Effects of genetic background on adaptive evolution

项目摘要 适应性进化在多大程度上是可预测的？尽管它在理解人类疾病的生物学方面很重要， 包括病毒和细菌病原体的进化，对遗传适应的动态仍然知之甚少。在 特别是，很少有例子已经被解剖到分子水平。自然适应的例子 系统和模式生物的使用是强大的工具，可以结合起来，使这一困难的进展。 问题我们采用了“平行进化”的例子，涉及物种的集合，经历了一个共同的进化过程。 自然选择制度，以评估适应过程的多种结果。有了这些信息，我们可以 学习适应的经常性特征，并推断适应过程中的约束和不确定性。我们的工作 专注于Na+，K +-ATP酶及其调节类固醇糖苷之间的生物医学重要相互作用， 大量的植物和动物用它作为毒素来保护自己不被吃掉。利用一组不同的动物 已经独立进化出对类固醇糖苷毒性的抗性，包括昆虫和脊椎动物，我们 我发现这些不同的物种最经常通过少数可能的选择（即只涉及 蛋白质中41个可能位点中的3个可被修饰以赋予抗性）。这些发现表明， 进化往往是路径依赖的，这意味着适应的各个组成部分必须按照规定的方式进化， 最终是可预测的秩序他们还提出了许多关于这种路径依赖性质的问题，包括 在何种程度上它出现从蛋白质内的残基之间的相互作用，或从基因组的背景， 物种;它是否同样限制了适应在短期和长期的时间尺度，以及如何普遍适用于 适应性蛋白质进化在这里，我们提出了三个目标，解决这些问题，通过结合的方法， 进化基因组学和分子遗传学。在目标1中，我们将在果蝇中使用基因组工程来阐明 类固醇-糖苷抗性适应在其主要靶点（Na+，K +-ATP酶）水平上的路径依赖性 在整个基因组的水平上。在目标2中，我们将确定哪些基因组因素以及有多少基因组因素有助于 果蝇种群中自然发生的变异。这些信息将揭示内部的关系- 种群和种间遗传变异是同一性状的基础，连接了种群的短期和长期动态。 适应过程。在目标3中，我们将使用从Na+，K +-ATP酶的分子适应中学到的原理， 计算预测和使用基因组工程功能验证路径依赖的适应动力学， 果蝇具有多种蛋白质，其中许多蛋白质（如Na+，K +-ATP酶）在神经系统中具有重要作用。 发育和体内平衡。总之，这项工作将大大增加我们对适应性约束的理解。 蛋白质进化和新表型出现的遗传变化的可预测性。

Project Summary To what extent is adaptive evolution predictable? Despite its importance in understanding the biology of human diseases, including the evolution of viral and bacterial pathogens, the dynamics of genetic adaptation are still poorly understood. In particular, few examples yet exist that have been dissected to the molecular level. Examples of adaptations from natural systems and the use of model organisms are powerful tools that can be combined to make progress on this difficult question. We have employed instances of “parallel evolution”, involving assemblages of species experiencing a common regime of natural selection, to evaluate multiple outcomes of the process of adaptation. With this information, we can learn about recurrent features of adaptation and infer constraints and regularities in the adaptive process. Our work focused on the biomedically-important interaction between Na+,K+-ATPases and their regulatory steroidal-glycosides that a large variety of plants and animals use as toxins to defend themselves from being eaten. Using a diverse set of animals that have independently evolved resistance to steroidal-glycoside toxicity, including insects and vertebrates, we discovered these diverse species most often evolve resistance via a small number of possible options (i.e. involving only three of 41 possible sites in the protein that could be modified to confer resistance). These findings suggest that adaptive evolution is often path-dependent, implying that the individual components of an adaptation must evolve in a prescribed, and ultimately predictable, order. They also raise numerous questions about the nature of this path dependency, including the extent to which it emerges from interactions among residues within a protein, or from the genomic background of the species; whether it similarly constrains adaptation over short and longer time scales, and how generally it applies in adaptive protein evolution. Here we propose three aims that address these questions, by combining approaches from evolutionary genomics and molecular genetics. In Aim 1, we will use genome engineering in Drosophila to elucidate the path dependence of the steroidal-glycoside resistance adaptation both at the level of its primary target (Na+,K+-ATPase) and at the level of the whole genome. In Aim 2, we will determine which and how many genomic factors contribute to naturally occurring variation within Drosophila populations. This information will reveal the relationship between within- population and between-species genetic variation underlying the same trait, connecting short- and long-term dynamics of the adaptive process. In Aim 3, we will use principles learned from molecular adaptation at Na+,K+-ATPase to computationally predict and use genome engineering to functionally validate path dependent adaptation dynamics in Drosophila for a diverse group of proteins, many of which (like Na+,K+-ATPase) have important roles in neurological development and homeostasis. Together this work will greatly increase our understanding of the constraints on adaptive protein evolution and the predictability of the genetic changes by which novel phenotypes emerge.