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Reconstruction and analysis of transcriptome regulatory network of Methanobrevibacter ruminantium M1. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2021.101489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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2
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Castellana S, Biagini T, Parca L, Petrizzelli F, Bianco SD, Vescovi AL, Carella M, Mazza T. A comparative benchmark of classic DNA motif discovery tools on synthetic data. Brief Bioinform 2021; 22:6341664. [PMID: 34351399 DOI: 10.1093/bib/bbab303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/08/2021] [Accepted: 07/15/2021] [Indexed: 01/01/2023] Open
Abstract
Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.
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Affiliation(s)
- Stefano Castellana
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Tommaso Biagini
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Luca Parca
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Francesco Petrizzelli
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy.,Department of Experimental Medicine, Sapienza University of Rome, Rome 00161, Italy
| | | | - Angelo Luigi Vescovi
- ISBReMIT Institute for Stem Cell Biology, Regenerative Medicine and Innovative Therapies, IRCSS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), 71013, Italy
| | - Massimo Carella
- Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Tommaso Mazza
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
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3
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Cui J, Shu J. Circulating microRNA trafficking and regulation: computational principles and practice. Brief Bioinform 2020; 21:1313-1326. [PMID: 31504144 PMCID: PMC7412956 DOI: 10.1093/bib/bbz079] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 01/18/2023] Open
Abstract
Rapid advances in genomics discovery tools and a growing realization of microRNA's implication in intercellular communication have led to a proliferation of studies of circulating microRNA sorting and regulation across cells and different species. Although sometimes, reaching controversial scientific discoveries and conclusions, these studies have yielded new insights in the functional roles of circulating microRNA and a plethora of analytical methods and tools. Here, we consider this body of work in light of key computational principles underpinning discovery of circulating microRNAs in terms of their sorting and targeting, with the goal of providing practical guidance for applications that is focused on the design and analysis of circulating microRNAs and their context-dependent regulation. We survey a broad range of informatics methods and tools that are available to the researcher, discuss their key features, applications and various unsolved problems and close this review with prospects and broader implication of this field.
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Affiliation(s)
- Juan Cui
- Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Jiang Shu
- Systems Biology and Biomedical Informatics Laboratory, Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
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Prathiviraj R, Chellapandi P. Modeling a global regulatory network of Methanothermobacter thermautotrophicus strain ∆H. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s13721-020-0223-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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5
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Chahal G, Tyagi S, Ramialison M. Navigating the non-coding genome in heart development and Congenital Heart Disease. Differentiation 2019; 107:11-23. [PMID: 31102825 DOI: 10.1016/j.diff.2019.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 01/14/2019] [Accepted: 05/06/2019] [Indexed: 12/12/2022]
Abstract
Congenital Heart Disease (CHD) is characterised by a wide range of cardiac defects, from mild to life-threatening, which occur in babies worldwide. To date, there is no cure to CHD, however, progress in surgery has reduced its mortality allowing children affected by CHD to reach adulthood. In an effort to understand its genetic basis, several studies involving whole-genome sequencing (WGS) of patients with CHD have been undertaken and generated a great wealth of information. The majority of putative causative mutations identified in WGS studies fall into the non-coding part of the genome. Unfortunately, due to the lack of understanding of the function of these non-coding mutations, it is challenging to establish a causal link between the non-coding mutation and the disease. Thus, here we review the state-of-the-art approaches to interpret non-coding mutations in the context of CHD and address the following questions: What are the non-coding sequences important for cardiac function? Which technologies are used to identify them? Which resources are available to analyse them? What mutations are expected in these non-coding sequences? Learning from developmental process, what is their expected role in CHD?
