Mapping E. coli RNA Polymerase and Associated Transcription Factors and Identifying Promoters Genome-Wide

Davis, Sarah E.; Mooney, Rachel A.; Kanin, Elenita I.; Grass, Jeff; Landick, Robert; Ansari, Aseem Z.

doi:10.1016/b978-0-12-385120-8.00020-6

Cited by 22 publications

(20 citation statements)

References 25 publications

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“…ChIP assays were performed as previously described [33], except that the glycine, the formaldehyde, and the sodium phosphate mix were sparged with argon gas for minutes before use to maintain anaerobic conditions when required. Samples were immunoprecipitated using of RNA Polymerase antibody from NeoClone (W0004).…”

Section: Methodsmentioning

confidence: 99%

dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data

et al. 2013

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Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as to resolution. Applications of dPeak to ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.

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Section: Methodsmentioning

confidence: 99%

dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data

et al. 2013

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“…Comparison of fixed-cell images with images from live-cell procedures, including the use of microfluidic device, not only validates the fixed-cell technique, but also highlights the advantage and necessity in using the fixed-cell procedure to probe the organizations of the transcription machinery and the nucleoid in fast-growing cells (Jin et al, 2015). Note that the formaldehyde-fixed cells procedure has also been widely used in ChIP-chip assays to study the distribution of E. coli RNAP genome wide under different growth conditions (Davis et al, 2011; Grainger et al, 2005; Herring et al, 2005). Importantly, the fixed-cell images are consistent with extensive studies of E. coli genetics and physiology (Jin et al, 2013).…”

Section: Developments Of Imaging Tools and Procedures In Studying Thementioning

confidence: 99%

Nucleolus-like compartmentalization of the transcription machinery in fast-growing bacterial cells

Jin

Martín-Franco

Sun

et al. 2016

Critical Reviews in Biochemistry and Molecular Biology

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Overview. We have learned a great deal about RNA polymerase (RNA Pol), transcription factors, and the transcriptional regulation mechanisms in prokaryotes for specific genes, operons, or transcriptomes. However, we have only begun to understand how the transcription machinery is 3-dimensionally (3D) organized into bacterial chromosome territories to orchestrate the transcription process and to maintain harmony with the replication machinery in the cell. Much progress has been made recently in our understanding of the spatial organization of the transcription machinery in fast-growing Escherichia coli cells using state-of-the-art superresolution imaging techniques. Co-imaging of RNA polymerase (RNA Pol) with DNA and transcription elongation factors involved in ribosomal RNA (rRNA) synthesis and ribosome biogenesis has revealed similarities between bacteria and eukaryotes in the spatial organization of the transcription machinery for growth genes, most of which are rRNA genes. Evidence supports the notion that RNA Pol molecules are concentrated, forming foci at the clustering of rRNA operons resembling the eukaryotic nucleolus. RNA Pol foci are proposed to be active transcription factories for both rRNA genes expression and ribosome biogenesis to support maximal growth in optimal growing conditions. Thus, in fast-growing bacterial cells, RNA Pol foci mimic eukaryotic Pol I activity, and transcription factories resemble nucleolus-like compartmentation. In addition, the transcription and replication machineries are mostly segregated in space to avoid the conflict between the two major cellular functions in fast-growing cells.

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“…Several methods and review articles describe in detail how to perform ChIP-chip and ChIP-seq experiments [30-33], and we encourage readers to also review these publications. The methodology described in these articles differs and while each has been used to produced high-quality ChIP-seq data, we have found success following the method described by Davis et al [33]. Briefly, cells are grown in desired conditions to a designated growth phase and formaldehyde is added to crosslink proteins to DNA.…”

Section: : Chip-seq Sample Preparation and Data Generationmentioning

confidence: 99%

“…The method used for immunoprecipitation of the cross-linked DNA bound transcription was described by Davis et al [33]. Since library preparation and sequencing are still expensive and can take weeks to obtain data depending on access to sequencing cores, pilot experiments to examine the efficiency of ChIP before subjecting the samples to ChIP-seq are critical.…”

Section: : Chip-seq Sample Preparation and Data Generationmentioning

confidence: 99%

“…If targets of the TF are known, amplifying targets of the TF of interest, including high-affinity targets, moderate-affinity targets, and low-affinity targets as well as regions of the genome not bound by the TF of interest should provide sufficient information to optimize conditions. By comparing the enrichment by qPCR of target sites to those not bound by the TF, the efficacy and efficiency of the ChIP experiment can be evaluated [33]. However, if the TF being studied lacks known target sites, one approach is to use the promoter region of the TF gene, since autoregulation is a common feature of bacterial TFs [34].…”

Section: : Chip-seq Sample Preparation and Data Generationmentioning

confidence: 99%

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Defining bacterial regulons using ChIP-seq

Myers

Park

Beauchene

et al. 2015

Methods

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Chromatin immunoprecitation followed by high-throughput sequencing (ChIP-seq) is a powerful method that identifies protein-DNA binding sites in vivo. Recent studies have illustrated the value of ChIP-seq in studying transcription factor binding in various bacterial species under a variety of growth conditions. These results show that in addition to identifying binding sites, correlation of ChIP-seq data with expression data can reveal important information about bacterial regulons and regulatory networks. In this chapter, we provide an overview of the current state of knowledge about ChIP-seq methodology in bacteria, from sample preparation to raw data analysis. We also describe visualization and various bioinformatic analyses of processed ChIP-seq data.

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Mapping E. coli RNA Polymerase and Associated Transcription Factors and Identifying Promoters Genome-Wide

Cited by 22 publications

References 25 publications

dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data

dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data

Nucleolus-like compartmentalization of the transcription machinery in fast-growing bacterial cells

Defining bacterial regulons using ChIP-seq

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