Selective Enrichment of Angiomirs in Extracellular Vesicles Released from Ischemic Skeletal Muscles: Potential Role in Angiogenesis and Neovascularization
Bibliographic record
Abstract
MicroRNAs (miRs) regulate physiological and pathological processes, including ischemia-induced angiogenesis and neovascularization. They can be transferred between cells by extracellular vesicles (EVs). However, the specific miRs that are packaged in EVs released from skeletal muscles, and how this process is modulated by ischemia, remain to be determined. We used a mouse model of hindlimb ischemia and next generation sequencing (NGS) to perform a complete profiling of miR expression and determine the effect of ischemia in skeletal muscles, and in EVs of different sizes (microvesicles (MVs) and exosomes) released from these muscles. Ischemia significantly modulated miR expression in whole muscles and EVs, increasing the levels of several miRs that can have pro-angiogenic effects (angiomiRs). We found that specific angiomiRs are selectively enriched in MVs and/or exosomes in response to ischemia. In silico approaches indicate that these miRs modulate pathways that play key roles in angiogenesis and neovascularization, including HIF1/VEGF signaling, regulation of actin cytoskeleton and focal adhesion, NOTCH, PI3K/AKT, RAS/MAPK, JAK/STAT, TGFb/SMAD signaling and the NO/cGMP/PKG pathway. Thus, we show for the first time that angiomiRs are selectively enriched in MVs and exosomes released from ischemic muscles. These angiomiRs could be targeted in order to improve the angiogenic function of EVs for potential novel therapeutic applications in patients with severe ischemic vascular diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".