Quantitative Studies of Metabolic Switches in enteric bacteria

肠道细菌代谢开关的定量研究

• 批准号: 10241397
• 负责人: TERENCE HWA
• 金额: $ 37.57万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-17 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241397
• 关键词: Animal Model; Anti-Bacterial Agents; Antibiotic Therapy; Bacteria; Behavior; Biochemical; Biophysics; Biotechnology; Bypass; Catalogs; Cell physiology; Cells; Characteristics; Communities; Complement; Complex; Data; Dependence; Diagnosis; Diagnostic; Engineering; Enterobacteriaceae; Environment; Escherichia coli; Gene Expression; Gene Expression Process; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Goals; Grain; Grant; Growth; Individual; Kinetics; Knowledge; Laws; Link; Mediating; Messenger RNA; Methodology; Modeling; Modernization; Molecular; Nutrient; Organism; Outcome; Output; Phase; Physiology; Play; Post-Transcriptional Regulation; Process; Progress Reports; Proteins; Proteome; Proteomics; Publishing; Regulation; Research; Ribosomes; Sigma Factor; Signal Transduction; Speed; Stress; System; Systems Biology; Transcript; Transcriptional Regulation; Translations; Work; cell behavior; environmental change; experience; genetic regulatory protein; genome-wide; in vivo; kinetic model; metabolic abnormality assessment; metabolomics; model building; molecular scale; mutant; novel strategies; phenomenological models; predictive modeling; promoter; response; stem; success; transcription factor; transcriptome; transcriptome sequencing; translational model

Project Summary Attaining quantitative, predictive understanding of cellular behaviors from the knowledge of molecular parts and interactions is one of the foremost challenges of systems biology. In the previous grant period, we established a kinetic model to predict the proteome dynamics in the model bacterium E. coli, in response to changing environmental conditions. In this next grant period, we propose to extend this work to predicting the dynamics of the transcriptome. This is a much more challenging task than predicting the proteome dynamics, because unlike the proteome, even the steady- state characteristics of the transcriptome have not been understood at a quantitative level; in particular the link between the transcriptome and proteome is poorly understood. Our preliminary data identified a previously unknown global transcriptional regulation in E. coli as the missing link. We propose to establish this global regulatory effect quantitatively in different growth conditions, and to elucidate the molecular mechanism and strategy underlying this regulation. We will validate and exploit the predicted coordination between transcriptional and translational capacities provided by this global regulation to establish quantitative links between transcriptional regulation and cellular mRNA and protein levels for many genes in E. coli. By incorporating the knowledge on transcriptional regulation into the kinetic model of proteome dynamics developed so far, we will establish a framework to predict the dynamics of the transcriptome during growth transitions. Experimental components of this research involve a combination of modern ‘omic methodologies and classical biochemical analysis. Specifically, RNA-seq data will be collected for a broad range of growth conditions (various types of nutrient limitations, antibiotic treatment, transient shifts) and for strains with different genetic backgrounds including titratable mutants. The RNA-seq data will be further complemented by the absolute determination of total mRNA abundances and fluxes to enable comparison across conditions. The data will then be integrated with quantitative proteomic and metabolomic data we have already collected across the same growth conditions, so that they can be related to cellular physiology and enable quantitative analysis and model building. The latter will combine the unique experiences available at the PI’s lab, involving detailed quantitative modeling of transcriptional and post-transcriptional regulation for specific genes and mRNAs on the one hand, and coarse-grained modeling of genome-scale dynamics on the other hand.

项目摘要 从分子部分的知识和 相互作用是系统生物学的首要挑战之一。在上一个赠款期间，我们建立了一个动力学 用于预测模型细菌大肠杆菌中蛋白质组动力学的模型，以响应不断变化的环境 状况。在下一个赠款期间，我们建议扩展这项工作，以预测转录组的动态。这 比预测蛋白质组动力学要多得多，因为与蛋白质组，甚至稳定不同 转录组的状态特征尚未在定量水平上理解。特别是 转录组和蛋白质组知之甚少。我们的初步数据确定了以前未知的全局 大肠杆菌中的转录调节作为缺失的链接。我们建议定量建立这种全球监管效果 在不同的生长条件下，并阐明了该调节的分子机制和策略。我们将 验证和利用此全局提供的转录和翻译能力之间的预测协调 调节以转录调节与细胞mRNA和蛋白质水平之间建立定量联系 大肠杆菌中的许多基因。通过将转录调节的知识编码为蛋白质组动力学模型 到目前为止开发的动态，我们将建立一个框架来预测增长过程中转录组的动态 过渡。 这项研究的实验组成部分涉及现代'imic方法和经典的组合 生化分析。具体而言，将在广泛的生长条件（各种类型）中收集RNA-seq数据 营养局限性，抗生素治疗，瞬时转移）和具有不同遗传背景的菌株（包括 可滴定突变体。 RNA-seq数据将通过绝对确定总mRNA进一步完成 抽象和通量可在条件下进行比较。然后，数据将与定量集成 蛋白质组学和代谢组数据我们已经在相同的生长条件下收集了 进行细胞生理学并启用定量分析和模型构建。后者将结合独特的体验 可在PI实验室获得，涉及转录和转录后调节的详细定量建模 另一方面，特定的基因和mRNA，另一方面是基因组尺度动力学的粗粒化建模。