Genome-wide measurement of bacterial transcriptional regulatory states

细菌转录调控状态的全基因组测量

• 批准号: 8993954
• 负责人: Lydia Freddolino
• 金额: $ 24.87万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-20 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8993954
• 关键词: Antibiotics; Award; Bacteria; Bacterial Genome; Behavior; Binding; Binding Sites; Biology; Carbon; Cell Communication; Cells; Chloroform; Communities; Complex; Computing Methodologies; DNA; DNA Binding; DNA-Binding Proteins; DNase I hypersensitive sites sequencing; Data; Detection; Development; Environment; Environmental Hazards; Escherichia coli; Eukaryota; Evaluation; Evolution; Exclusion; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Genome; Genomics; Goals; Grant; Heart; High-Throughput Nucleotide Sequencing; Housekeeping; Human; Individual; Interphase; Knowledge; Life; Link; Location; Logic; Maps; Measurement; Measures; Mentors; Messenger RNA; Methods; Microbe; Modeling; Molecular Models; Mutation; Organism; Output; Phase; Phenols; Phenotype; Physiological; Play; Population; Process; Proteins; Proteomics; Regulatory Element; Research; Resistance; Resolution; Role; Signal Transduction; Site; Source; Staging; Statistical Models; Stimulus; Systems Biology; Technology; Testing; Time; Tissue Differentiation; Transcriptional Regulation; Validation; Vertebral column; Work; abstracting; antimicrobial drug; aqueous; base; chromatin immunoprecipitation; computer framework; computerized data processing; computerized tools; crosslink; density; directed evolution; drug development; extracellular; follow-up; genetic regulatory protein; genome-wide; improved; information gathering; insight; metabolomics; microbial community; molecular modeling; novel; preference; programs; rapid technique; research study; resistance mechanism; response; rho; termination factor; tool; transcription factor; ultraviolet irradiation

Abstract The regulation of gene expression plays a pivotal role in all aspects of biology, from the manner in which bacteria respond to their environment to the differentiation of tissues in higher eukaryotes. In the era of genomics, proteomics, and metabolomics, however, biologists are still bereft of a generally applicable method for rapid determination of the regulatory logic underlying the pattern of gene expression in a cell under a given set of conditions. This logic arises in large part from the binding of transcription factors (TFs) which can either repress or activate expression of nearby genes. The K99/R00 project proposed here aims to contribute a method, termed IPODHR, for obtaining a genome-wide snapshot of the transcriptional regulatory state of the cell, by providing the locations and identities of all transcription factors bound to the genome under physiological conditions. Understanding and quantitatively modeling the regulatory networks of bacterial cells is crucial both for the successful development of new antibiotics, and for the rational manipulation of microbial communities such as that in the human gut. IPODHR is superficially similar to chromatin immunoprecipitation (ChIP) experiments, but instead of isolat- ing a single protein (and any DNA bound to it), IPODHR isolates all protein-DNA complexes from crosslinked lysates, using the fact that these complexes partition to the organic-aqueous interphase during phenol-chloroform extraction. High throughput sequencing is used to reveal the locations of DNA-bound TFs. The resulting sig- nal, representing overall protein occupancy throughout the genome, is then split during data processing into contributions from different TFs and other DNA binding proteins, using a computational method that is currently under development. Thus, unlike ChIP, only one experiment is required to study the entire regulatory state of the cell under a given condition, and prior knowledge of the relevant TFs is not required. At present, my ongoing research (including plans for the mentored phase of the award) is focused on completing the experimental and computational aspects of the IPODHR framework. For the experimental com- ponent, only small refinements appear necessary to improve spatial resolution further; validation experiments and pilot applications will then be performed to confirm the sensitivity and specificity of the method to changing physiological conditions. The computational methods required for partitioning the IPODHR binding profile are also under active development, using a statistical model to assign peaks in the IPODHR density to particular factors. In the process of these development and validation experiments, follow-ups will target TF binding sites and specificities inferred from IPODHR data but not yet characterized in detail, further expanding our knowledge of the E. coli transcriptional regulatory network by revealing new TFs and interactions. Successful completion and application of IPODHR will provide the community with a transformative new tool to measure the transcrip- tional regulatory logic of bacteria without detailed prior knowledge of the transcription factors involved. Research planned for the independent phase will focus on the use of IPODHR, alongside other established methods in bacterial systems biology, to obtain a complete understanding of how rewiring transcriptional net- works can allow cells to adapt to novel conditions without the acquisition of new enzymatic capacities. I will focus initially on a previously discovered mutation of the termination factor Rho that improves cellular fitness under a variety of conditions, and appears to be representative of a broad class of mutations to housekeep- ing proteins that occur in evolving bacterial populations. IPODHR will allow measurement of the changes in transcriptional logic giving rise to previously observed adaptive outputs, and thus provide insight into the ex- act mechanisms through which the perturbations under study alter TF behavior to give rise to the observed changes in phenotype. As the rho mutation in question renders cells somewhat resistant to several classes of antibiotics, it will be particularly useful to compare the mechanisms of this resistance with other known paths to antibiotic tolerance. If progress on the proposed aims is sufficiently rapid, near the end of the grant period adaptation of IPODHR for use in bacteria other than E. coli may also begin. The massive scope of information provided by the method, and lack of any need for specific prior knowledge or manipulation of the target organism, mean that IPODHR has the promise to provide a huge advance in the understanding of transcriptional regulation in poorly studied microbes. These applications of IPODHR will form the backbone of an R01 proposal to be prepared during the late stages of the independent R00 phase.

