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Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.

Recent advances in sequencing methods allow the analysis of an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three technologies for amplicon analysis: (i) Next Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry of base-specific cleavage reactions of a target sequence. Hypervariable region 1 of hepatitis C virus was analyzed using these three technologies to assess diversity and genetic relatedness of intra-host viral populations in specimens obtained from 38 patients. Estimates of population heterogeneity varied among technologies. However, all three technologies were equally accurate in identification of genetic relatedness among viral strains, supporting their application in molecular epidemiology for tracking viral variants and detecting transmission events.

Hepatitis C virus (HCV) is a major cause of liver disease worldwide. Current interferon and ribavirin (IFN/RBV) therapy is effective in 50%–60% of patients. HCV exists in infected patients as a large viral population of intra-host variants (quasispecies), which form a certain topological structure (sequence space) and may be differentially resistant to interferon treatment. We present a method for measuring differential interferon resistance of HCV quasispecies based on mathematical modeling and analysis of HCV population dynamics during the first hours of interferon therapy. The mathematical models showed that individual intra-host HCV variants have a wide range of resistance to IFN treatment in each patient. Analysis of differential IFN resistance among intra-host HCV variants allows to accurately predict outcome of IFN therapy. The models strongly suggest that resistance to interferon may vary broadly among closely related variants in infected hosts and therapy outcome may be defined by a single or a few variants irrespective of their frequency in the intra-host HCV population before treatment.