Proteomic analysis reveals changes in energy metabolism of skeletal muscle in beef cattle supplemented with vitamin A
Bibliographic record
Abstract
BACKGROUND: Vitamin A has been reported as a factor influencing marbling deposition in meat from animals. Although the mechanisms by which vitamin A regulates lipid metabolism in mature adipocytes are already well-established, information regarding molecular mechanisms underlying the effects of vitamin A on the regulation of intramuscular fat deposition in beef cattle still remains limited. The present study aimed to assess the molecular mechanisms involved in the intramuscular fat deposition in beef cattle supplemented with vitamin A during the fattening phase using a proteomic approach. RESULTS: Vitamin A supplementation during the fattening phase decreased intramuscular fat deposition in beef cattle. Proteome and phospho-proteome analysis together with biological and networking analysis of the protein differentially abundant between treatments indicated that Vitamin A supplementation affects the overall energy metabolism of skeletal muscle, impairing lipid biosynthesis in skeletal muscle. CONCLUSION: Vitamin A supplementation at fattening phase impairs intramuscular fat deposition in beef cattle likely by changing the energy metabolism of skeletal muscle. The interaction of retinoic acid and heat shock 70-kDa protein may play a pivotal role in intramuscular fat deposition as a consequence of vitamin A supplementation by impairing de novo fatty acid synthesis as a result of a possible decrease in insulin sensitivity in the skeletal muscle. © 2020 Society of Chemical Industry.
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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 | 0.000 |
| 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) | 0.001 | 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".