Longissimus dorsi muscle transcriptomic analysis of Yunling and Chinese simmental cattle differing in intramuscular fat content and fatty acid composition
Bibliographic record
Abstract
Intramuscular fat (IMF) content and fatty acid (FA) composition vary significantly across beef cattle breeds, which play an important role in taste and nutritional value. However, the molecular mechanisms underlying these phenotypic differences remain unknown. The present study compared meat quality traits between Yunling cattle and Chinese Simmental cattle. Yunling cattle showed a lower IMF content and proportion of monounsaturated fatty acids (MUFA), as well as higher proportions of saturated fatty acids (SFA), polyunsaturated fatty acids (PUFA), and short-chain fatty acids (sc-FA) in the longissimus dorsi (LD) muscle than Chinese Simmental cattle. To further identify the candidate genes and pathways responsible for these phenotypic differences, the transcriptome of LD muscle from the two breeds were measured using RNA-seq. A total of 1347 differentially expressed genes were identified. The major metabolic pathways that were differentially modulated were lipolysis and glycometabolism. Yunling cattle showed a higher expression of lipolysis genes (ALDH9A1, ACSL5, ACADM, ACAT2, ACOT2) and a lower expression of genes related to glycometabolism (PGM1, GALM, PGM1, GPI, LDHA). This research identified candidate genes and pathways for IMF content and FA composition in the LD muscle of beef cattle, which may facilitate the design of new selection strategies to improve meat quality.
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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 | 0.001 |
| 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".