scholarly journals Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture

2009 ◽  
Vol 5 (1) ◽  
pp. 294 ◽  
Author(s):  
Raja Jothi ◽  
S Balaji ◽  
Arthur Wuster ◽  
Joshua A Grochow ◽  
Jörg Gsponer ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Network Architecture ◽  
Genomic Analysis

scholarly journals The transcription factor basal regulatory network of Homo sapiens and Saccharomyces cerevisiae: uncovering the relationship between topology and phenotype

2019 ◽  
Author(s):  
JL Hernández-Domínguez ◽  
A. Brass ◽  
EM Navarro-López
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Network Architecture ◽  
Allelic Variation ◽  
Budding Yeast ◽  
Genetic Diseases ◽  
Local Network ◽  
Topological Features ◽  
Rare Genetic Diseases

AbstractTranscription factors play a key role in controlling which proteins are made by a cell. As transcription factors are themselves proteins, they are part of a complex interconnected and self-regulated network. We define the transcription factor basal regulatory network (TFBRN) as the network formed by the interactions between transcription factors (TFs) as proteins acting on target genes which are themselves TFs. The question then becomes as to whether topological features of this network are important in determining phenotypes caused by perturbations in TFs. To explore this, we developed two simple TFBRN models; one based on data from human TFs, and the other on the budding yeast. Even from this basic model we did find some very clear correlations between local topological measures and phenotypes seen in cancer and rare genetic diseases. This strongly suggests that the local network architecture of the TFBRN provides important information around the roles of transcription factors and the impacts to an organisation of their perturbation.Author SummaryThe human body is controlled by proteins whose production is coordinated by proteins known as transcription factors. These transcription factors can control multiple proteins, including other transcription factors. Does this network itself play any role in determining the properties of the transcription factors and their roles in cancer and disease? In this paper we find that there is a relationship between the local structures in the network and processes such as cancer and rare genetic diseases. We also found a similar relationship between local network characteristics and budding yeast phenotypes. This work therefore shows that simple properties of the network of interactions between transcription factors and their targets can be useful in determining the effects caused by changes in transcription factors (whether through deletion or allelic variation).

Construction of transcription factor mutual regulatory network based on a new database

2014 ◽  
Author(s):  
Zhenping Yang ◽  
Wei Wang ◽  
Yang Yang ◽  
Hongfei Chen ◽  
Jinke Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network

scholarly journals Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Albert T. Young ◽  
Xavier Carette ◽  
Michaela Helmel ◽  
Hanno Steen ◽  
Robert N. Husson ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Protein Interaction Data ◽  
Kinase Inhibition ◽  
Protein Protein Interaction ◽  
Downstream Effects ◽  
Regulate Cell Growth ◽  
Regulatory Effects

AbstractThe ability of Mycobacterium tuberculosis (Mtb) to adapt to diverse stresses in its host environment is crucial for pathogenesis. Two essential Mtb serine/threonine protein kinases, PknA and PknB, regulate cell growth in response to environmental stimuli, but little is known about their downstream effects. By combining RNA-Seq data, following treatment with either an inhibitor of both PknA and PknB or an inactive control, with publicly available ChIP-Seq and protein–protein interaction data for transcription factors, we show that the Mtb transcription factor (TF) regulatory network propagates the effects of kinase inhibition and leads to widespread changes in regulatory programs involved in cell wall integrity, stress response, and energy production, among others. We also observe that changes in TF regulatory activity correlate with kinase-specific phosphorylation of those TFs. In addition to characterizing the downstream regulatory effects of PknA/PknB inhibition, this demonstrates the need for regulatory network approaches that can incorporate signal-driven transcription factor modifications.

scholarly journals Regulatory Network Analysis in Estradiol-Treated Human Endothelial Cells

