MEK–ERK Inhibition Corrects the Defect in VLDL Assembly in HepG2 Cells
Bibliographic record
Abstract
OBJECTIVE: Hepatic VLDL assembly is defective in HepG2 cells, resulting in the secretion of immature triglyceride-poor LDL-sized apoB particles. We investigated the mechanisms underlying defective VLDL assembly in HepG2 and have obtained evidence implicating the MEK-ERK pathway. METHODS AND RESULTS: HepG2 cells exhibited considerably higher levels of the ERK1/2 mass and activity compared with primary hepatocytes. Inhibition of ERK1/2 using the MEK1/MEK2 inhibitor, U0126 (but not the inactive analogue) led to a significant increase in apoB secretion. In the presence of oleic acid, ERK1/2 inhibition caused a major shift in the lipoprotein distribution with a majority of particles secreted as VLDL, an effect independent of insulin. In contrast, overexpression of constitutively active MEK1 decreased apoB and large VLDL secretion. MEK1/2 inhibition significantly increased both cellular and microsomal TG mass, and mRNA levels for DGAT-1 and DGAT-2. In contrast to ERK, modulation of the PI3-K pathway or inhibition of the p38 MAP kinase, had no effect on lipoprotein density profile. Modulation of the MEK-ERK pathway in primary hamster hepatocytes led to changes in apoB secretion and altered the density profile of apoB-containing lipoproteins. CONCLUSIONS: Inhibition of the overactive ras-MEK-ERK pathway in HepG2 cells can correct the defect in VLDL assembly leading to the secretion of large, VLDL-sized particles, similar to primary hepatocytes, implicating the MEK-ERK cascade in VLDL assembly in the HepG2 model. Modulation of this pathway in primary hepatocytes also regulates apoB secretion and appears to alter the formation of VLDL-1 sized particles.
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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) | 0.001 | 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 | 0.001 |
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".