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起着关键作用，从细菌 在高等真核生物中，它们对环境的反应是组织的分化。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， 在给定的条件下，确定细胞中基因表达模式的调控逻辑 的条件。这种逻辑在很大程度上源于转录因子（TF）的结合， 抑制或激活附近基因的表达。这里提出的K99/R 00项目旨在为 方法，称为IPODHR，用于获得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， 条件理解和定量建模细菌细胞的调控网络是至关重要的， 成功开发新的抗生素，合理控制微生物群落 例如在人的肠道中。 IPODHR与染色质免疫沉淀（ChIP）实验非常相似，但不是分离的， IPODHR使用单个蛋白质（以及与之结合的任何DNA），从交联的DNA中分离出所有蛋白质-DNA复合物。 裂解物，利用这些复合物在苯酚-氯仿过程中分配到有机-水界面的事实， 萃取高通量测序用于揭示DNA结合的TF的位置。结果是， 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成 来自不同TF和其他DNA结合蛋白的贡献，使用目前 正在开发中。因此，与ChIP不同，只需要一个实验来研究整个调控状态。 在给定的条件下的小区，并且不需要相关TF的先验知识。 目前，我正在进行的研究（包括该奖项的指导阶段的计划）的重点是 完成IPODHR框架的实验和计算方面。对于实验性的COM- 组件，只有小的改进似乎需要进一步提高空间分辨率;验证实验 然后将进行试点应用，以确认该方法对变化的敏感性和特异性 生理条件。划分IPODHR绑定配置文件所需的计算方法是 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 因素在这些开发和验证实验的过程中，后续将针对TF结合位点 从IPODHR数据推断但尚未详细描述的特异性，进一步扩展了我们的知识 的大肠大肠杆菌转录调控网络，揭示新的转录因子和相互作用。成功完成 IPODHR的应用将为社区提供一个变革性的新工具来衡量转录， 在没有涉及的转录因子的详细先验知识的情况下，细菌的常规调控逻辑。 计划在独立阶段进行的研究将侧重于使用国际人权观察站，以及其他已建立的 方法在细菌系统生物学，以获得如何重新布线转录网络的完整理解， 工程可以使细胞适应新的条件，而无需获得新的酶能力。我会 首先关注先前发现的终止因子Rho的突变，该突变提高了细胞的适应性 在各种条件下，似乎是一个广泛的一类突变管家的代表- 在进化的细菌种群中出现的蛋白质。IPODHR将允许测量 转录逻辑引起先前观察到的适应性输出，从而提供了对前 研究中的扰动改变TF行为的作用机制， 表型的变化。由于所讨论的rho突变使细胞对几类药物具有一定的抗性， 抗生素，将这种耐药性的机制与其他已知的途径进行比较将是特别有用的 抗生素耐受性的问题 如果拟议目标的进展足够迅速，在赠款期结束时， 用于除大肠杆菌以外的细菌。大肠杆菌也可以开始。该方法所提供的信息的巨大范围， 并且不需要对目标生物体的特定先验知识或操作，这意味着IPODHR 有希望提供一个巨大的进步，在理解转录调控的研究不足， 微生物IPODHR的这些应用程序将构成R 01提案的基础，该提案将在2008年12月15日至2009年12月31日期间编写。 独立R 00阶段的后期。