Quantifying evolutionary solutions to fitness tradeoffs in fluctuating environments

量化波动环境中适应性权衡的进化解决方案

基本信息

批准号：
10534955
负责人：
Clare Isabel Abreu
金额：
$ 6.76万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-05 至 2024-08-04
项目状态：
已结题

项目摘要

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.

项目概要实验进化的一个教训是，有益的、大效应的突变在简单的过程中迅速出现。鉴于这种突变是可能的，因此尚未在实验室环境中修复。野生种群，它们很可能帮助生物体专门适应特定的环境并且可能会表现出多效性权衡，这使得它们在其他环境中有害。多效性很常见，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问题，以便在间歇性的恶劣条件下持续生存。尽管环境波动对生物学和医学的影响至关重要进化，该领域受到跟踪多种进化路径的能力的限制。在这里，我们建议通过使用高通量系统来解决这个问题。通过在施加压力的环境中进化酿酒酵母种群。已知的生物权衡其适应度优化，并通过跟踪的出现和传播在波动和恒定的环境中存在数十万个适应性突变，我们将了解对波动环境的适应与组件常数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：波动的环境会导致通才克服困难。通过优化跨条件的平均适应度进行生物权衡，而恒定的环境会产生更适合特定环境但平均适应度较低的专家。通才和专家，我们将在 Aim 2 中重新对他们的集合进行条形码编码，以供后续发展在时间和空间上波动的环境，使突变体要么进一步多样化，要么这些实验将检验角色位移和角色定位的假设。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以下交叉领域的领导者我最终将应用从这些实验中获得的见解。复杂的多物种群落的高精度模型系统我的博士训练是在社区中进行的。生态学，在这里我将学习使用尖端的条形码技术，以及研究分子此外，我将参加专业发展活动并做。公共宣传。