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Parise D, Parise MTD, Kataka E, Kato RB, List M, Tauch A, Azevedo VADC, Baumbach J. On the Consistency between Gene Expression and the Gene Regulatory Network of Corynebacterium glutamicum. NETWORK AND SYSTEMS MEDICINE 2021; 4:51-59. [PMID: 33796877 PMCID: PMC8006670 DOI: 10.1089/nsm.2020.0014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Transcriptional regulation of gene expression is crucial for the adaptation and survival of bacteria. Regulatory interactions are commonly modeled as Gene Regulatory Networks (GRNs) derived from experiments such as RNA-seq, microarray and ChIP-seq. While the reconstruction of GRNs is fundamental to decipher cellular function, even GRNs of economically important bacteria such as Corynebacterium glutamicum are incomplete. Materials and Methods: Here, we analyzed the predictive power of GRNs if used as in silico models for gene expression and investigated the consistency of the C. glutamicum GRN with gene expression data from the GEO database. Results: We assessed the consistency of the C. glutamicum GRN using real, as well as simulated, expression data and showed that GRNs alone cannot explain the expression profiles well. Conclusion: Our results suggest that more sophisticated mechanisms such as a combination of transcriptional, post-transcriptional regulation and signaling should be taken into consideration when analyzing and constructing GRNs.
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Affiliation(s)
- Doglas Parise
- TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Mariana Teixeira Dornelles Parise
- TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Evans Kataka
- TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Rodrigo Bentes Kato
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Markus List
- TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Andreas Tauch
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
| | | | - Jan Baumbach
- TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany
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Parise D, Teixeira Dornelles Parise M, Pinto Gomide AC, Figueira Aburjaile F, Bentes Kato R, Salgado-Albarrán M, Tauch A, Ariston de Carvalho Azevedo V, Baumbach J. The Transcriptional Regulatory Network of Corynebacterium pseudotuberculosis. Microorganisms 2021; 9:microorganisms9020415. [PMID: 33671149 PMCID: PMC7923171 DOI: 10.3390/microorganisms9020415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/11/2021] [Accepted: 02/14/2021] [Indexed: 12/26/2022] Open
Abstract
Corynebacterium pseudotuberculosis is a Gram-positive, facultative intracellular, pathogenic bacterium that infects several different hosts, yielding serious economic losses in livestock farming. It causes several diseases including oedematous skin disease (OSD) in buffaloes, ulcerative lymphangitis (UL) in horses, and caseous lymphadenitis (CLA) in sheep, goats and humans. Despite its economic and medical-veterinary importance, our understanding concerning this organism’s transcriptional regulatory mechanisms is still limited. Here, we review the state of the art knowledge on transcriptional regulatory mechanisms of this pathogenic species, covering regulatory interactions mediated by two-component systems, transcription factors and sigma factors. Key transcriptional regulatory players involved in virulence and pathogenicity of C. pseudotuberculosis, such as the PhoPR system and DtxR, are in the focus of this review, as these regulators are promising targets for future vaccine design and drug development. We conclude that more experimental studies are needed to further understand the regulatory repertoire of this important zoonotic pathogen, and that regulators are promising targets for future vaccine design and drug development.
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Affiliation(s)
- Doglas Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
- Correspondence: or
| | - Mariana Teixeira Dornelles Parise
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Anne Cybelle Pinto Gomide
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | | | - Rodrigo Bentes Kato
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Marisol Salgado-Albarrán
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Departamento de Ciencias Naturales, Universidad Autónoma Metropolitana Cuajimalpa, Mexico City 05348, Mexico
| | - Andreas Tauch
- Center for Biotechnology (CeBiTec), Bielefeld University, 33615 Bielefeld, Germany;
| | - Vasco Ariston de Carvalho Azevedo
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil; (A.C.P.G.); (R.B.K.); (V.A.d.C.A.)
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany; (M.T.D.P.); (M.S.-A.); (J.B.)
- Computational BioMedicine lab, Institute of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
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Parise MTD, Parise D, Kato RB, Pauling JK, Tauch A, Azevedo VADC, Baumbach J. CoryneRegNet 7, the reference database and analysis platform for corynebacterial gene regulatory networks. Sci Data 2020; 7:142. [PMID: 32393779 PMCID: PMC7214426 DOI: 10.1038/s41597-020-0484-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/15/2020] [Indexed: 12/21/2022] Open
Abstract
We present the newest version of CoryneRegNet, the reference database for corynebacterial regulatory interactions, available at www.exbio.wzw.tum.de/coryneregnet/. The exponential growth of next-generation sequencing data in recent years has allowed a better understanding of bacterial molecular mechanisms. Transcriptional regulation is one of the most important mechanisms for bacterial adaptation and survival. These mechanisms may be understood via an organism's network of regulatory interactions. Although the Corynebacterium genus is important in medical, veterinary and biotechnological research, little is known concerning the transcriptional regulation of these bacteria. Here, we unravel transcriptional regulatory networks (TRNs) for 224 corynebacterial strains by utilizing genome-scale transfer of TRNs from four model organisms and assigning statistical significance values to all predicted regulations. As a result, the number of corynebacterial strains with TRNs increased twenty times and the back-end and front-end were reimplemented to support new features as well as future database growth. CoryneRegNet 7 is the largest TRN database for the Corynebacterium genus and aids in elucidating transcriptional mechanisms enabling adaptation, survival and infection.
