EVOLUTIONARY DYNAMICS OF GENE-FOR-GENE SYSTEMS

这一建议将疾病基因的分子生物学与 决定这些基因和基因频率的种群水平过程 它们的进化史。寄主是植物拟南芥 病原菌为黄绿假单胞菌。该提案将重点放在宿主位置上 影响抵抗力或敏感度，以及影响 毒力(感染成功)或无毒(感染失败)。 第一个目标是测量感染的表型模式。 不同寄主植物和病原菌分离物的成功或抗性。在每一个中 在10个人群中，调查人员将分析感染的结果 50个病原菌分别对10个寄主基因型进行了测试。 接下来，将克隆三个影响毒力的病原菌基因座。三个基因座 将根据他们在测试时的不同感染成功进行选择 已经在序列水平上很好地描述了宿主基因类型，并且 为以后的分析提供有用的分子工具。 一旦选择了三个病原体基因座，调查人员将搜索 对于寄主植物中与病原菌相互作用的三个匹配的基因座 毒力基因座。植物与病原菌之间匹配的基因对基因的相互作用 经常被观察到，而且很可能在这种情况下被发现。 第一个目标的最后一步是测量自然感染的流行病学方面。 人口。特别是，调查人员将研究各种自然 人口感染率、宿主人口密度和迁移率， 以及三个寄主和病原体基因座的DNA多态模式 都被克隆了。 第二个目标是开发数学模型。这些模型将用于 提出关于种群生物学如何影响 寄主和病原菌等位基因频率与分子进化模式 克隆的基因座上。这些模型强调了艾滋病的流行病学过程 感染频率和疾病传播影响基因频率的各个方面 和分子进化。 第三个目标将使用生成的数据来估计 数学模型。估计包括流行性感冒的流行传播率 疾病，具有或不具有特定特性的植物之间的适应性差异 抗性等位基因(抗性代价)、适合度之间的差异 具有或缺乏允许它们攻击特定植物的等位基因的病原体 基因类型(毒力成本)、种群间迁移率以及 环境(如湿度)和寄主密度对病原率的影响 变速箱。 第四个目的是利用参数估计来扩展数学模型 并研究流行病学和进化论的各个方面。例如，如果 天气对传播有重大影响，那么这个因素将是 纳入了该模型的流行病学组成部分。该模型将 接受测试的意义是，调查人员将寻找一致的 解释了观察到的多态模式，分子进化， 和流行病学相吻合。例如，流行病学和成本 抗性和毒力的变化可以估计等位基因频率的趋势 随着时间的推移波动。等位基因频率波动的趋势是，在 反过来，对预期的分子进化模式的后果。因此， 调查人员可以使用他们数据的特定方面来估计流程 如等位基因频率有波动的趋势。然后他们就可以使用 他们对等位基因频率波动的估计，以做出可检验的预测 关于可观察的模式，例如核苷酸的分布 多态现象。

This proposal links the molecular biology of disease genes to the population-level processes that determine the frequencies of these genes and their evolutionary history. The host organism is the plant Arabidopsis thaliana and the pathogen is Pseudomonas viridiflava. The proposal focuses on host loci that affect resistance or susceptibility and pathogen loci that affect virulence (successful infection) or avirulence (unsuccessful infection). The first aim begins with measuring the phenotypic patterns of infection success or resistance for different host plants and pathogen isolates. In each of 10 populations, the investigators will analyze the outcome of infection for 50 pathogen isolates tested against each of 10 host genotypes. Next, three pathogen loci that affect virulence will be cloned. The three loci will be chosen based on their different infection successes when tested against host genotypes that have been well characterized at the sequence level and that provide helpful molecular tools for later analysis. Once the three pathogen loci have been chosen, the investigators will search for three matching loci in the host plant that interact with the pathogen virulence loci. Matching gene-for-gene interactions between plant and pathogen have been frequently observed and are likely to be found in this case. The final step for the first aim measures epidemiological aspects of natural populations. In particular, the investigators will study various natural populations for infection rates, host population densities and migration rates, and patterns of DNA polymorphism in the three host and pathogen loci that have been cloned. The second aim develops mathematical models. These models will be used to formulate hypotheses about how the population biology influences the frequencies of host and pathogen alleles and the pattern of molecular evolution at the cloned loci. The models emphasize how epidemiological processes of infection frequency and spread of disease influence aspects of gene frequency and molecular evolution. The third aim will use the data generated to estimate parameters of the mathematical models. Estimates include rates of epidemiological spread of disease, the fitness differences between plants that have or lack particular resistance alleles (cost of resistance), the fitness differences between pathogens that have or lack alleles that allow them to attack particular plant genotypes (costs of virulence), migration rates between populations, and the effects of environment (e.g., humidity) and host density on rates of pathogen transmission. The fourth aim uses the parameter estimates to expand the mathematical models and to study various aspects of epidemiology and evolution. For example, if weather has a significant impact on transmission, then that factor will be incorporated into the epidemiological components of the model. The model will be tested in the sense that the investigators will search for consistent explanations for how observed patterns of polymorphism, molecular evolution, and epidemiology fit together. For example, the epidemiology along with costs of resistance and virulence allow estimates for tendency of allele frequencies to fluctuate over time. The tendency of allele frequencies to fluctuate has, in turn, consequences for the expected patterns of molecular evolution. Thus, the investigators can use a particular aspect of their data to estimate processes such as the tendency of allele frequencies to fluctuate. They can then use their estimate of allele frequency fluctuations to make testable predictions about observable patterns, such as the distribution of nucleotide polymorphisms.