Literature DB >> 22246825

Discordance in HIV-1 co-receptor use prediction by different genotypic algorithms and phenotype assay: intermediate profile in relation to concordant predictions.

Mary-Anne Trabaud1, Vinca Icard, Caroline Scholtes, Thomas Perpoint, Joseph Koffi, Laurent Cotte, Djamila Makhloufi, Jean Claude Tardy, Patrice André.   

Abstract

Concordant and discordant genotypic predictions of HIV-1 co-receptor tropism were analyzed. V3 region was sequenced from plasma samples of patients screened for R5 tropism by the Trofile® assay, before CCR5 antagonist prescription. Ten tools including geno2pheno, PSSM, an "11/25" and "net charge" rule, and other published algorithms were used. Patients were grouped according to concordance or discordance between tools and Trofile® result. Trofile® tropism reports from 50 patient samples were R5 in 38 and Dual/Mixed (DM) in 12. Prediction with the genotypic tools were concordant for 23 R5 samples, and discordant for the 15 other ones. From Trofile® DM strains were concordant in 6 and discordant in 6. V3 sequences were not clearly distinct between R5 and DM strains, except a greater diversity in the later. Discordances were found with any tool or combination of them, so that no one can be proposed as better than the others. Predictive values of each algorithm were similar and rather good (efficacy ranged from 74% to 84%), but the rate of non-confirmed prediction is greater when compelling the results of all tools with each individual sample. The mean of quantitative values obtained with one tool when another tool give the opposite prediction were different from those obtained when all tools agree with that prediction. The two discordant groups were often not distinguishable from each other. These results suggest that viruses giving discordant prediction with bioinformatic tools could be functionally distinct and/or in a different evolutionary state compared to those with concordant prediction.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22246825     DOI: 10.1002/jmv.23209

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   2.327


  8 in total

Review 1.  Bioinformatic analysis of HIV-1 entry and pathogenesis.

Authors:  Benjamas Aiamkitsumrit; Will Dampier; Gregory Antell; Nina Rivera; Julio Martin-Garcia; Vanessa Pirrone; Michael R Nonnemacher; Brian Wigdahl
Journal:  Curr HIV Res       Date:  2014       Impact factor: 1.581

2.  Copy Number Variation within Human β-Defensin Gene Cluster Influences Progression to AIDS in the Multicenter AIDS Cohort Study.

Authors:  Rajeev K Mehlotra; Jean-Eudes Dazard; Bangan John; Peter A Zimmerman; Aaron Weinberg; Richard J Jurevic
Journal:  J AIDS Clin Res       Date:  2012

3.  Profile of HIV type 1 coreceptor tropism among Kenyan patients from 2009 to 2010.

Authors:  Anthony Kebira Nyamache; Anne W T Muigai; Zipporah Ng'ang'a; Samoel A Khamadi
Journal:  AIDS Res Hum Retroviruses       Date:  2013-05-21       Impact factor: 2.205

4.  Sensitive cell-based assay for determination of human immunodeficiency virus type 1 coreceptor tropism.

Authors:  Jan Weber; Ana C Vazquez; Dane Winner; Richard M Gibson; Ariel M Rhea; Justine D Rose; Doug Wylie; Kenneth Henry; Alison Wright; Kevin King; John Archer; Eva Poveda; Vicente Soriano; David L Robertson; Paul D Olivo; Eric J Arts; Miguel E Quiñones-Mateu
Journal:  J Clin Microbiol       Date:  2013-03-13       Impact factor: 5.948

5.  Use of four next-generation sequencing platforms to determine HIV-1 coreceptor tropism.

Authors:  John Archer; Jan Weber; Kenneth Henry; Dane Winner; Richard Gibson; Lawrence Lee; Ellen Paxinos; Eric J Arts; David L Robertson; Larry Mimms; Miguel E Quiñones-Mateu
Journal:  PLoS One       Date:  2012-11-14       Impact factor: 3.240

6.  Prediction of coreceptor usage by five bioinformatics tools in a large Ethiopian HIV-1 subtype C cohort.

Authors:  Amare Worku Kalu; Nigus Fikrie Telele; Solomon Gebreselasie; Daniel Fekade; Samir Abdurahman; Gaetano Marrone; Anders Sönnerborg
Journal:  PLoS One       Date:  2017-08-25       Impact factor: 3.240

7.  Sensitive deep-sequencing-based HIV-1 genotyping assay to simultaneously determine susceptibility to protease, reverse transcriptase, integrase, and maturation inhibitors, as well as HIV-1 coreceptor tropism.

Authors:  Richard M Gibson; Ashley M Meyer; Dane Winner; John Archer; Felix Feyertag; Ezequiel Ruiz-Mateos; Manuel Leal; David L Robertson; Christine L Schmotzer; Miguel E Quiñones-Mateu
Journal:  Antimicrob Agents Chemother       Date:  2014-01-27       Impact factor: 5.191

8.  Determination of viral tropism by genotyping and phenotyping assays in Brazilian HIV-1-infected patients.

Authors:  Liã Bárbara Arruda; Marilia Ladeira de Araújo; Maira Luccia Martinez; Claudio Roberto Gonsalez; Alberto José da Silva Duarte; Eoin Coakley; Yolanda Lie; Jorge Casseb
Journal:  Rev Inst Med Trop Sao Paulo       Date:  2014 Jul-Aug       Impact factor: 1.846

  8 in total

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