MicroRNA-590 Inhibits Lipoprotein Lipase Expression and Prevents Atherosclerosis in apoE Knockout Mice
Bibliographic record
Abstract
Recent studies have suggested that miR-590 may play critical roles in cardiovascular disease. This study was designed to determine the effects of miR-590 on lipoprotein lipase (LPL) expression and development of atherosclerosis in apolipoprotein E knockout (apoE-/-) mice and explore the potential mechanisms. En face analysis of the whole aorta revealed that miR-590 significantly decreased aortic atherosclerotic plaque size and lipid content in apoE-/- mice. Double immunofluorescence staining in cross-sections of the proximal aorta showed that miR-590 agomir reduced CD68 and LPL expression in macrophages in atherosclerotic lesions. MiR-590 agomir down-regulated LPL mRNA and protein expression as analyzed by RT-qPCR and western blotting analyses, respectively. Consistently, miR-590 decreased the expression of CD36 and scavenger receptor A1 (SRA1) mRNA and protein. High-performance liquid chromatography (HPLC)analysis confirmed that treatment with miR-590 agomir reduced lipid levels either in plasma orinabdominal cavity macrophages of apoE-/- mice. ELISA analysis showed that miR-590 agomir decreased plasma levels of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), monocyte chemotactic protein-1 (MCP-1), interleukin-1β (IL-1β)and interleukin-6 (IL-6). In contrast, treatment with miR-590 antagomir prevented or reversed these effects. Taken together, these results reveal a novel mechanism of miR-590 effects, and may provide new insights into the development of strategies for attenuating lipid accumulation and pro-inflammatory cytokine secretion.
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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".