Phosphoproteomic analysis identifies differentially expressed phosphorylation sites that affect muscle fiber type in pigs
Bibliographic record
Abstract
Skeletal muscle of livestock is composed of both fast- and slow-twitch muscle fibers, which are key factors in their meat quality. However, the role of protein phosphorylation in muscle fiber type is not completely understood. Here, a fast-twitch (biceps femoris, BF) and slow-twitch (soleus, SOL) muscle tissue sample was collected from three male offspring of Duroc and Meishan pigs. We demonstrate that the meat quality of SOL muscle is significantly better than that of BF muscle. We further used phosphoproteomic profiling of BF and SOL muscles to identify differences between these muscle types. A total of 2,327 phosphorylation sites from 770 phosphoproteins were identified. Among these sites, 287 differentially expressed phosphorylation sites (DEPSs) were identified between BF and SOL. GO and KEGG enrichment analysis of proteins containing DEPSs showed that these phosphorylated proteins were enriched in the glycolytic process GO term and the AMPK signaling pathway. A protein-protein interaction (PPI) analysis reveals that these phosphorylated proteins interact with each other to regulate the transformation of muscle fiber type. These analyses reveal that protein phosphorylation modifications are involved in porcine skeletal muscle fiber type transformation. This study provides new insights into the molecular mechanisms by which protein phosphorylation regulates muscle fiber type transformation and meat quality in pigs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".