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Zhou B, Lin X, Li Z, Yao Y, Yang J, Zhu Y. Structure‒function‒pathogenicity analysis of C-terminal myocilin missense variants based on experiments and 3D models. Front Genet 2022; 13:1019208. [PMID: 36267417 PMCID: PMC9577182 DOI: 10.3389/fgene.2022.1019208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
MYOC is a common pathogenic gene for primary open-angle glaucoma and encodes the protein named myocilin. Multiple MYOC variations have been found, with different clinical significance. However, the pathogenesis of glaucoma induced by MYOC mutations has not been fully clarified. Here, we analyze the molecular and cellular biological differences caused by multiple variant myocilins, including protein secretion characteristics, structural changes, subcellular localization, cellular autophagic activity and oxidative stress. Denaturing and nondenaturing electrophoresis showed myocilin to be a secreted protein with the tendency to self-oligomerize. The full-length myocilin and its C-terminal cleavage fragment are secreted. Secretion analysis of 23 variant myocilins indicated that secretion defects are closely related to the pathogenicity of MYOC variants. Structural analysis showed that the alteration of steric clash is associated with the secretion characteristics and pathogenicity of myocilin variants. Immunocytochemistry results demonstrated that mutated myocilins are retained in the endoplasmic reticulum and disrupt autophagy. MTT assay, MitoTracker staining, and DCFH-DA staining showed increased oxidative injury in cells expressing MYOC mutants. Taken together, MYOC mutations are able to induce cell dysfunction via secretion defects and intracellular accumulation resulting from steric clash alterations.
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Affiliation(s)
- Biting Zhou
- Department of Ophthalmology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiaojia Lin
- Department of Ophthalmology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Zhong Li
- Department of Bioengineering and Biopharmaceutics, School of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Yihua Yao
- Department of Ophthalmology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Juhua Yang
- Department of Bioengineering and Biopharmaceutics, School of Pharmacy, Fujian Medical University, Fuzhou, China
- *Correspondence: Yihua Zhu, ; Juhua Yang,
| | - Yihua Zhu
- Department of Ophthalmology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- *Correspondence: Yihua Zhu, ; Juhua Yang,
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2
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The economy of chromosomal distances in bacterial gene regulation. NPJ Syst Biol Appl 2021; 7:49. [PMID: 34911953 PMCID: PMC8674286 DOI: 10.1038/s41540-021-00209-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 11/12/2021] [Indexed: 12/04/2022] Open
Abstract
In the transcriptional regulatory network (TRN) of a bacterium, the nodes are genes and a directed edge represents the action of a transcription factor (TF), encoded by the source gene, on the target gene. It is a condensed representation of a large number of biological observations and facts. Nonrandom features of the network are structural evidence of requirements for a reliable systemic function. For the bacterium Escherichia coli we here investigate the (Euclidean) distances covered by the edges in the TRN when its nodes are embedded in the real space of the circular chromosome. Our work is motivated by 'wiring economy' research in Computational Neuroscience and starts from two contradictory hypotheses: (1) TFs are predominantly employed for long-distance regulation, while local regulation is exerted by chromosomal structure, locally coordinated by the action of structural proteins. Hence long distances should often occur. (2) A large distance between the regulator gene and its target requires a higher expression level of the regulator gene due to longer reaching times and ensuing increased degradation (proteolysis) of the TF and hence will be evolutionarily reduced. Our analysis supports the latter hypothesis.
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3
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Ishihama A, Shimada T. Hierarchy of transcription factor network in Escherichia coli K-12: H-NS-mediated silencing and Anti-silencing by global regulators. FEMS Microbiol Rev 2021; 45:6312496. [PMID: 34196371 DOI: 10.1093/femsre/fuab032] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022] Open
Abstract
Transcriptional regulation for genome expression determines growth and adaptation of single-cell bacteria that are directly exposed to environment. The transcriptional apparatus in Escherichia coli K-12 is composed of RNA polymerase core enzyme and two groups of its regulatory proteins, seven species of promoter-recognition subunit sigma and about 300 species of transcription factors. The identification of regulatory targets for all these regulatory proteins is critical toward understanding the genome regulation as a whole. For this purpose, we performed a systematic search in vitro of the whole set of binding sites for each factor by gSELEX system. This review summarizes the accumulated knowledge of regulatory targets for more than 150 TFs from E. coli K-12. Overall TFs could be classified into four families: nucleoid-associated bifunctional TFs; global regulators; local regulators; and single-target regulators, in which the regulatory functions remain uncharacterized for the nucleoid-associated TFs. Here we overview the regulatory targets of two nucleoid-associated TFs, H-NS and its paralog StpA, both together playing the silencing role of a set of non-essential genes. Participation of LeuO and other global regulators have been indicated for the anti-silencing. Finally, we propose the hierarchy of TF network as a key framework of the bacterial genome regulation.
