MicroRNA-27 Prevents Atherosclerosis by Suppressing Lipoprotein Lipase-Induced Lipid Accumulation and Inflammatory Response in Apolipoprotein E Knockout Mice
Bibliographic record
Abstract
Atherosclerotic lesions are lipometabolic disorder characterized by chronic progressive inflammation in arterial walls. Previous studies have shown that macrophage-derived lipoprotein lipase (LPL) might be a key factor that promotes atherosclerosis by accelerating lipid accumulation and proinflammatory cytokine secretion. Increasing evidence indicates that microRNA-27 (miR-27) has beneficial effects on lipid metabolism and inflammatory response. However, it has not been fully understood whether miR-27 affects the expression of LPL and subsequent development of atherosclerosis in apolipoprotein E knockout (apoE KO) mice. To address these questions and its potential mechanisms, oxidized low-density lipoprotein (ox-LDL)-treated THP-1 macrophages were transfected with the miR-27 mimics/inhibitors and apoE KO mice fed high-fat diet were given a tail vein injection with miR-27 agomir/antagomir, followed by exploring the potential roles of miR-27. MiR-27 agomir significantly down-regulated LPL expression in aorta and peritoneal macrophages by western blot and real-time PCR analyses. We performed LPL activity assay in the culture media and found that miR-27 reduced LPL activity. ELISA showed that miR-27 reduced inflammatory response as analyzed in vitro and in vivo experiments. Our results showed that miR-27 had an inhibitory effect on the levels of lipid both in plasma and in peritoneal macrophages of apoE KO mice as examined by HPLC. Consistently, miR-27 suppressed the expression of scavenger receptors associated with lipid uptake in ox-LDL-treated THP-1 macrophages. In addition, transfection with LPL siRNA inhibited the miR-27 inhibitor-induced lipid accumulation and proinflammatory cytokines secretion in ox-LDL-treated THP-1 macrophages. Finally, systemic treatment revealed that miR-27 decreased aortic plaque size and lipid content in apoE KO mice. The present results provide evidence that a novel antiatherogenic role of miR-27 was closely related to reducing lipid accumulation and inflammatory response via downregulation of LPL gene expression, suggesting a potential strategy to the diagnosis and treatment of atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".