Autoantibodies to Arginine-rich Sequences Mimicking Epstein-Barr Virus in Post-COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome
Bibliographic record
Abstract
Abstract Background Epstein-Barr virus (EBV) infection is a known trigger and risk factor for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and post-COVID syndrome (PCS). In previous studies, we found enhanced IgG reactivity to EBV EBNA4 and EBNA6 arginine-rich sequences in postinfectious ME/CFS (piME/CFS). Objective This study aims to investigate IgG responses to arginine-rich (poly-R) EBNA4 and EBNA6 sequences and homologous human sequences in PCS and ME/CFS. Methods The IgG responses against poly-R EBNA4 and EBNA6 and corresponding homologous human 15-mer peptides and respective full-length proteins were analyzed using a cytometric bead array (CBA) and a multiplex dot-blot assay. Sera of 45 PCS patients diagnosed according to WHO criteria, with 26 patients fulfilling the Canadian Consensus criteria for ME/CFS (pcME/CFS), 36 patients with non-COVID post-infectious ME/CFS (piME/CFS), and 34 healthy controls (HC) were investigated. Results Autoantibodies to poly-R peptide sequences of the neuronal antigen SRRM3, the ion channel SLC24A3, TGF-β signaling regulator TSPLY2, angiogenic regulator TSPYL5, as well as to full-length α-adrenergic receptor (ADRA) proteins were more frequent in patients. Several autoantibodies were positively associated with key symptoms of autonomic dysfunction, fatigue, cognition, and pain. Conclusion Collectively, we identified autoantibodies with new antigen specificities with a potential role in PCS and ME/CFS. Clinical Implication These finding should prompt further studies on the function of these autoantibodies, their exploitation for diagnostic use, and of drugs targeting autoantibodies. Capsule summary Our study reveals elevated autoantibodies to EBV-related poly-R sequences and their human homologues in PCS and ME/CFS patients associated with symptom severity, suggesting a potential role in disease pathogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".