Hedskog Charlotte, Chodavarapu Krishna, Ku Karin S, Xu Simin, Martin Ross, Miller Michael D, Mo Hongmei, Svarovskaia Evguenia
Gilead Sciences, Inc., Foster City, California, USA.
Gilead Sciences, Inc., Foster City, California, USA
J Clin Microbiol. 2015 Jul;53(7):2049-59. doi: 10.1128/JCM.02624-14. Epub 2015 Apr 15.
Hepatitis C virus (HCV) exhibits a high genetic diversity and is classified into 6 genotypes, which are further divided into 66 subtypes. Current sequencing strategies require prior knowledge of the HCV genotype and subtype for efficient amplification, making it difficult to sequence samples with a rare or unknown genotype and/or subtype. Here, we describe a subtype-independent full-genome sequencing assay based on a random amplification strategy coupled with next-generation sequencing. HCV genomes from 17 patient samples with both common subtypes (1a, 1b, 2a, 2b, and 3a) and rare subtypes (2c, 2j, 3i, 4a, 4d, 5a, 6a, 6e, and 6j) were successfully sequenced. On average, 3.7 million reads were generated per sample, with 15% showing HCV specificity. The assembled consensus sequences covered 99.3% to 100% of the HCV coding region, and the average coverage was 6,070 reads/position. The accuracy of the generated consensus sequence was estimated to be >99% based on results from in vitro HCV replicon amplification, with the same extrapolated amount of input RNA molecules as that for the patient samples. Taken together, the HCV genomes from 17 patient samples were successfully sequenced, including samples with subtypes that have limited sequence information. This method has the potential to sequence any HCV patient sample, independent of genotype or subtype. It may be especially useful in confounding cases, like those with rare subtypes, intergenotypic recombination, or multiple genotype infections, and may allow greater insight into HCV evolution, its genetic diversity, and drug resistance development.
丙型肝炎病毒(HCV)具有高度的遗传多样性,可分为6个基因型,进一步又分为66个亚型。目前的测序策略需要事先了解HCV的基因型和亚型才能进行有效扩增,这使得对具有罕见或未知基因型和/或亚型的样本进行测序变得困难。在此,我们描述了一种基于随机扩增策略结合下一代测序的不依赖亚型的全基因组测序方法。来自17例患者样本的HCV基因组成功测序,这些样本既有常见亚型(1a、1b、2a、2b和3a),也有罕见亚型(2c、2j、3i、4a、4d、5a、6a、6e和6j)。每个样本平均产生370万个读数,其中15%显示出HCV特异性。组装的共有序列覆盖了HCV编码区的99.3%至100%,平均覆盖度为6070个读数/位置。根据体外HCV复制子扩增的结果,估计所生成共有序列的准确性>99%,输入RNA分子的外推量与患者样本相同。总之,17例患者样本的HCV基因组成功测序,包括那些具有有限序列信息的亚型样本。该方法有可能对任何HCV患者样本进行测序,而不依赖于基因型或亚型。它在复杂病例中可能特别有用,如那些具有罕见亚型、基因型间重组或多重基因型感染的病例,并且可能有助于更深入地了解HCV的进化、其遗传多样性和耐药性的发展。