Searching for interferon-induced genes that inhibit hepatitis B virus replication in transgenic mouse hepatocytes.

We have previously shown that alpha/beta interferon (IFN-alpha/beta) and IFN-gamma inhibit hepatitis B virus (HBV) replication noncytopathically in the livers of HBV transgenic mice and in hepatocyte cell lines derived from these mice. The present study was designed to identify transcriptionally controlled hepatocellular genes that are tightly associated with the inhibition of HBV replication and that might, therefore, mediate the antiviral effect of these cytokines. Twenty-nine genes were identified, many of which have known or potential antiviral activity. Notably, multiple components of the immunoproteasome and ubiquitin-like proteins were strongly induced by both IFN-alpha/beta and IFN-gamma, as were a number of GTP-binding proteins, including GTPases with known antiviral activity, chemokines, signaling molecules, and miscellaneous genes associated with antigen processing, DNA-binding, or cochaperone activity and several expressed sequence tags. The results suggest that one or more members of this relatively small subset of genes may mediate the antiviral effect of IFN-alpha/beta and IFN-gamma against HBV. We have already exploited this information by demonstrating that the antiviral activity of IFN-alpha/beta and IFN-gamma is proteasome dependent.

Large-scale analysis of the human and mouse transcriptomes.

High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.