EVOLUTIONARY DYNAMICS OF GENE-FOR-GENE SYSTEMS

这一提议将疾病基因的分子生物学与 决定这些基因频率的群体水平过程， 它们的进化史宿主生物体是植物拟南芥 病原菌为绿黄假单胞菌。该提案侧重于宿主基因座 影响抗性或易感性的病原体基因座 毒力（成功感染）或无毒力（不成功感染）。 第一个目标始于测量感染的表型模式 不同寄主植物和病原分离物的成功或抗性。在每个 在10个人群中，研究人员将分析感染的结果， 针对10种宿主基因型中的每一种测试50个病原体分离物。 接下来，将克隆影响毒力的三个病原体基因座。三位点 将根据其不同的感染成功率进行选择， 在序列水平上已被充分表征的宿主基因型， 为以后的分析提供有用的分子工具。 一旦选定了三个病原体位点，研究人员将搜索 与病原体相互作用的寄主植物中的三个匹配位点 毒力位点植物与病原菌的基因对基因互作匹配 经常被观察到，并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，研究人员将研究各种自然 感染率、宿主人口密度和迁移率， 和模式的DNA多态性在三个主机和病原体基因座， 被克隆了 第二个目标是建立数学模型。这些模型将用于 提出关于种群生物学如何影响 寄主和病原体等位基因频率和分子进化模式 在克隆的基因座上。这些模型强调， 感染频率和疾病传播影响基因频率方面 和分子进化。 第三个目标将使用生成的数据来估计 数学模型估计数包括流行病传播率 疾病，植物之间的健康差异，有或缺乏特定的 抗性等位基因（抗性成本）， 具有或缺乏使它们能够攻击特定植物的等位基因的病原体 基因型（毒力成本），种群间迁移率， 环境的影响（例如，湿度）和寄主密度对病原菌侵染率的影响 传输 第四个目标是利用参数估计对数学模型进行扩展 并研究流行病学和进化的各个方面。例如如果 天气对传播有重大影响，那么这个因素将是 纳入模型的流行病学组成部分。该模型将 在某种意义上说，调查人员将寻找一致的 解释了观察到的多态性，分子进化， 和流行病学联系在一起例如，流行病学沿着成本 抗性和毒力的关系可以估计等位基因频率的趋势 随着时间的推移而波动。等位基因频率波动的趋势， 反过来，预期的分子进化模式的后果。因此 研究人员可以使用他们的数据的一个特定方面来估计过程 例如等位基因频率波动的趋势。然后他们可以使用 他们对等位基因频率波动的估计， 关于可观察的模式，例如核苷酸的分布， 多态性

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.