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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".