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Affiliation(s)
- Mariana Teixeira Dornelles Parise
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
| | - Doglas Parise
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Rodrigo Bentes Kato
- Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Josch Konstantin Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Andreas Tauch
- Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
| | | | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
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Galán-Vásquez E, Sánchez-Osorio I, Martínez-Antonio A. Transcription Factors Exhibit Differential Conservation in Bacteria with Reduced Genomes. PLoS One 2016; 11:e0146901. [PMID: 26766575 PMCID: PMC4713081 DOI: 10.1371/journal.pone.0146901] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/23/2015] [Indexed: 11/18/2022] Open
Abstract
The description of transcriptional regulatory networks has been pivotal in the understanding of operating principles under which organisms respond and adapt to varying conditions. While the study of the topology and dynamics of these networks has been the subject of considerable work, the investigation of the evolution of their topology, as a result of the adaptation of organisms to different environmental conditions, has received little attention. In this work, we study the evolution of transcriptional regulatory networks in bacteria from a genome reduction perspective, which manifests itself as the loss of genes at different degrees. We used the transcriptional regulatory network of Escherichia coli as a reference to compare 113 smaller, phylogenetically-related γ-proteobacteria, including 19 genomes of symbionts. We found that the type of regulatory action exerted by transcription factors, as genomes get progressively smaller, correlates well with their degree of conservation, with dual regulators being more conserved than repressors and activators in conditions of extreme reduction. In addition, we found that the preponderant conservation of dual regulators might be due to their role as both global regulators and nucleoid-associated proteins. We summarize our results in a conceptual model of how each TF type is gradually lost as genomes become smaller and give a rationale for the order in which this phenomenon occurs.
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Affiliation(s)
- Edgardo Galán-Vásquez
- Center for Research and Advanced Studies of the National Polytechnic Institute, Campus Irapuato, Genetic Engineering Department, Cinvestav, Km. 9.6 Libramiento Norte Carr. Irapuato-León 36821, Irapuato Gto, México
| | - Ismael Sánchez-Osorio
- Center for Research and Advanced Studies of the National Polytechnic Institute, Campus Irapuato, Genetic Engineering Department, Cinvestav, Km. 9.6 Libramiento Norte Carr. Irapuato-León 36821, Irapuato Gto, México
| | - Agustino Martínez-Antonio
- Center for Research and Advanced Studies of the National Polytechnic Institute, Campus Irapuato, Genetic Engineering Department, Cinvestav, Km. 9.6 Libramiento Norte Carr. Irapuato-León 36821, Irapuato Gto, México
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Barah P, B N MN, Jayavelu ND, Sowdhamini R, Shameer K, Bones AM. Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses. Nucleic Acids Res 2015; 44:3147-64. [PMID: 26681689 PMCID: PMC4838348 DOI: 10.1093/nar/gkv1463] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 11/28/2015] [Indexed: 11/25/2022] Open
Abstract
Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.
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Affiliation(s)
- Pankaj Barah
- Cell, Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and Technology, Trondheim N-7491, Norway
| | - Mahantesha Naika B N
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bangalore 560 065, India
| | - Naresh Doni Jayavelu
- Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim N-7491, Norway
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bangalore 560 065, India
| | - Khader Shameer
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bangalore 560 065, India
| | - Atle M Bones
- Cell, Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and Technology, Trondheim N-7491, Norway
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Abreu VAC, Almeida S, Tiwari S, Hassan SS, Mariano D, Silva A, Baumbach J, Azevedo V, Röttger R. CMRegNet-An interspecies reference database for corynebacterial and mycobacterial regulatory networks. BMC Genomics 2015; 16:452. [PMID: 26062809 PMCID: PMC4464113 DOI: 10.1186/s12864-015-1631-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 05/14/2015] [Indexed: 11/10/2022] Open
Abstract
Background Organisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations. One of the most important mechanisms is transcriptional gene regulation. In-depth study of the transcriptional gene regulatory network can lead to various practical applications, creating a greater understanding of how organisms control their cellular behavior. Description In this work, we present a new database, CMRegNet for the gene regulatory networks of Corynebacterium glutamicum ATCC 13032 and Mycobacterium tuberculosis H37Rv. We furthermore transferred the known networks of these model organisms to 18 other non-model but phylogenetically close species (target organisms) of the CMNR group. In comparison to other network transfers, for the first time we utilized two model organisms resulting into a more diverse and complete network of the target organisms. Conclusion CMRegNet provides easy access to a total of 3,103 known regulations in C. glutamicum ATCC 13032 and M. tuberculosis H37Rv and to 38,940 evolutionary conserved interactions for 18 non-model species of the CMNR group. This makes CMRegNet to date the most comprehensive database of regulatory interactions of CMNR bacteria. The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet.
