Integrative transcriptomic, proteomic and metabolomic analyses yields insights into muscle fiber type in cattle
Bibliographic record
Abstract
Muscle fiber is an important factor in beef quality. Here, we compared fast-type longissimus dorsi muscle and slow-type psoas major muscle from cattle using transcriptomic, proteomic and metabolomic analyses. A total of 1717 differentially expressed genes (DEGs), 297 differentially abundant proteins (DAPs) and 193 differentially abundant metabolites (DAMs) were identified between LD and PM tissue, respectively. For verification, we selected 10 DEGs for qRT-PCR and 6 DAPs for western blotting, and showed they were consistent between the two approaches. GO and KEGG enrichment analyses revealed that some DEGs, DAPs and DAMs were enriched in muscle fiber type-associated GO terms and pathways. Many of them are involved in glycolysis, TCA and fatty acid metabolism. Integrated multi-omics analysis showed a correlation coefficient of 0.6244 between the transcriptome and proteome. This study provides a new understanding of molecular mechanisms involved in the determination of bovine muscle fiber type and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".