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

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.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

scholarly journals Hub Genes Related to the Pathogenesis and Progression of Colorectal Cancer and Adenoma by Integrative Bioinformatics Approaches

2021 ◽  
Author(s):  
Yuxuan HUANG ◽  
Ge CUI
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Aims: To utilize the bioinformatics to analyze the differentially expressed genes (DEGs), interaction proteins, perform gene enrichment analysis, protein-protein interaction network (PPI) and map the hub genes between colorectal cancer(CRC) and colorectal adenocarcinomas(CA).Methods: We analyzed a microarray dataset (GSE32323 and GSE4183) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in tumor tissues and non-cancerous tissues were identified using the dplyr and Venn diagram packages of the R Studio software. Functional annotation of the DEGs was performed using the Gene Ontology (GO) website. Pathway enrichment (KEGG) used the WebGestalt to analyze the data and R Studio to generate the graph. We constructed a protein–protein interaction (PPI) network of DEGs using STRING and Cytoscape software was used for visualization. Survival analysis of the hub genes and was performed using the online platform GEPIA to determine the prognostic value of the expression of hub genes in cell lines from CRC patients. The expression of molecules with prognostic values was validated on the UALCAN database. The expression of hub genes was examined using the Human Protein Atlas. Results: Applying the GEO2R analysis and R studio, we identified a total of 471 upregulated and 278 downregulated DEGs. By using the online database WebGestalt, we identified the most relevant biological networks involving DEGs with statistically significant differences in expression were mainly associated with biological processes involved in the cell proliferation, cell cycle transition, cell homeostasis and indicated the role of each DEGs in cell cycle regulation pathways. We found 10 hub genes with prognostic values were overexpressed in the CRC and CA samples.Conclusion: we found out ten hub genes and three core genes closely associated with the pathogenesis and prognosis of CRC and CA, which is of great significance for colorectal tumor early detection and prognosis evaluation.

Optimizing Modifiable and Lifestyle-related Factors in the Prevention of Dementia Disorders with Special Reference to Alzheimer, Parkinson and Autism Diseases

2020 ◽  
Vol 16 (6) ◽  
pp. 900-911
Author(s):  
Umesh C. Gupta ◽  
Subhas C. Gupta
Keyword(s):  
Physical Activity ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Environmental Pollutants ◽  
Memory Loss ◽  
Tau Proteins ◽  
Body Parts ◽  
Related Factors ◽  
Age Related ◽  
Major Factors

Dementia is a syndrome and an umbrella term that encompasses Alzheimer, Parkinson and autism diseases. These diseases are by far the most common cause of dementia; therefore this investigation will chiefly include these disorders, with a limited discussion of few other disorders related to dementia. Alzheimer’s disease (AD) is characterized by the accumulation of cerebral β-amyloid plaques, tau proteins and memory loss; Parkinson by the deterioration of brain cells which regulate the movement of body parts and produce dopamine; and autism by abnormalities of social disorder and difficulty in communicating and forming relationships. Alzheimer’s disease and cognitive impairment in dementia are age-related and manageable only with early diagnosis and prevention. Data based on several decades of research has shown that the major factors responsible for the induction of inflammation in dementia and many chronic diseases are infections, obesity, alcohol, radiation, environmental pollutants, improper nutrition, lack of physical activity, depression, anxiety, genetic factors, and sleep deprivation. There are some studied preventive measures for dementia including continued physical activity and consuming predominantly a plant-based Mediterranean diet comprising olive oil and foods containing flavonoids and other phytochemicals having strong antioxidant and anti-inflammatory properties and along with management of chronic conditions.

scholarly journals Identification of Key Genes Related with Alzheimer’s Disease Treatment Through Bioinformatics Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 78
Author(s):  
Liqun Wang ◽  
Hongjia Qian ◽  
Liqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Amyloid Β ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Tg2576 Mice

T0901317, a live X receptor agonist, can reduce amyloid β generation in vitro and in a mouse Alzheimer’s disease (AD) model. To investigate the global molecular effects of T0901317 in mouse hippocampus, we downloaded public GSE31624 generated from the hippocampus of wild-type mice, Tg2576 mice and T0901317-treated Tg2576 mice. Differentially-expressed genes (DEGs) were identified on LIMMA of R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment were analyzed through DAVID. Protein- protein interaction and hub genes were obtained based on STRING and Cytoscape. Nine downregulated and 68 upregulated DEGs in T0901317-treated Tg2576 were identified in comparison with untreated Tg2576 mice. Annotation analyses showed these DEGs correlated with transport (BP), membrane (CC) and binding (MF) terms and the dopaminergic synapse pathway. Protein-protein interaction network was built to find out some hub genes by maximal clique centrality. Discs large homolog 4 (Dlg4), the most outstanding gene, was associated with cognition improvement in aged AD mice. T0901317 may impact the development by regulating the Dlg4 expression. In conclusion, we investigated effects of T0901317 therapy on gene expression profiles in the hippocampus of Tg2576 mice and found Dlg4 may serve as putative therapeutics target for AD treatment.

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

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.

scholarly journals Ultrasensitive assays for detection of plasma tau and phosphorylated tau 181 in Alzheimer’s disease: a systematic review and meta-analysis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xulong Ding ◽  
Shuting Zhang ◽  
Lijun Jiang ◽  
Lu Wang ◽  
Tao Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Tau Proteins ◽  
Phosphorylated Tau ◽  
Diagnosis And Prognosis ◽  
Recent Advancement ◽  
Protein Assays ◽  
Phosphorylated Tau 181

AbstractA lack of convenient and reliable biomarkers for diagnosis and prognosis is a common challenge for neurodegenerative diseases such as Alzheimer’s disease (AD). Recent advancement in ultrasensitive protein assays has allowed the quantification of tau and phosphorylated tau proteins in peripheral plasma. Here we identified 66 eligible studies reporting quantification of plasma tau and phosphorylated tau 181 (ptau181) using four ultrasensitive methods. Meta-analysis of these studies confirmed that the AD patients had significantly higher plasma tau and ptau181 levels compared with controls, and that the plasma tau and ptau181 could predict AD with high-accuracy area under curve of the Receiver Operating Characteristic. Therefore, plasma tau and plasma ptau181 can be considered as biomarkers for AD diagnosis.

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