Dataset Information

Shared Molecular Mechanisms between Alzheimer's Disease and Periodontitis Revealed by Transcriptomic Analysis.

ABSTRACT:

Objective

To investigate the genetic crosstalk mechanisms that link periodontitis and Alzheimer's disease (AD).

Background

Periodontitis, a common oral infectious disease, is associated with Alzheimer's disease (AD) and considered a putative contributory factor to its progression. However, a comprehensive investigation of potential shared genetic mechanisms between these diseases has not yet been reported.

Methods

Gene expression datasets related to periodontitis were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis was performed to identify differentially expressed genes (DEGs). Genes associated with AD were downloaded from the DisGeNET database. Overlapping genes among the DEGs in periodontitis and the AD-related genes were defined as crosstalk genes between periodontitis and AD. The Boruta algorithm was applied to perform feature selection from these crosstalk genes, and representative crosstalk genes were thus obtained. In addition, a support vector machine (SVM) model was constructed by using the scikit-learn algorithm in Python. Next, the crosstalk gene-TF network and crosstalk gene-DEP (differentially expressed pathway) network were each constructed. As a final step, shared genes among the crosstalk genes and periodontitis-related genes in DisGeNET were identified and denoted as the core crosstalk genes.

Results

Four datasets (GSE23586, GSE16134, GSE10334, and GSE79705) pertaining to periodontitis were included in the analysis. A total of 48 representative crosstalk genes were identified by using the Boruta algorithm. Three TFs (FOS, MEF2C, and USF2) and several pathways (i.e., JAK-STAT, MAPK, NF-kappa B, and natural killer cell-mediated cytotoxicity) were identified as regulators of these crosstalk genes. Among these 48 crosstalk genes and the chronic periodontitis-related genes in DisGeNET, C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 were shared and identified as the most pivotal candidate links between periodontitis and AD.

Conclusions

Exploration of available transcriptomic datasets revealed C4A, C4B, CXCL12, FCGR3A, IL1B, and MMP3 as the top candidate molecular linkage genes between periodontitis and AD.

SUBMITTER: Jin J

PROVIDER: S-EPMC8032519 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveThis study investigated the nature of shared transcriptomic alterations in PBMs from periodontitis and atherosclerosis to unravel molecular mechanisms underpinning their association.MethodsGene expression data from PBMs from patients with periodontitis and those with atherosclerosis were each downloaded from the GEO database. Differentially expressed genes (DEGs) in periodontitis and atherosclerosis were identified through differential gene expression analysis. The disease-related known genes related to periodontitis and atherosclerosis each were downloaded from the DisGeNET database. A Venn diagram was constructed to identify crosstalk genes from four categories: DEGs expressed in periodontitis, periodontitis-related known genes, DEGs expressed in atherosclerosis, and atherosclerosis-related known genes. A weighted gene coexpression network analysis (WGCNA) was performed to identify significant coexpression modules, and then, coexpressed gene interaction networks belonging to each significant module were constructed to identify the core crosstalk genes.ResultsFunctional enrichment analysis of significant modules obtained by WGCNA analysis showed that several pathways might play the critical crosstalk role in linking both diseases, including bacterial invasion of epithelial cells, platelet activation, and Mitogen-Activated Protein Kinases (MAPK) signaling. By constructing the gene interaction network of significant modules, the core crosstalk genes in each module were identified and included: for GSE23746 dataset, RASGRP2 in the blue module and VAMP7 and SNX3 in the green module, as well as HMGB1 and SUMO1 in the turquoise module were identified; for GSE61490 dataset, SEC61G, PSMB2, SELPLG, and FIBP in the turquoise module were identified.ConclusionExploration of available transcriptomic datasets revealed core crosstalk genes (RASGRP2, VAMP7, SNX3, HMGB1, SUMO1, SEC61G, PSMB2, SELPLG, and FIBP) and significant pathways (bacterial invasion of epithelial cells, platelet activation, and MAPK signaling) as top candidate molecular linkage mechanisms between atherosclerosis and periodontitis.