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Affiliation(s)
- Vinicius A C Abreu
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Sintia Almeida
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Sandeep Tiwari
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Syed Shah Hassan
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Diego Mariano
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Artur Silva
- Institute of Biological Sciences, Federal University of Pará, Belém, Pará, Brazil.
| | - Jan Baumbach
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
| | - Vasco Azevedo
- Graduate Program in Bioinformatics, Institute of Biological Sciences, Federal University of Minas Gerais (Universidade Federal de Minas Gerais), Belo Horizonte, Minas Gerais, Brazil.
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark. .,Computational Systems Biology, Max Planck Institute for Informatics, Campus E 2.1, 66123, Saarbrucken, Germany.
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Every Site Counts: Submitting Transcription Factor-Binding Site Information through the CollecTF Portal. J Bacteriol 2015; 197:2454-7. [PMID: 26013488 DOI: 10.1128/jb.00031-15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Experimentally verified transcription factor-binding sites represent an information-rich and highly applicable data type that aptly summarizes the results of time-consuming experiments and inference processes. Currently, there is no centralized repository for this type of data, which is routinely embedded in articles and extremely hard to mine. CollecTF provides the first standardized resource for submission and deposition of these data into the NCBI RefSeq database, maximizing its accessibility and prompting the community to adopt direct submission policies.
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López García de Lomana A, Schäuble S, Valenzuela J, Imam S, Carter W, Bilgin DD, Yohn CB, Turkarslan S, Reiss DJ, Orellana MV, Price ND, Baliga NS. Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii. BIOTECHNOLOGY FOR BIOFUELS 2015; 8:207. [PMID: 26633994 PMCID: PMC4667458 DOI: 10.1186/s13068-015-0391-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 11/17/2015] [Indexed: 05/08/2023]
Abstract
BACKGROUND Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii. RESULTS We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal. CONCLUSIONS In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.
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Affiliation(s)
| | - Sascha Schäuble
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Jena University Language and Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, Jena, Germany
- />Research Group Theoretical Systems Biology, Friedrich-Schiller-University Jena, Jena, Germany
| | - Jacob Valenzuela
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Saheed Imam
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Warren Carter
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | | | | | - Serdar Turkarslan
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - David J. Reiss
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
| | - Mónica V. Orellana
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Polar Science Center, University of Washington, Seattle, WA USA
| | - Nathan D. Price
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Departments of Bioengineering and Computer Science and Engineering, University of Washington, Seattle, WA USA
- />Molecular and Cellular Biology Program, University of Washington, Seattle, WA USA
| | - Nitin S. Baliga
- />Institute for Systems Biology, 401 Terry Ave N, Seattle, 98109 WA USA
- />Departments of Biology and Microbiology, University of Washington, Seattle, WA USA
- />Molecular and Cellular Biology Program, University of Washington, Seattle, WA USA
- />Lawrence Berkeley National Lab, Berkeley, CA USA
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Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression. PLoS One 2013; 8:e82457. [PMID: 24349289 PMCID: PMC3857773 DOI: 10.1371/journal.pone.0082457] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Accepted: 10/23/2013] [Indexed: 12/17/2022] Open
Abstract
Neuroblastoma is the most common extracranial tumor and a major cause of infant cancer mortality worldwide. Despite its importance, little is known about its molecular mechanisms. A striking feature of this tumor is its clinical heterogeneity. Possible outcomes range from aggressive invasion to other tissues, causing patient death, to spontaneous disease regression or differentiation into benign ganglioneuromas. Several efforts have been made in order to find tumor progression markers. In this work, we have reconstructed the neuroblastoma regulatory network using an information-theoretic approach in order to find genes involved in tumor progression and that could be used as outcome predictors or as therapeutic targets. We have queried the reconstructed neuroblastoma regulatory network using an aggressive neuroblastoma metastasis gene signature in order to find its master regulators (MRs). MRs expression profiles were then investigated in other neuroblastoma datasets so as to detect possible clinical significance. Our analysis pointed MAX as one of the MRs of neuroblastoma progression. We have found that higher MAX expression correlated with favorable patient outcomes. We have also found that MAX expression and protein levels were increased during neuroblastoma SH-SY5Y cells differentiation. We propose that MAX is involved in neuroblastoma progression, possibly increasing cell differentiation by means of regulating the availability of MYC:MAX heterodimers. This mechanism is consistent with the results found in our SH-SY5Y differentiation protocol, suggesting that MAX has a more central role in these cells differentiation than previously reported. Overexpression of MAX has been identified as anti-tumorigenic in other works, but, to our knowledge, this is the first time that the link between the expression of this gene and malignancy was verified under physiological conditions.
