Murine Double Minute‐2 is a new regulator of physiopathological angio‐adaptation in cardiac and skeletal muscles
Bibliographic record
Abstract
Maintenance of the established vasculature and angiogenesis is critical for vascular homeostasis in adults. Murine double minute-2 (Mdm2) oncoprotein was shown to regulate the expression of the pro-angiogenic Vascular Endothelial Growth Factor (VEGF) in vitro and in tumors. Here we aimed to determine if Mdm2 regulates muscle angio-adaptation in vivo. Methods and main results Mdm2 protein positively correlated with high levels of capillarization and VEGF expression in cardiac and skeletal muscles. Mdm2 and VEGF expression, and capillarization were decreased in diabetic skeletal muscle and failing heart. Interestingly, physical exercise prevented or restored such alterations. In transgenic mice with hypomorphic and knockout alleles for Mdm2, muscles possess 20% less capillaries and exercise-induced VEGF expression is inhibited. Our 3D angiogenesis assay enables us to study the angiogenic activity of endothelial cells in cultured muscle explants. Stimulation of explants with muscle homogenates obtained from exercised animals enhanced endothelial cell migration. This was suppressed in explants obtained from Mdm2 hypomorphic mice or wild-type animals treated with Mdm2 inhibitor Nutlin-3. Conclusion We identified a complex relationship between Mdm2 and VEGF that regulates cardiac and skeletal muscle angio-adaptation. Research support: Natural Sciences and Engineering Research Council of Canada.
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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".