PKA promotes MEF2/HDAC repression of the c‐Jun promoter in vascular smooth muscle cells
Bibliographic record
Abstract
Vascular smooth muscle cells (VSMCs) do not terminally differentiate, but can modulate their phenotype in response to extracellular stimuli. Although proliferative VSMCs are required during vascular repair, the activated phenotype also plays a role in vascular disease. To investigate the role of myocyte enhancer factor 2 (MEF2) in the induction of the growth‐responsive gene, c‐Jun, we utilized the A10 line. Mitogenic stimulation by platelet derived growth factor (PDGF) resulted in marked induction of c‐Jun protein and promoter activity. This induction was attenuated by rottlerin and KN‐62. Given that these signaling pathways have been shown to relieve the repressive effects of histone deacetylases (HDACs) on MEF2 proteins, we overexpressed HDAC4, which repressed the c‐Jun promoter. Mutation of the MEF2 binding site in the c‐Jun promoter resulted in activation during quiescent conditions, while treatment with trichostatin A, increased c‐Jun protein. Interestingly, activation of protein kinase A (PKA) prevented PDGF induction of c‐Jun, and repressed a MEF2‐dependent reporter gene. PKA also caused nuclear accumulation of HDAC4 and stabilized the interaction of HDAC4 with MEF2D. Thus, it appears that MEF2 and HDAC4 act to repress c‐Jun expression in quiescent VSMCs, PKA enhances this repression, and PDGF derepresses through CaMKs and novel PKCs. Supported by CIHR and the Heart and Stroke Foundation of Canada.
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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 | 0.001 |
| 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".