Glucocorticoid-induced transcriptional regulators in human airway cells and airways
Bibliographic record
Abstract
Introduction: Inhaled glucocorticoids (corticosteroids) act on the glucocorticoid receptor (GR, NR3C1) to control inflammation in asthma by reducing the expression of inflammatory genes. This may involve direct repression of inflammatory gene transcription by GR and induction of anti-inflammatory genes. However, GR is a transcription factor that induces the expression of many genes, including other transcription factors. Aims: To characterize the expression of transcriptional regulators that are induced by glucocorticoids in airway epithelial cells and in the airways. Methods: Gene expression profiling of RNA from: i) primary human bronchial epithelial cells; ii) pulmonary A549 epithelial cells; and iii) BEAS-2B cells following budesonide treatment was performed using Affymetrix PrimeView microarrays. Data was compared to that from biopsies of healthy individuals 6 h following a single dose of inhaled budesonide. Selected genes were validated by qPCR. Results: Gene ontology showed up to 20% of genes upregulated by budesonide treatment in the human airways as being involved in transcription control and many of these were common with the epithelial cells. Expression of CEBPD, FOXO3, HIF3A, KLF9, KLF15, PER1, TFCP2L1, TSC22D3 and ZBTB16 were significantly upregulated in the biopsy samples and in HBE cells, with some variability between A549 and BEAS-2B, or other structural cells. Conclusion: The large number of transcriptional regulators induced by glucocorticoids indicates key roles in mediating downstream responses. Mechanistic studies are required to identify roles for these factors in the desirable therapeutic effects and/or unwanted side effects of glucocorticoids.
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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 | 0.001 |
| 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) | 0.003 | 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".