Key gene network related to primary ciliary dyskinesia in hippocampus of patients with Alzheimer’s disease revealed by weighted gene co-expression network analysis

Abstract

Background Alzheimer’s disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main brain regions involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little is known about its association with primary ciliary dyskinesia (PCD). Material and Methods The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network. Results Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g., DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients. Conclusion Our study demonstrated that AD and PCD have common metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD. Subjects Bioinformatics, Cell Biology, Molecular Biology, Neurology.

Advances in Integrative Medicine for Neurodegenerative Diseases: From Basic Research to Clinical Practice

“…In the 16S rRNA analysis, the operational taxonomic units (OTUs) were subjected to alpha diversity and beta diversity analysis. Communities or species that significantly differentially affected sample division were estimated using linear discriminant analysis (LDA) (Wang et al, 2022). For metabolomic analysis, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to screen differentially abundant metabolites.…”

“…Variable importance of projection (VIP) > 1 and P < 0.05 were used as thresholds to identify differentially abundant metabolites. To prevent overfitting, we used sevenfold cross validation and 200response permutation testing (RPT) to assess the quality of the model (Wang et al, 2022). Finally, association analysis was performed using the Spearman correlation analysis method (Lu et al, 2022).…”

“…To prevent overfitting, we used sevenfold cross validation and 200response permutation testing (RPT) to assess the quality of the model (Wang et al, 2022). Finally, association analysis was performed using the Spearman correlation analysis method (Lu et al, 2022).…”

Identification of deregulated lncRNAs in Alzheimer’s disease: an integrated gene co-expression network analysis of hippocampus and fusiform gyrus RNA-seq datasets

“…Several co-expression and differential co-expression network analyses have already been applied on RNA-seq data from hippocampus and fusiform gyrus, leading to the identification of co-expressed networks and genes associated with AD ( Sato et al, 2019 ; Crist et al, 2021 ; Xia et al, 2022 ; Ribeiro-dos-Santos et al, 2023 ). As our aim was the identification of lncRNAs that could take part of the pathologic molecular mechanisms of AD, we prioritized modules comprising the majority of lncRNAs which could be identified as driver genes and could be postulated to influence the expression of other genes or that could be directly involved in the causal mechanisms of AD.…”

Identification of key genes and signaling pathways associated with dementia with Lewy bodies and Parkinson's disease dementia using bioinformatics

“…The R software (version 4.2.1) was used for the GO annotation, the KEGG pathway enrichment analysis, and the visualization of DEGs ( 13 ). An online analysis tool Metascape website ( http://metascape.org ) was used for GO and KEGG analyses of gene modules selected by weighted gene co-expression network analysis (WGCNA) ( 14 ).…”