Differential effects of γc cytokines on postselection differentiation of CD8 thymocytes
Bibliographic record
Abstract
The primary consequence of positive selection is to render thymocytes responsive to cytokines and chemokines expressed in the thymic medulla. In the present study, our main objective was to discover which cytokines could support the differentiation of positively selected thymocytes. To this end, we have developed an in vitro model suitable for high-throughput analyses of positive selection and CD8 T-cell differentiation. The model involves coculture of TCR(hi)CD5(int)CD69(-) double-positive (DP) thymocytes with peptide-pulsed OP9 cells and γc-cytokines. We report that IL-4, IL-7, and IL-21 have nonredundant effects on positively selected DP thymocytes. IL-7 signaling phosphorylates STAT5 and ERK; induces Foxo1, Klf2, and S1pr1; and supports the differentiation of classic CD8 T cells. IL-4 activates STAT6 and ERK and supports the differentiation of CD8(int)PD-L1(hi)CD44(hi)EOMES(+) innate CD8 T cells. IL-21 is produced by thymic epithelial cells and the IL-21 receptor-α is strongly induced on DP thymocytes undergoing positive selection. IL-21 signaling phosphorylates STAT3 and STAT5, but not ERK, and does not support CD8 T-cell differentiation. However, IL-21 has a unique ability to up-regulate BCL-6, expand DP thymocytes undergoing positive selection, and increase the production of mature T cells. Our data suggest that injection of recombinant IL-21 might enhance thymic output in subjects with age- or disease-related thymic atrophy.
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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".