Abstract 102: MicroRNA-33 Inhibition Reprograms Monocyte/macrophage Dynamics in Atherosclerosis to Promote Plaque Regression
Bibliographic record
Abstract
The inhibition of microRNA (miR)-33 promotes regression of atherosclerosis by increasing plasma HDL and reverse cholesterol transport levels, and reducing plaque inflammation. We sought to understand how anti-miR-33 treatment impacts monocyte and macrophage dynamics in the plaque to promote inflammation resolution. Ldlr -/- mice were fed a western diet for 14 weeks to establish plaques, after which mice were switched to chow diet to halt atherosclerosis progression and treated with anti-miR-33 or control anti-miR oligonucleotides for 4 weeks. As we reported previously, anti-miR-33 treatment increased plasma levels of HDL cholesterol by 30%, and concurrently reduced plaque size (-25%) and macrophage content (-30%), compared to control anti- miR treatment. Analysis of monocyte kinetic processes revealed that anti-miR-33 treatment normalized hypercholesterolemia associated monocytosis and reduced common myeloid progenitor (CMP) cells in the splenic reservoir. Interestingly, despite a 40% reduction in circulating monocytes, monocyte tracking assays revealed an increase in Ly6C hi monocyte recruitment into plaques of anti-miR-33 treated mice. These findings are consistent with our recent study showing that Ly6C hi monocytes are required for atherosclerosis regression and are a source of tissue reparative M2 macrophages. Indeed, we find that M2 macrophages, as well as atheroprotective regulatory T cells are enriched in plaques of anti-miR-33 treated mice. Finally, although the proliferation of macrophages in plaques did not change, we observed a 40% increase in the number of apoptotic cells in anti- miR33 treated mice, and an increase in macrophage efferocytosis (clearance of apoptotic cells) - markers that are consistent with resolution of inflammation. Collectively, our results provide insight into the mechanisms underlying anti-miR- 33’s atheroprotective actions, which now include reprogramming of monocyte/ macrophage dynamics to resolve inflammation in the plaque.
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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) | 0.004 | 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".