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？尽管它对理解人类疾病的生物学很重要， 包括病毒和细菌病原体的进化，遗传适应的动态仍然知之甚少。在……里面 具体地说，目前还没有多少例子被剖析到分子水平。改编自自然界的例子 系统和模型生物体的使用是强大的工具，可以结合起来在这一困难的问题上取得进展 问题。我们使用了“平行进化”的例子，涉及经历了共同的 自然选择制度，以评价适应进程的多重成果。有了这些信息，我们就可以 了解适应的反复出现的特征，并推断适应过程中的约束和规律。我们的工作 重点研究了Na+，K+-ATPase及其调节的类固醇糖苷之间的生物医学上的重要相互作用 种类繁多的动植物用毒素来保护自己不被吃掉。使用不同种类的动物 已经独立进化出对类固醇-糖苷毒性的抵抗力，包括昆虫和脊椎动物，我们 发现这些不同的物种最常通过少数可能的选择(即只涉及 蛋白质中41个可能的位点中有3个可以被修饰来赋予抗药性)。这些发现表明，适应性 进化通常是路径依赖的，这意味着适应的各个组成部分必须按照规定的、 最终可以预测的是秩序。他们还对这种路径依赖的性质提出了许多问题，包括 从蛋白质内部残基之间的相互作用中或从蛋白质的基因组背景中出现的程度 物种；它是否同样在较短和较长的时间尺度上限制适应，以及它在 适应性蛋白质进化。在这里，我们提出了三个目标，通过结合以下方法来解决这些问题 进化基因组学和分子遗传学。在目标1中，我们将利用果蝇的基因组工程来阐明 在主要靶点(Na~+，K~+-ATPase)水平上的类固醇-糖苷抗性适应的路径依赖性 在整个基因组的水平上。在目标2中，我们将确定哪些和多少基因组因素对 果蝇种群中自然发生的变异。这一信息将揭示内部-- 同一性状下的群体和种间遗传变异，连接了 适应过程。在目标3中，我们将使用从Na+，K+-ATPase的分子适应中学到的原理来 计算预测和使用基因组工程在功能上验证路径依赖的适应动力学 果蝇有一组不同的蛋白质，其中许多(如Na+，K+-ATPase)在神经学中具有重要作用 发展和动态平衡。综上所述，这项工作将极大地增加我们对自适应限制的理解 蛋白质进化和新表型产生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.