Atorvastatin Inhibits Myocardin Expression in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Atorvastatin (ATV), an inhibitor of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, is widely prescribed as a lipid-lowering drug. It also inhibits the RhoA-Rho-associated kinase pathway in vascular smooth muscle (SM) cells and critically inhibits SM function. Myocardin is a coactivator of serum response factor, which upregulates SM contractile proteins. The RhoA-Rho-associated kinase pathway, which directly triggers SM contraction, also increases myocardin gene expression. Therefore, we investigated whether ATV inhibits myocardin gene expression in SM cells. In mice injected with ATV (IP 20 μg/g per day) for 5 days, myocardin gene expression was significantly downregulated in aortic and carotid arterial tissues with decreased expression of myocardin target genes SM α-actin and SM22. Correspondingly, the contractility of aortic rings in mice treated with ATV or the Rho-associated kinase inhibitor Y-27632 was reduced in response to treatment with either KCl or phenylephrine. In cultured mouse and human aortic SM cells, KCl treatment stimulated the expression of myocardin, SM α-actin, and SM22. These stimulatory effects were prevented by ATV treatment. ATV-induced inhibition of myocardin expression was prevented by pretreatment with either mevalonate or geranylgeranylpyrophosphate but not farnesylpyrophosphate. Treatment with Y-27632 mimicked ATV effects on the gene expression of myocardin, SM α-actin, and SM22, further suggesting a role for the RhoA-Rho-associated kinase pathway in ATV effects. Furthermore, ATV treatment inhibited RhoA membrane translocation and activation; these effects were prevented by pretreatment with mevalonate. We conclude that ATV inhibits myocardin gene expression in vivo and in vitro, suggesting a novel mechanism for ATV inhibition of vascular contraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".