Integration of T‐cell receptor, Notch and cytokine signals programs mouse γδ T‐cell effector differentiation
Bibliographic record
Abstract
Abstract γδ T‐cells perform a wide range of tissue‐ and disease‐specific functions that are dependent on the effector cytokines produced by these cells. However, the aggregate signals required for the development of interferon‐γ (IFNγ) and interleukin‐17 (IL‐17) producing γδ T‐cells remain unknown. Here, we define the cues involved in the functional programming of γδ T‐cells, by examining the roles of T‐cell receptor (TCR), Notch, and cytokine‐receptor signaling. KN6 γδTCR‐transduced Rag2−/− T‐cell progenitors were cultured on stromal cells variably expressing TCR and Notch ligands, supplemented with different cytokines. We found that distinct combinations of these signals are required to program IFNγ versus IL‐17 producing γδ T‐cell subsets, with Notch and weak TCR ligands optimally enabling development of γδ17 cells in the presence of IL‐1β, IL‐21 and IL‐23. Notably, these cytokines were also shown to be required for the intrathymic development of γδ17 cells. Together, this work provides a framework of how signals downstream of TCR, Notch and cytokine receptors integrate to program the effector function of IFNγ and IL‐17 producing γδ T‐cell subsets.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".