Laboratório de Retrovírus, Centro de Virologia, Instituto Adolfo Lutz, São Paulo, Brazil.
Intervirology. 2013;56(4):217-23. doi: 10.1159/000348511. Epub 2013 May 9.
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.
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.
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.
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.
已经开发出用于从序列提交中解释耐药性的生物信息学算法,这支持临床决策。本研究评估了两种常用算法在解释基因分型方面的差异,使用包含逆转录酶基因插入/缺失的序列。
从 2004 年至 2011 年接受高效抗逆转录病毒治疗失败的患者的病毒 RNA 中获得序列。使用包括亚型 B 共识和 HXB2 的 Clustal W 进行比对。将具有插入/缺失证据的序列提交给斯坦福耐药数据库和 Geno2Pheno,以定位插入/缺失位置并确定耐药谱。
共评估了 1959 个部分逆转录酶序列,主要是亚型 B(74%)。分别在 0.9%和 0.6%的序列中观察到插入和缺失。对于大多数(90%)插入序列,插入位置存在不一致,缺失的不一致率为 27%。当评估缺失序列时,一些抗逆转录病毒药物的耐药性存在差异,主要是 TDF、d4T 和 ETV。
插入/缺失的定位及其对药物敏感性的影响因算法而异,这可能会影响临床决策。对具有手动比对的插入/缺失序列进行批判性分析很重要,并且与不同算法一起使用可能有助于更好地理解基因型耐药性预测的结果。