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.

scholarly journals Transcriptional regulatory networks that promote and restrict identities and functions of intestinal innate lymphoid cells

2018 ◽  
Author(s):  
Maria Pokrovskii ◽  
Jason A. Hall ◽  
David E. Ochayon ◽  
Ren Yi ◽  
Natalia S. Chaimowitz ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Inflammatory Diseases ◽  
Innate Lymphoid Cells ◽  
Chromatin Accessibility ◽  
Immune Defense ◽  
Lymphoid Cells ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

SummaryInnate lymphoid cells (ILCs) can be subdivided into several distinct cytokine-secreting lineages that promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. Accumulating evidence suggests that ILCs, similarly to other immune populations, are capable of phenotypic and functional plasticity in response to infectious or environmental stimuli. Yet the transcriptional circuits that control ILC identity and function are largely unknown. Here we integrate gene expression and chromatin accessibility data to infer transcriptional regulatory networks within intestinal type 1, 2, and 3 ILCs. We predict the “core” sets of transcription-factor (TF) regulators driving each ILC subset identity, among which only a few TFs were previously known. To assist in the interpretation of these networks, TFs were organized into cooperative clusters, or modules that control gene programs with distinct functions. The ILC network reveals extensive alternative-lineage-gene repression, whose regulation may explain reported plasticity between ILC subsets. We validate new roles for c-MAF and BCL6 as regulators affecting the type 1 and type 3 ILC lineages. Manipulation of TF pathways identified here might provide a novel means to selectively regulate ILC effector functions to alleviate inflammatory disease or enhance host tolerance to pathogenic microbes or noxious stimuli. Our results will enable further exploration of ILC biology, while our network approach will be broadly applicable to identifying key cell state regulators in otherin vivocell populations.

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 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.

scholarly journals CoryneRegNet 2: An Integrative Bioinformatics Approach for Reconstruction and Comparison of Transcriptional Regulatory Networks in Prokaryotes

2006 ◽  
Vol 3 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Jan Baumbach ◽  
Karina Brinkrolf ◽  
Tobias Wittkop ◽  
Andreas Tauch ◽  
Sven Rahmann
Keyword(s):  
Transcription Factor ◽  
Data Warehouse ◽  
Binding Sites ◽  
Regulatory Network ◽  
High Performance ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Search Tool ◽  
Gene Regulatory

SummaryCoryneRegNet is an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Now we integrated the genomes and transcriptional interactions of three other corynebacteria, C. diphtheriae, C. efficiens, and C. jeikeium into CoryneRegNet; providing comparative analysis and visualization with GraphVis. We also integrated the high-performance PSSM search tool PoSSuM search to detect potential transcription factor binding sites within and across species. As an application, we reconstruct in silico the regulatory network of the iron metabolism regulator DtxR in the four corynebacteria.CoryneRegNet is freely accessible at https://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/. The final slash (/) is mandatory. In order to use the GraphVis feature, Java (at least version 1.4.2) is required.

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 Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jason K. Sa ◽  
Nakho Chang ◽  
Hye Won Lee ◽  
Hee Jin Cho ◽  
Michele Ceccarelli ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Complex Disease ◽  
Target Genes ◽  
Mammalian Target Of Rapamycin ◽  
Differentially Expressed Gene ◽  
Treatment Resistance ◽  
Tumor Associated Macrophages ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Abstract Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCOhigh TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCOhigh TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

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].

Sign in / Sign up

Export Citation Format

Share Document