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.

P4-140: Biological knowledge-driven approach to gene-gene interaction analysis in the Alzheimer's Disease Genetics Consortium

2012 ◽  
Vol 8 (4S_Part_19) ◽  
pp. P681-P682
Author(s):  
Tricia Thornton-Wells ◽  
Kristin Brown-Gentry ◽  
Allison Baker ◽  
Eric Torstenson ◽  
Scott Dudek ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Interaction Analysis ◽  
Gene Interaction ◽  
Biological Knowledge

ACE I/D polymorphism in Alzheimer’s disease

2008 ◽  
Vol 3 (1) ◽  
pp. 49-54
Author(s):  
Marianna Trebunova ◽  
Eva Slaba ◽  
Viera Habalova ◽  
Zuzana Gdovinova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Interaction Analysis ◽  
Neurological Diseases ◽  
Apoe Genotype ◽  
Gene Interaction ◽  
Genotype Distribution ◽  
Ace Gene ◽  
Increased Risk ◽  
E4 Allele

AbstractAngiotensin-converting enzyme (ACE) has been reported to show altered activity in patients with neurological diseases. The recent studies found that a 287 bp insertion/deletion (I/D) polymorphism of the ACE gene may be associated with susceptibility to Alzheimer’s disease (AD) but the results have been heterogenous between studies in Europe. In the present study we examined for the first time the association of ACE I/D polymorphism along with APOE genotype in 70 sporadic AD and 126 control subjects in Slovak Caucasians (Central Europe). An increased risk for AD was observed in subjects with at least one APOE*E4 allele (OR=3.99, 95% CI=1.97–8.08). No significant differences for the genotype distribution or the allele frequency were revealed comparing controls and patients for ACE gene. Gene-gene interaction analysis showed increase of the risk to develop AD in subjects carrying both the ACE DD genotype and the APOE*E4 allele (OR=10.32, 95% C.I. 2.67–39.81).

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 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 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 MicroRNA-Target Interaction Regulatory Network in Alzheimer’s Disease

2021 ◽  
Vol 11 (12) ◽  
pp. 1275
Author(s):  
Aleksander Turk ◽  
Tanja Kunej ◽  
Borut Peterlin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Interaction Analysis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Microrna Target ◽  
Target Interaction ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Effect Of Nhexane Extract Of Caryota No Seed On Markers Of Neurodegeneration And Fecundity In Drosophila Melanogaster

2020 ◽  
Vol 6 (5) ◽  
pp. 1-7
Author(s):  
Chinonye A Maduagwuna ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drosophila Melanogaster ◽  
Neurodegenerative Diseases ◽  
Cognitive Functions ◽  
The Elderly ◽  
Common Cause ◽  
Chronic Neuroinflammation

Study background: Chronic neuroinflammation is a common emerging hallmark of several neurodegenerative diseases. Alzheimer’s Disease (AD) is the most common cause of dementia among the elderly and is characterized by loss of memory and other cognitive functions.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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