Enhanced anti-inflammatory gene expression in humans following inhaled budesonide
Bibliographic record
Abstract
RATIONALE: Inhaled corticosteroids (ICS) are the cornerstone of asthma treatment and act on the glucocorticoid receptor (GR) to repress inflammatory gene expression. Nevertheless, incomplete understanding of GR action hinders the development of improved anti-inflammatory ligands. While many studies focus on direct GR repression of inflammatory gene transcription ( transrepression ), corticosteroids also bind GR to enhance anti-inflammatory gene expression ( transactivation ). However, it is unclear whether a clinical dose of ICS elicits this effect in humans. METHODS: Twelve healthy, non-smoking, non-atopic males with normal lung function were recruited to a prospective double-blind, placebo-controlled, randomised, two-period cross-over study. A single dose of inhaled placebo or budesonide (1600 µg) was administered by Turbuhaler®. Bronchoscopy was performed after 5-6 h and endobronchial brushings and biopsies were obtained and processed for gene expression analysis. RESULTS: Inhaled budesonide significantly enhanced mRNA expression of multiple genes including; FKBP5 / FKBP51 , TSC22D3 / GILZ , DUSP1 / MKP1 , ZFP36 / TTP , RGS2 , CDKN1C / p57Kip2 and NFKBIA / IKBA in the biopsy samples. With the exception of RGS2 , these mRNAs were also upregulated in cells obtained from brushings. CONCLUSIONS: This study confirms enhanced expression of multiple corticosteroid-induced genes by a single, clinical dose of inhaled budesonide. The data obtained illustrate the therapeutic importance of ICS-induced transactivation since TSC22D3 , DUSP1 , ZFP36 and NFKBIA all repress inflammatory genes, FKBP5 provides feedback control, CDKN1C (a cell-cycle inhibitor) may attenuate Jun N-terminal kinase activity while RGS2 is bronchoprotective.
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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) | 0.001 | 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 | 0.000 |
| 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".