Nicotinate-Curcumin Impedes Foam Cell Formation from THP-1 Cells through Restoring Autophagy Flux
Bibliographic record
Abstract
Our previous studies have indicated that a novel curcumin derivate nicotinate-curcumin (NC) has beneficial effects on the prevention of atherosclerosis, but the precise mechanisms are not fully understood. Given that autophagy regulates lipid metabolism, the present study was designed to investigate whether NC decreases foam cell formation through restoring autophagy flux in oxidized low-density lipoprotein (ox-LDL)-treated THP-1 cells. Our results showed that ox-LDL (100 μg/ml) was accumulated in THP-1 cells and impaired autophagy flux. Ox-LDL-induced impairment of autophagy was enhanced by treatment with the autophagy inhibitor chloroquine (CQ) and rescued by the autophagy inducer rapamycin. The aggregation of ox-LDL was increased by CQ, but decreased by rapamycin. In addition, colocalization of lipid droplets with LC3-II was remarkably reduced in ox-LDL group. In contrast, NC (10 μM) rescued the impaired autophagy flux by significantly increasing level of LC3-II, the number of autophagolysosomes, and the degradation of p62 in ox-LDL-treated THP-1 cells. Inhibition of the PI3K-Akt-mTOR signaling was required for NC-rescued autophagy flux. Notably, our results showed that NC remarkably promoted the colocalization of lipid droplets with autophagolysosomes, increased efflux of cholesterol, and reduced ox-LDL accumulation in THP-1 cells. However, treatment with 3-methyladenine (3-MA) or CQ reduced the protective effects of NC on lipid accumulation. Collectively, the findings suggest that NC decreases lipid accumulation in THP-1 cells through restoring autophagy flux, and further implicate that NC may be a potential therapeutic reagent to reverse 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) | 0.001 | 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".