Abstract 620: miRNA Nanoparticles Targeting Cholesterol Efflux: a Promising Tool for the Treatment of Atherosclerosis
Bibliographic record
Abstract
The prevention and treatment of cardiovascular diseases (CVD) has largely focused on lowering circulating LDL cholesterol, yet a significant burden of atherosclerotic disease remains even with low LDL. Recently, microRNAs (miRNAs) have emerged as exciting therapeutic targets for CVD. miRNAs are small noncoding RNAs that post-transcriptionally regulate gene expression by degradation or translational inhibition of target mRNAs. A number of miRNAs have been found to modulate all stages of atherosclerosis, particularly those that promote cholesterol efflux from lipid laden macrophages in the vessel wall. However, one of the major challenges of miRNA-based therapy is to achieve tissue-specific, efficient and safe delivery of miRNAs in vivo . Objective: We therefore sought to develop chitosan/miRNA nanoparticles, deliver them to the plaque, and determine if these miRNAs can promote cholesterol efflux to decrease atherosclerosis. Results: We conjugated negatively charged miRNAs with tripolyphosphate (TPP) to support crosslinks between polymeric and nucleic acid units, which were then mixed with varying ratios of chitosan polymer to form nanoparticles that ranged from 150-180nm in size. We next optimized the efficiency of intracellular delivery of different chitosan/miRNA ratios to mouse macrophages (MΦ). We find chitosan nanoparticles can protect as well as transfer exogenous miR-33 to naïve MΦ and reduce mRNA and protein expression of its target gene, ABCA1, confirming that miRNAs delivered via nanoparticle can escape the endosomal system and function in the RISC complex. Because ABCA1 plays a key role in stimulating the efflux of cholesterol from MΦ, we also confirmed that MΦ treated with chitosan/miR-33 nanoparticles exhibited reduced cholesterol efflux to Apolipoprotein A1, further confirming functional delivery of the miRNA. Using this formulation, we have developed a panel of miRNA nanoparticles delivering miRNAs that enhance ABCA1 expression and promote cholesterol efflux. Conclusions: miRNAs can be efficiently delivered to macrophages via nanoparticles where they can function to regulate ABCA1 expression and cholesterol efflux, suggesting that these miRNA-nanoparticles can be used in vivo to target atherosclerotic lesions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".