Topology Analysis of a Metabolic Functional Gene Transcriptional Regulatory Network of Escherichia Coli

2013 ◽  
Vol 461 ◽  
pp. 648-653
Author(s):  
Qing Yu Zou ◽  
Fu Liu ◽  
Hou Tao
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Topological Analysis ◽  
Model Organisms ◽  
Transcriptional Regulatory Networks ◽  
Statistical Characterization ◽  
Functional Annotations ◽  
Physiological Processes ◽  
Transcriptional Regulatory

Under the perspectives of network science and systems biology, the characterizations of transcriptional regulatory networks (TRNs) beyond the context of model organisms have been studied extensively. However, little is still known about the structure and functionality of TRNs that control metabolic physiological processes. In this study, we present a newly version of the TRN of E.coli controlling metabolism based on functional annotations from GeneProtEC and Gene Ontology (GO). We also present an exhaustive topological analysis of the metabolic transcriptional regulatory network (MTRN), focusing on the main statistical characterization describing the topological structure and the comparison with TRN. From the results in this paper we infer that TRN and MTRN have very similar characteristic distribution.

scholarly journals Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

2017 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Daniel J H Nightingale ◽  
Valerie Wood ◽  
Kathryn S Lilley ◽  
Stephen G Oliver
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Topological Analysis ◽  
Network Size ◽  
Transcriptional Level ◽  
Transcriptional Regulatory Networks ◽  
Network Function ◽  
Network Analyses ◽  
Transcriptional Regulatory

AbstractThe topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

scholarly journals Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulator ◽  
Cell Analysis ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

Breast Cancer Transcriptional Regulatory Network Reprogramming by using the CRISPR/Cas9 system: An Oncogenetics Perspective

2021 ◽  
Vol 21 ◽  
Author(s):  
Desh Deepak Singh ◽  
Ravi Verma ◽  
Subhash K. Tripathi ◽  
Rajnish Sahu ◽  
Poonam Trivedi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Treatment Options ◽  
Diagnostic Tools ◽  
Transcriptional Regulatory Networks ◽  
Development Of Resistance ◽  
Transcriptional Regulatory ◽  
Network Reprogramming

: Breast cancer (BC) is the second most commonly diagnosed cancer in the world. BC develops due to dysregulation of transcriptional profiles, substantial interpatient variations, genetic mutations, and dysregulation of signaling pathways in breast cells. These events are regulated by many genes such as BRCA1/2, PTEN, TP53, mTOR, TERT, AKT, PI3K and others genes. Treatment options for BC remain a hurdle, which warrants a comprehensive understanding that establishes an interlinking connection between these genes in BC tumorigenesis. Consequently, there is an increasing demand for alternative treatment approaches and the design of more effective treatments. In this regard, it is crucial to build the corresponding transcriptional regulatory networks governing BC by using advanced genetic tools and techniques. In the past, several molecular editing technologies have been used to edit genes with several limitations. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated Protein 9 (CRISPR/Cas9) recently received a profound attention due to its potential in biomedical and therapeutic applications. Here, we review the role of various molecular signalling pathways dysregulated in BC development such as PTEN/PI3K/AKT/mTOR as well as BRCA1/BRCA2/TP53/TERT and their interplay between the related gene networks in BC initiation, progression and development of resistance against available targeted therapeutic agents. Use of CRISPR/Cas9 gene-editing technology to generate BC gene-specific transgenic cell lines and animal models to decipher their role and interactions with other gene products has been employed successfully. Moreover, the significance of using CRISPR/Cas9 technology to develop early BC diagnostic tools and treatments is discussed here.

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 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.

scholarly journals Network design principle for robust oscillatory behaviors with respect to biological noise

2021 ◽  
Author(s):  
Lingxia Qiao ◽  
Zhi-Bo Zhang ◽  
Wei Zhao ◽  
Ping Wei ◽  
Lei Zhang
Keyword(s):  
Chemical Reactions ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Intrinsic Noise ◽  
Biological Noise ◽  
Extrinsic Noise ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Activator Inhibitor ◽  
Oscillatory Behaviors

Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive auto-regulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive auto-regulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies, and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive auto-regulation, improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

Current and emerging approaches to define intestinal epithelium-specific transcriptional networks

2012 ◽  
Vol 302 (3) ◽  
pp. G277-G286 ◽  
Author(s):  
Anders Krüger Olsen ◽  
Mette Boyd ◽  
Erik Thomas Danielsen ◽  
Jesper Thorvald Troelsen
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Tissue Type ◽  
Intestinal Cell ◽  
Future Research ◽  
Transcriptional Networks ◽  
Differentiation Process ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Upon developmental or environmental cues, the composition of transcription factors in a transcriptional regulatory network is deeply implicated in controlling the signature of the gene expression and thereby specifies the cell or tissue type. Novel methods including ChIP-chip and ChIP-Seq have been applied to analyze known transcription factors and their interacting regulatory DNA elements in the intestine. The intestine is an example of a dynamic tissue where stem cells in the crypt proliferate and undergo a differentiation process toward the villus. During this differentiation process, specific regulatory networks of transcription factors are activated to target specific genes, which determine the intestinal cell fate. The expanding genomewide mapping of transcription factor binding sites and construction of transcriptional regulatory networks provide new insight into how intestinal differentiation occurs. This review summarizes the current overview of the transcriptional regulatory networks driving epithelial differentiation in adult intestine. The novel technologies that have been implied to study these networks are presented and their prospects for implications in future research are also addressed.

Computational approaches to study transcriptional regulation

2008 ◽  
Vol 36 (4) ◽  
pp. 758-765 ◽  
Author(s):  
M. Madan Babu
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Model Organisms ◽  
Emerging Pathogens ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Interactions ◽  
Regulatory Circuits ◽  
Transcriptional Regulatory ◽  
The Individual

In recent years, a number of technical and experimental advances have allowed us to obtain an unprecedented amount of information about living systems on a genomic scale. Although the complete genomes of many organisms are available due to the progress made in sequencing technology, the challenge to understand how the individual genes are regulated within the cell remains. Here, I provide an overview of current computational methods to investigate transcriptional regulation. I will first discuss how representing protein–DNA interactions as a network provides us with a conceptual framework to understand the organization of regulatory interactions in an organism. I will then describe methods to predict transcription factors and cis-regulatory elements using information such as sequence, structure and evolutionary conservation. Finally, I will discuss approaches to infer genome-scale transcriptional regulatory networks using experimentally characterized interactions from model organisms and by reverse-engineering regulatory interactions that makes use of gene expression data and genomewide location data. The methods summarized here can be exploited to discover previously uncharacterized transcriptional pathways in organisms whose genome sequence is known. In addition, such a framework and approach can be invaluable to investigate transcriptional regulation in complex microbial communities such as the human gut flora or populations of emerging pathogens. Apart from these medical applications, the concepts and methods discussed can be used to understand the combinatorial logic of transcriptional regulation and can be exploited in biotechnological applications, such as in synthetic biology experiments aimed at engineering regulatory circuits for various purposes.

Sign in / Sign up

Export Citation Format

Share Document