X Jin1, Y-F Ye, S-H Chen, C-H Yu, J Liu, Y-M Li. 1. Digestive Department, The First Affiliated Hospital, Medical School, Zhejiang University, Zhejiang, PR China.
Abstract
BACKGROUND/AIMS: To explore the unique microRNA expression pattern of nonalcoholic fatty liver disease in a rat model, and search for targets of certain dysregulated microRNAs. METHODS: Microarray and stem-loop RT-PCR were utilized to detect dysregulated microRNAs in a rat model. Significance Analysis of Microarray, Prediction Analysis of Microarray and clustering analysis were implemented to calculate significantly aberrantly expressed microRNAs. TargetScan, miRanda and PicTar were jointly used to predict targets of microRNAs. RESULTS: Confirmed by Significance Analysis of Microarray and predicted by Prediction Analysis of Microarray, portfolios of 27 and 21 microRNAs were selected as an accurate molecular signature in distinguishing steatosis and steatohepatitis from normal rat liver. Besides, a panel of microRNA-target pairs that may be involved in lipid and glucose metabolism and inflammation process was delineated. CONCLUSION: This is by far the first report on the dysregulated microRNAs expression pattern in nonalcoholic fatty liver disease. The successful differentiation of steatosis and steatohepatitis from normal liver hints to the potential of using lists of dysregulated microRNAs for diagnosis, though many problems need to be solved. Besides, these data will guide further studies of the contribution of microRNAs to the pathogenesis of nonalcoholic fatty liver disease while disease-specific microRNAs might become potential targets for therapeutic intervention.
BACKGROUND/AIMS: To explore the unique microRNA expression pattern of nonalcoholic fatty liver disease in a rat model, and search for targets of certain dysregulated microRNAs. METHODS: Microarray and stem-loop RT-PCR were utilized to detect dysregulated microRNAs in a rat model. Significance Analysis of Microarray, Prediction Analysis of Microarray and clustering analysis were implemented to calculate significantly aberrantly expressed microRNAs. TargetScan, miRanda and PicTar were jointly used to predict targets of microRNAs. RESULTS: Confirmed by Significance Analysis of Microarray and predicted by Prediction Analysis of Microarray, portfolios of 27 and 21 microRNAs were selected as an accurate molecular signature in distinguishing steatosis and steatohepatitis from normal rat liver. Besides, a panel of microRNA-target pairs that may be involved in lipid and glucose metabolism and inflammation process was delineated. CONCLUSION: This is by far the first report on the dysregulated microRNAs expression pattern in nonalcoholic fatty liver disease. The successful differentiation of steatosis and steatohepatitis from normal liver hints to the potential of using lists of dysregulated microRNAs for diagnosis, though many problems need to be solved. Besides, these data will guide further studies of the contribution of microRNAs to the pathogenesis of nonalcoholic fatty liver disease while disease-specific microRNAs might become potential targets for therapeutic intervention.
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