The Role of SHIP in the Development and Activation of Mouse Mucosal and Connective Tissue Mast Cells
Bibliographic record
Abstract
Although SHIP is a well-established suppressor of IgE plus Ag-induced degranulation and cytokine production in bone marrow-derived mast cells (BMMCs), little is known about its role in connective tissue (CTMCs) or mucosal (MMCs) mast cells. In this study, we compared SHIP's role in the development as well as the IgE plus Ag and TLR-induced activation of CTMCs, MMCs, and BMMCs and found that SHIP delays the maturation of all three mast cell subsets and, surprisingly, that it is a positive regulator of IgE-induced BMMC survival. We also found that SHIP represses IgE plus Ag-induced degranulation of all three mast cell subsets and that TLR agonists do not trigger their degranulation, whether SHIP is present or not, nor do they enhance IgE plus Ag-induced degranulation. In terms of cytokine production, we found that in MMCs and BMMCs, which are poor producers of TLR-induced cytokines, SHIP is a potent negative regulator of IgE plus Ag-induced IL-6 and TNF-α production. Surprisingly, however, in splenic or peritoneal derived CTMCs, which are poor producers of IgE plus Ag-induced cytokines, SHIP is a potent positive regulator of TLR-induced cytokine production. Lastly, cell signaling and cytokine production studies with and without LY294002, wortmannin, and PI3Kα inhibitor-2, as well as with PI3K p85α(-/-) BMMCs and CTMCs, are consistent with SHIP positively regulating TLR-induced cytokine production via an adaptor-mediated pathway while negatively regulating IgE plus Ag-induced cytokine production by repressing the PI3K pathway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".