Transactivation and transrepression in the repression of inflammatory gene expression by dexamethasone
Bibliographic record
Abstract
BACKGROUND: The anti-inflammatory activities of glucocorticoids are attributed to the repression of inflammatory gene expression. Dogma states that this occurs via direct repression (transrepression) by the glucocorticoid receptor (GR) of transcription factors, such as NF-κB. However, evidence also suggests that gene induction (transactivation) by GR is important for repression. AIMS: To assess the roles of transrepression and transactivation in the repression of inflammatory gene expression by dexamethasone (Dex). METHODS AND RESULTS: The effect of Dex on 39 IL-1β-induced genes was examined in human pulmonary A549 cells by real-time PCR. Dex showed a range of activity in terms of the extent (E max ) and potency (EC 50 ) of repression on these genes. These parameters correlated, such that the most highly repressed genes were also the most potently repressed. While all 39 genes were NF-κB-dependent, this did not correlate with repression by Dex. Finally, inhibition of protein translation by cycloheximide (CHX) reduced IL-1β-induced expression of 19 genes (secondary response genes). Of the remaining 21 genes, CHX significantly prevented the Dex-dependent repression of 11 (∼50%), suggesting a role for transactivation. These 11 genes were significantly more sensitive (E max and EC 50 ) to repression by dexamethasone when compared to genes showing repression that was insensitive to CHX (and which may represent a classical transrepression mechanism). CONCLUSIONS: Repression of inflammatory gene expression by dexamethasone involves multiple mechanisms. Transactivation appears to play a significant role showing both high potency and high level of repression on target genes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".