Dataset Information

Network analysis of endogenous gene expression profiles after polyethyleneimine-mediated DNA delivery.

ABSTRACT: DNA delivery systems, which transport exogenous DNA to cells, have applications that include gene therapy, tissue engineering and medical devices. Although the cationic nonviral DNA carrier polyethyleneimine (PEI) has been widely studied, the molecular factors and pathways underlying PEI-mediated DNA transfer remain largely unknown, preventing the design of more efficient delivery systems.HEK 293?T cells were treated with polyplexes formed with PEI and pEGFPLuc encoding for green fluorescent protein (GFP). Transfected cells expressing GFP were flow-separated from treated, untransfected cells. Gene expression profiles were obtained using Affymetrix HG-U133 2.0 microarrays and differentially expressed genes were identified using R/Bioconductor. Gene network analysis using EGAN (exploratory gene association network) bioinformatics tools was then used to find interaction among genes and enriched gene ontology (GO) terms related to transfection. Genes identified by this method were perturbed using pharmacologic activators or inhibitors to assess their effect on DNA transfer.Microarray analysis comparing transfected cells to untransfected cells revealed 215 genes to be differentially expressed, with the majority enriched to GO processes including metabolism, response to stimulus, cell cycle, biological regulation and cellular component organization or biogenesis pathways. Gene network analysis revealed a coordinated induction of RAP1A, SCG5, PGAP1, ATF3 and NEB genes implicated in cell stress, cell cycle and cytoskeletal processes. Altering pathways with pharmacologic agents confirmed the potential role of RAP1A, SCG5 and ATF3 in transfection.Microarray and gene network analyses of the sorted, transfected cell population can identify potential mediators of transfection, providing a basis for the design of improved delivery systems.

SUBMITTER: Martin TM

PROVIDER: S-EPMC4115370 | biostudies-literature | 2013 Mar-Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Network analysis of endogenous gene expression profiles after polyethyleneimine-mediated DNA delivery.

Martin Timothy M TM Plautz Sarah A SA Pannier Angela K AK

The journal of gene medicine 20130301 3-4

<h4>Background</h4>DNA delivery systems, which transport exogenous DNA to cells, have applications that include gene therapy, tissue engineering and medical devices. Although the cationic nonviral DNA carrier polyethyleneimine (PEI) has been widely studied, the molecular factors and pathways underlying PEI-mediated DNA transfer remain largely unknown, preventing the design of more efficient delivery systems.<h4>Methods</h4>HEK 293 T cells were treated with polyplexes formed with PEI and pEGFPLuc ...[more]

PMID: 23526566

Similar Datasets

Project description:BackgroundThe incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis.MethodsThe gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database.ResultsWe identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA.ConclusionsOur study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

Project description:Background: Currently, disease control in patients with severe eosinophilic asthma is not optimistic. Competing endogenous RNA (ceRNA) networks have been found to play a key role in asthma in recent years. However, it is unclear whether ceRNA networks play an important part in severe eosinophilic asthma. Methods: Firstly, gene expression profiles related to severe eosinophilic asthma were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, the key modules were identified by the weighted gene co-expression network analysis (WGCNA). Thirdly, genes in modules highly associated with severe eosinophilic asthma were selected for further construction of the ceRNA network. Fourthly, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on hub genes. Finally, the results of this study were validated on the GSE143303, GSE137268, and GSE147878 datasets. Results: 22 severe eosinophilic asthmatics and 13 healthy controls were extracted for WGCNA. We found that the genes in the black module (r = -0.75, p < 0.05) and yellow module (r = 0.65, p < 0.05) were highly associated with severe eosinophilic asthma. EP300 was discovered to serve the key connecting function in the ceRNA network. Surprisingly, lncRNAs seem to eliminate the role of EP300 in the black module and we discovered that CCT8 and miRNA-mRNA formed a circRNA-miRNA-mRNA network in the yellow module. We found that EP300 and FOXO3 in the black module were regulated by steroid hormones in the enrichment analysis, which were related to the medication used by the patient. Through validation of other datasets, we found that the hub genes in the yellow module were the key genes in the treatment of severe eosinophilic asthma. In particular, RPL17 and HNRNPK might specifically regulate severe eosinophilic asthma. Conclusion: RPL17 and HNRNPK might particularly regulate severe eosinophilic asthma. Our results could be useful to provide potential immunotherapy targets and prognostic markers for severe eosinophilic asthma.

Project description:BACKGROUND:Ovarian cancer is an epithelial malignancy that intrigues people for its poor outcome and lack of efficient treatment, while methylation is an important mechanism that have been recognized in many malignancies. In this study, we attempt to assess abnormally methylated gene markers and pathways in ovarian cancer by integrating three microarray datasets. METHODS:Three datasets including expression (GSE26712 and GSE66957) and methylation (GSE81224) datasets were accessed. GEO2R platform was used to detect abnormally methylated-differentially expressed genes. Protein-protein interaction (PPI) networks were built and analysed for hypermethylated and hypermethylated differentially expressed genes using Cytoscape software and Mcode app. GEPIA and cBioPortal platforms were used to validate the expression of the hub genes and the correlation between their mRNA expressions and methylation levels. Kaplan Meier-plotter platform were used to assess the prognostic significance of the hub genes. RESULTS:Six hundred eighty-one hypomethylated-upregulated genes were detected and involved in Rap1 signaling pathway, biosynthesis of amino acids, endocrine resistance, apoptosis, pathways in cancer. The hub genes were TNF, UBC, SRC, ESR1, CDK1, PECAM1, CXCR4, MUC1, IKBKG. Additionally, 337 hypermethylated-downregulated genes were detected and involved in pathways in cancer, focal adhesion, sphingolipid signaling pathway, EGFR tyrosine kinase inhibitor resistance, cellular senescence. The hub genes were BDNF, CDC42, CD44, PPP2R5C, PTEN, UBB, BMP2, FOXO1, KLHL2. TNF, ESR1, MUC1, CD44, PPP2R5C, PTEN, UBB and FOXO1 showed significant negative correlation between their mRNA expressions and methylation levels. TNF, ESR1 and FOXO1 showed prognostic significance. CONCLUSIONS:Two novel gene networks were found for ovarian cancer. TNF, ESR1, MUC1 and FOXO1 are our candidate genes that might take part in ovarian cancer progression in an epigenetic approach, TNF, ESR1 and FOXO1 may serve as potential markers for ovarian cancer prognosis evaluation.

Dataset Information

Network analysis of endogenous gene expression profiles after polyethyleneimine-mediated DNA delivery.

Publications

Network analysis of endogenous gene expression profiles after polyethyleneimine-mediated DNA delivery.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets