Abstract 18676: Anti-miR33 Therapy Induces PGC1a Expression to Regulate Immunometabolic Pathways in Atherosclerosis and Obesity
Bibliographic record
Abstract
Atherosclerosis and obesity are epidemic causes of morbidity and mortality, and immunometabolic mechanisms are at the intersection of both diseases. Recently, microRNA 33 (miR33) was identified as a novel and critical regulator of lipid metabolism and a therapeutic target for reducing atherosclerosis. However, the exact mechanism(s) by which anti-miR33 exerts these beneficial effects are unclear. Comprehensive microarray profiling of mRNA from atherosclerotic plaque macrophages of anti-miR33 treated mice identified PPARγ coactivator 1α (PGC1α) as a novel miR33 target gene that is markedly de-repressed. PGC1α is a key regulator of mitochondrial function and energy metabolism, and promoting its activity improves metabolic syndrome (e.g. insulin resistance, obesity, inflammation). Here, we determine if anti-miR33 therapy upregulates PGC1α and affects energy metabolism in atherosclerosis. Results: miR33 overexpression reduced the 3’UTR activity of PGC1α by 40% (p≤0.05), confirming it was a direct and specific target of miR33. Transfection of macrophages with either miR33 mimics or anti-miR33 in vitro resulted in a decrease or increase of Pgc1α mRNA and protein respectively. Using the Seahorse XF Flux Analyzer, we showed that anti-miR33 treatment boosted maximal mitochondrial oxygen consumption rate, confirming that anti-miR33 impacts positively on mitochondrial respiration. Anti-miR33 treated macrophages also increased the expression of known functional mitochondrial genes (e.g. Slc25a25, Nrf1, Bid and Mtch2), implying miR33 controls several genes in the mitochondrial metabolic pathway. Inhibition of miR33 in vivo in Ldlr-/- mice upregulated Pgc1α mRNA expression in adipose tissue, liver and macrophages. Adipose tissue from anti-miR33 treated obese mice also showed upregulation of so-called “browning” genes (e.g. Ucp-1, Elovl3) that contribute to increased energy utilization of white adipose tissue, possibly improving whole-body metabolism. Conclusion: Anti-miR33 therapy specifically targets PGC1α in macrophages and adipose tissue and improves mitochondrial function, all of which can positively regulate inflammatory and energy utilization processes in cardiometabolic diseases (eg. atherosclerosis and obesity).
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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) | 0.005 | 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".