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Affiliation(s)
- Akira Ishihama
- Hosei University, Research Institute for Micro-Nano Technology, Koganei, Tokyo 184-0003, Japan
| | - Tomohiro Shimada
- Meiji University, School of Agriculture, Kawasaki, Kanagawa 214-8571, Japan
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4
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Nagy-Staron A, Tomasek K, Caruso Carter C, Sonnleitner E, Kavčič B, Paixão T, Guet CC. Local genetic context shapes the function of a gene regulatory network. eLife 2021; 10:e65993. [PMID: 33683203 PMCID: PMC7968929 DOI: 10.7554/elife.65993] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic GRN to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one GRN with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit (TU) within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual TUs, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of GRNs.
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Affiliation(s)
- Anna Nagy-Staron
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - Kathrin Tomasek
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | | | - Elisabeth Sonnleitner
- Department of MicrobiologyImmunobiology and Genetics, Max F. Perutz Laboratories, Center Of Molecular Biology, University of ViennaViennaAustria
| | - Bor Kavčič
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - Tiago Paixão
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - Calin C Guet
- Institute of Science and Technology AustriaKlosterneuburgAustria
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5
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Chaiboonchoe A, Ghamsari L, Dohai B, Ng P, Khraiwesh B, Jaiswal A, Jijakli K, Koussa J, Nelson DR, Cai H, Yang X, Chang RL, Papin J, Yu H, Balaji S, Salehi-Ashtiani K. Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. MOLECULAR BIOSYSTEMS 2017; 12:2394-407. [PMID: 27357594 DOI: 10.1039/c6mb00237d] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.
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Affiliation(s)
- Amphun Chaiboonchoe
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Bushra Dohai
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Patrick Ng
- Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
| | - Basel Khraiwesh
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Ashish Jaiswal
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Kenan Jijakli
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Joseph Koussa
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - David R Nelson
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Hong Cai
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Roger L Chang
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Jason Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
| | - Santhanam Balaji
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE. and Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA and MRC Laboratory of Molecular Biology, Cambridge, UK.
| | - Kourosh Salehi-Ashtiani
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE. and Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
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6
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Beber ME, Muskhelishvili G, Hütt MT. Effect of database drift on network topology and enrichment analyses: a case study for RegulonDB. Database (Oxford) 2016; 2016:baw003. [PMID: 26980514 PMCID: PMC4792529 DOI: 10.1093/database/baw003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 12/12/2015] [Accepted: 01/11/2016] [Indexed: 12/11/2022]
Abstract
RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information. Database URL: www.regulondb.ccg.unam.mx.
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Affiliation(s)
- Moritz E Beber
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and Bioinformatics Group, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin 14195, Germany
| | - Georgi Muskhelishvili
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and
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7
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Van Landeghem S, Van Parys T, Dubois M, Inzé D, Van de Peer Y. Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks. BMC Bioinformatics 2016; 17:18. [PMID: 26729218 PMCID: PMC4700732 DOI: 10.1186/s12859-015-0863-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 12/17/2015] [Indexed: 11/30/2022] Open
Abstract
Background Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. Results In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Availability Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0863-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sofie Van Landeghem
- Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. .,Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium.
| | - Thomas Van Parys
- Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. .,Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium.
| | - Marieke Dubois
- Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. .,Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium.
| | - Dirk Inzé
- Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. .,Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium.
| | - Yves Van de Peer
- Department of Plant Systems Biology, VIB, Technologiepark 927, Ghent, 9052, Belgium. .,Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, Ghent, 9052, Belgium. .,Genomics Research Institute, University of Pretoria, Private bag X200028, Pretoria, South Africa.
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8
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Santolaya-Forgas J, Townsend R, Santolaya JL, Patel P, Herrera-Garcia G, Castracane VD. The Microbiota and Transgenomic Networks: Potential Implications for Maternal-Fetal Medicine. Fetal Diagn Ther 2016; 39:1-3. [DOI: 10.1159/000441452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Accepted: 09/23/2015] [Indexed: 01/12/2023]
Abstract
The maternal microbiota has long been considered a potential cause for adverse perinatal outcomes. Gene expression regulators in prokaryotic and eukaryotic cells are influenced by changes in their microenvironments. We propose the novel idea that during in utero development, an adaptive and dynamic gene-regulatory cross talk might exist between the host genome and the maternal microbiota. Understanding these cross talks could increase the appreciation for the discovery of new diagnostics and therapeutics in maternal-fetal medicine.
