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      Molecular evolution in court: analysis of a large hepatitis C virus outbreak from an evolving source

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          Abstract

          Background

          Molecular phylogenetic analyses are used increasingly in the epidemiological investigation of outbreaks and transmission cases involving rapidly evolving RNA viruses. Here, we present the results of such an analysis that contributed to the conviction of an anesthetist as being responsible for the infection of 275 of his patients with hepatitis C virus.

          Results

          We obtained sequences of the NS5B and E1-E2 regions in the viral genome for 322 patients suspected to have been infected by the doctor, and for 44 local, unrelated controls. The analysis of 4,184 cloned sequences of the E1-E2 region allowed us to exclude 47 patients from the outbreak. A subset of patients had known dates of infection. We used these data to calibrate a relaxed molecular clock and to determine a rough estimate of the time of infection for each patient. A similar analysis led to an estimate for the time of infection of the source. The date turned out to be 10 years before the detection of the outbreak. The number of patients infected was small at first, but it increased substantially in the months before the detection of the outbreak.

          Conclusions

          We have developed a procedure to integrate molecular phylogenetic reconstructions of rapidly evolving viral populations into a forensic setting adequate for molecular epidemiological analysis of outbreaks and transmission events. We applied this procedure to a large outbreak of hepatitis C virus caused by a single source and the results obtained played a key role in the trial that led to the conviction of the suspected source.

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          Most cited references36

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          MEGA2: molecular evolutionary genetics analysis software.

          We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets; (2) enabling creation and analyses of groups of sequences; (3) enabling specification of domains and genes; (4) expanding the repertoire of statistical methods for molecular evolutionary studies; and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform. http://www.megasoftware.net. s.kumar@asu.edu
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            Many-core algorithms for statistical phylogenetics.

            Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/).
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              The changing epidemiology of hepatitis C virus infection in Europe.

              The epidemic of hepatitis C virus (HCV) infection in Europe is continuously evolving and epidemiological parameters (prevalence, incidence, disease transmission patterns and genotype distribution) have changed substantially during the last 15 years. Four main factors contribute to such changes: increased blood transfusion safety, improvement of healthcare conditions, continuous expansion of intravenous drug use and immigration to Europe from endemic areas. As a result, intravenous drug use has become the main risk factor for HCV transmission, prevalent infections have increased and genotype distribution has changed and diversified. Hence, prevalence data from studies conducted a decade ago may not be useful to estimate the current and future burden of HCV infection and additional epidemiological studies should be conducted, as well as new preventive strategies implemented to control the silent epidemic. This review summarizes recently published data on the epidemiology of HCV infection in Europe focusing on the factors currently shaping the epidemic.
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                Author and article information

                Contributors
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central
                1741-7007
                2013
                19 July 2013
                : 11
                : 76
                Affiliations
                [1 ]Joint Research Unit ‘Genómica y Salud’ CSISP (FISABIO), Instituto Cavanilles/Universidad de Valencia, c/ Catedrático José Beltrán, 2 46980-Paterna, Valencia, Spain
                [2 ]Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Valencia, Spain
                [3 ]Department of Genetics and Marine Biotechnology, Institute of Oceanology, Polish Academy of Sciences, Powstańców Warszawy 55, 81-712 Sopot, Poland
                [4 ]Laboratory of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań, Umultowska 89, 61-614 Poznań, Poland
                Article
                1741-7007-11-76
                10.1186/1741-7007-11-76
                3717074
                23870105
                c473f8eb-4974-45b7-89f6-d65ba74f758f
                Copyright © 2013 González-Candelas et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 July 2012
                : 24 May 2013
                Categories
                Research Article

                Life sciences
                hcv,outbreak,forensics,molecular epidemiology,nosocomial transmission,compartmentalization,maximum likelihood,dating transmission events,viral evolution

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