Qiu Ping, Stevens Richard, Wei Bo, Lahser Fred, Howe Anita Y M, Klappenbach Joel A, Marton Matthew J
Molecular Biomarker and Diagnostics, Merck Research Laboratories, Rahway, New Jersey, United States of America.
Target & Pathway Biology, Merck Research Laboratories, Boston, Massachusetts, United States of America.
PLoS One. 2015 Apr 1;10(4):e0122082. doi: 10.1371/journal.pone.0122082. eCollection 2015.
Genotyping of hepatitis C virus (HCV) plays an important role in the treatment of HCV. As new genotype-specific treatment options become available, it has become increasingly important to have accurate HCV genotype and subtype information to ensure that the most appropriate treatment regimen is selected. Most current genotyping methods are unable to detect mixed genotypes from two or more HCV infections. Next generation sequencing (NGS) allows for rapid and low cost mass sequencing of viral genomes and provides an opportunity to probe the viral population from a single host. In this paper, the possibility of using short NGS reads for direct HCV genotyping without genome assembly was evaluated. We surveyed the publicly-available genetic content of three HCV drug target regions (NS3, NS5A, NS5B) in terms of whether these genes contained genotype-specific regions that could predict genotype. Six genotypes and 38 subtypes were included in this study. An automated phylogenetic analysis based HCV genotyping method was implemented and used to assess different HCV target gene regions. Candidate regions of 250-bp each were found for all three genes that have enough genetic information to predict HCV genotypes/subtypes. Validation using public datasets shows 100% genotyping accuracy. To test whether these 250-bp regions were sufficient to identify mixed genotypes, we developed a random primer-based method to sequence HCV plasma samples containing mixtures of two HCV genotypes in different ratios. We were able to determine the genotypes without ambiguity and to quantify the ratio of the abundances of the mixed genotypes in the samples. These data provide a proof-of-concept that this random primed, NGS-based short-read genotyping approach does not need prior information about the viral population and is capable of detecting mixed viral infection.
丙型肝炎病毒（HCV）基因分型在HCV治疗中起着重要作用。随着新的基因型特异性治疗方案的出现，获取准确的HCV基因型和亚型信息以确保选择最合适的治疗方案变得越来越重要。目前大多数基因分型方法无法检测来自两种或更多HCV感染的混合基因型。新一代测序（NGS）允许对病毒基因组进行快速且低成本的大规模测序，并提供了从单个宿主中探测病毒群体的机会。在本文中，评估了使用短NGS读数进行直接HCV基因分型而无需基因组组装的可能性。我们调查了三个HCV药物靶标区域（NS3、NS5A、NS5B）的公开可用遗传内容，以确定这些基因是否包含可预测基因型的基因型特异性区域。本研究纳入了六种基因型和38个亚型。实施了一种基于系统发育分析的自动化HCV基因分型方法，并用于评估不同的HCV靶基因区域。在所有三个基因中都发现了每个250bp的候选区域，这些区域具有足够的遗传信息来预测HCV基因型/亚型。使用公共数据集进行的验证显示基因分型准确率为100%。为了测试这些250bp区域是否足以识别混合基因型，我们开发了一种基于随机引物的方法，对含有不同比例两种HCV基因型混合物的HCV血浆样本进行测序。我们能够明确确定基因型，并对样本中混合基因型的丰度比例进行定量。这些数据提供了一个概念验证，即这种基于随机引物、NGS的短读长基因分型方法不需要关于病毒群体的先验信息，并且能够检测混合病毒感染。