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Chandrasekaran S, Price ND. Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput Biol 2013; 9:e1003370. [PMID: 24348226 PMCID: PMC3857774 DOI: 10.1371/journal.pcbi.1003370] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Accepted: 10/16/2013] [Indexed: 01/01/2023] Open
Abstract
There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10−172), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10−14) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/ Cellular networks, such as metabolic and transcriptional regulatory networks (TRNs), do not operate independently but work together in unison to determine cellular phenotypes. Further, the phenotype and architecture of one network constrains the topology of other networks. Hence, it is critical to study network components and interactions in the context of the entire cell. Typically, efforts to reconstruct TRNs focus only on immediately proximal data such as gene co-expression and transcription factor (TF)-binding. Herein, we take a different strategy by linking candidate TRNs with the metabolic network to predict systems-level responses such as growth phenotypes of TF knockout strains, and compare predictions with experimental phenotype data to select amongst the candidate TRNs. Our approach goes beyond traditional data integration approaches for network inference and refinement by using a predictive network model (metabolism) to refine another network model (regulation) – thus providing an alternative avenue to this area of research. Understanding how the networks function together in a cell will pave the way for synthetic biology and has a wide-range of applications in biotechnology, drug discovery and diagnostics. Further we demonstrate how metabolic models can integrate and reconcile inconsistencies across different data-types.
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Affiliation(s)
- Sriram Chandrasekaran
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign, Illinois, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign, Illinois, United States of America
- * E-mail:
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11
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Leyn SA, Kazanov MD, Sernova NV, Ermakova EO, Novichkov PS, Rodionov DA. Genomic reconstruction of the transcriptional regulatory network in Bacillus subtilis. J Bacteriol 2013; 195:2463-73. [PMID: 23504016 PMCID: PMC3676070 DOI: 10.1128/jb.00140-13] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 03/11/2013] [Indexed: 12/26/2022] Open
Abstract
The adaptation of microorganisms to their environment is controlled by complex transcriptional regulatory networks (TRNs), which are still only partially understood even for model species. Genome scale annotation of regulatory features of genes and TRN reconstruction are challenging tasks of microbial genomics. We used the knowledge-driven comparative-genomics approach implemented in the RegPredict Web server to infer TRN in the model Gram-positive bacterium Bacillus subtilis and 10 related Bacillales species. For transcription factor (TF) regulons, we combined the available information from the DBTBS database and the literature with bioinformatics tools, allowing inference of TF binding sites (TFBSs), comparative analysis of the genomic context of predicted TFBSs, functional assignment of target genes, and effector prediction. For RNA regulons, we used known RNA regulatory motifs collected in the Rfam database to scan genomes and analyze the genomic context of new RNA sites. The inferred TRN in B. subtilis comprises regulons for 129 TFs and 24 regulatory RNA families. First, we analyzed 66 TF regulons with previously known TFBSs in B. subtilis and projected them to other Bacillales genomes, resulting in refinement of TFBS motifs and identification of novel regulon members. Second, we inferred motifs and described regulons for 28 experimentally studied TFs with previously unknown TFBSs. Third, we discovered novel motifs and reconstructed regulons for 36 previously uncharacterized TFs. The inferred collection of regulons is available in the RegPrecise database (http://regprecise.lbl.gov/) and can be used in genetic experiments, metabolic modeling, and evolutionary analysis.
