Phylogenetic analysis of full-length, early infection, hepatitis C virus genomes among people with intravenous drug use: the InC3 Study.

Cross-continental phylogenetic analysis is important to understand subtle molecular differences of currently circulating hepatitis C virus (HCV) subtypes. Existence of such differences can be crucial in pursuing a universal hepatitis C vaccine. We characterized molecular epidemiology of early HCV infections identified across nine cohorts [North America (n=4), Australia (n=4) and Europe (n=1)] in the International Collaborative of Incident HIV and Hepatitis C in Injecting Cohorts (InC3 ). One hundred and ninety-two full-length HCV genomes were amplified from plasma of incident infections and subjected to next generation sequencing to establish the largest cross-continental, full-length acute HCV genomic data set available to date. Genomes from the most common subtypes (1a: n=94, 2b: n=15 and 3a: n=68) were used in phylogenetic analysis. Using full genome trees, 78 sequences (44%) were found to lie within 29 phylogenetic clusters/pairs defined on the basis of molecular similarity of consensus sequences. Of these, 26 each had exclusively Australian or North American sequences indicating a strong geographical bias for molecular similarity. On further analysis of behavioural and demographic associations, binary logistic regression analysis showed that older age and non-Caucasian ethnicity were significantly associated with clustering. HCV probably evolves in micro-epidemics within geographically isolated communities.

Analysis of resistance-associated substitutions in acute hepatitis C virus infection by deep sequencing across six genotypes and three continents.

Several direct-acting antivirals (DAAs) have been approved for the treatment of chronic hepatitis C virus (HCV) infections, opening the door to highly effective interferon-free treatment regimens. Resistance-associated substitutions (RASs) have been reported both in treatment-naïve patients and following treatment with protease (NS3), phosphoprotein (NS5A) and polymerase (NS5B) inhibitors. The prevalence of naturally occurring RASs in untreated HCV-infected individuals has mostly been analysed in those infected with genotype 1 (GT1), in the late phase of infection, and only within limited regions of the genome. Furthermore, the geographic distribution of RASs remains poorly characterized. In this study, we used next-generation sequencing to analyse full-length HCV genomes for the prevalence of RASs in acute HCV infections identified in nine international prospective cohorts. RASs were analysed in 179 participants infected with all six major HCV genotypes (GT1-GT6), and the geographic distribution of RASs was assessed in 107 GT1a and GT3a samples. While RASs were detected at varied frequencies across the three genomic regions, and between genotypes, RASs relevant to multiple DAAs in the leading IFN-free regimens were rarely detected in combination. Low-frequency RASs (<10% of the viral population) were also shown to have a GT-specific distribution. The main RASs with geographic associations were NS3 Q80K in GT1a samples and NS5B N142T in GT3a. These data provide the backdrop for prospective surveillance of RASs during DAA treatment scale-up.

Why replication is important in landscape genetics: American black bear in the Rocky Mountains.

We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation, altitude, variation in elevation and road coverage. In all but one of the study areas, isolation by landscape resistance was more supported than IBD suggesting gene flow is likely influenced by elevation, forest cover, and roads. However, the landscape features influencing gene flow varied among study areas. Using subsets of loci usually gave models with the very similar landscape features influencing gene flow as with all loci, suggesting the landscape features influencing gene flow were correctly identified. To test if the cause of the variability of supported landscape features in study areas resulted from landscape differences among study areas, we conducted a limiting factor analysis. We found that features were supported in landscape models only when the features were highly variable. This is perhaps not surprising but suggests an important cautionary note - that if landscape features are not found to influence gene flow, researchers should not automatically conclude that the features are unimportant to the species' movement and gene flow. Failure to investigate multiple study areas that have a range of variability in landscape features could cause misleading inferences about which landscape features generally limit gene flow. This could lead to potentially erroneous identification of corridors and barriers if models are transferred between areas with different landscape characteristics.