Differences in histomorphology and expression of key lipid regulated genes of four adipose tissues from Tibetan pigs

Abstract

Tibetan pigs, an indigenous pig breed in China, have high overall fat deposition and flavorful and tasty meat. They are thus good models for studying adipogenesis. Few studies have been conducted focusing on expression of lipid regulated genes in different adipose tissues of Tibetan pigs. Therefore, we compared the difference of histomorphology and expression level of lipid regulated genes through qPCR and western blot in subcutaneous fat, perirenal fat, omental adipose tissue, and inguinal fat of Tibetan pigs. Our results showed that the area of subcutaneous adipocytes in Tibetan pigs was smaller, while the other three adipose tissues (perirenal fat, greater omentum fat, inguinal fat) had cell areas of similar size. The gene expression of FABP4, FASN, FABP3, and ME1 in subcutaneous fat was significantly higher than that in perirenal fat. Furthermore, the protein expression of FABP4 was significantly lower in subcutaneous fat than in perirenal fat (p < 0.05), and the expression of FASN was higher in greater omentum fat than in subcutaneous fat (p = 0.084). The difference in adipocyte cell size and expression of lipid-regulated genes in adipose tissues from the various parts of the pig body is likely due to the different cellular lipid metabolic processes. Specially, FABP4 and FASN may be involved in the regulation of fat deposition in different adipose tissues of Tibetan pigs.

Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

“…Fifty-two DEGs were filtered out and 18 down- and 34 up-regulated genes were presented in the IMF group. Functional analysis of these DEGs showed that these targets were primarily associated with lipid metabolism, energy metabolites and amino acid biosynthesis and degradation, such as lipid metabolic process [ 71 ], valine, leucine and isoleucine degradation [ 72 ], peroxisome [ 73 ], apelin signaling pathway [ 74 ]. Subsequently, ACTA2 and HMGCL were screened as potential biomarkers specific to IMF deposition through bioinformatics and three machine learning approaches, with ACTA2 in particular identified as the most potentially sensitive biomarker.…”