ABI Innovation: A framework to predictably manipulate a microbial gene regulatory program

ABI Innovation：可预测地操纵微生物基因调控程序的框架

• 批准号: 1565166
• 负责人: Nitin Baliga
• 金额: $ 154.19万
• 依托单位: Institute for Systems Biology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565166&HistoricalAwards=false
• 关键词: ABI; Innovation; framework; predictably; manipulate

Living organisms have to adjust to changes in their environment in order to optimize their use of resources, minimize stress and maintain stability. This proposal aims to uncover the basic principles that direct how organisms tailor their physiological responses to environmental changes. Understanding why certain responses occur requires a precise map showing which genes are regulated in a given response and how they are coordinated. Using prior NSF ABI support, the Baliga Laboratory at the Institute for Systems Biology developed an approach to create precise maps of gene regulation for any microbial species. Using this approach, they mapped gene regulation in a set of important organisms including uranium-reducing Desulfovibrio vulgaris, lipid-accumulating Chlamydomonas reinhardtii, yeast, diatoms, and Mycobacterium tuberculosis. The next goal is to see how well the maps work as tools to predict the results when manipulating complex behaviors. Effective tools have wide-ranging implications for biotechnology, agriculture and medicine. Initially, new algorithms and software will be developed to further refine the gene regulation maps and identify factors affecting gene regulation during environmental changes. The focus will be understanding how organisms switch on or off specific behaviors in response to environmental cues. The work will be performed in two organisms - E. coli, a well-known, widely studied bacterium, and Halobacterium salinarum, an extremophile that thrives in high salt environments; it will be readily applicable to all sequenced microorganisms that are of significant industrial, agricultural, and medical importance. Part of the project is to develop and disseminate new high school curriculum to introduce the importance of computational modeling in solving real world problems such as food scarcity and climate change. Diverse populations of students and teachers from a variety of backgrounds, including those currently underrepresented in science, technology, engineering and math (STEM), will receive training and sustained support as they learn this interdisciplinary science. This curriculum and training is part of a program called Systems Education Experiences (SEE) that reaches thousands of students and teachers each month. SEE works toward cultivating systems thinkers who can tackle problems, contribute to a STEM-literate citizenry, and help build a more diverse population of STEM professionals.The primary objective of this project is to develop a framework to elucidate and predictably manipulate the gene regulatory program of any microbe. In previous ABI-funded research, the Baliga lab developed a systems approach to reverse engineer the environment and gene regulatory influence network v2.0 (EGRIN 2.0) model directly from a compendium of transcriptome profiles. The EGRIN 2.0 model elucidates mechanisms for environment-specific transcriptional regulation of all genes with unprecedented nucleotide-level resolution, at canonical promoter locations and even within coding sequences and inside operons. Here, an approach will be developed to elucidate transcription factor interactions within EGRIN 2.0 and characterize how the topology of these interactions (i.e., 'network motifs') generates genome-wide, temporally coordinated transcriptional responses. These studies will be performed in the context of understanding how two phylogenetically distant organisms --Escherichia coli (a bacterium) and Halobacterium salinarum (an archaeaon)-- use distinct regulators to mediate physiologically different yet phenotypically similar transitions from aerobic growth to anaerobic quiescence. First, an approach will be developed to precisely map conditional binding of transcription factors to sequence elements within promoters of all genes in the genome (EGRIN 3.0). Next, EGRIN 3.0 will be used to identify, characterize, and manipulate topologies of transcription factor interactions (i.e., network motifs) to predictably alter oxygen (O2)-responsive state transitions in H. salinarum and E. coli. In addition to developing a generalized framework for manipulating a microbial gene regulatory program within any organism, the activities will test the hypothesis that similar environmental forcing drives convergent evolution of topologically similar network motifs in phylogenetically distant organisms. The high-level thinking and process used by this interdisciplinary group will be translated into curriculum and training experiences in the form of real-word cases studies for high school teachers and students. One of the goals will be for students to use experimentation and modeling to better understand the influence of environmental parameters (such as oxygen, nitrates, pH, light, etc.) on productivity and stability of food systems, such as aquaponic systems. Students, teachers, and STEM professionals will work together to iteratively develop and test curriculum and experiences through a modified Dick and Carey Instructional Design model. All curricula will be integrated with published national education standards. All needed technology, software, lesson plans and learning aides will be provided to teachers and students through multiple online sources, resource centers, and in-person and online trainings. For further information about this project and its products, visit the Baliga Laboratory's website at http://baliga.systemsbiology.net and http://see.systemsbiology.net.

活生物体必须适应其环境变化，以优化其利用资源，最大程度地减少压力并保持稳定性。该建议旨在揭示指导生物如何量身定制其生理反应对环境变化的基本原则。 了解为什么发生某些响应需要一个精确的图，以表明在给定响应中调节哪些基因以及如何协调。 使用先前的NSF ABI支持，系统生物学研究所的BALIGA实验室开发了一种为任何微生物物种创建基因调节的精确地图的方法。使用这种方法，他们在一组重要生物中绘制了基因调节，包括减少铀的desulfovibrio dulgaris，脂质蓄积的衣原体Reinhardtii，酵母菌，硅藻和结核分枝杆菌。下一个目标是查看地图作为操纵复杂行为时预测结果的工具的工作状况。有效的工具对生物技术，农业和医学具有广泛的影响。最初，将开发新的算法和软件，以进一步完善基因调节图并确定影响环境变化期间基因调节的因素。重点将是了解生物如何按照环境线索打开或关闭特定行为。这项工作将在两个生物体中进行 - 大肠杆菌，一种众所周知的，广泛研究的细菌和盐酸盐盐，这是一种在高盐环境中繁衍生息的极端细菌。它将很容易适用于所有具有重要工业，农业和医学重要性的测序微生物。该项目的一部分是开发和传播新的高中课程，以介绍计算建模在解决现实世界中问题（例如粮食稀缺和气候变化）中的重要性。来自各种背景的学生和教师的多样化人群，包括当前在科学，技术，工程和数学（STEM）中的人数不足的学生，他们将在学习这种跨学科科学时获得培训和持续的支持。该课程和培训是一个名为“系统教育经验”计划的一部分，该课程每月覆盖成千上万的学生和老师。参见培养系统思想家的著作，他们可以解决问题，促进茎识别的公民并帮助建立更多样化的STEM专业人员。该项目的主要目标是开发一个框架，以阐明并可以预测地操纵任何微生物的基因调节计划。在以前的ABI资助研究中，Baliga Lab开发了一种系统方法，可以直接从转录组曲线汇编的汇编中逆转环境和基因调节网络v2.0（Egrin 2.0）模型。 Egrin 2.0模型阐明了所有具有前所未有的核苷酸水平分辨率的基因环境特异性转录调控的机制，在规范启动子位置，甚至在编码序列和操纵子内。在这里，将开发一种方法来阐明Egrin 2.0中的转录因子相互作用，并表征这些相互作用的拓扑（即“网络基序”）如何产生全基因组，时间协调的转录响应。这些研究将在理解两个系统发育较远的生物-Escherichia coli（A细菌）和盐酸盐盐（一种古细菌）（一种古细菌）中如何进行 - 使用不同的调节剂来介导生理上不同但在表型上相似的相似性过渡，从有氧质量到厌氧菌生长到厌氧生长。首先，将开发一种方法，以精确绘制转录因子与基因组所有基因启动子内的序列元件的条件结合（Egrin 3.0）（Egrin 3.0）。接下来，Egrin 3.0将用于识别，表征和操纵转录因子相互作用的拓扑（即网络基序），以可预测地改变H. salinarum和E. Coli中的氧气（O2）响应状态转变。除了开发一个通用框架以操纵任何生物体中的微生物基因调节程序外，这些活动还将检验以下假设：类似的环境迫使在系统发育远处生物体中拓扑相似的网络基序的收敛进化。该跨学科小组使用的高级思维和过程将以高中教师和学生的现实案例研究形式转化为课程和培训经验。一个目标之一是学生使用实验和建模来更好地了解环境参数（例如氧，硝酸盐，pH，光等）对食品系统的生产力和稳定性（例如Aquaponic Systems）的影响。学生，老师和STEM专业人员将通过修改后的Dick和Carey教学设计模型一起迭代开发和测试课程和经验。所有课程将与已出版的国家教育标准融合。所有需要的技术，软件，课程计划和学习助手都将通过多个在线资源，资源中心以及面对面和在线培训提供给教师和学生。有关该项目及其产品的更多信息，请访问Baliga实验室的网站http://baliga.systemsbiology.net和http://see.syeystemsbiology.net。