Abstract 640: Role of MicroRNA-146a in Vascular Inflammation and Atherosclerosis
Bibliographic record
Abstract
Prolonged activation of inflammatory pathways in the endothelium leads to vascular diseases, including sepsis and atherosclerosis. During this process, activated endothelial cells recruit circulating leukocytes by expressing adhesion molecules and chemoattractants, such as vascular cellular adhesion molecule-1 (VCAM-1) and monocyte chemoattractant protein-1 (MCP-1), respectively. Down-regulation of the anti-adhesion molecule, nitric oxide, which is synthesized by endothelial nitric oxide synthase (eNOS), is also a critical step during endothelial activation. Here we demonstrate that microRNA-146a and microRNA-146b are upregulated in endothelial cells upon exposure to the pro-inflammatory cytokine, interleukin-1-beta (IL-1β). Over expression of miR-146 in endothelial cells dampens the inflammatory response induced by IL-1β and conversely inhibition of endogenous miR-146 in vitro or deletion of miR-146a in vivo exacerbates the inflammatory response to IL-1β. We found that miR-146 represses VCAM-1 and MCP-1 expression by impinging on the canonical NF-kB pathway by targeting the upstream adaptor proteins TRAF6 and IRAK1. In addition, we identified the RNA-binding protein, HuR, as a novel miR-146 target. Knock-down of HuR results in an increase in eNOS expression and a corresponding decrease in leukocyte adhesion, which parallels with the miR-146 over expression phenotype. Considering the involvement of miR-146a in regulating vascular inflammation, miR-146a-/-; Ldlr-/- mice (DKO) were generated to assess atherogenesis. After 12 weeks on high cholesterol diet, the DKO mice developed more lipid-rich plaques in the lesser curvature of the aortic arch compared to Ldlr-/- mice. This suggests that miR-146a may have atheroprotective properties in addition to its anti-inflammatory role in acute inflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".