No abstract

Alzheimer’s disease (AD) is a common neurodegenerative disease. Its onset is insidious and its progression is slow, making diagnosis difficult. In addition, its underlying molecular and cellular mechanisms remain unclear. In this study, clustering analysis was performed on single-cell RNA sequencing (scRNA-seq) data from the prefrontal cortex of 48 AD patients. Each sample module was identified to be a specific AD cell type, eight main brain cell types were identified, and the dysfunctional evolution of each cell type was further explored by pseudo-time analysis. Correlation analysis was then used to explore the relationship between AD cell types and pathological characteristics. In particular, intercellular communication between neurons and glial cells in AD patients was investigated by cell communication analysis. In patients, neuronal cells and glial cells significantly correlated with pathological features, and glial cells appear to play a key role in the development of AD through ligand-receptor axis communication. Marker genes involved in communication between these two cell types were identified using five types of modeling: logistic regression, multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). LASSO modeling identified CXCR4, EGFR, MAP4K4, and IGF1R as key genes in this communication. Our results support the idea that microglia play a role in the occurrence and development of AD through ligand-receptor axis communication. In particular, our analyses identify CXCR4, EGFR, MAP4K4, and IGF1R as potential biomarkers and therapeutic targets in AD.

Vascular dementia (VD) and Alzheimer’s disease (AD) are common types of dementia for which no curative therapies are known. In this study, we identified hub genes associated with AD and VD in order to explore new potential therapeutic targets. Genes differentially expressed in VD and AD in all three datasets (GSE122063, GSE132903, and GSE5281) were identified and used to construct a protein–protein interaction network. We identified 10 modules containing 427 module genes in AD and VD. Module genes showing an area under the diagnostic curve > 0.60 for AD or VD were used to construct a least absolute shrinkage and selection operator model and were entered into a support vector machine-recursive feature elimination algorithm, which identified REPS1 as a hub gene in AD and VD. Furthermore, REPS1 was associated with activation of pyruvate metabolism and inhibition of Ras signaling pathway. Module genes, together with differentially expressed microRNAs from the dataset GSE46579, were used to construct a regulatory network. REPS1 was predicted to bind to the microRNA hsa_miR_5701. Single-sample gene set enrichment analysis was used to explore immune cell infiltration, which suggested a negative correlation between REPS1 expression and infiltration by plasmacytoid dendritic cells in AD and VD. In conclusion, our results suggest core pathways involved in both AD and VD, and they identify REPS1 as a potential biomarker of both diseases. This protein may aid in early diagnosis, monitoring of treatment response, and even efforts to prevent these debilitating disorders.

The incidence of Alzheimer’s disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each “typing pathway” and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each “typing module,” a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications.

BackgroundAlzheimer’s disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.MethodsDifferentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.ResultsDEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.ConclusionAD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.