Infection, Genetics and Evolution 54 (2017) 486–490

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Short communication

Heterogeneous recombination among Hepatitis B virus genotypes Nadine Castelhano

a,b

c

, Natalia M. Araujo , Miguel Arenas

MARK

a,b,d,⁎

a

Instituto de Investigação e Inovação em Saúde (i3S), University of Porto, Porto, Portugal Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal Laboratory of Molecular Virology, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro, Brazil d Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Hepatitis B virus Recombination HBV genome HBV genotypes HBV evolution

The rapid evolution of Hepatitis B virus (HBV) through both evolutionary forces, mutation and recombination, allows this virus to generate a large variety of adapted variants at both intra and inter-host levels. It can, for instance, generate drug resistance or the diverse viral genotypes that currently exist in the HBV epidemics. Concerning the latter, it is known that recombination played a major role in the emergence and genetic diversification of novel genotypes. In this regard, the quantification of viral recombination in each genotype can provide relevant information to devise expectations about the evolutionary trends of the epidemic. Here we measured the amount of this evolutionary force by estimating global and local recombination rates in > 4700 HBV complete genome sequences corresponding to nine (A to I) HBV genotypes. Counterintuitively, we found that genotype E presents extremely high levels of recombination, followed by genotypes B and C. On the other hand, genotype G presents the lowest level, where recombination is almost negligible. We discuss these findings in the light of known characteristics of these genotypes. Additionally, we present a phylogenetic network to depict the evolutionary history of the studied HBV genotypes. This network clearly classified all genotypes into specific groups and indicated that diverse pairs of genotypes are derived from a common ancestor (i.e., C–I, D–E and, F–H) although still the origin of this virus presented large uncertainty. Altogether we conclude that the amount of observed recombination is heterogeneous among HBV genotypes and that this heterogeneity can influence on the future expansion of the epidemic.

1. Introduction Hepatitis B virus (HBV) infection is a major cause of chronic liver disease, including cirrhosis and hepatocellular carcinoma, affecting 257 million people worldwide (WHO, 2017). HBV is a unique enveloped double-stranded DNA (dsDNA) virus that employs an error-prone reverse transcriptase to replicate its genome. Indeed, the estimated nucleotide substitution rate is higher than that observed in most dsDNA viruses, ranging between 10− 6 and 10− 4 substitutions per site per year (Osiowy et al., 2006; Paraskevis et al., 2015; Zhou and Holmes, 2007). As expected, the genetic diversity of the current HBV strains results from accumulating mutation and recombination events (Simmonds and Midgley, 2005; Zhou and Holmes, 2007). At present, human HBV is classified into 10 genotypes (A to J) and a growing number of subgenotypes based on nucleotide sequence divergence of the entire genome by 8% and 4%, respectively (Araujo et al., 2011; Tong and Revill, 2016). Despite established rules, HBV subgenotyping is not straightforward and a number of misclassifications have been reported (Pourkarim et al., 2010; Schaefer et al., 2009; Shi et al., 2013). HBV ⁎

genotypes have distinct geographical distributions and have been increasingly associated with differences in clinical and virological responses, such as severity of liver disease and resistance to antiviral therapies (McMahon, 2009). Novel HBV variants generated by recombination have been documented worldwide. Some of them are now endemic in certain geographical regions, indicating the potential for spreading in a wide range of human populations and developing their own epidemiology (Araujo, 2015). The relevance of recombination in virus evolution [establishing new chances for viruses to escape from selective pressures caused by the immune system or antiviral therapies (Arenas, 2015; Fraser, 2005; Levy et al., 2004)] leads to a need for not only detecting its presence (i.e., by identifying the distribution of recombination hotspots), but also for quantifying its extent (Perez-Losada et al., 2015). In this concern, recombination rate is often used to appraise the frequency of recombination events in the studied data (McVean et al., 2004). Multiple studies have estimated this parameter to investigate the evolution of diverse viruses such as HIV-1 or HSV-1 (Arenas et al., 2016; Bowden et al., 2004; Lopes et al., 2014). However, to our knowledge, the

Corresponding author at: Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo 36310, Spain. E-mail addresses: [email protected] (N. Castelhano), nmaraujo@ioc.fiocruz.br (N.M. Araujo), [email protected] (M. Arenas).

http://dx.doi.org/10.1016/j.meegid.2017.08.015 Received 5 May 2017; Received in revised form 13 August 2017; Accepted 17 August 2017 Available online 18 August 2017 1567-1348/ © 2017 Elsevier B.V. All rights reserved.

