Functional Characterization of EBV-Encoded Nuclear Antigen 1–Specific CD4+ Helper and Regulatory T Cells Elicited by<i>In vitro</i>Peptide Stimulation
Bibliographic record
Abstract
CD4(+) helper and regulatory T (Treg) cells play important but opposing roles in regulating host immune responses against cancer and other diseases. However, very little is known about the antigen specificity of CD4(+) Treg cells. Here we describe the generation of a panel of EBV-encoded nuclear antigen 1 (EBNA1)-specific CD4(+) T-cell lines and clones that recognize naturally processed EBNA1-P(607-619) and -P(561-573) peptides in the context of HLA-DQ2 and HLA-DR11, -DR12, and -DR13 molecules, respectively. Phenotypic and functional analyses of these CD4(+) T cells revealed that they represent EBNA1-specific CD4(+) T helper as well as Treg cells. CD4(+) Treg cells do not secrete interleukin (IL)-10 and transforming growth factor beta cytokines but express CD25, the glucocorticoid-induced tumor necrosis factor receptor-related protein (GITR), and Forkhead Box P3 (Foxp3), and are capable of suppressing the proliferative responses of naive CD4(+) and CD8(+) T cells to stimulation with mitogenic anti-CD3 antibody. The suppressive activity of these CD4(+) Treg cells is mediated via cell-cell contact or in part by a cytokine-dependent manner. Importantly, these Treg cells suppress IL-2 secretion by CD4(+) effector T cells specific for either EBNA1 or a melanoma antigen, suggesting that these CD4(+) Treg cells induce immune suppression. These observations suggest that the success of peptide-based vaccines against EBV-associated cancer and other diseases may likely depend upon our ability to identify antigens/peptides that preferentially activate helper T cells and/or to design strategies to regulate the balance between CD4(+) helper and Treg cells.
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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 | 0.000 |
| 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) | 0.001 | 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 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".