Yu‐Tsueng Liu

Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

Because of the constant threat posed by emerging infectious diseases and the limitations of existing approaches used to identify new pathogens, there is a great demand for new technological methods for viral discovery. We describe herein a DNA microarray-based platform for novel virus identification and characterization. Central to this approach was a DNA microarray designed to detect a wide range of known viruses as well as novel members of existing viral families; this microarray contained the most highly conserved 70mer sequences from every fully sequenced reference viral genome in GenBank. During an outbreak of severe acute respiratory syndrome (SARS) in March 2003, hybridization to this microarray revealed the presence of a previously uncharacterized coronavirus in a viral isolate cultivated from a SARS patient. To further characterize this new virus, approximately 1 kb of the unknown virus genome was cloned by physically recovering viral sequences hybridized to individual array elements. Sequencing of these fragments confirmed that the virus was indeed a new member of the coronavirus family. This combination of array hybridization followed by direct viral sequence recovery should prove to be a general strategy for the rapid identification and characterization of novel viruses and emerging infectious disease.

IFN-alpha is used to suppress the replication of hepatitis C virus (HCV) in chronically infected patients with partial success. Here we present evidence showing that a ligand of Toll-like receptor 7 (TLR7) can induce anti-HCV immunity not only by IFN induction, but also through an IFN-independent mechanism. Human hepatocyte line Huh-7 carrying an HCV replicon expressed TLR7, and activation of the receptor induced several antiviral genes including IFN regulatory factor-7. Inhibitors of the enzyme inosine monophosphate dehydrogenase augmented both IFN-dependent and -independent antiviral effect. Prolonged exposure of Huh-7 cells to a TLR7 ligand [SM360320 (9-benzyl-8-hydroxy-2-(2-methoxyethoxy)adenine)], alone or in combination with an inosine monophosphate dehydrogenase inhibitor, reduced HCV levels dose dependently. Immunohistochemical analysis of livers shows that TLR7 is expressed in hepatocytes of normal or HCV-infected people. Because TLR7 agonists can impede HCV infection both via type I IFN and independently of IFN, they may be considered as an alternative treatment of chronic HCV infection, especially in IFN-alpha-resistant patients.