Identification of MicroRNA-mRNA Regulatory Networks in Periodontitis by Bioinformatics Analysis

Abstract Background: Periodontitis is a complex infectious disease with various causes and contributing factors. In recent years, microRNAs (miRNAs) have been commonly accepted as having key regulatory functions in periodontal disease. The aim of this study was to identify miRNAs and hub genes involved in periodontal disease pathogenesis using a miRNA-mRNA interaction network.Methods: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using P <0.05 and |log FC| ≥1. Potential upstream transcription factors and downstream target genes of candidate DEMis were predicted using the FunRich and miRNet programs, respectively. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba plugin was used to screen out key hub mRNAs. The key genes in the miRNA-mRNA regulatory network were extracted by intersecting the target genes of candidate DEMis and DEMs. Cytoscape software was used to visualise the interaction between miRNAs and mRNAs and to predict the hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyse the key genes in the regulatory network.Results: Ten DEMis and 161 DEMs were filtered out, from which we constructed a miRNA-mRNA network consisting of six miRNAs and 32 mRNAs. KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. Hsa-miR-203/CXCL8, hsa-miR-203/BTG2, and hsa-miR-203/DNAJB9 were identified as four potential regulatory pathways for periodontitis. Conclusion: In this study, a potential miRNA–mRNA regulatory network was first constructed and four regulatory pathways were identified for periodontitis to help clarify the aetiology of the disease and provide potential therapeutic targets.

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Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04894-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ying Jiang ◽

Yi Shen ◽

Liyan Ding ◽

Shengli Xia ◽

Liying Jiang

Keyword(s):

Regulatory Networks ◽

Target Genes ◽

Therapeutic Targets ◽

Microarray Dataset ◽

Integrated Analysis ◽

Rna Seq ◽

Hub Genes ◽

Serum Samples ◽

Regulatory Pathways ◽

Novel Mirna

Abstract Backgrounds As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. Methods Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). Results Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. Conclusions We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

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Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

10.21203/rs.3.rs-50763/v1 ◽

2020 ◽

Author(s):

Tong Sun ◽

Haiyang Yu ◽

Jianhua Fu

Keyword(s):

Gene Expression ◽

Preterm Infants ◽

Bronchopulmonary Dysplasia ◽

Regulatory Networks ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Regulatory Pathways ◽

Ppi Networks ◽

Key Genes

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

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Construction of a circRNA-miRNA-mRNA network based on differentially co-expressed circular RNA in gastric cancer tissue and plasma by bioinformatics analysis

10.21203/rs.3.rs-22009/v1 ◽

2020 ◽

Author(s):

Yu Gong ◽

Xiaoyang Qi ◽

Jinjin Fu ◽

Jun Qian ◽

Yuwen Jiao ◽

...

Keyword(s):

Gastric Cancer ◽

Signaling Pathway ◽

Cancer Progression ◽

Regulatory Network ◽

Target Genes ◽

Human Cancer ◽

Microarray Dataset ◽

Circular Rnas ◽

Cancer Tissue ◽

Hub Genes

Abstract Background: Increasing evidence implicates circular RNAs (circRNAs) have been involved in human cancer progression. However, the mechanism remains unclear. In this study, we identified novel circRNAs related to gastric cancer and constructed a circRNA-miRNA-mRNA network.Methods: Microarray dataset GSE83521 and GSE93541 were obtained from Gene Expression Omnibus (GEO). Then, we used computational biology to select differentially co-expressed circRNAs in GC tissue and plasma and detected the expression of selected circRNAs in gastric cell lines by quantitative real‑time polymerase chain reaction (qRT‑PCR). We also chose the candidate miRNAs and their target genes for circRNAs through online tools. Combining the predictions of miRNAs and target mRNAs, a competing endogenous RNA regulatory network was established. Functional and pathway enrichment analyses were performed, and interactions between proteins were predicted by using String and Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the possible functions of these differentially expressed circRNAs.Results: The regulatory network constructed using the microarray datasets (GSE83521 and GSE93541) contained three differentially co-expressed circRNAs (DECs). A circRNA-miRNA-mRNA network was constructed based on 3 circRNAs, 43 miRNAs and 119 mRNAs. GO and KEGG analysis showed that regulation of apoptotic signaling pathway and PI3K−Akt signaling pathway were highest degrees of enrichment respectively. We established a protein-protein interaction (PPI) network consisting of 165 nodes and 170 edges and identified hub genes by MCODE plugin in Cytoscape. Furthermore, a core circRNA-miRNA-mRNA network was constructed base on hub genes. Hsa_circ_0001013 was finally determined to play an important role in the pathogenesis of GC according to the core circRNA-miRNA-mRNA network.Conclusions: We propose a new circRNA-miRNA-mRNA network associated with the pathogenesis of GC. The network may become a new molecular biomarker and be used to develop potential therapeutic strategies for gastric cancer.

