High-Resolution Quantification of Hepatitis C Virus Genome-Wide Mutation Load and Its Correlation with the Outcome of Peginterferon-Alpha2a and Ribavirin Combination Therapy

Hepatitis C virus (HCV) is a highly mutable RNA virus and circulates as a heterogeneous population in individual patients. The magnitude of such population heterogeneity has long been proposed to be linked with diverse clinical phenotypes, including antiviral therapy. Yet data accumulated thus far are fairly inconclusive. By the integration of long RT-PCR with 454 sequencing, we have built a pipeline optimized for the quantification of HCV genome-wide mutation load at 1% resolution of mutation frequency, followed by a retrospective study to examine the role of HCV mutation load in peginterferon-alpha2a and ribavirin combination antiviral therapy. Genome-wide HCV mutation load varied widely with a range from 92 to 1639 mutations and presented a Poisson distribution among 56 patients (Kolmogorov-Smirnov statistic = 0.078, p = 0.25). Patients achieving sustained virological response (n = 26) had significantly lower mutation loads than that in null responders (n = 30) (mean and standard derivation: 524±279 vs. 805±271, p = 0.00035). All 36,818 mutations detected in 56 patients displayed a power-law distribution in terms of mutation frequency in viral population. The low-frequency mutation load, but not the high-frequency load, was proportional firmly to the total mutation load. In-depth analyses revealed that intra-patient HCV population structure was shaped by multiple factors, including immune pressure, strain difference and genetic drift. These findings explain previous conflicting reports using low-resolution methods and highlight a dominant role of natural selection in response to therapeutic intervention. By attaining its signatures from complex interaction between host and virus, the high-resolution quantification of HCV mutation load predicts outcomes from interferon-based antiviral therapy and could also be a potential biomarker in other clinical settings.