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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".