Systems biology and the evolutionary dynamics in a synthetic microbial community

合成微生物群落的系统生物学和进化动力学

• 批准号: 9381496
• 负责人: William Harcombe
• 金额: $ 30.7万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9381496
• 关键词: Bacteria; Behavior; Biochemical Pathway; Biological Assay; Biological Models; Cells; Communities; Data; Engineering; Enzymes; Escherichia coli; Evolution; Foundations; Frequencies; Generations; Genes; Genetic; Genome; Genomics; Goals; Growth; Health; Human; Human body; Knock-out; Laboratories; Libraries; Link; Malignant Neoplasms; Massive Parallel Sequencing; Metabolic; Metabolic Pathway; Metabolism; Methylobacterium; Methylobacterium extorquens; Modeling; Mutagenesis; Mutation; Nutrient; Pattern; Phenotype; Preparation; Prevalence; Process; Property; Reaction; Research; Salmonella enterica; System; Systems Biology; Testing; Translating; Work; base; disorder prevention; genetic element; genome sequencing; genome-wide; genomic data; insight; laboratory experiment; microbial; microbial community; microbiome; microorganism interaction; novel; obesity prevention; personalized medicine; synergism; tool

Project Summary The ability to engineer the microbiome could transform treatment and prevention of diseases from obesity to cancer. The promise of designer microbiomes is largely constrained by lack of understanding of how community composition and function are encoded in the genomes present in a system. The long-term goal is to develop systems-level, metabolically-based approaches to connect genomic data to microbial community function and dynamics. Metabolic mechanisms provide a broadly applicable foundation for understanding and managing microbial systems as metabolic enzymes can be identified from sequence data, and intracellular metabolism drives many of the microbial interactions that generate community behavior. The proposed research will computationally predict and experimentally test the quantitative connection between genome sequence, metabolic mechanisms, and community properties in a microbial community. A model microbial community has been engineered in the laboratory with defined metabolic interactions between Escherichia coli, Salmonella enterica, and Methylobacterium extorquens. Further a computational platform has been developed that uses genome-scale metabolic models to simulate growth and metabolic interactions and community function. These cutting-edge tools will be combined to achieve the following specific aims: Aim 1 – Identify all metabolic and genetic elements that contribute to growth in a defined community. Genome-scale knockout libraries will be evaluated computationally and empirically. Aim 2 – Determine how evolution changes community composition and function. High-throughput phenotypic assays and genome sequencing will be used to identify the changes that have evolved in eight replicate communities over 400 generations. Metabolic constraints on evolution will be computationally investigated. Aim 3 – Test the prevalence of genetic interactions in a microbial community. Genetic interactions within and between genomes will be determined by the frequency with which the effect of a mutation changes in the presence of other mutations. The proposed work will generate the first systems-level data on the genomic basis of microbial community function. It will provide valuable insights into the metabolic and genetic mechanisms underlying dynamics in multi-species systems and the extent to which the effects of genetic changes are context dependent. Finally, the work will enable quantitative prediction of evolutionary trajectories from genome-scale metabolic models. As we strive to engineer microbiomes it is critical to characterize how genomic changes translate to changes in the community. Quantitatively connecting genome sequence to community function is a vital step in the ultimate goal of understanding and rationally managing microbial communities.

项目摘要 改造微生物组的能力可以将疾病的治疗和预防从肥胖转变为 癌症。设计者微生物群的前景在很大程度上受到缺乏对如何 群落的组成和功能编码在系统中存在的基因组中。长期目标是 开发系统级、基于新陈代谢的方法，将基因组数据与微生物群落联系起来 功能和动力学。代谢机制提供了一个广泛适用的基础，以了解和 将微生物系统作为代谢酶进行管理可以从序列数据中识别，并在细胞内 新陈代谢推动了许多微生物的相互作用，从而产生了社区行为。建议数 研究将通过计算预测和实验测试基因组之间的定量联系 微生物群落中的序列、代谢机制和群落特性。一种典型的微生物 已经在实验室中利用大肠杆菌之间明确的代谢相互作用构建了群落， 肠沙门氏菌和甲氧基洛布氏菌。此外，还开发了一个计算平台 它使用基因组规模的代谢模型来模拟生长和代谢的相互作用和群落 功能。这些尖端工具将结合在一起，实现以下具体目标： 目标1-确定在特定社区中有助于成长的所有代谢和遗传因素。 基因组规模的基因敲除文库将通过计算和经验进行评估。 目标2-确定进化如何改变群落的组成和功能。 将使用高通量表型分析和基因组测序来确定 在超过400代人的8个复制群落中进化。新陈代谢对进化的制约 将通过计算进行调查。 目标3-测试微生物群落中遗传交互作用的流行度。 基因组内部和基因组之间的遗传相互作用将由 当存在其他突变时，突变的影响会发生变化。 拟议的工作将产生关于微生物群落基因组基础的第一个系统级数据 功能。它将提供有价值的见解，以新陈代谢和遗传机制潜在的动态 多物种系统以及遗传变化的影响依赖于环境的程度。最后， 这项工作将使从基因组规模的新陈代谢模型中定量预测进化轨迹成为可能。 当我们努力设计微生物群时，表征基因组变化如何转化为 社区。定量地将基因组序列与群落功能联系起来是 了解和合理管理微生物群落的最终目标。