Inhibition of Apolipoprotein A‐I Expression by TNF‐Alpha in HepG2 Cells: Requirement for c‐<i>jun</i>
Bibliographic record
Abstract
Tumor necrosis factor alpha (TNF α) signals in part through the mitogen activated protein (MAP) kinase c-jun-N-terminal kinase (JNK). Activation of JNK has been shown to promote insulin resistance and dyslipidemia, including reductions in plasma high-density lipoprotein (HDL) and apolipoprotein A-I (apo A-I). To examine how TNF α-mediated JNK activation inhibits hepatic apo A-I production, the effects of c-jun activation on apo A-I gene expression were examined in HepG2 cells. Apo A-I gene expression and promoter activity were measured by Northern and Western blotting and transient transfection. Transient transfection and siRNA were used to specifically over-express or knockout c-jun, c-jun-N-terminal kinase-1 and -2 (JNK1 and JNK2, respectively) and mitogen-activated protein kinase-4 (MKK4). TNF α-treatment of HepG2 cells induced rapid phosphorylation of c-jun on serine 63. In cells treated with phorbol-12-myristate-13-acetate (PMA), apo A-I gene promoter activity was inhibited and apo A-I mRNA content and apo A-I protein secretion decreased. Likewise, over-expression of JNK1 and JNK2 inhibited apo A-I promoter activity. Over-expression of constitutively active MKK4, an upstream protein kinase that directly activates JNK, also inhibited apo A-I promoter activity, while over-expression of a dominant-negative MKK4 de-repressed apo A-I promoter activity in TNF α-treated cells. Inhibition of c-jun synthesis using siRNA but not a control siRNA prevented TNF α-mediated inhibition of apo A-I. These results suggest that the MKK4/JNK/c-jun signaling pathway mediates TNF α-dependent inhibition of apo A-I synthesis.
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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 | 0.001 |
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".