Sequential bottlenecks drive viral evolution in early acute hepatitis C virus infection

Rowena A Bull, Fabio Luciani, Kerensa McElroy, Silvana Gaudieri, Son T Pham, Abha Chopra, Barbara Cameron, Lisa Maher, Gregory J Dore, Peter A White, Andrew R Lloyd, Rowena A Bull, Fabio Luciani, Kerensa McElroy, Silvana Gaudieri, Son T Pham, Abha Chopra, Barbara Cameron, Lisa Maher, Gregory J Dore, Peter A White, Andrew R Lloyd

Abstract

Hepatitis C is a pandemic human RNA virus, which commonly causes chronic infection and liver disease. The characterization of viral populations that successfully initiate infection, and also those that drive progression to chronicity is instrumental for understanding pathogenesis and vaccine design. A comprehensive and longitudinal analysis of the viral population was conducted in four subjects followed from very early acute infection to resolution of disease outcome. By means of next generation sequencing (NGS) and standard cloning/Sanger sequencing, genetic diversity and viral variants were quantified over the course of the infection at frequencies as low as 0.1%. Phylogenetic analysis of reassembled viral variants revealed acute infection was dominated by two sequential bottleneck events, irrespective of subsequent chronicity or clearance. The first bottleneck was associated with transmission, with one to two viral variants successfully establishing infection. The second occurred approximately 100 days post-infection, and was characterized by a decline in viral diversity. In the two subjects who developed chronic infection, this second bottleneck was followed by the emergence of a new viral population, which evolved from the founder variants via a selective sweep with fixation in a small number of mutated sites. The diversity at sites with non-synonymous mutation was higher in predicted cytotoxic T cell epitopes, suggesting immune-driven evolution. These results provide the first detailed analysis of early within-host evolution of HCV, indicating strong selective forces limit viral evolution in the acute phase of infection.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1. RNA level, Shannon entropy, and…
Figure 1. RNA level, Shannon entropy, and antibody titers over time for the four early infection subjects.
Panels A and B show two subjects who developed chronic infection (240_Ch, 23_Ch) followed from pre-seroconversion timepoints. Panels C and D show the infection dynamics for two subjects who ultimately cleared the infection (360_Cl, 686_Cl). Red dots represent viremic time points analyzed via next generation sequencing (NGS). The solid line represents the RNA level. The dashed and dotted lines represent the interpolation of the Shannon entropy calculated across the genome at each time point using NGS data. Entropy was calculated using all mutated sites (dotted line), or with only non-synonymous sites (dashed line). The shaded area represents semi-quantitative estimates of the anti-HCV antibody titer (OD: cut-off). Note the varied ranges in the x- and y-axes.
Figure 2. Distribution of the non-synonymous substitutions…
Figure 2. Distribution of the non-synonymous substitutions detected across the genome over the course of the infection.
Panels A and B show the distributions of non-synonymous substitutions in two subjects who developed chronic infection (240_Ch, 23_Ch). Panels C and D show two subjects who ultimately cleared the infection (360_Cl, 686_Cl). Each panel shows the longitudinal analysis of the distribution of non-synonymous substitutions for each subject. In subjects who cleared the infection, substitutions sporadically emerged at low frequency (99%). Colors represent the time course post-infection (see legend).
Figure 3. Founder virus analysis based on…
Figure 3. Founder virus analysis based on partial E2 region of the viral genome.
