Regulation of the Growth Arrest and DNA Damage-Inducible Gene 45 (GADD45) by Peroxisome Proliferator-Activated Receptor γ in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR) gamma is activated by thiazolidinediones (TZDs), widely used as insulin-sensitizing agents for the treatment of type 2 diabetes. TZDs have been shown to induce apoptosis in a variety of mammalian cells. In vascular smooth muscle cells (VSMCs), proliferation and apoptosis may be competing processes during the formation of restenotic and atherosclerotic lesions. The precise molecular mechanisms by which TZDs induce apoptosis in VSMCs, however, remain unclear. In the present study, we demonstrate that the TZDs rosiglitazone (RSG), troglitazone (TRO), and a novel non-TZD partial PPARgamma agonist (nTZDpa) induce caspase-mediated apoptosis of human coronary VSMCs. Induction of VSMC apoptosis correlated closely with an upregulation of growth arrest and DNA damage-inducible gene 45 (GADD45) mRNA expression and transcription, a well-recognized modulator of cell cycle arrest and apoptosis. Using adenoviral-mediated overexpression of a constitutively active PPARgamma mutant and the irreversible PPARgamma antagonist GW9662, we provide evidence that PPARgamma ligands induce caspase-mediated apoptosis and GADD45 expression through a receptor-dependent pathway. Deletion analysis of the GADD45 promoter revealed that a 153-bp region between -234 and -81 bp proximal to the transcription start site, containing an Oct-1 element, was crucial for the PPARgamma ligand-mediated induction of the GADD45 promoter. PPARgamma activation induced Oct-1 protein expression and DNA binding and stimulated activity of a reporter plasmid driven by multiple Oct-1 elements. These findings suggest that activation of PPARgamma can lead to apoptosis and growth arrest in VSMCs, at least in part, by inducing Oct-1-mediated transcription of GADD45. The full text of this article is available online at http://www.circresaha.org.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".