Bioinformatic Analysis Using Complex Networks and Clustering Proteins Linked with Alzheimer’s Disease

2016 ◽  
pp. 219-230 ◽  
Author(s):  
Suthinan Rujirapipat ◽  
Ken McGarry ◽  
David Nelson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Networks ◽  
Bioinformatic Analysis

Profiling Basal Forebrain Cholinergic Neurons Reveals a Molecular Basis for Vulnerability Within the Ts65Dn Model of Down Syndrome and Alzheimer’s Disease

2020 ◽  
Author(s):  
Melissa J. Alldred ◽  
Sai C. Penikalapati ◽  
Sang Han Lee ◽  
Adriana Heguy ◽  
Panos Roussos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Down Syndrome ◽  
Basal Forebrain ◽  
Differentially Expressed Gene ◽  
Cholinergic Neurons ◽  
Bioinformatic Analysis ◽  
Selective Vulnerability ◽  
Neuronal Population ◽  
Single Population

Abstract Background: Basal forebrain cholinergic neuron (BFCN) degeneration is a hallmark of Down syndrome (DS) and Alzheimer’s disease (AD). Current therapeutics have been unsuccessful in slowing disease progression, likely due to complex pathological interactions and dysregulated pathways that are poorly understood. The Ts65Dn trisomic mouse model recapitulates both cognitive and morphological deficits of DS and AD, including BFCN degeneration. Methods: We utilized Ts65Dn mice to understand mechanisms underlying BFCN degeneration to identify novel targets for therapeutic intervention. We performed high-throughput, single population RNA sequencing (RNA-seq) to interrogate transcriptomic changes within medial septal nucleus (MSN) BFCNs, using laser capture microdissection to individually isolate ~500 choline acetyltransferase-immunopositive neurons in Ts65Dn and normal disomic (2N) mice at 6 months of age (MO). Results: Ts65Dn mice had unique MSN BFCNs transcriptomic profiles at ~6 MO clearly differentiating them from 2N mice. Leveraging Ingenuity Pathway Analysis and KEGG analysis, we linked differentially expressed gene (DEG) changes within MSN BFCNs to several canonical pathways and aberrant physiological functions. The dysregulated transcriptomic profile of trisomic BFCNs provides key information underscoring selective vulnerability within the septohippocampal circuit. Conclusions: We propose both expected and novel therapeutic targets for DS and AD, including specific DEGs within cholinergic, glutamate, GABAergic, and neurotrophin pathways, as well as select targets for repairing oxidative phosphorylation status in neurons. We demonstrate and validate an interrogative quantitative bioinformatic analysis of a key dysregulated neuronal population linking single population transcript changes to an established pathological hallmark associated with cognitive decline for therapeutic development in human DS and AD.

scholarly journals Potential hippocampal genes and pathways involved in Alzheimer’s disease: a bioinformatic analysis

2015 ◽  
Vol 14 (2) ◽  
pp. 7218-7232 ◽  
Author(s):  
L. Zhang ◽  
X.Q. Guo ◽  
J.F. Chu ◽  
X. Zhang ◽  
Z.R. Yan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bioinformatic Analysis

Use of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Disease

2019 ◽  
pp. 115-126 ◽  
Author(s):  
Aruane Mello Pineda ◽  
Fernando M. Ramos ◽  
Luiz Eduardo Betting ◽  
Andriana S. L. O. Campanharo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Networks ◽  
Automatic Detection

scholarly journals The role of microRNAs regulatory network in Alzheimer’s disease: a bioinformatics analysis

2021 ◽  
Author(s):  
Chenjing Sun ◽  
Xiaokun Qi ◽  
Jianguo Liu ◽  
Feng Duan ◽  
Lin Cong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bioinformatics Analysis ◽  
Time Window ◽  
Bioinformatic Analysis ◽  
Preclinical Phase ◽  
Wide Range ◽  
Important Time ◽  
Brain Tissues

IntroductionAlzheimer’s disease (AD) is a neurodegenerative disease which presents with an earlier onset age and increased symptom severity. The objective of this study was to evaluate the relationship between regulation of miRNAs and AD.Material and methodsWe completed the bioinformatic analysis of miRNAs-AD studies through multiple databases such as TargetScan, Database for Annotation, Visualization and Integrated Discovery (DAVID), FunRich and String and assessed which miRNAs are commonly elevated or decreased in brain tissues, cerebrospinal fluid (CSF) and blood of AD. All identified articles were assessed using specific inclusion and exclusion criteria.ResultsMiRNAs related to AD of twenty-eight studies were assessed in this study. A wide range of miRNAs were up-regulated or down-regulated in tissues of AD patient’s brain, blood and CSF. 27 differentially dysregulated miRNAs have identified involved in amyloidogenesis, inflammation, tau-phosphorylation, apoptosis, synaptogenesis, neurotrophism, neurons degradation, activates cell cycle entry. Additionally, our bioinformatics analysis identified the top ten functions of common miRNAs in candidate studies. The function of common up-regulated miRNAs primarily target nucleus and common down-regulated miRNAs primarily target transcription, DNA-templated.ConclusionsComprehensive analysis of all miRNAs studies reveals cooperation in miRNA signatures whether in brain tissues or in CSF and peripheral blood. More and more studies suggest that miRNAs may play crucial roles as diagnostic biomarkers and/or as new therapeutic targets in AD. According to biomarkers, we can identify the preclinical phase early that provides an important time-window for therapeutic intervention.

A 3-Gene-Based Diagnostic Signature in Alzheimer’s Disease

2021 ◽  
pp. 1-8
Author(s):  
Huimin Wang ◽  
Yanqiu Zhang ◽  
Chengyao Zheng ◽  
Songqi Yang ◽  
Xiuju Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Diagnostic Methods ◽  
Sample Type ◽  
Ppi Network

<b><i>Background:</i></b> Alzheimer’s disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. <b><i>Methods:</i></b> All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. <b><i>Results:</i></b> Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. <b><i>Conclusion:</i></b> The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.

Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph

2020 ◽  
Vol 17 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Zhongke Gao ◽  
Yanhua Feng ◽  
Chao Ma ◽  
Kai Ma ◽  
Qing Cai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Complex Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Visibility Graph ◽  
Time Dependent ◽  
Biological Information ◽  
Local Complex

Background: Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD. Methods: We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences. Results: According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD. Conclusion: These results would be helpful in revealing the underlying pathological mechanism of the disease.

Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhengqiang He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Actin Filament ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction

Abstract More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

scholarly journals The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer’s Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaocong Pang ◽  
Ying Zhao ◽  
Jinhua Wang ◽  
Qimeng Zhou ◽  
Lvjie Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecules ◽  
Entorhinal Cortex ◽  
Drug Targets ◽  
Target Genes ◽  
Bioinformatic Analysis ◽  
Hub Genes ◽  
Pathway Enrichment

Aim. The incidence of Alzheimer’s disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods. We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results. We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion. These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.

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