MAP kinases mediate interleukin-13 effects on calcium signaling in human airway smooth muscle cells
Bibliographic record
Abstract
Interleukin-13 (IL-13) has been strongly implicated in the pathogenesis of allergic asthma through animal models that have shown that IL-13 is both necessary and sufficient to cause airway hyperresponsiveness (AHR). Airway smooth muscle (ASM) is a primary effector of AHR, and IL-13 increases the responsiveness of ASM, by increasing Ca(2+) release intracellularly, to bronchoconstrictors such as histamine. The mechanisms and signaling pathways mediating this effect are incompletely understood. We have investigated the pathways through which IL-13 regulates the Ca(2+) response to histamine in primary human ASM cell cultures. Functional IL-13 receptors were demonstrated by IL-13-mediated phosphorylation of signal transducer and activator of transcription 6 (STAT6) and mitogen-activated protein kinases (MAPKs). IL-13 increased Ca(2+) responses to histamine. The augmentation of Ca(2+) signaling was not affected by inhibition of STAT6 or p38 MAPK signaling but was prevented by concurrent inhibition of c-jun N-terminal kinase (JNK) and extracellular signal-related kinase (ERK) MAPKs. This inhibition did not affect the IL-13-induced increase in histamine receptors. We conclude that IL-13 induces potentiation of Ca(2+) responses to contractile agonists by affecting mechanisms downstream of receptors. JNK and ERK MAPKs modulate these mechanisms.
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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) | 0.003 | 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".