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      Evolutionary Analysis Provides Insight Into the Origin and Adaptation of HCV

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          Abstract

          Hepatitis C virus (HCV) belongs to the Hepacivirus genus and is genetically heterogeneous, with seven major genotypes further divided into several recognized subtypes. HCV origin was previously dated in a range between ∼200 and 1000 years ago. Hepaciviruses have been identified in several domestic and wild mammals, the largest viral diversity being observed in bats and rodents. The closest relatives of HCV were found in horses/donkeys (equine hepaciviruses, EHV). However, the origin of HCV as a human pathogen is still an unsolved puzzle. Using a selection-informed evolutionary model, we show that the common ancestor of extant HCV genotypes existed at least 3000 years ago (CI: 3192–5221 years ago), with the oldest genotypes being endemic to Asia. EHV originated around 1100 CE (CI: 291–1640 CE). These time estimates exclude that EHV transmission was mainly sustained by widespread veterinary practices and suggest that HCV originated from a single zoonotic event with subsequent diversification in human populations. We also describe a number of biologically important sites in the major HCV genotypes that have been positively selected and indicate that drug resistance-associated variants are significantly enriched at positively selected sites. HCV exploits several cell-surface molecules for cell entry, but only two of these (CD81 and OCLN) determine the species-specificity of infection. Herein evolutionary analyses do not support a long-standing association between primates and hepaciviruses, and signals of positive selection at CD81 were only observed in Chiroptera. No evidence of selection was detected for OCLN in any mammalian order. These results shed light on the origin of HCV and provide a catalog of candidate genetic modulators of HCV phenotypic diversity.

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          Most cited references84

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          Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence.

          In efforts to inform public health decision makers, the Global Burden of Diseases, Injuries, and Risk Factors 2010 (GBD2010) Study aims to estimate the burden of disease using available parameters. This study was conducted to collect and analyze available prevalence data to be used for estimating the hepatitis C virus (HCV) burden of disease. In this systematic review, antibody to HCV (anti-HCV) seroprevalence data from 232 articles were pooled to estimate age-specific seroprevalence curves in 1990 and 2005, and to produce age-standardized prevalence estimates for each of 21 GBD regions using a model-based meta-analysis. This review finds that globally the prevalence and number of people with anti-HCV has increased from 2.3% (95% uncertainty interval [UI]: 2.1%-2.5%) to 2.8% (95% UI: 2.6%-3.1%) and >122 million to >185 million between 1990 and 2005. Central and East Asia and North Africa/Middle East are estimated to have high prevalence (>3.5%); South and Southeast Asia, sub-Saharan Africa, Andean, Central, and Southern Latin America, Caribbean, Oceania, Australasia, and Central, Eastern, and Western Europe have moderate prevalence (1.5%-3.5%); whereas Asia Pacific, Tropical Latin America, and North America have low prevalence (<1.5%). The high prevalence of global HCV infection necessitates renewed efforts in primary prevention, including vaccine development, as well as new approaches to secondary and tertiary prevention to reduce the burden of chronic liver disease and to improve survival for those who already have evidence of liver disease. Copyright © 2012 American Association for the Study of Liver Diseases.
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            An algorithm for progressive multiple alignment of sequences with insertions.

            Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.
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              Estimating maximum likelihood phylogenies with PhyML.

              Our understanding of the origins, the functions and/or the structures of biological sequences strongly depends on our ability to decipher the mechanisms of molecular evolution. These complex processes can be described through the comparison of homologous sequences in a phylogenetic framework. Moreover, phylogenetic inference provides sound statistical tools to exhibit the main features of molecular evolution from the analysis of actual sequences. This chapter focuses on phylogenetic tree estimation under the maximum likelihood (ML) principle. Phylogenies inferred under this probabilistic criterion are usually reliable and important biological hypotheses can be tested through the comparison of different models. Estimating ML phylogenies is computationally demanding, and careful examination of the results is warranted. This chapter focuses on PhyML, a software that implements recent ML phylogenetic methods and algorithms. We illustrate the strengths and pitfalls of this program through the analysis of a real data set. PhyML v3.0 is available from (http://atgc_montpellier.fr/phyml/).
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                01 May 2018
                2018
                : 9
                : 854
                Affiliations
                [1] 1Bioinformatics Laboratory, Scientific Institute IRCCS E.Medea , Bosisio Parini, Italy
                [2] 2Department of Biotechnology and Biosciences, University of Milan-Bicocca , Milan, Italy
                [3] 3Department of Pathophysiology and Transplantation, University of Milan , Milan, Italy
                [4] 4Don C. Gnocchi Foundation Onlus, IRCCS , Milan, Italy
                Author notes

                Edited by: Masako Nomaguchi,Tokushima University, Japan

                Reviewed by: Fernando Gonzalez-Candelas, Universitat de València, Spain; Navaneethan Palanisamy, Albert-Ludwigs-Universität Freiburg, Germany

                *Correspondence: Manuela Sironi, manuela.sironi@ 123456bp.lnf.it

                This article was submitted to Virology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2018.00854
                5938362
                29765366
                5fc33e5e-8bf8-4100-bc5a-2d6efad82fef
                Copyright © 2018 Forni, Cagliani, Pontremoli, Pozzoli, Vertemara, De Gioia, Clerici and Sironi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 February 2018
                : 13 April 2018
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 119, Pages: 16, Words: 0
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                hepatitis c virus,equine hepacivirus,molecular dating,tmrca,positive selection,resistance-associated amino acid variants,cd81

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