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Carson MB, Gu J, Yu G, Lu H. Identification of cancer‐related genes and motifs in the human gene regulatory network. IET Syst Biol 2015; 9:128-34. [DOI: 10.1049/iet-syb.2014.0058] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Matthew B. Carson
- Department of BioengineeringUniversity of Illinois at Chicago835 S. WolcottChicagoIL60612USA
- Division of Health and Biomedical InformaticsDepartment of Preventive MedicineNorthwestern University Feinberg School of Medicine750 N. Lake Shore DriveChicagoIL60611USA
- Center for Healthcare StudiesInstitute for Public Health and MedicineNorthwestern University Feinberg School of Medicine633 N. Saint ClairChicagoIL60611USA
| | - Jianlei Gu
- Shanghai Institute of Medical Genetics & Shanghai Laboratory of Embryo and Reproduction EngineeringShanghai200040People's Republic of China
- Shanghai Children's HospitalShanghai Jiaotong UniversityShanghai200040People's Republic of China
| | - Guangjun Yu
- Shanghai Children's HospitalShanghai Jiaotong UniversityShanghai200040People's Republic of China
| | - Hui Lu
- Department of BioengineeringUniversity of Illinois at Chicago835 S. WolcottChicagoIL60612USA
- Shanghai Institute of Medical Genetics & Shanghai Laboratory of Embryo and Reproduction EngineeringShanghai200040People's Republic of China
- Shanghai Children's HospitalShanghai Jiaotong UniversityShanghai200040People's Republic of China
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10
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Imam S, Noguera DR, Donohue TJ. Global analysis of photosynthesis transcriptional regulatory networks. PLoS Genet 2014; 10:e1004837. [PMID: 25503406 PMCID: PMC4263372 DOI: 10.1371/journal.pgen.1004837] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2014] [Accepted: 10/20/2014] [Indexed: 12/18/2022] Open
Abstract
Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis. Photosynthetic organisms are among the most abundant life forms on earth. Their unique ability to harvest solar energy and use it to fix atmospheric carbon dioxide is at the foundation of the global food chain. This paper reports the first comprehensive analysis of networks that control expression of photosynthesis genes using Rhodobacter sphaeroides, a microbe that has been studied for decades as a model of solar energy capture and other aspects of the photosynthetic lifestyle. We find a previously unappreciated complexity in the level of control of photosynthetic genes, while identifying new links between photosynthesis and central processes like iron availability. This organism is an ancestor of modern day plants, so our data can inform studies in other photosynthetic organisms and improve our ability to harness solar energy for food and industrial processes.
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Affiliation(s)
- Saheed Imam
- Program in Cellular and Molecular Biology, University of Wisconsin – Madison, Madison, Wisconsin, United States of America
- Department of Bacteriology, University of Wisconsin – Madison, Wisconsin Energy Institute, Madison, Wisconsin, United States of America
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin – Madison, Madison, Wisconsin, United States of America
| | - Daniel R. Noguera
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin – Madison, Madison, Wisconsin, United States of America
- Department of Civil and Environmental Engineering, University of Wisconsin – Madison, Madison, Wisconsin, United States of America
| | - Timothy J. Donohue
- Department of Bacteriology, University of Wisconsin – Madison, Wisconsin Energy Institute, Madison, Wisconsin, United States of America
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin – Madison, Madison, Wisconsin, United States of America
- * E-mail:
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11
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Ferreira AJS, Siam R, Setubal JC, Moustafa A, Sayed A, Chambergo FS, Dawe AS, Ghazy MA, Sharaf H, Ouf A, Alam I, Abdel-Haleem AM, Lehvaslaiho H, Ramadan E, Antunes A, Stingl U, Archer JAC, Jankovic BR, Sogin M, Bajic VB, El-Dorry H. Core microbial functional activities in ocean environments revealed by global metagenomic profiling analyses. PLoS One 2014; 9:e97338. [PMID: 24921648 PMCID: PMC4055538 DOI: 10.1371/journal.pone.0097338] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 04/17/2014] [Indexed: 11/19/2022] Open
Abstract
Metagenomics-based functional profiling analysis is an effective means of gaining deeper insight into the composition of marine microbial populations and developing a better understanding of the interplay between the functional genome content of microbial communities and abiotic factors. Here we present a comprehensive analysis of 24 datasets covering surface and depth-related environments at 11 sites around the world's oceans. The complete datasets comprises approximately 12 million sequences, totaling 5,358 Mb. Based on profiling patterns of Clusters of Orthologous Groups (COGs) of proteins, a core set of reference photic and aphotic depth-related COGs, and a collection of COGs that are associated with extreme oxygen limitation were defined. Their inferred functions were utilized as indicators to characterize the distribution of light- and oxygen-related biological activities in marine environments. The results reveal that, while light level in the water column is a major determinant of phenotypic adaptation in marine microorganisms, oxygen concentration in the aphotic zone has a significant impact only in extremely hypoxic waters. Phylogenetic profiling of the reference photic/aphotic gene sets revealed a greater variety of source organisms in the aphotic zone, although the majority of individual photic and aphotic depth-related COGs are assigned to the same taxa across the different sites. This increase in phylogenetic and functional diversity of the core aphotic related COGs most probably reflects selection for the utilization of a broad range of alternate energy sources in the absence of light.
