HDAC4 contributes to IL‐1‐induced mPGES‐1 expression in human synovial fibroblasts through up‐regulation of Egr‐1 transcriptional activity
Bibliographic record
Abstract
Microsomal prostaglandin E synthase-1 (mPGES-1) catalyzes the terminal step in the biosynthesis of PGE(2), which contributes to many physiopathological processes. We show here that inhibitors of histone deacetylase (HDAC) activity, trichostatin A (TSA), butyric acid (BA), and valproic acid (BA) prevented IL-1-induced mPGES-1 protein expression in human synovial fibroblasts. TSA also inhibited IL-1-induced mPGES-1 mRNA expression and promoter activation. Overexpression of HDAC4, but not of HDAC1, 2, 3, 5, or 6 enhanced, whereas HDAC4 silencing with small interfering RNA (siRNA) reduced, IL-1-induced mPGES-1 promoter activation, implying that HDAC4 contributes to mPGES-1 gene expression. Consistently, IL-1-induced mPGES-1 protein expression was prevented by siRNA for HDAC4. We also demonstrate that IL-1-induced HDAC4 recruitment to the mPGES-1 promoter. This recruitment was not accompanied by deacetylation of histones H3 and H4, suggesting that HDAC4 contributes to mPGES-1 induction independently of local deacetylation of histones H3 and H4. We then investigated whether HDAC4 regulates mPGES-1 expression by modulating the activity of Egr-1, a key transcription factor in IL-1-induced mPGES-1 expression. We found that HDAC4 overexpression enhances, whereas HDAC4 knockdown by siRNA reduces Egr-1-mediated activation of the mPGES-1 promoter. Together these data indicate that HDAC4 contributes to transcriptional induction of mPGES-1 by IL-1 through a mechanism involving up-regulation of Egr-1 transcriptional activity.
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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 | 0.000 |
| 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".