BackgroundAlzheimer's disease (AD) is becoming a more prevalent public health issue in today's culture. The experimental study of Coptidis Rhizoma (CR) and its chemical components in AD treatment has been widely reported, but the principle of multi-level and multi-mechanism treatment of AD urgently needs to be clarified.ObjectiveThis study focuses on network pharmacology to clarify the mechanism of CR's multi-target impact on Alzheimer's disease.MethodsThe Phytochemical-compounds of CR have been accessed from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Symmap database or HPLC determination. The values of Oral Bioavailability (OB) ≥ 30% and Drug Like (DL) ≥ 0.18 or blood ingredient were used to screen the active components of CR; the interactive network of targets and compounds were constructed by STRING and Cytoscape platform, and the network was analyzed by Molecular Complex Detection (MCODE); Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) and metabolic pathway enrichment of targets were carried out with Metascape, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and MetaboAnalyst platform; Based on CytoHubba, the potential efficient targets were screened by Maximal Clique Centrality (MCC) and Degree, the correlation between potential efficient targets and amyloid β-protein (Aβ), Tau pathology was analyzed by Alzdata database, and the genes related to aging were analyzed by Aging Altas database, and finally, the core targets were obtained; the binding ability between ingredients and core targets evaluated by molecular docking, and the clinical significance of core targets was assessed with Gene Expression Omnibus (GEO) database.Results19 active components correspond to 267 therapeutic targets for AD, of which 69 is potentially effective; in module analysis, RELA, TRAF2, STAT3, and so on are the critical targets of each module; among the six core targets, RELA, MAPK8, STAT3, and TGFB1 have clinical therapeutic significance; GO function, including 3050 biological processes (BP), 257 molecular functions (MF), 184 cellular components (CC), whose functions are mainly related to antioxidation, regulation of apoptosis and cell composition; the HIF-1 signaling pathway, glutathione metabolism is the most significant result of 134 KEGG signal pathways and four metabolic pathways, respectively; most of the active components have an excellent affinity in docking with critical targets.ConclusionThe pharmacological target prediction of CR based on molecular network pharmacology paves the way for a multi-level networking strategy. The study of CR in AD treatment shows a bright prospect for curing neurodegenerative diseases.

BackgroundAlzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.PurposeThe primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.MethodsDownload the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein–protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).ResultsThe subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.ConclusionThe common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases.