Induction of MicroRNA-1 by Myocardin in Smooth Muscle Cells Inhibits Cell Proliferation
Bibliographic record
Abstract
OBJECTIVE: Myocardin is a cardiac- and smooth muscle-specific transcription co-factor that potently activates the expression of downstream target genes. Previously, we demonstrated that overexpression of myocardin inhibited the proliferation of smooth muscle cells (SMCs). Recently, myocardin was reported to induce the expression of microRNA-1 (miR-1) in cardiomyocytes. In this study, we investigated whether myocardin induces miR-1 expression to mediate its inhibitory effects on SMC proliferation. METHODS AND RESULTS: Using tetracycline-regulated expression (T-REx) inducible system expressing myocardin in human vascular SMCs, we found that overexpression of myocardin resulted in significant induction of miR-1 expression and inhibition of SMC proliferation, which was reversed by miR-1 inhibitors. Consistently, introduction of miR-1 into SMCs inhibited their proliferation. We isolated spindle-shaped and epithelioid human SMCs and demonstrated that spindle-shaped SMCs were more differentiated and less proliferative. Correspondingly, spindle-shaped SMCs had significantly higher expression levels of both myocardin and miR-1 than epithelioid SMCs. We identified Pim-1, a serine/threonine kinase, as a target gene for miR-1 in SMCs. Western blot and luciferase reporter assays further confirmed that miR-1 targeted Pim-1 directly. Furthermore, neointimal lesions of mouse carotid arteries displayed downregulation of myocardin and miR-1 with upregulation of Pim-1. CONCLUSIONS: Our data demonstrate that miR-1 participates in myocardin-dependent of SMC proliferation inhibition.
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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) | 0.001 | 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".