Transcriptome profile of skeletal muscle at different developmental stages in Large White and Mashen pigs
Bibliographic record
Abstract
From the perspectives of promoting individual growth and development, increasing pork yield, and improving feed utilization, it is desirable to screen candidate genes underlying pig muscle growth and regulation. In this study, we investigated transcriptome differences at 1, 90, and 180 d of age in Large White and Mashen pigs, characterized differentially expressed genes (DEGs), and screened candidate genes affecting skeletal muscle growth and development. RNA-seq was applied to analyze the transcriptome of the longissimus dorsi (LD) in the two breeds. In LD samples from the two breeds at three growth stages, 7215, 6332, 237, 3935, 3404, and 846 DEGs were obtained for L01 vs. L90, L01 vs. L180, L90 vs. L180, MS01 vs. MS90, MS01 vs. MS180, and MS90 vs. MS180, respectively. Significant tendencies in DEG expression could be grouped into eight profiles. Based on the functional analysis of DEGs, 16 candidate genes related to skeletal muscle growth and development were identified, including PCK2, GNAS, ADCY2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PHKA1, PHKA2, PHKG1, PHKG2, ITPR3, IGF1R, FGFR4, FGF1, and FGF18. The results of this study thus provide a theoretical basis for the mechanisms and candidate genes underlying skeletal muscle development in pigs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".