Coevolution in action in host and parasite genomes

宿主和寄生虫基因组中的共同进化

• 批准号: 274542535
• 负责人: Professor Dr. Aurélien Tellier
• 金额: --
• 依托单位: Professur für Populationsgenetik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/274542535?language=en
• 关键词: Coevolution; action; host; parasite; genomes

When hosts and pathogens exert strong reciprocal selection pressure, the genes underpinning the interaction coevolve over short time scales, while populations undergo steep year-to-year size fluctuations. Surprisingly, however, no method was ever developed to track genes undergoing coevolutionary dynamics using serial sampling of local host and pathogen populations over time. We have developed a first Approximate Bayesian Computation (ABC) method which allows to reveal the genetic underpinnings of coevolutionary dynamics using jointly host and parasite whole-genome polymorphism data. The coevolutionary dynamics of important genes for the interaction can be distinguished from demographic dynamics of host and pathogen populations by assessing and modelling genetic diversity over time and over the whole genome. The method is thus applicable to serial samplings of one given coevolving host and pathogen population.The goal of this project is to extend this method to study more complex but also realistic set ups of coevolution: 1) host and parasite spatial structure with and without heterogeneity in selection, and 2) violation of the classic Wright-Fisher model assumption in parasite species. First, we study coevolution in space by using host and parasite genome polymorphism data from several populations. We will build a spatial homogeneous and heterogeneous host and parasite population model, in which migration and local changes in population size can be inferred using full genome data. The ABC method will then be used to search for genes under coevolution and infer the parameters of the geographic mosaic of coevolution. It is of special interest to assess if sampling of several populations in space is equivalent to time sampling used in our current method.Second, parasite due to their life cycles may violate the assumptions of our models in two ways: 1) parasites show large variation in their population size over time generating recurrent bottlenecks, and 2) parasite species can exhibit large variance in offspring distribution. Our inference method may be biased when dealing with parasites such as viruses or bacteria. We will therefore integrate these two key aspects of parasite life-cycles into our model and our ABC inference method, and study how they affect the coevolutionary dynamics and polymorphism at the coevolving loci and genome-wide. We therefore aim to provide a novel inference method applicable to most host-parasite systems for which full genome data are available for several populations and several time points.

当宿主和病原体施加强大的相互选择压力时，支撑相互作用的基因在短时间内共同进化，而种群则经历逐年的急剧波动。然而，令人惊讶的是，没有一种方法可以通过对当地宿主和病原体种群进行连续采样来跟踪经历共同进化动力学的基因。我们开发了第一种近似贝叶斯计算（ABC）方法，该方法可以利用宿主和寄生虫的全基因组多态性数据来揭示共同进化动力学的遗传基础。通过评估和模拟遗传多样性随时间和整个基因组的变化，可以将相互作用的重要基因的共同进化动态与宿主和病原体种群的人口统计学动态区分开来。因此，该方法适用于一个给定的共同进化宿主和病原体群体的连续采样。本项目的目标是将该方法扩展到更复杂但也更现实的共同进化设置：1)宿主和寄生虫的空间结构在选择中具有或不具有异质性；2)寄生虫物种中违反经典的Wright-Fisher模型假设。首先，我们利用来自多个种群的宿主和寄生虫基因组多态性数据来研究空间中的共同进化。我们将建立一个空间同质和异质宿主和寄生虫种群模型，在该模型中，可以使用全基因组数据推断迁移和种群规模的局部变化。ABC方法将用于搜索共同进化下的基因，并推断共同进化的地理马赛克参数。评估在空间中对几个种群进行抽样是否等同于我们目前方法中使用的时间抽样，这是特别有趣的。其次，由于寄生虫的生命周期，它们可能在两个方面违反我们的模型假设：1)寄生虫的种群规模随着时间的推移而发生很大的变化，从而产生周期性的瓶颈；2)寄生虫物种在后代分布上可能表现出很大的差异。我们的推理方法在处理诸如病毒或细菌之类的寄生虫时可能会有偏差。因此，我们将把寄生虫生命周期的这两个关键方面整合到我们的模型和ABC推断方法中，并研究它们如何影响共同进化位点和全基因组的共同进化动力学和多态性。因此，我们的目标是提供一种新的推理方法，适用于大多数宿主-寄生虫系统，其中多个种群和多个时间点的全基因组数据可用。