Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in muscle growth in the large Diqing Tibetan pig
Bibliographic record
Abstract
Pig growth involves multiple genes and coordinated regulatory mechanisms. The large Diqing Tibetan pigs (TPs) are a unique plateau pig breed in China. Data on the mechanisms of muscle growth and development in TPs are limited, and its transcriptional regulation mechanism remains unclear. This study identifies important genes and proteins involved in muscle growth in TPs. We obtained transcriptomic and proteomic data from the longissimus dorsi muscle tissues of TPs and Duroc pigs (DPs) via RNA-seq and isobaric tags for relative and absolute quantitation analyses, respectively. Comparative analyses of TPs and DPs yielded 557 differentially expressed genes (DEGs) and 56 differentially abundant proteins (DAPs). Functional annotation of these DEGs and DAPs was enriched in metabolic processes, metabolic pathways, cytoskeletal protein binding, AMPK signaling pathway, insulin signaling pathway, PPAR signaling pathway, and other related pathways. Ten genes were identified as key candidate regulators ( FASN, PPARG, PCK1, ACTA2, TXN, SNU13, APOA1, ATP8, ALDH2, and IGFN1) that may play important roles in the muscle growth traits of TPs. This study provides a reference for analyzing the genetic regulation mechanism underlying muscle growth in pigs and improving the meat yield of TPs via molecular marker-assisted selection.
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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 | 0.001 | 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) | 0.001 | 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".