Intra-host evolutionary dynamics of the hepatitis C virus among people who inject drugs.

Most individuals chronically infected with hepatitis C virus (HCV) are asymptomatic during the initial stages of infection and therefore the precise timing of infection is often unknown. Retrospective estimation of infection duration would improve existing surveillance data and help guide treatment. While intra-host viral diversity quantifications such as Shannon entropy have previously been utilized for estimating duration of infection, these studies characterize the viral population from only a relatively short segment of the HCV genome. In this study intra-host diversities were examined across the HCV genome in order to identify the region most reflective of time and the degree to which these estimates are influenced by high-risk activities including those associated with HCV acquisition. Shannon diversities were calculated for all regions of HCV from 78 longitudinally sampled individuals with known seroconversion timeframes. While the region of the HCV genome most accurately reflecting time resided within the NS3 gene, the gene region with the highest capacity to differentiate acute from chronic infections was identified within the NS5b region. Multivariate models predicting duration of infection from viral diversity significantly improved upon incorporation of variables associated with recent public, unsupervised drug use. These results could assist the development of strategic population treatment guidelines for high-risk individuals infected with HCV and offer insights into variables associated with a likelihood of transmission.

A systematic, deep sequencing-based methodology for identification of mixed-genotype hepatitis C virus infections.

Hepatitis C virus (HCV) mixed genotype infections can affect treatment outcomes and may have implications for vaccine design and disease progression. Previous studies demonstrate 0-39% of high-risk, HCV-infected individuals harbor mixed genotypes however standardized, sensitive methods of detection are lacking. This study compared PCR amplicon, random primer (RP), and probe enrichment (PE)-based deep sequencing methods coupled with a custom sequence analysis pipeline to detect multiple HCV genotypes. Mixed infection cutoff values, based on HCV read depth and coverage, were identified using receiver operating characteristic curve analysis. The methodology was validated using artificially mixed genotype samples and then applied to two clinical trials of HCV treatment in high-risk individuals (ACTIVATE, 114 samples from 90 individuals; DARE-C II, 26 samples from 18 individuals) and a cohort of HIV/HCV co-infected individuals (Canadian Coinfection Cohort (CCC), 3 samples from 2 individuals with suspected mixed genotype infections). Amplification bias of genotype (G)1b, G2, G3 and G5 was observed in artificially mixed samples using the PCR method while no genotype bias was observed using RP and PE. RP and PE sequencing of 140 ACTIVATE and DARE-C II samples identified the following primary genotypes: 15% (n = 21) G1a, 76% (n = 106) G3, and 9% (n = 13) G2. Sequencing of ACTIVATE and DARE-C II demonstrated, on average, 2% and 1% of HCV reads mapping to a second genotype using RP and PE, respectively, however none passed the mixed infection cutoff criteria and phylogenetics confirmed no mixed infections. From CCC, one mixed infection was confirmed while the other was determined to be a recombinant genotype. This study underlines the risk for false identification of mixed HCV infections and stresses the need for standardized methods to improve prevalence estimates and to understand the impact of mixed infections for management and elimination of HCV.

Differentiation of acute from chronic hepatitis C virus infection by nonstructural 5B deep sequencing: a population-level tool for incidence estimation.

UNLABELLED: The ability to classify acute versus chronic hepatitis C virus (HCV) infections at the time of diagnosis is desirable to improve the quality of surveillance information. The aim of this study was to differentiate acute from chronic HCV infections utilizing deep sequencing. HCV nonstructural 5B (NS5B) amplicons (n = 94) were generated from 77 individuals (13 acute and 64 chronic HCV infections) in British Columbia, Canada, with documented seroconversion time frames. Amplicons were deep sequenced and HCV genomic diversity was measured by Shannon entropy (SE) and a single nucleotide variant (SNV) analysis. The relationship between each diversity measure and the estimated days since infection was assessed using linear mixed models, and the ability of each diversity measure to differentiate acute from chronic infections was assessed using generalized estimating equations. Both SE and the SNV diversity measures were significantly different for acute versus chronic infections (P < 0.009). NS5B nucleotide diversity continued to increase for at least 3 years postinfection. Among individuals with the least uncertainty with regard to duration of infection (n = 39), the area under the receiver operating characteristic curve (AUROC) was high (0.96 for SE; 0.98 for SNV). Although the AUROCs were lower (0.86 for SE; 0.80 for SNV) when data for all individuals were included, they remain sufficiently high for epidemiological purposes. Synonymous mutations were the primary discriminatory variable accounting for over 78% of the measured genetic diversity. CONCLUSIONS: NS5B sequence diversity assessed by deep sequencing can differentiate acute from chronic HCV infections and, with further validation, could become a powerful population-level surveillance tool for incidence estimation.