Parsing the microRNA genetics basis regulating skeletal muscle fiber types and meat quality traits in pigs.
A JiangD YinL ZhangB LiR LiX ZhangZ ZhangH LiuK KimWangjun WuPublished in: Animal genetics (2021)
Muscle fibers are closely related to human diseases and livestock meat quality. However, the genetics basis of microRNAs (miRNAs) in regulating muscle fibers is not completely understood. In this study, we constructed the whole genome-wide miRNA expression profiles of porcine fast-twitch muscle [biceps femoris (Bf)] and slow-twitch muscle [soleus (Sol)], and identified hundreds of miRNAs, including four skeletal muscle-highly expressed miRNAs, ssc-miR-378, ssc-let-7f, ssc-miR-26a, and ssc-miR-27b-3p. Moreover, we identified 63 differentially expressed (DE) miRNAs between biceps femoris vs. soleus, which are the key candidate miRNAs regulating the skeletal muscle fiber types. In addition, we found that the expression of DE ssc-miR-499-5p was significantly correlated to the expression of Myoglobin (r = 0.6872, P < 0.0001) and Myosin heavy chain 7 (MYH7; r = 0.5408, P = 0.0020), and pH45 min (r = 0.3806, P = 0.0380) and glucose content (r = -0.4382, P = 0.0154); while the expression of DE ssc-miR-499-3p was significantly correlated to the expression of Myoglobin (r = 0.5340, P = 0.0024) and pH45 min (r = 0.4857, P = 0.0065). Taken together, our data established a sound foundation for further studies on the regulatory mechanisms of miRNAs in skeletal muscle fiber conversion and meat quality traits in livestock, and could provide a genetic explanation of the role of miRNAs in human muscular diseases.
Keyphrases
- skeletal muscle
- poor prognosis
- genome wide
- insulin resistance
- long non coding rna
- endothelial cells
- binding protein
- cell proliferation
- dna methylation
- metabolic syndrome
- heart failure
- type diabetes
- wastewater treatment
- long noncoding rna
- machine learning
- transcription factor
- big data
- blood pressure
- quality improvement
- copy number
- hypertrophic cardiomyopathy
- pluripotent stem cells
- electronic health record
- body composition
- deep learning
- weight loss