scholarly journals Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

2017 ◽  
Author(s):  
Kentaro Kawata ◽  
Katsuyuki Yugi ◽  
Atsushi Hatano ◽  
Masashi Fujii ◽  
Yoko Tomizawa ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Insulin Action ◽  
Regulatory Networks ◽  
Dynamic Range ◽  
Transcriptional Regulatory Network ◽  
Integrated Analysis ◽  
List Type ◽  
Fasting Insulin ◽  
Cellular Functions ◽  
Insulin Signal

SUMMARYThe concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks—a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.HighlightsWe constructed a trans-omic network of insulin action using multi-omic data.The trans-omic network integrates phosphorylation, transcription, and metabolism.We classified signaling, transcriptome, and metabolome by sensitivity to insulin.We identified fed and fasting insulin signal flow across the trans-omic network.

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 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 TopicNet: a framework for measuring transcriptional regulatory network change

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i474-i481
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Xiangmeng Kong ◽  
Jing Zhang ◽  
Jason Liu ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transcriptional Regulatory Network ◽  
Network Connectivity ◽  
Supplementary Information ◽  
Differential Survival ◽  
Network Change ◽  
Low Dimensional

Abstract Motivation Recently, many chromatin immunoprecipitation sequencing experiments have been carried out for a diverse group of transcription factors (TFs) in many different types of human cells. These experiments manifest large-scale and dynamic changes in regulatory network connectivity (i.e. network ‘rewiring’), highlighting the different regulatory programs operating in disparate cellular states. However, due to the dense and noisy nature of current regulatory networks, directly comparing the gains and losses of targets of key TFs across cell states is often not informative. Thus, here, we seek an abstracted, low-dimensional representation to understand the main features of network change. Results We propose a method called TopicNet that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF. We then define a rewiring score to quantify regulatory-network changes in terms of the topic changes for this TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states (such as observed in oncogenesis). Also, incorporating gene expression data, we define a topic activity score that measures the degree to which a given topic is active in a particular cellular state. And we show how activity differences can indicate differential survival in various cancers. Availability and Implementation The TopicNet framework and related analysis were implemented using R and all codes are available at https://github.com/gersteinlab/topicnet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Predicting Evolution Using Regulatory Architecture

2020 ◽  
Vol 49 (1) ◽  
pp. 181-197 ◽  
Author(s):  
Philippe Nghe ◽  
Marjon G.J. de Vos ◽  
Enzo Kingma ◽  
Manjunatha Kogenaru ◽  
Frank J. Poelwijk ◽  
...  
Keyword(s):  
Systems Biology ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cellular Biology ◽  
Evolutionary Constraint ◽  
Cellular Functions ◽  
Biological Organization ◽  
Cell Quantification ◽  
Regulatory Architecture ◽  
Different Levels

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization—in molecular recognition, within a single regulatory network, and between different networks—providing first indications of predictable features of evolutionary constraint.

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 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 Identifying Genes Devoted to the Cell Death Process in the Gene Regulatory Network of Ustilago maydis

2021 ◽  
Vol 12 ◽  
Author(s):  
Cinthia V. Soberanes-Gutiérrez ◽  
Ernesto Pérez-Rueda ◽  
José Ruíz-Herrera ◽  
Edgardo Galán-Vásquez
Keyword(s):  
Cell Death ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Pathogenic Fungi ◽  
Ustilago Maydis ◽  
Death Process ◽  
Dimorphic Fungus ◽  
Cell Death Process ◽  
Gene Regulatory

Cell death is a process that can be divided into three morphological patterns: apoptosis, autophagy and necrosis. In fungi, cell death is induced in response to intracellular and extracellular perturbations, such as plant defense molecules, toxins and fungicides, among others. Ustilago maydis is a dimorphic fungus used as a model for pathogenic fungi of animals, including humans, and plants. Here, we reconstructed the transcriptional regulatory network of U. maydis, through homology inferences by using as templates the well-known gene regulatory networks (GRNs) of Saccharomyces cerevisiae, Aspergillus nidulans and Neurospora crassa. Based on this GRN, we identified transcription factors (TFs) as hubs and functional modules and calculated diverse topological metrics. In addition, we analyzed exhaustively the module related to cell death, with 60 TFs and 108 genes, where diverse cell proliferation, mating-type switching and meiosis, among other functions, were identified. To determine the role of some of these genes, we selected a set of 11 genes for expression analysis by qRT-PCR (sin3, rlm1, aif1, tdh3 [isoform A], tdh3 [isoform B], ald4, mca1, nuc1, tor1, ras1, and atg8) whose homologues in other fungi have been described as central in cell death. These genes were identified as downregulated at 72 h, in agreement with the beginning of the cell death process. Our results can serve as the basis for the study of transcriptional regulation, not only of the cell death process but also of all the cellular processes of U. maydis.