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Affiliation(s)
- Ari J. S. Ferreira
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Rania Siam
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - João C. Setubal
- Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, Brazil
| | - Ahmed Moustafa
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Ahmed Sayed
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Felipe S. Chambergo
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, Brazil
| | - Adam S. Dawe
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Mohamed A. Ghazy
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Hazem Sharaf
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Amged Ouf
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Intikhab Alam
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Alyaa M. Abdel-Haleem
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - Heikki Lehvaslaiho
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Eman Ramadan
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
| | - André Antunes
- Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Portugal
| | - Ulrich Stingl
- Red Sea Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - John A. C. Archer
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Boris R. Jankovic
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Mitchell Sogin
- Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
| | - Vladimir B. Bajic
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
| | - Hamza El-Dorry
- Department of Biology and the Science and Technology Research Center, School of Sciences and Engineering, The American University in Cairo, Cairo, New Cairo, Egypt
- * E-mail:
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12
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Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology. Proc Natl Acad Sci U S A 2013; 110:7754-9. [PMID: 23610404 DOI: 10.1073/pnas.1217630110] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Cis-regulatory networks (CRNs) play a central role in cellular decision making. Like every other biological system, CRNs undergo evolution, which shapes their properties by a combination of adaptive and nonadaptive evolutionary forces. Teasing apart these forces is an important step toward functional analyses of the different components of CRNs, designing regulatory perturbation experiments, and constructing synthetic networks. Although tests of neutrality and selection based on molecular sequence data exist, no such tests are currently available based on CRNs. In this work, we present a unique genotype model of CRNs that is grounded in a genomic context and demonstrate its use in identifying portions of the CRN with properties explainable by neutral evolutionary forces at the system, subsystem, and operon levels. We leverage our model against experimentally derived data from Escherichia coli. The results of this analysis show statistically significant and substantial neutral trends in properties previously identified as adaptive in origin--degree distribution, clustering coefficient, and motifs--within the E. coli CRN. Our model captures the tightly coupled genome-interactome of an organism and enables analyses of how evolutionary events acting at the genome level, such as mutation, and at the population level, such as genetic drift, give rise to neutral patterns that we can quantify in CRNs.
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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: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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14
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Architecture of the human regulatory network derived from ENCODE data. Nature 2012; 489:91-100. [PMID: 22955619 DOI: 10.1038/nature11245] [Citation(s) in RCA: 1111] [Impact Index Per Article: 85.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 05/22/2012] [Indexed: 12/21/2022]
Abstract
Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.
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15
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Kosti I, Radivojac P, Mandel-Gutfreund Y. An integrated regulatory network reveals pervasive cross-regulation among transcription and splicing factors. PLoS Comput Biol 2012; 8:e1002603. [PMID: 22844237 PMCID: PMC3405991 DOI: 10.1371/journal.pcbi.1002603] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Accepted: 05/24/2012] [Indexed: 11/19/2022] Open
Abstract
Traditionally the gene expression pathway has been regarded as being comprised of independent steps, from RNA transcription to protein translation. To date there is increasing evidence of coupling between the different processes of the pathway, specifically between transcription and splicing. To study the interplay between these processes we derived a transcription-splicing integrated network. The nodes of the network included experimentally verified human proteins belonging to three groups of regulators: transcription factors, splicing factors and kinases. The nodes were wired by instances of predicted transcriptional and alternative splicing regulation. Analysis of the network indicated a pervasive cross-regulation among the nodes; specifically, splicing factors are significantly more connected by alternative splicing regulatory edges relative to the two other subgroups, while transcription factors are more extensively controlled by transcriptional regulation. Furthermore, we found that splicing factors are the most regulated of the three regulatory groups and are subject to extensive combinatorial control by alternative splicing and transcriptional regulation. Consistent with the network results, our bioinformatics analyses showed that the subgroup of kinases have the highest density of predicted phosphorylation sites. Overall, our systematic study reveals that an organizing principle in the logic of integrated networks favor the regulation of regulatory proteins by the specific regulation they conduct. Based on these results, we propose a new regulatory paradigm postulating that gene expression regulation of the master regulators in the cell is predominantly achieved by cross-regulation.
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Affiliation(s)
- Idit Kosti
- Faculty of Biology, Technion – Israel Institute of Technology, Haifa, Israel
| | - Predrag Radivojac
- School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America
| | - Yael Mandel-Gutfreund
- Faculty of Biology, Technion – Israel Institute of Technology, Haifa, Israel
- * E-mail:
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16
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van Dijk ADJ, van Mourik S, van Ham RCHJ. Mutational robustness of gene regulatory networks. PLoS One 2012; 7:e30591. [PMID: 22295094 PMCID: PMC3266278 DOI: 10.1371/journal.pone.0030591] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Accepted: 12/19/2011] [Indexed: 11/18/2022] Open
Abstract
Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.
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Affiliation(s)
- Aalt D J van Dijk
- Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.
