Inhibition of NF-κB-dependent Transcription by MKP-1
Bibliographic record
Abstract
Acting via the glucocorticoid receptor (GR), glucocorticoids exert potent anti-inflammatory effects partly by repressing inflammatory gene transcription occurring via factors such as NF-kappaB. In the present study, the synthetic glucocorticoid, dexamethasone, induces expression of MKP-1 (mitogen-activated protein kinase (MAPK) phosphatase-1) in human bronchial epithelial (BEAS-2B) and pulmonary (A549) cells. This correlates with reduced TNFalpha-stimulated p38 MAPK phosphorylation. Since NF-kappaB-dependent transcription and IL-8 protein, mRNA, and unspliced RNA (a surrogate of transcription rate) are sensitive to p38 MAPK inhibitors (SB203580 and SB239063), we explored the role of MKP-1 in repression of these outputs. Repression of TNFalpha-induced p38 MAPK phosphorylation, NF-kappaB-dependent transcription, and IL-8 expression by dexamethasone are sensitive to transcriptional or translational inhibitors. This indicates a role for de novo gene synthesis. Adenoviral expression of MKP-1 profoundly reduces p38 MAPK phosphorylation and IL-8 expression. Similarly, NF-kappaB-dependent transcription is significantly reduced to levels consistent with maximal p38 MAPK inhibition. Thus, MKP-1 attenuates TNFalpha-dependent activation of p38 MAPK, induction of IL-8 expression, and NF-kappaB-dependent transcription. Small interfering RNA knockdown of dexamethasone-induced MKP-1 expression partially reverses the repression of TNFalpha-activated p38 MAPK, demonstrating that MKP-1 participates in the dexamethasone-dependent repression of this pathway. In the presence of MKK6 (MAPK kinase 6), a p38 MAPK activator, dexamethasone dramatically represses TNFalpha-induced NF-kappaB-dependent transcription, and this is significantly reversed by MKP-1-targeting small interfering RNA. This reveals an important and novel role for transcriptional activation (transactivation) of MKP-1 in the repression of NF-kappaB-dependent transcription by glucocorticoids. We conclude that GR transactivation is essential to the anti-inflammatory properties of GR ligands.
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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 | 0.000 |
| 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) | 0.002 | 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".