Deferoxamine stimulates LDLR expression and LDL uptake in HepG2 cells
Bibliographic record
Abstract
SCOPE: Iron overload contributes to the pathogenesis of atherosclerosis and iron chelators are beneficial through their antioxidant properties. Hepatic iron loading increases cholesterol synthesis. Whether iron depletion could affect hepatic cholesterol metabolism is unknown. METHODS AND RESULTS: We examined the effect of the iron chelator deferoxamine (DFO) on mRNA expression of genes involved in cholesterol metabolism and/or cholesterol uptake. Our results revealed that DFO increases LDL receptor (LDLR) mRNA levels in human hepatocyte-derived cell lines HepG2 and Huh7 cells, and in K562 cells. In HepG2 cells, we observed that DFO increases (i) LDLR-mRNA levels in a time- and dose-dependent manner, (ii) LDLR-protein levels; (iii) cell surface LDLR; and (iv) LDL uptake. In contrast, the mRNA levels of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, sterol regulatory element-binding proteins, and the mRNA/protein levels of proprotein convertase subtilisin-kexin 9 were not modulated by DFO, suggesting that the LDLR regulation by DFO is not at the transcriptional or posttranslational levels. Since LDLR-mRNA was stabilized by DFO, a posttranscriptional mechanism is suggested for the DFO-mediated upregulation of LDLR. CONCLUSION: DFO induced an increase in LDLR expression by a posttranscriptional mechanism resulting in an enhancement of LDL uptake in HepG2 cells, suggesting increased LDLR activity as one of the underlying causes of the hypocholesterolemic effect of iron reduction.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".