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Affiliation(s)
- Gulrez Chahal
- Australian Regenerative Medicine Institute (ARMI), 15 Innovation Walk, Monash University, Wellington Road, Clayton, 3800, VIC, Australia; Systems Biology Institute (SBI), Wellington Road, Clayton, 3800, VIC, Australia
| | - Sonika Tyagi
- School of Biological Sciences, Monash University, Wellington Road, Clayton, 3800, VIC, Australia; Australian Genome Research Facility, 305 Grattan Street, Melbourne, VIC, 3000, Australia.
| | - Mirana Ramialison
- Australian Regenerative Medicine Institute (ARMI), 15 Innovation Walk, Monash University, Wellington Road, Clayton, 3800, VIC, Australia; Systems Biology Institute (SBI), Wellington Road, Clayton, 3800, VIC, Australia.
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6
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A developed system based on nature-inspired algorithms for DNA motif finding process. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3398-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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7
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Abstract
Gene organization and control are described by models conceived in the 1960s. These models explain basic gene regulatory mechanisms and underpin current genome annotation. However, such models struggle to explain recent genome-scale observations. For example, accounts of RNA synthesis initiating within genes, widespread antisense transcription and non-canonical DNA binding by gene regulatory proteins are difficult to reconcile with traditional thinking. As a result, unexpected observations have often been dismissed and downstream consequences ignored. In this paper I will argue that, to fully understand the biology of bacterial chromosomes, we must embrace their hidden layers of complexity.
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Affiliation(s)
- David C Grainger
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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8
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Maynou J, Pairó E, Marco S, Perera A. Sequence information gain based motif analysis. BMC Bioinformatics 2015; 16:377. [PMID: 26553056 PMCID: PMC4640167 DOI: 10.1186/s12859-015-0811-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Accepted: 10/30/2015] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. RESULTS This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70% of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions. CONCLUSIONS Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://b2slab.upc.edu.
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Affiliation(s)
- Joan Maynou
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Pau Gargallo, 5, Barcelona, 08028, Spain.
- CIBER de Bioingeniería, Biomateriales y Biomedicina, Spain.
| | - Erola Pairó
- Institute for BioEngineering of Catalonia, balidiri Reixach 4-6, Barcelona, 08028, Spain.
- Electronics Department in the University of Barcelona (UB), Martí i Franquès, 1, Barcelona, 08028, Spain.
| | - Santiago Marco
- Institute for BioEngineering of Catalonia, balidiri Reixach 4-6, Barcelona, 08028, Spain.
- Electronics Department in the University of Barcelona (UB), Martí i Franquès, 1, Barcelona, 08028, Spain.
| | - Alexandre Perera
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Pau Gargallo, 5, Barcelona, 08028, Spain.
- CIBER de Bioingeniería, Biomateriales y Biomedicina, Spain.
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De Witte D, Van de Velde J, Decap D, Van Bel M, Audenaert P, Demeester P, Dhoedt B, Vandepoele K, Fostier J. BLSSpeller: exhaustive comparative discovery of conserved cis-regulatory elements. Bioinformatics 2015; 31:3758-66. [PMID: 26254488 PMCID: PMC4653392 DOI: 10.1093/bioinformatics/btv466] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 08/03/2015] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The accurate discovery and annotation of regulatory elements remains a challenging problem. The growing number of sequenced genomes creates new opportunities for comparative approaches to motif discovery. Putative binding sites are then considered to be functional if they are conserved in orthologous promoter sequences of multiple related species. Existing methods for comparative motif discovery usually rely on pregenerated multiple sequence alignments, which are difficult to obtain for more diverged species such as plants. As a consequence, misaligned regulatory elements often remain undetected. RESULTS We present a novel algorithm that supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in O.sativa and Zea mays. Furthermore, the method is shown to recover experimentally profiled ga2ox1-like KN1 binding sites in Z.mays. AVAILABILITY AND IMPLEMENTATION BLSSpeller was written in Java. Source code and manual are available at http://bioinformatics.intec.ugent.be/blsspeller CONTACT Klaas.Vandepoele@psb.vib-ugent.be or jan.fostier@intec.ugent.be. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dieter De Witte
