scholarly journals Effects of DISC1 on Alzheimer's disease cell models assessed by iTRAQ proteomic analysis

2022 ◽  
Author(s):  
Jiajie Lu ◽  
Rihong Huang ◽  
Yuecheng Peng ◽  
Haojian Wang ◽  
Zejia Feng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ectopic Expression ◽  
Enrichment Analysis ◽  
The Elderly ◽  
Gene Set Enrichment Analysis ◽  
Absolute Quantitation ◽  
Protein Protein Interaction ◽  
Isobaric Tag ◽  
Underlying Mechanisms

Alzheimer’s disease (AD) is a form of neurodegenerative disease in the elderly with no cure at present. In a previous study, we found that the scaffold protein DISC1 is downregulated in the AD brains, and ectopic expression of DISC1 can delay the progression of AD by protecting synaptic plasticity and down-regulating BACE1. However, the underlying mechanisms remain not to be elucidated. In the present study, we compared the proteomes of normal and DISC1high AD cells expressing the amyloid precursor protein (APP) using isobaric tag for relative and absolute quantitation (iTRAQ) and mass spectrometry (MS). The differentially expressed proteins (DEPs) were identified, and the protein-protein interaction (PPI) network was constructed to identify the interacting partners of DISC1. Based on the interaction scores, NDE1, GRM3, PTGER3 and KATNA1 were identified as functionally or physically related to DISC1, and may therefore regulate AD development. The DEPs were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases with the DAVID software, and the eggNOG database was used to determine their evolutionary relationships. The DEPs were significantly enriched in microtubules and mitochondria-related pathways. Gene set enrichment analysis (GSEA) was performed to identify genes and pathways that are activated when DISC1 is overexpressed. Our findings provide novel insights into the regulatory mechanisms underlying DISC1 function in AD.

scholarly journals Identification of the Hub Genes in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiwen Gui ◽  
Qi Gong ◽  
Jun Jiang ◽  
Mei Liu ◽  
Huanyin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Elderly ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

scholarly journals A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease

2020 ◽  
Vol 21 (5) ◽  
pp. 1766 ◽  
Author(s):  
Wenyong Wu ◽  
Zijia Zhang ◽  
Feifei Li ◽  
Yanping Deng ◽  
Min Lei ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Biological Activities ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Protein Protein Interaction ◽  
Novel Strategy ◽  
Underlying Mechanisms

Uncaria alkaloids are the major bioactive chemicals found in the Uncaria genus, which have a long history of clinical application in treating cardiovascular and mental diseases in traditional Chinese medicine (TCM). However, there are gaps in understanding the multiple targets, pathways, and biological activities of Uncaria alkaloids. By constructing the interactions among drug-targets-diseases, network pharmacology provides a systemic methodology and a novel perspective to present the intricate connections among drugs, potential targets, and related pathways. It is a valuable tool for studying TCM drugs with multiple indications, and how these multi-indication drugs are affected by complex interactions in the biological system. To better understand the mechanisms and targets of Uncaria alkaloids, we built an integrated analytical platform based on network pharmacology, including target prediction, protein–protein interaction (PPI) network, topology analysis, gene enrichment analysis, and molecular docking. Using this platform, we revealed the underlying mechanisms of Uncaria alkaloids’ anti-hypertensive effects and explored the possible application of Uncaria alkaloids in preventing Alzheimer’s disease. These results were further evaluated and refined using biological experiments. Our study provides a novel strategy for understanding the holistic pharmacology of TCM, as well as for exploring the multi-indication properties of TCM beyond its traditional applications.

scholarly journals Study on the Mechanism of Acori Graminei Rhizoma in the Treatment of Alzheimer’s Disease Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Kuan Du ◽  
Yue Xiao ◽  
Shao Min Zhong ◽  
Yi Xing Huang ◽  
Qian Wen Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
The Elderly ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Active Components

