TNF-α and IFN-γ inversely modulate expression of the IL-17E receptor in airway smooth muscle cells
Bibliographic record
Abstract
The interleukin-17B receptor (IL-17BR) is expressed in a variety of tissues and is upregulated under inflammatory conditions. This receptor binds both its cognate ligand IL-17B and IL-17E/IL-25, a novel cytokine known to promote Th2 responses. The present study shows that airway smooth muscle cells express IL-17BR in vitro and that its expression is upregulated by TNF-alpha and downregulated by IFN-gamma. Our data indicate that TNF-alpha upregulates IL-17BR mainly through nuclear factor-kappaB as assessed with the IkappaB kinase 2 inhibitor AS-602868. In addition, both IFN-gamma and dexamethasone are able to antagonize a TNF-alpha-induced IL-17BR increase in mRNA expression. The mitogen-activated protein kinase kinase inhibitor U0126 totally reversed the inhibition observed with IFN-gamma, suggesting the involvement of the extracellular signal-regulated kinase pathway in this effect. In addition, on stimulation with IL-17E, airway smooth muscle cells increase their expression of ECM components, namely procollagen-alphaI and lumican mRNA. Furthermore, immunohistochemical analysis of biopsies from asthmatic subjects reveals that this receptor is abundant in smooth muscle layers. This is the first report showing IL-17BR receptor in structural cells of the airways. Our results suggest a potential proremodeling effect of IL-17E on airway smooth muscle cells through the induction of ECM and that its receptor is upregulated by proinflammatory conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".