Role of Extracellular MicroRNAs in Atherosclerosis
Bibliographic record
Abstract
Atherosclerosis is a maladaptive inflammatory disease driven by the interplay between excess cholesterol accumulation in the arterial wall and the immune system. Macrophages are fundamental to the propagation of atherosclerosis due to their capacity to engulf modified lipoproteins and induce a pro‐inflammatory state. Macrophages can secrete miRNAs in exosomes as a form of intercellular communication during inflammation and infection. miRNAs are small noncoding RNAs that post‐transcriptionally regulate gene expression. Herein, we hypothesize that atherogenic macrophages secrete miRNAs in exosomes to mediate cell‐cell communication, which regulates the atherogenic response. To test this hypothesis, we first examined the miRNA expression profile of exosomes from control and atherogenic macrophages. Among the 88 differentially expressed miRNAs, 68 were down‐regulated whereas 20 were up‐regulated in exosomes from cholesterol‐loaded macrophages as compared with the control. qPCR confirmed the enrichment of several miRNAs such as miR‐146a, miR‐128, miR‐185, and miR‐503 in exosomes derived from atherogenic macrophages, whereas miR‐150 was downregulated in these exosomes. Bioinformatic pathway analysis suggests that atherogenic exosomal miRNAs may regulate cell migration and adhesion pathways via targeting the 3′UTR of migration/adhesion genes (i.e. integrin α3, VE‐cadherin, CXCR4). Interestingly, live cell imaging showed that exosomes secreted from cholesterol‐loaded macrophages were internalized by naïve macrophages and exogenous C. elegans miR‐39 could transfer from atherogenic macrophages to naïve cells. Thus, miRNAs secreted from atherogenic macrophages may promote atherosclerosis by regulating macrophage migration in the vessel wall and the role of these exo‐miRNAs will be tested in vitro and in vivo .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".