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17
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Iyer LM, Aravind L. Insights from the architecture of the bacterial transcription apparatus. J Struct Biol 2011; 179:299-319. [PMID: 22210308 DOI: 10.1016/j.jsb.2011.12.013] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Revised: 12/14/2011] [Accepted: 12/18/2011] [Indexed: 10/14/2022]
Abstract
We provide a portrait of the bacterial transcription apparatus in light of the data emerging from structural studies, sequence analysis and comparative genomics to bring out important but underappreciated features. We first describe the key structural highlights and evolutionary implications emerging from comparison of the cellular RNA polymerase subunits with the RNA-dependent RNA polymerase involved in RNAi in eukaryotes and their homologs from newly identified bacterial selfish elements. We describe some previously unnoticed domains and the possible evolutionary stages leading to the RNA polymerases of extant life forms. We then present the case for the ancient orthology of the basal transcription factors, the sigma factor and TFIIB, in the bacterial and the archaeo-eukaryotic lineages. We also present a synopsis of the structural and architectural taxonomy of specific transcription factors and their genome-scale demography. In this context, we present certain notable deviations from the otherwise invariant proteome-wide trends in transcription factor distribution and use it to predict the presence of an unusual lineage-specifically expanded signaling system in certain firmicutes like Paenibacillus. We then discuss the intersection between functional properties of transcription factors and the organization of transcriptional networks. Finally, we present some of the interesting evolutionary conundrums posed by our newly gained understanding of the bacterial transcription apparatus and potential areas for future explorations.
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Affiliation(s)
- Lakshminarayan M Iyer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, Room 5N50, Bethesda, MD 20894, USA
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18
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Domenzain C, Camarena L, Osorio A, Dreyfus G, Poggio S. Evolutionary origin of the Rhodobacter sphaeroides specialized RpoN sigma factors. FEMS Microbiol Lett 2011; 327:93-102. [PMID: 22093079 DOI: 10.1111/j.1574-6968.2011.02459.x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Revised: 11/01/2011] [Accepted: 11/02/2011] [Indexed: 11/29/2022] Open
Abstract
Gene duplication and horizontal gene transfer (HGT) are two events that enable the generation of new genes. Rhodobacter sphaeroides (WS8 and 2.4.1 strains) has four copies of the rpoN gene that are not functionally interchangeable. Until now, this is the only example of specialization of this sigma factor. In this work, we aimed to determine whether the multiple copies of this gene originated from HGT or through gene duplication. Our results suggest a multiplication origin of the different rpoN copies that occurred after the Rhodobacter clade separated. Functional tests indicate that the specialization of the rpoN genes is not restricted to R. sphaeroides. We propose that the rpoN copy involved in nitrogen fixation is the ancestral gene and that the other rpoN genes have acquired new specificities.
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Affiliation(s)
- Clelia Domenzain
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, México City, México
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19
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Ghamsari L, Balaji S, Shen Y, Yang X, Balcha D, Fan C, Hao T, Yu H, Papin JA, Salehi-Ashtiani K. Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii. BMC Genomics 2011; 12 Suppl 1:S4. [PMID: 21810206 PMCID: PMC3223727 DOI: 10.1186/1471-2164-12-s1-s4] [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] [Indexed: 11/25/2022] Open
Abstract
Background Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale. Results We assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST. In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate. Conclusions We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.
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Affiliation(s)
- Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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20
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Babu MM. Early Career Research Award Lecture. Structure, evolution and dynamics of transcriptional regulatory networks. Biochem Soc Trans 2010; 38:1155-78. [PMID: 20863280 DOI: 10.1042/bst0381155] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.
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Affiliation(s)
- M Madan Babu
- MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 0QH, UK.
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21
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Theodorou EC, Theodorou MC, Samali MN, Kyriakidis DA. Activation of the AtoSC two-component system in the absence of the AtoC N-terminal receiver domain in E. coli. Amino Acids 2010; 40:421-30. [DOI: 10.1007/s00726-010-0652-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2010] [Accepted: 06/02/2010] [Indexed: 10/19/2022]
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22
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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: 33] [Impact Index Per Article: 2.2] [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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23
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Fu C, Li J, Wang E. Signaling network analysis of ubiquitin-mediated proteins suggests correlations between the 26S proteasome and tumor progression. MOLECULAR BIOSYSTEMS 2010; 5:1809-16. [PMID: 19593471 DOI: 10.1039/b905382d] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We performed a comprehensive analysis of a literature-mined human signaling network by integrating data on ubiquitin-mediated protein half-lives. We found that proteins with very long half-lives are connected to form a network backbone, while proteins with short and medium half-lives preferentially attach to the network backbone and scatter throughout the network. Furthermore, proteins with short and medium half-lives are mutually exclusive in network neighbors. Short half-life proteins are enriched in the upstream portion of the network, suggesting that ubiquitination might help initiate signal processing and specificity. We also discovered that ubiquitination preferentially occurs in positive regulatory loops. Furthermore, these loops predominately induce or positively regulate apoptosis, a major component in cancer signaling. These results lead us to discover that the highly expressed genes involved in the common machinery of ubiquitination, the 26S proteasome genes, are significantly correlated with tumor progression and metastasis. Furthermore, expression of the 26S proteasome gene set predicts the clinical outcome of breast cancer patients. Our findings have implications for the development of cancer treatments and prognostic markers focused on the ubiquitination machinery.
