Role of Calcium Signaling Pathway‐Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single‐Cell Analysis

Abstract

Background. This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis. Methods. The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions. Results. The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex. Conclusions. Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

Single-Cell Transcriptome Analysis Reveals the Importance of IRF1/FSTL1 in Synovial Fibroblast Subsets for the Development of Rheumatoid Arthritis

“…The regulatory interactions between regulators and their potential targets constitute a gene regulatory network. From the gene regulatory network, we can mine the key TFs that regulate the genes of interest [ 22 ]. Based on the gene regulatory network, the regulon activity score (RAS) was calculated to quantify the regulatory capacity of regulons in the cell.…”

Potential Mechanism Underlying Exercise Upregulated Circulating Blood Exosome miR-215-5p to Prevent Necroptosis of Neuronal Cells and a Model for Early Diagnosis of Alzheimer’s Disease

“…SCENIC supports the analysis of positive transcriptional regulation, which implies that it can screen transcription factors that regulate miRNAs during positive transcription. An improved version of the SCENIC method was used to screen the key transcription factors in skeletal muscles, as described previously ( Suo et al, 2018 ; Chen Y. et al, 2021 ; Lin et al, 2021a , b ). Moreover, to quantify the cell-type specificity of a regulon, we adapted an entropy-based strategy that was previously used for the analysis of gene expression data ( Cabili et al, 2011 ).…”

Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer’s Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology

“…The log fold change (logFC) and mean expression values of all genes were also recorded. Adjusted p -values less than 0.05 were considered to be statistically significantly different ( Costa-Silva et al, 2017 ; Chen et al, 2021 ; Lin W. et al, 2021 ; Shi et al, 2021 ; Wu et al, 2021 ). The “pheatmap” package of the R software was used for heat m APP ing.…”