Abstract 14936: Profiling of Microrna Expression in Extracellular Vesicles Released From Ischemic Skeletal Muscles in Hypercholesterolemic Conditions
Bibliographic record
Abstract
Introduction: Hypercholesterolemia (HC) is an important cardiovascular risk factor associated with impaired neovascularization in response to ischemia. We found that specific microRNAs (miRs) whose levels are reduced in ischemic skeletal muscles of mice exposed to HC can rescue endothelial cell function and neovascularization. miRs can be transferred between cells via extracellular vesicles (EVs). EVs are involved in several biological functions including the response to ischemia and angiogenesis. Hypothesis: Here we tested the hypothesis that HC causes alterations in the miR content of EVs related to ischemia/neovascularization. Methods: We used a mouse model of peripheral artery disease to induce severe ischemia in the hindlimbs of hypercholesterolemic ApoE -/- and control C57Bl/6J mice. Microvesicles (MVs) and exosomes (exo) were isolated from ischemic skeletal muscles by differential centrifugation. Total RNA was extracted from these 2 types of EVs, and the miR content was analyzed using next generation sequencing. Results: Bioinformatic analysis of the 100 most expressed miRs in EVs showed an enrichment of 38 miRs in MVs compared to ischemic skeletal muscles, and an enrichment of 44 miRs in exo compared to ischemic skeletal muscles. Among enriched miRs, 27 are common to both MVs and exo. We identified specific miRs enriched in EVs that are known to modulate angiogenesis, including several miRs that were altered by HC. For example, among enriched miRs downregulated by HC in EVs, the let-7 family and miR-29a are known to be pro-angiogenic via inhibition of members of the TIMP family. On the other hand, among enriched miRs upregulated by HC in EVs, miR-199a and miR-16-5p are known to be anti-angiogenic via inhibition of VEGF. Conclusions: This study describes for the first time the effect of HC on the modulation of miR profile in EVs following skeletal muscle ischemia. We found that several angiogenesis-modulating miRs are enriched in EVs and altered by HC. Our findings constitute a solid framework for the identification of miRs that could be targeted to modify EV content in atherosclerotic conditions. Eventually these engineered EVs could serve as vectors to promote neovascularization and reduce ischemic damages in severe vascular diseases.
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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".