Development of promyelocytic leukemia zinc finger-expressing innate CD4 T cells requires stronger T-cell receptor signals than conventional CD4 T cells
Bibliographic record
Abstract
MHC class II-expressing thymocytes and thymic epithelial cells can mediate CD4 T-cell selection resulting in functionally distinct thymocyte-selected CD4 (T-CD4) and epithelial-selected CD4 (E-CD4) T cells, respectively. However, little is known about how T-cell receptor (TCR) signaling influences the development of these two CD4 T-cell subsets. To study TCR signaling for T-CD4 T-cell development, we used a GFP reporter system of Nur77 in which GFP intensity directly correlates with TCR signaling strength. T-CD4 T cells expressed higher levels of GFP than E-CD4 T cells, suggesting that T-CD4 T cells received stronger TCR signaling than E-CD4 T cells during selection. Elimination of Ras GTPase-activating protein enhanced E-CD4 but decreased T-CD4 T-cell selection efficiency, suggesting a shift to negative selection. Conversely, the absence of IL-2-inducible T-cell kinase that causes poor E-CD4 T-cell selection due to insufficient TCR signaling improved T-CD4 T-cell generation, consistent with rescue from negative selection. Strong TCR signaling during T-CD4 T-cell development correlates with the expression of the transcription factor promyelocytic leukemia zinc finger protein. However, although modulation of the signaling strength affected the efficiency of T-CD4 T-cell development during positive and negative selection, the signaling strength is not as important for the effector function of T-CD4 T cells. These findings indicate that innate T-CD4 T cells, together with invariant natural killer T cells and γδ T cells, receive strong TCR signals during their development and that signaling requirements for the development and the effector functions are distinct.
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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) | 0.002 | 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".