A Shared TCR Bias toward an Immunogenic EBV Epitope Dominates in HLA-B*07:02–Expressing Individuals
Bibliographic record
Abstract
Abstract EBV is one of the most common viruses found in humans and is prototypic of a persistent viral infection characterized by periods of latency. Across many HLA class I molecules, the latent-specific CD8+ T cell response is focused on epitopes derived from the EBNA-3 protein family. In the case of HLA-B*07:02 restriction, a highly frequent class I allele, the T cell response is dominated by an epitope spanning residues 379–387 of EBNA-3 (RPPIFIRRL [EBVRPP]). However, little is known about either the TCR repertoire specific for this epitope or the molecular basis for this observed immunodominance. The EBVRPP CD8+ T cell response was common among both EBV-seropositive HLA-B*07:02+ healthy and immunocompromised individuals. Similar TCRs were identified in EBVRPP–specific CD8+ T cell repertoires across multiple HLA-B7+ individuals, indicating a shared Ag-driven bias in TCR usage. In particular, TRBV4-1 and TRAV38 usage was observed in five out of six individuals studied. In this study, we report the crystal structure of a TRBV4-1+ TCR–HLA-B*07:02/EBVRPP complex, which provides a molecular basis for the observed TRBV4-1 bias. These findings enhance our understanding of the CD8+ T cell response toward a common EBV determinant in HLA-B*07:02+ individuals.
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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) | 0.004 | 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".