Skeletal muscle protein synthesis and the abundance of the mRNA translation initiation repressor PDCD4 are inversely regulated by fasting and refeeding in rats
Bibliographic record
Abstract
Optimal skeletal muscle mass is vital to human health, because defects in muscle protein metabolism underlie or exacerbate human diseases. The mammalian target of rapamycin complex 1 is critical in the regulation of mRNA translation and protein synthesis. These functions are mediated in part by the ribosomal protein S6 kinase 1 (S6K1) through mechanisms that are poorly understood. The tumor suppressor programmed cell death 4 (PDCD4) has been identified as a novel substrate of S6K1. Here, we examined 1) the expression of PDCD4 in skeletal muscle and 2) its regulation by feed deprivation (FD) and refeeding. Male rats (~100 g; n = 6) were subjected to FD for 48 h; some rats were refed for 2 h. FD suppressed muscle fractional rates of protein synthesis and Ser(67) phosphorylation of PDCD4 (-50%) but increased PDCD4 abundance (P < 0.05); refeeding reversed these changes (P < 0.05). Consistent with these effects being regulated by S6K1, activation of this kinase was suppressed by FD (-91%, P < 0.05) but was increased by refeeding. Gavaging rats subjected to FD with a mixture of amino acids partially restored muscle fractional rates of protein synthesis and reduced PDCD4 abundance relative to FD. Finally, when myoblasts were grown in amino acid- and serum-free medium, phenylalanine incorporation into proteins in cells depleted of PDCD4 more than doubled the values in cells with a normal level of PDCD4 (P < 0.0001). Thus feeding stimulates fractional protein synthesis in skeletal muscle in parallel with the reduction of the abundance of this mRNA translation inhibitor.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".