PGC‐1α‐mediated Changes in Phospholipid Profiles of Exercise‐trained Skeletal Muscle
Bibliographic record
Abstract
Exercise training influences phospholipid fatty acid composition in skeletal muscle and these changes are associated with physiological phenotypes; however, the molecular mechanism of this influence on compositional changes is poorly understood. Peroxisome proliferator‐activated receptor γ coactivator 1α (PGC‐1α), a nuclear receptor coactivator, promotes mitochondrial biogenesis, the fiber‐type switch to oxidative fibers, and angiogenesis in skeletal muscle. Because exercise training induces these adaptations, together with increased PGC‐1α, PGC‐1α may contribute to the exercise‐mediated change in phospholipid fatty acid composition. To determine the role of PGC‐1α, we performed lipidomics analyses of skeletal muscle from genetically modified mice that overexpress PGC‐1α in skeletal muscle or that carry knockout alleles of PGC‐1α. The overexpression of PGC‐1α in the skeletal muscle caused a significant change in the phosphatidylcholine (PC) and phosphatidylethanolamine (PE) profiles of the muscle. The PC and PE profile of EDL from PGC‐1α‐Tg mice, which showed oxidative characteristics, was similar to the profile of the originally oxidative muscle, such as the soleus. We also found that PGC‐1α increased several phospholipid species in glycolytic muscle, namely PC (18:0/22:6) and PE (18:0/22:6). Exercise training increased PC (18:0/22:6) and PE (18:0/22:6) in glycolytic muscle and that PGC‐1α was required for these alterations. Since phospholipid fatty acid composition influences cell permeability and receptor stability at the cell membrane, these phospholipids may contribute to exercise training‐mediated functional changes in the skeletal muscle. Support or Funding Information This study was supported by the Council for Science, Technology and Innovation (CSTI), Cross‐ministerial Strategic Innovation Promotion Program (SIP, No.14533567), and “Technologies for creating next‐generation agriculture, forestry and fisheries” (funding agency: Bio‐oriented Technology Research Advancement Institution, NARO), The Tojuro Iijima Foundation for Food Science and Technology (Chiba, Japan), Grants‐in‐Aid for Scientific Research (KAKENHI, No. 26282184, 26560400, 21300240) from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT, Tokyo), The Uehara Memorial Foundation (Tokyo, Japan), The Kao Research Council for the Study of Healthcare Science (Tokyo, Japan, No. A‐31006), and University of Shizuoka Grant for Scientific and Educational Research.
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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) | 0.001 | 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".