
pmid: 31593492
To explore the expression changes of genes and the pathological processes-related genetic information in Parkinson's disease (PD) model induced by rotenone. The microarray data set "GSE37178" was downloaded from Gene Expression Omnibus database. Differentially expression genes (DEGs) at different concentration and time points were examined and clustered using Mfuzz. Functional enrichment was analyzed with The Database for Annotation, Visualization and Integrated Discovery. Search Tool for the Retrieval of Interacting Genes was used to perform the protein-protein interaction (PPI) networks, and functional module analysis of PPI was constructed with Cytoscape. Moreover, transcription factors (TFs) and microRNA (miRNA) target were screened with TRRUST and WebGestalt GAST, respectively. In total, 680 DEGs were examined in the group with rotenone treatment. Clustering analysis revealed that 115 genes presented a consistent rising trend, and 138 genes presented a falling trend. Functional enrichment analysis uncovered that the upregulated genes associated with "type I interferon signaling pathway," and the downregulated genes were related to "proteasome-mediated ubiquitin-dependent protein catabolic process." The PPI network included 156 nodes and 298 interactions, and ISG15, RRM2, FBXW11, and FOXM1 were the hub genes. Meanwhile, 38 TF-target and 269 miRNA-target interactions were obtained; the mRNAs of the MIR-181 family have more target genes, such as TRIM13. Our study showed that aberrant expression of ISG15, RRM2, FBXW11, FOXM1, and MIR-181 family were associated with pathological processes in PD, and they could be the research focuses to further investigate the mechanism of PD.
MicroRNAs, Gene Expression Regulation, Gene Expression Profiling, Rotenone, Cluster Analysis, Humans, Gene Regulatory Networks, Parkinson Disease, Protein Interaction Maps, Microarray Analysis, Transcription Factors
MicroRNAs, Gene Expression Regulation, Gene Expression Profiling, Rotenone, Cluster Analysis, Humans, Gene Regulatory Networks, Parkinson Disease, Protein Interaction Maps, Microarray Analysis, Transcription Factors
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