scholarly journals The G-box transcriptional regulatory code in Arabidopsis

2017 ◽  
Author(s):  
Daphne Ezer ◽  
Samuel JK Shepherd ◽  
Anna Brestovitsky ◽  
Patrick Dickinson ◽  
Sandra Cortijo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Environmental Stressors ◽  
Sequence Motifs ◽  
Transcription Pattern ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Flanking Sequences ◽  
Gene Regulatory

ABSTRACTPlants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes—these expansions are linked to adaptation to environmental stressors (1, 2). Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determine that the flanking sequences near G-boxes help determine in vitro specificity, but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we construct a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the gene expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a website that provides interactive visualisations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

scholarly journals DksA-dependent regulation of RpoS contributes to Borrelia burgdorferi tick-borne transmission and mammalian infectivity

2020 ◽  
Author(s):  
William K. Boyle ◽  
Crystal L. Richards ◽  
Daniel P. Dulebohn ◽  
Amanda K. Zalud ◽  
Jeff A. Shaw ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Lyme Disease ◽  
Borrelia Burgdorferi ◽  
Rna Polymerase ◽  
Nutrient Limitation ◽  
Expression Of Genes ◽  
Gene Regulatory ◽  
Transcription Assays

ABSTRACTThroughout its enzootic cycle, the Lyme disease spirochete Borreliella (Borrelia) burgdorferi, senses and responds to changes in its environment by using a small repertoire of transcription factors which coordinate the expression of genes required for infection of Ixodes ticks and various mammalian hosts. Among these transcription factors, the DnaK suppressor protein (DksA) plays a pivotal role in regulating gene expression in B. burgdorferi during periods of nutrient limitation and is required for mammalian infectivity. In many pathogenic bacteria, the gene regulatory activity of DksA along with the alarmone guanosine penta- and tetra-phosphate ((p)ppGpp) coordinates the stringent response to various environmental stresses including nutrient limitation. In this study, we sought to characterize the role of DksA in regulating the transcriptional activity of RNA polymerase and in the regulation of RpoS-dependent gene expression required for B. burgdorferi infectivity. Using in vitro transcription assays, we observed recombinant DksA inhibits RpoD-dependent transcription by B. burgdorferi RNA polymerase independent of ppGpp Additionally, we determined the pH-inducible expression of RpoS-dependent genes relies on DksA, but is independent of (p)ppGpp produced by Relbbu. Subsequent transcriptomic and western blot assays indicated DksA regulates the expression of BBD18, a protein previously implicated in the post-transcriptional regulation of RpoS. Moreover, we observed DksA was required for infection of mice following intraperitoneal inoculation or for transmission of B. burgdorferi by Ixodes scapularis nymphs. Together, these data suggest DksA plays a central role in coordinating transcriptional responses of B. burgdorferi required for infectivity through its interactions with RNA polymerase and post-transcriptional control of RpoS.Author SummaryLyme disease, caused by the spirochetal bacteria Borrelia burgdorferi, is the most common vector-borne illness in North America. The ability of B. burgdorferi to establish infection is predicated by its ability to coordinate the expression of virulence factors in response to diverse environmental stimuli encountered within Ixodes ticks and mammalian hosts. Previous studies have shown an essential role for the alternative sigma factor RpoS in regulating the expression of genes required for the successful transmission of B. burgdorferi by Ixodes ticks and infection of mammalian hosts. The DnaK suppressor protein (DksA) is a global gene regulator in B. burgdorferi that also contributes to the expression of RpoS-dependent genes. In this study, we determined DksA exerts its gene regulatory function through direct interactions with the B. burgdorferi RNA polymerase using in vitro transcription assays and controls the expression of RpoS-dependent genes required for mammalian infection by post-transcriptionally regulating cellular levels of RpoS. Our results demonstrate the utility of in vitro transcription assays to determine how gene regulatory proteins like DksA control gene expression in B. burgdorferi, and reveal a novel role for DksA in the infectious cycle of B. burgdorferi.

scholarly journals Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

2020 ◽  
Author(s):  
Ian Leifer ◽  
Mishael Sanchez ◽  
Cecilia Ishida ◽  
Hernan Makse
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Conceptual Tool ◽  
Gene Coexpression ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Gene Regulatory

Abstract Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the transcriptional regulatory networks (TRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models -- with gene input functions differencing between genes -- predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the transcription networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

scholarly journals Study on the Relationship between the miRNA-centered ceRNA Regulatory Network and Fatigue

2021 ◽  
Author(s):  
Xingzhe Yang ◽  
Feng Li ◽  
Jie Ma ◽  
Yan Liu ◽  
Xuejiao Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Hot Spot ◽  
Genetic Research ◽  
Noncoding Rnas ◽  
Messenger Rnas ◽  
Effective Prevention ◽  
Gene Regulatory ◽  
The Relationship

