Metabolic Regulations by lncRNA, miRNA, and ceRNA Under Grass-Fed and Grain-Fed Regimens in Angus Beef Cattle
- PMID: 33747033
- PMCID: PMC7969984
- DOI: 10.3389/fgene.2021.579393
Metabolic Regulations by lncRNA, miRNA, and ceRNA Under Grass-Fed and Grain-Fed Regimens in Angus Beef Cattle
Abstract
Beef cattle raised under grass-fed and grain-fed have many differences, including metabolic efficiency and meat quality. To investigate these two regimens' intrinsic influence on beef cattle, we used high-throughput sequencing and metabolomics analyses to explore differentially expressed genes (DEGs) and metabolimic networks in the liver. A total of 200 DEGs, 76 differentially expressed miRNAs (DEmiRNAs), and two differentially expressed lncRNAs (DElncRNAs) were detected between regimen groups. Metabolic processes and pathways enriched functional genes including target genes of miRNAs and lncRNAs. We found that many genes were involved in energy, retinol and cholesterol metabolism, and bile acid synthesis. Combined with metabolites such as low glucose concentration, high cholesterol concentration, and increased primary bile acid concentration, these genes were mainly responsible for lowering intramuscular fat, low cholesterol, and yellow meat in grass-fed cattle. Additionally, we identified two lncRNAs and eight DEGs as potential competing endogenous RNAs (ceRNAs) to bind miRNAs by the interaction network analysis. These results revealed that the effects of two feeding regimens on beef cattle were mainly induced by gene expression changes in metabolic pathways mediated via lncRNAs, miRNAs, and ceRNAs, and contents of metabolites in the liver. It may provide a clue on feeding regimens inducing the metabolic regulations.
Keywords: beef cattle; ceRNAs; feeding regimens; lncRNAs; metabolic regulations; miRNAs.
Copyright © 2021 Jia, Bai, Liu, Cai, Liu, He and Song.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Figures
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References
-
- Andrews S., Fast Q. C. (2010). A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed April 26, 2010).
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