IL-17A Mediates a Selective Gene Expression Profile in Asthmatic Human Airway Smooth Muscle Cells
Bibliographic record
Abstract
Airway smooth muscle (ASM) cells are thought to contribute to the pathogenesis of allergic asthma by orchestrating and perpetuating airway inflammation and remodeling responses. In this study, we evaluated the IL-17RA signal transduction and gene expression profile in ASM cells from subjects with mild asthma and healthy individuals. Human primary ASM cells were treated with IL-17A and probed by the Affymetrix GeneChip array, and gene targets were validated by real-time quantitative RT-PCR. Genomic analysis underlined the proinflammatory nature of IL-17A, as multiple NF-κB regulatory factors and chemokines were induced in ASM cells. Transcriptional regulators consisting of primary response genes were overrepresented and displayed dynamic expression profiles. IL-17A poorly enhanced IL-1β or IL-22 gene responses in ASM cells from both subjects with mild asthma and healthy donors. Interestingly, protein modifications to the NF-κB regulatory network were not observed after IL-17A stimulation, although oscillations in IκBε expression were detected. ASM cells from subjects with mild asthma up-regulated more genes with greater overall variability in response to IL-17A than from healthy donors. Finally, in response to IL-17A, ASM cells displayed rapid activation of the extracellular signal-regulated kinase/ribosomal S6 kinase signaling pathway and increased nuclear levels of phosphorylated extracellular signal-regulated kinase. Taken together, our results suggest that IL-17A mediated modest gene expression response, which, in cooperation with the NF-κB signaling network, may regulate the gene expression profile in ASM cells.
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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 | 0.000 |
| 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".