A Systematic Bioinformatics Workflow With Meta-Analytics Identified Potential Pathogenic Factors of Alzheimer’s Disease

Potential pathogenic factors, other than well-known , , and , can be further identified from transcriptomics studies of differentially expressed genes (DEGs) that are specific for Alzheimer's disease (AD), but findings are often inconsistent or even contradictory. Evidence corroboration by comb...

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Veröffentlicht in:Frontiers in neuroscience Jg. 14; S. 209
Hauptverfasser: Yuen, Sze Chung, Zhu, Hongmei, Leung, Siu-wai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 13.03.2020
Frontiers Media S.A
Schlagworte:
Algorithms
Alzheimer's disease
Amyloid
Bioinformatics
Brain
Datasets
Dementia
Deoxyribonucleic acid
DNA
DNA microarrays
DNA sequencing
Drugs
Epidermal growth factor receptors
Fyn protein
Gene expression
Homeostasis
Hypotheses
Kinases
Meta-analysis
microarray analysis
Neurodegenerative diseases
Neuroscience
Neurotoxicity
Phosphorylation
Proteins
Ribonucleic acid
RNA
RNA sequence analysis
Statistical analysis
Studies
Transcriptomics
meta-analysis
microarray analysis
Alzheimer’s disease
RNA sequence analysis
bioinformatics
ISSN:1662-453X, 1662-4548, 1662-453X
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Zusammenfassung:Potential pathogenic factors, other than well-known , , and , can be further identified from transcriptomics studies of differentially expressed genes (DEGs) that are specific for Alzheimer's disease (AD), but findings are often inconsistent or even contradictory. Evidence corroboration by combining meta-analysis and bioinformatics methods may help to resolve existing inconsistencies and contradictions. This study aimed to demonstrate a systematic workflow for evidence synthesis of transcriptomic studies using both meta-analysis and bioinformatics methods to identify potential pathogenic factors. Transcriptomic data were assessed from GEO and ArrayExpress after systematic searches. The DEGs and their dysregulation states from both DNA microarray and RNA sequencing datasets were analyzed and corroborated by meta-analysis. Statistically significant DEGs were used for enrichment analysis based on KEGG and protein-protein interaction network (PPIN) analysis based on STRING. AD-specific modules were further determined by the DIAMOnD algorithm, which identifies significant connectivity patterns between specific disease-associated proteins and non-specific proteins. Within AD-specific modules, the nodes of highest degrees (>95th percentile) were considered as potential pathogenic factors. After systematic searches of 225 datasets, extensive meta-analyses among 25 datasets (21 DNA microarray datasets and 4 RNA sequencing datasets) identified 9,298 DEGs. The dysregulated genes and pathways in AD were associated with impaired amyloid-β (Aβ) clearance. From the AD-specific module, Fyn, and EGFR were the most statistically significant and biologically relevant. This meta-analytical study suggested that the reduced Aβ clearance in AD pathogenesis was associated with the genes encoding Fyn and EGFR, which were key receptors in Aβ downstream signaling.
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Edited by: Woon-Man Kung, Chinese Culture University, Taiwan
This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience
Reviewed by: Chung-Feng Kao, National Chung Hsing University, Taiwan; Judith Potashkin, Rosalind Franklin University of Medicine and Science, United States
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2020.00209