Glycogenin protein and mRNA expression in response to changing glycogen concentration in exercise and recovery
Bibliographic record
Abstract
Glycogenin (GN-1) is essential for the formation of a glycogen granule; however, rarely has it been studied when glycogen concentration changes in exercise and recovery. It is unclear whether GN-1 is degraded or is liberated and exists as apoprotein (apo)-GN-1 (unglycosylated). To examine this, we measured GN-1 protein and mRNA level at rest, at exhaustion (EXH), and during 5 h of recovery in which the rate of glycogen restoration was influenced by carbohydrate (CHO) provision. Ten males cycled (65% VO2 max) to volitional EXH (117.8 +/- 4.2 min) on two separate occasions. Subjects were administered carbohydrate (CHO; 1 g.kg(-1).h(-1) Gatorlode) or water [placebo (PL)] during 5 h of recovery. Muscle biopsies were taken at rest, at EXH, and following 30, 60, 120, and 300 min of recovery. At EXH, total glycogen concentration was reduced (P < 0.05). However, GN-1 protein and mRNA content did not change. By 5 h of recovery, glycogen was resynthesized to approximately 60% of rest in the CHO trial and remained unchanged in the PL trial. GN-1 protein and mRNA level did not increase during recovery in either trial. We observed modest amounts of apo-GN-1 at EXH, suggesting complete degradation of some granules. These data suggest that GN-1 is conserved, possibly as very small, or nascent, granules when glycogen concentration is low. This would provide the ability to rapidly restore glycogen during early recovery.
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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) | 0.002 | 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".