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      The Influence of Hepatitis C Virus Genetic Region on Phylogenetic Clustering Analysis

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          Abstract

          Sequencing is important for understanding the molecular epidemiology and viral evolution of hepatitis C virus (HCV) infection. To date, there is little standardisation among sequencing protocols, in-part due to the high genetic diversity that is observed within HCV. This study aimed to develop a novel, practical sequencing protocol that covered both conserved and variable regions of the viral genome and assess the influence of each subregion, sequence concatenation and unrelated reference sequences on phylogenetic clustering analysis. The Core to the hypervariable region 1 (HVR1) of envelope-2 (E2) and non-structural-5B (NS5B) regions of the HCV genome were amplified and sequenced from participants from the Australian Trial in Acute Hepatitis C (ATAHC), a prospective study of the natural history and treatment of recent HCV infection. Phylogenetic trees were constructed using a general time-reversible substitution model and sensitivity analyses were completed for every subregion. Pairwise distance, genetic distance and bootstrap support were computed to assess the impact of HCV region on clustering results as measured by the identification and percentage of participants falling within all clusters, cluster size, average patristic distance, and bootstrap value. The Robinson-Foulds metrics was also used to compare phylogenetic trees among the different HCV regions. Our results demonstrated that the genomic region of HCV analysed influenced phylogenetic tree topology and clustering results. The HCV Core region alone was not suitable for clustering analysis; NS5B concatenation, the inclusion of reference sequences and removal of HVR1 all influenced clustering outcome. The Core-E2 region, which represented the highest genetic diversity and longest sequence length in this study, provides an ideal method for clustering analysis to address a range of molecular epidemiological questions.

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          Most cited references38

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          Genetic diversity and evolution of hepatitis C virus--15 years on.

          In the 15 years since the discovery of hepatitis C virus (HCV), much has been learned about its role as a major causative agent of human liver disease and its ability to persist in the face of host-cell defences and the immune system. This review describes what is known about the diversity of HCV, the current classification of HCV genotypes within the family Flaviviridae and how this genetic diversity contributes to its pathogenesis. On one hand, diversification of HCV has been constrained by its intimate adaptation to its host. Despite the >30 % nucleotide sequence divergence between genotypes, HCV variants nevertheless remain remarkably similar in their transmission dynamics, persistence and disease development. Nowhere is this more evident than in the evolutionary conservation of numerous evasion methods to counteract the cell's innate antiviral defence pathways; this series of highly complex virus-host interactions may represent key components in establishing its 'ecological niche' in the human liver. On the other hand, the mutability and large population size of HCV enables it to respond very rapidly to new selection pressures, manifested by immune-driven changes in T- and B-cell epitopes that are encountered on transmission between individuals with different antigen-recognition repertoires. If human immunodeficiency virus type 1 is a precedent, future therapies that target virus protease or polymerase enzymes may also select very rapidly for antiviral-resistant mutants. These contrasting aspects of conservatism and adaptability provide a fascinating paradigm in which to explore the complex selection pressures that underlie the evolution of HCV and other persistent viruses.
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            Inferring species phylogenies from multiple genes: concatenated sequence tree versus consensus gene tree.

