miR-33a inhibits ATP-binding cassette (ABC)A1 and ABCG1 to reduce cellular cholesterol efflux. Studies in mice treated with anti-miR-33a or in genetic miR-33a-deficient mice showed miR-33a antagonism induced ABCA1 in macrophages and liver, increased serum high-density lipoprotein (HDL) levels, and promoted macrophage-to-feces reverse cholesterol transport.[12]

Additionally, miR-33a antagonism promoted regression of atherosclerosis in mice and nonhuman primates.[13, 14] These studies suggest that miR-33a acts in a synergistic manner with SREBP2 to regulate cellular cholesterol BVD-523 price homeostasis. The aim of this study was to investigate the potential effect of stimulation of bile acid synthesis on hepatic lipid metabolism using Cyp7a1-tg mice as a model. Here, we report that bile acid synthesis plays an important role in integrating intracellular cholesterol sensing and homeostasis by modulating the liver SREBP2/miR-33a axis. Our study suggests the antagonism of miRNA-33a to induce CYP7A1 and bile acid synthesis may be a potential therapeutic approach to treat

NAFLD and diabetes. Cyp7a1-tg mice overexpressing rat Cyp7a1 complementary DNA under an ApoE3 hepatic control region have been described previously.[6] “Humanized” CYP7A1 mice expressing human CYP7A1 from a BAC clone on a mouse cyp7a1 knockout background were generated as described previously.[15] Mice were 上海皓元医药股份有限公司 maintained under a 12-hour light (6 a.m. to 6 p.m.) Target Selective Inhibitor Library manufacturer and 12-hour dark (6 p.m. to 6 a.m.) cycle. Male wild-type (WT) and Cyp7a1-tg mice were fed chow or Western diet (WD; 42% fat calories, 0.2% cholesterol, Harlan-Teklad 88137; Harlan Teklad, Madison, WI) for 4 months. The local institutional animal care and use committee approved all animal protocols. A MouseRef-8 v2.0 Expression BeadChip kit (BD-202-0202; Illumina, San Diego, CA) was used for microarray analysis. Raw microarray

data were log2 transformed and processed with background correction and quintile normalization. Quality control analyses were applied to detect outlier samples. Expression signals with an Illumina detection threshold <0.05 across all samples were used. Linear models and the empirical Bayes method in Limma[16] were used to access differential expression between the control and transgenic groups. Those genes that satisfied the false discovery rate adjusted P value <0.05 or raw P value of <0.001, whichever was more stringent (Benjamini-Hochberg’s method), and fold-change threshold of 1.5 were identified for inclusion in the functional pathway and network analysis. Functional profiling of differentially affected biological processes and pathways between transgenic and control mice were evaluated using publicly available tools (e.g.