Glucose represses PPARαgene expression via AMP‐activated protein kinase but not via p38 mitogen‐activated protein kinase in the pancreatic β‐cell
Bibliographic record
Abstract
BACKGROUND: Peroxisome proliferator-activated receptor α (PPARα) regulates the expression of fatty acid metabolism genes and is thought to play a role in the regulation of insulin secretion and lipid detoxification. We have examined the mechanism whereby glucose decreases PPARα gene expression in the pancreatic β-cell. METHODS: INS832/13 β-cell and isolated rat islets were incubated at 3 and 20 mM glucose for 18 h in the absence or presence of adenosine monophosphate (AMP)-activated protein kinase (AMPK) activators and inhibitors, as well as p38 mitogen-activated protein kinase (p38 MAPK) inhibitors. In another set of experiments, INS832/13 were infected with an adenovirus expressing a dominant-negative form of AMPK. PPARα expression levels were measured by reverse transcription polymerase chain reaction and Western blot. RESULTS: Elevated glucose reduced the abundance of the PPARα transcript and protein, and its target genes acyl-coenzyme A (CoA) oxidase (ACO) and uncoupling protein 2 (UCP-2) in INS832/13 β-cell and isolated rat islets. Glucose reduced AMPK activity, while the AMPK activators 5-amino-4-imidazolecarboxamide riboside and metformin increased PPARα expression and suppressed the action of glucose. By contrast, the AMPK inhibitor compound C mimicked the glucose effect. A dominant negative form of AMPKα reduced the PPARα, ACO and UCP-2 transcripts to the same extent as elevated glucose. Pharmacological evidence indicated that glucose-regulated PPARα expression does not involve p38 MAPK, a target of AMPK in several cell types. CONCLUSIONS: The results indicate that glucose represses PPARα gene expression via AMPK, but not via p38 MAPK in the β-cell.
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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) | 0.003 | 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".