Application of iTRAQ to catalogue the skeletal muscle proteome in pigs and assessment of effects of gender and diet dephytinization
Bibliographic record
Abstract
In this study iTRAQ was used to produce a highly confident catalogue of 542 proteins identified in porcine muscle (false positive<5%). To our knowledge this is the largest reported set of skeletal muscle proteins in livestock. Comparison with human muscle proteome demonstrated a low level of false positives with 83% of the proteins common to both proteomes. In addition, for the first time we assess variations in the muscle proteome caused by sexually dimorphic gene expression and diet dephytinization. Preliminary analysis identified 19 skeletal muscle proteins differentially expressed between male and female pigs (> or = 1.2-fold, p<0.05), but only one of them, GDP-dissociation inhibitor 1, was significant (p<0.05) after false discovery rate correction. Diet dephytinization affected expression of 20 proteins (p<0.05). This study would contribute to an evaluation of the suitability of the pig as a model to study human gender-related differences in gene expression. Transgenic pigs used in this study might also serve as a useful model to understand changes in human physiology resulting from diet dephytinization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".