Novel susceptibility genes and molecular mechanisms identified in relation to mitochondrial dysfunctions in Parkinson’s disease

Abstract Background : Mitochondrial dysfunction is one of the major contributors in the pathogenesis of Parkinson’s disease (PD). However, the mechanisms and genes involved in mitochondrial dysfunction in PD have still not been fully uncovered. Methods : The PD associated datasets were accessed via the gene expression omnibus (GEO) database, and we extracted 1870 mitochondrial dysfunction related genes (MDRGs) via the Genecards database. First, differentially expressed genes (DEGs) between PD and control samples were screened out by differential expression analysis, and samples (PD and control) were considered as traits for weighted gene co-expression network analysis (WGCNA) to yield the key modules. Then, we took the intersection of DEGs and genes from key modules to acquire a set of intersected genes, which were then intersected with MDRGs to obtain candidate genes. Next, the protein-protein interaction (PPI) analysis was implemented on candidate genes for identifying the hub genes, and they were subjected to single gene set enrichment analysis (GSEA). Finally, we constructed the drug-gene, transcription factor (TF)-mRNA-miRNA networks, and LASSO-Logistic regression model. Results : Between the PD and control samples, 945 DEGs were filtered out. Subsequently, MEblue and MEyellow modules all had a strong correlation with traits, therefore they were considered as the key modules. We also yielded 444 intersected genes and 31 candidate genes, respectively. In addition, 7 hub genes: DVL2, DNMT1, ABL1, RAF1, NOTCH1, RELA, and PDGFRB were identified via the protein-protein interaction analysis. These genes were found to be related to ‘Parkinson's disease’, ‘oxidative phosphorylation’ and other functional pathways. Likewise, drugs such as resveratrol and bortezomib were predicted according to the hub genes, and the TF-mRNA-miRNA network consisting of relationship pairs, such as SREBF1-ABL1, and ‘hsa-mir-23b-3p’-NOTCH1, was constructed. Ultimately, we found that the LASSO-Logistic regression model had an excellent ability to distinguish PD samples from control samples. Conclusion : Through bioinformatic analyses, we identified novel mitochondrial dysfunction related hub genes including DVL2, DNMT1, ABL1, RAF1, NOTCH1, RELA, and PDGFRB in PD, and explored their mechanisms of action in the tricarboxylic acid cycle and neuroactive ligand receptor interaction signaling pathways, providing new insights for studying the pathogenesis of PD.