MicroRNA26 attenuates vascular smooth muscle maturation via endothelial BMP signalling
Bibliographic record
Abstract
As small regulatory transcripts, microRNAs (miRs) act as genetic 'fine tuners' of posttranscriptional events, and as genetic switches to promote phenotypic switching. The miR miR26a targets the BMP signalling effector, smad1. We show that loss of miR26a leads to hemorrhage (a loss of vascular stability) in vivo, suggesting altered vascular differentiation. Reduction in miR26a levels increases smad1 mRNA and phospho-Smad1 (pSmad1) levels. We show that increasing BMP signalling by overexpression of smad1 also leads to hemorrhage. Normalization of Smad1 levels through double knockdown of miR26a and smad1 rescues hemorrhage, suggesting a direct relationship between miR26a, smad1 and vascular stability. Using an in vivo BMP genetic reporter and pSmad1 staining, we show that the effect of miR26a on smooth muscle differentiation is non-autonomous; BMP signalling is active in embryonic endothelial cells, but not in smooth muscle cells. Nonetheless, increased BMP signalling due to loss of miR26a results in an increase in acta2-expressing smooth muscle cell numbers and promotes a differentiated smooth muscle morphology. Similarly, forced expression of smad1 in endothelial cells leads to an increase in smooth muscle cell number and coverage. Furthermore, smooth muscle phenotypes caused by inhibition of the BMP pathway are rescued by loss of miR26a. Taken together, our data suggest that miR26a modulates BMP signalling in endothelial cells and indirectly promotes a differentiated smooth muscle phenotype. Our data highlights how crosstalk from BMP-responsive endothelium to smooth muscle is important for smooth muscle differentiation.
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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".