Beta‐GPA‐induced changes in mitochondrial biogenesis are associated with a muscle type‐dependent decrease in RIP140
Bibliographic record
Abstract
Beta‐guanidopropionic acid (β‐GPA) is a creatine analogue that leads to reductions in ~P concentrations and increases in markers of mitochondrial content in rodent skeletal muscle. Prior evidence attributes these changes to activation of the 5'AMP pathway; however data suggest RIP140, a negative downstream regulator of PPAR gamma co‐activator 1 alpha (PGC‐1α), is a likely player in the regulation of mitochondrial biogenesis (MB). The aim of this study was to determine if β‐GPA feeding decreases RIP140 protein in skeletal muscle in a fiber type‐specific manner and if it is associated with an increase in mitochondrial content. Male Wistar rats were fed a semi‐purified diet supplemented with (n=12) or without (n=12) 1% β‐GPA for 6 weeks. β‐GPA feeding induced skeletal muscle MB in tricep as shown by increases in CORE1 and COXIV protein and citrate synthase activity. In contrast, no such changes were seen in soleus from β‐GPA‐fed rats. β‐GPA feeding led to a ~75% increase in PGC1α messenger RNA expression in tricep and ~140% increase in soleus. RIP140 protein decreased ~40% in tricep while it was unchanged in the soleus. In summary, β‐GPA feeding led to decreases in RIP140 in rat tricep only, correlating to increases in MB in only this muscle type. This suggests that RIP140 may play a key role in regulating MB downstream of PGC1α and provides a mechanism for fiber type differences in muscle adaptation. Supported by NSERC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".