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

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.

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

10.7287/peerj.preprints.27699v1 ◽

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.

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

10.7287/peerj.preprints.27699 ◽

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

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Identification of the TF-miRNA-mRNA Co-regulatory Networks Involved in Sepsis

10.21203/rs.3.rs-801025/v1 ◽

2021 ◽

Author(s):

Xiaoqian Luo ◽

Weina Lu ◽

Jianfeng Zhao ◽

Jun Hu ◽

Enjiang Chen ◽

...

Keyword(s):

Expression Analysis ◽

Regulatory Network ◽

Regulatory Networks ◽

Mirna Target ◽

Target Genes ◽

Medical Condition ◽

Differentially Expressed ◽

Biological Targets ◽

Differentially Expressed Mirnas

Abstract BackgroundSepsis is a life-threatening medical condition caused by a dysregulated host response to infection. Recent studies have found that the expression of miRNAs is associated with the pathogenesis of sepsis and septic shock. Our study aimed to reveal which miRNAs may be involved in the dysregulated immune response in sepsis and how these miRNAs interact with transcription factors (TFs) using a computational approach with in vitro validation studies. MethodsTo determine the network of TFs, miRNAs and target genes involved in sepsis, GEO datasets GSE94717 and GSE131761 were used to identify differentially expressed miRNAs and DEGs. TargetScan and miRWalk databases were used to predict biological targets that overlap with the identified DEGs of differentially expressed miRNAs. The TransmiR database was used to predict the differential miRNA TFs that overlap with the identified DEGs. The TF-miRNA-mRNA network was constructed and visualized. Finally, qRT-PCR was used to verify the expression of TFs and miRNA in HUVECs. ResultBetween the healthy and sepsis groups, there were 146 upregulated and 98 downregulated DEGs in the GSE131761 dataset, and there were 1 upregulated and 183 downregulated DEMs in the GSE94717 dataset. A regulatory network of the TF-miRNA-target genes was established. According to the experimental results, RUNX3 was found to be downregulated while MAPK14 was upregulated, which corroborates the result of the computational expression analysis. In a HUVECs model, miR-19b-1-5p and miR-5009-5p were found to be significantly downregulated. Other TFs and miRNAs did not correlate with our bioinformatics expression analysis. ConclusionWe constructed a TF-miRNA-target gene regulatory network and identified potential treatment targets RUNX3, MAPK14, miR-19b-1-5p and miR-5009-5p. This information provides an initial basis for understanding the complex sepsis regulatory mechanisms.

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Regulatory Network Analysis in Estradiol-Treated Human Endothelial Cells

International Journal of Molecular Sciences ◽

10.3390/ijms22158193 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8193

Author(s):

Daniel Pérez-Cremades ◽

Ana B. Paes ◽

Xavier Vidal-Gómez ◽

Ana Mompeón ◽

Carlos Hermenegildo ◽

...

Keyword(s):

Endothelial Cells ◽

Transcription Factor ◽

Transcription Factors ◽

Regulatory Network ◽

Mirna Target ◽

Target Genes ◽

Functional Characterization ◽

Human Endothelial Cells ◽

Mrna Targets ◽

Canonical Pathways

Background/Aims: Estrogen has been reported to have beneficial effects on vascular biology through direct actions on endothelium. Together with transcription factors, miRNAs are the major drivers of gene expression and signaling networks. The objective of this study was to identify a comprehensive regulatory network (miRNA-transcription factor-downstream genes) that controls the transcriptomic changes observed in endothelial cells exposed to estradiol. Methods: miRNA/mRNA interactions were assembled using our previous microarray data of human umbilical vein endothelial cells (HUVEC) treated with 17β-estradiol (E2) (1 nmol/L, 24 h). miRNA–mRNA pairings and their associated canonical pathways were determined using Ingenuity Pathway Analysis software. Transcription factors were identified among the miRNA-regulated genes. Transcription factor downstream target genes were predicted by consensus transcription factor binding sites in the promoter region of E2-regulated genes by using JASPAR and TRANSFAC tools in Enrichr software. Results: miRNA–target pairings were filtered by using differentially expressed miRNAs and mRNAs characterized by a regulatory relationship according to miRNA target prediction databases. The analysis identified 588 miRNA–target interactions between 102 miRNAs and 588 targets. Specifically, 63 upregulated miRNAs interacted with 295 downregulated targets, while 39 downregulated miRNAs were paired with 293 upregulated mRNA targets. Functional characterization of miRNA/mRNA association analysis highlighted hypoxia signaling, integrin, ephrin receptor signaling and regulation of actin-based motility by Rho among the canonical pathways regulated by E2 in HUVEC. Transcription factors and downstream genes analysis revealed eight networks, including those mediated by JUN and REPIN1, which are associated with cadherin binding and cell adhesion molecule binding pathways. Conclusion: This study identifies regulatory networks obtained by integrative microarray analysis and provides additional insights into the way estradiol could regulate endothelial function in human endothelial cells.

