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A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

ABSTRACT: BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

SUBMITTER: Liu CH

PROVIDER: S-EPMC5436445 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

Liu Chun-Han CH Liu Lian L

Medical science monitor : international medical journal of experimental and clinical research 20170508

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subs ...[more]

PMID: 28479591

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Project description:Alzheimer's disease (AD) is a progressive, neurodegenerative disorder with insidious onset. Some scholars believe that there is a close relationship between pyroptosis and AD. However, studies with evidence supporting this relationship are lacking. The microarray data of AD were retrieved from the Gene Expression Omnibus (GEO) database with the datasets merged using the R package inSilicoMerging. R software package Limma was used to perform the differential expression analysis to identify the differentially expressed genes (DEGs). We further performed the enrichment analyses of the DEGs based on Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to identify the metabolic pathways with a significant difference. The Gene Set Enrichment Analysis (GSEA) was applied to identify the significant pathways. The protein-protein interaction (PPI) network was constructed based on the STRING database with the hub genes identified. Quantitative real-time PCR (qRT-PCR) analyses based on HT22 cells were performed to validate the findings based on the microarray analysis. Gene expression correlation heatmaps were generated to evaluate the relationships among the genes. A new dataset was derived by merging 4 microarray datasets in the hippocampus of AD patients in the GEO database. Differential gene expression analysis yielded a volcano plot of a total of 20 DEGs (14 up-regulated and 6 down-regulated). GO analysis revealed a group of GO terms with a significant difference, e.g., cytoplasmic vesicle membrane, vesicle membrane, and monocyte chemotaxis. KEGG analysis detected the metabolic pathways with a significant difference, e.g., Rheumatoid arthritis and Fluid shear stress and atherosclerosis. The results of the Gene Set Enrichment Analysis of the microarray data showed that gene set ALZHEIMER_DISEASE and the gene set PYROPTOSIS were both up-regulated. PPI network showed that pyroptosis-related genes were divided into two groups. In the Aβ-induced HT22 cell model, three genes (i.e., BAX, IL18, and CYCS) were revealed with significant differences. Gene expression correlation heatmaps revealed strong correlations between pyroptotic genes and AD-related genes. The pyroptosis-related genes BAX, IL18, and CYCS were significantly different between AD patients and normal controls.

Project description:BackgroundDespite decades of research, no precise mechanisms of Alzheimer's disease (AD) development have been elucidated. This study aimed to investigate novel diagnostic biomarkers in both peripheral blood cells and hippocampus tissue, and the pathogenesis of memory impairment in AD.MethodsmRNA microarray data, including hippocampus samples (GSE1297 and GSE5281) and peripheral blood mononuclear cells (PBMCs) (GSE63060 and GSE63061), associated with AD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AD and normal-aging samples were screened through a comprehensive analysis of multiple gene expression spectra after gene reannotation and batch normalization. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to analyze hub genes and to discover potential biomarkers related to AD. Protein-protein interaction (PPI) network maps were constructed to visualize the correlation between possible genes. The CIBERSORT algorithm was built to explore the patterns of PBMC infiltration to investigate the role of inflammation in the pathogenesis of AD.ResultsThe bioinformatics analysis indicated 1,261 DEGs in the hippocampal samples and 290 in PBMCs when comparing patients with AD with normal-aging individuals. We selected 28 genes co-expressed in the hippocampus and PBMCs. A functional analysis of differential genes revealed that they were primarily involved in neuronal death, immune response, and mitochondrial function. Further, immune cell infiltration patterns demonstrated that the levels of naive CD4+ T cells, resting natural killer cells, M0 macrophages, and activated mast cells were higher in the peripheral blood of patients with AD, while resting memory CD4+ T cells were significantly lower.ConclusionsThe key gene changes present in both the hippocampus and PBMCs highly suggest their utility as an AD biomarker. In addition, according to our present results, immune abnormalities may have an important role in AD pathophysiology. When patients display these peripheral blood immune abnormalities, they may be recognized as being at high risk of developing AD.

Dataset Information

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

Publications

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

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