Quantifying evolutionary solutions to fitness tradeoffs in fluctuating environments

项目摘要 实验进化的一个教训是，有益的，大效应的突变在简单的基因组中迅速出现。 实验室环境。鉴于这种突变是可能的，因此还没有固定在 野生种群，很可能是它们帮助生物体专门化到一个特定的环境中。 并且可能显示出使它们在其它环境中有害的多效性折衷。证据 多效性是常见的，与恒定的实验室环境不同，自然环境可能需要 生物体解决进化权衡，以持续通过间歇性的恶劣条件。 尽管环境波动对生物学和医学的影响至关重要， 进化，该领域已被限制的能力，以跟踪大量的进化路径，在多个 环境条件在这里，我们建议通过使用高通量系统来解决这个问题 DNA条形码酵母。通过在环境中进化酿酒酵母种群， 已知的生物权衡其适应性优化，并通过跟踪的出现和传播， 成千上万的适应性突变在波动和恒定的环境中，我们将 了解如何适应波动的环境不同的战略，出现在组件常数 环境. 在目标1中，我们将检验一个假设，即波动的环境会导致通才， 生物权衡通过优化平均适应性的条件，而恒定的环境产生 在特定环境中更适合但平均适应度较低的专家。在确定 通才和专家，我们将在Aim 2中重新标记他们的集合，以便在 在时间和空间上都波动的环境，允许突变体进一步多样化， 侵入对方的小天地这些实验将检验角色置换和 专业化应该随着竞争而发展。 这项研究和培训计划将使我成为交叉领域的领导者， 微生物生态学与进化我将最终应用我从这些实验中获得的见解， 高度精确的模型系统，以复杂的多物种群落。我的博士课程是在社区 生态学，在这里我将学习与尖端的条形码技术，以及研究分子 生物学和群体遗传学。此外，我将参加专业发展活动， 公众宣传。

Project Summary One lesson of experimental evolution is that beneficial, large-effect mutations appear rapidly in simple laboratory environments. Given that such mutations are possible and thus have not already fixed in wild populations, it is likely that they help the organism to specialize to a particular environment and may display pleiotropic tradeoffs that make them deleterious in other environments. Evidence for pleiotropy is common, and unlike constant laboratory environments, natural settings likely require organisms to solve evolutionary tradeoffs in order to persist through intermittently harsh conditions. Despite the central importance to biology and medicine of the effect of environmental fluctuations on evolution, the field has been limited by the ability to track large numbers of evolutionary paths in multiple environmental conditions. Here, we propose to address this problem through use of a high-throughput system of DNA-barcoded yeast. By evolving Saccharomyces cerevisiae populations in environments that impose known biological tradeoffs on their fitness optimization, and by tracking the emergence and spread of hundreds of thousands of adaptive mutations in both fluctuating and constant environments, we will learn how adaptation to fluctuating environments differs from strategies that appear in the component constant environments. In Aim 1, we will test the hypothesis that fluctuating environments lead to generalists that overcome biological tradeoffs by optimizing mean fitness across conditions, while constant environments produce specialists that are more fit in particular environments but have lower mean fitness. After identifying generalists and specialists, we will re-barcode a collection of them in Aim 2 for subsequent evolution in environments that fluctuate both temporally and spatially, allowing the mutants to either diversify further or invade each other’s niches. These experiments will test the hypothesis that character displacement and specialization should evolve as a result of competition. This research and training plan will prepare me to become a leader in the intersecting fields of microbial ecology and evolution. I will ultimately apply the insights I gain from these experiments with a highly precise model system to complex multi-species communities. My PhD training was in community ecology, and here I will learn to work with a cutting-edge barcoding technology, as well as study molecular biology and population genetics. Additionally, I will participate in professional development activities and do public outreach.