SLAM/ SAP signaling positively regulates the differentiation of TH17 cells and experimental autoimmune encephalitis (163.15)
Bibliographic record
Abstract
Abstract Mutations in SAP (SLAM-associated protein) underlie the majority of cases of X-linked lymphoproliferative disease (XLP), a rare congenital disorder that often leads to fatal complications upon infection with Epstein-Barr virus. The small SH2-containing adaptor SAP transmits signaling via SLAM (signaling lymphocytic activation molecule) family receptors and has been shown to be critical for development and function of multiple immune cell types. Here, we have investigated the role of SLAM and SAP signaling in regulating the differentiation of naïve CD4 T cells into IL-17 secreting effectors (TH17 cells), a pro-inflammatory lineage implicated in host defense as well as autoimmune diseases. T cell receptor activation along with co-stimulating SLAM antibodies was found to augment TH17 cell differentiation in wild type but not SAP-deficient splenic T cells under IL-17 polarizing conditions. Furthermore, SAP-/- mice were protected from experimental autoimmune encephalomyelitis (EAE), exhibiting greatly decreased numbers of CNS-infiltrating TH17 cells and dramatically reduced disease severity. Collectively, these results suggest that SLAM-SAP interactions promote the differentiation of IL-17 secreting effector CD4 T cells both in vitro and 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 | 0.001 |
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".