RasGRP1 Signalling Programs Developing γδ-Thymocytes towards the γδT17 Lineage Through Control of c-Maf Expression
Bibliographic record
Abstract
Abstract The γδ TCR instructively directs both lineage specification and effector programming of developing γδ T cells. However, the manner in which different TCR signal strengths and other auxiliary signals coordinate downstream of the γδ TCR to regulate γδ T cell development remains unclear. In this study we characterized the role of Ras guanyl-releasing protein 1 (RasGRP1) in the development and effector programming of γδ T cells. While RasGRP1 was not necessary for bulk γδ T cell generation, we found it required for efficient generation of Vγ4 + thymocytes, and lineage-committed CD73 + γδ T cells in the thymus and periphery. Despite a decrease in immature CD73 + γδ thymocytes, we report an expansion of the perinatal wave of CD8 + IFNγ + γδ T cell population in the absence of RasGRP1. IL-17 producing γδ T cells were significantly reduced in RasGRP1 KO mice, with a specific loss of Vγ2 + γδ T cells that corresponds to a loss of c-Maf expression as early as the DN1d thymocyte stage. Critically, these adult-programmed γδT17s could express c-Maf in response to CCR9 stimulation, with RasGRP1 being required for CCR9-induced c-Maf expression. Thus, RasGRP1 activation serves as an important signalling hub in the effector programming of γδ T cells, which integrates signals from both non-TCR and TCR inputs to direct differentiation.
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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 | 0.001 |
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, 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".