Genetics of immune response to Epstein-Barr virus: prospects for multiple sclerosis pathogenesis
Bibliographic record
Abstract
Epstein-Barr virus (EBV) infection has been advocated as a prerequisite for developing multiple sclerosis (MS) and possibly the propagation of the disease. However, the precise mechanisms for such influences are still unclear. A large-scale study investigating the host genetics of EBV serology and related clinical manifestations, such as infectious mononucleosis (IM), may help us better understand the role of EBV in MS pathogenesis. This study evaluates the host genetic factors that influence serological response against EBV and history of IM and cross-evaluates them with MS risk and genetic susceptibility in the Swedish population. Plasma IgG antibody levels against EBV nuclear antigen-1 [EBNA-1, truncated = amino acids (aa) (325-641), peptide = aa(385-420)] and viral capsid antigen p18 (VCAp18) were measured using bead-based multiplex serology for 8744 MS cases and 7229 population-matched control subjects. The MS risk association for high/low EBV antibody levels and history of IM was compared to relevant clinical measures along with sex, age at sampling, and associated HLA allele variants. Genome-wide and HLA allele association analyses were also performed to identify genetic risk factors for EBV antibody response and IM history. Higher antibody levels against VCAp18 [odds ratio (OR) = 1.74, 95% confidence interval (CI) = 1.60-1.88] and EBNA-1, particularly the peptide (OR = 3.13, 95% CI = 2.93-3.35), were associated with an increased risk for MS. The risk increased with higher anti-EBNA-1 IgG levels up to 12× the reference risk. We also identified several independent HLA haplotypes associated with EBV serology overlapping with known MS risk alleles (e.g. DRB1*15:01). Although there were several candidates, no variants outside the HLA region reached genome-wide significance. Cumulative HLA risk for anti-EBNA-1 IgG levels, particularly the peptide fragment, was strongly associated with MS. In contrast, the genetic risk for high anti-VCAp18 IgG levels was not as strongly associated with MS risk. IM history was not associated with class II HLA genes but negatively associated with A*02:01, which is protective against MS. Our findings emphasize that the risk association between anti-EBNA-1 IgG levels and MS may be partly due to overlapping HLA associations. Additionally, the increasing MS risk with increasing anti-EBNA-1 levels would be consistent with a pathogenic role of the EBNA-1 immune response, perhaps through molecular mimicry. Given that high anti-EBNA-1 antibodies may reflect a poorly controlled T-cell defence against the virus, our findings would be consistent with DRB1*15:01 being a poor class II antigen in the immune defence against EBV. Last, the difference in genetic control of IM supports the independent roles of EBNA-1 and IM in MS susceptibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".