scholarly journals Shared Molecular Mechanisms between Alzheimer’s Disease and Periodontitis Revealed by Transcriptomic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Jieqi Jin ◽  
Mengkai Guang ◽  
Anthony Chukwunonso Ogbuehi ◽  
Simin Li ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Killer Cell ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Support Vector ◽  
Contributory Factor ◽  
Boruta Algorithm

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.

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

scholarly journals Transcriptional, Behavioral and Biochemical Profiling in the 3xTg-AD Mouse Model Reveals a Specific Signature of Amyloid Deposition and Functional Decline in Alzheimer’s Disease

2020 ◽  
Vol 14 ◽  
Author(s):  
Wencheng Yin ◽  
Navei Cerda-Hernández ◽  
Atahualpa Castillo-Morales ◽  
Mayra L. Ruiz-Tejada-Segura ◽  
Jimena Monzón-Sandoval ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Transcriptional Profiling ◽  
Differential Expression Analysis ◽  
Functional Decline ◽  
Insular Cortex ◽  
Learning Performance ◽  
Significant Enrichment ◽  
Transcriptional Changes

Alzheimer’s disease (AD)-related degenerative decline is associated to the presence of amyloid beta (Aβ) plaque lesions and neuro fibrillary tangles (NFT). However, the precise molecular mechanisms linking Aβ deposition and neurological decline are still unclear. Here we combine genome-wide transcriptional profiling of the insular cortex of 3xTg-AD mice and control littermates from early through to late adulthood (2–14 months of age), with behavioral and biochemical profiling in the same animals to identify transcriptional determinants of functional decline specifically associated to build-up of Aβ deposits. Differential expression analysis revealed differentially expressed genes (DEGs) in the cortex long before observed onset of behavioral symptoms in this model. Using behavioral and biochemical data derived from the same mice and samples, we found that down but not up-regulated DEGs show a stronger average association with learning performance than random background genes in control not seen in AD mice. Conversely, these same genes were found to have a stronger association with Aβ deposition than background genes in AD but not in control mice, thereby identifying these genes as potential intermediaries between abnormal Aβ/NFT deposition and functional decline. Using a complementary approach, gene ontology analysis revealed a highly significant enrichment of learning and memory, associative, memory, and cognitive functions only among down-regulated, but not up-regulated, DEGs. Our results demonstrate wider transcriptional changes triggered by the abnormal deposition of Aβ/NFT occurring well before behavioral decline and identify a distinct set of genes specifically associated to abnormal Aβ protein deposition and cognitive decline.

scholarly journals A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Aimin Hu ◽  
Zheng Wei ◽  
Zuxiang Zheng ◽  
Bichao Luo ◽  
Jieming Yi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Computational Framework ◽  
Normal Tissues

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies worldwide. Although there have been extensive studies on the molecular mechanisms of its carcinogenesis, FDA-approved drugs for HCC are rare. Side effects, development time, and cost of these drugs are the major bottlenecks, which can be partially overcome by drug repositioning. In this study, we developed a computational framework to study the mechanisms of HCC carcinogenesis, in which drug perturbation-induced gene expression signatures were utilized for repositioning of potential drugs. Specifically, we first performed differential expression analysis and coexpression network module analysis on the HCC dataset from The Cancer Genome Atlas database. Differential gene expression analysis identified 1,337 differentially expressed genes between HCC and adjacent normal tissues, which were significantly enriched in functions related to various pathways, including α-adrenergic receptor activity pathway and epinephrine binding pathway. Weighted gene correlation network analysis (WGCNA) suggested that the number of coexpression modules was higher in HCC tissues than in normal tissues. Finally, by correlating differentially expressed genes with drug perturbation-related signatures, we prioritized a few potential drugs, including nutlin and eribulin, for the treatment of hepatocellular carcinoma. The drugs have been reported by a few experimental studies to be effective in killing cancer cells.

scholarly journals Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer's Disease

2020 ◽  
Author(s):  
Manish D Paranjpe ◽  
Stella Belonwu ◽  
Jason K Wang ◽  
Tomiko Oskotsky ◽  
Aarzu Gupta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
B Cells ◽  
Whole Blood ◽  
Meta Analysis ◽  
The United States ◽  
Support Vector ◽  
Cell Type ◽  
Study Gene Expression

Abstract Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the apolipoprotein ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD signature. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusions: These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

scholarly journals Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer's Disease

2020 ◽  
Author(s):  
Manish Paranjpe ◽  
Stella Belonwu ◽  
Jason K Wang ◽  
Tomiko Oskotsky ◽  
Aarzu Gupta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
B Cells ◽  
Whole Blood ◽  
Meta Analysis ◽  
The United States ◽  
Support Vector ◽  
Cell Type ◽  
Study Gene Expression

Abstract Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the apolipoprotein ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD signature. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusions: These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

scholarly journals Discovering Biomarkers and Pathways Shared by Alzheimer’s Disease and Ischemic Stroke to Identify Novel Therapeutic Targets

Medicina ◽  
2019 ◽  
Vol 55 (5) ◽  
pp. 191 ◽  
Author(s):  
Md. Rezanur Rahman ◽  
Tania Islam ◽  
Md. Shahjaman ◽  
Toyfiquz Zaman ◽  
Hossain Md. Faruquee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Mapk Signaling ◽  
Protein Protein Interactions ◽  
Microarray Gene Expression ◽  
Pathway Analyses

Background and objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein–protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein–drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.

scholarly journals Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer’s disease

2018 ◽  
Author(s):  
Mikko Konki ◽  
Maia Malonzo ◽  
Ida K. Karlsson ◽  
Noora Lindgren ◽  
Bishwa Ghimire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Peripheral Blood ◽  
Molecular Mechanisms ◽  
Bisulfite Sequencing ◽  
Disease Risk ◽  
Gene Array ◽  
Discordant Twin

ABSTRACTAlzheimer’s disease (AD) results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into the disease process would be highly valuable. As life style factors largely modulate the disease risk, we hypothesised that the disease associated DNA methylation signatures are detectable in the peripheral blood of discordant twin pairs. Reduced Representation Bisulfite Sequencing, single cell RNA-sequencing and gene array data were utilised to examine DNA methylation signatures and associated gene expression changes in blood and hippocampus, and targeted bisulfite sequencing in cross cohort validation. Our results reveal that discordant twin pairs have disease associated differences in their peripheral blood epigenomes. A subset of affected genes, e.g.ADARB2contain differentially methylated sites also in anterior hippocampus. The DNA methylation differences seem to influence gene expression in brain rather than in blood cells. The affected genes are associated with neuronal functions and pathologies. These DNA methylation signatures are valuable disease marker candidates and may provide insights into the molecular mechanisms of pathogenesis.

scholarly journals Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Manish D. Paranjpe ◽  
Stella Belonwu ◽  
Jason K. Wang ◽  
Tomiko Oskotsky ◽  
Aarzu Gupta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
B Cells ◽  
Whole Blood ◽  
Meta Analysis ◽  
The United States ◽  
Support Vector ◽  
Cell Type ◽  
Apoe Ε4

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive.Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males.Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

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