Abstract 422: Metabolic Regulation by miR-33 in Macrophages Controls Immune Effector Responses
Bibliographic record
Abstract
Cellular metabolism is increasingly recognized to control immune cell fate and functions. MicroRNA-33 (miR-33) is a central regulator of cellular lipid metabolism that represses genes involved in cholesterol efflux and HDL biogenesis (Abca1, Abcg1) and fatty acid oxidation (Cpt1a, Crot, Ampk). Here we show that by altering the balance of aerobic glycolysis and mitochondrial oxidative phosphorylation, miR-33 inhibition instructs macrophage polarization to an M2 phenotype and shapes innate and adaptive immune responses. Targeted deletion of miR-33 in macrophages increases oxidative phosphorylation, enhances spare respiratory capacity, and induces the expression of genes that define M2 macrophage polarization (Arg1, Fizz1, Cd206, Ym1). Furthermore, inhibition of miR-33 in Abca1-/- macrophages showed that these changes are independent of effects on cholesterol efflux, but instead require miR-33 targeting of the energy sensor AMP-activated protein kinase (AMPK). Notably, inhibition of miR-33 markedly increased macrophage expression of the retinoic acid-producing enzyme Aldh1a2 and retinal dehydrogenase activity both in vitro and in vivo. Consistent with the ability of retinoic acid to foster inducible regulatory T cells, these macrophages had an enhanced capacity to induce FoxP3 expression in naïve CD4+ T cells. Finally, treatment of western diet-fed Ldlr-/- mice with miR-33 inhibitors for 8 weeks (conditions that do not alter HDL cholesterol levels) reduced atherosclerosis progression by 40%, and promoted the accumulation of M2 macrophages and FoxP3+ T regulatory cells in plaques. Collectively, these results identify a novel role for miR-33 in the regulation of macrophage inflammation and show that antagonism of miR-33 is atheroprotective, in part, by reducing plaque inflammation by promoting M2 macrophage polarization and regulatory T cell 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".