Genome-wide measurement of bacterial transcriptional regulatory states

细菌转录调控状态的全基因组测量

基本信息

批准号：
9008046
负责人：
Lydia Freddolino
金额：
$ 24.89万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
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/9008046
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 Directed Molecular Evolution 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 drug development extracellular follow-up genetic regulatory protein genome-wide improved information gathering insight metabolomics microbial community molecular modeling novel predicting response preference programs rapid technique research study resistance mechanism response rho termination factor tool transcription factor ultraviolet irradiation

项目摘要

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.

抽象的基因表达的调控在生物学的各个方面都发挥着关键作用，从细菌的表达方式到在基因组学时代，它们的环境对高等真核生物的组织分化做出反应。然而，生物学家仍然缺乏一种普遍适用的快速分析方法确定给定组下细胞中基因表达模式的调控逻辑这种逻辑在很大程度上源于转录因子（TF）的结合，转录因子可以这里提出的 K99/R00 项目旨在抑制或激活附近基因的表达。称为IPODHR的方法，用于获得转录调控状态的全基因组快照细胞，通过提供生理条件下与基因组结合的所有转录因子的位置和身份了解细菌细胞的调节网络并对其进行定量建模至关重要。成功开发新抗生素，合理调控微生物群落就像人类肠道中的那样。 IPODHR 表面上类似于染色质免疫沉淀 (ChIP) 实验，但不是分离的通过单个蛋白质（以及与其结合的任何 DNA），IPODHR 可将所有蛋白质-DNA 复合物从交联中分离出来裂解物，利用这些复合物在苯酚-氯仿过程中分配到有机-水相间的事实高通量测序用于揭示 DNA 结合 TF 的位置。 nal 代表整个基因组中蛋白质的总体占用率，然后在数据处理过程中分为来自不同 TF 和其他 DNA 结合蛋白的贡献，使用目前的计算方法因此，与 ChIP 不同，只需一项实验即可研究整个调控状态。给定条件下的单元格，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）主要集中在完成IPODHR框架的实验和计算方面的实验。观点认为，只需进行小的改进即可进一步提高验证实验；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划分IPODHR 结合谱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在这些开发和验证实验的过程中，后续将针对 TF 结合位点。以及从 IPODHR 数据推断但尚未详细表征的特异性，进一步扩展了我们的知识通过揭示新的 TF 和相互作用，成功完成了大肠杆菌转录调控网络的研究。 IPODHR 的应用将为社区提供一种变革性的新工具来衡量成绩单在没有详细了解所涉及的转录因子的情况下，研究细菌的调节逻辑。独立阶段计划的研究将重点关注 IPODHR 以及其他已建立的项目的使用细菌系统生物学中的方法，以获得对如何重新布线转录网络的完整理解我会的工作可以让细胞适应新的条件，而无需获得新的酶能力。首先关注先前发现的终止因子 Rho 的突变，该突变可改善细胞健康在各种条件下，并且似乎代表了一大类管家突变分析发生在进化的细菌群体中的蛋白质将允许测量变化。转录逻辑产生先前观察到的适应性输出，从而提供对前研究中的扰动改变 TF 行为以产生观察到的行为机制表型的变化，因为所讨论的 rho 突变使细胞对几类病毒有一定的抵抗力。抗生素，将这种耐药机制与其他已知途径进行比较特别有用对抗生素的耐受性。如果拟议目标的进展足够快，则在 IPODHR 的资助期即将结束时进行调整该方法也可以开始用于大肠杆菌以外的细菌，并且不需要任何特定的先验知识或对目标生物体的操作，意味着IPODHR 有希望为人们对研究不足的转录调控的理解提供巨大的进步 IPODHR 的这些应用将构成 R01 提案的支柱。独立R00阶段的后期阶段。