RasGRP1 signaling is required for Vγ2+ thymocyte c-Maf expression and γδT17 lineage programming
Bibliographic record
Abstract
The γδ TCR instructively directs both lineage specification and effector programming of developing γδ T cells. However, the way 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 defined 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 was 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, there was an expansion of the perinatally derived CD8+IFNγ+ γδ T-cell population in the absence of RasGRP1. IL-17-producing γδ T cells were significantly reduced in RasGRP1 knockout mice, with a specific loss of Vγ2+ γδ T cells that corresponded to a loss of c-Maf expression as early as the DN1d thymocyte stage. Critically, these cells undergoing γδT17 programming in adults could express c-Maf in response to CCR9 stimulation, with RasGRP1 but not MEK activity being required for CCR9-induced c-Maf expression. Thus, RasGRP1 activation serves as an important signaling 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) | 0.001 | 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, 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".