVID and ME/CFS
None of this shows that targeting LAG3 or any other gene would help patients. The authors call these genes candidates that need testing in labs and clinical trials. They also did not run a formal statistical test of whether the network overlap exceeds what chance alone could produce.
Still, the authors argue the approach holds promise, writing that these DNA-folding markers “offer significant potential for objective blood-based diagnostics.” If the shared networks hold up under further study, they could eventually support blood tests that work across several of these conditions at once. A treatment that targets an immune pathway involved in rheumatoid arthritis, for instance, might someday be worth investigating for ME/CFS or long COVID too, if researchers confirm that the same pathway plays a role in those illnesses.
Genes linked to each disease may differ, but the networks they connect to appear to have more in common than scientists previously recognized. Whether that circuitry can actually be fixed is the next, much harder, question.
Disclaimer: This article is for informational purposes only and is not medical advice. Findings from a single study are early and should be interpreted with caution. Anyone with questions about a health condition or treatment should speak with a qualified healthcare professional.
Paper Notes
Limitations
The analysis combined genetic datasets that differ in size, ancestry, and how each original study defined its patients, so it inherits their weaknesses. Most of the 3D folding patterns were predicted by software from genetic variants rather than measured in patients’ blood cells, and the 96% accuracy figure comes from a small earlier validation group. The researchers did not run a formal statistical test comparing the network overlap against chance. The network links are candidate relationships, not proof of cause and effect, and the highlighted hub genes (including LAG3, RUNX1, PPARGC1A, CDH2, NRP1, and PLCG2) have not been independently validated in lab experiments. The study grouped people by diagnosis rather than measuring fatigue itself, so the shared patterns may reflect broader overlapping biology. Longer-term studies are needed to learn whether these 3D genome signatures stay stable over the course of illness and treatment.
Funding and Disclosures
The work was funded by Oxford BioDynamics plc. Eleven authors (Ewan Hunter, Dominik Vugrinec, Shekinah Bautista, Abel Gebregzabhar, Anya Virdi, Joseph Croxford, Ann Dring, Ryan Powell, Matthew Salter, Jayne Green, and Alexandre Akoulitchev) are full-time employees of the company, which also offers commercial tests built on the EpiSwitch platform. The remaining authors reported no competing interests.
Publication Details
Title: Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis | Authors: Ewan Hunter, Heba Alshaker, Dominik Vugrinec, Shekinah Bautista, Abel Gebregzabhar, Anya Virdi, Joseph Croxford, Ann Dring, Ryan Powell, Matthew Salter, Caroline Kingdon, Jayne Green, Alexandre Akoulitchev, and Dmitri Pchejetski (corresponding author) | Affiliations: Oxford BioDynamics Plc (Oxford, U.K.); Norwich Medical School, University of East Anglia; London School of Hygiene & Tropical Medicine; Cornwall Partnership NHS Foundation Trust | Journal: Journal of Translational Medicine, 2026, volume 24, article 1134 (open access; received June 16, accepted August 19, published online August 22, 2026) | DOI: 10.1186/s12967-026-08874-9







