OBJECTIVES: Evaluation of the reliability of several V3-based genotypic predictors to infer viral tropism in patients infected with B and non-B strains of HIV-1. METHODS: Several genotypic tropism predictors were evaluated in plasma (RNA) samples from 198 HIV-1-infected patients, taking as gold standard the results of the phenotypic recombinant virus assay Phenoscript((R)). In addition, for 37 B subtype HIV-1 patients the phenotypic results from plasma samples were also compared with tropism predictions based on V3 amplification from paired peripheral blood mononuclear cells (PBMCs). RESULTS: A total of 150 paired genotypic/phenotypic results were obtained from plasma specimens. Concordance values ranged from 63% to 85%, depending on the genotypic algorithm used. The best predictors in terms of sensitivity/specificity to detect X4 variants were WebPSSM(X4/R5) (77%/87%), Geno2pheno(FPR) (=) (5%) (80%/77%) and an algorithm combining the '11/25' and 'Net charge' rules, termed Garrido's rule (80%/79%). The performance of genotypic predictors was better testing B than non-B clades. The overall sensitivity ranged from 28% to 94%, reaching 100% in subtype B antiretroviral-experienced patients using WebPSSM(SI/NSI), Geno2pheno(FPR) (> or =) (5%) and Garrido's rule. Conversely, the sensitivity when testing non-B subtypes was poorer, ranging from 17% to 67%. Interestingly, the correlation between genotypic and phenotypic results was better when testing PBMCs than plasma using all genotypic predictors. CONCLUSIONS: Genotypic tools based on V3 sequences may provide reliable information on HIV-1 tropism when testing clade B viruses, especially in antiretroviral-experienced patients. The sensitivity to detect X4 variants using genotypic tools may improve by testing proviral DNA instead of plasma RNA.
OBJECTIVES: Evaluation of the reliability of several V3-based genotypic predictors to infer viral tropism in patients infected with B and non-B strains of HIV-1. METHODS: Several genotypic tropism predictors were evaluated in plasma (RNA) samples from 198 HIV-1-infectedpatients, taking as gold standard the results of the phenotypic recombinant virus assay Phenoscript((R)). In addition, for 37 B subtype HIV-1patients the phenotypic results from plasma samples were also compared with tropism predictions based on V3 amplification from paired peripheral blood mononuclear cells (PBMCs). RESULTS: A total of 150 paired genotypic/phenotypic results were obtained from plasma specimens. Concordance values ranged from 63% to 85%, depending on the genotypic algorithm used. The best predictors in terms of sensitivity/specificity to detect X4 variants were WebPSSM(X4/R5) (77%/87%), Geno2pheno(FPR) (=) (5%) (80%/77%) and an algorithm combining the '11/25' and 'Net charge' rules, termed Garrido's rule (80%/79%). The performance of genotypic predictors was better testing B than non-B clades. The overall sensitivity ranged from 28% to 94%, reaching 100% in subtype B antiretroviral-experienced patients using WebPSSM(SI/NSI), Geno2pheno(FPR) (> or =) (5%) and Garrido's rule. Conversely, the sensitivity when testing non-B subtypes was poorer, ranging from 17% to 67%. Interestingly, the correlation between genotypic and phenotypic results was better when testing PBMCs than plasma using all genotypic predictors. CONCLUSIONS: Genotypic tools based on V3 sequences may provide reliable information on HIV-1 tropism when testing clade B viruses, especially in antiretroviral-experienced patients. The sensitivity to detect X4 variants using genotypic tools may improve by testing proviral DNA instead of plasma RNA.
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Authors: Gabriela Bastos Cabral; João Leandro de Paula Ferreira; Luana Portes Osório Coelho; Mylva Fonsi; Denise Lotufo Estevam; Jaqueline Souza Cavalcanti; Luis Fernando de Macedo Brígido Journal: AIDS Res Hum Retroviruses Date: 2011-12-02 Impact factor: 2.205
Authors: David J H F Knapp; Rachel A McGovern; Winnie Dong; Art F Y Poon; Luke C Swenson; Xiaoyin Zhong; Conan K Woods; P Richard Harrigan Journal: J Clin Microbiol Date: 2012-11-21 Impact factor: 5.948
Authors: Ron M Kagan; Erik P Johnson; Martin Siaw; Pinaki Biswas; Douglass S Chapman; Zhaohui Su; Jamie L Platt; Rick L Pesano Journal: PLoS One Date: 2012-09-27 Impact factor: 3.240