Temporal, TCR signal strength, and Notch signaling requirements for γδ T-cell functional programming and execution <i>in vivo</i>
Bibliographic record
Abstract
Abstract γδ T-cells are critical mediators of both protective and destructive immunity. In this study, we aimed to understand how ontogeny, T-cell receptor (TCR) signal strength, and Notch signaling dictate γδ T-cell functional programming and execution. We employed RBPJ-inducible (RBPJind) mice, which enabled control of the initiation and duration of Notch responsiveness in hematopoietic cells. Our results revealed that generation of γδ T-cell IL-17 producers occurred more readily during fetal life. However, while generation of lymph node IL-17 producers occurred exclusively during fetal life, generation of lung IL-17 producers still occurred postnatally. Using KN6-transgenic (KN6tg) and RBPJind mice, we observed that strong TCR signals programmed γδ T-cells towards the IL-4 fate. Conversely, weak TCR signals programmed γδ T-cells towards the IL-17 fate, and was required for the generation of innate-like IL-17 producers. Notch signaling played a role in promoting the IL-4 fate but, contrary to previous reports, did not play a role in dictating the IL-17 fate. To investigate the role of ontogeny and Notch signaling in the ability of lung γδ T-cells to execute their IL-17 function during infection, we employed an M. tuberculosis model using trehalose dimycolate (TDM). We show that fetal-derived γδ T-cells produced IL-17 in response to TDM while adult-derived γδ T-cells did not. Notch was dispensable for the fetal-derived lung γδ T-cells to produce IL-17, contrary to previous reports suggesting that Notch was required in the periphery for γδ T-cell IL-17 production. Taken together, this study revealed the precise temporal, TCR signal strength, and Notch signaling requirements for γδ T-cell functional programming and execution in vivo.
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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".