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      A genotypic method for determining HIV-2 coreceptor usage enables epidemiological studies and clinical decision support

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          Abstract

          Background

          CCR5-coreceptor antagonists can be used for treating HIV-2 infected individuals. Before initiating treatment with coreceptor antagonists, viral coreceptor usage should be determined to ensure that the virus can use only the CCR5 coreceptor (R5) and cannot evade the drug by using the CXCR4 coreceptor (X4-capable). However, until now, no online tool for the genotypic identification of HIV-2 coreceptor usage had been available. Furthermore, there is a lack of knowledge on the determinants of HIV-2 coreceptor usage. Therefore, we developed a data-driven web service for the prediction of HIV-2 coreceptor usage from the V3 loop of the HIV-2 glycoprotein and used the tool to identify novel discriminatory features of X4-capable variants.

          Results

          Using 10 runs of tenfold cross validation, we selected a linear support vector machine (SVM) as the model for geno2pheno[coreceptor-hiv2], because it outperformed the other SVMs with an area under the ROC curve (AUC) of 0.95. We found that SVMs were highly accurate in identifying HIV-2 coreceptor usage, attaining sensitivities of 73.5% and specificities of 96% during tenfold nested cross validation. The predictive performance of SVMs was not significantly different (p value 0.37) from an existing rules-based approach. Moreover, geno2pheno[coreceptor-hiv2] achieved a predictive accuracy of 100% and outperformed the existing approach on an independent data set containing nine new isolates with corresponding phenotypic measurements of coreceptor usage. geno2pheno[coreceptor-hiv2] could not only reproduce the established markers of CXCR4-usage, but also revealed novel markers: the substitutions 27K, 15G, and 8S were significantly predictive of CXCR4 usage. Furthermore, SVMs trained on the amino-acid sequences of the V1 and V2 loops were also quite accurate in predicting coreceptor usage (AUCs of 0.84 and 0.65, respectively).

          Conclusions

          In this study, we developed geno2pheno[coreceptor-hiv2], the first online tool for the prediction of HIV-2 coreceptor usage from the V3 loop. Using our method, we identified novel amino-acid markers of X4-capable variants in the V3 loop and found that HIV-2 coreceptor usage is also influenced by the V1/V2 region. The tool can aid clinicians in deciding whether coreceptor antagonists such as maraviroc are a treatment option and enables epidemiological studies investigating HIV-2 coreceptor usage. geno2pheno[coreceptor-hiv2] is freely available at http://coreceptor-hiv2.geno2pheno.org.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12977-016-0320-7) contains supplementary material, which is available to authorized users.

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          Most cited references76

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              Identification of common molecular subsequences.

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                Author and article information

                Contributors
                mdoering@mpi-inf.mpg.de
                pborrego@ff.ulisboa.pt
                buech@mpi-inf.mpg.de
                andreiamartins@ff.ul.pt
                gfried@mpi-inf.mpg.de
                ricardojorge.camacho@rega.kuleuven.be
                eberle@mvp.uni-muenchen.de
                rolf.kaiser@uk-koeln.de
                lengauer@mpi-inf.mpg.de
                ntaveira@ff.ul.pt
                npfeifer@mpi-inf.mpg.de
                Journal
                Retrovirology
                Retrovirology
                Retrovirology
                BioMed Central (London )
                1742-4690
                20 December 2016
                20 December 2016
                2016
                : 13
                : 85
                Affiliations
                [1 ]Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarland Informatics Campus, Campus E 1 4, 66123 Saarbrücken, Germany
                [2 ]Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, Av. Professor Gama Pinto, 1649-003 Lisbon, Portugal
                [3 ]Centro de Administração e Políticas Públicas (CAPP), Instituto Superior de Ciências Sociais e Políticas (ISCSP), University of Lisbon, Rua Almerindo Lessa, 1300-663 Lisbon, Portugal
                [4 ]Rega Institute for Medical Research, Clinical and Epidemiological Virology, Department of Microbiology and Immunology, KU Leuven-University of Leuven, Minderbroedersstraat 10, 3000 Louvain, Belgium
                [5 ]Department of Virology, Max von Pettenkofer-Institut, Ludwig-Maximilians-University, Pettenkoferstraße 9a, 80336 Munich, Germany
                [6 ]Institute for Virology, University of Cologne, Fürst-Pückler-Str. 56, 50935 Cologne, Germany
                [7 ]Instituto Superior de Ciências da Saúde Egas Moniz (ISCSEM), Campus Universitário, Quinta da Granja, Monte de Caparica, 2829-511 Caparica, Portugal
                Author information
                http://orcid.org/0000-0002-4261-9210
                Article
                320
                10.1186/s12977-016-0320-7
                5168878
                27998283
                346fa264-1c14-4a06-9a87-848d88e7154e
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 28 July 2016
                : 28 November 2016
                Funding
                Funded by: German Ministry of Health
                Award ID: Master-HIV/HEP
                Award Recipient :
                Funded by: EucoHIV
                Funded by: FundRef http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: PhD studentship
                Award ID: Postdoctoral fellowship
                Award Recipient :
                Funded by: Ministry of Health, Portugal
                Award ID: VIH/SAU/0029/2011
                Award Recipient :
                Funded by: EuResist
                Award ID: EEIG
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Microbiology & Virology
                human immunodeficiency virus type 2,hiv-2,coreceptor,chemokine receptor,prediction,statistical learning,v3,v1,v2,coreceptor antagonists

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