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.

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

scholarly journals Class I TCP transcription factors regulate trichome branching and cuticle development in Arabidopsis

2020 ◽  
Vol 71 (18) ◽  
pp. 5438-5453
Author(s):  
Alejandra Camoirano ◽  
Agustín L Arce ◽  
Federico D Ariel ◽  
Antonela L Alem ◽  
Daniel H Gonzalez ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Class I ◽  
Branch Number ◽  
Plant Organ ◽  
Cuticle Formation ◽  
Cuticle Development ◽  
Different Levels

Abstract Trichomes and the cuticle are two specialized structures of the aerial epidermis that are important for plant organ development and interaction with the environment. In this study, we report that Arabidopsis thaliana plants affected in the function of the class I TEOSINTE BRANCHED 1, CYCLOIDEA, PCF (TCP) transcription factors TCP14 and TCP15 show overbranched trichomes in leaves and stems and increased cuticle permeability. We found that TCP15 regulates the expression of MYB106, a MIXTA-like transcription factor involved in epidermal cell and cuticle development, and overexpression of MYB106 in a tcp14 tcp15 mutant reduces trichome branch number. TCP14 and TCP15 are also required for the expression of the cuticle biosynthesis genes CYP86A4, GPAT6, and CUS2, and of SHN1 and SHN2, two AP2/EREBP transcription factors required for cutin and wax biosynthesis. SHN1 and CUS2 are also targets of TCP15, indicating that class I TCPs influence cuticle formation acting at different levels, through the regulation of MIXTA-like and SHN transcription factors and of cuticle biosynthesis genes. Our study indicates that class I TCPs are coordinators of the regulatory network involved in trichome and cuticle development.

scholarly journals Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yuming Zhao ◽  
Fang Wang ◽  
Su Chen ◽  
Jun Wan ◽  
Guohua Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Prediction Models ◽  
Promoter Prediction ◽  
Sequencing Data ◽  
Protein Coding ◽  
Mirna Genes ◽  
Intergenic Regions

MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.

scholarly journals Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Juha Mehtonen ◽  
Susanna Teppo ◽  
Mari Lahnalampi ◽  
Aleksi Kokko ◽  
Riina Kaukonen ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Leukemic Cell ◽  
Immune Microenvironment ◽  
Ets Transcription Factors ◽  
Lineage Differentiation ◽  
B Lineage

Abstract Background Tight regulatory loops orchestrate commitment to B cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. Methods We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. Results We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment resistance, we show that inhibition of ETS-transcription factors reduced cell viability and resolved pathways contributing to this using scRNA-seq. Conclusions Our data provide a detailed picture of the transcription factor activities characterizing both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

Neighbourhoods in the yeast regulatory network in different physiological states

2020 ◽  
Author(s):  
Arthur M Lesk ◽  
Arun S Konagurthu
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Dna Damage ◽  
Transcription Factor ◽  
Stress Response ◽  
Local Structure ◽  
Regulatory Network ◽  
Diauxic Shift ◽  
Central Node ◽  
Similarities And Differences

Abstract Motivation The gene expression regulatory network in yeast controls the selective implementation of the information contained in the genome sequence. We seek to understand how, in different physiological states, the network reconfigures itself to produce a different proteome. Results This article analyses this reconfiguration, focussing on changes in the local structure of the network. In particular, we define, extract and compare the 1-neighbourhoods of each transcription factor, where a 1-neighbourhood of a node in a network is the minimal subgraph of the network containing all nodes connected to the central node by an edge. We report the similarities and differences in the topologies and connectivities of these neighbourhoods in five physiological states for which data are available: cell cycle, DNA damage, stress response, diauxic shift and sporulation. Based on our analysis, it seems apt to regard the components of the regulatory network as ‘software’, and the responses to changes in state, ‘reprogramming’.

scholarly journals A Novel microRNA and transcription factor mediated regulatory network in schizophrenia

2010 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
An-Yuan Guo ◽  
Jingchun Sun ◽  
Peilin Jia ◽  
Zhongming Zhao
Keyword(s):  
Transcription Factor ◽  
Regulatory Network
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