Panels A and B show the analyses for two subjects who developed chronic infection (240_Ch, 23_Ch) followed from pre-seroconversion timepoints. Panels C and D show the analysis for two subjects who cleared the infection (360_Cl, 686_Cl). Phylogenetic reconstructions and highlighter plots are shown, illustrating the genetic relatedness between HCV variant sequences. Names of each sequence are labeled with a letter (H for haplotype, and C for clone), with the first number representing the sampling timepoint and with the second number representing either the prevalence of the haplotype or the clone number. The phylogenetic trees of subjects 686_Cl, 360_Cl and 240_Ch (panels A, C, D) are consistent with an infection arising from a single founder. The fit with a Poisson model is also consistent with a single founder (p-value > 0.1, see text). As shown by the highlighter plots, founder viruses are identified as the consensus sequence and coincided with the most prevalent variant reconstructed from NGS data, (e.g. for subject 686_Cl H1_0.60 is identical to the consensus sequence and to clone C_12b). The highlighter plots also show the random distribution of mutated sites with respect to the founder sequence (master), which is consistent with a star-like phylogeny. The phylogenetic analysis in 23_Ch (panel B) is consistent with an infection originated from two founder viruses (indicated with an asterisk in the highlighter plot) giving rise to two major clusters, 23A and 23B. This is consistent with the rejection of the Poisson model (p-value = 0). Phylogenetic trees were obtained using PhyML, with Maximum Likelihood methods using a GTR model of substitution as suggested by model testing.
Figure 4. Evolutionary dynamics of HCV variants…
Figure 4. Evolutionary dynamics of HCV variants over the partial E2 region of the genome.
Sequence analyses of the two subjects who developed chronic infection, 240_Ch (A), and 23_Ch (B) revealed the presence of selective sweeps. These sweeps led to the emergence of new variants that replaced the founder viruses (identified with an asterisk). Phylogenetic trees (left panels in A and B) display nucleotide sequences of reconstructed haplotypes derived from NGS data and clonal sequences. Names of each sequence are labeled with a letter (H for haplotype and C for clone), with the first number representing the sampling timepoint and with the second number representing either the prevalence of the haplotype or the clone number. Colors are also used to portray the sampling timepoint (see legend). Infection dynamics for subject 240_Ch are consistent with a single founder, identified with the most prevalent strain of cluster 240A (H2_0.85 and H3_0.97 at time-points 2 and 3 respectively), with clone C2_6, and with the consensus of the sequences from time-point 1. The pre-chronic phase (corresponding with the color-coded time ranges 5 and 6) of infection shows the emergence and dominance of a new subgroup of viruses, designated 240B. 23_Ch has at least two founder viruses that successfully initiated the infection (H1_0.26 and H1_0.16 within the two clusters 23AF and 23BF, respectively), A new cluster 23AC, termed after the dominant variant H5_0.54, emerged in the pre-chronic phase and replaced cluster 23AF. Trees are calculated using Maximum Likelihood method (implemented in PhyML).
Figure 5. Evolution of the partial E2…
Figure 5. Evolution of the partial E2 region of individual HCV variants at the amino acid level.
The plots in A (subject 240_Ch) and B (23_Ch) show the dynamics of the individual viral variants over time. In 240_Ch, infection was initiated with one founder variant, 240AF, which was then replaced sequentially by two related variants, 240AC1 and 240AC2, respectively. In 23_Ch, at least two founders initiated infection, 23AF and 23BF, which both dominated the early phase of infection before being replaced by a new variant in the pre-chronic phase of infection (23AC). The y-axis shows the contribution of each variant with respect to the RNA level. Below each graph is an amino acid alignment indicating the distinguishing residues for the different variants. The location of putative CTL (pink shading) and B cell (green shading) epitopes, and mutations with previously recorded viral fitness costs (light blue shading) are indicated. All the identified epitopes within this region carried at least one amino acid change. Two of these mutations (G483D for 240_Ch and T542I for 23_Ch) generated CTL epitopes with reduced binding CTL affinity, and both subjects showed a substitution at position Y443, known to be within a B cell epitope - all of which is suggestive of immune escape. In addition, in 240_Ch a potential fitness cost associated mutation was observed at T543A .
Figure 6. Demographic reconstruction of the viral…
Figure 6. Demographic reconstruction of the viral populations.
Demographic reconstruction from E1/HVR1 (A,B) and E2 (C,D) sequences for subjects who developed chronic infection, 23_Ch (A,C) and 240_Ch (B,D). In both subjects, and in both genomic regions, the founder effect and sequential bottleneck events are evident. The estimated effective population size (Nτ, the product of the effective population size and generation length in days) had a peak value of the order of 103, and then decreased to values of the order of 102. The longer estimate of tMRCA for 23_Ch when compared to those in Table 1 is likely to be due to the presence of two founder viruses in this subject.

References

    1. The Global Burden Of Hepatitis CWG. Global burden of disease (GBD) for hepatitis C. J Clin Pharmacol. 2004;44:20–29.
    1. Ascione A, Tartaglione T, Di Costanzo GG. Natural history of chronic hepatitis C virus infection. Dig Liv Dis. 2007;39(Suppl 1):S4–7.
    1. Bowen D, Walker C. Adaptive immune responses in acute and chronic hepatitis C virus infection. Nature. 2005;436:946–952.
    1. Post J, Ratnarajah S, Lloyd AR. Immunological determinants of the outcomes from primary hepatitis C infection. Cell Mol Life Sci. 2009;66:733–756.
    1. Strickland G, El-Kamary S, Klenerman P, Nicosia A. Hepatitis C vaccine: supply and demand. Lancet Infect Dis. 2008;8:379–386.
    1. Maher L, White B, Hellard M, Madden A, Prins M, et al. Candidate hepatitis C vaccine trials and people who inject drugs: challenges and opportunities. Vaccine. 2010;28:7273–7278.
    1. Torresi J, Johnson D, Wedemeyer H. Progress in the development of preventive and therapeutic vaccines for hepatitis C virus. J Hepatol. 2011;54:1273–1285.
    1. Sanjuan R, Moya A, Elena SF. The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. Proc Natl Acad Sci U S A. 2004;101:8396–8401.
    1. Neumann AU, Lam NP, Dahari H, Gretch DR, Wiley TE, et al. Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha therapy. Science. 1998;282:103–107.
    1. Guedj J, Rong L, Dahari H, Perelson AS. A perspective on modelling hepatitis C virus infection. J Viral Hepat. 2010;17:825–833.
    1. Duffy S, Shackelton LA, Holmes EC. Rates of evolutionary change in viruses: patterns and determinants. Nat Rev Genet. 2008;9:267–276.
    1. Elena SF, Sanjuán R. Virus evolution: insights from an experimental approach. Annu Rev Ecol Evol Syst. 2007;38:27–52.
    1. Boutwell CL, Rolland MM, Herbeck JT, Mullins JI, Allen TM. Viral evolution and escape during acute HIV-1 infection. J Infect Dis. 2010;202(Suppl 2):S309–314.
    1. Bar KJ, Li H, Chamberland A, Tremblay C, Routy JP, et al. Wide variation in the multiplicity of HIV-1 infection among injection drug users. J Virol. 2010;84:6241–6247.
    1. Keele BF, Giorgi EE, Salazar-Gonzalez JF, Decker JM, Pham KT, et al. Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc Natl Acad Sci U S A. 2008;105:7552–7557.
    1. Kouyos RD, Althaus CL, Bonhoeffer S. Stochastic or deterministic: what is the effective population size of HIV-1? Trends Microbiol. 2006;14:507–511.
    1. Koelle K, Cobey S, Grenfell B, Pascual M. Epochal evolution shapes the phylodynamics of interpandemic influenza A (H3N2) in humans. Science. 2006;314:1898–1903.
    1. Sheridan I, Pybus OG, Holmes EC, Klenerman P. High-resolution phylogenetic analysis of hepatitis C virus adaptation and its relationship to disease progression. J Virol. 2004;78:3447–3454.
    1. Farci P, Shimoda A, Coiana A, Diaz G, Peddis G, et al. The outcome of acute hepatitis C predicted by the evolution of the viral quasispecies. Science. 2000;288:339–344.
    1. Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, et al. Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J Virol. 1999;73:10489–10502.
    1. Goonetilleke N, Liu MK, Salazar-Gonzalez JF, Ferrari G, Giorgi E, et al. The first T cell response to transmitted/founder virus contributes to the control of acute viremia in HIV-1 infection. J Exp Med. 2009;206:1253–1272.
    1. Salazar-Gonzalez JF, Salazar MG, Keele BF, Learn GH, Giorgi EE, et al. Genetic identity, biological phenotype, and evolutionary pathways of transmitted/founder viruses in acute and early HIV-1 infection. J Exp Med. 2009;206:1273–1289.
    1. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005;437:376–380.
    1. Wang C, Mitsuya Y, Gharizadeh B, Ronaghi M, Shafer RW. Characterization of mutation spectra with ultra-deep pyrosequencing: application to HIV-1 drug resistance. Genome Res. 2007;17:1195–1201.
    1. Wang GP, Ciuffi A, Leipzig J, Berry CC, Bushman FD. HIV integration site selection: analysis by massively parallel pyrosequencing reveals association with epigenetic modifications. Genome Res. 2007;17:1186–1194.
    1. Hoffmann C, Minkah N, Leipzig J, Wang G, Arens MQ, et al. DNA bar coding and pyrosequencing to identify rare HIV drug resistance mutations. Nucleic Acids Res. 2007;35:e91.
    1. Le T, Chiarella J, Simen BB, Hanczaruk B, Egholm M, et al. Low-abundance HIV drug-resistant viral variants in treatment-experienced persons correlate with historical antiretroviral use. PLoS One. 2009;4:e6079.
    1. Tsibris AM, Korber B, Arnaout R, Russ C, Lo CC, et al. Quantitative deep sequencing reveals dynamic HIV-1 escape and large population shifts during CCR5 antagonist therapy in vivo. PLoS One. 2009;4:e5683.
    1. Johnson JA, Li JF, Wei X, Lipscomb J, Irlbeck D, et al. Minority HIV-1 drug resistance mutations are present in antiretroviral treatment-naive populations and associate with reduced treatment efficacy. PLoS Med. 2008;5:e158.
    1. Wang GP, Sherrill-Mix SA, Chang KM, Quince C, Bushman FD. Hepatitis C virus transmission bottlenecks analyzed by deep sequencing. J Virol. 2010;84:6218–6228.
    1. Solmone M, Vincenti D, Prosperi MC, Bruselles A, Ippolito G, et al. Use of massively parallel ultradeep pyrosequencing to characterize the genetic diversity of hepatitis B virus in drug-resistant and drug-naive patients and to detect minor variants in reverse transcriptase and hepatitis B S antigen. J Virol. 2009;83:1718–1726.
    1. Margeridon-Thermet S, Shulman NS, Ahmed A, Shahriar R, Liu T, et al. Ultra-deep pyrosequencing of hepatitis B virus quasispecies from nucleoside and nucleotide reverse-transcriptase inhibitor (NRTI)-treated patients and NRTI-naive patients. J Infect Dis. 2009;199:1275–1285.
    1. Wright CF, Morelli MJ, Thebaud G, Knowles NJ, Herzyk P, et al. Beyond the consensus: dissecting within-host viral population diversity of Foot-and-Mouth Disease Virus by using next-generation genome sequencing. J Virol. 2011;85:2266–2275.
    1. Zagordi O, Klein R, Daumer M, Beerenwinkel N. Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies. Nucleic Acids Res. 2010;38:7400–7409.
    1. Farci P, Purcell RH. Clinical significance of hepatitis C virus genotypes and quasispecies. Semin Liver Dis. 2000;20:103–126.
    1. Zagordi O, Geyrhofer L, Roth V, Beerenwinkel N. Deep sequencing of a genetically heterogeneous sample: local haplotype reconstruction and read error correction. J Comput Biol. 2010;17:417–428.
    1. Timm J, Lauer GM, Kavanagh DG, Sheridan I, Kim AY, et al. CD8 epitope escape and reversion in acute HCV infection. J Exp Med. 2004;200:1593–1604.
    1. Cox AL, Mosbruger T, Lauer GM, Pardoll D, Thomas DL, et al. Comprehensive analyses of CD8+ T cell responses during longitudinal study of acute human hepatitis C. Hepatology. 2005;42:104–112.
    1. Cox AL, Mosbruger T, Mao Q, Liu Z, Wang XH, et al. Cellular immune selection with hepatitis C virus persistence in humans. J Exp Med. 2005;201:1741–1752.
    1. Chen S, Wang YM. Evolutionary study of hepatitis C virus envelope genes during primary infection. Chin Med J (Engl) 2007;120:2174–2180.
    1. Ray SC, Fanning L, Wang XH, Netski DM, Kenny-Walsh E, et al. Divergent and convergent evolution after a common-source outbreak of hepatitis C virus. J Exp Med. 2005;201:1753–1759.
    1. Teutsch S, Luciani F, Scheuer N, McCredie L, Hosseiny P, et al. Incidence of primary hepatitis C infection and risk factors for transmission in an Australian prisoner cohort. BMC Public Health. 2010;10:633.
    1. Dolan K, Teutsch S, Scheuer N, Levy M, Rawlinson W, et al. Incidence and risk for acute hepatitis C infection during imprisonment in Australia. Eur J Epidemiol. 2010;25:143–148.
    1. Giorgi EE, Funkhouser B, Athreya G, Perelson AS, Korber BT, et al. Estimating time since infection in early homogeneous HIV-1 samples using a poisson model. BMC Bioinformatics. 11:532.
    1. Lee HY, Giorgi EE, Keele BF, Gaschen B, Athreya GS, et al. Modeling sequence evolution in acute HIV-1 infection. J Theor Biol. 2009;261:341–360.
    1. Drummond AJ, Rambaut A, Shapiro B, Pybus OG. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol Biol Evol. 2005;22:1185–1192.
    1. Zhang P, Zhong L, Struble EB, Watanabe H, Kachko A, et al. Depletion of interfering antibodies in chronic hepatitis C patients and vaccinated chimpanzees reveals broad cross-genotype neutralizing activity. Proc Natl Acad Sci U S A. 2009;106:7537–7541.
    1. Owsianka AM, Timms JM, Tarr AW, Brown RJ, Hickling TP, et al. Identification of conserved residues in the E2 envelope glycoprotein of the hepatitis C virus that are critical for CD81 binding. J Virol. 2006;80:8695–8704.
    1. Ruhl M, Knuschke T, Schewior K, Glavinic L, Neumann-Haefelin C, et al. The CD8+ T-Cell Response Promotes Evolution of Hepatitis C Virus Nonstructural Proteins. Gastroenterology. 2011;140:2064–2073.
    1. Merani S, Petrovic D, James I, Chopra A, Cooper D, et al. Effect of immune pressure on hepatitis C virus evolution: insights from a single-source outbreak. Hepatology. 2011;53:396–405.
    1. Gaudieri S, Rauch A, Park LP, Freitas E, Herrmann S, et al. Evidence of viral adaptation to HLA class I-restricted immune pressure in chronic hepatitis C virus infection. J Virol. 2006;80:11094–11104.
    1. Timm J, Li B, Daniels MG, Bhattacharya T, Reyor LL, et al. Human leukocyte antigen-associated sequence polymorphisms in hepatitis C virus reveal reproducible immune responses and constraints on viral evolution. Hepatology. 2007;46:339–349.
    1. Busch MP, Glynn SA, Wright DJ, Hirschkorn D, Laycock ME, et al. Relative sensitivities of licensed nucleic acid amplification tests for detection of viremia in early human immunodeficiency virus and hepatitis C virus infection. Transfusion. 2005;45:1853–1863.
    1. Fischer W, Ganusov VV, Giorgi EE, Hraber PT, Keele BF, et al. Transmission of single HIV-1 genomes and dynamics of early immune escape revealed by ultra-deep sequencing. PLoS One. 2010;5:e12303.
    1. Haaland RE, Hawkins PA, Salazar-Gonzalez J, Johnson A, Tichacek A, et al. Inflammatory genital infections mitigate a severe genetic bottleneck in heterosexual transmission of subtype A and C HIV-1. PLoS Pathog. 2009;5:e1000274.
    1. Zhu T, Mo H, Wang N, Nam DS, Cao Y, et al. Genotypic and phenotypic characterization of HIV-1 patients with primary infection. Science. 1993;261:1179–1181.
    1. Fafi-Kremer S, Fofana I, Soulier E, Carolla P, Meuleman P, et al. Viral entry and escape from antibody-mediated neutralization influence hepatitis C virus reinfection in liver transplantation. J Exp Med. 2010;207:2019–2031.
    1. McCaffrey K, Boo I, Poumbourios P, Drummer HE. Expression and characterization of a minimal hepatitis C virus glycoprotein E2 core domain that retains CD81 binding. J Virol. 2007;81:9584–9590.
    1. Morel V, Fournier C, Francois C, Brochot E, Helle F, et al. Genetic recombination of the hepatitis C virus: clinical implications. J Viral Hepat. 2011;18:77–83.
    1. Holt KE, Parkhill J, Mazzoni CJ, Roumagnac P, Weill FX, et al. High-throughput sequencing provides insights into genome variation and evolution in Salmonella Typhi. Nat Genet. 2008;40:987–993.
    1. Stiffler JD, Nguyen M, Sohn JA, Liu C, Kaplan D, et al. Focal distribution of hepatitis C virus RNA in infected livers. PLoS One. 2009;4:e6661.
    1. Bartenschlager R, Lohmann V. Replication of hepatitis C virus. J Gen Virol. 2000;81:1631–1648.
    1. Ma Y, Yates J, Liang Y, Lemon SM, Yi M. NS3 helicase domains involved in infectious intracellular hepatitis C virus particle assembly. J Virol. 2008;82:7624–7639.
    1. Busch MP. Insights into the epidemiology, natural history and pathogenesis of hepatitis C virus infection from studies of infected donors and blood product recipients. Transfus Clin Biol. 2001;8:200–206.
    1. Glynn SA, Wright DJ, Kleinman SH, Hirschkorn D, Tu Y, et al. Dynamics of viremia in early hepatitis C virus infection. Transfusion. 2005;45:994–1002.
    1. Page-Shafer K, Pappalardo BL, Tobler LH, Phelps BH, Edlin BR, et al. Testing strategy to identify cases of acute hepatitis C virus (HCV) infection and to project HCV incidence rates. J Clin Microbiol. 2008;46:499–506.
    1. Pham ST, Bull RA, Bennett JM, Rawlinson WD, Dore GJ, et al. Frequent multiple hepatitis C virus infections among injection drug users in a prison setting. Hepatology. 2010;52:1564–1572.
    1. Zagordi O, Klein R, Daumer M, Beerenwinkel N. Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies. Nucleic Acids Res. 2010;38:7400–7409.
    1. Zagordi O, Geyrhofer L, Roth V, Beerenwinkel N. Deep sequencing of a genetically heterogeneous sample: local haplotype reconstruction and read error correction. J Comput Biol. 2010;17:417–428.
    1. Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics. 2009;25:2283–2285.
    1. Giorgi EE, Funkhouser B, Athreya G, Perelson AS, Korber BT, et al. Estimating time since infection in early homogeneous HIV-1 samples using a poisson model. BMC Bioinformatics. 2010;11:532.
    1. Sanjuan R, Nebot MR, Chirico N, Mansky LM, Belshaw R. Viral mutation rates. J Virol. 2010;84:9733–9748.
    1. Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24:1596–1599.
    1. Guindon S, Gascuel O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003;52:696–704.
    1. Posada D, Buckley TR. Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. Syst Biol. 2004;53:793–808.
    1. Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:214.

Source: PubMed

3
Prenumerera