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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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Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

10.21203/rs.3.rs-95029/v1 ◽

2020 ◽

Author(s):

Harish Joshi ◽

Basavaraj Mallikarjunayya Vastrad ◽

Nidhi Joshi ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Expression Profiles ◽

Therapeutic Targets ◽

Rank Aggregation ◽

Receiver Operating Curve ◽

Progression Rate ◽

Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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POS0851 IDENTIFICATION OF HUB GENES AND PATHWAYS IN DERMATOMYOSITIS BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2026 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 680.1-680

Author(s):

C. Zheng ◽

S. X. Zhang ◽

R. Zhao ◽

L. Cheng ◽

T. Kong ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathway ◽

Muscle Tissue ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Shanxi Province ◽

Pathway Enrichment Analysis ◽

Type I ◽

Hub Genes ◽

Key Genes

Background:Dermatomyositis (DM) is a chronic systemic autoimmune disease characterized by inflammatory infiltrates in the skin and muscle1. The genes and pathways in the inflamed myopathies in patients with DM are poorly understood2.Objectives:To identify the key genes and pathways associated with DM and further discover its pathogenesis.Methods:Muscle tissue gene expression profile (GSE143323) were acquired from the GEO database, which included 39 DM samples and 20 normal samples. The differentially expressed genes (DEGs) in DM muscle tissue were screened by adopting the R software. Gene ontology (GO) and Kyoto Encyclopedia of Genome (KEGG) pathway enrichment analysis was performed by Metascape online analysis tool. A protein-protein interaction (PPI) network was then constructed by STRING software using the genes in significantly different pathways. Network of DEGs was analyzed by Cytoscape software. And degree of nodes was used to screen key genes.Results:Totally, 126 DEGs were obtained, which contained 122 up-regulated and 4 down-regulated. GO analysis revealed that most of the DEGs were significantly enriched in type I interferon signaling pathway, response to interferon-gamma, collagen-containing extracellular matrix, response to interferon-alpha and bacterium, positive regulation of cell death, leukocyte chemotaxis. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the hepatitis C, complement and coagulation cascades, p53 signaling pathway, RIG-I-like receptor signaling, Osteoclast differentiation, and AGE-RAGE signaling pathway. Ten hub genes were identified in DM, they were ISG15, IRF7, STAT1, MX1, OASL, OAS2, OAS1, OAS3, GBP1, and IRF9 according to the Cytoscape software and cytoHubba plugin.Conclusion:The findings from this bioinformatics network analysis study identified the key hub genes that might provide new molecular markers for its diagnosis and treatment.References:[1]Olazagasti JM, Niewold TB, Reed AM. Immunological biomarkers in dermatomyositis. Curr Rheumatol Rep 2015;17(11):68. doi: 10.1007/s11926-015-0543-y [published Online First: 2015/09/26].[2]Chen LY, Cui ZL, Hua FC, et al. Bioinformatics analysis of gene expression profiles of dermatomyositis. Mol Med Rep 2016;14(4):3785-90. doi: 10.3892/mmr.2016.5703 [published Online First: 2016/09/08].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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

10.1101/298919 ◽

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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Identification of Key Genes, miRNAs and miRNAs-mRNA Regulatory Pathways for Chemotherapy Resistance in Ovarian Cancer

10.21203/rs.3.rs-898505/v1 ◽

2021 ◽

Author(s):

Wenwen Wang

Keyword(s):

Ovarian Cancer ◽

Target Genes ◽

Chemotherapy Resistance ◽

Differentially Expressed ◽

Platinum Resistance ◽

Hub Genes ◽

Regulatory Pathways ◽

Qrt Pcr ◽

Platinum Resistant ◽

Key Genes

Abstract Background: Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer.Methods: One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software.Results: A total of 63 upregulated DEGs, 124 downregulated DEGs, 4 upregulated miRNAs and 6 downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494.Conclusions: The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

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Identification of differentially expressed genes in asthma by bioinformatics analysis

Medical Research ◽

10.6913/mrhk.202106_3(2).0004 ◽

2021 ◽

Vol 3 (2) ◽

pp. 28-36

Author(s):

Zhaojun Wang ◽

Zhifeng Mo ◽

Hongsen Liang ◽

Qiwei Zhang ◽

Wei Li ◽

...

Keyword(s):

Regulatory Networks ◽

Target Genes ◽

Microarray Dataset ◽

Underlying Mechanism ◽

Gene Expression Omnibus ◽

The Novel ◽

Hub Genes ◽

Protein Protein Interaction ◽

Healthy Donors

Objective Asthma is a common inflammatory disease of the airway, and its underlying mechanism is complex. The role of microRNAs (miRNAs) in asthma is unclear. The present study aimed to investigate miRNA-mRNA regulatory networks underlying asthma. Methods One microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) was identified in bronchial epithelial cells (BECs) isolated from healthy donors and patients with asthma. MiRTarBase, mirDIP, and miRDB were used to predict target genes, followed by protein-protein interaction (PPI) network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis; cytoHubba was used to predict the important nodes in the network. The miRNA-hub genes sub-network of interest was determined. Results This study constructed an asthma-associated miRNA-mRNA network, in which seven key miRNAs and 10 hub genes were identified. Conclusions The novel miRNAs and hub genes identified in the present study could be potential diagnostic and treatment biomarkers for asthma.

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Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses

10.1101/2020.12.20.423656 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad

Keyword(s):

Gastric Cancer ◽

Regulatory Network ◽

Target Genes ◽

Characteristic Curve ◽

Interaction Network ◽

Microarray Dataset ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Clinical Prognosis ◽

Protein Protein Interaction

AbstractThe high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a protein protein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes. The pathways and GO functions of the up and down regulated genes were mainly enriched in pyrimidine deoxyribonucleosides degradation, extracellular structure organization, allopregnanolone biosynthesis and digestion. FN1, PLK1, ANLN, MCM7, MCM2, EEF1A2, PTGER3, CKB, ERBB4 and PRKAA2 were identified as the most important genes of GC, and validated by TCGA database, The Human Protein Atlas database, receiver operating characteristic curve (ROC) analysis and RT-PCR. Bioinformatics analysis might be useful method to explore the molecular pathogensis of GC. In addition, FN1, PLK1, ANLN, MCM7, MCM2, EEF1A2, PTGER3, CKB, ERBB4 and PRKAA2 might be the most important genes of GC.

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