Identification of Candidate Circular RNAs Underlying Intramuscular Fat Content in the Donkey
Bibliographic record
Abstract
Intramuscular fat (IMF) content is a crucial indicator of meat quality. Circular RNAs (circRNAs) are a large class of endogenous RNAs that are involved in many physiological processes. However, the expression and function of circRNA in IMF in the donkey remains unresolved. Here we performed an expression profiling of circRNAs in the donkey longissimus dorsi muscle and identified 12,727 candidate circRNAs. Among these, 70% were derived from the exons of protein genes. Furthermore, a total of 127 differentially expressed (DE) circRNAs were identified in high (H) and low (L) IMF content groups, including 63 upregulated and 64 downregulated circRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the host genes of the DE circRNAs showed that the host genes were enriched in lipid metabolism related GO terms (e.g., fatty acid beta-oxidation using acyl-CoA dehydrogenase and MLL3/4 complex), and signaling pathways (e.g., TGF-beta and lysine degradation signaling pathway). Further analyses indicated that 127 DE circRNAs were predicted to potentially interact with miRNAs, leading to the construction of circRNA-miRNA regulatory network. Multiple circRNAs can potentially function as sponges of miRNAs that regulate the differentiation of adipocytes. Our results provide valuable expression profile information for circRNA in the donkey and new insight into the regulatory mechanisms of circRNAs in the regulation of IMF content.
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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 | 0.000 | 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".