Understanding Gene Regulatory Networks in Hypersaline-adapted Archaea: Toward Synthetic Biology for Industrial Applications

了解适应高盐的古细菌中的基因调控网络：面向工业应用的合成生物学

• 批准号: 1417750
• 负责人: Amy Schmid
• 金额: $ 70万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1417750&HistoricalAwards=false
• 关键词: Understanding; Gene; Regulatory; Networks; Hypersaline

The project seeks to understand how gene circuits function and evolve in microorganisms living under extreme conditions. This knowledge will be used to enable the construction of synthetic prototype strains that efficiently produce biodegradable plastic from inexpensive feedstock. Synthetic biology has yet to explore the use of archaeal genetic circuitry and metabolic pathways despite their potential value for applications in industrial chemical and biofuel production. Archaea are single-celled microbes that thrive at the limits of life, found in deep-sea hydrothermal vents under high pressure and temperature, saturated salt lakes, and polar icecaps. Survival in these environments requires unique genetic and metabolic strategies, the natural chemical byproducts of which are attractive to industry (biofuels, biodegradable plastics). Strategies that engineer archaeal gene circuits or swap these circuits between archaeal strains to boost chemical production are attractive for biomanufacturing of fuels and chemicals. The NSF funded research will also contribute to education, training, and outreach at the high school and undergraduate levels. Specifically, the PI will collaborate with teachers to continue offering weeklong science immersion courses for high school students from North Carolina School of Science and Math. This public high school in Durham, NC, draws the top students from each congressional district in the state, providing even representation across cultural and socioeconomic backgrounds. The PI's ongoing "Introduction to Systems Biology" will engage biology and engineering undergraduates in collaborative active learning projects to build and analyze gene networks from data generated through the research. Several interested students from the class, together with HBCU summer students recruited through established Duke programs, will engage in research projects in the PI's lab. Through these activities, high school and undergraduate students will contribute directly to generating and analyzing research data. This work will engage students in research earlier in their careers and retain them in STEM fields. These activities are expected to have lasting effects on the recruitment and retention of researchers, especially from underrepresented groups.TECHNICAL DESCRIPTION: Synthetic biology has yet to explore the use of archaeal transcriptional systems and metabolic pathways despite their potential value for applications in industry and biofuel production. The hypersaline-adapted group of archaeal microbes, hereafter referred to as halophiles, hold significant promise because they naturally produce chemicals attractive to industry (isoprenoid lipids for fuels, polyhydroxyalkanoate for biodegradable plastics). Strategies that engineer halophile gene regulatory networks (GRNs) or swap gene circuits between halophile strains are attractive for biofuel or other industrial applications. However, before halophile networks can be customized and controlled, additional basic understanding of GRNs and transcription factor (TF) function is required. This plan has three objectives: (1) Characterize the topology, dynamics, and phenotypic output of small-scale GRN motifs that regulate stress and metabolic responses in halophiles. GRN motifs known to regulate important metabolic pathways and extreme stress resistance across four related halophile species using time course gene expression and TF-DNA binding measurements. (2) Build predictive genome-scale statistical models for each halophile to quantify and compare GRN motif architecture and dynamics. Data generated from objective 1 will be integrated into predictive models at two levels of detail: small-scale dynamical models and genome-scale gene regulatory interaction network models. Models will be compared across organisms. (3) Test model predictions in proof-of-principle synthetic biology experiments. Model tests will include three stages of molecular biology experiments: promoter-reporter fusions to test predictions regarding TF-cis-regulatory sequence interactions, high-resolution time course gene expression experiments, and building prototype synthetic biology circuits for increasing production of polyhydroxyalkanoate in halophiles. The products of this research will include predictive GRN models for four related species and prototype synthetic circuits. These gene circuits will test model predictions and increase the production of biodegradable plastic in halophiles. In the long term, the PI aims to exploit halophile GRNs to extend options for biotechnology and bioenergy.

该项目试图了解基因回路如何在生活在极端条件下的微生物中发挥作用和发展。这些知识将用于实现合成原型菌株的构建，这些菌株可有效地从廉价的原料中产生可生物降解的塑料。合成生物学尚未探索古细菌遗传回路和代谢途径的使用，尽管它们在工业化学和生物燃料生产中的应用潜在价值。古细菌是单细胞的微生物，在高压和温度，饱和盐湖和极地冰op的深海水热通风孔中发现的生命极限。在这些环境中的生存需要独特的遗传和代谢策略，其天然化学副产品对工业有吸引力（生物燃料，可生物降解的塑料）。设计古细胞电路或交换古细菌菌株之间的这些电路以增强化学生产的策略对燃料和化学物质的生物制造有吸引力。 NSF资助的研究还将在高中和本科阶段为教育，培训和外展作出贡献。具体来说，PI将与老师合作，​​继续为北卡罗来纳州科学与数学学院的高中学生提供为期一周的科学浸入式课程。这所位于北卡罗来纳州达勒姆市的公立高中吸引了该州每个国会区的顶级学生，在文化和社会经济背景之间提供甚至提供代表。 PI正在进行的“系统生物学简介”将吸引生物学和工程本科生在协作活跃学习项目中，以从研究生成的数据建立和分析基因网络。来自班级的几位感兴趣的学生，以及通过既定的杜克大学计划招募的HBCU夏季学生将在PI实验室中从事研究项目。通过这些活动，高中和本科生将直接为生成和分析研究数据做出贡献。这项工作将使学生从事职业生涯的早期研究，并将其保留在STEM领域。预计这些活动将对研究人员的招聘和保留持续影响，尤其是来自代表性不足的群体。技术描述：合成生物学尚未探索古细菌转录系统和代谢途径的使用，尽管在工业和生物燃料生产中的应用潜在价值。催眠盐适应的古细菌群（以下称为卤素）具有巨大的希望，因为它们自然产生了对工业吸引的化学物质（用于燃料的类化学物质，可生物降解的多羟基烷烃用于可生物降解的塑料）。设计卤素基因调节网络（GRN）或悬谷菌株之间的掉期基因回路的策略对生物燃料或其他工业应用具有吸引力。但是，在可以自定义和控制卤素网络之前，需要对GRN和转录因子（TF）功能的其他基本理解。该计划具有三个目标：（1）表征小规模GRN基序的拓扑，动力学和表型输出，这些基序调节卤素中的应力和代谢反应。已知的GRN基序已知，使用时间过程基因表达和TF-DNA结合测量值，可以调节四个相关的卤素种类的重要代谢途径和极端的应激性。 （2）为每个卤素构建预测性基因组尺度统计模型，以量化和比较GRN图案结构和动力学。从目标1产生的数据将集成到两个详细级别的预测模型中：小规模的动力学模型和基因组规模调节性相互作用网络模型。将比较跨生物体的模型。 （3）基本合成生物学实验中的测试模型预测。模型测试将包括分子生物学实验的三个阶段：启动子重孢子融合，以测试有关TF-CIS调节序列相互作用的预测，高分辨率的时间过程基因表达实验以及构建原型合成生物学回路，以增加大量羟基烷酸盐中的生产。这项研究的产品将包括针对四个相关物种和原型合成电路的预测性GRN模型。这些基因电路将测试模型预测并增加卤素中可生物降解的塑料的产生。从长远来看，PI旨在利用Halophile Grns扩展生物技术和生物能源的选择。