The long term objective of this application is to better understand the viral-host interactions that determine why only ~50 percent of patients chronically-infected with HCV respond to combination therapy with interferon and ribavirin, the current standard of care. This objective is important because interferon therapy will remain the critical backbone of HCV therapy for the foreseeable future, even in the era of small molecule inhibitors. Understanding interferon therapy will also result in a better understanding of viral innate immune evasion mechanisms. Large clinical trials have revealed that patients infected with the various HCV genotypes require significantly different durations of therapy and achieve substantially different sustained virologic response rates. Our hypothesis is that (a) elements within the viral genome contribute to these variable responses to interferon therapy;and thus (b), our research design combines evolutionary analysis of full-length HCV genomes, genotype-specific clinical outcome data, and HCV protein structural information, to identify the key elements within the virus that contribute to inadequate response to interferon. Our preliminary data includes a phylogenomic analysis of HCV that reveals that HCV genotype age correlates with clinical response to interferon. Using this relationship, we have generated an in-silico database of viral mutations that correlate with clinical response to interferon. Using homology modeling, we have identified subsets of these mutations within the NS5A gene of HCV that alter its biophysical nature in a genotype-specific manner.
The SPECIFIC AIMS of this application are to (1) use site-directed mutagenesis to create an in-vitro library of HCV mutants, based on evolutionary, clinical outcome, and HCV protein structural information;2) to characterize the interferon sensitivity of these HCV mutants.
Over 170 million people worldwide are chronically infected with hepatitis C virus (HCV). In the US, HCV is the most common cause of liver cancer. The goal of this research is to better understand who will respond to current treatment and to use this information to design the next generation of anti-HCV drugs.
| Pang, Phillip S; Pham, Edward A; Elazar, Menashe et al. (2012) Structural map of a microRNA-122: hepatitis C virus complex. J Virol 86:1250-4 |
| Pang, Phillip S; Elazar, Menashe; Pham, Edward A et al. (2011) Simplified RNA secondary structure mapping by automation of SHAPE data analysis. Nucleic Acids Res 39:e151 |
| Dvory-Sobol, Hadas; Pang, Philip S; Glenn, Jeffrey S (2010) The Future of HCV Therapy: NS4B as an Antiviral Target. Viruses 2:2481-92 |
| Pang, Phillip S; Planet, Paul J; Glenn, Jeffrey S (2009) The evolution of the major hepatitis C genotypes correlates with clinical response to interferon therapy. PLoS One 4:e6579 |
