scholarly journals miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks

2019 ◽  
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Annotation ◽  
Service Platform ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Specific Subset ◽  
Pathway Gene

Background. MiRNA regulates cellular processes through acting on specific target genes. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, are often organized into pathways among genes and gene products. Large databases on protein-protein interactions (PPIs) are available. Here, we have integrated the information mentioned above to build a web service platform, miRNA Disease Regulatory Network, or miRDRN, for users to construct disease and tissue-specific miRNA-protein regulatory networks. Methods. Data on human protein interaction, disease-associated miRNA, tumor-associated gene, miRNA targeted gene, molecular interaction and reaction network or pathway, gene ontology, gene annotation and gene product information, and gene expression were collected from publicly available databases and integrated. A complete set of regulatory sub-pathways (RSPs) having the form (M, T, G1, G2) were built from the integrated data and stored in the database part of miRDRN, where M is a disease-associated miRNA, T is its regulatory target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results. A web service platform, miRDRN ( http://mirdrn.ncu.edu.tw/mirdrn/), was built to allow users to retrieve a disease and tissue-specific subset of RSPs, from which a miRNA regulatory network is constructed. miRDRN is a database that currently contains 6,973,875 p-valued sub-pathways associated with 119 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes, and a web tool that facilitates the construction and visualization of disease and tissue-specific miRNA-protein regulatory networks, for exploring single diseases, or for exploring the comorbidity of disease-pairs. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes (AD-T2D), in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals miRDRN – miRNA Disease Regulatory Network: A tool for exploring disease and tissue-specific microRNA regulatory networks

2019 ◽  
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Annotation ◽  
Service Platform ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Specific Subset ◽  
Pathway Gene

Background. MiRNA regulates cellular processes through acting on specific target genes. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. Cellular processes, including those related to disease, proceed through multiple interactions, are often organized into pathways among genes and gene products. Large databases on protein-protein interactions (PPIs) are available. Here, we have integrated the information mentioned above to build a web service platform, miRNA Disease Regulatory Network, or miRDRN, for users to construct disease and tissue-specific miRNA-protein regulatory networks. Methods. Data on human protein interaction, disease-associated miRNA, tumor-associated gene, miRNA targeted gene, molecular interaction and reaction network or pathway, gene ontology, gene annotation and gene product information, and gene expression were collected from publicly available databases and integrated. A complete set of regulatory sub-pathways (RSPs) having the form (M, T, G1, G2) were built from the integrated data and stored in the database part of miRDRN, where M is a disease-associated miRNA, T is its regulatory target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results. A web service platform, miRDRN ( http://mirdrn.ncu.edu.tw/mirdrn/), was built to allow users to retrieve a disease and tissue-specific subset of RSPs, from which a miRNA regulatory network is constructed. miRDRN is a database that currently contains 6,973,875 p-valued sub-pathways associated with 119 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes, and a web tool that facilitates the construction and visualization of disease and tissue-specific miRNA-protein regulatory networks, for exploring single diseases, or for exploring the comorbidity of disease-pairs. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes (AD-T2D), in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals miRDRN—miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7309
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Gene Annotation ◽  
Biological Pathways ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Disease Pair

Background MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action. Methods Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

scholarly journals Formalizing metabolic-regulatory networks by hybrid automata

2019 ◽  
Author(s):  
Lin Liu ◽  
Alexander Bockmayr
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Discrete Control ◽  
Hybrid Automata ◽  
Hybrid Automaton ◽  
Metabolic System ◽  
Computational Approaches ◽  
Cellular Processes ◽  
Dynamic Interplay

AbstractComputational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolic-regulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

scholarly journals Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

scholarly journals Kinetic networks identify key regulatory nodes and transcription factor functions in early adipogenesis

2021 ◽  
Author(s):  
Arun B Dutta ◽  
Bao Ngyuen ◽  
Warren D Anderson ◽  
Ninad M Walavalkar ◽  
Fabiana M Duarte ◽  
...  
Keyword(s):  
Chromatin Structure ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Activation ◽  
Compartment Model ◽  
Chromatin Accessibility ◽  
Regulate Gene Expression ◽  
Cellular Processes ◽  
Brown Adipose ◽  
Regulatory Cascades

Sequence-specific transcription factors (TFs) bind DNA, modulate chromatin structure, regulate gene expression, and orchestrate transcription cascades. Activation and repression of TFs drive tightly controlled regulatory programs that lead to cellular processes such as differentiation. We measured chromatin accessibility and nascent transcription at seven time points over the first four hours of induced adipogenesis of 3T3-L1 mouse preadipocytes to construct dynamic gene regulatory networks. Regulatory networks describe successive waves of TF binding and dissociation followed by direct regulation of proximal genes. We identified 14 families of TFs that coordinate with and antagonize each other to regulate early adipogenesis. We developed a compartment model to quantify individual TF contributions to RNA polymerase initiation and pause release rates. Network analysis showed that the glucocorticoid receptor and AP1 drive immediate gene activation, including induction of Twist2. Twist2 is a highly interconnected node within the network and its expression leads to repression of target genes. Although Twist2's role in adipogenesis has not been previously appreciated, both Twist2 knockout mice and Setleis syndrome (Twist2-/-) patients lack subcutaneous and brown adipose tissue. We found that kinetic networks integrating chromatin structure and nascent transcription dynamics identify key genes, TF functions, and coordinate interactions within regulatory cascades.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

scholarly journals Role of diversity-generating retroelements for regulatory pathway tuning in cyanobacteria

