Skeletal muscle fatty acid oxidation is not directly associated with AMPK or ACC2 phosphorylation
Bibliographic record
Abstract
Rescue of palmitate-induced insulin resistance has been linked with improvements in fatty acid oxidation, but importantly, not always with concurrently altered AMPK or ACC2 phosphorylation. Therefore, we examined the interrelationships among AMPK, ACC2, and fatty acid oxidation under 12 controlled conditions in isolated muscle. Incubation of soleus muscle (0-12 h) did not alter fatty acid oxidation, but did increase AMPK and ACC2 phosphorylation (24%-30%). Muscle incubation with palmitate (2 mmol·L(-1)) inhibited palmitate oxidation (∼55%), but paradoxically, this was associated with increased AMPK and ACC2 phosphorylation (∼50%). Addition of an AMPK activator (thujone) to control (no palmitate) muscle increased AMPK and ACC2 phosphorylation (∼25%) but did not alter palmitate oxidation. Addition of AMPK inhibitors, compound C (50 µmol·L(-1)) or adenine 9-β-d-arabinofuranoside (Ara; 2.5 mmol·L(-1)), to thujone-treated muscles (no palmitate) did not alter palmiate oxidation but reduced AMPK phosphorylation (32%-42%), while ACC2 phosphorylation remained above basal level (+14%-18%). Finally, in palmitate-treated muscle, thujone increased AMPK (+100%) and ACC2 phosphorylation (+52%) and restored palmitate oxidation. Compound C or Ara, administered along with thujone in palmitate-treated muscle, only partly blunted palmitate oxidation recovery despite inhibiting AMPK phosphorylation (-22%), although ACC2 phosphorylation remained upregulated (+33%). Among these experiments, AMPK phosphorylation and ACC2 phosphorylation were positively correlated. However, AMPK phosphorylation was not correlated with palmitate oxidation, and unexpectedly, palmitate oxidation was negatively correlated with ACC2 phosphorylation. Our study, in accordance with a growing body of evidence, indicates that neither AMPK phosphorylation nor ACC2 phosphorylation is by itself an appropriate marker of fatty acid oxidation, and further serves to question their regulatory role.
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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) | 0.001 | 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".