MicroRNA-33–dependent regulation of macrophage metabolism directs immune cell polarization in atherosclerosis
Bibliographic record
Abstract
Cellular metabolism is increasingly recognized as a controller of immune cell fate and function. MicroRNA-33 (miR-33) regulates cellular lipid metabolism and represses genes involved in cholesterol efflux, HDL biogenesis, and fatty acid oxidation. Here, we determined that miR-33-mediated disruption of the balance of aerobic glycolysis and mitochondrial oxidative phosphorylation instructs macrophage inflammatory polarization and shapes innate and adaptive immune responses. Macrophage-specific Mir33 deletion increased oxidative respiration, enhanced spare respiratory capacity, and induced an M2 macrophage polarization-associated gene profile. Furthermore, miR-33-mediated M2 polarization required miR-33 targeting of the energy sensor AMP-activated protein kinase (AMPK), but not cholesterol efflux. Notably, miR-33 inhibition increased macrophage expression of the retinoic acid-producing enzyme aldehyde dehydrogenase family 1, subfamily A2 (ALDH1A2) and retinal dehydrogenase activity both in vitro and in a mouse model. Consistent with the ability of retinoic acid to foster inducible Tregs, miR-33-depleted macrophages had an enhanced capacity to induce forkhead box P3 (FOXP3) expression in naive CD4(+) T cells. Finally, treatment of hypercholesterolemic mice with miR-33 inhibitors for 8 weeks resulted in accumulation of inflammation-suppressing M2 macrophages and FOXP3(+) Tregs in plaques and reduced atherosclerosis progression. Collectively, these results reveal that miR-33 regulates macrophage inflammation and demonstrate that miR-33 antagonism is atheroprotective, in part, by reducing plaque inflammation by promoting M2 macrophage polarization and Treg induction.
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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 | 0.001 |
| 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".