Relationship Between Alzheimer’s Disease and the Immune System: A Meta-Analysis of Differentially Expressed 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.

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GPR40 Agonist Ameliorate Pathological Neuroinflammation of Alzheimer’s Disease via the Modulation of Gut Microbiota and Immune System, a Mini-Review

Neurotoxicity Research ◽

10.1007/s12640-021-00408-z ◽

2021 ◽

Author(s):

Jianheng Chen ◽

Qian Li ◽

Jiang Zhu ◽

Zijing Yuan ◽

Tao Wang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Immune System ◽

Gut Microbiota ◽

System A

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Putative Factors Interfering Cell Cycle Re-Entry in Alzheimer’s Disease: An Omics Study with Differential Expression Meta-Analytics and Co-Expression Profiling

Journal of Alzheimer s Disease ◽

10.3233/jad-215349 ◽

2021 ◽

pp. 1-26

Author(s):

Sze Chung Yuen ◽

Simon Ming-Yuen Lee ◽

Siu-wai Leung

Keyword(s):

Alzheimer’S Disease ◽

Cell Cycle ◽

Alzheimer's Disease ◽

Expression Analysis ◽

Protein Interaction ◽

Meta Analysis ◽

Differentially Expressed ◽

Tau Proteins ◽

Related Factors ◽

Protein Protein Interaction

Background: Neuronal cell cycle re-entry (CCR) is a mechanism, along with amyloid-β (Aβ) oligomers and hyperphosphorylated tau proteins, contributing to toxicity in Alzheimer’s disease (AD). Objective: This study aimed to examine the putative factors in CCR based on evidence corroboration by combining meta-analysis and co-expression analysis of omic data. Methods: The differentially expressed genes (DEGs) and CCR-related modules were obtained through the differential analysis and co-expression of transcriptomic data, respectively. Differentially expressed microRNAs (DEmiRNAs) were extracted from the differential miRNA expression studies. The dysregulations of DEGs and DEmiRNAs as binary outcomes were independently analyzed by meta-analysis based on a random-effects model. The CCR-related modules were mapped to human protein-protein interaction databases to construct a network. The importance score of each node within the network was determined by the PageRank algorithm, and nodes that fit the pre-defined criteria were treated as putative CCR-related factors. Results: The meta-analysis identified 18,261 DEGs and 36 DEmiRNAs, including genes in the ubiquitination proteasome system, mitochondrial homeostasis, and CCR, and miRNAs associated with AD pathologies. The co-expression analysis identified 156 CCR-related modules to construct a protein-protein interaction network. Five genes, UBC, ESR1, EGFR, CUL3, and KRAS, were selected as putative CCR-related factors. Their functions suggested that the combined effects of cellular dyshomeostasis and receptors mediating Aβ toxicity from impaired ubiquitination proteasome system are involved in CCR. Conclusion: This study identified five genes as putative factors and revealed the significance of cellular dyshomeostasis in the CCR of AD.

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P4-049: IDENTIFICATION OF CELL-SPECIFIC DIFFERENTIALLY EXPRESSED GENES IN ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.2451 ◽

2006 ◽

Vol 14 (7S_Part_27) ◽

pp. P1452-P1452

Author(s):

Xue Wang ◽

Mariet Allen ◽

Minerva M. Carrasquillo ◽

Jeremy D. Burgess ◽

Shaoyu Li ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Differentially Expressed Genes ◽

Differentially Expressed

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Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer’s disease by meta-analysis and adaptive boosting ensemble learning

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00862-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Sze Chung Yuen ◽

Xiaonan Liang ◽

Hongmei Zhu ◽

Yongliang Jia ◽

Siu-wai Leung

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Ensemble Learning ◽

Target Genes ◽

Meta Analysis ◽

Biological Significance ◽

Neuronal Cell ◽

Differentially Expressed ◽

Learning Method ◽

Adaptive Boosting

Abstract Background Blood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method. Methods Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA–AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions. Results A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected. Conclusion This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

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