病毒群体中的抗原合作：宿主内病毒变体功能的转换
Antigenic cooperation in Viral Populations: Transformation of Functions of Intra-Host Viral Variants
AI总结：
本文提出抗原合作机制作为病毒免疫逃逸的替代方案，通过交叉免疫反应网络使宿主内病毒变体特化为互补角色，并解析和数值研究了新变体出现及宿主间传播引发的病毒生态系统功能转换。
摘要：
本文通过抗原合作研究宿主内病毒适应，这是一种免疫逃逸机制，可作为持续基因组多样化标准逃逸机制的替代方案，并有助于解释与高突变病毒建立慢性感染相关的一些实验观察结果。在该机制下，交叉免疫反应网络的拓扑结构迫使宿主内病毒变体特化以承担互补角色，并作为一个准社会生态系统适应宿主的免疫反应。在此，我们研究由进化和流行病学事件引起的免疫适应动态变化。首先，我们证明具有改变抗原特征的病毒变体的出现可能导致病毒生态系统的快速重组以及现有病毒变体所扮演角色发生变化。特别是，它可能推动处于基因组多样化免疫逃逸下的群体转向抗原合作的稳定适应状态。接着，我们研究两名慢性感染宿主间病毒传播的影响，这导致两个处于稳定免疫适应平衡状态的宿主内病毒群体发生合并。在这种情况下，我们还描述了新形成的病毒群体如何通过改变其成员功能来适应宿主环境。这些结果在最小交叉免疫反应网络下通过解析方法获得，并在更大群体中通过数值方法获得。
英文摘要：
In this paper we study intra-host viral adaptation by antigenic cooperation - a mechanism of immune escape that serves as an alternative to the standard mechanism of escape by continuous genomic diversification and allows to explain a number of experimental observations associated with the establishment of chronic infections by highly mutable viruses. Within this mechanism, the topology of a cross-immunoreactivity network forces intra-host viral variants to specialize for complementary roles and adapt to host's immune response as a quasi-social ecosystem. Here we study dynamical changes in immune adaptation caused by evolutionary and epidemiological events. First, we show that the emergence of a viral variant with altered antigenic features may result in a rapid re-arrangement of the viral ecosystem and a change in the roles played by existing viral variants. In particular, it may push the population under immune escape by genomic diversification towards the stable state of adaptation by antigenic cooperation. Next, we study the effect of a viral transmission between two chronically infected hosts, which results in merging of two intra-host viral populations in the state of stable immune-adapted equilibrium. In this case, we also describe how the newly formed viral population adapts to the host's environment by changing the functions of its members. The results are obtained analytically for minimal cross-immunoreactivity networks and numerically for larger populations.
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