miRNA transcriptome comparison between muscle and adipose tissues indicates potential miRNAs associated with intramuscular fat in Chinese swamp buffalo
Bibliographic record
Abstract
The amount of intramuscular fat (IMF) affects the tenderness and juiciness of beef and is an important indicator of beef quality. A few miRNAs involved in IMF deposition have been identified in other livestock. However, in the buffalo, the association between miRNA and IMF has not been reported and the miRNA expression profile remains poorly understood. In this study, small RNA sequencing was performed to characterize the miRNA expression pattern in muscle and adipose tissues using the Illumina platform. A total of 108 differentially expressed (DE) miRNAs were identified, including 98 known miRNAs and 10 novel miRNAs. A qRT-PCR experiment confirmed the quality of the DE analysis. Eight DE miRNAs showed high expression in adipose tissue and a considerable expression level in muscle tissue. Functional enrichment indicated that bta-miR-148a, bta-miR-143, bta-miR-10b, bta-let-7i, bta-let-7f, bta-let-7b, bta-miR-30a-5p, and bta-miR-100 were significantly associated with adipogenesis, suggesting these as candidate regulators for IMF deposition in buffalo. However, further functional validation is required. This is the first characterization of the miRNA expression profile in the muscle and adipose tissues of buffalo. These results provide information for the identification of miRNAs with potential effects on IMF deposition in buffalo.
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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".