scholarly journals Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer’s Disease Pathogenesis

2020 ◽  
Vol 12 ◽  
Author(s):  
Rui-ting Hu ◽  
Qian Yu ◽  
Shao-dan Zhou ◽  
Yi-xin Yin ◽  
Rui-guang Hu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Novel Genes ◽  
Independent Dataset ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Functional Pathways ◽  
Independent Cohort

Background: The pathogenesis of Alzheimer’s disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways.Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed.Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls.Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.

scholarly journals Network pharmacology-based elucidation of the molecular mechanism underlying the anti-migraine effect of Asari Radix et Rhizoma

2021 ◽  
Vol 18 (10) ◽  
pp. 2067-2074
Author(s):  
Yun-Bin Jiang ◽  
Mei Zhong ◽  
Ting Huang ◽  
Zhong-Hua Dai ◽  
Xing-Bao Tao ◽  
...  
Keyword(s):  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Tnf Gene

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine.

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.

Integrated Bioinformatics Analysis Identifies Hub Genes Associated with Viral Infection and Alzheimer’s Disease

2021 ◽  
pp. 1-9
Author(s):  
Xiaoru Sun ◽  
Hui Zhang ◽  
Dongdong Yao ◽  
Yaru Xu ◽  
Qi Jing ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Viral Infection ◽  
Bioinformatics Analysis ◽  
Expression Patterns ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Kegg Pathways ◽  
Barr Virus

Background: Alzheimer’s disease (AD) is a fatal neurodegenerative disease, the etiology of which is unclear. Previous studies have suggested that some viruses are neurotropic and associated with AD. Objective: By using bioinformatics analysis, we investigated the potential association between viral infection and AD. Methods: A total of 5,066 differentially expressed genes (DEGs) in the temporal cortex between AD and control samples were identified. These DEGs were then examined via weighted gene co-expression network analysis (WGCNA) and clustered into modules of genes with similar expression patterns. Of identified modules, module turquoise had the highest correlation with AD. The module turquoise was further characterized using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. Results: Our results showed that the KEGG pathways of the module turquoise were mainly associated with viral infection signaling, specifically Herpes simplex virus, Human papillomavirus, and Epstein-Barr virus infections. A total of 126 genes were enriched in viral infection signaling pathways. In addition, based on values of module membership and gene significance, a total of 508 genes within the module were selected for further analysis. By intersecting these 508 genes with those 126 genes enriched in viral infection pathways, we identified 4 hub genes that were associated with both viral infection and AD: TLR2, COL1A2, NOTCH3, and ZNF132. Conclusion: Through bioinformatics analysis, we demonstrated a potential link between viral infection and AD. These findings may provide a platform to further our understanding of AD pathogenesis.

Construction of Long Noncoding RNA-Associated ceRNA Networks Reveals Potential Biomarkers in Alzheimer’s Disease

2021 ◽  
pp. 1-15
Author(s):  
Guan-yong Ou ◽  
Wen-wen Lin ◽  
Wei-jiang Zhao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Noncoding Rna ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Potential Biomarkers

Background: Alzheimer’s disease (AD) is a chronic neurodegenerative disease that seriously impairs both cognitive and memory functions mainly in the elderly, and its incidence increases with age. Recent studies demonstrated that long noncoding RNAs (lncRNAs) play important roles in AD by acting as competing endogenous RNAs (ceRNAs). Objective: In this study, we aimed to construct lncRNA-associated ceRNA regulatory networks composed of potential biomarkers in AD based on the ceRNA hypothesis. Methods: A total of 20 genes (10 upregulated genes and 10 downregulated genes) were identified as the hub differentially expressed genes (DEGs). The functional enrichment analysis showed that the most significant pathways of DEGs involved include retrograde endocannabinoid signaling, synaptic vesicle circle, and AD. The upregulated hub genes were mainly enriched in the cytokine-cytokine receptor interaction pathway, whereas downregulated hub genes were involved in the neuroactive ligand-receptor interaction pathway. After convergent functional genomic (CFG) ranks and expression level analysis in different brain regions of hub genes, we found that CXCR4, GFAP, and GNG3 were significantly correlated with AD. We further identified crucial miRNAs and lncRNAs of targeted genes to construct lncRNA-associated ceRNA regulatory networks. Results: The results showed that two lncRNAs (NEAT1, MIAT), three miRNAs (hsa-miR-551a, hsa-miR-133b and hsa-miR-206), and two mRNA (CXCR4 and GNG3), which are highly related to AD, were preliminarily identified as potential AD biomarkers. Conclusion: Our study provides new insights for understanding the pathogenic mechanism underlying AD, which may potentially contribute to the ceRNA mechanism in AD.

A METHODOLOGY BASED ON MOLECULAR INTERACTIONS AND PATHWAYS TO FIND CANDIDATE GENES ASSOCIATED TO DISEASES: ITS APPLICATION TO SCHIZOPHRENIA AND ALZHEIMER'S DISEASE

2011 ◽  
Vol 09 (04) ◽  
pp. 541-557 ◽  
Author(s):  
MARÍA ELENA OCHAGAVÍA ◽  
JAMILET MIRANDA ◽  
MARCELO NAZÁBAL ◽  
ALEXANDER MARTIN ◽  
LIDIA INÉS NOVOA ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Reference Genes ◽  
Enrichment Analysis ◽  
Second Step ◽  
Proof Of Concept ◽  
Molecular Interaction Networks ◽  
Gene Enrichment ◽  
Single List

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.

scholarly journals Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis

2021 ◽  
Vol 13 ◽  
Author(s):  
Wuhan Yu ◽  
Weihua Yu ◽  
Yan Yang ◽  
Yang Lü
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Validation Dataset ◽  
Braak Stage ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Functional Features ◽  
Key Genes

BackgroundAlzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects.MethodWe identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein—protein interaction (PPI) network, and utilized Cytoscape to identify hub genes. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes.ResultWe identified 608 consensus DEGs, several dysregulated pathways, and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high area under the curve (AUC) value in both the validation dataset and combined dataset (AUC = 0.992 and AUC = 0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. Brain-derived neurotrophic factor (BDNF) and WW domain-containing transcription regulator protein 1 (WWTR1), which are associated with the Braak stage, Aβ 42 levels, and β-secretase activity, were identified as critical genes of AD.ConclusionOur study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.

scholarly journals Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingxing Zhao ◽  
Hongmei Yao ◽  
Xinyi Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Machine Learning Algorithms ◽  
Functional Enrichment ◽  
Control Group ◽  
Hub Genes ◽  
Expression Levels

Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets.

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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