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Table 1 Studied HBV data and estimated nucleotide diversity, GC content and global recombination rate. For each genotype, the table indicates the number of studied genomes (sample size), the observed nucleotide diversity (π), GC content, the score of the recombination test (Φw) (see also Fig. S2) and the global recombination rate (ρ) (see also Fig. 1). The presented values for ρ correspond to the average of values estimated with two independent MCMC runs. Genotype

Sample size

Nucleotide diversitya (π)

GC contenta,b

PHI (Φw)

Recombination ratec (ρ)

A B C D E F G H I

775 1009 1519 853 227 226 38 26 60

2.89 2.83 3.16 2.91 1.79 3.95 1.53 1.31 2.80

48.60 48.96 48.90 47.96 48.23 48.86 47.98 49.33 48.42

9.99 × 10− 1 9.92 × 10− 1 9.58 × 10− 1 1.00 1.00 6.17 × 10− 1 1.70 × 10− 5 5.99 × 10− 1 1.00

8.47 (7.94 − 9.00) 3191.54 (761.78 − 5634.78) 821.52 (498.92 −1595.15) 215.70 (172.50 −259.92) 8234.01 (3067.80 − 18,372.35) 16.67 (13.63 − 20.78) 3.30 × 10− 2 (3.30 × 10− 2 − 3.30 × 10− 2) 33.02 (27.38 − 40.10) 27.27 (21.97 − 32.53)

a b c

(1.68) (1.68) (1.68) (1.68) (1.70) (1.66) (1.70) (1.70) (1.68)

Nucleotide diversity and GC content are presented in percentage. 95% CI (confidence interval) among sites is shown in parenthesis. 95% HPDI (highest posterior density interval) is shown in parenthesis.

In order to explore the evolutionary history of all the studied HBV genotypes we inferred a phylogenetic network [note that recombination can bias phylogenetic tree inferences (Arenas, 2013; Arenas and Posada, 2010; Posada and Crandall, 2002; Schierup and Hein, 2000)] with the well-established program SplitsTree 4.1 (Huson and Bryant, 2006). Unfortunately, this program crashes (out of memory) with large datasets (higher than 1000 HBV genomes) and consequently, for this analyses we considered a maximum of 100 genomes per genotype that is a sample large enough to infer the phylogeny of 9 genotypes. The procedure to perform this analysis follows below. First, we identified the best-fitting model of nucleotide substitution for the genome sequences of all genotypes that are analyzed in subsequent steps. This task was performed with jModelTest 2.0 (Darriba et al., 2012) under the Bayesian Information Criterion (BIC), as recommended by Luo et al. (2010), to select the model SYM + G + I. Next, this substitution model was applied to infer phylogenetic networks under the Neighbor-Net method (Bryant and Moulton, 2004), considering a bootstrap based on 100 and 1000 replicates, implemented in SplitsTree 4.1.

