Epitope Density Influences CD8+ Memory T Cell Differentiation
Bibliographic record
Abstract
BACKGROUND: The generation of long-lived memory T cells is critical for successful vaccination but the factors controlling their differentiation are still poorly defined. We tested the hypothesis that the strength of T cell receptor (TCR) signaling contributed to memory CD8(+) T cell generation. METHODOLOGY/PRINCIPAL FINDINGS: We manipulated the density of antigenic epitope presented by dendritic cells to mouse naïve CD8(+) T cells, without varying TCR affinity. Our results show that a two-fold decrease in antigen dose selectively affects memory CD8(+) T cell generation without influencing T cell expansion and acquisition of effector functions. Moreover, we show that low antigen dose alters the duration of the interaction between T cells and dendritic cells and finely tunes the expression level of the transcription factors Eomes and Bcl6. Furthermore, we demonstrate that priming with higher epitope density results in a 2-fold decrease in the expression of Neuron-derived orphan nuclear receptor 1 (Nor-1) and this correlates with a lower level of conversion of Bcl-2 into a pro-apoptotic molecule and an increased number of memory T cells. CONCLUSIONS: Our results show that the amount of antigen encountered by naïve CD8(+) T cells following immunization with dendritic cells does not influence the generation of functional effector CD8(+) T cells but rather the number of CD8(+) memory T cells that persist in the host. Our data support a model where antigenic epitope density sensed by CD8(+) T cells at priming influences memory generation by modulating Bcl6, Eomes and Nor-1 expression.
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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 | 0.001 |
| 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".