20 Profiling circRNA and lncRNA Expression in Key Bovine Metabolic Tissues
Bibliographic record
Abstract
Abstract CircRNAs and lncRNAs are non-coding RNAs that regulate many biological processes at the cellular level. However, knowledge of their function and expression patterns in cattle remains scarce. We investigated circRNA and lncRNA expression profiles in four key metabolic tissues of beef cattle (rumen, liver, muscle, and subcutaneous adipose) using RNA-Seq. Bioinformatic analysis of 189 samples collected from 48 cattle identified 19,766 circRNAs and 10,615 lncRNAs across all four tissues. PCA revealed the expression profiles of both circ- and lncRNA were clearly separated by tissue, suggesting their expression patterns are tissue dependent. Moreover, both datasets contained large numbers of transcripts that were unique to each tissue, underlining the divergence in function across metabolic sites. Further functional analysis of tissue specific circRNAs using the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that lysine degradation, peroxisome, insulin resistance, and ubiquitin-mediated proteolysis unique to the rumen, liver, muscle, and adipose tissues, respectively. The same analysis of the lncRNAs revealed that purine metabolism, fatty acid degradation, cAMP signaling pathway, and AGE-RAGE signaling pathway in diabetic complications were uniquely enriched in the rumen, liver, muscle, and subcutaneous adipose tissues, respectively. Together, these results revealed tissue specific metabolic pathways that may be regulated by the multiple non-coding RNAs. More specifically, circDLG1 and lncMSTRG.12092.8 were predicted to regulate the expression of Lymphocyte Antigen 6 Family Member G5B (LY6G5B) and pyruvate kinase M1/2 (PKM), Adenylate Kinase 1 (AK1), and Mitofusin 2 (MFN2), respectively, indicating regulatory roles in host immune response, glycolysis, energy balance control, and mitochondrial fusion, all of which may contribute to regulation of energy metabolism. Our findings show that expression profiles of circRNAs and lncRNAs differ among metabolic tissues, and these transcripts may play crucial roles in regulating metabolism in 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".