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Affiliation(s)
- Semen A. Leyn
- Sanford-Burnham Medical Research Institute, La Jolla, California, USA
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Marat D. Kazanov
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Natalia V. Sernova
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina O. Ermakova
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | | | - Dmitry A. Rodionov
- Sanford-Burnham Medical Research Institute, La Jolla, California, USA
- A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
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12
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Faria JP, Overbeek R, Xia F, Rocha M, Rocha I, Henry CS. Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models. Brief Bioinform 2013; 15:592-611. [DOI: 10.1093/bib/bbs071] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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13
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High-resolution detection of DNA binding sites of the global transcriptional regulator GlxR in Corynebacterium glutamicum. Microbiology (Reading) 2013; 159:12-22. [DOI: 10.1099/mic.0.062059-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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14
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Balasubramanian D, Schneper L, Kumari H, Mathee K. A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence. Nucleic Acids Res 2012; 41:1-20. [PMID: 23143271 PMCID: PMC3592444 DOI: 10.1093/nar/gks1039] [Citation(s) in RCA: 317] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Pseudomonas aeruginosa is a metabolically versatile bacterium that is found in a wide range of biotic and abiotic habitats. It is a major human opportunistic pathogen causing numerous acute and chronic infections. The critical traits contributing to the pathogenic potential of P. aeruginosa are the production of a myriad of virulence factors, formation of biofilms and antibiotic resistance. Expression of these traits is under stringent regulation, and it responds to largely unidentified environmental signals. This review is focused on providing a global picture of virulence gene regulation in P. aeruginosa. In addition to key regulatory pathways that control the transition from acute to chronic infection phenotypes, some regulators have been identified that modulate multiple virulence mechanisms. Despite of a propensity for chaotic behaviour, no chaotic motifs were readily observed in the P. aeruginosa virulence regulatory network. Having a ‘birds-eye’ view of the regulatory cascades provides the forum opportunities to pose questions, formulate hypotheses and evaluate theories in elucidating P. aeruginosa pathogenesis. Understanding the mechanisms involved in making P. aeruginosa a successful pathogen is essential in helping devise control strategies.
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Affiliation(s)
- Deepak Balasubramanian
- Department of Biological Sciences, College of Arts and Science, Florida International University, Miami, FL 33199, USA
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15
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Tsoy OV, Pyatnitskiy MA, Kazanov MD, Gelfand MS. Evolution of transcriptional regulation in closely related bacteria. BMC Evol Biol 2012; 12:200. [PMID: 23039862 PMCID: PMC3735044 DOI: 10.1186/1471-2148-12-200] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Accepted: 09/26/2012] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The exponential growth of the number of fully sequenced genomes at varying taxonomic closeness allows one to characterize transcriptional regulation using comparative-genomics analysis instead of time-consuming experimental methods. A transcriptional regulatory unit consists of a transcription factor, its binding site and a regulated gene. These units constitute a graph which contains so-called "network motifs", subgraphs of a given structure. Here we consider genomes of closely related Enterobacteriales and estimate the fraction of conserved network motifs and sites as well as positions under selection in various types of non-coding regions. RESULTS Using a newly developed technique, we found that the highest fraction of positions under selection, approximately 50%, was observed in synvergon spacers (between consecutive genes from the same strand), followed by ~45% in divergon spacers (common 5'-regions), and ~10% in convergon spacers (common 3'-regions). The fraction of selected positions in functional regions was higher, 60% in transcription factor-binding sites and ~45% in terminators and promoters. Small, but significant differences were observed between Escherichia coli and Salmonella enterica. This fraction is similar to the one observed in eukaryotes.The conservation of binding sites demonstrated some differences between types of regulatory units. In E. coli, strains the interactions of the type "local transcriptional factor gene" turned out to be more conserved in feed-forward loops (FFLs) compared to non-motif interactions. The coherent FFLs tend to be less conserved than the incoherent FFLs. A natural explanation is that the former imply functional redundancy. CONCLUSIONS A naïve hypothesis that FFL would be highly conserved turned out to be not entirely true: its conservation depends on its status in the transcriptional network and also from its usage. The fraction of positions under selection in intergenic regions of bacterial genomes is roughly similar to that of eukaryotes. Known regulatory sites explain 20±5% of selected positions.
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Affiliation(s)
- Olga V Tsoy
- Institute for Information Transmission Problems, RAS, Bolshoi Karetny per. 19, Moscow 127994, Russia
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16
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Röttger R, Rückert U, Taubert J, Baumbach J. How little do we actually know? On the size of gene regulatory networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:1293-1300. [PMID: 22585140 DOI: 10.1109/tcbb.2012.71] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The National Center for Biotechnology Information (NCBI) recently announced the availability of whole genome sequences for more than 1,000 species. And the number of sequenced individual organisms is growing. Ongoing improvement of DNA sequencing technology will further contribute to this, enabling large-scale evolution and population genetics studies. However, the availability of sequence information is only the first step in understanding how cells survive, reproduce, and adjust their behavior. The genetic control behind organized development and adaptation of complex organisms still remains widely undetermined. One major molecular control mechanism is transcriptional gene regulation. The direct juxtaposition of the total number of sequenced species to the handful of model organisms with known regulations is surprising. Here, we investigate how little we even know about these model organisms. We aim to predict the sizes of the whole-organism regulatory networks of seven species. In particular, we provide statistical lower bounds for the expected number of regulations. For Escherichia coli we estimate at most 37 percent of the expected gene regulatory interactions to be already discovered, 24 percent for Bacillus subtilis, and <3% human, respectively. We conclude that even for our best researched model organisms we still lack substantial understanding of fundamental molecular control mechanisms, at least on a large scale.