            Phylogenetic trees from multiple genes can be obtained in two fundamentally different ways. In one, gene sequences are concatenated into a super-gene alignment, which is then analyzed to generate the species tree. In the other, phylogenies are inferred separately from each gene, and a consensus of these gene phylogenies is used to represent the species tree. Here, we have compared these two approaches by means of computer simulation, using 448 parameter sets, including evolutionary rate, sequence length, base composition, and transition/transversion rate bias. In these simulations, we emphasized a worst-case scenario analysis in which 100 replicate datasets for each evolutionary parameter set (gene) were generated, and the replicate dataset that produced a tree topology showing the largest number of phylogenetic errors was selected to represent that parameter set. Both randomly selected and worst-case replicates were utilized to compare the consensus and concatenation approaches primarily using the neighbor-joining (NJ) method. We find that the concatenation approach yields more accurate trees, even when the sequences concatenated have evolved with very different substitution patterns and no attempts are made to accommodate these differences while inferring phylogenies. These results appear to hold true for parsimony and likelihood methods as well. The concatenation approach shows >95% accuracy with only 10 genes. However, this gain in accuracy is sometimes accompanied by reinforcement of certain systematic biases, resulting in spuriously high bootstrap support for incorrect partitions, whether we employ site, gene, or a combined bootstrap resampling approach. Therefore, it will be prudent to report the number of individual genes supporting an inferred clade in the concatenated sequence tree, in addition to the bootstrap support. (c) 2005 Wiley-Liss, Inc.
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              Classification of hepatitis C virus into six major genotypes and a series of subtypes by phylogenetic analysis of the NS-5 region.

              Hepatitis C virus (HCV) showed substantial nucleotide sequence diversity distributed throughout the viral genome, with many variants showing only 68 to 79% overall sequence similarity to one another. Phylogenetic analysis of nucleotide sequences derived from part of the gene encoding a non-structural protein (NS-5) has provided evidence for six major genotypes of HCV amongst a worldwide collection of 76 samples from HCV-infected blood donors and patients with chronic hepatitis. Many of these HCV types comprised a number of more closely related subtypes, leading to a current total of 11 genetically distinct viral populations. Phylogenetic analysis of other regions of the viral genome produced relationships between published sequences equivalent to those found in NS-5, apart from the more highly conserved 5' non-coding region in which only the six major HCV types, but not subtypes, could be differentiated. A new nomenclature for HCV variants is proposed in this communication that reflects the two-tiered nature of sequence differences between different viral isolates. The scheme classifies all known HCV variants to date, and describes criteria that would enable new variants to be assigned within the classification as they are discovered.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 July 2015
                2015
                : 10
                : 7
                : e0131437
                Affiliations
                [1 ]The Kirby Institute, University of New South Wales Australia, Sydney, Australia
                [2 ]Inflammation and Infection Research Centre, School of Medical Sciences, University of New South Wales Australia, Sydney, Australia
                [3 ]Academic Medical Centre, Department of Medical Microbiology, Section of Clinical Virology, Amsterdam, The Netherlands
                [4 ]BC Centre for Excellence in HIV/AIDS, Vancouver, Canada
                [5 ]Department of Medicine, University of British Columbia, Vancouver, Canada
                [6 ]HIV/Immunology/Infectious Diseases Clinical Services Unit, St Vincent’s Hospital, Sydney, Australia
                Institut Pasteur, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: FL BJ GD JG TA. Performed the experiments: FL SB AW. Analyzed the data: FL. Wrote the paper: FL TA. Provided input in the analysis of data: BJ AP SB JA JG GD TA. Provided input in the writing of the paper: BJ JG RB JS. Critically reviewed the first draft of the article and approved the final version to be submitted: FL BJ SB RB AW JA JS AP GM JG GD TA. Provided clinical samples from the ATAHC study: GM.

                Article
                PONE-D-15-08556
                10.1371/journal.pone.0131437
                4507989
                26192190
                876f1b9b-9396-4783-9169-8a0b4cc7c990
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 26 February 2015
                : 1 June 2015
                Page count
                Figures: 10, Tables: 1, Pages: 22
                Funding
                This research was supported by National Health and Medical Research Council (NHMRC) grant HIV and HCV vaccines and immunopathogenesis (#510448) and NHMRC grant Hepatitis C infection: epidemiology, pathogenesis, and treatment (#1053206), Australia. http://www.nhmrc.gov.au/. The ATAHC study was funded by the National Institutes of Health grant R01 DA 15999-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                Data are from the ATAHC study whose authors may be contacted at recpt@ 123456nchecr.unsw.edu.au and phone: +61 (2) 9385 0900. The sequences are available in Genbank with accession number KR855579 to KR855628.

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