scholarly journals TopicNet: a framework for measuring transcriptional regulatory network change

2019 ◽  
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Xiangmeng Kong ◽  
Jing Zhang ◽  
Jason Liu ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Transcriptional Regulatory Network ◽  
Network Connectivity ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Differential Survival ◽  
Network Change

SummaryNext generation sequencing data highlights comprehensive and dynamic changes in the human gene regulatory network. Moreover, changes in regulatory network connectivity (network “rewiring”) manifest different regulatory programs in multiple cellular states. However, due to the dense and noisy nature of the connectivity in regulatory networks, directly comparing the gains and losses of targets of key TFs is not that informative. Thus, here, we seek a abstracted lower-dimensional representation to understand the main features of network change. In particular, we propose a method called TopicNet that applies latent Dirichlet allocation (LDA) to extract meaningful functional topics for a collection of genes regulated by a TF. We then define a rewiring score to quantify the large-scale changes in the regulatory network in terms of topic change for a TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states. This is particularly relevant in oncogenesis. Also, incorporating gene-expression data, we define a topic activity score that gives the degree that a topic is active in a particular cellular state. Furthermore, we show how activity differences can highlight differential survival in certain cancers.

scholarly journals Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liwei Yu ◽  
Tengfei Yao ◽  
Zhoulei Jiang ◽  
Tong Xu
Keyword(s):  
Gene Expression ◽  
Femoral Head ◽  
Expression Profile ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Differential Expression Analysis ◽  
Integrated Analysis ◽  
Transferase Activity ◽  
Bioinformatics Analyses ◽  
Molecular Abnormalities

Osteonecrosis of the femoral head (ONFH) accounts for as many as 18% of total hip arthroplasties. Knowledge of genetic changes and molecular abnormalities could help identify individuals considered to be at a higher risk of developing ONFH. In this study, we sought to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs) associated with ONFH by integrated bioinformatics analyses as well as to construct the miRNA-mRNA regulatory network involving in the pathogenesis of ONFH. We performed differential expression analysis using a gene expression profile GSE123568 and a miRNA expression profile GSE89587 deposited in the Gene Expression Omnibus and identified 47 DEmiRs (24 upregulated miRNAs and 23 downregulated miRNAs) and 529 DEGs (218 upregulated genes and 311 downregulated genes). Gene Ontology enrichment analyses of DEGs suggested that DEGs were significantly enriched in neutrophil activation, cytosol, and ubiquitin-protein transferase activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs revealed that DEGs were significantly enriched in transcriptional misregulation in cancer. DEGs-based miRNA-mRNA regulatory networks were obtained by searching miRNA-mRNA prediction databases, TargetScan, miTarBase, miRMap, miRDB, and miRanda databases. Then, overlapped miRNAs were selected between these putative miRNAs and DEmiRs between ONFH and non-ONFH, and pairs of the DEmiR-DEG regulatory network were finally depicted. There were 12 nodes and 64 interactions for upDEmiR-downDEG regulatory networks and 6 nodes and 16 interactions for downDEmiR-upDEG regulatory networks. Using the STRING database, we established a protein-protein interaction network based on the overlapped DEGs between ONFH and non-ONFH. C5AR1, CDC27, CDC34, KAT2B, CPPED1, TFDP1, and MX2 were identified as the hub genes. The present study characterizes the miRNA profile, gene profile, and miRNA-mRNA regulatory network in ONFH, which may contribute to the interpretation of the pathogenesis of ONFH and the identification of novel biomarkers and therapeutic targets for ONFH.

A Synthesis Method of Gene Regulatory Networks based on Gene Expression by Network Learning

2010 ◽  
pp. 266-288
Author(s):  
Yoshihiro Mori ◽  
Yasuaki Kuroe
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Synthesis Method ◽  
Gene Expression Pattern ◽  
Cellular Functions ◽  
Network Learning ◽  
Complementary Approach ◽  
Gene Regulatory

Investigating gene regulatory networks is important to understand mechanisms of cellular functions. Recently, the synthesis of gene regulatory networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene regulatory networks, and it could be the first step in controlling living cells. In this chapter, we discuss a synthesis problem in gene regulatory networks by network learning. The problem is to determine parameters of a gene regulatory network such that it possesses given gene expression pattern sequences as desired properties. We also discuss a controller synthesis method of gene regulatory networks. Some experiments illustrate the performance of this method.

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