Assessingin vivomutation frequencies and creating a high-resolution genome-wide map of fitness costs of Hepatitis C virus

Abstract

Studying in vivo fitness costs of mutations in viruses provides important insights into their evolutionary dynamics, which can help decipher how they adapt to host immune systems and develop drug resistance. However, studying fitness costs in natural populations is difficult, and is often conducted in vitro where evolutionary dynamics differ from in vivo. We aimed to understand in vivo fitness costs of mutations in Hepatitis C virus using next generation sequencing data. Hepatitis C virus is a positive-sense single-stranded RNA virus, and like many RNA viruses, has extremely high mutation and replication rates, making it ideal for studying mutational fitness costs. Using the ‘frequency-based approach’, we estimated genome-wide in vivo mutation frequencies at mutation-selection equilibrium, and inferred fitness costs (selection coefficients) at every genomic position using data from 195 patients. We applied a beta regression model to estimate the effects and the magnitudes of different factors on fitness costs. We generated a high-resolution genome-wide map of fitness costs in Hepatitis C virus for the first time. Our results revealed that costs of nonsynonymous mutations are three times higher than those of synonymous mutations, and mutations at nucleotides A/T have higher costs than those at C/G. Genome location had a modest effect, which is a clear contrast from previously reported in vitro findings, and highlights host immune selection. We inferred the strongest negative selection on the Core and NS5B proteins. We also found widespread natural prevalence of known drug resistance-associated variants in treatment naive patients, despite high fitness costs of these resistance sites. Our results indicate that in vivo evolutionary patterns and associated mutational costs are dynamic and can be virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.Author SummaryUnderstanding how viruses evolve within patients is important for combatting viral diseases, yet studying viruses within patients is difficult. Laboratory experiments are often used to understand the evolution of viruses, in place of assessing the evolution in natural populations (patients), but the dynamics will be different. In this study, we aimed to understand the within-patient evolution of Hepatitis C virus, which is an RNA virus that replicates and mutates extremely quickly, by taking advantage of high-throughput next generation sequencing. Here, we describe the evolutionary patterns of Hepatitis C virus from 195 patients: We analyzed mutation frequencies and estimated how costly each mutation was. We also assessed what factors made a mutation more costly, including the costs associated with drug resistance mutations. We were able to create a genome-wide fitness map of within-patient mutations in Hepatitis C virus which proves that, with technological advances, we can deepen our understanding of within-patient viral evolution, which can contribute to develop better treatments and vaccines.

Comparative Analysis of Within-Host Mutation Patterns and Diversity of Hepatitis C Virus Subtypes 1a, 1b, and 3a

“…Our study confirmed that results from our previous study on subtype 1a [ 19 ] generally also hold for subtypes 1b and 3a. Genetic diversity is the lowest in the 5′ UTR (the 3′ UTR was not included in our study) and, by far, the highest in the Hyper Variable Region 1 (HVR1), which is part of the E2 gene.…”

“…Multiple factors simultaneously drive and constrain mutation frequencies, and these effects were explored using beta-regression models to understand the driving force of similarities and differences in mutation patterns across subtypes. We explored the effects of ancestral nucleotide, mutation type (synonymous/nonsynonymous, whether a mutation creates a CpG-site or not or causes a drastic amino acid (AA) change or not), location in the genome, and interactions of these factors, as they were known to shape mutation patterns in HCV [ 19 ]. The best-fit model included a total of 15 factors ( Table S3 ).…”

“…Viral RNA samples were obtained and sequenced, as described in Tisthammer et al [ 19 ]. Briefly, plasma samples were obtained from HCV treatment-naïve and viremic HCV/HIV co-infected participants from sites in the Canadian Co-infection Cohort (CCC) [ 22 ].…”

“…All sequence reads of virus isolates were quality-filtered and trimmed using BBTools [ 23 ], by following the methods of Tisthammer et al [ 19 ]. We incorporated several processes to increase the confidence in calling minor variants, as well as compared the outcomes of different processes to ensure that results did not change.…”

“…Raghwani et al [ 18 ] assessed within-host population dynamics in four patients over time, suggesting within-host HCV evolutionary dynamics are shaped by population structure and strong genetic linkage. We recently analyzed within-host mutations and their fitness costs in 195 patients with HCV 1a [ 19 ].…”