Antibody reactivity against EBNA1 and GlialCAM differentiates multiple sclerosis patients from healthy controls
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune demyelinating disorder of the central nervous system (CNS), which is linked to Epstein–Barr virus (EBV) infection, preceding the disease. The molecular mechanisms underlying this connection are only partially understood. We previously described molecular mimicry between the EBV transcription factor EBV nuclear antigen 1 (EBNA1) and three human CNS proteins: anoctamin-2 (ANO2), alpha-B crystallin (CRYAB), and glial cellular adhesion molecule (GlialCAM). Here, we investigated antibody responses against EBNA1 and GlialCAM in a large cohort of 650 MS patients and 661 matched population controls and compared them to responses against CRYAB and ANO2. We confirmed that elevated IgG responses against EBNA1 and all three CNS-mimic antigens associate with increased MS risk. Blocking experiments confirmed the presence of cross-reactive antibodies and molecular mimicry between EBNA1 and GlialCAM, and accompanying antibody responses against adjacent peptide regions of GlialCAM suggest epitope spreading. Antibody responses against EBNA1, GlialCAM, CRYAB, and ANO2 are elevated in MS patients carrying the main risk allele HLA-DRB1*15:01, and combinations of HLA-DRB1*15:01 with anti-EBNA1 and anti-GlialCAM antibodies increase MS risk significantly and in an additive fashion. In addition, antibody reactivities against more than one EBNA1 peptide and more than one CNS-mimic increase the MS risk significantly but modestly. Overall, we show that molecular mimicry between EBNA1 and GlialCAM is likely an important molecular mechanism contributing to MS pathology.
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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 | 0.001 |
| 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 | 0.001 | 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".