Abstract 13102: Microrna-150 Modulates Ischemia-induced Neovascularization in Atherosclerotic Conditions
Bibliographic record
Abstract
Background: Hypercholesterolemia is an atherosclerotic condition that is associated with impaired neovascularization in response to ischemia. This study sought to define the role of microRNAs (miRNAs) in that pathophysiology. Methods and results: Next generation sequencing and qRT-PCR analyses identified miR-150 as a pro-angiogenic miRNA which expression is significantly reduced in the ischemic muscles of hypercholesterolemic ApoE-deficient (ApoE -/- ) mice, and in HUVECs exposed to oxLDL. Forced expression of miR-150 using a miR mimic could rescue oxLDL-mediated impairment of endothelial cell migration and tubule formation in vitro. In a mouse model of hindlimb ischemia, intramuscular injection of miR-150 mimic restored blood flow recuperation, vascular densities in ischemic muscles, and functional mobility in ApoE -/- mice. Treatment of ApoE -/- mice with miR-150 also increased the number and the activities of pro-angiogenic cells (PACs). miR-150 targets SRC kinase signaling inhibitor 1 (SRCIN1), an important regulator of Src activity. Here we found that hypercholesterolemia and oxLDL exposure are associated with increased SRCIN1 expression and decreased Src activity. However, treatment with miR-150 mimic reduces SRCIN1 expression and restores Src and downstream eNOS and Akt activities both in vitro and in vivo. We also demonstrate that the angiogenic effects of miR-150 are abolished when endothelial cells are treated with an inhibitor of Src. Conclusions: Hypercholesterolemia is associated with reduced expression of miR-150, impaired Src signaling and inefficient neovascularisation in response to ischemia. Forced expression of miR-150 using a miR mimic could constitute a novel therapeutic strategy to improve ischemia-induced neovascularization in atherosclerotic conditions.
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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) | 0.003 | 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".