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Alec Vallota-Eastman ◽  
Eleanor C. Arrington ◽  
Siobhan Meeken ◽  
Simon Roux ◽  
Krishna Dasari ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Environmental Stress ◽  
Regulatory Networks ◽  
Target Genes ◽  
Cell Attachment ◽  
Past Research ◽  
Reverse Transcriptases ◽  
Cellular Processes ◽  
Binding Domains ◽  
Signal Response

Abstract Background Cyanobacteria maintain extensive repertoires of regulatory genes that are vital for adaptation to environmental stress. Some cyanobacterial genomes have been noted to encode diversity-generating retroelements (DGRs), which promote protein hypervariation through localized retrohoming and codon rewriting in target genes. Past research has shown DGRs to mainly diversify proteins involved in cell-cell attachment or viral-host attachment within viral, bacterial, and archaeal lineages. However, these elements may be critical in driving variation for proteins involved in other core cellular processes. Results Members of 31 cyanobacterial genera encode at least one DGR, and together, their retroelements form a monophyletic clade of closely-related reverse transcriptases. This class of retroelements diversifies target proteins with unique domain architectures: modular ligand-binding domains often paired with a second domain that is linked to signal response or regulation. Comparative analysis indicates recent intragenomic duplication of DGR targets as paralogs, but also apparent intergenomic exchange of DGR components. The prevalence of DGRs and the paralogs of their targets is disproportionately high among colonial and filamentous strains of cyanobacteria. Conclusion We find that colonial and filamentous cyanobacteria have recruited DGRs to optimize a ligand-binding module for apparent function in signal response or regulation. These represent a unique class of hypervariable proteins, which might offer cyanobacteria a form of plasticity to adapt to environmental stress. This analysis supports the hypothesis that DGR-driven mutation modulates signaling and regulatory networks in cyanobacteria, suggestive of a new framework for the utility of localized genetic hypervariation.

scholarly journals Comprehensive Annotation and Functional Exploration of MicroRNAs in Lettuce

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Deng ◽  
Yajuan Qin ◽  
Pan Yang ◽  
Jianjun Du ◽  
Zheng Kuang ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Computational Prediction ◽  
Regulatory Relationship ◽  
Specific Expression ◽  
Economic Significance ◽  
Specific Expression Pattern

MicroRNA (miRNA) is an important endogenous post-transcriptional regulator, while lettuce (Lactuca sativa) is a leafy vegetable of global economic significance. However, there are few studies on miRNAs in lettuce, and research on miRNA regulatory network in lettuce is absent. In this study, through deep sequencing of small RNAs in different tissues, together with a reference genome, 157 high-confidence miRNA loci in lettuce were comprehensively identified, and their expression patterns were determined. Using a combination of computational prediction and high-throughput experimental verification, a set of reliable lettuce miRNA targets were obtained. Furthermore, through RNA-Seq, the expression profiles of these targets and a comprehensive view of the negative regulatory relationship between miRNAs and their targets was acquired based on a correlation analysis. To further understand miRNA functions, a miRNA regulatory network was constructed, with miRNAs at the core and combining transcription factors and miRNA target genes. This regulatory network, mainly composed of feed forward loop motifs, greatly increases understanding of the potential functions of miRNAs, and many unknown potential regulatory links were discovered. Finally, considering its specific expression pattern, Lsa-MIR408 as a hub gene was employed to illustrate the function of the regulatory network, and genetic experiments revealed its ability to increase the fresh weight and achene size of lettuce. In short, this work lays a solid foundation for the study of miRNA functions and regulatory networks in lettuce.

scholarly journals MicroRNAs reinforce repression of PRC2 transcriptional targets independently and through a feed-forward regulatory network

2018 ◽  
Author(s):  
Haridha Shivram ◽  
Steven V. Le ◽  
Vishwanath R. Iyer
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Target Genes ◽  
Significant Proportion ◽  
Feed Forward ◽  
Transcriptional Targets ◽  
Polycomb Repressive Complex 2 ◽  
Small Regulatory Rnas

AbstractGene expression can be regulated at multiple levels, but it is not known if and how there is broad coordination between regulation at the transcriptional and post-transcriptional levels. Transcription factors and chromatin regulate gene expression transcriptionally, while microRNAs (miRNAs) are small regulatory RNAs that function post-transcriptionally. Systematically identifying the post-transcriptional targets of miRNAs and the mechanism of transcriptional regulation of the same targets can shed light on regulatory networks connecting transcriptional and post-transcriptional control. We used iCLIP (individual crosslinking and immunoprecipitation) for the RISC (RNA-induced silencing complex) component AGO2 and global miRNA depletion to identify genes directly targeted by miRNAs. We found that PRC2 (Polycomb repressive complex 2) and its associated histone mark, H3K27me3, is enriched at hundreds of miRNA-repressed genes. We show that these genes are directly repressed by PRC2 and constitute a significant proportion of direct PRC2 targets. For just over half of the genes co-repressed by PRC2 and miRNAs, PRC2 promotes their miRNA-mediated repression by increasing expression of the miRNAs that are likely to target them. miRNAs also repress the remainder of the PRC2 target genes, but independently of PRC2. Thus, miRNAs post-transcriptionally reinforce silencing of PRC2-repressed genes that are inefficiently repressed at the level of chromatin, by either forming a feed-forward regulatory network with PRC2 or repressing them independently of PRC2.

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