IL-17A Induces Eotaxin-1/CC Chemokine Ligand 11 Expression in Human Airway Smooth Muscle Cells: Role of MAPK (Erk1/2, JNK, and p38) Pathways
Bibliographic record
Abstract
Recently, IL-17A has been shown to be expressed in higher levels in respiratory secretions from asthmatics and correlated with airway hyperresponsiveness. Although these studies raise the possibility that IL-17A may influence allergic disease, the mechanisms remain unknown. In this study, we investigated the molecular mechanisms involved in IL-17A-mediated CC chemokine (eotaxin-1/CCL11) production from human airway smooth muscle (ASM) cells. We found that incubation of human ASM cells with rIL-17A resulted in a significant increase of eotaxin-1/CCL11 release from ASM cells that was reduced by neutralizing anti-IL-17A mAb. Moreover, IL-17A significantly induced eotaxin-1/CCL11 release and mRNA expression, an effect that was abrogated with cycloheximide and actinomycin D treatment. Furthermore, transfection studies using a luciferase-driven reporter construct containing eotaxin-1/CCL11 proximal promoter showed that IL-17A induced eotaxin-1/CCL11 at the transcriptional level. IL-17A also enhanced significantly IL-1beta-mediated eotaxin-1/CCL11 mRNA, protein release, and promoter activity in ASM cells. Primary human ASM cells pretreated with inhibitors of MAPK p38, p42/p44 ERK, JNK, or JAK but not PI3K, showed a significant decrease in eotaxin-1/CCL11 release upon IL-17A treatment. In addition, IL-17A mediated rapid phosphorylation of MAPK (p38, JNK, and p42/44 ERK) and STAT-3 but not STAT-6 or STAT-5 in ASM cells. Taken together, our data provide the first evidence of IL-17A-induced eotaxin-1/CCL11 expression in ASM cells via MAPK (p38, p42/p44 ERK, JNK) signaling pathways. Our results raise the possibility that IL-17A may play a role in allergic asthma by inducing eotaxin-1/CCL11 production.
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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 | 0.001 |
| 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 | 0.001 |
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".