Transcriptional suppression of human apolipoproteina4 and apolipoproteinc3 genes by phorbol myristate acetate in hepatic and intestinal cells
Bibliographic record
Abstract
BACKGROUND: Genetic, epidemiological and clinical evidence has demonstrated the importance of the human apolipoproteinA5 (apoA5), apolipoproteinA4 (apoA4), apolipoproteinC3 (apoC3), and apolipoproteinA1 (apoA1) genes in the control of the triglyceride and cholesterol concentrations in the blood. However, little is known about the mechanism by which protein kinase C (PKC) regulates the expression of these genes in hepatic and intestinal cells. The aim of this study was to explore the regulatory role of PKC on the expression of apoA5, apoA4, apoC3 and apoA1. METHODS: Hepatic HepG2 and intestinal Caco-2 cells were treated with a potent PKC activator, Phorbol myristate acetate (PMA). The real time quantitative RT-PCR (qRT-PCR) technique was used to evaluate the effects of PMA on the expression of apoA1, apoA4, apoA5 and apoC3 genes. Nuclear run on assay was used to determine whether the effect of PMA on apoA4 and apoC3 was due to its ability to regulate the transcription of these genes. RESULTS: PMA specifically down-regulated the transcription of apoA4 and apoC3, but exhibited no effects on apoA1 or apoA5 in either HepG2 or Caco-2 cells. Further study by nuclear run on assay proved that the suppressive effect of PMA on apoA4 and apoC3 resulted from PMA's regulation of the transcription rate of the two genes. CONCLUSIONS: PMA down-regulated transcription of apoA4 and apoC3 possibly through the common regulatory element shared by these two genes, suggesting a suppressive role of PKC on the transcriptional regulation of specific apolipoproteins in hepatic and intestinal cells.
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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) | 0.002 | 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".