The MicroRNA-130/301 Family Controls Vasoconstriction in Pulmonary Hypertension
Bibliographic record
Abstract
Pulmonary hypertension (PH) is a complex disorder, spanning several known vascular cell types. Recently, we identified the microRNA-130/301 (miR-130/301) family as a regulator of multiple pro-proliferative pathways in PH, but the true breadth of influence of the miR-130/301 family across cell types in PH may be even more extensive. Here, we employed targeted network theory to identify additional pathogenic pathways regulated by miR-130/301, including those involving vasomotor tone. Guided by these predictions, we demonstrated, via gain- and loss-of-function experimentation in vitro and in vivo , that miR-130/301-specific control of the peroxisome proliferator-activated receptor γ regulates a panel of vasoactive factors communicating between diseased pulmonary vascular endothelial and smooth muscle cells. Of these, the vasoconstrictive factor endothelin-1 serves as an integral point of communication between the miR-130/301-peroxisome proliferator-activated receptor γ axis in endothelial cells and contractile function in smooth muscle cells. Thus, resulting from an in silico analysis of the architecture of the PH disease gene network coupled with molecular experimentation in vivo , these findings clarify the expanded role of the miR-130/301 family in the global regulation of PH. They further emphasize the importance of molecular cross-talk among the diverse cellular populations involved in PH.The microRNA-130/301 family regulates pulmonary hypertension (PH), but its breadth of activity remains undefined. Results Predicted by network analysis, microRNA-130/301 members regulate vasoactive factors such as endothelin-1 for pulmonary vascular cross-talk. Conclusion The microRNA-130/301 family promotes vasoconstriction in PH. Significance This microRNA-based mechanism of vascular cross-talk is central to the systems-wide actions of microRNA-130/301 in PH.
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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 | 0.000 |
| 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".