Role of the AMP-activated protein kinase in regulating fatty acid metabolism during exerciseThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
During moderate-intensity exercise, fatty acids are the predominant substrate for working skeletal muscle. The release of fatty acids from adipose tissue stores, combined with the ability of skeletal muscle to actively fine tune the gradient between fatty acid and carbohydrate metabolism, depending on substrate availability and energetic demands, requires a coordinated system of metabolic control. Over the past decade, since the discovery that AMP-activated protein kinase (AMPK) was increased in accordance with exercise intensity, there has been significant interest in the proposed role of this ancient stress-sensing kinase as a critical integrative switch controlling metabolic responses during exercise. In this review, studies examining the role of AMPK as a regulator of fatty acid metabolism in both adipose tissue and skeletal muscle during exercise will be discussed. Exercise induces activation of AMPK in adipocytes and regulates triglyceride hydrolysis and esterfication through phosphorylation of hormone sensitive lipase (HSL) and glycerol-3-phosphate acyl-transferase, respectively. In skeletal muscle, exercise-induced activation of AMPK is associated with increases in fatty acid uptake, phosphorylation of HSL, and increased fatty acid oxidation, which is thought to occur via the acetyl-CoA carboxylase-malony-CoA-CPT-1 signalling axis. Despite the importance of AMPK in regulating fatty acid metabolism under resting conditions, recent evidence from transgenic models of AMPK deficiency suggest that alternative signalling pathways may also be important for the control of fatty acid metabolism during exercise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".