Altered MicroRNA Expression in Bovine Subcutaneous and Visceral Adipose Tissues from Cattle under Different Diet
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are a class of molecular regulators found to participate in numerous biological processes, including adipogenesis in mammals. This study aimed to evaluate the differences of miRNA expression between bovine subcutaneous (backfat) and visceral fat depots (perirenal fat) and the dietary effect on miRNA expression in these fat tissues. METHODOLOGY/PRINCIPAL FINDINGS: Fat tissues were collected from 16 Hereford×Aberdeen Angus cross bred steers (15.5 month old) fed a high-fat diet (5.85% fat, n = 8) or control diet (1.95% fat, n = 8). Total RNA from each animal was subjected to miRNA microarray analysis using a customized Agilent miRNA microarray containing 672 bovine miRNA probes. Expression of miRNAs was not equal between fat depots as well as diets: 207 miRNAs were detected in both fat depots, while 37 of these were found to be tissue specific; and 169 miRNAs were commonly expressed under two diets while 75 were diet specific. The number of miRNAs detected per animal fed the high fat diet was higher than those fed control diet (p = 0.037 in subcutaneous fat and p = 0.002 visceral fat). Further qRT-PCR analysis confirmed that the expression of some miRNAs was highly influenced by diet (miR-19a, -92a, -92b, -101, -103, -106, -142-5p, and 296) or fat depot (miR-196a and -2454). CONCLUSIONS/SIGNIFICANCE: Our results revealed that the miRNA may differ among adipose depots and level of fat in the diet, suggesting that miRNAs may play a role in the regulation of bovine adipogenesis.
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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) | 0.001 | 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".