The long‐acting β<sub>2</sub>‐adrenoceptor agonist, indacaterol, enhances glucocorticoid receptor‐mediated transcription in human airway epithelial cells in a gene‐ and agonist‐dependent manner
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Inhaled glucocorticoid (ICS)/long-acting β2 -adrenoceptor agonist (LABA) combination therapy is a recommended treatment option for patients with moderate/severe asthma in whom adequate control cannot be achieved by an ICS alone. Previously, we discovered that LABAs can augment dexamethasone-inducible gene expression and proposed that this effect may explain how these two drugs interact to deliver superior clinical benefit. Herein, we extended that observation by analysing, pharmacodynamically, the effect of the LABA, indacaterol, on glucocorticoid receptor (GR)-mediated gene transcription induced by seven ligands with intrinsic activity values that span the spectrum of full agonism to antagonism. EXPERIMENTAL APPROACH: BEAS-2B human airway epithelial cells stably transfected with a 2× glucocorticoid response element luciferase reporter were used to model gene transcription together with an analysis of several glucocorticoid-inducible genes. KEY RESULTS: Indacaterol augmented glucocorticoid-induced reporter activation in a manner that was positively related to the intrinsic activity of the GR agonist. This effect was demonstrated by an increase in response maxima without a change in GR agonist affinity or efficacy. Indacaterol also enhanced glucocorticoid-inducible gene expression. However, the magnitude of this effect was dependent on both the GR agonist and the gene of interest. CONCLUSIONS AND IMPLICATIONS: These data suggest that indacaterol activates a molecular rheostat, which increases the transcriptional competency of GR in an agonist- and gene-dependent manner without apparently changing the relationship between fractional GR occupancy and response. These findings provide a platform to rationally design ICS/LABA combination therapy that is based on the generation of agonist-dependent gene expression profiles in target and off-target tissues.
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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) | 0.002 | 0.000 |
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".