IL-9-Mediated Induction of Eotaxin1/CCL11 in Human Airway Smooth Muscle Cells
Bibliographic record
Abstract
Recent work has shown the potential importance of IL-9 in allergic diseases. The development of transgenic mice overexpressing IL-9 has suggested a key role for this cytokine in the development of the asthmatic phenotype including airway eosinophilia. In this study, we evaluated the expression of the IL-9R and the effects of IL-9 on human ASM cells by examining the release of Th2-associated chemokines (eotaxin1/CCL11 and thymus- and activation-regulated chemokine (TARC)/CCL17). IL-9R alpha-chain mRNA and surface expression were detected in cultured human airway smooth muscle (ASM) cells. In addition, primary cultured ASM cells, as well as bronchial smooth muscle cells within biopsies of asthmatics and not control subjects, revealed IL-9R protein expression. IL-9 stimulation of human ASM cells resulted in release of eotaxin1/CCL11, but had no effect on the release of TARC/CCL17, in time- and dose-dependent manner. Moreover, in vitro chemotaxis assay demonstrated that conditioned medium from IL-9-stimulated ASM cells attracted human eosinophils. Neutralizing Abs to IL-9, but not to IL-4 or IL-13, reduced significantly IL-9-induced production of eotaxin1/CCL11 from ASM cells. Interestingly, real-time RT-PCR showed that IL-9 up-regulated eotaxin1/CCL11 mRNA expression, but had no effect on TARC/CCL17. Treatment with Act D abrogates IL-9-induced eotaxin1/CCL11 mRNA and protein release by ASM cells. Finally, transfection study using eotaxin1/CCL11 promoter luciferase construct confirmed that IL-9 induced eotaxin1/CCL11 at the transcriptional level. Taken together, these data provide new evidence demonstrating that IL-9-dependent activation of ASM cells contributes to eosinophilic inflammation observed in asthma.
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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".