Critical Role for SLAM/SAP Signaling in the Thymic Developmental Programming of IL-17– and IFN-γ–Producing γδ T Cells
Bibliographic record
Abstract
Abstract During thymic development, mouse γδ T cells commit to either an IFN-γ– or an IL-17–producing phenotype through mechanisms that remain unclear. In this study, we investigated the extent to which the SLAM/SAP signaling pathway regulates the functional programming of γδ T cells. Characterization of SLAM family receptor expression revealed that thymic γδ T cell subsets were each marked by distinct coexpression profiles of SLAMF1, SLAMF4, and SLAMF6. In the thymus, Vγ1 and Vγ4 T cells that exhibited an SLAMF1+SLAMF6+ double positive phenotype were largely contained within immature CD24+CD73− and CD24+CD73+ subsets, whereas SLAMF1 single positive, SLAMF6 single positive, or SLAMF1SLAMF6 double negative cells were found within mature CD24−CD73+ and CD24−CD73− subsets. In the periphery, SLAMF1 and SLAMF6 expression distinguished IL-17– and IFN-γ–producing γδ T cells, respectively. Disruption of SLAM family receptor signaling through deletion of SAP resulted in impaired thymic Vγ1 and Vγ4 T cell maturation at the CD24+CD73−SLAMF1+SLAMF6+ double positive stage that was associated with a decreased frequency of CD44+RORγt+ γδ T cells. Impaired development was in turn associated with decreased γδ T cell IL-17 and IFN-γ production in the thymus as well as in peripheral tissues. The role for SAP was subset-specific, as Vγ1Vδ6.3, Vγ4, Vγ5, but not Vγ6 subsets were SAP-dependent. Together, these data suggest that the SLAM/SAP signaling pathway plays a larger role in γδ T cell development than previously appreciated and represents a critical checkpoint in the functional programming of both IL-17– and IFN-γ–producing γδ T cell subsets.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".