Potential Role of Glycogen Synthase Kinase‐3β in Regulation of Myocardin Activity in Human Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Glycogen synthase kinase (GSK)-3β, a serine/threonine kinase with an inhibitory role in glycogen synthesis in hepatocytes and skeletal muscle, is also expressed in cardiac and smooth muscles. Inhibition of GSK-3β results in cardiac hypertrophy through reducing phosphorylation and increasing transcriptional activity of myocardin, a transcriptional co-activator for serum response factor. Myocardin plays critical roles in differentiation of smooth muscle cells (SMCs). This study, therefore, aimed to examine whether and how inhibition of GSK-3β regulates myocardin activity in human vascular SMCs. Treatment of SMCs with the GSK-3β inhibitors AR-A014418 and TWS 119 significantly reduced endogenous myocardin activity, as indicated by lower expression of myocardin target genes (and gene products), CNN1 (calponin), TAGLN1 (SM22), and ACTA2 (SM α-actin). In human SMCs overexpressing myocardin through the T-REx system, treatment with either GSK-3β inhibitor also inhibited the expression of CNN1, TAGLN1, and ACTA2. These effects of GSK-3β inhibitors were mimicked by transfection with GSK-3β siRNA. Notably, both AR-A014418 and TWS 119 decreased the serine/threonine phosphorylation of myocardin. The chromatin immunoprecipitation assay showed that AR-A014418 treatment reduced myocardin occupancy of the promoter of the myocardin target gene ACTA2. Overexpression of a dominant-negative GSK-3β mutant in myocardin-overexpressing SMCs reduced the expression of calponin, SM22, and SM α-actin. As expected, overexpression of constitutively active or wild-type GSK-3β in SMCs without myocardin overexpression increased expression of these proteins. In summary, our results indicate that inhibition of GSK-3β reduces myocardin transcriptional activity, suggesting a role for GSK-3β in myocardin transcriptional activity and smooth muscle differentiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".