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
| | - Jan Van de Velde
- Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Dries Decap
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
| | - Michiel Van Bel
- Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Pieter Audenaert
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
| | - Piet Demeester
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
| | - Bart Dhoedt
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
| | - Klaas Vandepoele
- Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Jan Fostier
- Department of Information Technology (INTEC), Ghent University-iMinds, Ghent, Belgium
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Szostak N, Royo F, Rybarczyk A, Szachniuk M, Blazewicz J, del Sol A, Falcon-Perez JM. Sorting signal targeting mRNA into hepatic extracellular vesicles. RNA Biol 2014; 11:836-44. [PMID: 24921245 DOI: 10.4161/rna.29305] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Intercellular communication mediated by extracellular vesicles has proved to play an important role in normal and pathological scenarios. However not too much information about the sorting mechanisms involved in loading the vesicles is available. Recently, our group has characterized the mRNA content of vesicles released by hepatic cellular systems, showing that a set of transcripts was particularly enriched in the vesicles in comparison with their intracellular abundance. In the current work, based on in silico bioinformatics tools, we have mapped a novel sequence of 12 nucleotides C[TA]G[GC][AGT]G[CT]C[AT]GG[GA], which is significantly enriched in the set of mRNAs that accumulate in extracellular vesicles. By including a 3'-UTR containing this sequence in a luciferase mRNA reporter, we have shown that in a hepatic cellular system this reporter mRNA was incorporated into extracellular vesicles. This study identifies a sorting signal in mRNAs that is involved in their enrichment in EVs, within a hepatic non-tumoral cellular model.
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Affiliation(s)
- Natalia Szostak
- Institute of Computing Science; Poznan University of Technology; Poznan, Poland
| | - Felix Royo
- Metabolomics Unit; CIC bioGUNE; CIBERehd; Bizkaia Technology Park; Derio, Bizkaia Spain
| | - Agnieszka Rybarczyk
- Institute of Computing Science; Poznan University of Technology; Poznan, Poland; Institute of Bioorganic Chemistry; Polish Academy of Sciences; Poznan, Poland
| | - Marta Szachniuk
- Institute of Computing Science; Poznan University of Technology; Poznan, Poland; Institute of Bioorganic Chemistry; Polish Academy of Sciences; Poznan, Poland
| | - Jacek Blazewicz
- Institute of Computing Science; Poznan University of Technology; Poznan, Poland; Institute of Bioorganic Chemistry; Polish Academy of Sciences; Poznan, Poland
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB); University of Luxembourg; Luxembourg
| | - Juan M Falcon-Perez
- Metabolomics Unit; CIC bioGUNE; CIBERehd; Bizkaia Technology Park; Derio, Bizkaia Spain; IKERBASQUE; Basque Foundation for Science; Bilbao, Spain
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11
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Pairó E, Maynou J, Vallverdú M, Caminal P, Marco S, Perera A. MEET: motif elements estimation toolkit. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2011:6483-6486. [PMID: 22255823 DOI: 10.1109/iembs.2011.6091600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
MEET is an R package that integrates a set of algorithms for the detection of transcription factor binding sites (TFBS). The MEET R package includes five motif searching algorithms: MEME/MAST(Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) and MATCH. In addition MEET allows the user to work with different alignment algorithms: MUSCLE (Multiple Sequence Comparison by Log-Expectation), ClustalW and MEME. The package can work in two modes, training and detection. The training mode allows the user to choose the best parameters of a detector. Once the parameters are chosen, the detection mode allows to analyze a genome looking for binding sites. Both modes can combine the different alignment and detection methods, offering multiple possibilities. Combining the alignments and the detection algorithms makes possible the comparison between detection models at the same level, without having to care about the differences produced during the alignment process. The MEET R package can be downloaded from http://sisbio.recerca.upc.edu/R/MEET_1.0. tar.gz.
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Affiliation(s)
- Erola Pairó
- Institut of BioEngineering of Catalonia, IBEC balidiri REixach 4-6, 08028 Barcelona, Spain.
