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Quantitative Studies of Metabolic Switches in enteric bacteria

肠道细菌代谢开关的定量研究

基本信息

批准号：
10461919
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
项目类别：
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/10461919
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

项目摘要

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.

项目摘要从分子组成部分的知识中获得对细胞行为的定量、预测性理解，相互作用是系统生物学最重要的挑战之一。在上一个资助期，我们建立了一个动力学模型，模型预测蛋白质组动力学在模式细菌E。大肠杆菌，以应对不断变化的条件在下一个资助期，我们建议将这项工作扩展到预测转录组的动态。这是一项比预测蛋白质组动态更具挑战性的任务，因为与蛋白质组不同，即使是稳定的转录组的状态特征还没有在定量水平上被理解;特别是对转录组和蛋白质组的了解很少。我们的初步数据确定了一个以前未知的全球 E.缺失的一环。我们建议定量地建立这种全球监管效应在不同的生长条件下，并阐明这种调控的分子机制和策略。我们将验证和利用由这种全球性的转录和翻译能力之间的预测协调，调节，以建立转录调节与细胞mRNA和蛋白质水平之间的定量联系， E.杆菌通过将转录调控的知识融入蛋白质组动力学模型，动力学发展到目前为止，我们将建立一个框架来预测转录组在生长过程中的动力学过渡。这项研究的实验部分涉及现代组学方法和经典的生化分析具体地，将针对广泛的生长条件（各种类型的生长条件）收集RNA-seq数据。营养限制、抗生素治疗、瞬时变化）以及具有不同遗传背景的菌株，可滴定突变体。RNA-seq数据将通过总mRNA的绝对测定进一步补充丰度和通量，以便能够在各种条件下进行比较。然后，将数据与定量我们已经在相同的生长条件下收集了蛋白质组学和代谢组学数据，因此它们可以相互关联。细胞生理学，使定量分析和模型的建立。后者将结合联合收割机的独特经验，可在PI的实验室，涉及转录和转录后调控的详细定量建模，一方面是特定基因和mRNA，另一方面是基因组规模动态的粗粒度建模。