SHIP Negatively Regulates IgE + Antigen-Induced IL-6 Production in Mast Cells by Inhibiting NF-κB Activity
Bibliographic record
Abstract
We demonstrate in this study that IgE + Ag-induced proinflammatory cytokine production is substantially higher in Src homology-2-containing inositol 5'-phosphatase (SHIP)(-/-) than in SHIP(+/+) bone marrow-derived mast cells (BMMCs). Focusing on IL-6, we found that the repression of IL-6 mRNA and protein production in SHIP(+/+) BMMCs requires the enzymatic activity of SHIP, because SHIP(-/-) BMMCs expressing wild-type, but not phosphatase-deficient (D675G), SHIP revert the IgE + Ag-induced increase in IL-6 mRNA and protein down to levels seen in SHIP(+/+) BMMCs. Comparing the activation of various signaling pathways to determine which ones might be responsible for the elevated IL-6 production in SHIP(-/-) BMMCs, we found the phosphatidylinositol 3-kinase/protein kinase B (PKB), extracellular signal-related kinase (Erk), p38, c-Jun N-terminal kinase, and protein kinase C (PKC) pathways are all elevated in IgE + Ag-induced SHIP(-/-) cells. Moreover, inhibitor studies suggested that all these pathways play an essential role in IL-6 production. Looking downstream, we found that IgE + Ag-induced IL-6 production is dependent on the activity of NF-kappa B and that I kappa B phosphorylation/degradation and NF-kappa B translocation, DNA binding and transactivation are much higher in SHIP(-/-) BMMCs. Interestingly, using various pathway inhibitors, it appears that the phosphatidylinositol 3-kinase/PKB and PKC pathways elevate IL-6 mRNA synthesis, at least in part, by enhancing the phosphorylation of I kappa B and NF-kappa B DNA binding while the Erk and p38 pathways enhance IL-6 mRNA synthesis by increasing the transactivation potential of NF-kappa B. Taken together, our data are consistent with a model in which SHIP negatively regulates NF-kappa B activity and IL-6 synthesis by reducing IgE + Ag-induced phosphatidylinositol-3,4,5-trisphosphate levels and thus PKB, PKC, Erk, and p38 activation.
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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 | 0.000 |
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".