SLAP Is a Negative Regulator of FcεRI Receptor-Mediated Signaling and Allergic Response
Bibliographic record
Abstract
synthesis of cytokines causing allergic reactions. Src-like adapter protein (SLAP) functions co-operatively with c-Cbl to negatively regulate signaling downstream of the T cell receptor, B cell receptor, and receptor tyrosine kinases (RTK). Here, we investigated the role of SLAP in FcεRI-mediated mast cell signaling, using bone marrow derived mast cells (BMMCs) from SLAP knock out (SLAP KO) mice. Mature SLAP-KO BMMCs displayed significantly enhanced antigen induced degranulation and synthesis of IL-6, TNFα, and MCP-1 compared to wild type (WT) BMMCs. In addition, SLAP KO mice displayed an enhanced passive cutaneous anaphylaxis response. In agreement with a negative regulatory role, SLAP KO BMMCs showed enhanced FcεRI-mediated signaling to downstream effector kinases, Syk, Erk, and Akt. Recombinant GST-SLAP protein binds to the FcεRIβ chain and to the Cbl-b in mast cell lysates, suggesting a role in FcεRI down regulation. In addition, the ubiquitination of FcεRIγ chain and antigen mediated down regulation of FcεRI is impaired in SLAP KO BMMCs compared to the wild type. In line with these findings, stimulation of peripheral blood human basophils with FcεRIα antibody, or a clinically relevant allergen, resulted in increased SLAP expression. Together, these results indicate that SLAP is a dynamic regulator of IgE-FcεRI signaling, limiting allergic responses.
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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 | 0.001 |
| 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".