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      Genomic Analysis of Hepatitis B Virus Reveals Antigen State and Genotype as Sources of Evolutionary Rate Variation

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          Abstract

          Hepatitis B virus (HBV) genomes are small, semi-double-stranded DNA circular genomes that contain alternating overlapping reading frames and replicate through an RNA intermediary phase. This complex biology has presented a challenge to estimating an evolutionary rate for HBV, leading to difficulties resolving the evolutionary and epidemiological history of the virus. Here, we re-examine rates of HBV evolution using a novel data set of 112 within-host, transmission history (pedigree) and among-host genomes isolated over 20 years from the indigenous peoples of the South Pacific, combined with 313 previously published HBV genomes. We employ Bayesian phylogenetic approaches to examine several potential causes and consequences of evolutionary rate variation in HBV. Our results reveal rate variation both between genotypes and across the genome, as well as strikingly slower rates when genomes are sampled in the Hepatitis B e antigen positive state, compared to the e antigen negative state. This Hepatitis B e antigen rate variation was found to be largely attributable to changes during the course of infection in the preCore and Core genes and their regulatory elements.

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          An exact nonparametric method for inferring mosaic structure in sequence triplets.

          Maciej Boni, David Posada, Marcus Feldman … (2007)
          Statistical tests for detecting mosaic structure or recombination among nucleotide sequences usually rely on identifying a pattern or a signal that would be unlikely to appear under clonal reproduction. Dozens of such tests have been described, but many are hampered by long running times, confounding of selection and recombination, and/or inability to isolate the mosaic-producing event. We introduce a test that is exact, nonparametric, rapidly computable, free of the infinite-sites assumption, able to distinguish between recombination and variation in mutation/fixation rates, and able to identify the breakpoints and sequences involved in the mosaic-producing event. Our test considers three sequences at a time: two parent sequences that may have recombined, with one or two breakpoints, to form the third sequence (the child sequence). Excess similarity of the child sequence to a candidate recombinant of the parents is a sign of recombination; we take the maximum value of this excess similarity as our test statistic Delta(m,n,b). We present a method for rapidly calculating the distribution of Delta(m,n,b) and demonstrate that it has comparable power to and a much improved running time over previous methods, especially in detecting recombination in large data sets.
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            Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data.

            Alexei J. Drummond, Geoff Nicholls, Allen Rodrigo … (2002)
            Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.
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              Bayesian selection of continuous-time Markov chain evolutionary models.

              M. Suchard, R Weiss, J Sinsheimer (2001)
              We develop a reversible jump Markov chain Monte Carlo approach to estimating the posterior distribution of phylogenies based on aligned DNA/RNA sequences under several hierarchical evolutionary models. Using a proper, yet nontruncated and uninformative prior, we demonstrate the advantages of the Bayesian approach to hypothesis testing and estimation in phylogenetics by comparing different models for the infinitesimal rates of change among nucleotides, for the number of rate classes, and for the relationships among branch lengths. We compare the relative probabilities of these models and the appropriateness of a molecular clock using Bayes factors. Our most general model, first proposed by Tamura and Nei, parameterizes the infinitesimal change probabilities among nucleotides (A, G, C, T/U) into six parameters, consisting of three parameters for the nucleotide stationary distribution, two rate parameters for nucleotide transitions, and another parameter for nucleotide transversions. Nested models include the Hasegawa, Kishino, and Yano model with equal transition rates and the Kimura model with a uniform stationary distribution and equal transition rates. To illustrate our methods, we examine simulated data, 16S rRNA sequences from 15 contemporary eubacteria, halobacteria, eocytes, and eukaryotes, 9 primates, and the entire HIV genome of 11 isolates. We find that the Kimura model is too restrictive, that the Hasegawa, Kishino, and Yano model can be rejected for some data sets, that there is evidence for more than one rate class and a molecular clock among similar taxa, and that a molecular clock can be rejected for more distantly related taxa.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Viruses
                Title: Viruses
                Publisher: Molecular Diversity Preservation International (MDPI)
                ISSN (Electronic): 1999-4915
                Publication date (Electronic): 25 January 2011
                Publication date Collection: February 2011
                Volume: 3
                Issue: 2
                Pages: 83-101
                Affiliations
                [1 ] Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, South Parks Road, Oxford OX1 3SY, UK
                [2 ] Fiji School of Medicine, Suva, Fiji; E-Mails: pryor.jan@ 123456gmail.com (J.P.); joji.malani@ 123456fnu.ac.fj (J.M.)
                [3 ] Department of Microbiology and Immunology, Rega Institute, K.U. Leuven 3000, Belgium; E-Mail: philippe.lemey@ 123456rega.kuleuven.be
                [4 ] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK; E-Mail: meh@ 123456sanger.ac.uk
                [5 ] The Hepatitis Foundation of New Zealand, Ohope, Whakatane 3121, New Zealand; E-Mail: chris.moyes@ 123456bopdhb.govt.nz
                [6 ] Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany; E-Mail: horn@ 123456eva.mpg.de
                [7 ] School of Medicine and Health Sciences, University of Papua New Guinea, P.O. Box 5623, Boroko, Port Moresby, NCD, Papua New Guinea; E-Mails: sapuri@ 123456daltron.com.pg (M.S.); masta@ 123456daltron.com.pg (A.M.)
                [8 ] Nawerwere Hospital, Kiribati Ministry of Health, Tawara, Kiribati; E-Mails: dhsmhms@ 123456yahoo.com (B.T.); tebtoatu@ 123456yahoo.com (T.T.)
                [9 ] Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North 4442, New Zealand; E-Mail: d.penny@ 123456massey.ac.nz
                [10 ] Ashworth Laboratories, Institute of Evolutionary Biology, King’s Buildings, Edinburgh, EH8 3JT, UK; E-Mail: a.rambaut@ 123456ed.ac.uk
                [11 ] Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
                [12 ] Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
                Author notes
                [* ]Authors to whom correspondence should be addressed; E-Mail: a.g.l.harrison@ 123456gmail.com ; Tel.: +44-(0)-1865-281532; Fax: +44-(0)-1865-281890 (A.H.); E-Mail: beth.shapiro@ 123456psu.edu ; Tel.: +1-814-863-9178; Fax: +1-814-865-9131 (B.S.).
                Article
                Publisher ID: viruses-03-00083
                DOI: 10.3390/v3020083
                PMC ID: 3136878
                PubMed ID: 21765983
                SO-VID: 2a8d73a1-2d96-46c6-90f4-236200fc4a13
                Copyright © © 2011 by the authors; licensee MDPI, Basel, Switzerland.
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                Date received : 3 December 2010
                Date revision received : 6 January 2011
                Date accepted : 6 January 2011
                Categories
                Subject: Article

                ScienceOpen disciplines: Microbiology & Virology
                Keywords: bayesian phylogenetics,hepatitis b virus,molecular clock
                Data availability:
                ScienceOpen disciplines: Microbiology & Virology
                Keywords: bayesian phylogenetics, hepatitis b virus, molecular clock

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