Endothelial cells suppress monocyte activation through secretion of extracellular vesicles containing antiinflammatory microRNAs
Bibliographic record
Abstract
The blood contains high concentrations of circulating extracellular vesicles (EVs), and their levels and contents are altered in several disease states, including cardiovascular disease. However, the function of circulating EVs, especially the microRNAs (miRNAs) that they contain, are poorly understood. We sought to determine the effect of secreted vesicles produced by quiescent endothelial cells (ECs) on monocyte inflammatory responses and to assess whether transfer of microRNAs occurs between these cells. We observed that monocytic cells cocultured (but not in contact) with ECs were refractory to inflammatory activation. Further characterization revealed that endothelium-derived EVs (EC-EVs) suppressed monocyte activation by enhancing immunomodulatory responses and diminishing proinflammatory responses. EVs isolated from mouse plasma also suppressed monocyte activation. Importantly, injection of EC-EVs in vivo repressed monocyte/macrophage activation, confirming our in vitro findings. We found that several antiinflammatory microRNAs were elevated in EC-EV-treated monocytes. In particular, miR-10a was transferred to monocytic cells from EC-EVs and could repress inflammatory signaling through the targeting of several components of the NF-κB pathway, including IRAK4. Our findings reveal that ECs secrete EVs that can modulate monocyte activation and suggest that altered EV secretion and/or microRNA content may affect vascular inflammation in the setting of cardiovascular disease.
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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".