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Evolutionary dynamics of hepatitis C virus envelope genes during chronic infection

Richard J. P. Brown^1,

Vicky S. Juttla^1,

¹ Microbiology and Infectious Diseases, Institute of Infection, Immunity and Inflammation, The University of Nottingham, Queen's Medical Centre, Nottingham NG7 2UH, UK
² The Edward Jenner Institute for Vaccine Research, Compton, Newbury, Berkshire RG20 7NN, UK

Correspondence
Jonathan K. Ball
jonathan.ball{at}nottingham.ac.uk

Hepatitis C virus (HCV) envelope glycoproteins E1 and E2 are important targets for the host immune response. The genes encoding these proteins exhibit a high degree of variability that gives rise to differing phenotypic traits, including alterations in receptor-binding affinity and immune recognition and escape. In order to elucidate patterns of adaptive evolution during chronic infection, a panel of full-length E1E2 clones was generated from sequential serum samples obtained from four chronically infected individuals. By using likelihood-based methods for phylogenetic inference, the evolutionary dynamics of circulating HCV quasispecies populations were assessed and a site-by-site analysis of the d_N/d_S ratio was performed, to identify specific codons undergoing diversifying positive selection. HCV phylogenies, coupled with the number and distribution of selected sites, differed markedly between patients, highlighting that HCV evolution during chronic infection is a patient-specific phenomenon. This analysis shows that purifying selection is the major force acting on HCV populations in chronic infection. Whilst no significant evidence for positive selection was observed in E1, a number of sites under positive selection were identified within the ectodomain of the E2 protein. All of these sites were located in regions hypothesized to be exposed to the selective environment of the host, including a number of functionally defined domains that have been reported to be involved in immune evasion and receptor binding. Dated-tip methods for estimation of underlying HCV mutation rates were also applied to the data, enabling prediction of the most recent common ancestor for each patient's quasispecies.

The GenBank/EMBL/DDBJ accession numbers for the sequence data reported here are AY957985–AY958064.

These authors contributed equally to this work.

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