SLAM–SAP Signaling Promotes Differentiation of IL-17–Producing T Cells and Progression of Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
IL-17 plays critical roles in host defenses, combating bacterial and fungal infections, as well as the pathogenesis of autoimmune diseases such as experimental autoimmune encephalomyelitis (EAE). The signaling adaptor SAP is essential for normal immune homeostasis and mutations within SH2D1A, the locus encoding this protein, result in serious and sometimes fatal syndromes, including X-linked lymphoproliferative disease and severe cases of common variable immunodeficiency. However, the precise cellular basis of how SAP deficiency contributes to immune dysfunction remains incompletely understood. In this study, we found that CD4 and CD8 T cells lacking SAP had a diminished capacity to differentiate into IL-17-producing Th17 and T cytotoxic (Tc17) cells relative to wild-type lymphocytes. The use of costimulating SLAM Abs was found to augment the differentiation of IL-17-secreting effectors in wild-type but not Sh2d1a(-/-) splenic T cells under IL-17-polarizing conditions. In addition, SAP's regulation of IL-17-secreting T cells was shown to be a T cell-intrinsic role, as purified naive Sh2d1a(-/-) CD4 and CD8 T cells were inherently defective at converting into Th17 and Tc17 cells in vitro and in vivo. Furthermore, Sh2d1a(-/-) mice were protected from EAE and exhibited greatly decreased numbers of CNS-infiltrating Th17 and Tc17 effector T cells and reduced disease severity. Collectively, these results suggest that SLAM-SAP signaling drives the differentiation and function of Th17 and Tc17 cells in vitro and in vivo and contributes to the pathogenesis of autoimmunity in EAE.
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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) | 0.001 | 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 | 0.002 |
| 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".