Elucidation of Molecular Mechanisms of Physiological Variations between Bovine Subcutaneous and Visceral Fat Depots under Different Nutritional Regimes
Bibliographic record
Abstract
Adipose tissue plays a critical role in energy homeostasis and metabolism. There is sparse understanding of the molecular regulation at the protein level of bovine adipose tissues, especially within different fat depots under different nutritional regimes. The objective of this study was to analyze the differences in protein expression between bovine subcutaneous and visceral fat depots in steers fed different diets and to identify the potential regulatory molecular mechanisms of protein expression. Subcutaneous and visceral fat tissues were collected from 16 British-continental steers (15.5 month old) fed a high-fat diet (7.1% fat, n=8) or a control diet (2.7% fat, n=8). Protein expression was profiled using label free quantification LC-MS/MS and expression of selected transcripts was evaluated using qRT-PCR. A total of 682 proteins were characterized and quantified with fat depot having more impact on protein expression, altering the level of 51.0% of the detected proteins, whereas diet affected only 5.3%. Functional analysis revealed that energy production and lipid metabolism were among the main functions associated with differentially expressed proteins between fat depots, with visceral fat being more metabolically active than subcutaneous fat as proteins associated with lipid and energy metabolism were upregulated. The expression of several proteins was significantly correlated to subcutaneous fat thickness and adipocyte size, indicating their potential as adiposity markers. A poor correlation (r=0.245) was observed between mRNA and protein levels for 9 genes, indicating that many proteins may be subjected to post-transcriptional regulation. A total of 8 miRNAs were predicted to regulate more than 20% of lipid metabolism proteins differentially expressed between fat depots, suggesting that miRNAs play a role in adipose tissue regulation. Our results show that proteomic changes support the distinct metabolic and physiological characteristics observed between subcutaneous and visceral adipose tissue depots in cattle.
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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 | 0.000 |
| 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) | 0.000 | 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 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".