Muscle-specific differences in the response of mitochondrial proteins to β-GPA feeding: an evaluation of potential mechanisms
Bibliographic record
Abstract
Beta-Guanadinopropionic acid (beta-GPA) feeding leads to reductions in skeletal muscle phosphagen concentrations and has been used as a tool with which to study the effects of energy charge on skeletal muscle metabolism. Supplementing standard rodent diets with beta-GPA leads to increases in mitochondrial enzyme content in fast but not slow-twitch muscles from male rats. Given this apparent discrepancy between muscle types we used beta-GPA feeding as a model to study signaling pathways involved in mitochondrial biogenesis. We hypothesized that beta-GPA feeding would result in a preferential activation of p38 MAPK and AMPK signaling and reductions in RIP140 protein content in triceps but not soleus muscle. Despite similar reductions in high-energy phosphate concentrations, 6 wk of beta-GPA feeding led to increases in mitochondrial proteins in triceps but not soleus muscles. Differences in the response of mitochondrial proteins to beta-GPA feeding did not seem to be related to a differential activation of p38 MAPK and AMPK signaling pathways or discrepancies in the induction of PPARgamma coactivator (PGC)-1alpha and -1beta. The protein content and expression of the nuclear corepressor RIP140 was reduced in triceps but not soleus muscle. Collectively our results indicate that chronic reductions in high-energy phosphates lead to the activation of p38 MAPK and AMPK signaling and increases in the expression of PGC-1alpha and -1beta in both soleus and triceps muscles. The lack of an effect of beta-GPA feeding on mitochondrial proteins in the soleus muscles could be related to a fiber type-specific effect of beta-GPA on RIP140 protein content.
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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) | 0.002 | 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".