Therapeutic Inhibition of miR-33 Promotes Fatty Acid Oxidation but Does Not Ameliorate Metabolic Dysfunction in Diet-Induced Obesity
Bibliographic record
Abstract
OBJECTIVE: miR-33 has emerged as an important regulator of lipid homeostasis. Inhibition of miR-33 has been demonstrated as protective against atherosclerosis; however, recent studies in mice suggest that miR-33 inhibition may have adverse effects on lipid and insulin metabolism. Given the therapeutic interest in miR-33 inhibitors for treating atherosclerosis, we sought to test whether pharmacologically inhibiting miR-33 at atheroprotective doses affected metabolic parameters in a mouse model of diet-induced obesity. APPROACH AND RESULTS: High-fat diet (HFD) feeding in conjunction with treatment of male mice with 10 mg/kg control anti-miR or anti-miR33 inhibitors for 20 weeks promoted equivalent weight gain in all groups. miR-33 inhibitors increased plasma total cholesterol and decreased serum triglycerides compared with control anti-miR, but not compared with PBS-treated mice. Metrics of insulin resistance were not altered in anti-miR33-treated mice compared with controls; however, respiratory exchange ratio was decreased in anti-miR33-treated mice. Hepatic expression of miR-33 targets Abca1 and Hadhb were derepressed on miR-33 inhibition. In contrast, protein levels of putative miR-33 target gene SREBP-1 or its downstream targets genes Fasn and Acc were not altered in anti-miR33-treated mice, and hepatic lipid accumulation did not differ between groups. In the adipose tissue, anti-miR33 treatment increased Ampk gene expression and markers of M2 macrophage polarization. CONCLUSIONS: We demonstrate in a mouse model of diet-induced obesity that therapeutic silencing of miR-33 may promote whole-body oxidative metabolism but does not affect metabolic dysregulation. This suggests that pharmacological inhibition of miR-33 at doses known to reduce atherosclerosis may be a safe future therapeutic.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".