Evolution of within-host variants of the hepatitis C virus.

INTRODUCTION

Comprehensive investigation of the within-host evolution of hepatitis C virus (HCV) variants has been difficult without high coverage deep sequencing data and bioinformatics tools to characterise these variants. With the advent of high throughput, long-read sequencing platforms such as Oxford Nanopore Technology (ONT), capturing within-host evolution of HCV using full genome sequences has become feasible. This study aimed to provide the proof of concept that within-host evolutionary analysis of HCV using near-full-length genomes, is achievable.

METHODS

Five treatment naïve subjects with chronic HCV infection were sampled longitudinally from 6 months to 5 years post-infection, with 3-5 sampling timepoints per subject. Near full-length sequences generated using the ONT platform encompassing within-host HCV variants were analysed using an in-house bioinformatic tool. A 200-sequence proxy alignment of the viral variants was made for each subject and timepoint, proportionately representing the observed within-host variants. This alignment was then used in a Bayesian evolutionary analysis using BEAST software suite (v1.8).

RESULTS

The estimated within-host substitution rates ranged between 0.89 and 6.19 × 10-5 substitutions/site/day. At most timepoints, observed viral lineages were closely related to those from the immediately preceding timepoint, and genetic diversity bottlenecks were observed at intervals in both the acute and chronic phases of infection. The highest within-host mutation rates were observed in the Envelope-P7 and NS5 regions while the Core region was the most conserved.

CONCLUSION

This study demonstrates the feasibility of studying within-host evolution of near-full-length HCV genomes, using long-read sequencing platforms. When considered in conjunction with meta-data such as the host immune response, these methods may offer high resolution insights into immune escape (in vivo or in vitro) to inform vaccine design and to predict spontaneous clearance.