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Identification of differentially expressed genes profiles in a combined mouse model of Parkinsonism and colitis.

ABSTRACT: Different cellular mechanisms have been described as being potentially involved in the progression of neurodegeneration in Parkinson's disease, although their role is still unclear. The present study aimed to identify in detail, through differentially expressed genes analysis by bioinformatics approaches, the molecular mechanisms triggered after a systemic insult in parkinsonian mice. To address this objective, we combined a dextran sodium sulfate (DSS)-induced ulcerative colitis experimental mice model with an acute 1-methyl-4-phenyl-1,2,3,6-tetradropyridine (MPTP) intoxication. The animals were divided into four experimental groups based on the different treatments: (i) control, (ii) DSS, (iii) MPTP and (iv) MPTP?+?DSS. The data obtained by microarray and functional enrichment analysis point out the implication of different molecular mechanisms depending on the experimental condition. We see, in the striatum of animals intoxicated only with DSS, dysfunction processes related to the blood. On the other hand, oxidative stress processes are more prominent at the MPTP intoxicated mice. Finally, differentially expressed genes within the MPTP?+?DSS show functional enrichment in inflammation and programmed cell death. Interestingly, we identify a significant synergistic negative effect of both toxins since the expression of differentially expressed genes (DEGs) related to balanced cellular homeostasis was not enough to prevent processes associated with cell death. This work provides detailed insights into the involvement of systemic inflammation, triggered after an insult in the colon, in the progression of the degeneration in Parkinsonism. In this way, we will be able to identify promising therapeutic targets that prevent the contribution of inflammatory processes in the progression of Parkinson's disease.

SUBMITTER: Gil-Martinez AL

PROVIDER: S-EPMC7403295 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of differentially expressed genes profiles in a combined mouse model of Parkinsonism and colitis.

Gil-Martinez A L AL Cuenca-Bermejo L L Gonzalez-Cuello A M AM Sanchez-Rodrigo C C Parrado A A Vyas S S Fernandez-Villalba E E Herrero M T MT

Scientific reports 20200804 1

Different cellular mechanisms have been described as being potentially involved in the progression of neurodegeneration in Parkinson's disease, although their role is still unclear. The present study aimed to identify in detail, through differentially expressed genes analysis by bioinformatics approaches, the molecular mechanisms triggered after a systemic insult in parkinsonian mice. To address this objective, we combined a dextran sodium sulfate (DSS)-induced ulcerative colitis experimental mi ...[more]

PMID: 32753609

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Project description:BackgroundUlcerative colitis (UC) is an inflammatory bowel disease that is difficult to diagnose and treat. To date, the degree of inflammation in patients with UC has mainly been determined by measuring the levels of nonspecific indicators, such as C-reactive protein and the erythrocyte sedimentation rate, but these indicators have an unsatisfactory specificity. In this study, we performed bioinformatics analysis using data from the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) databases and verified the selected core genes in a mouse model of dextran sulfate sodium (DSS)-induced colitis.AimTo identify UC-related differentially expressed genes (DEGs) using a bioinformatics analysis and verify them in vivo and to identify novel biomarkers and the underlying mechanisms of UC.MethodsTwo microarray datasets from the NCBI-GEO database were used, and DEGs between patients with UC and healthy controls were analyzed using GEO2R and Venn diagrams. We annotated these genes based on their functions and signaling pathways, and then protein-protein interactions (PPIs) were identified using the Search Tool for the Retrieval of Interacting Genes. The data were further analyzed with Cytoscape software and the Molecular Complex Detection (MCODE) app. The core genes were selected and a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed. Finally, colitis model mice were established by administering DSS, and the top three core genes were verified in colitis mice using real-time polymerase chain reaction (PCR).ResultsOne hundred and seventy-seven DEGs, 118 upregulated and 59 downregulated, were initially identified from the GEO2R analysis and predominantly participated in inflammation-related pathways. Seven clusters with close interactions in UC formed: Seventeen core genes were upregulated [C-X-C motif chemokine ligand 13 (CXCL13), C-X-C motif chemokine receptor 2 (CXCR2), CXCL9, CXCL5, C-C motif chemokine ligand 18, interleukin 1 beta, matrix metallopeptidase 9, CXCL3, formyl peptide receptor 1, complement component 3, CXCL8, CXCL1, CXCL10, CXCL2, CXCL6, CXCL11 and hydroxycarboxylic acid receptor 3] and one was downregulated [neuropeptide Y receptor Y1 (NYP1R)] in the top cluster according to the PPI and MCODE analyses. These genes were substantially enriched in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The top three core genes (CXCL13, NYP1R, and CXCR2) were selected and verified in a mouse model of colitis using real-time PCR Increased expression was observed compared with the control mice, but only CXCR2 expression was significantly different.ConclusionCore DEGs identified in UC are related to inflammation and immunity inflammation, indicating that these reactions are core features of the pathogenesis of UC. CXCR2 may reflect the degree of inflammation in patients with UC.

Project description:Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC. Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs. Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

Project description:Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

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Identification of differentially expressed genes profiles in a combined mouse model of Parkinsonism and colitis.

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Identification of differentially expressed genes profiles in a combined mouse model of Parkinsonism and colitis.

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