GSE110478
Genome-wide identification and comparison of mRNAs, lncRNAs, and circRNAs in porcine intramuscular, subcutaneous, retroperitoneal, and mesenteric adipose tissues
Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), which were proved to play a crucial role in regulating cell differentiation and tissue development. However, the expression profiles of different RNAs, such as mRNA, lncRNAs, and circRNAs in different adipose tissues, are still largely unclear. To shed light on this issue, we performed the systematic analysis of mRNAs, lncRNAs, and circRNAs obtained from intramuscular adipose tissues (IAT), subcutaneous adipose tissues (SAT), visceral adipose tissues (RPAT), and mesenteric adipose tissues (MAT) tissues of Chinese Erhualian pigs using high-throughput sequencing. A total of 1,695, 1,878, 494, 1,300, 902, and 355 differentially expressed (DE) mRNAs, 318, 399, 192, 251, 249, and 347 DE lncRNAs, 1,367, 1,007, 999, 1,153, 1,256, and 837 DE circRNAs were identified in IAT versus SAT, IAT versus RPAT, SAT versus RPAT, MAT versus IAT, MAT versus SAT, and MAT versus RPAT, respectively. Gene ontology (GO) enrichment analyses showed these DE genes were mainly involved in lipid metabolic and immune response. Next the co-expression network construction of mRNAs-lncRNAs revealed several key lncRNAs such as MSTRG.604204 and MSTRG.604206 might associated with lipid metabolic. Tissue specific analysis indicated that circRNA exhibited the highest tissue specificity than lncRNA and mRNA, and the IAT had the most tissue specific lncRNA, mRNA, and circRNA compared with other tissues. Taken together, this study gives us a novel clue to the role of mRNAs, lncRNAs and circRNAs in fat metabolism, thus improving our understanding of the regulation mechanism of different gene expression profiles in adipose deposition Overall design: mRNA, lncRNA, and circRNA profiles of intramuscular adipose (IAT), subcutaneous adipose (SAT),retroperitoneal adipose (RPAT), and mesenteric adipose (MAT) of Erhualian pigs were generated by RNA-Seq using Illumina HiSeq X Ten