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Affiliation(s)
- Cong Fu
- College of Life Sciences, Beijing Normal University, Beijing, China
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24
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Martínez-Núñez MA, Pérez-Rueda E, Gutiérrez-Ríos RM, Merino E. New insights into the regulatory networks of paralogous genes in bacteria. MICROBIOLOGY-SGM 2009; 156:14-22. [PMID: 19850620 DOI: 10.1099/mic.0.033266-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Extensive genomic studies on gene duplication in model organisms such as Escherichia coli and Saccharomyces cerevisiae have recently been undertaken. In these models, it is commonly considered that a duplication event may include a transcription factor (TF), a target gene, or both. Following a gene duplication episode, varying scenarios have been postulated to describe the evolution of the regulatory network. However, in most of these, the TFs have emerged as the most important and in some cases the only factor shaping the regulatory network as the organism responds to a natural selection process, in order to fulfil its metabolic needs. Recent findings concerning the regulatory role played by elements other than TFs have indicated the need to reassess these early models. Thus, we performed an exhaustive review of paralogous gene regulation in E. coli and Bacillus subtilis based on published information, available in the NCBI PubMed database and in well-established regulatory databases. Our survey reinforces the notion that despite TFs being the most prominent components shaping the regulatory networks, other elements are also important. These include small RNAs, riboswitches, RNA-binding proteins, sigma factors, protein-protein interactions and DNA supercoiling, which modulate the expression of genes involved in particular metabolic processes or induce a more complex response in terms of the regulatory networks of paralogous genes in an integrated interplay with TFs.
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Affiliation(s)
- Mario A Martínez-Núñez
- Departamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
| | - Ernesto Pérez-Rueda
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
| | - Rosa María Gutiérrez-Ríos
- Departamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
| | - Enrique Merino
- Departamento de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
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25
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Ciandrini L, Maffi C, Motta A, Bassetti B, Cosentino Lagomarsino M. Feedback topology and XOR-dynamics in Boolean networks with varying input structure. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2009; 80:026122. [PMID: 19792215 DOI: 10.1103/physreve.80.026122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2008] [Revised: 05/28/2009] [Indexed: 05/28/2023]
Abstract
We analyze a model of fixed in-degree random Boolean networks in which the fraction of input-receiving nodes is controlled by the parameter gamma. We investigate analytically and numerically the dynamics of graphs under a parallel XOR updating scheme. This scheme is interesting because it is accessible analytically and its phenomenology is at the same time under control and as rich as the one of general Boolean networks. We give analytical formulas for the dynamics on general graphs, showing that with a XOR-type evolution rule, dynamic features are direct consequences of the topological feedback structure, in analogy with the role of relevant components in Kauffman networks. Considering graphs with fixed in-degree, we characterize analytically and numerically the feedback regions using graph decimation algorithms (Leaf Removal). With varying gamma , this graph ensemble shows a phase transition that separates a treelike graph region from one in which feedback components emerge. Networks near the transition point have feedback components made of disjoint loops, in which each node has exactly one incoming and one outgoing link. Using this fact, we provide analytical estimates of the maximum period starting from topological considerations.
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Affiliation(s)
- L Ciandrini
- Dip. di Fisica Nucleare e Teorica, Università di Pavia, Via Bassi 6, 27100 Pavia, Italy.
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26
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Fadda A, Fierro AC, Lemmens K, Monsieurs P, Engelen K, Marchal K. Inferring the transcriptional network of Bacillus subtilis. MOLECULAR BIOSYSTEMS 2009; 5:1840-52. [PMID: 20023724 DOI: 10.1039/b907310h] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The adaptation of bacteria to the vigorous environmental changes they undergo is crucial to their survival. They achieve this adaptation partly via intricate regulation of the transcription of their genes. In this study, we infer the transcriptional network of the Gram-positive model organism, Bacillus subtilis. We use a data integration workflow, exploiting both motif and expression data, towards the generation of condition-dependent transcriptional modules. In building the motif data, we rely on both known and predicted information. Known motifs were derived from DBTBS, while predicted motifs were generated by a de novo motif detection method that utilizes comparative genomics. The expression data consists of a compendium of microarrays across different platforms. Our results indicate that a considerable part of the B. subtilis network is yet undiscovered; we could predict 417 new regulatory interactions for known regulators and 453 interactions for yet uncharacterized regulators. The regulators in our network showed a preference for regulating modules in certain environmental conditions. Also, substantial condition-dependent intra-operonic regulation seems to take place. Global regulators seem to require functional flexibility to attain their roles by acting as both activators and repressors.
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Affiliation(s)
- Abeer Fadda
- Department of Microbial and Molecular Systems, KULeuven, Kasteelpark Arenberg 20, 3001 Heverlee, Belgium
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27
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Janky R, Helden JV, Babu MM. Investigating transcriptional regulation: from analysis of complex networks to discovery of cis-regulatory elements. Methods 2009; 48:277-86. [PMID: 19450688 DOI: 10.1016/j.ymeth.2009.04.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2009] [Revised: 04/17/2009] [Accepted: 04/18/2009] [Indexed: 10/20/2022] Open
Abstract
Regulation of gene expression at the transcriptional level is a fundamental mechanism that is well conserved in all cellular systems. Due to advances in large-scale experimental analyses, we now have a wealth of information on gene regulation such as mRNA expression level across multiple conditions, genome-wide location data of transcription factors and data on transcription factor binding sites. This knowledge can be used to reconstruct transcriptional regulatory networks. Such networks are usually represented as directed graphs where regulatory interactions are depicted as directed edges from the transcription factor nodes to the target gene nodes. This abstract representation allows us to apply graph theory to study transcriptional regulation at global and local levels, to predict regulatory motifs and regulatory modules such as regulons and to compare the regulatory network of different genomes. Here we review some of the available computational methodologies for studying transcriptional regulatory networks as well as their interpretation.
