IL-9 Induces CCL11 Expression via STAT3 Signalling in Human Airway Smooth Muscle Cells
Bibliographic record
Abstract
BACKGROUND: Previous findings support the concept that IL-9 may play a significant role in mediating both pro-inflammatory and changes in airway responsiveness that characterizes the atopic asthmatic state. We previously demonstrated that human airway smooth muscle (ASM) cells express a functional IL-9R that mediate CCL11 expression. However, the signaling pathway governing this effect is not well understood. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we showed that IL-9 mediated CCL11 expression in ASM cells does not rely on STAT6 or STAT5 but on STAT3 pathway. IL-9 induced rapid STAT3 activation in primary ASM cells that was not observed in case of STAT6 or STAT5. STAT3 binding to CCL11 promoter was also observed in vivo upon IL-9 stimulation of ASM cells. Disruption of STAT3 activity with SH2 domain binding inhibitory peptide results in significant reduction of IL-9 mediated CCL11 promoter activity. DN STAT3beta over-expression in ASM cells, but not Ser 727 STAT3 or STAT6 DN, abolishes IL-9 mediated CCL11 promoter activity. Finally, STAT3 but not STAT6 silenced ASM cells showed significant reduction in IL-9 mediated CCL11 promoter activity and mRNA expression. CONCLUSION/SIGNIFICANCE: Taken together, our results indicate that IL-9 mediated CCL11 via STAT3 signalling pathway may play a crucial role in airway inflammatory responses.
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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".