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.

项目摘要实验进化的一堂课是，有益，大效应突变在简单中迅速出现实验室环境。鉴于这种突变是可能的，因此尚未固定野生人群，他们很可能会帮助有机体专门针对特定环境并可能显示出多效的权衡，使它们在其他环境中有害。证据多效性很常见，与恒定的实验室环境不同，自然环境可能需要有机体解决进化折衷，以通过间歇性的伤害条件持续存在。尽管对生物学和医学对环境波动的影响至关重要进化，该领域受到跟踪多个进化路径的能力的限制环境条件。在这里，我们建议通过使用高通量系统解决此问题 DNA-barcoded酵母。通过不断发展的环境中的酿酒酵母种群在其健身优化方面的已知生物折衷方案，以及通过跟踪的出现和传播在波动和恒定环境中，成千上万的自适应突变，我们将了解适应对环境的波动如何与出现在组件常数中的策略有所不同环境。在AIM 1中，我们将检验以下假设，即波动的环境导致了克服的通才主义者生物折衷通过在条件上优化平均健身，而持续的环境产生在特定环境中更适合但平均健身较低的专家。识别后通才和专家，我们将在AIM 2中重新计算它们的集合，以便随后的演变临时和空间上波动的环境，使突变体能够进一步多样化或互相入侵。这些实验将测试字符位移和专业化应由于竞争而发展。该研究和培训计划将使我成为我成为相交领域的领导者微生物生态学和进化。我最终将利用这些实验中获得的见解高度精确的模型系统，用于复杂的多物种社区。我的博士培训是在社区中生态学，在这里我将学会使用尖端的条形码技术，并研究分子生物学和种群遗传学。此外，我将参加专业发展活动并做公共宣传。