Extracellular Vesicles Secreted by Atherogenic Macrophages Transfer MicroRNA to Inhibit Cell Migration
Bibliographic record
Abstract
OBJECTIVE: During inflammation, macrophages secrete vesicles carrying RNA, protein, and lipids as a form of extracellular communication. In the vessel wall, extracellular vesicles (EVs) have been shown to be transferred between vascular cells during atherosclerosis; however, the role of macrophage-derived EVs in atherogenesis is not known. Here, we hypothesize that atherogenic macrophages secrete microRNAs (miRNAs) in EVs to mediate cell-cell communication and promote proinflammatory and proatherogenic phenotypes in recipient cells. APPROACH AND RESULTS: We isolated EVs from mouse and human macrophages treated with an atherogenic stimulus (oxidized low-density lipoprotein) and characterized the EV miRNA expression profile. We confirmed the enrichment of miR-146a, miR-128, miR-185, miR-365, and miR-503 in atherogenic EVs compared with controls and demonstrate that these EVs are taken up and transfer exogenous miRNA to naive recipient macrophages. Bioinformatic pathway analysis suggests that atherogenic EV miRNAs are predicted to target genes involved in cell migration and adhesion pathways, and indeed delivery of EVs to naive macrophages reduced macrophage migration both in vitro and in vivo. Inhibition of miR-146a, the most enriched miRNA in atherogenic EVs, reduced the inhibitory effect of EVs on macrophage migratory capacity. EV-mediated delivery of miR-146a repressed the expression of target genes IGF2BP1 (insulin-like growth factor 2 mRNA-binding protein 1) and HuR (human antigen R or ELAV-like RNA-binding protein 1) in recipient cells, and knockdown of IGF2BP1 and HuR using short interfering RNA greatly reduced macrophage migration, highlighting the importance of these EV-miRNA targets in regulating macrophage motility. CONCLUSIONS: EV-derived miRNAs from atherogenic macrophages, in particular miR-146a, may accelerate the development of atherosclerosis by decreasing cell migration and promoting macrophage entrapment in the vessel wall.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".