Role of the atypical protein kinase Cζ in regulation of 5′-AMP-activated protein kinase in cardiac and skeletal muscle
Bibliographic record
Abstract
During metabolic stress, phosphorylation and activation of 5'-AMP-activated protein kinase (AMPK) becomes a major regulator of cellular energy metabolism in heart and skeletal muscle. Despite this, the upstream regulation of AMPK in both heart and muscle is poorly understood. Recent work has implicated the atypical protein kinase Czeta (PKCzeta) as a regulator of AMPK in endothelial cells via phosphorylation of LKB1, an upstream AMPK kinase (AMPKK). Our goal was to determine the potential role PKCzeta plays in regulating AMPK in cardiac and skeletal muscle. Cultures of H9c2 myocytes (cardiac) and C(2)C(12) myotubes (skeletal muscle) were pretreated with a selective PKCzeta pseudosubstrate peptide inhibitor and treated with various AMPK activating agents to determine whether PKCzeta regulates AMPK. PKCzeta activity was also examined in isolated working rat hearts subjected to ischemia. We show that PKCzeta is not involved in regulating threonine 172 AMPK phosphorylation induced by metformin or phenformin in either cardiac or skeletal muscle cells but is involved in 5-aminoimidazole-4-carboxamine-1-beta-D-ribofuranoside (AICAR)-induced AMPK phosphorylation in cardiac muscle cells. Activation of PKCzeta with high palmitate concentrations is also insufficient to increase AMPK phosphorylation. Furthermore, we show that the ischemia-induced activation of AMPK is not accompanied by increased PKCzeta activity. Finally, we show that PKCzeta may actually be a downstream target of AMPK in skeletal muscle, since adenoviral expression of a dominant-negative mutant of AMPK prevented metformin- and AICAR-induced phosphorylation of PKCzeta. We conclude that PKCzeta plays a very minor role in the regulation of AMPK in cardiac and skeletal muscle and may actually be a downstream target of AMPK in skeletal muscle.
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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) | 0.001 | 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".