Transcriptome and translational signaling following endurance exercise in trained skeletal muscle: impact of dietary protein
Bibliographic record
Abstract
Postexercise protein feeding regulates the skeletal muscle adaptive response to endurance exercise, but the transcriptome guiding these adaptations in well-trained human skeletal muscle is uncharacterized. In a crossover design, eight cyclists ingested beverages containing protein, carbohydrate and fat (PTN: 0.4, 1.2, 0.2 g/kg, respectively) or isocaloric carbohydrate and fat (CON: 1.6, 0.2 g/kg) at 0 and 1 h following 100 min of cycling. Biopsies of the vastus lateralis were collected at 3 and 48 h following to determine the early and late transcriptome and regulatory signaling responses via microarray and immunoblot. The top gene ontology enriched by PTN were: muscle contraction, extracellular matrix--signaling and structure, and nucleoside, nucleotide, and nucleic acid metabolism (3 and 48 h); developmental processes, immunity, and defense (3 h); glycolysis, lipid and fatty acid metabolism (48 h). The transcriptome was also enriched within axonal guidance, actin cytoskeletal, Ca2+, cAMP, MAPK, and PPAR canonical pathways linking protein nutrition to exercise-stimulated signaling regulating extracellular matrix, slow-myofibril, and metabolic gene expression. At 3 h, PTN attenuated AMPKα1Thr172 phosphorylation but increased mTORC1Ser2448, rps6Ser240/244, and 4E-BP1-γ phosphorylation, suggesting increased translation initiation, while at 48 h AMPKα1Thr172 phosphorylation and PPARG and PPARGC1A expression increased, supporting the late metabolic transcriptome, relative to CON. To conclude, protein feeding following endurance exercise affects signaling associated with cell energy status and translation initiation and the transcriptome involved in skeletal muscle development, slow-myofibril remodeling, immunity and defense, and energy metabolism. Further research should determine the time course and posttranscriptional regulation of this transcriptome and the phenotype responding to chronic postexercise protein feeding.
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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".