MiR-145-5p modulates lipid metabolism and M2 macrophage polarization by targeting PAK7 and regulating β-catenin signaling in hyperlipidemia
Bibliographic record
Abstract
The present study aims to explore the role of microRNA 145-5p (miR-145-5p) in hyperlipidemia. Using bioinformatics tools and a wide range of function and mechanism assays, we attempted to understand the specific function and potential mechanism of miR-145-5p in hyperlipidemia. A cholesterol-enriched diet induced an increase of serum cholesterol and triacylglycerol but a decrease of serum high-density lipoprotein. MiR-145-5p level was decreased in hyperlipidemia rat models. MiR-145-5p regulated lipid metabolism by antagonizing the alteration of high-density lipoprotein, cholesterol, and triacylglycerol in serum mediated by a cholesterol-enriched diet. In mechanism, miR-145-5p directly bound with p21 protein (RAC1)-activated kinase 7 (PAK7) and negatively regulated mRNA and protein levels of PAK7 in THP-1 cells. Furthermore, miR-145-5p level was negatively associated with PAK7 level in rat cardiac tissues. Finally, overexpression of PAK7 reversed the effects of miR-145-5p on β-catenin activation and M2 macrophages polarization in THP-1 cells. In conclusion, MiR-145-5p modulated lipid metabolism and M2 macrophage polarization by targeting PAK7 and regulating β-catenin signaling in hyperlipidemia, which may provide a potential biomarker for the treatment of hyperlipidemia-induced cardiovascular diseases.
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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 | 0.001 |
| 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 | 0.001 |
| 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".