Causes and consequences of regulatory network rewiring under extreme environmental selection

极端环境选择下监管网络重布线的原因和后果

基本信息

批准号：
1936024
负责人：
Amy Schmid
金额：
$ 90万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936024&HistoricalAwards=false
关键词：
Causes consequences regulatory network rewiring

项目摘要

This project seeks to understand how gene circuits evolve under extreme conditions. Microorganisms that live in extreme environments, called extremophiles, are remarkable examples of life's resilience, thriving in hot springs at boiling temperatures, in brine lakes saturated with salt, and in deserts once thought to be sterile. This research uses halophiles, extremophiles that live in high salt, as a test system to map complex gene circuits that enable survival. Circuit maps are compared across halophile species resistant to different levels of salt and stress to understand how extreme conditions rewire gene circuits, and how rewiring enables adaptation in the face of stress. The high salt extremophiles of interest are members of the domain of life Archaea. Because the molecules that make up gene circuits in archaea resemble those of other domains of life, this research has the potential to reveal general principles of gene circuit evolution across the tree of life. The education plan involves collaborating with students to build solutions for analysis and visualization of archaeal data. The PI and her group mentor undergraduates through the summer Duke Data+ program to create interactive web-accessible tools for data analysis and visualization. Teams of Duke students contribute directly to the research by developing these tools. The resultant graphical user interface (GUI) serve as an entry point for experimental biologists into computational biology in archaea, reducing the barrier for otherwise intimidating genome-scale analyses. Duke Masters in Data Science (MIDS) students and graduate students from the PI's lab co-mentor the Data+ team and provide support for these tools throughout the academic year. This vertically integrated mentorship structure provides critical training in research mentorship, team project management, and communication skills. The team-based learning approach encourages recruitment and retention of underrepresented groups in STEM. The overarching goal of this project is to understand how extreme environments select for regulatory network architecture and function. Transcription regulatory networks (TRNs) vary gene expression dynamically in response to stress. Such expression adapts physiology to improve fitness in the short term and leads to phenotypic diversity over evolutionary time scales. However, the selective forces causing such rewiring of gene circuits, and whether such rewiring is adaptive, remain unclear. In recent work on halophiles, hypersaline-adapted representatives of the archaeal domain of life, the PI discovered archaeal TRNs that regulate critical cellular decisions such as nutrient use and damage repair. Halophiles provide a unique model for investigating the evolution of TRNs given their experimental tractability in the lab and adaptability during continual exposure to multiple extreme conditions in the natural environment. Previous research compared the architecture and dynamic function of TRNs across related species of halophiles. More recent research has led to the hypothesis that extreme conditions select for more highly interconnected TRNs, enabling rapid physiological adjustment in response to variable environments. To test this hypothesis, the research: (a) uses an integrated experimental and computational systems biology approach pioneered in the PI's lab to map and compare small-scale TRNs across four species of halophiles; (b) jointly infers global TRNs across species using multi-task machine learning to ctract-Sompare genome-scale networks; and (c) forces TRN rewiring with in-lab evolution experiments. This approach combines genetics, genomics, quantitative phenotyping, and statistical modeling, yielding rapid and unprecedented insight into the dynamic function of archaeal regulatory networks and their impact on cell physiology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目试图了解基因电路如何在极端条件下发展。生活在极端环境中的微生物（称为极端粒子）是生命韧性的显着例子，在沸腾温度下在温泉中繁荣起来，在盐水中充满盐的盐湖，以及曾经被认为是无菌的沙漠。这项研究使用了卤素，居住在高盐中的极端细胞作为测试系统，以绘制能够生存的复杂基因电路。比较了在抗不同水平的盐和压力的卤素物种之间比较电路图，以了解极端条件的重新连接基因电路，以及如何在应力面对压力下进行适应。感兴趣的高盐极端粒子是生命古细菌领域的成员。由于构成基因回路的分子古细菌类似于生命的其他领域的分子，因此这项研究有可能揭示整个生命树的基因回路演化的一般原理。该教育计划涉及与学生合作，以构建用于分析和可视化古细菌数据的解决方案。 PI和她的小组导师通过夏季Duke Data+计划为您创建交互式Web访问工具，以进行数据分析和可视化。杜克大学学生团队通过开发这些工具直接为研究做出了贡献。所得的图形用户界面（GUI）是实验生物学家进入古细菌计算生物学的入口处，从而减少了以外的障碍，以吓到基因组规模分析。 PI的LAB Co-Mentor Data+ The Data+ Team的数据科学硕士（MIDS）学生和研究生，并在整个学年为这些工具提供支持。这种垂直整合的指导结构为研究指导，团队项目管理和沟通技巧提供了重要的培训。基于团队的学习方法鼓励STEM中代表性不足的群体的招募和保留。该项目的总体目标是了解极端环境如何选择监管网络体系结构和功能。转录调节网络（TRN）随着应力的反应而动态地变化基因表达。这种表达适应生理，以在短期内提高适应性，并导致进化时间尺度上的表型多样性。但是，导致这种基因回路的选择性力量，以及这种重新布线是否具有适应性，尚不清楚。在最近关于卤素的工作，是生命古细菌领域的高盐适应的代表，PI发现了调节关键细胞决策（例如营养用途和损害修复）的古细菌TRN。卤素提供了一个独特的模型，用于研究TRN在实验室中的实验性障碍，并在自然环境中持续暴露于多个极端条件的过程中的适应性。先前的研究比较了相关卤素物种中TRN的结构和动态功能。最近的研究导致了一个假设，即极端条件选择更高度相互联系的TRN，从而可以响应可变环境来快速生理调整。为了检验这一假设，研究：（a）使用在PI实验室中开创的集成实验和计算系统生物学方法，以绘制和比较四种卤素种类的小规模TRN；（b）使用多任务机器学习来cractsportapare基因组尺度网络，共同渗透整个物种的全球TRN；（c）通过LAB内进化实验重新布线TRN。这种方法结合了遗传学，基因组学，定量表型和统计建模，对古细胞监管网络的动态功能及其对细胞生理的影响产生了快速而前所未有的见解。该奖项反映了NSF的法规任务，并被认为是通过基金会的知识优点和广泛的Cromitia Cromitia Cromitia Cromitia crocritia crocritia crocritia crocritia crocritia crocritia crocritia crocritia crocritia crocritia crocritia crocritia cromitia cromitia crocritia cromitia cromitia cromitia cromitia scritia cromitia均值得一评论。