Alzheimer’s disease is a common neurodegenerative disease in the elderly. This study explored the curative effect and possible mechanism of Acori graminei rhizoma on Alzheimer’s disease. In this paper, 8 active components of Acori graminei rhizoma were collected by consulting literature and using the TCMSP database, and 272 targets were screened using the PubChem and Swiss Target Prediction databases. Introduce it into the software of Cytoscape 3.7.2 and establish the graph of “drug-active ingredient-ingredient target.” A total of 276 AD targets were obtained from OMIM, Gene Cards, and DisGeNET databases. Import the intersection targets of drugs and diseases into STRING database for enrichment analysis, and build PPI network in the Cytoscape 3.7.2 software, whose core targets involve APP, AMPK, NOS3, etc. GO analysis and KEGG analysis showed that there were 195 GO items and 30 AD-related pathways, including Alzheimer’s disease pathway, serotonin synapse, estrogen signaling pathway, dopaminergic synapse, and PI3K-Akt signaling pathway. Finally, molecular docking was carried out to verify the binding ability between Acori graminei rhizoma and core genes. Our results predict that Acori graminei rhizoma can treat AD mainly by mediating Alzheimer’s signal pathway, thus reducing the production of Aβ, inhibiting the hyperphosphorylation of tau protein, regulating neurotrophic factors, and regulating the activity of kinase to change the function of the receptor.

Screening novel drug candidates for Alzheimer’s disease by an integrated network and transcriptome analysis

2020 ◽  
Vol 36 (17) ◽  
pp. 4626-4632
Author(s):  
Yonglin Peng ◽  
Meng Yuan ◽  
Juncai Xin ◽  
Xinhua Liu ◽  
Ju Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Supplementary Information ◽  
Integrated Network ◽  
Drug Candidates ◽  
Novel Drug

Abstract Motivation Alzheimer’s disease (AD) is a serious degenerative brain disease and the most common cause of dementia. The current available drugs for AD provide symptomatic benefit, but there is no effective drug to cure the disease. The emergence of large-scale genomic, pharmacological data provides new opportunities for drug discovery and drug repositioning as a promising strategy in searching novel drug for AD. Results In this study, we took advantage of our increasing understanding based on systems biology approaches on the pathway and network levels and perturbation datasets from the Library of Integrated Network-Based Cellular Signatures to introduce a systematic computational process to discover new drugs implicated in AD. First, we collected 561 genes that have reported to be risk genes of AD, and applied functional enrichment analysis on these genes. Then, by quantifying proximity between 5595 molecule drugs and AD based on human interactome, we filtered out 1092 drugs that were proximal to the disease. We further performed an Inverted Gene Set Enrichment analysis on these drug candidates, which allowed us to estimate effect of perturbations on gene expression and identify 24 potential drug candidates for AD treatment. Results from this study also provided insights for understanding the molecular mechanisms underlying AD. As a useful systematic method, our approach can also be used to identify efficacious therapies for other complex diseases. Availability and implementation The source code is available at https://github.com/zer0o0/drug-repo.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-Wide Integrative Analysis Reveals Common Molecular Signatures in Blood and Brain of Alzheimer’s Disease

2020 ◽  
Vol 11 (2) ◽  
pp. 8686-8701
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Integrative Analysis ◽  
Gene Set Enrichment Analysis ◽  
Brain Gene Expression ◽  
Sensitivity Specificity ◽  
Eqtl Data ◽  
Brain Tissues

The currently utilized neuroimaging and cerebrospinal fluid-based detection of Alzheimer’s disease (AD) suffer several limitations, including sensitivity, specificity, and cost. Therefore, the identification of AD by analyzing blood gene expression may ameliorate the early diagnosis of the AD. We aimed to identify common genes commonly deregulated in blood and brain in AD. Comprehensive analysis of blood and brain gene expression datasets of AD, eQTL, and epigenetics data was analyzed by the integrative bioinformatics approach. The integrative analysis showed nine differentially expressed genes common to blood cells and brain (CNBD1, SUCLG2-AS1, CCDC65, PDE4D, MTMR1, C3, SLC6A15, LINC01806, and FRG1JP). Analysis of SNP and cis-eQTL data showed 18 genes; namely, HSD17B1, GAS5, RPS5, VKORC1, GLE1, WDR1, RPL12, MORN1, RAD52, SDR39U1, NPHP4, MT1E, SORD, LINC00638, MCM3AP-AS1, GSDMD, RPS9, and GNL2 were observed deregulated AD blood and brain tissues. Functional gene set enrichment analysis demonstrated a significant association of these genes in neurodegeneration-associated molecular pathways. Integrative biomolecular networks revealed dysregulation of several hub transcription factors and microRNAs in AD. Moreover, hub genes were observed associated with significant histone modification. This study detected common molecular biomarkers and pathways in blood and brain tissues in AD that may be potential biomarkers and therapeutic targets in AD.

