The prediction workflow has been performed by considering only those miRNAs found significantly deregulated (i.e. by a fold-change of at least two). For each miRNA, we obtained three lists that we matched to consider all the possible intersections. We finally retained only those genes predicted by all three algorithms. We assumed that the predictions obtained following this criteria, should have a minor degree of uncertainty since they originate from algorithms with different underlying principles. To reduce complexity, C646 chemical structure we also filtered out every gene found in both upregulated and downregulated miRNAs lists, considering only those genes with

unambiguous behavior. This improvement would have certainly helped Jin et al. to avoid misleading overlap of functional categories in their lists. Finally,

to take into account the cooperative effect of different miRNAs on the same target gene, we ranked a list of genes targeted by two or more miRNAs. GPCR Compound Library The list of target genes that we obtained following this bioinformatics workflow was finally subjected to functional classification using DAVID.16 In summary, both the paper by Jin et al.12 and our own work (Alisi et al.7)) provide clear examples of how many data are contained in “simple” miRNA expression profile experiments, and the difficulty in developing an understanding of the pathogenic mechanism leading to the transition from steatosis to NASH. In conclusion, we believe that an integrated approach of differentbioinformatics analyses for microRNA expression and regulation studies, although remaining predictive without appropriate experimental validations of predicted target genes, could lead to a deeper understanding of gene expression patterns and their regulation

in the pathogenesis of important liver diseases like NAFLD. “
“Host factors play an important role in all facets of the hepatitis medchemexpress C virus (HCV) life cycle and one such host factor is signal transducer and activator of transcription 3 (STAT3). The HCV core protein has been shown to directly interact with and activate STAT3, while oxidative stress generated during HCV replication in a replicon-based model also induced STAT3 activation. However, despite these findings the precise role of STAT3 in the HCV life cycle remains unknown. We have established that STAT3 is actively phosphorylated in the presence of replicating HCV. Furthermore, expression of a constitutively active form of STAT3 leads to marked increases in HCV replication, whereas, conversely, chemical inhibition and small interfering RNA (siRNA) knockdown of STAT3 leads to significant decreases in HCV RNA levels. This strongly implicates STAT3 as a proviral host factor.