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Affiliation(s)
- Richard Röttger
- Computational Systems Biology Group, Max Planck Institute for Informatics, Saarbrucken 66123, Germany.
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17
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Lo K, Raftery AE, Dombek KM, Zhu J, Schadt EE, Bumgarner RE, Yeung KY. Integrating external biological knowledge in the construction of regulatory networks from time-series expression data. BMC SYSTEMS BIOLOGY 2012; 6:101. [PMID: 22898396 PMCID: PMC3465231 DOI: 10.1186/1752-0509-6-101] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2012] [Accepted: 07/24/2012] [Indexed: 01/27/2023]
Abstract
BACKGROUND Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. RESULTS We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. CONCLUSIONS We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.
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Affiliation(s)
- Kenneth Lo
- Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA
| | - Adrian E Raftery
- Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA
| | - Kenneth M Dombek
- Department of Biochemistry, University of Washington, Box 357350, Seattle, WA, 98195, USA
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA
| | - Roger E Bumgarner
- Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA
| | - Ka Yee Yeung
- Department of Microbiology, University of Washington, Box 358070, Seattle, WA, 98195, USA
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18
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Cornish JP, Matthews F, Thomas JR, Erill I. Inference of self-regulated transcriptional networks by comparative genomics. Evol Bioinform Online 2012; 8:449-61. [PMID: 23032607 PMCID: PMC3422134 DOI: 10.4137/ebo.s9205] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The assumption of basic properties, like self-regulation, in simple transcriptional regulatory networks can be exploited to infer regulatory motifs from the growing amounts of genomic and meta-genomic data. These motifs can in principle be used to elucidate the nature and scope of transcriptional networks through comparative genomics. Here we assess the feasibility of this approach using the SOS regulatory network of Gram-positive bacteria as a test case. Using experimentally validated data, we show that the known regulatory motif can be inferred through the assumption of self-regulation. Furthermore, the inferred motif provides a more robust search pattern for comparative genomics than the experimental motifs defined in reference organisms. We take advantage of this robustness to generate a functional map of the SOS response in Gram-positive bacteria. Our results reveal definite differences in the composition of the LexA regulon between Firmicutes and Actinobacteria, and confirm that regulation of cell-division inhibition is a widespread characteristic of this network among Gram-positive bacteria.
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Affiliation(s)
- Joseph P Cornish
- Department of Biological Sciences, University of Maryland Baltimore County
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19
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Ullah MO, Müller M, Hooiveld GJEJ. An Integrated Statistical Approach to Compare Transcriptomics Data Across Experiments: A Case Study on the Identification of Candidate Target Genes of the Transcription Factor PPARα. Bioinform Biol Insights 2012; 6:145-54. [PMID: 22783064 PMCID: PMC3388742 DOI: 10.4137/bbi.s9529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
An effective strategy to elucidate the signal transduction cascades activated by a transcription factor is to compare the transcriptional profiles of wild type and transcription factor knockout models. Many statistical tests have been proposed for analyzing gene expression data, but most tests are based on pair-wise comparisons. Since the analysis of microarrays involves the testing of multiple hypotheses within one study, it is generally accepted that one should control for false positives by the false discovery rate (FDR). However, it has been reported that this may be an inappropriate metric for comparing data across different experiments. Here we propose an approach that addresses the above mentioned problem by the simultaneous testing and integration of the three hypotheses (contrasts) using the cell means ANOVA model. These three contrasts test for the effect of a treatment in wild type, gene knockout, and globally over all experimental groups. We illustrate our approach on microarray experiments that focused on the identification of candidate target genes and biological processes governed by the fatty acid sensing transcription factor PPARα in liver. Compared to the often applied FDR based across experiment comparison, our approach identified a conservative but less noisy set of candidate genes with same sensitivity and specificity. However, our method had the advantage of properly adjusting for multiple testing while integrating data from two experiments, and was driven by biological inference. Taken together, in this study we present a simple, yet efficient strategy to compare differential expression of genes across experiments while controlling for multiple hypothesis testing.
