The Importance of Exogenous Antigen in Priming the Human CD8+ T Cell Response: Lessons from the EBV Nuclear Antigen EBNA1
Bibliographic record
Abstract
Mouse models suggest that the processing of exogenous Ag by dendritic cells can be important for priming the CD8(+) CTL response. To study the situation in humans, we have exploited the CTL response to EBV infection. In this context EBV expresses eight latent proteins, of which EBV-encoded nuclear Ag (EBNA) 3A, 3B, and 3C appear to be immunodominant for CTL responses, whereas another nuclear Ag, EBNA1, which is completely protected from endogenous presentation via the MHC class I pathway, is thought to induce responses rarely, if ever. Here, using EBNA1 peptides and/or EBNA1 protein-loaded dendritic cells as in vitro stimuli, we have identified memory CTL responses to HLA-B*3501, -B7, and -B53-restricted EBNA1 epitopes that can be as strong as those seen in immunodominant epitopes from the "conventionally processed"" EBNA3 Ags. Furthermore, we used HLA-peptide tetramers to show that the primary response to one such EBNA1 epitope constituted up to 5% of the CD8(+) T cells in infectious mononucleosis blood, the strongest latent Ag-specific response yet detected in this setting. We conclude that exogenous protein represents a significant source of Ag for priming the human CTL response.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".