RESUMEN
Increasing scientific evidence demonstrates that the gut microbiota influences normal physiological homeostasis and contributes to pathogenesis, ranging from obesity to neurodegenerative diseases, such as Alzheimer's disease (AD). Gut microbiota can interact with the central nervous system (CNS) through the microbiota-gut-brain axis. The interaction is mediated by microbial secretions, metabolic interventions, and neural stimulation. Here, we review and summarize the regulatory pathways (immune, neural, neuroendocrine, or metabolic systems) in the microbiota-gut-brain axis in AD pathogenesis. Besides, we highlight the significant roles of the intestinal epithelial barrier and blood-brain barrier (BBB) in the microbiota-gut-brain axis. During the progression of AD, there is a gradual shift in the gut microbiota and host co-metabolic relationship, leading to gut dysbiosis, and the imbalance of microbial secretions and metabolites, such as lipopolysaccharides (LPS) and short-chain fatty acids (SCFAs). These products may affect the CNS metabolic state and immune balance through the microbiota-gut-brain axis. Further, we summarize the potential microbiota-gut-brain axis-targeted therapy including carbohydrates, probiotics, dietary measures, and propose new strategies toward the development of anti-AD drugs. Taken together, the data in this review suggest that remodeling the gut microbiota may present a tractable strategy in the management and development of new therapeutics against AD and other neurodegenerative diseases.
RESUMEN
Objective Alzheimer's disease (AD) is the most common cause of dementia. The pathophysiology of the disease mostly remains unearthed, thereby challenging drug development for AD. This study aims to screen high throughput gene expression data using weighted co-expression network analysis (WGCNA) to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus (GEO) database. Normalization, quality control, filtration, and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules. Furthermore, the correlation coefficients between the modules and clinical traits were computed to identify the key modules. Gene ontology and pathway enrichment analyses were performed on the key module genes. The STRING database was used to construct the protein-protein interaction (PPI) networks, which were further analyzed by Cytoscape app (MCODE). Finally, validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules, among which 6 modules were identified as the key module relating to AD occurrence. These key modules are primarily involved in chemical synaptic transmission (GO:0007268), the tricarboxylic acid (TCA) cycle and respiratory electron transport (R-HSA-1428517). WDR47, OXCT1, C3orf14, ATP6V1A, SLC25A14, NAPB were found as the hub genes and their expression were validated by external datasets.Conclusions Through modules co-expression network analyses and PPI network analyses, we identified the hub genes of AD, including WDR47, OXCT1, C3orf14, ATP6V1A, SLC25A14 and NAPB. Among them, three hub genes (ATP6V1A, SLC25A14, OXCT1) might contribute to AD pathogenesis through pathway of TCA cycle.