quantification of recombination (i.e., by estimated recombination rates) was not investigated so far in HBV. Here we estimated and discussed global (entire genome) and local (variation along the genome) recombination rates for the different HBV genotypes. We also inferred and discussed the evolutionary history of HBV genotypes with a recombination-based phylogenetic network. We conclude that recombination is heterogeneous among HBV genotypes and that the future expansion of the HBV epidemic could be highly influenced by this heterogeneity. 2. Materials and methods 2.1. Genomic data and nucleotide diversity We downloaded all HBV full-length genome sequences available in the HBVdb database (Hayer et al., 2013) (Table 1). These sequences correspond to nine HBV genotypes (from A to I, J was excluded due to the lack of information with only one sequence in the database). Next, we obtained a multiple sequence alignment (MSA) of all sequences with MAFFT (Katoh and Standley, 2013) and trimmed poorly aligned regions (gaps in > 95% of sequences) with trimAl (Capella-Gutierrez et al., 2009). The resulting MSA presented an overall coverage of 99.4% (its variation along the genome is shown in Fig. S1; Supplementary material). Finally, we calculated nucleotide diversity (π) (Nei and Li, 1979) and GC content for each genotype.

3. Results and discussion We found that recombination is highly heterogeneous among HBV genotypes. The estimated global recombination rate was extremely high in genotype E (ρ > 8000), followed by genotypes B and C (ρ > 3000 and 800, respectively). Next, genotype D presented an intermediate recombination rate (ρ > 200). Genotypes A, F, G, H and I presented lower recombination rates (ρ < 50), where genotype G indicated almost negligible recombination (Table 1 and Fig. 1). We obtained similar findings with the recombination test PHI (Table 1), actually results from these analytical methods presented a high correlation (Fig. S2; Supplementary material). Concerning nucleotide diversity, genotypes F and C presented the highest levels of diversity whilst genotypes G and H showed the lowest levels (Table 1). Since recombination could be biased by low genetic diversity (Posada and Crandall, 2001), we explored a possible correlation between these parameters. We found lack of correlation between recombination rate and nucleotide diversity (Fig. S3; Supplementary material) suggesting that our recombination analyses are not influenced by the level of genetic diversity present in the data. The GC content did not significantly vary across HBV genotypes (Table 1 and Fig. S4; Supplementary material), which indicates lack of interrelationship between recombination and GC content. One could expect high levels of recombination in genotypes A, B and C since most of known intergenotypes are derived from them (Araujo, 2015; Shi et al., 2012; Yang et al., 2006). However, these genotypes can present a higher number of descendant recombinant forms because they are the oldest genotypes (Paraskevis et al., 2015) and not necessarily because of a larger recombination rate. Indeed, analyzing

2.2. Recombination analyses For each genotype we performed two quantitative measures of recombination, (i) estimation of recombination rates at both global and local levels, and (ii) identification of recombination with a robust recombination test. We finally inferred an implicit phylogenetic network to explore the evolutionary history of HBV genotypes. We applied straightforward and well-established methods for investigating recombination in viruses (Perez-Losada et al., 2015). We estimated global and local population recombination rates (ρ) with RDP 4 (Martin et al., 2015), which incorporates a sophisticated version of the MCMC approach implemented in LDHat (McVean et al., 2004). We performed two independent runs per alignment, each based on 10,000,000 iterations. Convergence between runs was assessed as the overlapping of the estimated recombination rates within the 95% highest posterior density interval (HPDI). Additionally, as a sanity check of the estimates obtained from RDP, we analyzed genetic signatures of recombination with the statistical test PHI (Φw, pairwise homoplasy index), implemented in PHIPack (Bruen et al., 2006). We selected this specific test due to its robustness with respect to other recombination tests and because it can be applied to large datasets (Bruen et al., 2006). 487

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Fig. 1. Global recombination rate in each HBV genotype. Error bars indicate 95% HPDI (highest posterior density interval).

hotspots did not correlate with the GC content, note that the studied genotypes presented similar levels of GC content (Fig. S4) but distinct recombination hotspots (Fig. S5) along the genome, suggesting that the GC content is not a cause of the observed recombination. A recombination hotspot region was detected in positions 1500–2600 [P, X and pre-C/C genes (Liang, 2009)] in genotypes B, C and H. This region was extended to 800–2600 [also P, X and pre-C/C genes] in genotypes D and I. Additionally, genotype E presented large recombination in more HBV genomic regions [S, P, X, pre-C/C and Pre-S1 genes (Liang, 2009)]. These genomic regions are in agreement with other studies related with the detection of recombination breakpoints (Araujo, 2015; Hino et al., 1991; Simmonds and Midgley, 2005). It is known that recombination events involving these genomic regions can transfer several clinically relevant mutations from one strain to another, such as mutations related to vaccine escape (S gene), antiviral resistance (P gene), and hepatocarcinogenesis (pre-C/C and X genes). Finally, genotypes A and G presented homogeneous recombination along the genome, probably related with their small recombination rates (Table 1). We inferred a phylogenetic network to decipher the evolutionary history and genetic relationships of HBV genotypes (Figs. 2 and S6; Supplementary material). Notably, we could classify all genotypes into specific groups. The origin of all genotypes still presents large uncertainty probably due to the high level of reticulation (recombination) at the onset of first divergences. However, we could identify that some genotypes share common ancestors (C–I, D–E and, F–H), as also noted in other studies (Jiang et al., 2013; Paraskevis et al., 2015; Tran et al., 2008). Interestingly, genotype G (that presented recombination rates close to 0) accordingly showed long non-reticulated branches from its divergence with little reticulation near the present. We conclude that recombination is heterogeneous among HBV

recombination between different combinations of genotypes is complex due to the lack of data and the problematic characterization of a large variety of intergenotypes (Pourkarim et al., 2010; Schaefer et al., 2009; Shi et al., 2013). Thus here we only focus on intragenotype recombination, where results can be more clearly interpreted. Unexpectedly, we found that genotype E presents the highest levels of intragenotype recombination. The low nucleotide diversity of this genotype (Table 1) suggests a short evolutionary history and a late introduction of this genotype into the African population. However, this genotype is presenting a recent extensive spread throughout the African continent, which is probably the result of unsafe mass injection campaigns (Andernach et al., 2009; Andernach et al., 2013). Our hypothesis is that this efficient horizontal transmission of genotype E would accelerate viral replication (Andernach et al., 2013) and, subsequently, increase the generation of recombination forms (Delviks-Frankenberry et al., 2011; Perez-Losada et al., 2015; Weller and Sawitzke, 2014). On the other hand, the low recombination rate estimated for genotype G may conflict with the worldwide detection of several intergenotypes derived from this genotype (Araujo, 2015). However, genotype G is found almost exclusively in co-infection with other genotypes that can supply the protein HBeAg (note that this genotype does not produce HBeAg by itself) (Kato et al., 2002; Stuyver et al., 2000) increasing the chances of intergenotype recombination, and being intragenotype recombination disfavored. Interestingly, a recent study showed that the replication capacity of genotype G is similar to that of genotype A (Peiffer et al., 2015), which could explain the similar (low) recombination levels that we found in these genotypes. Our estimated recombination rates were not uniform along genomes. We identified several regions with high levels of recombination (hotspot regions) and their location in the genome varied among genotypes (Fig. S5; Supplementary material). These recombination

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Fig. 2. Phylogenetic network of HBV genotypes. Phylogenetic network based on the Neighbor-Net method and considering a bootstrap of 100 replicates. Genotypes are depicted with different colors. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

doi.org/10.1016/j.meegid.2017.08.015.

genotypes and that, considering the estimated recombination rates, we expect a generation of new recombinant forms in most of genotypes, especially in genotype E. This heterogeneous evolution among genotypes can strongly influence on the future expansion of the HBV epidemic. We suggest that the design of future large-scale therapies, such as vaccines, could be more realistic, and thus more effective, by considering evolutionary scenarios of recombination heterogeneity.

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Competing interests The authors have declared that no competing interests exist. Acknowledgements NC was supported by a fellowship derived from the Project NORTE01-0145-FEDER-000029 funded by NORTE2020 (Portugal). NMA was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grant number 444071/2014-8. MA was initially supported by the FCT Starting Grant IF/00955/2014 from the Portuguese Government and finally supported by “Ramón y Cajal” Grant RYC-2015-18241 from the Spanish Government. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. 489

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