Inhibition of IKK down-regulates antigen + IgE-induced TNF production by mast cells: a role for the IKK-IκB-NF-κB pathway in IgE-dependent mast cell activation
Bibliographic record
Abstract
Mast cells (MC) are major effector cells for allergic diseases. Cross-linking of immunoglobulin E (IgE) and its high-affinity receptor, FcepsilonRI, by antigen initiates a cascade of signaling events leading to nuclear factor (NF)-kappaB activation and tumor necrosis factor (TNF) production. Here, we demonstrated that inhibition of inhibitor of kappaB (IkappaB) kinase (IKK) by a peptide IKK inhibitor or by four individual chemical IKK inhibitors including 15-deoxy-prostaglandin J(2), BMS-345541, SC-514, or sulindac significantly blocked IgE + trinitrophenyl (TNP)-induced TNF production by mouse bone marrow-derived MC (BMMC). Moreover, IgE + TNP induced a rapid phosphorylation of IKKalpha but not IKKbeta in BMMC. IgE + TNP-induced phosphorylation of IKKalpha was accompanied with phosphorylation and degradation of IkappaBalpha, subsequent NF-kappaB activation, and TNF production. Inhibition of IKK by sulindac decreased IKKalpha phosphorylation, IkappaBalpha phosphorylation and degradation, NF-kappaB activation, and TNF production by BMMC. It is interesting that IgE + TNP stimulation also induced a prominent synthesis of IKKalpha and IkappaBalpha. Inhibition of NF-kappaB activity by pyrrolidine dithiocarbomate (PDTC) blocked IgE + TNP-induced IkappaBalpha synthesis. NF-kappaB activity and TNF production were also inhibited when PDTC was used even after IgE + TNP stimulation, suggesting a potential role for the newly synthesized IkappaBalpha in MC activation. In addition, IgE + TNP-induced IKKalpha and IkappaBalpha phosphorylation was inhibited by a protein kinase C (PKC) inhibitor Ro 31-8220. Taken together, our results support a role for the IKK-IkappaB-NF-kappaB pathway, which likely involves PKC in IgE-dependent TNF production by MC. Thus, IKK may serve as a new target for the regulation of MC function in allergy.
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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".