Pharmacological AMP-kinase activators have compartment-specific effects on cell physiology
Bibliographic record
Abstract
5'-AMP-activated kinase (AMPK) regulates numerous biological events and is an essential target for the treatment of type 2 diabetes. The objectives of the present study were first to determine the compartment-specific effects of three established AMPK activators on Thr172 phosphorylation of the α-subunit, an indicator of AMPK activation. Second, we examined how cytoplasmic and nuclear processes are modulated by pharmacological AMPK activators. Specifically, the impact of phenformin, resveratrol, and 5-aminoimidazole-4-carboxamide riboside (AICAR) on Thr172 phosphorylation in the cytoplasm and nucleus was quantified by different methods. To analyze how these activators change cell physiology, we measured the inactivation of acetyl-CoA-carboxylase 1, a predominantly cytoplasmic enzyme that is crucial for lipid metabolism. As a criterion for activities associated with the nucleus, de novo RNA synthesis in nucleoli was quantified. Our studies demonstrate that pharmacological activators of AMPK can alter the balance between nuclear and cytoplasmic AMPK pools. Thus, phenformin and resveratrol caused a strong activation of AMPK in the cytoplasm, whereas the effect was less pronounced in nuclei. By contrast, AICAR elicited a comparable rise in Thr172 phosphorylation in both compartments. Notably, these activators differed drastically in their effects on physiological processes that are located in distinct subcellular compartments. All compounds led to a substantial inactivation of acetyl-CoA-carboxylase 1 in the cytoplasm, with only minor changes to the nuclear enzyme. In the nucleolus, transcription was strongly inhibited by resveratrol, while a moderate inhibition was observed with phenformin and AICAR. Taken together, the compartment-specific phosphorylation of AMPK and downstream events are determined by the activator.
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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".