Molecular Epidemiology & Bioinformatics Laboratory, Laboratory Branch, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA.
J Virol. 2011 Apr;85(7):3649-63. doi: 10.1128/JVI.02197-10. Epub 2011 Jan 19.
Genotype-specific sensitivity of the hepatitis C virus (HCV) to interferon-ribavirin (IFN-RBV) combination therapy and reduced HCV response to IFN-RBV as infection progresses from acute to chronic infection suggest that HCV genetic factors and intrahost HCV evolution play important roles in therapy outcomes. HCV polyprotein sequences (n = 40) from 10 patients with unsustainable response (UR) (breakthrough and relapse) and 10 patients with no response (NR) following therapy were identified through the Virahep-C study. Bayesian networks (BNs) were constructed to relate interrelationships among HCV polymorphic sites to UR/NR outcomes. All models showed an extensive interdependence of HCV sites and strong connections (P ≤ 0.003) to therapy response. Although all HCV proteins contributed to the networks, the topological properties of sites differed among proteins. E2 and NS5A together contributed ∼40% of all sites and ∼62% of all links to the polyprotein BN. The NS5A BN and E2 BN predicted UR/NR outcomes with 85% and 97.5% accuracy, respectively, in 10-fold cross-validation experiments. The NS5A model constructed using physicochemical properties of only five sites was shown to predict the UR/NR outcomes with 83.3% accuracy for 6 UR and 12 NR cases of the HALT-C study. Thus, HCV adaptation to IFN-RBV is a complex trait encoded in the interrelationships among many sites along the entire HCV polyprotein. E2 and NS5A generate broad epistatic connectivity across the HCV polyprotein and essentially shape intrahost HCV evolution toward the IFN-RBV resistance. Both proteins can be used to accurately predict the outcomes of IFN-RBV therapy.
基因型特异性对丙型肝炎病毒 (HCV) 对干扰素 - 利巴韦林 (IFN-RBV) 联合治疗的敏感性以及随着感染从急性转为慢性,HCV 对 IFN-RBV 的反应降低,表明 HCV 遗传因素和宿主内 HCV 进化在治疗结果中发挥重要作用。通过 Virahep-C 研究,从 10 名持续反应不良(UR)(突破和复发)和 10 名治疗后无反应(NR)的患者中鉴定出 HCV 多蛋白序列(n = 40)。构建贝叶斯网络(BNs)以将 HCV 多态性位点之间的相互关系与 UR/NR 结果相关联。所有模型均显示 HCV 位点之间存在广泛的相互依赖性和强连接(P ≤ 0.003)与治疗反应。尽管所有 HCV 蛋白都有助于网络,但蛋白之间的拓扑性质有所不同。E2 和 NS5A 共同为多蛋白 BN 贡献了所有位点的约 40%和约 62%的所有连接。在 10 倍交叉验证实验中,NS5A BN 和 E2 BN 分别以 85%和 97.5%的准确度预测 UR/NR 结果。使用仅五个位点的物理化学性质构建的 NS5A 模型,用于预测 HALT-C 研究中的 6 个 UR 和 12 个 NR 病例的 UR/NR 结果,准确率为 83.3%。因此,HCV 对 IFN-RBV 的适应性是一个复杂的特征,由整个 HCV 多蛋白中许多位点之间的相互关系编码。E2 和 NS5A 在整个 HCV 多蛋白中产生广泛的上位连接,并基本上使宿主内 HCV 进化朝着 IFN-RBV 耐药性发展。这两种蛋白都可用于准确预测 IFN-RBV 治疗的结果。