Project description:Background: Periodontitis is associated with periodontal tissue damage and teeth loss. Nonalcoholic fatty liver disease (NAFLD) has an intimate relationship with periodontitis. Nevertheless, interacted mechanisms between them have not been clear. This study was intended for the exploration of shared gene signatures and latent therapeutic targets in periodontitis and NAFLD. Methods: Microarray datasets of periodontitis and NAFLD were obtained from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was utilized for the acquisition of modules bound up with NAFLD and periodontitis. We used ClueGO to carry out biological analysis on shared genes to search their latent effects in NAFLD and periodontitis. Another cohort composed of differential gene analysis verified the results. The common microRNAs (miRNAs) in NAFLD and periodontitis were acquired in the light of the Human microRNA Disease Database (HMDD). According to miRTarbase, miRDB, and Targetscan databases, latent target genes of miRNAs were forecasted. Finally, the miRNAs-mRNAs network was designed. Results: Significant modules with periodontitis and NAFLD were obtained via WGCNA. GO enrichment analysis with GlueGo indicated that damaged migration of dendritic cells (DCs) might be a common pathophysiologic feature of NAFLD and periodontitis. In addition, we revealed common genes in NAFLD and periodontitis, including IGK, IGLJ3, IGHM, MME, SELL, ENPP2, VCAN, LCP1, IGHD, FCGR2C, ALOX5AP, IGJ, MMP9, FABP4, IL32, HBB, FMO1, ALPK2, PLA2G7, MNDA, HLA-DRA, and SLC16A7. The results of differential analysis in another cohort were highly accordant with the findings of WGCNA. We established a comorbidity model to explain the underlying mechanism of NAFLD secondary to periodontitis. Finally, the analysis of miRNA pointed out that hsa-mir-125b-5p, hsa-mir-17-5p, and hsa-mir-21-5p might provide potential therapeutic targets. Conclusion: Our study initially established a comorbidity model to explain the underlying mechanism of NAFLD secondary to periodontitis, found that damaged migration of DCs might be a common pathophysiological feature of NAFLD and periodontitis, and provided potential therapeutic targets.

Project description:Aim Alzheimer’s disease (AD) and ischemic stroke (IS), two major neurological diseases, are suggested to be associated in clinical and pathophysiological levels. Previous studies have provided some insights into the possible genetic mechanisms behind the correlation between AD and IS, but this issue is still not clear. We implemented transcriptomic analysis to detect common hub genes and pathways to help promote the understanding of this issue. Materials and methods Four gene expression profiling datasets (GSE16561, GSE58294, GSE63060, and GSE63061) of peripheral whole blood, which contain 108 IS samples, 284 AD samples, and 285 matched controls, were employed to detect differentially expressed genes (DEGs) for AD and IS, which were further analyzed for shared biological pathways, candidate drugs, and transcription factors. Protein-protein interaction (PPI) network and drug-target interaction analysis were applied to identify hub genes and drug targets, respectively. Result verification was done with other independent datasets (GSE37587, GSE46480, and GSE140829). The difference in proportions of various immune cells in the peripheral blood of AD and IS patients were evaluated using CIBERSORT. Results We identified 74 DEGs and 18 biological processes with statistical significance shared by AD and IS, 9 of which were immune-related pathways. Five hub genes scored high in the topological analysis of the PPI network, and we also found eight drug target genes and candidate drugs which were associated with AD and IS. As for immunological changes, an increase in the proportion of M0 macrophages was found in the peripheral circulation of both AD and IS patients, and SOD1 expression was significantly correlated with this change. Conclusion Collectively, the common DEGs and shared pathways found in this study suggest a potential shared etiology between AD and IS, behind which immune system, particularly the M0 macrophage elevation, might have important roles. While, the shared hub genes, potential therapeutic gene targets and drugs reported in this study provide promising treatment strategies for AD and IS.

Project description:Background Hepatocellular carcinoma (HCC) is one of the leading causes of death from cancer worldwide. The histopathological features, risk factors, and prognosis of HCC caused by nonalcoholic fatty liver disease (NAFLD) appear to be significantly different from those of HCC caused by other etiologies of liver disease. Objective This article explores the shared gene and molecular mechanism between NAFLD and HCC through bioinformatics technologies such as weighted gene co-expression network analysis (WGCNA), so as to provide a reference for comprehensive understanding and treatment of HCC caused by NAFLD. Methods NAFLD complementary deoxyribonucleic acid microarrays (GSE185051) from the Gene Expression Omnibus database and HCC ribonucleic acid (RNA)-sequencing data (RNA-seq data) from The Cancer Genome Atlas database were used to analyze the differentially expressed genes (DEGs) between NAFLD and HCC. Then, the clinical traits and DEGs in the two disease data sets were analyzed by WGCNA to obtain W-DEGs, and cross-W-DEGs were obtained by their intersection. We performed subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses of the cross-W-DEGs and established protein-protein interaction networks. Then, we identified the hub genes in them by Cytoscape and screened out the final candidate genes. Finally, we validated candidate genes by gene expression, survival, and immunohistochemical analyses. Results The GO analysis of 79 cross-W-DEGs showed they were related mainly to RNA polymerase II (RNAP II) and its upstream transcription factors. KEGG analysis revealed that they were enriched predominantly in inflammation-related pathways (tumor necrosis factor and interleukin-17). Four candidate genes (JUNB, DUSP1, NR4A1, and FOSB) were finally screened out from the cross-W-DEGs. Conclusion JUNB, DUSP1, NR4A1, and FOSB inhibit NAFLD and HCC development and progression. Thus, they can serve as potential useful biomarkers for predicting and treating NAFLD progression to HCC.