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 Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae

mBio ◽  
2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Guodong Liu ◽  
David Bergenholm ◽  
Jens Nielsen
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Network ◽  
Deletion Mutant ◽  
Transcriptional Regulatory Network ◽  
Biological Processes ◽  
Genome Wide ◽  
Transcriptional Regulatory

ABSTRACT In the model eukaryote Saccharomyces cerevisiae , the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103 , encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae . IMPORTANCE Transcription factors regulate the activity of various biological processes through binding to specific DNA sequences. Therefore, the determination of binding positions is important for the understanding of the regulatory effects of transcription factors. In the model eukaryote Saccharomyces cerevisiae , the transcription factor Cst6p has been reported to regulate several biological processes, while its genome-wide targets remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. We show that the binding of Cst6p to its target promoters is condition dependent and explain the mechanism for the retarded growth of the CST6 deletion mutant on ethanol. Furthermore, we demonstrate that Cst6p is a new member of a stress-responsive transcriptional regulatory network. These results provide deeper understanding of the function of the dynamic transcriptional regulatory network in S. cerevisiae .

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

scholarly journals Identification of a GrgA-Euo-HrcA Transcriptional Regulatory Network in Chlamydia

mSystems ◽  
2021 ◽  
Author(s):  
Wurihan Wurihan ◽  
Yi Zou ◽  
Alec M. Weber ◽  
Korri Weldon ◽  
Yehong Huang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Bacterial Pathogen ◽  
Developed Countries ◽  
Developmental Cycle ◽  
Content Type ◽  
Sexually Transmitted ◽  
Transcriptional Regulatory ◽  
Preventable Blindness

Chlamydia trachomatis is the most prevalent sexually transmitted bacterial pathogen worldwide and is a leading cause of preventable blindness in underdeveloped areas as well as some developed countries. Chlamydia carries genes that encode a limited number of known transcription factors. While Euo is thought to be critical for early chlamydial development, the functions of GrgA and HrcA in the developmental cycle are unclear.

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.

scholarly journals Coregulation of transcription factors and microRNAs in human transcriptional regulatory network

2011 ◽  
Vol 12 (Suppl 1) ◽  
pp. S41 ◽  
Author(s):  
Cho-Yi Chen ◽  
Shui-Tein Chen ◽  
Chiou-Shann Fuh ◽  
Hsueh-Fen Juan ◽  
Hsuan-Cheng Huang
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulatory

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 Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators, and role in key physiological response

2020 ◽  
Vol 117 (29) ◽  
pp. 17228-17239 ◽  
Author(s):  
Saugat Poudel ◽  
Hannah Tsunemoto ◽  
Yara Seif ◽  
Anand V. Sastry ◽  
Richard Szubin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Mueller Hinton ◽  
Transcriptional Regulatory ◽  
Its Gene ◽  
Low Dimensional ◽  
Regulatory Organization

The ability ofStaphylococcus aureusto infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from twoS. aureusclinical strains (TCH1516 and LAC). ICA decomposed theS. aureustranscriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules inS. aureus, and a platform enabling its full elucidation.

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 Rapid reorganization of the transcriptional regulatory network after genome duplication in yeast

2009 ◽  
Vol 277 (1683) ◽  
pp. 869-876 ◽  
Author(s):  
Gavin C. Conant
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Whole Genome Duplication ◽  
Genome Duplication ◽  
Transcriptional Regulatory Network ◽  
Whole Genome ◽  
Functional Roles ◽  
Transcriptional Regulatory ◽  
Dosage Balance ◽  
Tf Gene

I study the reorganization of the yeast transcriptional regulatory network after whole-genome duplication (WGD). Individual transcription factors (TFs) were computationally removed from the regulatory network, and the resulting networks were analysed. TF gene pairs that survive in duplicate from WGD show detectable redundancy as a result of that duplication. However, in most other respects, these duplicated TFs are indistinguishable from other TFs in the genome, suggesting that the duplicate TFs produced by WGD were rapidly diverted to distinct functional roles in the regulatory network. Separately, I find that genes targeted by many TFs appear to be preferentially retained in duplicate after WGD, an effect I attribute to selection to maintain dosage balance in the regulatory network after WGD.

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