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Affiliation(s)
- Rekin's Janky
- Structural Studies Division, Medical Research Council - Laboratory of Molecular Biology, Hills Road, Cambridge CB20QH, United Kingdom.
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28
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Bacha J, Brodie JS, Loose MW. myGRN: a database and visualisation system for the storage and analysis of developmental genetic regulatory networks. BMC DEVELOPMENTAL BIOLOGY 2009; 9:33. [PMID: 19500400 PMCID: PMC2702357 DOI: 10.1186/1471-213x-9-33] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2009] [Accepted: 06/06/2009] [Indexed: 11/23/2022]
Abstract
BACKGROUND Biological processes are regulated by complex interactions between transcription factors and signalling molecules, collectively described as Genetic Regulatory Networks (GRNs). The characterisation of these networks to reveal regulatory mechanisms is a long-term goal of many laboratories. However compiling, visualising and interacting with such networks is non-trivial. Current tools and databases typically focus on GRNs within simple, single celled organisms. However, data is available within the literature describing regulatory interactions in multi-cellular organisms, although not in any systematic form. This is particularly true within the field of developmental biology, where regulatory interactions should also be tagged with information about the time and anatomical location of development in which they occur. DESCRIPTION We have developed myGRN (http://www.myGRN.org), a web application for storing and interrogating interaction data, with an emphasis on developmental processes. Users can submit interaction and gene expression data, either curated from published sources or derived from their own unpublished data. All interactions associated with publications are publicly visible, and unpublished interactions can only be shared between collaborating labs prior to publication. Users can group interactions into discrete networks based on specific biological processes. Various filters allow dynamic production of network diagrams based on a range of information including tissue location, developmental stage or basic topology. Individual networks can be viewed using myGRV, a tool focused on displaying developmental networks, or exported in a range of formats compatible with third party tools. Networks can also be analysed for the presence of common network motifs. We demonstrate the capabilities of myGRN using a network of zebrafish interactions integrated with expression data from the zebrafish database, ZFIN. CONCLUSION Here we are launching myGRN as a community-based repository for interaction networks, with a specific focus on developmental networks. We plan to extend its functionality, as well as use it to study networks involved in embryonic development in the future.
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Affiliation(s)
- Jamil Bacha
- Institute of Genetics, University of Nottingham, Nottingham, UK
| | - James S Brodie
- Institute of Genetics, University of Nottingham, Nottingham, UK
| | - Matthew W Loose
- Institute of Genetics, University of Nottingham, Nottingham, UK
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29
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Baumbach J, Wittkop T, Kleindt CK, Tauch A. Integrated analysis and reconstruction of microbial transcriptional gene regulatory networks using CoryneRegNet. Nat Protoc 2009; 4:992-1005. [DOI: 10.1038/nprot.2009.81] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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30
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Lemmens K, De Bie T, Dhollander T, De Keersmaecker SC, Thijs IM, Schoofs G, De Weerdt A, De Moor B, Vanderleyden J, Collado-Vides J, Engelen K, Marchal K. DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli. Genome Biol 2009; 10:R27. [PMID: 19265557 PMCID: PMC2690998 DOI: 10.1186/gb-2009-10-3-r27] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2008] [Revised: 01/15/2009] [Accepted: 03/06/2009] [Indexed: 11/13/2022] Open
Abstract
DISTILLER, a data integration framework for the inference of transcriptional module networks, is presented and used to investigate the condition dependency and modularity in Escherichia coli networks. We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.
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Affiliation(s)
- Karen Lemmens
- Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
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Balleza E, López-Bojorquez LN, Martínez-Antonio A, Resendis-Antonio O, Lozada-Chávez I, Balderas-Martínez YI, Encarnación S, Collado-Vides J. Regulation by transcription factors in bacteria: beyond description. FEMS Microbiol Rev 2009; 33:133-51. [PMID: 19076632 PMCID: PMC2704942 DOI: 10.1111/j.1574-6976.2008.00145.x] [Citation(s) in RCA: 138] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.
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Affiliation(s)
- Enrique Balleza
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico
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Lagomarsino MC, Bassetti B, Castellani G, Remondini D. Functional models for large-scale gene regulation networks: realism and fiction. MOLECULAR BIOSYSTEMS 2009; 5:335-44. [PMID: 19396369 DOI: 10.1039/b816841p] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.
