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

• 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

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.

这个项目试图了解基因电路在极端条件下是如何进化的。生活在极端环境中的微生物，称为极端微生物，是生命弹性的显著例子，它们在沸腾的温度下的温泉、饱和盐湖和曾经被认为是贫瘠的沙漠中茁壮成长。这项研究使用嗜盐菌，即生活在高盐环境中的极端嗜盐菌作为测试系统，绘制出使生存得以实现的复杂基因电路。对耐不同水平的盐和压力的嗜盐物种的电路图进行了比较，以了解极端条件如何重新连接基因电路，以及重新连接如何在面对压力时实现适应。感兴趣的高盐极端微生物是生命古生界的成员。由于古生物中构成基因回路的分子与其他生命领域的分子相似，这项研究有可能揭示整个生命树上基因回路进化的一般原理。该教育计划涉及与学生合作，为分析和可视化古代数据建立解决方案。PI和她的团队通过暑期Duke Data+计划指导本科生创建交互式网络可访问工具，用于数据分析和可视化。杜克大学的学生团队通过开发这些工具直接为研究做出了贡献。由此产生的图形用户界面(GUI)为实验生物学家进入古生物中的计算生物学提供了一个切入点，减少了原本令人生畏的基因组规模分析的障碍。杜克大学数据科学硕士(MIDS)学生和PI实验室的研究生共同指导Data+团队，并在整个学年为这些工具提供支持。这种垂直整合的导师结构提供了研究导师、团队项目管理和沟通技能方面的关键培训。以团队为基础的学习方法鼓励招募和留住STEM中任职人数不足的群体。本项目的首要目标是了解极端环境如何选择监管网络架构和功能。转录调控网络(TRN)动态地改变基因的表达以响应胁迫。这种表达使生理学在短期内改善了适应性，并导致了进化时间尺度上的表型多样性。然而，导致这种基因电路重新连接的选择性作用力，以及这种重新连接是否是适应性的，仍然不清楚。在最近对嗜盐生物的研究中，PI发现了古细菌TRN，它们调控着营养物质的使用和损伤修复等关键的细胞决策。嗜盐微生物为研究TRN的进化提供了一个独特的模型，因为它们在实验室中的实验可控性和在自然环境中持续暴露于多种极端条件下的适应性。以前的研究比较了嗜盐相关物种中TRNS的结构和动态功能。最近的研究导致了一种假设，即极端条件选择更高度相互关联的TRN，使其能够对变化的环境做出快速的生理调整。为了验证这一假设，这项研究：(A)使用PI实验室首创的集成实验和计算系统生物学方法来绘制和比较四种嗜盐生物的小规模TRN；(B)使用多任务机器学习来联合推断跨物种的全球TRN，以提取基因组规模的网络；以及(C)强制TRN与实验室内的进化实验重新连接。这种方法结合了遗传学、基因组学、定量表型和统计建模，对古生物调控网络的动态功能及其对细胞生理学的影响产生了快速和前所未有的洞察。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。