Hepatic expression of inflammatory genes and microRNAs in pigs with high "cholesteryl ester transfer protein" (CETP) activity

Human obesity and obesity-related diseases (ORD) are growing health problems worldwide and represent a major public health challenge. Most of these diseases are complex conditions, influenced by many genes (including microRNAs) and environmental factors. Many metabolic perturbations are associated with obesity; e.g., low levels of high-density lipoproteins (HDL) are high risk factors of cardiovascular events. A number of genetic, lifestyle, and environmental factors have been shown to contribute to the lowering of HDL-cholesterol. One of these factors is cholesteryl ester transfer protein (CETP) promoting the redistribution of cholesteryl esters, triglycerides, and phospholipids between plasma proteins. Moreover, obesity and ORD are often linked with chronic low-grade inflammation leading to insulin resistance and endothelial and microvascular dysfunctions. The aim of this study was to detect differences in the hepatic expression of genes involved in low-grade inflammation and of obesity- and cholesterol-related microRNAs in two mixed breed populations of pigs (Yorkshire-Gottingen minipig, YM and Duroc-Gottingen minipig, DM) including males and females, with extreme phenotypes for CETP activity levels (designated as CETP-high and CETP-low, respectively). Furthermore, breed and gender differences were also investigated. We found significant difference (P < 0.05) in hepatic expression levels of several mRNAs and microRNAs between the CETP-high and -low groups (C5, IL1RN, IL18, and miR-223-5p); between the two mixed breeds (IL1RAP and miR-140-5p); and between gender (APOA1, IL1RN, and FBLN1). Furthermore, when taking breed into account we show that the transcriptional levels of TNF, miR20a, miR33b, and miR130a differed between the two CETP groups. We conclude that increased CETP activity is accompanied by a modest differential hepatic expression of several microRNAs and inflammatory-related genes. Furthermore, our study demonstrates that when modeling the analysis of expression data, it is important to take gender- and breed-specific effects into account.