AbstractIn recent years, the incidence of fatigue has been increasing, and the effective prevention and treatment of fatigue has become an urgent problem. As a result, the genetic research of fatigue has become a hot spot. Transcriptome-level regulation is the key link in the gene regulatory network. The transcriptome includes messenger RNAs (mRNAs) and noncoding RNAs (ncRNAs). MRNAs are common research targets in gene expression profiling. Noncoding RNAs, including miRNAs, lncRNAs, circRNAs and so on, have been developed rapidly. Studies have shown that miRNAs are closely related to the occurrence and development of fatigue. MiRNAs can regulate the immune inflammatory reaction in the central nervous system (CNS), regulate the transmission of nerve impulses and gene expression, regulate brain development and brain function, and participate in the occurrence and development of fatigue by regulating mitochondrial function and energy metabolism. LncRNAs can regulate dopaminergic neurons to participate in the occurrence and development of fatigue. This has certain value in the diagnosis of chronic fatigue syndrome (CFS). CircRNAs can participate in the occurrence and development of fatigue by regulating the NF-κB pathway, TNF-α and IL-1β. The ceRNA hypothesis posits that in addition to the function of miRNAs in unidirectional regulation, mRNAs, lncRNAs and circRNAs can regulate gene expression by competitive binding with miRNAs, forming a ceRNA regulatory network with miRNAs. Therefore, we suggest that the miRNA-centered ceRNA regulatory network is closely related to fatigue. At present, there are few studies on fatigue-related ncRNA genes, and most of these limited studies are on miRNAs in ncRNAs. However, there are a few studies on the relationship between lncRNAs, cirRNAs and fatigue. Less research is available on the pathogenesis of fatigue based on the ceRNA regulatory network. Therefore, exploring the complex mechanism of fatigue based on the ceRNA regulatory network is of great significance. In this review, we summarize the relationship between miRNAs, lncRNAs and circRNAs in ncRNAs and fatigue, and focus on exploring the regulatory role of the miRNA-centered ceRNA regulatory network in the occurrence and development of fatigue, in order to gain a comprehensive, in-depth and new understanding of the essence of the fatigue gene regulatory network.

scholarly journals Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Drosophila Embryo ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

What are Gene Regulatory Networks?

2010 ◽  
pp. 1-27 ◽  
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Biochemical Processes ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

This book deals with algorithms for inferring and analyzing Gene Regulatory Networks using mainly gene expression data. What precisely are the Gene Regulatory Networks that are inferred by such algorithms from this type of data? There is still much confusion in the current literature and it is important to start a book about computational methods for Gene Regulatory Networks with a definition that is as unambiguous as possible. In this chapter, I provide a definition and try to clearly explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. To do the latter in a formal way, I will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed’ into the more compact description of Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, such as Transcriptional Regulatory Networks and Co-Expression Networks, will be highlighted.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

2019 ◽  
Vol 17 (06) ◽  
pp. 1950035
Author(s):  
Huiqing Wang ◽  
Yuanyuan Lian ◽  
Chun Li ◽  
Yue Ma ◽  
Zhiliang Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Knockout ◽  
Cell Heterogeneity ◽  
State Information ◽  
Gene Regulatory ◽  
Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

scholarly journals BCL2 and hsa-miR-181a-5p are potential biomarkers associated with papillary thyroid cancer based on bioinformatics analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Cong Zhang ◽  
Chunrui Bo ◽  
Lunhua Guo ◽  
Pingyang Yu ◽  
Susheng Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Gene Regulatory Network ◽  
Papillary Thyroid Cancer ◽  
Regulatory Network ◽  
Mirna Gene ◽  
Cancer Type ◽  
Papillary Thyroid ◽  
Ppi Network ◽  
Gene Regulatory

Abstract Background The morbidity of thyroid carcinoma has been rising worldwide and increasing faster than any other cancer type. The most common subtype with the best prognosis is papillary thyroid cancer (PTC); however, the exact molecular pathogenesis of PTC is still not completely understood. Methods In the current study, 3 gene expression datasets (GSE3678, GSE3467, and GSE33630) and 2 miRNA expression datasets (GSE113629 and GSE73182) of PTC were selected from the Gene Expression Omnibus (GEO) database and were further used to identify differentially expressed genes (DEGs) and deregulated miRNAs between normal thyroid tissue samples and PTC samples. Then, Gene Ontology (GO) and pathway enrichment analyses were conducted, and a protein-protein interaction (PPI) network was constructed to explore the potential mechanism of PTC carcinogenesis. The hub gene detection was performed using the CentiScaPe v2.0 plugin, and significant modules were discovered using the MCODE plugin for Cytoscape. In addition, a miRNA-gene regulatory network in PTC was constructed using common deregulated miRNAs and DEGs. Results A total of 263 common DEGs and 12 common deregulated miRNAs were identified. Then, 6 significant KEGG pathways (P < 0.05) and 82 significant GO terms were found to be enriched, indicating that PTC was closely related to amino acid metabolism, development, immune system, and endocrine system. In addition, by constructing a PPI network and miRNA-gene regulatory network, we found that hsa-miR-181a-5p regulated the most DEGs, while BCL2 was targeted by the most miRNAs. Conclusions The results of this study suggested that hsa-miR-181a-5p and BCL2 and their regulatory networks may play important roles in the pathogenesis of PTC.

scholarly journals Finding gene regulatory network candidates using the gene expression knowledge base

2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Aravind Venkatesan ◽  
Sushil Tripathi ◽  
Alejandro Sanz de Galdeano ◽  
Ward Blondé ◽  
Astrid Lægreid ◽  
...  
Keyword(s):  
Gene Expression ◽  
Knowledge Base ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Regulatory
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