2021 ◽  
Vol 22 (15) ◽  
pp. 8193
Author(s):  
Daniel Pérez-Cremades ◽  
Ana B. Paes ◽  
Xavier Vidal-Gómez ◽  
Ana Mompeón ◽  
Carlos Hermenegildo ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Mirna Target ◽  
Target Genes ◽  
Functional Characterization ◽  
Human Endothelial Cells ◽  
Mrna Targets ◽  
Canonical Pathways

Background/Aims: Estrogen has been reported to have beneficial effects on vascular biology through direct actions on endothelium. Together with transcription factors, miRNAs are the major drivers of gene expression and signaling networks. The objective of this study was to identify a comprehensive regulatory network (miRNA-transcription factor-downstream genes) that controls the transcriptomic changes observed in endothelial cells exposed to estradiol. Methods: miRNA/mRNA interactions were assembled using our previous microarray data of human umbilical vein endothelial cells (HUVEC) treated with 17β-estradiol (E2) (1 nmol/L, 24 h). miRNA–mRNA pairings and their associated canonical pathways were determined using Ingenuity Pathway Analysis software. Transcription factors were identified among the miRNA-regulated genes. Transcription factor downstream target genes were predicted by consensus transcription factor binding sites in the promoter region of E2-regulated genes by using JASPAR and TRANSFAC tools in Enrichr software. Results: miRNA–target pairings were filtered by using differentially expressed miRNAs and mRNAs characterized by a regulatory relationship according to miRNA target prediction databases. The analysis identified 588 miRNA–target interactions between 102 miRNAs and 588 targets. Specifically, 63 upregulated miRNAs interacted with 295 downregulated targets, while 39 downregulated miRNAs were paired with 293 upregulated mRNA targets. Functional characterization of miRNA/mRNA association analysis highlighted hypoxia signaling, integrin, ephrin receptor signaling and regulation of actin-based motility by Rho among the canonical pathways regulated by E2 in HUVEC. Transcription factors and downstream genes analysis revealed eight networks, including those mediated by JUN and REPIN1, which are associated with cadherin binding and cell adhesion molecule binding pathways. Conclusion: This study identifies regulatory networks obtained by integrative microarray analysis and provides additional insights into the way estradiol could regulate endothelial function in human endothelial cells.

The regulatory network architecture of cardiometabolic diseases

2022 ◽  
Vol 54 (1) ◽  
pp. 2-3
Author(s):  
Harald H. H. W. Schmidt ◽  
Jörg Menche
Keyword(s):  
Regulatory Network ◽  
Network Architecture ◽  
Cardiometabolic Diseases

scholarly journals Zebrafish Transcription Factor ORFeome for Gene Discovery and Regulatory Network Elucidation

Zebrafish ◽  
2018 ◽  
Vol 15 (2) ◽  
pp. 202-205 ◽  
Author(s):  
Justin King ◽  
Justin Foster ◽  
James M. Davison ◽  
John F. Rawls ◽  
Ghislain Breton
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Gene Discovery

Deciphering the transcription factor-microRNA-target gene regulatory network associated with graphene oxide cytotoxicity

2018 ◽  
Vol 12 (9) ◽  
pp. 1014-1026 ◽  
Author(s):  
Masoumeh Farahani ◽  
Mostafa Rezaei–Tavirani ◽  
Hakimeh Zali ◽  
Afsaneh Arefi Oskouie ◽  
Meisam Omidi ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Graphene Oxide ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Microrna Target ◽  
Gene Regulatory

Volumetric compression develops noise-driven single-cell heterogeneity

2021 ◽  
Vol 118 (51) ◽  
pp. e2110550118
Author(s):  
Xing Zhao ◽  
Jiliang Hu ◽  
Yiwei Li ◽  
Ming Guo
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Regulatory Network ◽  
Network Architecture ◽  
Cell Heterogeneity ◽  
Cell Lung Carcinoma ◽  
Homogeneous Population ◽  
Mesenchymal Transition ◽  
Signature Genes

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non–small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial–mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.

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