Inhibition of Net HepG2 Cell Apolipoprotein B Secretion by the Citrus Flavonoid Naringenin Involves Activation of Phosphatidylinositol 3-Kinase, Independent of Insulin Receptor Substrate-1 Phosphorylation
Bibliographic record
Abstract
The flavonoid naringenin improves hyperlipidemia and hyperglycemia in streptozotocin-treated rats. In HepG2 human hepatoma cells, naringenin inhibits apolipoprotein B (apoB) secretion primarily by inhibiting microsomal triglyceride transfer protein and enhances LDL receptor (LDLr)-mediated apoB-containing lipoprotein uptake. Phosphatidylinositol 3-kinase (PI3K) activation by insulin increases sterol regulatory element-binding protein (SREBP)-1 and LDLr expression and inhibits apoB secretion in hepatocytes. Thus, we determined whether naringenin activates this pathway. Insulin and naringenin induced PI3K-dependent increases in cytosolic and nuclear SREBP-1 and LDLr expression. Similar PI3K-mediated increases in SREBP-1 were observed in McA-RH7777 rat hepatoma cells, which express predominantly SREBP-1c. Reductions in HepG2 cell media apoB with naringenin were partially attenuated by wortmannin, whereas the effect of insulin was completely blocked. Both treatments reduced apoB100 secretion in wild-type and LDLr(-/-) mouse hepatocytes to the same extent. Insulin and naringenin increased HepG2 cell PI3K activity and decreased insulin receptor substrate (IRS)-2 levels. In sharp contrast to insulin, naringenin did not induce tyrosine phosphorylation of IRS-1. We conclude that naringenin increases LDLr expression in HepG2 cells via PI3K-mediated upregulation of SREBP-1, independent of IRS-1 phosphorylation. Although this pathway may not regulate apoB secretion in primary hepatocytes, PI3K activation by this novel mechanism may explain the insulin-like effects of naringenin in vivo.
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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 | 0.000 |
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".