Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, chose an appropriate cut-off, and create a list of candidate genes. This approach has been criticized for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, many of these methods seem overly complicated. Furthermore, the most popular method, Gene Set Enrichment Analysis (GSEA), is based on a statistical test known for its lack of sensitivity. In this paper we compare the performance of a simple alternative to GSEA. We find that this simple solution clearly outperforms GSEA. We demonstrate this with eight different microarray datasets.

Seneca Valley virus (SVV) is an oncolytic RNA virus belonging to the Picornaviridae family. Its nucleotide sequence is highly similar to those of members of the Cardiovirus genus. SVV is also a neuroendocrine cancer-selective oncolytic picornavirus that can be used for anticancer therapy. However, the interaction between SVV and its host is yet to be fully characterized. In this study, SVV inhibited antiviral type I interferon (IFN) responses by targeting different host adaptors, including mitochondrial antiviral signaling (MAVS), Toll/interleukin 1 (IL-1) receptor domain-containing adaptor inducing IFN-␤ (TRIF), and TRAF family member-associated NF-B activator (TANK), via viral 3C protease (3C pro ). SVV 3C pro mediated the cleavage of MAVS, TRIF, and TANK at specific sites, which required its protease activity. The cleaved MAVS, TRIF, and TANK lost the ability to regulate pattern recognition receptor (PRR)-mediated IFN production. The cleavage of TANK also facilitated TRAF6-induced NF-B activation. SVV was also found to be sensitive to IFN-␤. Therefore, SVV suppressed antiviral IFN production to escape host antiviral innate immune responses by cleaving host adaptor molecules. IMPORTANCE Host cells have developed various defenses against microbial pathogen infection. The production of IFN is the first line of defense against microbial infection. However, viruses have evolved many strategies to disrupt this host defense. SVV, a member of the Picornavirus genus, is an oncolytic virus that shows potential functions in anticancer therapy. It has been demonstrated that IFN can be used in anticancer therapy for certain tumors. However, the relationship between oncolytic virus and innate immune response in anticancer therapy is still not well known. In this study, we showed that SVV has evolved as an effective mechanism to inhibit host type I IFN production by using its 3C pro to cleave the molecules MAVS, TRIF, and TANK directly. These molecules are crucial for the Toll-like receptor 3 (TLR3)-mediated and retinoic acid-inducible gene I (RIG-I)-like receptor (RLR)-mediated signaling pathway. We also found that SVV is sensitive to IFN-␤. These findings increase our understanding of the interaction between SVV and host innate immunity.

The presence of K103N mutant virus in plasma above 2000 copies/ml prior to therapy in treatment-naive individuals correlated with increased risk of virologic failure of these efavirenz-containing triple-drug regimens.

BackgroundSubcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.DescriptionWe have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website.ConclusionDBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at .

The members of tripartite-motif containing (TRIM) protein participate in various cellular processes and play an important role in host antiviral function. TRIM proteins exert their antiviral activity either directly by degrading viral proteins through their E3 ligase activity, or indirectly by promoting host innate immunity. This study demonstrated for the first time that TRIM52 is a novel antiviral TRIM protein against Japanese encephalitis virus (JEV) infection. Overexpression of TRIM52 restricted JEV replication in BHK-21 and 293T cells. In addition, JEV nonstructural protein 2A (NS2A) is a protein that interacts with TRIM52. Their interaction degraded NS2A in a proteasome-dependent manner via the E3 ligase activity of TRIM52. Thus, TRIM52 is a novel antiviral TRIM protein, and it exerted antiviral activity against JEV infection by targeting and degrading viral NS2A.