1. Discrepancies of HIV-1 reverse transcriptase resistance interpretation of insertions and deletions between two genotypic algorithms.
- Author
-
López-Lopes GI, Lança AM, de Paula Ferreira JL, Souza LO, and de Macedo Brígido LF
- Subjects
- Adolescent, Adult, Algorithms, Child, Female, Genotype, HIV-1 isolation & purification, Humans, INDEL Mutation, Male, Microbial Sensitivity Tests methods, Middle Aged, RNA, Viral genetics, Young Adult, Computational Biology methods, Drug Resistance, Viral, HIV Infections virology, HIV Reverse Transcriptase genetics, HIV-1 drug effects, HIV-1 genetics, Reverse Transcriptase Inhibitors pharmacology
- Abstract
Background: Bioinformatics algorithms have been developed for the interpretation of resistance from sequence submission, which supports clinical decision making. This study evaluated divergences of the interpretation of the genotyping in two commonly used algorithms, using sequences with indels of reverse transcriptase genes., Methods: Sequences were obtained from virus RNA of patients failing highly active antiretroviral therapy from 2004 to 2011. Alignments were obtained using Clustal W including subtype B consensus and HXB2. Sequences with evidence of indels were submitted to the Stanford Resistance Database and to the Geno2Pheno to locate indel positioning and determine the resistance profile., Results: A total of 1,959 partial reverse transcriptase sequences were assessed, mostly subtype B (74%). Insertions and deletions were observed in 0.9 and 0.6% of sequences, respectively. Discordant insert positioning was assigned for most (90%) insertion sequences, with 27% discordances for deletions. Susceptibility differed for some antiretroviral drugs, predominantly for TDF, d4T and ETV, when sequences with deletions were evaluated., Conclusion: Both indel positioning and its impact on drug susceptibility varies depending on the algorithm, a fact that might influence the clinical decision. Critical analysis of indel sequences with manual alignments is important, and its use alongside different algorithms may be important to better understand the outcomes of genotypic resistance prediction., (Copyright © 2013 S. Karger AG, Basel.)
- Published
- 2013
- Full Text
- View/download PDF