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Affiliation(s)
- Mohammad Ohid Ullah
- Nutrition, Metabolism and Genomics group, Division of Human Nutrition, Wageningen University, The Netherlands
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20
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Leuze MR, Karpinets TV, Syed MH, Beliaev AS, Uberbacher EC. Binding Motifs in Bacterial Gene Promoters Modulate Transcriptional Effects of Global Regulators CRP and ArcA. GENE REGULATION AND SYSTEMS BIOLOGY 2012; 6:93-107. [PMID: 22701314 PMCID: PMC3370831 DOI: 10.4137/grsb.s9357] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Bacterial gene regulation involves transcription factors (TF) that bind to DNA recognition sequences in operon promoters. These recognition sequences, many of which are palindromic, are known as regulatory elements or transcription factor binding sites (TFBS). Some TFs are global regulators that can modulate the expression of hundreds of genes. In this study we examine global regulator half-sites, where a half-site, which we shall call a binding motif (BM), is one half of a palindromic TFBS. We explore the hypothesis that the number of BMs plays an important role in transcriptional regulation, examining empirical data from transcriptional profiling of the CRP and ArcA regulons. We compare the power of BM counts and of full TFBS characteristics to predict induced transcriptional activity. We find that CRP BM counts have a nonlinear effect on CRP-dependent transcriptional activity and predict this activity better than full TFBS quality or location.
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Affiliation(s)
- Michael R. Leuze
- Computer Science and Mathematics Division, Oak Ridge National
Laboratory, Oak Ridge, TN, USA
| | - Tatiana V. Karpinets
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN,
USA
- Department of Plant Sciences, University of Tennessee, Knoxville,
TN, USA
| | - Mustafa H. Syed
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN,
USA
| | - Alexander S. Beliaev
- Biological Sciences Division, Pacific Northwest National Laboratory,
Richland, WA, USA
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21
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Fu Q, Lemmens K, Sanchez-Rodriguez A, Thijs IM, Meysman P, Sun H, Fierro AC, Engelen K, Marchal K. Directed module detection in a large-scale expression compendium. Methods Mol Biol 2012; 804:131-165. [PMID: 22144152 DOI: 10.1007/978-1-61779-361-5_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Public online microarray databases contain tremendous amounts of expression data. Mining these data sources can provide a wealth of information on the underlying transcriptional networks. In this chapter, we illustrate how the web services COLOMBOS and DISTILLER can be used to identify condition-dependent coexpression modules by exploring compendia of public expression data. COLOMBOS is designed for user-specified query-driven analysis, whereas DISTILLER generates a global regulatory network overview. The user is guided through both web services by means of a case study in which condition-dependent coexpression modules comprising a gene of interest (i.e., "directed") are identified.
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Affiliation(s)
- Qiang Fu
- Centre of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Heverlee, Belgium
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22
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Baumbach J. On the power and limits of evolutionary conservation--unraveling bacterial gene regulatory networks. Nucleic Acids Res 2010; 38:7877-84. [PMID: 20699275 PMCID: PMC3001071 DOI: 10.1093/nar/gkq699] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Center for Biotechnology Information (NCBI) recently announced ‘1000 prokaryotic genomes are now completed and available in the Genome database’. The increasing trend will provide us with thousands of sequenced microbial organisms over the next years. However, this is only the first step in understanding how cells survive, reproduce and adapt their behavior while being exposed to changing environmental conditions. One major control mechanism is transcriptional gene regulation. Here, striking is the direct juxtaposition of the handful of bacterial model organisms to the 1000 prokaryotic genomes. Next-generation sequencing technologies will further widen this gap drastically. However, several computational approaches have proven to be helpful. The main idea is to use the known transcriptional regulatory network of reference organisms as template in order to unravel evolutionarily conserved gene regulations in newly sequenced species. This transfer essentially depends on the reliable identification of several types of conserved DNA sequences. We decompose this problem into three computational processes, review the state of the art and illustrate future perspectives.
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Affiliation(s)
- Jan Baumbach
- Algorithms Group, International Computer Science Institute, Berkeley, USA.
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23
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Built shallow to maintain homeostasis and persistent infection: insight into the transcriptional regulatory network of the gastric human pathogen Helicobacter pylori. PLoS Pathog 2010; 6:e1000938. [PMID: 20548942 PMCID: PMC2883586 DOI: 10.1371/journal.ppat.1000938] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Transcriptional regulatory networks (TRNs) transduce environmental signals into coordinated output expression of the genome. Accordingly, they are central for the adaptation of bacteria to their living environments and in host-pathogen interactions. Few attempts have been made to describe a TRN for a human pathogen, because even in model organisms, such as Escherichia coli, the analysis is hindered by the large number of transcription factors involved. In light of the paucity of regulators, the gastric human pathogen Helicobacter pylori represents a very appealing system for understanding how bacterial TRNs are wired up to support infection in the host. Herein, we review and analyze the available molecular and "-omic" data in a coherent ensemble, including protein-DNA and protein-protein interactions relevant for transcriptional control of pathogenic responses. The analysis covers approximately 80% of the annotated H. pylori regulators, and provides to our knowledge the first in-depth description of a TRN for an important pathogen. The emerging picture indicates a shallow TRN, made of four main modules (origons) that process the physiological responses needed to colonize the gastric niche. Specific network motifs confer distinct transcriptional response dynamics to the TRN, while long regulatory cascades are absent. Rather than having a plethora of specialized regulators, the TRN of H. pylori appears to transduce separate environmental inputs by using different combinations of a small set of regulators.