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Formalizing metabolic-regulatory networks by hybrid automata

10.1101/586198 ◽

2019 ◽

Cited By ~ 1

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.

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

10.1101/2020.06.16.155481 ◽

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.

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

10.1101/2021.11.17.469040 ◽

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.

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PUMA: PANDA Using MicroRNA Associations

Bioinformatics ◽

10.1093/bioinformatics/btaa571 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4765-4773

Author(s):

Marieke L Kuijjer ◽

Maud Fagny ◽

Alessandro Marin ◽

John Quackenbush ◽

Kimberly Glass

Keyword(s):

Messenger Rna ◽

Message Passing ◽

Mirna Target ◽

Target Prediction ◽

Network Models ◽

Tissue Expression ◽

Supplementary Information ◽

Tissue Specific ◽

Cellular Processes ◽

Regulatory Processes

Abstract Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.

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hsa-miR-106b-5p participates in the development of chronic thromboembolic pulmonary hypertension via targeting matrix metalloproteinase 2

Pulmonary Circulation ◽

10.1177/2045894020928300 ◽

2020 ◽

Vol 10 (3) ◽

pp. 204589402092830

Author(s):

Ran Miao ◽

Xingbei Dong ◽

Juanni Gong ◽

Ying Wang ◽

Xiaojuan Guo ◽

...

Keyword(s):

Gene Expression ◽

Pulmonary Hypertension ◽

Regulatory Network ◽

Mirna Target ◽

Target Genes ◽

Chronic Thromboembolic Pulmonary Hypertension ◽

Luciferase Assay ◽

Matrix Metalloproteinase 2 ◽

Qrt Pcr ◽

Thromboembolic Pulmonary Hypertension

Background Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by elevated pressure in pulmonary arteries. This study was performed to explore the critical miRNAs and genes affecting the pathogenesis of CTEPH. Methods GSE56914 dataset (10 CTEPH whole blood samples and 10 control samples) was downloaded from the Gene Expression Omnibus database. Using limma package, the differentially expressed miRNAs (DE-miRNAs) were acquired. After miRNA-target pairs were obtained using miRWalk2.0 tool, a miRNA-target regulatory network was built by Cytoscape software. Using DAVID tool, significantly enriched pathways involving the target genes were identified. Moreover, the protein–protein interaction network and transcription factor-target regulatory network were built by the Cytoscape software. Additionally, quantitative real-time PCR (qRT-PCR) experiments and luciferase assay were conducted to validate miRNA/gene expression and miRNA–target regulatory relationship, respectively. Results There were 25 DE-miRNAs (8 up-regulated and 17 down-regulated) between CTEPH and control groups. The target genes of has-let-7b-3p, has-miR-17-5p, has-miR-3202, has-miR-106b-5p, and has-miR-665 were enriched in multiple pathways such as “Insulin secretion”. qRT-PCR analysis confirmed upregulation of hsa-miR-3202, hsa-miR-665, and matrix metalloproteinase 2 ( MMP2) as well as downregulation of hsa-let-7b-3p, hsa-miR-17-5p, and hsa-miR-106b-5p. Luciferase assay indicated that MMP2 was negatively mediated by hsa-miR-106b-5p. Conclusions These miRNAs and genes were associated with the pathogenesis of CTEPH. Besides, hsa-miR-106b-5p was involved in the development of CTEPH via targeting MMP2.

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

eLife ◽

10.7554/elife.51254 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 16

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.

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