Identification of circRNA-associated ceRNA networks in the longissimus dorsi of yak under different feeding systems
Bibliographic record
Abstract
BACKGROUND: Yaks (Bos grunniens), prized for their ability to thrive in high-altitude environments, are indispensable livestock in the plateau region. Modifying their feeding systems holds significant promise for improving their growth and meat quality. Tenderness, a key determinant of yak meat quality and consumer appeal, is demonstrably influenced by dietary regimen. Indoor feeding regimes have been shown to enhance tenderness by lowering shear stress and optimizing pH values. CircRNAs, well-known modulators of circulatory function, also play a crucial role in skeletal muscle development across various animal species. However, their functional significance in yak skeletal muscle remains largely unexplored. RESULTS: In this study, we identified a total of 5,534 circRNAs within the longissimus dorsi muscle, and we found 51 differentially expressed circRNAs (20 up-regulated and 31 down-regulated) between the two feeding groups. Constructing a comprehensive ceRNA network illuminated intricate regulatory mechanisms, with PGP and circRNA_0617 converging on bta-miR-2285q, mirrored by KLF15/circRNA_0345/bta-miR-20b and CTSF/circRNA_0348/bta-miR-146a. These findings shed light on the potential of circRNAs to influence yak muscle development and meat quality, offering valuable insights for future research. CONCLUSIONS: This investigation unraveled a complex interaction network between circRNAs、mRNAs and miRNAs in yak skeletal muscle. We further elucidated the target genes regulated by these target genes within the network, offering valuable insights into the potential regulatory mechanisms governing muscle development and meat quality-related traits in yaks.
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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 | 0.001 |
| 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".