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Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms. BMC SYSTEMS BIOLOGY 2009; 3:8. [PMID: 19146695 PMCID: PMC2653031 DOI: 10.1186/1752-0509-3-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2008] [Accepted: 01/15/2009] [Indexed: 11/10/2022]
Abstract
BACKGROUND Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus. RESULTS Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for ~40% of the common transcription factors, compared to ~5% for which knowledge was available before. CONCLUSION Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation.
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Sellerio A, Bassetti B, Isambert H, Cosentino Lagomarsino M. A comparative evolutionary study of transcription networks. The global role of feedback and hierachical structures. ACTA ACUST UNITED AC 2009; 5:170-9. [DOI: 10.1039/b815339f] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Baumbach J, Tauch A, Rahmann S. Towards the integrated analysis, visualization and reconstruction of microbial gene regulatory networks. Brief Bioinform 2008; 10:75-83. [PMID: 19074493 DOI: 10.1093/bib/bbn055] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
To handle changing environmental surroundings and to manage unfavorable conditions, microbial organisms have evolved complex transcriptional regulatory networks. To comprehensively analyze these gene regulatory networks, several online available databases and analysis platforms have been implemented and established. In this article, we address the typical cycle of scientific knowledge exploration and integration in the area of procaryotic transcriptional gene regulation. We briefly review five popular, publicly available systems that support (i) the integration of existing knowledge, (ii) visualization capabilities and (iii) computer analysis to predict promising wet lab targets. We exemplify the benefits of such integrated data analysis platforms by means of four application cases exemplarily performed with the corynebacterial reference database CoryneRegNet.
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Affiliation(s)
- Jan Baumbach
- International Computer Science Institute, Berkeley, USA.
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Abstract
Progress in the reconstruction of genome-wide metabolic maps has led to the development of network-based computational approaches for linking an organism with its biochemical habitat.
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Tanous C, Soutourina O, Raynal B, Hullo MF, Mervelet P, Gilles AM, Noirot P, Danchin A, England P, Martin-Verstraete I. The CymR regulator in complex with the enzyme CysK controls cysteine metabolism in Bacillus subtilis. J Biol Chem 2008; 283:35551-60. [PMID: 18974048 DOI: 10.1074/jbc.m805951200] [Citation(s) in RCA: 84] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Several enzymes have evolved as sensors in signal transduction pathways to control gene expression, thereby allowing bacteria to adapt efficiently to environmental changes. We recently identified the master regulator of cysteine metabolism in Bacillus subtilis, CymR, which belongs to the poorly characterized Rrf2 family of regulators. We now report that the signal transduction mechanism controlling CymR activity in response to cysteine availability involves the formation of a stable complex with CysK, a key enzyme for cysteine biosynthesis. We carried out a comprehensive quantitative characterization of this regulator-enzyme interaction by surface plasmon resonance and analytical ultracentrifugation. We also showed that O-acetylserine plays a dual role as a substrate of CysK and as an effector modulating the CymR-CysK complex formation. The ability of B. subtilis CysK to bind to CymR appears to be correlated to the loss of its capacity to form a cysteine synthase complex with CysE. We propose an original model, supported by the determination of the intracellular concentrations of the different partners, by which CysK positively regulates CymR in sensing the bacterial cysteine pool.
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Affiliation(s)
- Catherine Tanous
- Institut Pasteur, UnitédeGénétique des Génomes Bactériens, Plate-forme de Biophysique des Macromolécules et de leurs Interactions, 75724 Paris cedex 15, France
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Janga SC, Salgado H, Martínez-Antonio A, Collado-Vides J. Coordination logic of the sensing machinery in the transcriptional regulatory network of Escherichia coli. Nucleic Acids Res 2007; 35:6963-72. [PMID: 17933780 PMCID: PMC2175315 DOI: 10.1093/nar/gkm743] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The active and inactive state of transcription factors in growing cells is usually directed by allosteric physicochemical signals or metabolites, which are in turn either produced in the cell or obtained from the environment by the activity of the products of effector genes. To understand the regulatory dynamics and to improve our knowledge about how transcription factors (TFs) respond to endogenous and exogenous signals in the bacterial model, Escherichia coli, we previously proposed to classify TFs into external, internal and hybrid sensing classes depending on the source of their allosteric or equivalent metabolite. Here we analyze how a cell uses its topological structures in the context of sensing machinery and show that, while feed forward loops (FFLs) tightly integrate internal and external sensing TFs connecting TFs from different layers of the hierarchical transcriptional regulatory network (TRN), bifan motifs frequently connect TFs belonging to the same sensing class and could act as a bridge between TFs originating from the same level in the hierarchy. We observe that modules identified in the regulatory network of E. coli are heterogeneous in sensing context with a clear combination of internal and external sensing categories depending on the physiological role played by the module. We also note that propensity of two-component response regulators increases at promoters, as the number of TFs regulating a target operon increases. Finally we show that evolutionary families of TFs do not show a tendency to preserve their sensing abilities. Our results provide a detailed panorama of the topological structures of E. coli TRN and the way TFs they compose off, sense their surroundings by coordinating responses.
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Affiliation(s)
- Sarath Chandra Janga
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, 62100, México.
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