Bovine hepatic miRNAome profiling and differential miRNA expression analyses between beef steers with divergent feed efficiency phenotypes
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small RNA molecules involved in regulation of multiple biological processes through modulating expression of their target genes. Here we employed RNAseq to profile liver tissue miRNAome of 60 steers from Angus, Charolais, and Kinsella Composite (KC) populations. Of these animals, 36 animals (n = 12 for each breed) were utilized to identify differentially expressed (DE) miRNAs between animals with high (n = 6) or low (n = 6) phenotypic values of residual feed intake (RFI), a common measurement of feed efficiency. At a threshold of fold-change > 1.5 and P-value < 0.05, we detected 12 (7 up- and 5 downregulated in low-RFI animals), 18 (12 up- and 6 downregulated), and 13 (8 up- and 5 downregulated) DE miRNAs for Angus, Charolais, and KC steers, respectively. Most of the DE miRNAs were breed specific, with bta-miR-449a and bta-miR-AB-2 being differentially expressed in all three breeds. The predicted target genes of the identified DE miRNA are mainly involved in cell cycle, cell death and survival, cell signaling, cellular growth and proliferation, protein trafficking, cell morphology, cell-to-cell signaling and interaction, cellular development, molecular transport, post-translational modification, as well as nutrient metabolism (lipids, carbohydrates, protein and amino acid). Our results provide insights into the bovine hepatic miRNAome and their potential roles in molecular regulation of RFI in beef 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 | 0.001 | 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".