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

卷:24

Research

Ewan Hunter¹  Heba Alshaker²  Dominik Vugrinec¹  Shekinah Bautista¹  Abel Gebregzabhar¹  Anya Virdi¹  Joseph Croxford¹  Ann Dring¹  Ryan Powell¹  Matthew Salter¹  Caroline Kingdon³  Jayne Green¹  Alexandre Akoulitchev¹  Dmitri Pchejetski^2,4 

[1] The London School of Hygiene & Tropical Medicine, London, UK

[2] Cornwall Partnership NHS Foundation Trust, Bodmin, UK

[3] Oxford BioDynamics Plc, Oxford, UK

[4] Norwich Medical School, University of East Anglia, Norwich, UK

关键词: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS);    Long COVID;    Post-traumatic stress disorder;    Multiple sclerosis;    Rheumatoid arthritis;    3D genomics;    Chromosome conformation capture;    Chromatin architecture;    Epigenetic biomarkers;    Systems biology;    Network biology;    Protein–protein interaction networks;    Biomarker discovery;    Precision medicine;   

DOI  :  10.1186/s12967-026-08874-9

 received in 2026-06-16, accepted in 2026-08-19,  发布年份 2026

来源: Springer

【 摘 要 】

BackgroundMyalgic 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.MethodsThe 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.ResultsAnalysis 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.ConclusionsThese 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.

【 授权许可】

CC BY-NC-ND   
© The Author(s) 2026

【 图 表 】

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Figure 4. Tissue preparation and addition to the XF24 Islet Capture microplate.