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Oberto J. FITBAR: a web tool for the robust prediction of prokaryotic regulons. BMC Bioinformatics 2010; 11:554. [PMID: 21070640 PMCID: PMC3098098 DOI: 10.1186/1471-2105-11-554] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2010] [Accepted: 11/11/2010] [Indexed: 11/24/2022] Open
Abstract
Background The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale. Results FITBAR (Fast Investigation Tool for Bacterial and Archaeal Regulons) is a web service designed to identify new protein binding sites on fully sequenced prokaryotic genomes. This tool consists in a workbench where the significance of the predictions can be compared using different statistical methods, a feature not found in existing resources. The Local Markov Model and the Compound Importance Sampling algorithms have been implemented to compute the P-value of newly discovered binding sites. In addition, FITBAR provides two optimized genomic scanning algorithms using either log-odds or entropy-weighted position-specific scoring matrices. Other significant features include the production of a detailed genomic context map for each detected binding site and the export of the search results in spreadsheet and portable document formats. FITBAR discovery of a high affinity Escherichia coli NagC binding site was validated experimentally in vitro as well as in vivo and published. Conclusions FITBAR was developed in order to allow fast, accurate and statistically robust predictions of prokaryotic regulons. This feature constitutes the main advantage of this web tool over other matrix search programs and does not impair its performance. The web service is available at http://archaea.u-psud.fr/fitbar.
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Affiliation(s)
- Jacques Oberto
- Université Paris-Sud 11, Centre National de la Recherche Scientifique, UMR 8621, Institut de Génétique et Microbiologie, Orsay, France.
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13
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Abstract
Background Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. Methods We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Results Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. Conclusion We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of PWM (position weight matrix) motif model.
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Taylor RC, Singhal M, Weller J, Khoshnevis S, Shi L, McDermott J. A network inference workflow applied to virulence-related processes in Salmonella typhimurium. Ann N Y Acad Sci 2009; 1158:143-58. [PMID: 19348639 DOI: 10.1111/j.1749-6632.2008.03762.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene-expression data now available for that organism and describe our results obtained by following this workflow. The primary tool is one of the network-inference algorithms deployed in the Software Environment for Biological Network Inference (SEBINI). Specifically, we selected the algorithm called context likelihood of relatedness (CLR), which uses the mutual information contained in the gene-expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological Interaction Networks (CABIN) tool for further postanalysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.
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Affiliation(s)
- Ronald C Taylor
- Computational Biology & Bioinformatics Group, Pacific Northwest National Laboratory (U.S. Dept of Energy), Richland, Washington, USA.
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Shimada T, Ishihama A, Busby SJW, Grainger DC. The Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions. Nucleic Acids Res 2008; 36:3950-5. [PMID: 18515344 PMCID: PMC2475637 DOI: 10.1093/nar/gkn339] [Citation(s) in RCA: 121] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The Escherichia coli RutR protein is the master regulator of genes involved in pyrimidine catabolism. Here we have used chromatin immunoprecipitation in combination with DNA microarrays to measure the binding of RutR across the chromosome of exponentially growing E. coli cells. Twenty RutR-binding targets were identified and analysis of these targets generated a DNA consensus logo for RutR binding. Complementary in vitro binding assays showed high-affinity RutR binding to 16 of the 20 targets, with the four low-affinity RutR targets lacking predicted key binding determinants. Surprisingly, most of the DNA targets for RutR are located within coding segments of the genome and appear to have little or no effect on transcript levels in the conditions tested. This contrasts sharply with other E. coli transcription factors whose binding sites are primarily located in intergenic regions. We suggest that either RutR has yet undiscovered function or that evolution has been slow to eliminate non-functional DNA sites for RutR because they do not have an adverse effect on cell fitness.
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Affiliation(s)
- Tomohiro Shimada
- Department of Frontier Bioscience and Micro-Nano Technology Research Centre, Hosei University, Koganei, Tokyo 184-8584, Nippon Institute for Biological Science, Ome, Tokyo 198-0024, Japan
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