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. 2024 Apr;52(2):83-98.
doi: 10.62641/aep.v52i2.1601.

Bioinformatic Identification of Signaling Pathways and Hub Genes in Vascular Dementia

Affiliations

Bioinformatic Identification of Signaling Pathways and Hub Genes in Vascular Dementia

Yuanhua Wu et al. Actas Esp Psiquiatr. 2024 Apr.

Abstract

Background: Vascular dementia (VaD) is a prevalent neurodegenerative disease characterized by cognitive impairment due to cerebrovascular factors, affecting a significant portion of the aging population and highlighting the critical need to understand specific targets and mechanisms for effective prevention and treatment strategies. We aimed to identify pathways and crucial genes involved in the progression of VaD through bioinformatics analysis and subsequently validate these findings.

Methods: We conducted differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) analysis. We utilized pheochromocytoma 12 (PC12) cells to create an in vitro oxygen-glucose deprivation (OGD) model. We investigated the impact of overexpression and interference of adrenoceptor alpha 1D (ADRA1D) on OGD PC12 cells using TdT-mediated dUTP nick-end labeling (TUNEL), reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blot (WB), and Fluo-3-pentaacetoxymethyl ester (Fluo-3 AM) analysis.

Results: We found 187 differentially expressed genes (DEGs) in the red module that were strongly associated with VaD and were primarily enriched in vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathway, and cell adhesion. Among these pathways, we identified ADRA1D as a gene shared by vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction. The TUNEL assay revealed a significant decrease in PC12 cell apoptosis with ADRA1D overexpression (p < 0.01) and a significant increase in apoptosis upon silencing ADRA1D (p < 0.01). RT-qPCR and WB analysis revealed elevated ADRA1D expression (p < 0.001) and decreased phospholipase C beta (PLCβ) and inositol 1,4,5-trisphosphate receptor (IP3R) expression (p < 0.05) with ADRA1D overexpression. Moreover, the Fluo-3 AM assessment indicated significantly lower intracellular Ca2+ levels with ADRA1D overexpression (p < 0.001). Conversely, interference with ADRA1D yielded opposite results.

Conclusion: Our study provides a new perspective on the pathogenic mechanisms of VaD and potential avenues for therapeutic intervention. The results highlight the role of ADRA1D in modulating cellular responses to OGD and VaD, suggesting its potential as a target for VaD treatment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Analysis of differentially expressed genes (DEGs) in the dataset GSE122063. (A) Volcano plot of DEGs. Colors toward blue indicate downregulated DEGs, colors toward red indicate upregulated DEGs, and gray represents non-significantly DEGs. (B) Hierarchical clustering heatmap of the top 50 DEGs. Colors toward blue indicate downregulated DEGs, colors toward red indicate upregulated DEGs. (C) Box plot of the DEGs expression data after normalization. (D) Principal component analysis (PCA) plot. The blue points represent the control samples, and the red points represent Vascular dementia (VaD) samples. VaD, vascular dementia samples; con, control samples.
Fig. 2.
Fig. 2.
Construction of gene co-expression networks using Weighted Gene Co-expression Network Analysis (WGCNA). (A) Analysis of scale independence. (B) Mean connectivity for different soft threshold powers. (C) Cluster dendrogram of all differentially expressed genes clustered based on a dissimilarity measure.
Fig. 3.
Fig. 3.
Identification of modules associated with the clinical traits of VaD. (A) Heatmap of the correlation between module eigengenes and clinical traits of VaD. (B) There is a significant correlation between the key module (red module) and VaD. (C) Venn diagram of red module genes with DEGs.
Fig. 4.
Fig. 4.
Gene ontology and pathway enrichment analysis of red module genes. (A) The bar chart of the top 10 Gene Ontology (GO) enrichment terms. BP, biological processes; CC, cellular components; MF, molecular functions. (B) The dot chart of the top 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
Fig. 5.
Fig. 5.
Network plot of key genes in the red module; nodes indicate genes.
Fig. 6.
Fig. 6.
Impact of overexpression and interference of adrenoceptor alpha 1D (ADRA1D) on apoptosis and Ca2+ homeostasis in oxygen-glucose deprivation (OGD) model pheochromocytoma 12 (PC12) cells. (A) TdT-mediated dUTP nick end labeling (TUNEL) assay depicting PC12 cell apoptosis. (B) Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) examining the influence of overexpression and interference of ADRA1D on mRNA expression of ADRA1D and phospholipase C beta (PLCβ) in PC12 cells. (C) Western blot assessing the impact of overexpression and silencing of ADRA1D on protein expression of ADRA1D, inositol 1,4,5-trisphosphate receptor (IP3R), and PLCβ in PC12 cells. *p < 0.05, **p < 0.01, ***p < 0.001 compared to OE-ADRA1D NC or sh-ADRAlD NC. n = 3. GAPDH, Glyceraldehyde-3-phosphate dehydrogenase.
Fig. 7.
Fig. 7.
Fluo-3-pentaacetoxymethyl ester (Fluo-3AM) measurement illustrates the effect of overexpression and silencing of ADRA1D on intracellular calcium ion levels in PC12 cells. ***p < 0.001 compared to OE-ADRA1D NC or sh-ADRA1D NC. n = 3.

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