Circular RNA Profiling Identifies Novel circPPARA that Promotes Intramuscular Fat Deposition in Pigs
Bibliographic record
Abstract
Intramuscular fat (IMF) content plays an important role in pork quality. Circular RNAs (circRNAs) implicate various biological processes; however, the regulatory mechanisms and functions of circRNAs in porcine IMF remains elusive. Hence, the study assessed the circRNA expression profiling in the longissimus dorsi muscle of pigs with high (H) and low (L) IMF content to unravel their regulatory functions in improving meat quality. The RNA sequencing analysis identified 29,732 circRNAs from six sampled pigs, most of which were exon-derived. In the muscle, 336 were differentially expressed (DE) between the H and L IMF groups; 196 circRNAs were upregulated, and 140 were downregulated. Subsequent qRT-PCR validation of 10 DE circRNAs revealed expression patterns consistent with the RNA-seq data. Gene ontology and KEGG enrichment analysis revealed that most significantly enriched DE circRNAs' host genes were linked to lipid metabolism and adipogenesis processes. The circRNA-miRNA regulatory network analysis found several circRNAs targeting miRNAs associated with adipogenesis. Finally, a novel circRNA, circPPARA, was identified with the expression positively correlated with the IMF content. Detailed analysis revealed that circPPARA was formed via head-to-tail splicing and was more stable than the linear PPARA, predominantly located in the cytoplasm. Functional studies using overexpression and siRNA constructs demonstrated that circPPARA promotes differentiation and hinders the proliferation of porcine intramuscular preadipocytes. Moreover, the dual-luciferase assay revealed that circPPARA adsorbed miR-429 and miR-200b, thereby promoting intramuscular adipogenesis in pigs. Our results identified a candidate circRNA, circPPARA, that affects porcine IMF content. The study provides knowledge of the regulatory functions of circRNAs in intramuscular adipogenesis and abundant resource for future research on circRNAs in pigs.
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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".