Hart Gregory R, Ferguson Andrew L
Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, United States of America. Present address: Department of Therapeutic Radiology, Yale University, 202 LLCI, 15 York Street, New Haven, CT 96510, United States of America.
Phys Biol. 2018 Nov 28;16(1):016004. doi: 10.1088/1478-3975/aaeec0.
Hepatitis C virus (HCV) afflicts 170 million people and kills 700 000 annually. Vaccination offers the most realistic and cost effective hope of controlling this epidemic, but despite 25 years of research, no vaccine is available. A major obstacle is HCV's extreme genetic variability and rapid mutational escape from immune pressure. Coupling maximum entropy inference with population dynamics simulations, we have employed a computational approach to translate HCV sequence databases into empirical landscapes of viral fitness and simulate the intrahost evolution of the viral quasispecies over these landscapes. We explicitly model the coupled host-pathogen dynamics by combining agent-based models of viral mutation with stochastically-integrated coupled ordinary differential equations for the host immune response. We validate our model in predicting the mutational evolution of the HCV RNA-dependent RNA polymerase (protein NS5B) within seven individuals for whom longitudinal sequencing data is available. We then use our approach to perform exhaustive in silico evaluation of putative immunogen candidates to rationally design tailored vaccines to simultaneously cripple viral fitness and block mutational escape within two selected individuals. By systematically identifying a small number of promising vaccine candidates, our empirical fitness landscapes and host-pathogen dynamics simulator can guide and accelerate experimental vaccine design efforts.
丙型肝炎病毒（HCV）感染着1.7亿人，每年导致70万人死亡。疫苗接种是控制这一流行病最现实且最具成本效益的希望，但尽管经过了25年的研究，仍没有可用的疫苗。一个主要障碍是HCV的极端基因变异性以及其能迅速从免疫压力下发生突变逃逸。我们将最大熵推断与种群动态模拟相结合，采用了一种计算方法，将HCV序列数据库转化为病毒适应性的经验景观，并在这些景观上模拟病毒准种的宿主内进化。我们通过将基于主体的病毒突变模型与宿主免疫反应的随机积分耦合常微分方程相结合，明确地对宿主 - 病原体的耦合动态进行建模。我们利用有纵向测序数据的7名个体的HCV RNA依赖性RNA聚合酶（蛋白NS5B）的突变进化预测来验证我们的模型。然后，我们使用我们的方法对假定的免疫原候选物进行详尽的计算机模拟评估，以合理设计定制疫苗，同时削弱两个选定个体内的病毒适应性并阻止突变逃逸。通过系统地识别少数有前景的疫苗候选物，我们的经验适应性景观和宿主 - 病原体动态模拟器可以指导并加速实验性疫苗设计工作。
