Cytokine-induced glucocorticoid resistance: Effect on GILZ expression and reversal by formoterol
Bibliographic record
Abstract
The most effective treatment for asthma is inhaled glucocorticoid (GC), whose anti-inflammatory activity can be synergistically enhanced by addition of a long-acting β 2 -adrenoceptor agonist (LABA), e.g. formoterol. However, in severe asthma enhanced production of cytokines, such as interleukin (IL)-1β and tumor necrosis factor α (TNF), appears to contribute to GC resistance. We therefore examined cytokine-induced resistance to the GC dexamethasone (Dex) in human pulmonary cells and evaluated the effects of formoterol. METHODS: Type II A549 epithelial cells were pre-treated for 2 h with IL-1β (1 ng/ml), before Dex (1 µM) addition, and harvested 2 or 4 h later for microarray analysis. Primary human airway smooth muscle (ASM) and bronchial epithelial (HBE) cells, or BEAS-2B and A549 cells with 2×glucocorticoid response element (GRE) reporter, were pre-treated with TNF (10 ng/ml) or IL-1β for 1 h prior to 6 h Dex and/or formoterol (10 nM) treatment, followed by RT-PCR analysis of GILZ and GAPDH expression or luciferase assay. RESULTS: Expression of a majority of Dex-induced genes on the microarray was repressed by IL-1β. Both IL-1β and TNF repressed 2×GRE reporter activation in A549 cells, while formoterol functionally reversed TNF-induced repression in BEAS-2B 2×GRE cells. Expression of GILZ, a GC inducible gene, was significantly repressed in BEAS-2B, ASM and HBE cells. However, this repression was modestly reversed by formoterol addition in ASM and BEAS-2B cells. CONCLUSIONS: IL-1β and TNF induce GC resistance in multiple lung cells relevant to asthma, in a manner that is reversed by formoterol. This provides support for the use of GC/LABA combinations in severe asthma.
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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 | 0.001 |
| 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 | 0.001 |
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".