Antagonism of Betulinic Acid on LPS-Mediated Inhibition of ABCA1 and Cholesterol Efflux through Inhibiting Nuclear Factor-kappaB Signaling Pathway and miR-33 Expression
Bibliographic record
Abstract
ATP-binding cassette transporter A1 (ABCA1) is critical in exporting cholesterol from macrophages and plays a protective role in the development of atherosclerosis. The purpose of this study was to investigate the effects of betulinic acid (BA), a pentacyclic triterpenoid, on ABCA1 expression and cholesterol efflux, and to further determine the underlying mechanism. BA promoted ABCA1 expression and cholesterol efflux, decreased cellular cholesterol and cholesterol ester content in LPS-treated macrophages. Furthermore, we found that BA promoted ABCA1 expression via down-regulation of miR-33s. The inhibition of LPS-induced NF-κB activation further decreased miR-33s expression and enhanced ABCA1 expression and cholesterol efflux when compared with BA only treatment. In addition, BA suppressed IκB phosphorylation, p65 phosphorylation and nuclear translocation, and the transcription of NF-κB-dependent related gene. Moreover, BA reduced atherosclerotic lesion size, miR-33s levels and NF-κB activation, and promoted ABCA1 expression in apoE(-/-) mice. Taken together, these results reveal a novel mechanism for the BA-mediated ABCA1 expression, which may provide new insights for developing strategies for modulating vascular inflammation and atherosclerosis.
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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) | 0.002 | 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 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".