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Hunter, Ewan, Alshaker, Heba, Vugrinec, Dominik, Bautista, Shekinah, Gebregzabhar, Abel, Virdi, Anya, Croxford, Joseph, Dring, Ann, Powell, Ryan, Salter, Matthew, Kingdon, Caroline, Green, Jayne, Akoulitchev, Alexandre and Pchejetski, Dmitri 
ORCID: https://orcid.org/0000-0003-1074-9261
(2026)
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.
Journal of Translational Medicine, 24 (1).
ISSN 1479-5876
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Abstract
Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. Methods: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein–protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. Results: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. Conclusions: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability: Knowledge graphs were generated using the EpiSwitch 3D-Genomics large language model (LLM), which employs a unique embedding approach. This platform integrates advanced semantic parsing with over 1.5 billion experimentally-verified 3D genome interactions. Leveraging proprietary AI models trained on chromatin architecture, it enables mapping of regulatory circuitry, identification of causal mechanisms, and prediction of the effects of non-coding and structural variants within their native 3D genomic context. These tools are deployed on Google Cloud in collaboration with Google. Data is available on request https://www.oxfordbiodynamics.com/contact-us#contact-us-form-wrapper. |
| Uncontrolled Keywords: | 3d genomics,biomarker discovery,chromatin architecture,chromosome conformation capture,epigenetic biomarkers,long covid,multiple sclerosis,cfs),network biology,post-traumatic stress disorder,precision medicine,protein–protein interaction networks,rheumatoid arthritis,systems biology,general medicine,general biochemistry,genetics and molecular biology,das in place,contact-us#contact-us-form-wrapper.,2026 ,/dk/atira/pure/subjectarea/asjc/2700/2700 |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 09 Nov 2026 10:51 |
| Last Modified: | 09 Nov 2026 10:51 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/105010 |
| DOI: | 10.1186/s12967-026-08874-9 |
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