摘要 基因表达的调节在生物学的各个方面都扮演着关键的角色，从细菌 在高等真核生物中对其环境的组织分化作出反应。在基因组学时代， 蛋白质组学和代谢组学，然而，生物学家仍然缺乏一种普遍适用的方法来快速 确定在给定集合下细胞中基因表达模式的潜在调控逻辑 条件的问题。这种逻辑在很大程度上源于转录因子(TF)的结合，转录因子可以是 抑制或激活邻近基因的表达。这里建议的K99/R00项目旨在为 一种称为IPODHR的方法，用于获得该基因转录调控状态的全基因组快照 细胞，通过提供在生理条件下与基因组结合的所有转录因子的位置和身份 条件。理解细菌细胞的调控网络并对其进行定量建模是至关重要的 为了成功地开发新的抗生素，以及合理地操纵微生物群落 比如在人类的肠道里。 IPODHR在本质上类似于染色质免疫沉淀(ChIP)实验，但不是分离的fi- IPODHR通过对单个蛋白质(以及与其结合的任何DNA)进行分离，将所有蛋白质-DNA复合体从交联体中分离出来 裂解产物，利用这些络合物在苯酚-氯仿过程中分配到有机-水界面的事实 拔牙。高通量测序用于揭示DNA结合的转录因子的位置。由此产生的信号- NAL代表整个基因组的总蛋白质占有率，然后在数据处理过程中被分解为 来自不同的转录因子和其他DNA结合蛋白的贡献，使用目前 正在开发中。因此，与芯片不同，只需要一个实验就可以研究整个监管状态 在给定条件下的单元格，并且不需要相关函数的先验知识。 目前，我正在进行的研究(包括奖项指导阶段的计划)集中在 完成IPODHR框架的实验和计算方面的工作。对于实验性的COM- Ponent，只有小的元素似乎是进一步提高空间分辨率所必需的；验证实验 然后将执行试点应用，以控制该方法对改变的敏感性和特异性(fifiCity 生理条件。划分IPODHR绑定PROfiLE所需的计算方法如下 也在积极开发中，使用统计模型将IPODHR密度的峰值分配给特定的 各种因素。在这些开发和验证实验的过程中，后续将以TF结合位点为目标 和从IPODHR数据推断的特殊fi城市，但尚未详细描述，进一步扩大了我们的知识 通过揭示新的转录因子和相互作用来研究大肠杆菌转录调控网络。成功完成 IPODHR的应用将为社区提供一种革命性的新工具来衡量转录- 细菌的常规调控逻辑，对所涉及的转录因子没有详细的先验知识。 计划在独立阶段进行的研究将侧重于IPODHR的使用，以及其他已建立的 方法在细菌系统生物学中，获得一个完整的理解如何重新连接转录网络- Works可以使细胞在不获得新的酶能力的情况下适应新的条件。这就做 最初关注之前发现的改善细胞fi完整性的终止因子Rho的突变 在不同的条件下，似乎代表了一大类对家务管理的突变- 出现在进化中的细菌种群中的ING蛋白质。IPODHR将允许测量以下方面的变化 转录逻辑，引起以前观察到的适应性输出，从而提供对前- 研究中的扰动通过改变TF行为以产生观察到的 表型的变化。因为有问题的Rho突变使细胞对几类 对于抗生素，将这种耐药性的机制与其他已知途径进行比较将特别有用。 抗生素耐受性。 如果在拟议的AIMS方面取得的进展确实很快，则在授权期接近尾声时对fi进行调整 用于除大肠杆菌以外的细菌也可能开始。该方法提供的信息范围很广， 以及不需要任何特定的fic先验知识或对目标生物体的操纵，意味着IPODHR 有希望为理解转录调控提供一个巨大的进步，在研究较少的领域 微生物。IPODHR的这些应用将构成将在#年期间编写的R01提案的主干 独立R00阶段的后期。