microRNA-33 Regulates Macrophage Autophagy in Atherosclerosis
Bibliographic record
Abstract
Objective— Defective autophagy in macrophages leads to pathological processes that contribute to atherosclerosis, including impaired cholesterol metabolism and defective efferocytosis. Autophagy promotes the degradation of cytoplasmic components in lysosomes and plays a key role in the catabolism of stored lipids to maintain cellular homeostasis. microRNA-33 (miR-33) is a post-transcriptional regulator of genes involved in cholesterol homeostasis, yet the complete mechanisms by which miR-33 controls lipid metabolism are unknown. We investigated whether miR-33 targeting of autophagy contributes to its regulation of cholesterol homeostasis and atherogenesis. Approach and Results— Using coherent anti-Stokes Raman scattering microscopy, we show that miR-33 drives lipid droplet accumulation in macrophages, suggesting decreased lipolysis. Inhibition of neutral and lysosomal hydrolysis pathways revealed that miR-33 reduced cholesterol mobilization by a lysosomal-dependent mechanism, implicating repression of autophagy. Indeed, we show that miR-33 targets key autophagy regulators and effectors in macrophages to reduce lipid droplet catabolism, an essential process to generate free cholesterol for efflux. Notably, miR-33 regulation of autophagy lies upstream of its known effects on ABCA1 (ATP-binding cassette transporter A1)-dependent cholesterol efflux, as miR-33 inhibitors fail to increase efflux upon genetic or chemical inhibition of autophagy. Furthermore, we find that miR-33 inhibits apoptotic cell clearance via an autophagy-dependent mechanism. Macrophages treated with anti-miR-33 show increased efferocytosis, lysosomal biogenesis, and degradation of apoptotic material. Finally, we show that treating atherosclerotic Ldlr −/− mice with anti-miR-33 restores defective autophagy in macrophage foam cells and plaques and promotes apoptotic cell clearance to reduce plaque necrosis. Conclusions— Collectively, these data provide insight into the mechanisms by which miR-33 regulates cellular cholesterol homeostasis and 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 | 0.000 |
| 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 | 0.001 |
| 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".