Macrophage mitochondrial energy status regulates cholesterol efflux and is enhanced by anti‐miR33 in atherosclerosis
Bibliographic record
Abstract
Therapeutically targeting macrophage reverse cholesterol transport is a promising approach to treat atherosclerosis and miR‐33 has emerged as novel regulator of cholesterol homeostasis. Cellular energy status can significantly influence macrophage function, and bioinformatic analysis predicts that miR‐33 represses a cluster of genes controlling energy metabolism that may contribute to macrophage cholesterol efflux. We hypothesize that miR‐33 represses mitochondria metabolic pathways to reduce cholesterol efflux. Here, we show that macrophage cholesterol efflux is regulated by mitochondrial ATP production and that miR‐33 controls a network of genes that synchronize mitochondrial function. Macrophage cholesterol efflux capacity was markedly reduced by ATP synthase inhibition, confirming the importance of mitochondria in the efflux of excess cholesterol. Specifically, anti‐miR33 derepressed the novel target genes PGC‐1α, PDK4 and SLC25A25 and boosted mitochondrial respiration and ATP production, and mitochondrial respiration was key to the pro‐efflux effects of anti‐miR33. Anti‐miR33 therapy in atherosclerotic Apoe ‐/‐ mice reduced aortic sinus lesion area, despite no changes in HDL‐C. Also, this therapy increased expression of mitochondrial target genes in vivo , suggesting that the regulation of mitochondrial genetic networks occur in atherosclerotic lesions. Conclusion We showed that anti‐miR33 therapy de‐represses genes that enhance mitochondrial respiration and ATP production, which in conjunction with increased ABCA1 expression, promotes macrophage cholesterol efflux and reduces 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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".