Shin Jeewoen, MacCarthy Thomas
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
BMC Evol Biol. 2016 Oct 26;16(1):233. doi: 10.1186/s12862-016-0804-z.
Host resistance and viral pathogenicity are determined by molecular interactions that are part of the evolutionary arms race between viruses and their hosts. Viruses are obligate intracellular parasites and entry to the host cell is the first step of any virus infection. Commonly, viruses enter host cells by binding cell surface receptors. We adopt a computational modeling approach to study the evolution of the first infection step, where we consider two possible levels of resistance mechanism: at the level of the binding interaction between the host receptor and a virus binding protein, and at the level of receptor protein expression where we use a standard gene regulatory network model. At the population level we adopted the Susceptible-Infected-Susceptible (SIS) model. We used our multi-scale model to understand what conditions might determine the balance between use of resistance mechanisms at the two different levels.
We explored a range of different conditions (model parameters) that affect host evolutionary dynamics and, in particular, the balance between the use of different resistance mechanisms. These conditions include the complexity of the receptor binding protein-protein interaction, selection pressure on the host population (pathogenicity), and the number of expressed cell-surface receptors. In particular, we found that as the receptor binding complexity (understood as the number of amino acids involved in the interaction between the virus entry protein and the host receptor) increases, viruses tend to become specialists and target one specific receptor. At the same time, on the host side, the potential for resistance shifts from the changes at the level of receptor binding (protein-protein) interaction towards changes at the level of gene regulation, suggesting a mechanism for increased biological complexity.
Host resistance and viral pathogenicity depend on quite different evolutionary conditions. Viruses may evolve cell entry strategies that use small receptor binding regions, represented by low complexity binding in our model. Our modeling results suggest that if the virus adopts a strategy based on binding to low complexity sites on the host receptor, the host will select a defense strategy at the protein (receptor) level, rather than at the level of the regulatory network - a virus-host strategy that appears to have been selected most often in nature.
宿主抗性和病毒致病性由分子相互作用决定,这些相互作用是病毒与其宿主之间进化军备竞赛的一部分。病毒是专性细胞内寄生虫,进入宿主细胞是任何病毒感染的第一步。通常,病毒通过结合细胞表面受体进入宿主细胞。我们采用计算建模方法来研究首次感染步骤的进化,在此过程中我们考虑两种可能的抗性机制水平:宿主受体与病毒结合蛋白之间的结合相互作用水平,以及受体蛋白表达水平,我们使用标准基因调控网络模型。在种群水平上,我们采用了易感-感染-易感(SIS)模型。我们使用多尺度模型来了解哪些条件可能决定两种不同水平抗性机制使用之间的平衡。
我们探索了一系列影响宿主进化动态的不同条件(模型参数),特别是不同抗性机制使用之间的平衡。这些条件包括受体结合蛋白-蛋白相互作用的复杂性、宿主种群上的选择压力(致病性)以及表达的细胞表面受体数量。特别是,我们发现随着受体结合复杂性(理解为病毒进入蛋白与宿主受体之间相互作用中涉及的氨基酸数量)增加,病毒倾向于成为 specialists 并靶向一种特定受体。同时,在宿主方面,抗性潜力从受体结合(蛋白-蛋白)相互作用水平的变化转向基因调控水平的变化,这表明了生物复杂性增加的一种机制。
宿主抗性和病毒致病性取决于截然不同的进化条件。病毒可能进化出使用小受体结合区域的细胞进入策略,在我们的模型中以低复杂性结合为代表。我们的建模结果表明,如果病毒采用基于结合宿主受体上低复杂性位点的策略,宿主将在蛋白(受体)水平而非调控网络水平选择防御策略——这种病毒-宿主策略似乎在自然界中最常被选择。