Glycogenin activity in human skeletal muscle is proportional to muscle glycogen concentration
Bibliographic record
Abstract
The de novo biosynthesis of glycogen is catalyzed by glycogenin, a self-glucosylating protein primer. To date, the role of glycogenin in regulating glycogen metabolism and the attainment of maximal glycogen levels in skeletal muscle are unknown. We measured glycogenin activity after enzymatic removal of glucose by alpha-amylase, an indirect measure of glycogenin amount. Seven male subjects performed an exercise and dietary protocol that resulted in one high-carbohydrate leg (HL) and one low-carbohydrate leg (LL) before testing. Resting muscle biopsies were obtained and analyzed for total glycogen, proglycogen (PG), macroglycogen (MG), and glycogenin activity. Results showed differences (P < 0.05) between HL and LL for total glycogen (438.0 +/- 69.5 vs. 305.7 +/- 57.4 mmol glucosyl units/kg dry wt) and PG (311.4 +/- 38.1 vs. 227.3 +/- 33.1 mmol glucosyl units/kg dry wt). A positive correlation between total muscle glycogen content and glycogenin activity (r = 0.84, P < 0.001) was observed. Similar positive correlations (P < 0.05) were also evident between both PG and MG concentration and glycogenin activity (PG, r = 0.82; MG, r = 0.84). It can be concluded that glycogenin does display activity in human skeletal muscle and is proportional to glycogen concentration. Thus it must be considered as a potential regulator of glycogen synthesis in human skeletal muscle.
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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 | 0.001 |
| 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 | 0.001 |
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".