Nie Bin, Zhang Guangjiong, Guo Yongcan, Li Qingfeng, Liu Jinbo, Tu Zhiguang
The Key Laboratory of Laboratory Medical Diagnostics, Ministry of Education, Chongqing Medical University, Chongqing 400016, P.R. China.
Int J Mol Med. 2015 Oct;36(4):1028-34. doi: 10.3892/ijmm.2015.2321. Epub 2015 Aug 24.
The classification of hepatitis C virus (HCV) genotypes is of clinical importance as it may help to predict drug therapy responses and estimate treatment duration. The classical method of HCV subgenotype classification is whole genome sequencing (WGS). However, the high cost and time-consuming nature of WGS limits its usage in clinical practice. A number of studies have been conducted to confirm whether specific regions of HCV could replace WGS in the classification of HCV subgenotypes. In the present study, we used the HCV database to select HCV sequences from different countries. The neighbor-joining method was used to construct phylogenetic trees based on different regions of HCV (core, E1, E2 and NS5B), to confirm which region could replace WGS in subgenotype classification. Our results indicated that the core, E1 and E2 regions could not be used to classify the HCV subgenotype correctly (core failed to recognize subgenotypes c and a, E1 failed to discriminate between subgenotypes a and b, and E2 failed to identify subgenotypes a and c). The NS5B region provided the correct subgenotype classification. The HCV samples (n=153) collected from patients in Sichuan province, (Southwest China) were sequenced and classified based on the NS5B region. The results indicated that the major subgenotype of HCV in patients from Sichuan was 1b (51.6%, n=79); other subgenotypes included 3b (30.1%, n=46), 3a (7.8%, n=12), 6a (8.5%, n=13), 2a (n=2) and 6n (n=1). The data from our analysis may prove to be helpful in future epidemiological investigations of HCV, and may aid in the prevention and clinical treatment of HCV.
丙型肝炎病毒(HCV)基因型的分类具有临床重要性,因为它有助于预测药物治疗反应并估计治疗持续时间。HCV 亚型分类的经典方法是全基因组测序(WGS)。然而,WGS 的高成本和耗时特性限制了其在临床实践中的应用。已经进行了多项研究以确认 HCV 的特定区域是否可以在 HCV 亚型分类中取代 WGS。在本研究中,我们使用 HCV 数据库从不同国家选择 HCV 序列。采用邻接法基于 HCV 的不同区域(核心区、E1 区、E2 区和 NS5B 区)构建系统发育树,以确认哪个区域可以在亚型分类中取代 WGS。我们的结果表明,核心区、E1 区和 E2 区不能正确用于 HCV 亚型分类(核心区无法识别 c 和 a 亚型,E1 区无法区分 a 和 b 亚型,E2 区无法识别 a 和 c 亚型)。NS5B 区提供了正确的亚型分类。对从中国西南部四川省患者中收集的 153 份 HCV 样本基于 NS5B 区进行测序和分类。结果表明,四川省患者中 HCV 的主要亚型是 1b(51.6%,n = 79);其他亚型包括 3b(30.1%,n = 46)、3a(7.8%,n = 12)、6a(8.5%,n = 13)、2a(n = 2)和 6n(n = 1)。我们的分析数据可能有助于未来 HCV 的流行病学调查,并有助于 HCV 的预防和临床治疗。