miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer

Abstract Background Asthma is a chronic inflammatory disorder of the airways involving many different factors. This study aimed to screen for the critical genes using DNA methylation/CpGs and miRNAs involved in childhood atopic asthma. Methods DNA methylation and gene expression data (Access Numbers GSE40732 and GSE40576) were downloaded from the Gene Expression Omnibus database. Each set contains 194 peripheral blood mononuclear cell (PBMC) samples of 97 children with atopic asthma and 97 control children. Differentially expressed genes (DEGs) with DNA methylation changes were identified. Pearson correlation analysis was used to select genes with an opposite direction of expression and differences in methylation levels, and then Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Protein–protein interaction network and miRNA–target gene regulatory networks were then constructed. Finally, important genes related to asthma were screened. Results A total of 130 critical DEGs with DNA methylation changes were screened from children with atopic asthma and compared with control samples from healthy children. GO and KEGG pathway enrichment analysis found that critical genes were primarily related to 24 GO terms and 10 KEGG pathways. In the miRNA–target gene regulatory networks, 9 KEGG pathways were identified. Analysis of the miRNA–target gene network noted an overlapping KEGG signaling pathway, hsa04060: cytokine-cytokine receptor interaction, in which the gene CCL2, directly related to asthma, was involved. This gene is targeted by eight asthma related miRNAs (hsa-miR-206, hsa-miR-19a, hsa-miR-9,hsa-miR-22, hsa-miR-33b, hsa-miR-122, hsa-miR-1, and hsa-miR-23b). The genes IL2RG and CCl4 were also involved in this pathway. Conclusions The present study provides a novel insight into the underlying molecular mechanism of childhood atopic asthma.

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Discovering and Constructing ceRNA-miRNA-Target Gene Regulatory Networks during Anther Development in Maize

International Journal of Molecular Sciences ◽

10.3390/ijms20143480 ◽

2019 ◽

Vol 20 (14) ◽

pp. 3480 ◽

Cited By ~ 6

Author(s):

Ziwen Li ◽

Xueli An ◽

Taotao Zhu ◽

Tingwei Yan ◽

Suowei Wu ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Mirna Target ◽

Target Gene ◽

Target Genes ◽

Anther Development ◽

Small Rna Sequencing ◽

Mirna Target Gene ◽

Gene Pairs ◽

Gene Regulatory

The “competing endogenous RNA (ceRNA) hypothesis” has recently been proposed for a new type of gene regulatory model in many organisms. Anther development is a crucial biological process in plant reproduction, and its gene regulatory network (GRN) has been gradually revealed during the past two decades. However, it is still unknown whether ceRNAs contribute to anther development and sexual reproduction in plants. We performed RNA and small RNA sequencing of anther tissues sampled at three developmental stages in two maize lines. A total of 28,233 stably transcribed loci, 61 known and 51 potentially novel microRNAs (miRNAs) were identified from the transcriptomes. Predicted ceRNAs and target genes were found to conserve in sequences of recognition sites where their corresponding miRNAs bound. We then reconstructed 79 ceRNA-miRNA-target gene regulatory networks consisting of 51 known miRNAs, 28 potentially novel miRNAs, 619 ceRNA-miRNA pairs, and 869 miRNA-target gene pairs. More than half of the regulation pairs showed significant negative correlations at transcriptional levels. Several well-studied miRNA-target gene pairs associated with plant flower development were located in some networks, including miR156-SPL, miR159-MYB, miR160-ARF, miR164-NAC, miR172-AP2, and miR319-TCP pairs. Six target genes in the networks were found to be orthologs of functionally confirmed genes participating in anther development in plants. Our results provide an insight that the ceRNA-miRNA-target gene regulatory networks likely contribute to anther development in maize. Further functional studies on a number of ceRNAs, miRNAs, and target genes will facilitate our deep understanding on mechanisms of anther development and sexual plants reproduction.

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MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma

Journal of Oral Pathology and Medicine ◽

10.1111/jop.12650 ◽

2017 ◽

Vol 47 (1) ◽

pp. 78-85 ◽

Cited By ~ 6

Author(s):

Thaís dos Santos Fontes Pereira ◽

João Artur Ricieri Brito ◽

André Luiz Sena Guimarães ◽

Carolina Cavaliéri Gomes ◽

Júlio Cesar Tanos de Lacerda ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Target Gene ◽

Ossifying Fibroma ◽

Microrna Profiling ◽

Gene Regulatory

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Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

10.1101/2020.06.15.151712 ◽

2020 ◽

Author(s):

Xanthoula Atsalaki ◽

Lefteris Koumakis ◽

George Potamias ◽

Manolis Tsiknakis

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Systems Biology ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Integrative Approach ◽

Expression Data ◽

Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

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Soft computing methods to predict gene regulatory networks: An integrative approach on time-series gene expression data

Applied Soft Computing ◽

10.1016/j.asoc.2007.02.023 ◽

2008 ◽

Vol 8 (3) ◽

pp. 1189-1199 ◽

Cited By ~ 13

Author(s):

Zeke S.H. Chan ◽

Ilkka Havukkala ◽

Vishal Jain ◽

Yingjie Hu ◽

Nikola Kasabov

Keyword(s):

Gene Expression ◽

Time Series ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Computing Methods ◽

Integrative Approach ◽

Expression Data ◽

Time Series Gene Expression ◽

Soft Computing Methods ◽

Gene Regulatory

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Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders

BMC Medical Genomics ◽

10.1186/s12920-016-0202-9 ◽

2016 ◽

Vol 9 (S2) ◽

Cited By ~ 3

Author(s):

Dongchul Kim ◽

Mingon Kang ◽

Ashis Biswas ◽

Chunyu Liu ◽

Jean Gao

Keyword(s):

Psychiatric Disorders ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Integrative Approach ◽

Gene Regulatory

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Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

10.1101/2020.09.27.315382 ◽

2020 ◽

Author(s):

Juexin Wang ◽

Anjun Ma ◽

Qin Ma ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Neural Network ◽

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Target Gene ◽

Graph Embedding ◽

Expression Data ◽

Graph Classification ◽

Gene Regulatory

AbstractDiscovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.HighlightsWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding.Our method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons.Our results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks.GRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks.

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