An analysis of glucocorticoid receptor‐mediated gene expression in <scp>BEAS‐2B</scp> human airway epithelial cells identifies distinct, ligand‐directed, transcription profiles with implications for asthma therapeutics
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: International asthma guidelines recommend that inhaled glucocorticoids be used as a monotherapy in all patients with mild to moderate disease because of their ability to suppress airways inflammation. Current evidence suggests that the therapeutic benefit of glucocorticoids is due to the transactivation and transrepression of anti-inflammatory and pro-inflammatory genes respectively. However, the extent to which clinically relevant glucocorticoids are equivalent in their ability to modulate gene expression is unclear. EXPERIMENTAL APPROACH: A pharmacodynamics investigation of glucocorticoid receptor (GR)-mediated gene transactivation in BEAS-2B human airway epithelial cells was performed using a glucocorticoid response element luciferase reporter coupled with an analysis of glucocorticoid-inducible genes encoding proteins with anti-inflammatory and adverse-effect potential. KEY RESULTS: Using transactivation as a functionally relevant output, a given glucocorticoid displayed a unique, gene expression 'fingerprint' where intrinsic efficacy and GR density were essential determinants. We showed that depending on the gene selected for analysis, a given glucocorticoid can behave as an antagonist, partial agonist, full agonist or even 'super agonist'. In the likely event that different, tissue-dependent gene expression profiles are reproduced in vivo, then the anti-inflammatory and adverse-effect potential of many glucocorticoids currently available as asthma therapeutics may not be equivalent. CONCLUSIONS AND IMPLICATIONS: The generation of gene expression 'fingerprints' in target and off-target human tissues could assist the rational design of GR agonists with improved therapeutic ratios. This approach could identify compounds that are useful in the management of severe asthma and other inflammatory disorders where systemic exposure is desirable.
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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) | 0.001 | 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".