Asynchronous Differentiation of CD8 T Cells That Recognize Dominant and Cryptic Antigens
Bibliographic record
Abstract
Restriction of T cell responses to a few epitopes (immunodominance) is a central feature of immune responses. We analyzed the entire transcriptome of effector CD8 T cells specific for a dominant (H7(a)) and a cryptic (HY) mouse Ag and performed a longitudinal analysis of selected T cell differentiation markers. We found that Ag specificity had a relatively modest influence on the repertoire of genes that are transcriptionally modulated by the CD8 T cell differentiation program. Although the differentiation programs of anti-H7(a) and anti-HY T cells were similar, they did not progress simultaneously. The expansion peak of anti-H7(a) T cells was reached on day 10 while that of anti-HY T cells was attained on days 15-20. Between days 10 and 20, anti-H7(a) T cells were in the contraction phase and anti-HY T cells in the expansion phase. Furthermore, expansion and development of effector function were well-synchronized in anti-H7(a) T cells but were disconnected in anti-HY T cells. We propose that, by leading to selective expansion of the fittest CD8 T cells, immunodominance may be beneficial to the host. Inhibition of the T cell response to cryptic Ag would ensure that host resources (APC, cytokines) for which T cells compete are devoted to T cells with the best effector potential. One implication is that favoring expansion of the fittest effector T cells in general may be more important than increasing the diversity of the T cell repertoire.
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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) | 0.001 | 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".