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24
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Danielli A, Scarlato V. Regulatory circuits in Helicobacter pylori : network motifs and regulators involved in metal-dependent responses. FEMS Microbiol Rev 2010; 34:738-52. [PMID: 20579104 DOI: 10.1111/j.1574-6976.2010.00233.x] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The ability of Helicobacter pylori, one of the most successful human bacterial pathogens, to colonize the acidic gastric niche persistently, depends on the proper homeostasis of intracellular metal ions, needed as cofactors of essential metallo-proteins involved in acid acclimation, respiration and detoxification. This fundamental task is controlled at the transcriptional level mainly by the regulators Fur and NikR, involved in iron homeostasis and nickel response, respectively. Herein, we review the molecular mechanisms that underlie the activity of these key pleiotropic regulators. In addition, we will focus on their involvement in the transcriptional regulatory network of the bacterium, pinpointing a surprising complexity of network motifs that interconnects them and their gene targets. These motifs appear to confer versatile dynamics of metal-dependent responses by extensive horizontal connections between the regulators and feedback control of metal-cofactor availability.
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25
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Hawkins T, Chitale M, Kihara D. Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP. BMC Bioinformatics 2010; 11:265. [PMID: 20482861 PMCID: PMC2882935 DOI: 10.1186/1471-2105-11-265] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2009] [Accepted: 05/19/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. RESULTS Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. CONCLUSION The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.
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Affiliation(s)
- Troy Hawkins
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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26
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Madar A, Greenfield A, Vanden-Eijnden E, Bonneau R. DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS One 2010; 5:e9803. [PMID: 20339551 PMCID: PMC2842436 DOI: 10.1371/journal.pone.0009803] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2009] [Accepted: 01/18/2010] [Indexed: 01/10/2023] Open
Abstract
Background Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions. Methodology We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model. Conclusions/Significance Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. “who regulates who” (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.
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Affiliation(s)
- Aviv Madar
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
| | - Alex Greenfield
- Computational Biology Program, New York University School of Medicine, New York, New York, United States of America
| | - Eric Vanden-Eijnden
- Computational Biology Program, New York University School of Medicine, New York, New York, United States of America
- Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
| | - Richard Bonneau
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Computational Biology Program, New York University School of Medicine, New York, New York, United States of America
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, New York, United States of America
- * E-mail:
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Synthetic biology: tools to design, build, and optimize cellular processes. J Biomed Biotechnol 2010; 2010:130781. [PMID: 20150964 PMCID: PMC2817555 DOI: 10.1155/2010/130781] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2009] [Accepted: 10/28/2009] [Indexed: 11/17/2022] Open
Abstract
The general central
dogma frames the emergent properties of life,
which make biology both necessary and difficult
to engineer. In a process engineering paradigm,
each biological process stream and process unit
is heavily influenced by regulatory interactions
and interactions with the surrounding
environment. Synthetic biology is developing the
tools and methods that will increase control
over these interactions, eventually resulting in
an integrative synthetic biology that will allow
ground-up cellular optimization. In this review,
we attempt to contextualize the areas of
synthetic biology into three tiers: (1) the
process units and associated streams of the
central dogma, (2) the intrinsic regulatory
mechanisms, and (3) the extrinsic physical and
chemical environment. Efforts at each of these
three tiers attempt to control cellular systems
and take advantage of emerging tools and
approaches. Ultimately, it will be possible to
integrate these approaches and realize the
vision of integrative synthetic biology when
cells are completely rewired for
biotechnological goals. This review will
highlight progress towards this goal as well as
areas requiring further research.
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van Hijum SAFT, Medema MH, Kuipers OP. Mechanisms and evolution of control logic in prokaryotic transcriptional regulation. Microbiol Mol Biol Rev 2009; 73:481-509, Table of Contents. [PMID: 19721087 PMCID: PMC2738135 DOI: 10.1128/mmbr.00037-08] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.
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Affiliation(s)
- Sacha A F T van Hijum
- Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.
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