T Cell Cross-Reactivity between a Highly Immunogenic EBV Epitope and a Self-Peptide Naturally Presented by HLA-B*18:01+ Cells
Bibliographic record
Abstract
T cell cross-reactivity underpins the molecular mimicry hypothesis in which microbial peptides sharing structural features with host peptides stimulate T cells that cross-react with self-peptides, thereby initiating and/or perpetuating autoimmune disease. EBV represents a potentially important factor in the pathogenesis of several T cell-mediated autoimmune disorders, with molecular mimicry a likely mechanism. In this study, we describe a human self-peptide (DELEIKAY) that is a homolog of a highly immunogenic EBV T cell epitope (SELEIKRY) presented by HLA-B*18:01. This self-peptide was shown to bind stably to HLA-B*18:01, and peptide elution/mass spectrometric studies showed it is naturally presented by this HLA molecule on the surface of human cells. A significant proportion of CD8(+) T cells raised from some healthy individuals against this EBV epitope cross-reacted with the self-peptide. A diverse array of TCRs was expressed by the cross-reactive T cells, with variable functional avidity for the self-peptide, including some T cells that appeared to avoid autoreactivity by a narrow margin, with only 10-fold more of the self-peptide required for equivalent activation as compared with the EBV peptide. Structural studies revealed that the self-peptide-HLA-B*18:01 complex is a structural mimic of the EBV peptide-HLA-B*18:01 complex, and that the strong antiviral T cell response is primarily dependent on the alanine/arginine mismatch at position 7. To our knowledge, this is the first report confirming the natural presentation of a self-peptide cross-recognized in the context of self-HLA by EBV-reactive CD8(+) T cells. These results illustrate how aberrant immune responses and immunopathological diseases could be generated by EBV infection.
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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 | 0.000 | 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".