Dataset Information

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis.

ABSTRACT: BACKGROUND:Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. METHODS:An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein-protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. RESULTS:A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues. CONCLUSIONS:Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.

SUBMITTER: Karimizadeh E

PROVIDER: S-EPMC6935135 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis.

Karimizadeh Elham E Sharifi-Zarchi Ali A Nikaein Hassan H Salehi Seyedehsaba S Salamatian Bahar B Elmi Naser N Gharibdoost Farhad F Mahmoudi Mahdi M

BMC medical genomics 20191227 1

<h4>Background</h4>Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients.<h4>Methods</h4>An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to id ...[more]

PMID: 31881890

Similar Datasets

Project description:Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that involves the death of neurons. ALS is associated with many gene mutations as previously studied. In order to explore the molecular mechanisms underlying ALS with C9orf72 mutation, gene expression profiles of ALS fibroblasts and control fibroblasts were subjected to bioinformatics analysis. Genes with critical functional roles can be detected by a measure of node centrality in biological networks. In gene co-expression networks, highly connected genes called as candidate hubs have been associated with key disease-related pathways. Herein, this method was applied to find the hub genes related to ALS disease.Illumina HiSeq microarray gene expression dataset GSE51684 was retrieved from Gene Expression Omnibus (GEO) database which included four Sporadic ALS, twelve Familial ALS and eight control samples. Differentially Expressed Genes (DEGs) were identified using the Student's t test statistical method and gene co-expression networking. Gene ontology (GO) function and KEGG pathway enrichment analysis of DEGs were performed using the DAVID online tool. Protein-protein interaction (PPI) networks were constructed by mapping the DEGs onto protein-protein interaction data from publicly available databases to identify the pathways where DEGs are involved in. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape.The results revealed that the expression of DEGs was mainly involved in cell adhesion, cell-cell signaling, Extra cellular matrix region GO processes and focal adhesion, neuroactive ligand receptor interaction, Extracellular matrix receptor interaction. Tumor necrosis factor (TNF), Endothelin 1 (EDN1), Angiotensin (AGT) and many cell adhesion molecules (CAM) were detected as hub genes that can be targeted as novel therapeutic targets for ALS disease.These analyses and findings enhance the understanding of ALS pathogenesis and provide references for ALS therapy.

Project description:BACKGROUND: Parkinson's Disease (PD) is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions. RESULTS: Microarray based gene expression data and protein-protein interaction (PPI) databases were combined to construct the PPI networks of differentially expressed (DE) genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM), run separately to construct two Query-Query PPI (QQPPI) networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs) and High Betweenness Low Connectivity (bottlenecks) were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS) out of the 37 markers were found to be associated with several neurotransmitters including dopamine. CONCLUSION: This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network biomarkers may provide as potential therapeutic targets for PD applications development.

Project description:BackgroundAnti-CD20 is a highly effective therapy for multiple sclerosis (MS), a disease with multiple abnormalities in function of B and T cells and innate immune cells. Anti-CD20 therapy depletes B cells, which alters antibody production and has diverse effects on B cell immunity. These changes potentially affect immunity beyond B cells in MS.ObjectiveDetermine if anti-CD20 therapy effects non-B cell, as well as B cell, gene expression, and serum protein levels.MethodsSamples were collected from 10 healthy controls and from clinically stable relapsing-remitting MS - 10 untreated, 9 interferon-β-treated, and 15 ocrelizumab-treated patients were studied before, and 2 weeks and 6 months after, the first anti-CD20 infusion. Peripheral blood mononuclear cells (PBMC) were analyzed with sensitive, 135,000-transcript RNA expression microarrays, using stringent criteria. Gene expression was compared to 43 MS-relevant serum immune and neurotrophic proteins, using multiplex protein assays.ResultsAnti-CD20 therapy reduced expression of 413 total genes and 185 B-cell-regulated genes at 2 weeks vs. pre-therapy. Expression of 19 (15%) of these B cell genes returned toward baseline by 6 months, including genes for the B cell activation protein, CD79A, and for immunoglobulin A, D, and G heavy chains. Expression pathways for Th17 and CD4 regulatory T-cell (Treg) development, differentiation, and proliferation also quieted. In contrast, expression increased in Th1 and myeloid cell antiviral, pro-inflammatory, and toll-like receptor (TLR) gene pathways.ConclusionThese findings have clinical implications. B cell gene expression diminishes 2 weeks after anti-CD20 antibody infusion, but begins to rebound by 6 months. This suggests that the optimum time for vaccination is soon before reinfusion of anti-CD20 therapy. In addition, at 6 months, there is enhanced Th1 cell gene expression and induction of innate immune response genes and TLR expression, which can enhance anti-viral and anti-tumor immunity. This may compensate for diminished B cell gene expression after therapy. These data suggest that anti-CD20 therapy has dynamic effect on B cells and causes a compensatory rise in Th1 and myeloid immunity.

Dataset Information

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis.

Publications

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets