Peroxisome proliferator‐activated receptor‐γ abrogates Smad‐dependent collagen stimulation by targeting the p300 transcriptional coactivator
Bibliographic record
Abstract
Ligands of peroxisome proliferator-activated receptor-gamma (PPAR-gamma) abrogate the stimulation of collagen gene transcription induced by transforming growth factor-beta (TGF-beta). Here, we delineate the mechanisms underlying this important novel physiological function for PPAR-gamma in connective tissue homeostasis. First, we demonstrated that antagonistic regulation of TGF-beta activity by PPAR-gamma ligands involves cellular PPAR-gamma, since 15-deoxy-Delta12,14-prostaglandin J(2) (15d-PGJ(2)) failed to block TGF-beta-induced responses in either primary cultures of PPAR-gamma-null murine embryonic fibroblasts, or in normal human skin fibroblasts with RNAi-mediated knockdown of PPAR-gamma. Next, we examined the molecular basis underlying the abrogation of TGF-beta signaling by PPAR-gamma in normal human fibroblasts in culture. The results demonstrated that Smad-dependent transcriptional responses were blocked by PPAR-gamma without preventing Smad2/3 activation. In contrast, the interaction between activated Smad2/3 and the transcriptional coactivator and histone acetyltransferase p300 induced by TGF-beta, and the accumulation of p300 on consensus Smad-binding DNA sequences and histone H4 hyperacetylation at the COL1A2 locus, were all prevented by PPAR-gamma. Wild-type p300, but not a mutant form of p300 lacking functional histone acetyltransferase, was able to restore TGF-beta-induced stimulation of COL1A2 in the presence of PPAR-gamma ligands. Collectively, these results indicate that PPAR-gamma blocked Smad-mediated transcriptional responses by preventing p300 recruitment and histone H4 hyperacetylation, resulting in the inhibition of TGF-beta-induced collagen gene expression. Pharmacological activation of PPAR-gamma thus may represent a novel therapeutic approach to target p300-dependent TGF-beta profibrotic responses such as stimulation of collagen gene expression.
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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".