Activation of Apolipoprotein AI Gene Expression by Protein Kinase A and Kinase C through Transcription Factor, Sp1
Bibliographic record
Abstract
Our previous finding that insulin induces apolipoprotein AI (apoAI) transcription points to the participation of intracellular signaling. This finding prompted us to ask whether two classical G-protein-coupled signaling pathways requiring activated protein kinase A (PKA) or kinase C (PKC) may also regulate apoAI. Therefore, human hepatoma, Hep G2 cells stably transfected with pAI.474-CAT, a reporter construct spanning -474 to -7 of apoAI DNA fused to chloramphenicol acetyltransferase (CAT) were treated with 10 microm forskolin (FSK) or 50 nm phorbol dibutyrate (PDBu) to activate PKA and PKC, respectively. Results showed that the apoAI promoter activity increased 4-5-fold following 24 h of treatment with either FSK or PDBu. Induction by either agent was blocked with actinomycin D but not the protein synthesis inhibitor, cycloheximide. The PKA inhibitor, PKI 14-22 amide, abrogated induction by FSK, 100 microm 8-bromo-cAMP, or 100 ng/ml cholera toxin, but it had no effect on activation via PKC. Similarly, PDBu induction was attenuated by 2 microm of the PKC inhibitor, GF109203X, but it did not affect FSK activity. Next we used deletional constructs to show that the actions of FSK and PDBu required the insulin-responsive core element (IRCE). This motif matched the consensus binding site for the transcription factor, Sp1. The binding of Sp1 to the IRCE was confirmed by gel-retardation and supershift analysis. Site-directed mutagenesis of the IRCE eliminated Sp1 action and induction by FSK or PDBu. Whereas overexpression of Sp1 enhanced basal and FSK or PDBu induced promoter activity, transfection of an antisense oligomer against Sp1 mRNA attenuated both parameters. In summary, activation of PKA or PKC increases apoAI promoter activity. The activity of both signaling pathways is mediated by the IRCE, a motif that binds the transcription factor, Sp1.
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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) | 0.002 | 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".