Transmission and coevolutionary dynamics drive the evolution of generalist and specialist viruses

病毒在它们攻击的宿主中表现出巨大的多样性-通才和专家在自然界都很常见。与专家不同，多面手病毒能够感染一系列宿主物种，而专家感染单一宿主类型。如何解释宿主与病毒之间相互作用类型的差异？病毒和宿主是如何相互作用的？它们的互动方式会随着时间的推移而演变吗？这些问题的答案是理解疾病动力学的核心。控制病毒-宿主相互作用的基本过程是高度复杂的--从分子和细胞到种群和多营养水平--并且关键地取决于生态学和共同进化之间的相互作用，共同进化是宿主和病毒相互响应的同时进化过程。然而，这种复杂性给实现疾病动力学的机械理解带来了众多挑战。拟议的研究将调查专业和通用病毒的进化--从基因到细胞到种群再到社区。它将考虑宿主和病毒如何同时进化并相互响应，特别注意病毒是如何传播的。这里提出的新方法将实验进化和数学模型相结合，使用细菌，大肠杆菌和攻击细菌的病毒，噬菌体，作为一个模型系统，可以帮助我们了解自然界中的病毒进化。实验将实时记录共同进化，并直接测试理论框架提出的理论预测。与此同时，从实验中产生的数据将被纳入新一代的数学模型，这些模型将提供一系列关于专家和通用病毒进化的具体和一般预测。这项工作的结果将进一步促进我们对疾病动力学的全面理解。

Viruses show tremendous diversity in the hosts that they attack - both generalists and specialists are common in nature. Unlike specialists, generalist viruses are able to infect a range host species, whereas specialists infect a single host type. What explains this variation in the types of interactions between hosts and their viruses? How do viruses and their hosts interact? Can the way they interact evolve over time? Answers to these questions are central to understanding the dynamics of disease. The underlying processes governing viral-host interactions are highly complex - ranging from molecular and cellular through to population and multi-trophic levels - and crucially depend on the interplay between ecology and coevolution, a process of simultaneous evolution of hosts and viruses in response to each other. However, this complexity presents numerous challenges to achieving a mechanistic understanding of disease dynamics.The proposed research will investigate the evolution of specialist and generalist viruses - from genes to cells to populations to the community. It will consider how hosts and viruses evolve simultaneously and in response to each other, paying particular attention to how viruses are transmitted. The novel approach proposed here will intertwine experimental evolution and mathematical models, using the bacteria, Escherichia coli and viruses that attack bacteria, bacteriophages, as a model system that can help us understand virus evolution in nature. Experiments will document coevolution in real-time and directly test theoretical predictions put forth by the theoretical framework. At the same time, data generated from the experiments will be incorporated into a new generation of mathematical models that will provide a range of both specific and general predictions regarding the evolution of specialist and generalist viruses. The results of this work will further our general understanding of the dynamics of disease.