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、大影响的突变在简单的 实验室环境。假设这样的突变是可能的，因此还没有固定在 野生种群，它们很可能帮助有机体专门化到特定的环境 并可能表现出多效性的权衡，使它们在其他环境中是有害的。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 为了在间歇性恶劣的条件下坚持下去，生物体需要解决进化上的权衡。 尽管环境波动对生物和医学的影响具有核心重要性 进化论，该领域一直受到在多个 环境条件。在这里，我们建议通过使用高吞吐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 已知的生物权衡对它们的适应度优化，并通过跟踪出现和传播 在波动和恒定的环境中，成千上万的适应性突变，我们将 了解对多变环境的适应与分量常量中出现的策略有何不同 环境。 在目标1中，我们将检验这样一个假设，即波动的环境导致通才克服 通过优化不同条件下的平均适合度来进行生物权衡，而恒定的环境产生 在特定环境中更健康，但平均健康水平较低的专家。在识别出 多面手和专家，我们将在目标2中重新编码他们的集合，以便在 同时在时间和空间上波动的环境，允许突变体进一步多样化或 侵占彼此的壁龛。这些实验将检验这样一种假设，即角色位移和 专业化应该作为竞争的结果而演变。 这项研究和培训计划将使我成为以下交叉领域的领导者 微生物生态和进化。我最终将把我从这些实验中获得的见解应用到 复杂多物种群落的高精度模型系统。我的博士训练是在社区进行的 生态学，在这里我将学习使用尖端的条形码技术，以及研究分子 生物学和种群遗传学。此外，我还将参加职业发展活动，并 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.