Phosphatase inhibition potentiates IL-6 production by mast cells in response to FcɛRI-mediated activation: involvement of p38 MAPK
Bibliographic record
Abstract
Mast cells are crucial effector cells in the immune response through mediator secretion and release of cytokines. A coordinated balance between protein kinases and phosphatases plays an essential role in the regulation of mast cell mediator secretion. We have previously shown that treatment of mast cells with okadaic acid (OA), a protein phosphatase 2A (PP2A) inhibitor, results in a dose-dependent increase in interleukin (IL)-6 production. We show here for the first time a synergism between OA and immunoglobulin E (IgE)-mediated IL-6 secretion by murine bone marrow-derived mast cells (BMMC). Selective p38 mitogen-activated protein kinase (p38 MAPK) inhibition reduces OA and IgE-mediated IL-6 production. Regulation of p38 MAPK by PP2A was demonstrated, as OA treatment caused a dose-dependent increase in p38 MAPK phosphorylation. Antigen-mediated activation of murine mast cells also resulted in an increase in p38 MAPK phosphorylation, which was potentiated by cotreatment of the cells with OA. Lastly, in two mast cell lines (human mast cell-1 5C6 and murine MC/9) and primary-cultured murine BMMC, we show by coimmunoprecipitation an interaction between p38 MAPK and PP2A. These data support a role for PP2A through interaction with p38 MAPK in the regulation of IgE-dependent mast cell 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) | 0.001 | 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 | 0.001 |
| 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".