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

2019 ◽  
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 ◽  
Regulate Cell Growth ◽  
Regulatory Effects ◽  
Network Approaches

The 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 ef-fects. By combining RNA-Seq data, following treatment with either a PknA/PknB inhibitor or an inactive control, with publicly available ChIP-Seq and protein-protein interaction data, 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 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 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 Gene Expression and Regulatory Webwork of POLR2K in Bladder Carcinogenesis by Integrated Bioinformatics Approaches

2020 ◽  
Author(s):  
Liliang Yang ◽  
Kaizhen Wang ◽  
Wenjing Guo ◽  
Xian Chen ◽  
Qinglong Guo ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factor ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Transcription Factor Binding ◽  
Sequencing Data ◽  
Factor Binding

Abstract Background：RNA polymerase II subunit K (POLR2K) belongs to one of the multiple subunits of RNA polymerase II (Pol II), whose biological function is to synthesize mRNA. Aberrant POLR2K expression is related to carcinogenesis. However, POLR2K’s underlying role in bladder cancer has not been explored. In the current study, we intend to analyze the function of POLR2K and its regulatory network within bladder cancer.Methods: Public sequencing data was obtained from GEO and TCGA to investigate POLR2K expression and regulatory network within bladder cancer (BLCA) by using GEPIA and Oncomine as well as cBioPortal online tool. LinkedOmics was employed to identify genes displaying significantly differential expression patterns and to perform GO and KEGG analyses. After differential genes was assigned and ranked, GSEA analyses was performed to obtain target networks for transcription factors, miRNAs, and kinases that could regulate POLR2K–associated gene network. Subsequent functional webwork analyses were used to identify cancer-relevant pathways Moreover, POLR2K gene is verified, by ChIP-seq in MCF-7 cell line , with transcription factor binding evidence in the ENCODE Transcription Factor Binding Site Profiles dataset.Conclusions: The current study implies that POLR2K gene is overexpressed and often amplified in BLCA, providing the first evidence that POLR2K deregulation, in particular increased transcription, may promote BLCA. These findings uncover a unique expression patterns of POLR2K and its potential regulatory networks in BLCA, contributing greatly to study of the role of POLR2K in cancer development.

A Linear Programming Framework for Inferring Gene Regulatory Networks by Integrating Heterogeneous Data

2010 ◽  
pp. 450-475 ◽  
Author(s):  
Yong Wang ◽  
Rui-Sheng Wang ◽  
Trupti Joshi ◽  
Dong Xu ◽  
Xiang-Sun Zhang ◽  
...  
Keyword(s):  
Linear Programming ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction ◽  
Programming Framework ◽  
Gene Regulatory

There exist many heterogeneous data sources that are closely related to gene regulatory networks. These data sources provide rich information for depicting complex biological processes at different levels and from different aspects. Here, we introduce a linear programming framework to infer the gene regulatory networks. Within this framework, we extensively integrate the available information derived from multiple time-course expression datasets, ChIP-chip data, regulatory motif-binding patterns, protein-protein interaction data, protein-small molecule interaction data, and documented regulatory relationships in literature and databases. Results on synthetic and real experimental data both demonstrate that the linear programming framework allows us to recover gene regulations in a more robust and reliable manner.

scholarly journals NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks

2017 ◽  
Author(s):  
Yijie Wang ◽  
Dong-Yeon Cho ◽  
Hangnoh Lee ◽  
Justin Fear ◽  
Brian Oliver ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Tissue Type ◽  
Expression Data ◽  
Gene Regulatory ◽  
Context Specific ◽  
Tf Gene ◽  
Biological Context

AbstractUnderstanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Due to lack of the biological context specificity, these context-agnostic networks may not provide insight for revealing the precise actions of genes for a specific biological system under concern. Collecting multitude of features required for a reliable construction of GRNs such as physical features (TF binding, chromatin accessibility) and functional features (correlation of expression or chromatin patterns) for every context of interest is costly. Therefore we need methods that is able to utilize the knowledge about a context-agnostic network (or a network constructed in a related context) for construction of a context specific regulatory network.To address this challenge we developed a computational approach that utilizes expression data obtained in a specific biological context such as a particular development stage, sex, tissue type and a GRN constructed in a different but related context (alternatively an incomplete or a noisy network for the same context) to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis (NCA) that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, which is non-convex and non-smooth, we provide a general mathematical framework allowing use of the recently proposed Proximal Alternative Linearized Maximization technique and prove that our formulation has the properties required for convergence.We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from 99 hemizygotic lines of the Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method.

scholarly journals Constructing gene regulatory networks using epigenetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Abhijeet Rajendra Sonawane ◽  
Dawn L. DeMeo ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Message Passing ◽  
Regulatory Networks ◽  
Transcription Factor Binding ◽  
Network Reconstruction ◽  
Sequence Motif ◽  
Factor Binding ◽  
Gene Regulatory

AbstractThe biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

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 Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network

2014 ◽  
Vol 15 (11) ◽  
Author(s):  
Tige R Rustad ◽  
Kyle J Minch ◽  
Shuyi Ma ◽  
Jessica K Winkler ◽  
Samuel Hobbs ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Regulatory Network

scholarly journals Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

mSystems ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
David Bergenholm ◽  
Guodong Liu ◽  
Petter Holland ◽  
Jens Nielsen
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Lipid Metabolism ◽  
Chromatin Immunoprecipitation ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Lambda Exonuclease ◽  
Transcriptional Regulatory ◽  
Exonuclease Digestion

ABSTRACT To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor (e.g., glutamate biosynthesis as a target of Oaf1 and Pip2). Moreover, condition-dependent binding of transcription factors in response to cell metabolic state (e.g., differential binding of Ino2 between fermentative and respiratory metabolic conditions) was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks. IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism.

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