Next-generation sequencing reveals large connected networks of intra-host HCV variants

Abstract

Background: Next-generation sequencing (NGS) allows for sampling numerous viral variants from infected patients. This provides a novel opportunity to represent and study the mutational landscape of Hepatitis C Virus (HCV) within a single host.

Results: Intra-host variants of the HCV E1/E2 region were extensively sampled from 58 chronically infected patients. After NGS error correction, the average number of reads and variants obtained from each sample were 3202 and 464, respectively. The distance between each pair of variants was calculated and networks were created for each patient, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. The work focused on large components having > 5% of all reads, which in average account for 93.7% of all reads found in a patient.

Conclusions: Most intra-host variants are organized into distinct single-mutation components that are: well separated from each other, represent genetic distances between viral variants, robust to sampling, reproducible and likely seeded during transmission events. Facilitated by NGS, large components offer a novel evolutionary framework for genetic analysis of intra-host viral populations and understanding transmission, immune escape and drug resistance.

Figure 1

One-step components of a single patient. A) Largest one-step component of patient 14, where each node is a variant and two nodes are connected by a link if the nucleotide distance between them is 1. B) k-step network showing the three components of patient 14. Each one-step component is shown in a different colour and links among component connect the pairs of variants having the minimal distance found among components.

Figure 2

Scatterplot of hamming distances among the variants of the largest component of patient 14. A) Scatterplot of hamming distances (x-axis) and shortest path distances over the one-step component (y-axis). B) Scatterplot of hamming distances (x-axis) and shortest path distances over the Neighbor-Joining tree (y-axis). The R value is the correlation among the two distance matrices. The size of the circles indicate the number of points (pairwise comparisons) in those coordinates.

Figure 3

In silico sampling robustness of components. Mean over 1000 samples for each component and sampling level, with bars indicating the 99% confidence interval for the mean. The x-axis represents the percentage of reads removed from the original dataset. The y-axis represents the percentage of nodes in the subsample that are part of the largest component.

Figure 4

Component overlap. The x-axis represents the 35 components found in the first and second experimental sample. The y-axis shows the fraction of reads found in the first sample that were also found in the second sample.

Figure 5

Natural selection of components. The x-axis represents the 58 patients that were analysed. The y-axis represents the number of components. The blue colour shows the components with dN/dS ratio lower than 1, whereas the red colour indicates the components with dN/dS ratio higher than 1.