scholarly journals An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana Teixeira Dornelles Parise ◽  
Doglas Parise ◽  
Flavia Figueira Aburjaile ◽  
Anne Cybelle Pinto Gomide ◽  
Rodrigo Bentes Kato ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Small Rnas ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Bacterial Gene ◽  
Bacterial Gene Expression ◽  
Transcriptional Regulatory ◽  
Animal Pathogens ◽  
Gene Regulatory

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

What are Gene Regulatory Networks?

2010 ◽  
pp. 1-27 ◽  
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Biochemical Processes ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

This book deals with algorithms for inferring and analyzing Gene Regulatory Networks using mainly gene expression data. What precisely are the Gene Regulatory Networks that are inferred by such algorithms from this type of data? There is still much confusion in the current literature and it is important to start a book about computational methods for Gene Regulatory Networks with a definition that is as unambiguous as possible. In this chapter, I provide a definition and try to clearly explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. To do the latter in a formal way, I will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed’ into the more compact description of Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, such as Transcriptional Regulatory Networks and Co-Expression Networks, will be highlighted.

Condensing Biochemistry into Gene Regulatory Networks

2014 ◽  
Vol 4 (3) ◽  
pp. 1-25
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Modern Biology ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.

scholarly journals Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

2020 ◽  
Author(s):  
Ian Leifer ◽  
Mishael Sanchez ◽  
Cecilia Ishida ◽  
Hernan Makse
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Conceptual Tool ◽  
Gene Coexpression ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Gene Regulatory

Abstract Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the transcriptional regulatory networks (TRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models -- with gene input functions differencing between genes -- predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the transcription networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

scholarly journals Programmatic access to bacterial regulatory networks with regutools

2020 ◽  
Vol 36 (16) ◽  
pp. 4532-4534
Author(s):  
Joselyn Chávez ◽  
Carmina Barberena-Jonas ◽  
Jesus E Sotelo-Fonseca ◽  
José Alquicira-Hernández ◽  
Heladia Salgado ◽  
...  
Keyword(s):  
Data Structures ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
R Package ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Sites ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Programmatic Access

Abstract Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.

scholarly journals Programmatic access to bacterial regulatory networks with regutools

2020 ◽  
Author(s):  
Joselyn Chávez ◽  
Carmina Barberena-Jonas ◽  
Jesus E. Sotelo-Fonseca ◽  
José Alquicira-Hernández ◽  
Heladia Salgado ◽  
...  
Keyword(s):  
Data Structures ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Link Type ◽  
Dna Binding Sites ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Programmatic Access

AbstractSummaryRegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.Availability and Implementationregutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.Contactgithub.com/ComunidadBioInfo/regutools, [email protected], [email protected].

Transcriptional Control of Parturition: Insights from Gene Regulation Studies in the Myometrium

2021 ◽  
Author(s):  
Nawrah Khader ◽  
Virlana M Shchuka ◽  
Oksana Shynlova ◽  
Jennifer A Mitchell
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Progesterone Receptors ◽  
Activator Protein 1 ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Transcriptional Regulatory ◽  
Mechanistic Basis ◽  
Labour Onset

Abstract The onset of labour is a culmination of a series of highly coordinated and preparatory physiological events that take place throughout the gestational period. In order to produce the associated contractions needed for fetal delivery, smooth muscle cells in the muscular layer of the uterus (i.e. myometrium) undergo a transition from quiescent to contractile phenotypes. Here, we present the current understanding of the roles transcription factors play in critical labour-associated gene expression changes as part of the molecular mechanistic basis for this transition. Consideration is given to both transcription factors that have been well-studied in a myometrial context, i.e. activator protein 1 (AP-1), progesterone receptors (PRs), estrogen receptors (ERs), and nuclear factor kappa B (NF-κB), as well as additional transcription factors whose gestational event-driving contributions have been demonstrated more recently. These transcription factors may form pregnancy- and labour- associated transcriptional regulatory networks in the myometrium to modulate the timing of labour onset. A more thorough understanding of the transcription factor-mediated, labour-promoting regulatory pathways holds promise for the development of new therapeutic treatments that can be used for the prevention of preterm labour in at-risk women.

scholarly journals Dissecting the Repertoire of DNA-Binding Transcription Factors of the Archaeon Pyrococcus furiosus DSM 3638

Life ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 40 ◽  
Author(s):  
Antonia Denis ◽  
Mario Alberto Martínez-Núñez ◽  
Silvia Tenorio-Salgado ◽  
Ernesto Perez-Rueda
Keyword(s):  
Transcription Factors ◽  
Dna Binding ◽  
Regulatory Networks ◽  
Pyrococcus Furiosus ◽  
Structural Domain ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Domains ◽  
Binding Domains ◽  
Transcriptional Regulatory ◽  
Cellular Domain

In recent years, there has been a large increase in the amount of experimental evidence for diverse archaeal organisms, and these findings allow for a comprehensive analysis of archaeal genetic organization. However, studies about regulatory mechanisms in this cellular domain are still limited. In this context, we identified a repertoire of 86 DNA-binding transcription factors (TFs) in the archaeon Pyrococcus furiosus DSM 3638, that are clustered into 32 evolutionary families. In structural terms, 45% of these proteins are composed of one structural domain, 41% have two domains, and 14% have three structural domains. The most abundant DNA-binding domain corresponds to the winged helix-turn-helix domain; with few alternative DNA-binding domains. We also identified seven regulons, which represent 13.5% (279 genes) of the total genes in this archaeon. These analyses increase our knowledge about gene regulation in P. furiosus DSM 3638 and provide additional clues for comprehensive modeling of transcriptional regulatory networks in the Archaea cellular domain.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

scholarly journals Vegfaexpression is activated through positive and negative transcriptional regulatory networks controlled by the ETS factor Etv6in vivo

2018 ◽  
Author(s):  
Lei Li ◽  
Rossella Rispoli ◽  
Roger Patient ◽  
Aldo Ciau-Uitz ◽  
Catherine Porcher
Keyword(s):  
Regulatory Networks ◽  
Dual Function ◽  
Transcriptional Regulatory Networks ◽  
Pathological Angiogenesis ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Ets Transcription Factor ◽  
Endothelial Development ◽  
New Strategies

AbstractVEGFA signaling is crucial for physiological and pathological angiogenesis and hematopoiesis. Although many context-dependent signaling pathways downstream of VEGFA have been uncovered, vegfa transcriptional regulation in vivo remains unclear. Here we show that the ETS transcription factor, Etv6, positively regulates vegfa expression during Xenopus blood stem cell development through multiple transcriptional inputs. In agreement with its established repressive functions, Etv6 directly inhibits the expression of the vegfa repressor, foxo3. Surprisingly, it also directly activates the expression of the vegfa activator, klf4. Finally, it indirectly binds to the vegfa promoter where it co-localizes with Klf4. Klf4 deficiency downregulates vegfa expression and significantly decreases Etv6 binding to the vegfa promoter, indicating that Klf4 recruits Etv6 to the vegfa promoter. Thus, our work uncovers a dual function for Etv6, as both a transcriptional repressor and activator, in controlling a major signaling pathway involved in blood and endothelial development in vivo. Given the established relationships between development and cancer, this elaborate gene regulatory network may inform new strategies for the treatment of VEGFA-dependent tumorigenesis.

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