MicroRNA-33–dependent regulation of macrophage metabolism directs immune cell polarization in atherosclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it