MicroRNA regulation of BMP signaling; cross-talk between endothelium and vascular smooth muscle cells
Bibliographic record
Abstract
Abstract Vascular smooth muscle cells (vSMC) are essential to the integrity of blood vessels, and therefore an attractive target of therapeutics aimed at improving vascular function. Smooth muscle cells are one of the few cell types that maintain plasticity and can switch phenotypes from differentiated (contractile) to de-differentiated (synthetic) and vice versa. As small regulatory transcripts, miRNAs act as genetic ‘fine tuners’ of posttranscriptional events and can act as genetic switches promoting phenotypic switching. The microRNA miR26a targets the BMP signalling effector, smad1 . We show that loss of miR26 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 and that normalization of Smad1 levels through double knockdown of miR26 and smad1 rescues hemorrhage suggesting a direct relationship between miR26 and smad1 and vascular stability. Using a BMP genetic reporter and pSmad1 staining we show that the effect of miR26 on vascular instability 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 miR26 results in an increase in acta2 -expressing smooth muscle cell numbers and promotes a differentiated smooth muscle morphology. Taken together our data suggests that miR26 modulates BMP signalling in endothelial cells and indirectly promotes a differentiated smooth muscle phenotype. Our data also suggests that crosstalk from BMP-responsive endothelium to smooth muscle is important for its 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 | 0.000 |
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".