MicroRNA‐1 inhibits myocardin‐induced contractility of human vascular smooth muscle cells
Bibliographic record
Abstract
Myocardin, a cofactor of serum response factor (SRF), specifically induces the expression of contractile proteins to promote differentiation and contractile phenotype of smooth muscle cells (SMCs). SRF directly induces the transcription of microRNA-1 (miR-1) in cardiac and skeletal muscle precursor cells and miR-1 promotes the skeletal muscle differentiation and modulates cardiac hypertrophy. We aimed to examine whether miR-1 plays a role in the regulation of smooth muscle contractility. We found that miR-1 expression was induced by myocardin overexpression in human aortic SMCs. In a collagen lattice contraction assay using SMCs harboring a doxycycline-inducible expression system for myocardin, we found that myocardin expression increased the contractility of SMCs, which was significantly inhibited by exogenous miR-1. Our further studies revealed that exogenous miR-1, which did not affect myocardin or SRF expression, suppressed the expression of contractile proteins, such as alpha-SMA and SM22, and impaired the actin cytoskeletal organization. Taken together, our results have revealed that myocardin induces miR-1 expression, which represses the expression of contractile proteins and thereby inhibits the contractility of SMCs. Therefore, our findings suggest a role of miR-1 in the negative feedback loop in the regulation of contractility induced by myocardin.
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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 | 0.001 |
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".