Corticosteroid-dependent transcription is reduced by inflammatory stimuli in human airway epithelial cells: Rescue by long-acting β <sub>2</sub> -adrenoceptor agonists
Bibliographic record
Abstract
Rationale: Inhaled corticosteroids (glucocorticoids) are the most effective treatment for inflammatory diseases such as asthma. However, in some patients with severe disease, or who smoke, or suffer from COPD, these drugs are less effective. While many investigators focus on the repression of inflammatory gene expression, corticosteroids also induce the expression (transactivation) of numerous genes to elicit anti-inflammatory effects. Results: Using human bronchial airway epithelial, BEAS-2B, and pulmonary, A549, cells, we show that tumour necrosis factor (TNF) α, interleukin (IL)-1β, fetal calf serum (FCS), phorbol ester, cigarette smoke extract and a G q -linked G-protein coupled receptor agonist, all attenuate simple glucocorticoid response element (GRE)-dependent transcription. With TNFα and FCS, this was not overcome by increasing concentrations of dexamethasone, budesonide or fluticasone propionate. Thus, maximal GRE-dependent transcription was reduced and this was confirmed for the glucocorticoid-induced gene, p57KIP2. Long-acting β 2 -adrenoceptor agonists (LABAs), formoterol fumarate and salmeterol xinafoate, enhanced simple GRE-dependent transcription to a level that could not be achieved by glucocorticoid alone. In the presence of TNFα or FCS, which repressed corticosteroid responsiveness, LABAs restored corticosteroid-dependent transcription to that achieved by corticosteroid alone. Conclusions: The repression of transactivation represents a mechanism to explain corticosteroid resistance and its reversal may explain the clinical benefit of LABAs as an add-on therapy in asthma and COPD. Funded by AstraZeneca.
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) | 0.001 | 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 | 0.002 |
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".