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      HIV coreceptor tropism determination and mutational pattern identification

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          Abstract

          In the early stages of infection, Human Immunodeficiency Virus Type 1 (HIV-1) generally selects CCR5 as the primary coreceptor for entering the host cell. As infection progresses, the virus evolves and may exhibit a coreceptor-switch to CXCR4. Accurate determination coreceptor usage and identification key mutational patterns associated tropism switch are essential for selection of appropriate therapies and understanding mechanism of coreceptor change. We developed a classifier composed of two coreceptor-specific weight matrices (CMs) based on a full-scale dataset. For this classifier, we found an AUC of 0.97, an accuracy of 95.21% and an MCC of 0.885 (sensitivity 92.92%; specificity 95.54%) in a ten-fold cross-validation, outperforming all other methods on an independent dataset (13% higher MCC value than geno2pheno and 15% higher MCC value than PSSM). A web server ( http://spg.med.tsinghua.edu.cn/CM.html) based on our classifier was provided. Patterns of genetic mutations that occur along with coreceptor transitions were further identified based on the score of each sequence. Six pairs of one-AA mutational patterns and three pairs of two-AA mutational patterns were identified to associate with increasing propensity for X4 tropism. These mutational patterns offered new insights into the mechanism of coreceptor switch and aided in monitoring coreceptor switch.

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          Most cited references44

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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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            Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists.

            Chemokine receptors are critical regulators of cell migration in the context of immune surveillance, inflammation, and development. The G protein-coupled chemokine receptor CXCR4 is specifically implicated in cancer metastasis and HIV-1 infection. Here we report five independent crystal structures of CXCR4 bound to an antagonist small molecule IT1t and a cyclic peptide CVX15 at 2.5 to 3.2 angstrom resolution. All structures reveal a consistent homodimer with an interface including helices V and VI that may be involved in regulating signaling. The location and shape of the ligand-binding sites differ from other G protein-coupled receptors and are closer to the extracellular surface. These structures provide new clues about the interactions between CXCR4 and its natural ligand CXCL12, and with the HIV-1 glycoprotein gp120.
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              Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunodeficiency virus type 1 activity.

              Maraviroc (UK-427,857) is a selective CCR5 antagonist with potent anti-human immunodeficiency virus type 1 (HIV-1) activity and favorable pharmacological properties. Maraviroc is the product of a medicinal chemistry effort initiated following identification of an imidazopyridine CCR5 ligand from a high-throughput screen of the Pfizer compound file. Maraviroc demonstrated potent antiviral activity against all CCR5-tropic HIV-1 viruses tested, including 43 primary isolates from various clades and diverse geographic origin (geometric mean 90% inhibitory concentration of 2.0 nM). Maraviroc was active against 200 clinically derived HIV-1 envelope-recombinant pseudoviruses, 100 of which were derived from viruses resistant to existing drug classes. There was little difference in the sensitivity of the 200 viruses to maraviroc, as illustrated by the biological cutoff in this assay (= geometric mean plus two standard deviations [SD] of 1.7-fold). The mechanism of action of maraviroc was established using cell-based assays, where it blocked binding of viral envelope, gp120, to CCR5 to prevent the membrane fusion events necessary for viral entry. Maraviroc did not affect CCR5 cell surface levels or associated intracellular signaling, confirming it as a functional antagonist of CCR5. Maraviroc has no detectable in vitro cytotoxicity and is highly selective for CCR5, as confirmed against a wide range of receptors and enzymes, including the hERG ion channel (50% inhibitory concentration, >10 microM), indicating potential for an excellent clinical safety profile. Studies in preclinical in vitro and in vivo models predicted maraviroc to have human pharmacokinetics consistent with once- or twice-daily dosing following oral administration. Clinical trials are ongoing to further investigate the potential of using maraviroc for the treatment of HIV-1 infection and AIDS.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                17 February 2016
                2016
                : 6
                : 21280
                Affiliations
                [1 ]The Key Laboratory of Bioinformatics, Ministry of Education, School of Life Sciences, Tsinghua University , China
                [2 ]Department of Biostatistics, Saw Swee Hock School of Public Health, National University of Singapore , Singapore
                Author notes
                Article
                srep21280
                10.1038/srep21280
                4756667
                26883082
                915d023b-6771-48c2-9edd-13345556b94b
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 04 November 2015
                : 20 January 2016
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