scholarly journals Association Between Obstructive Sleep Apnea, Its Treatment, and Alzheimer's Disease: Systematic Mini-Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Chih-Yun Kuo ◽  
Hung-Ta Hsiao ◽  
Ing-Hsien Lo ◽  
Tomas Nikolai
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
The Elderly ◽  
Protein Levels ◽  
Obstructive Sleep ◽  
Increased Risk ◽  
Osa Treatment ◽  
Underlying Mechanisms

Obstructive sleep apnea (OSA) and Alzheimer's disease (AD) are common in the elderly population. Obstructive sleep apnea that may cause significant changes in the cerebrospinal fluid β-amyloid and T-tau and/or P-tau protein levels is often identified as a risk factor for development of AD. Although the underlying mechanisms of AD are still not fully understood, a hypothesis associating OSA with AD has been already proposed. In this systematic mini-review, we first discuss the recent findings supporting the association of OSA with an increased risk of AD and then provide evidence suggesting the positive effect of OSA treatment on a reduced risk of AD.

scholarly journals Gene Expression Meta-Analysis of Major Depressive Disorder and Its Relationship with Alzheimer’s Disease

2021 ◽  
Author(s):  
Ruihan WANG ◽  
Jinghao DUAN ◽  
Quan ZHENG ◽  
Xueping CHEN
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Ontology ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Meta Analysis ◽  
The Elderly ◽  
Major Depressive ◽  
Protein Protein Interaction ◽  
System Metabolism

Abstract Introduction: Major depressive disorder (MDD) and Alzheimer’s disease (AD) are often co-existing in the elderly and have been suggested to share common pathological and physiological links. Understanding the connections between these two diseases could benefit for revealing new possible strategies of early diagnosis and therapeutic intervention. Methods: we conducted a meta-analysis to identify differentially expressed genes (DEGs) in MDD microarray datasets including 180 MDD and 281 control prefrontal cortex of brain samples. Using identified DEGs, we performed gene ontology (GO), pathway and protein-protein interaction (PPI) analysis. Results: We identified 1400 DEGs, of which 846 were upregulated and 554 was downregulated in MDD. 198 DEGs were found over-lapping between AD and MDD compared with the previous study on AD. The over-lapping DEGs were particularly enriched in the protein binding of gene ontology and Signal Transduction, Immune System, Metabolism of proteins as for pathways. CDC42 was the most important gene in PPI network which had the most connections with other genes.Conclusion: Our study shows that MDD and AD share significant common DEGs and pathways and add some new potential perspectives to the comprehensive neurobiologic model between MDD and the development of AD.

scholarly journals Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis

2021 ◽  
Vol 17 ◽  
pp. 117693432110237
Author(s):  
Kailin Mao ◽  
Fang Lin ◽  
Yingai Zhang ◽  
Hailong Zhou
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Gefitinib Resistance

Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.

Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhengqiang He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Actin Filament ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction

Abstract More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

scholarly journals A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Jing ◽  
Hui Zhang ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Silu Cao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Interaction Analysis ◽  
Gene Interaction ◽  
The Elderly ◽  
Machine Learning Algorithms ◽  
Sustained Remission ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Normal Individuals

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In this study, we used comprehensive bioinformatics to determine 158 potential genes, whose expression levels changed between the entorhinal and temporal lobe cortex samples from cognitively normal individuals and patients with AD. Then, we clustered these genes in the protein-protein interaction analysis and identified six significant genes that had more biological functions. Besides, we conducted a drug-gene interaction analysis of module genes in the drug-gene interaction database and obtained 26 existing drugs that might be applied for the prevention and treatment of AD. In addition, a predictive model was built based on the selected genes using different machine learning algorithms to identify individuals with AD. These findings may provide new insights into AD therapy.

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