Bilateral BBSRC-NSF/BIO: Identifying Mechanisms for Environmental Adaptation in Bacteria

双边 BBSRC-NSF/BIO：确定细菌环境适应机制

• 批准号: 1518261
• 负责人: Nitin Baliga
• 金额: $ 90万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518261&HistoricalAwards=false
• 关键词: Bilateral; BBSRC; NSF; BIO; Identifying

This collaborative project engages researchers in the US (Institute for Systems Biology) and the UK (University of Birmingham) to enhance understanding of how gene regulation has evolved and functions across a group of related but nevertheless diverse microorganisms. Knowledge of how CMNR bacteria (Corynebacterium, Mycobacterium, Nocardia and Rhodococcus) adapt to new environments will be used to improve their use in remediating pollutants, producing bioenergy, and improving public health. CMNR bacteria inhabit soils, cooperate with plants, and include microbes and pathogens that are of great interest to many industries. The investigators in this project will examine the hypothesis that the diversity of habitats colonized by this group of bacteria is rooted in their ability to adapt to new environments by altering the arrangement of complex fat molecules in their cell envelope. The research will also expand the high school educational curriculum developed by the US team, which uses a systematic approach to explore the global issue of food security. Activities will be incorporated that model natural food production systems and that use network models to study ecosystem dynamics, functioning, and resilience. The curriculum will be aligned with the goals of the Next Generation Science Standards (US) and National Curriculum (UK). The curriculum will be widely disseminated to classrooms in the US and UK through a widely accessed education website. The computational tools and experimental methods to be developed will be useful to the broad research community and will be made available as well-documented, open-source resources.CMNR bacteria adapt to diverse habitats by using paralogous enzymes in different combinations to selectively catabolize different substrates and alter the composition of their cell envelope. To elucidate this combinatorial strategy of CMNR bacteria, the underlying conditionally active gene regulatory networks within Mycobacterium smegmatis will be reverse engineered using a systems biology approach. Comparative analysis across all CMNR bacterial genomes will facilitate inference of the gene regulatory networks and also aid in elucidating the evolutionarily conserved and unique features of the networks. Further, genes and interactions within sub-networks that effect specific changes in cell envelope composition will be identified by correlating changes in modular architecture of the M. smegmatis model during growth transitions between varying substrates and environmental conditions. Model predictions will be tested by analyzing consequences of specific gene deletions on cell envelope composition of representative CMNR bacteria under relevant conditions. This project will generate innovative approaches to construct and analyze a gene regulatory network model across related, but distinct, microorganisms. This collaborative US/UK project is supported by the US National Science Foundation and the UK Biotechnology and Biological Sciences Research Council.

该协作项目吸引了美国（系统生物学研究所）和英国（伯明翰大学）的研究人员，以增强对基因调节如何发展的理解，并在一组相关但多样化的微生物中发挥作用。了解CMNR细菌（Corynebacterius，分枝杆菌，Nocardia和Rhodococcus）如何适应新环境，以改善它们在修复污染物中的用途，产生生物能和改善公共卫生。 CMNR细菌居住在土壤中，与植物合作，包括许多行业引起的微生物和病原体。该项目中的研究者将研究以下假设：这组细菌殖民的栖息地的多样性植根于它们通过改变细胞包膜中复杂脂肪分子的排列来适应新环境的能力。这项研究还将扩大美国团队开发的高中教育课程，该课程采用系统的方法来探索全球粮食安全问题。活动将纳入自然食品生产系统并使用网络模型来研究生态系统动态，功能和弹性。该课程将与下一代科学标准（美国）和国家课程（英国）的目标保持一致。该课程将通过广泛访问的教育网站广泛传播到美国和英国的教室。要开发的计算工具和实验方法将对广泛的研究社区有用，并将提供充分的文献记载的开源资源。CMNR细菌通过使用不同组合中的副酶来选择性地分解不同的substrates并更改其细胞内置的组成，以适应不同的栖息地。为了阐明CMNR细菌的这种组合策略，将使用系统生物学方法进行逆向工程。所有CMNR细菌基因组的比较分析将促进基因调节网络的推断，并有助于阐明网络的进化保守和独特的特征。此外，将通过在变化的底物和环境条件之间生长过渡期间将Smegmatis模型的模块化结构的变化相关联，从而确定了子网络中的基因和相互作用。模型预测将通过分析特定基因缺失在相关条件下代表性CMNR细菌的细胞包膜组成的后果来测试。该项目将生成创新的方法，以构建和分析相关但独特的微生物的基因调节网络模型。美国/英国的合作项目得到了美国国家科学基金会和英国生物技术与生物科学研究委员会的支持。