Identifying and Ranking Potential Driver Genes of Alzheimer's Disease Using Weighted Co-Expression Network Analysis

Abstract Background: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Identification of AD-related genes from transcriptomics provided new direction to the mechanism for finding potential targets for drug therapy.Methods: We mined gene co-expression network modules from differentially expressed genes (DEGs) of AD and normal samples in multiple datasets by weighted gene co-expression network analysis (WGCNA). A convergent functional genomic (CFG) method was used to prioritize potential driver genes.Results: The 7567 DEGs were enriched significantly with 61 KEGG pathway and 242 GO terms. Then, the genes in 5 AD-specific modules obtained significantly from DEGs were interconnected with well-known AD risk genes in common PPI network. Remarkably, compared to the number of Tau production-related genes, Aβ play a more critical role. Lastly, the 23 potential driver genes was prioritized by CFG method from 5 AD-specific modules.Conclusions: Identification of AD-related genes could be useful for understanding pathophysiology of AD and looking for candidates drug targets.

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Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses

Journal of Alzheimer s Disease ◽

10.3233/jad-201235 ◽

2021 ◽

pp. 1-11

Author(s):

Qi-Shuai Zhuang ◽

Lei Meng ◽

Zhe Wang ◽

Liang Shen ◽

Hong-Fang Ji

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis ◽

Drug Targets ◽

Protective Factor ◽

Genetic Locus ◽

Meta Analysis ◽

Current Evidence ◽

Causal Association ◽

Ppi Network

Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. Results: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. Conclusion: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

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Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

Current Pharmaceutical Design ◽

10.2174/1381612826666200928161721 ◽

2020 ◽

Vol 26 ◽

Author(s):

Smriti Sharma ◽

Vinayak Bhatia

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Design ◽

In Silico ◽

Drug Targets ◽

Development Process ◽

Phytochemical Analysis ◽

Computational Drug Design ◽

Novel Drugs ◽

Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

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Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Current Gene Therapy ◽

10.2174/1566523219666190924113737 ◽

2019 ◽

Vol 19 (4) ◽

pp. 216-223 ◽

Cited By ~ 2

Author(s):

Tianyi Zhao ◽

Donghua Wang ◽

Yang Hu ◽

Ningyi Zhang ◽

Tianyi Zang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Targets ◽

Molecular Mechanisms ◽

Feature Vector ◽

Mirna Gene ◽

Interaction Network ◽

Gene Interaction ◽

Proteinprotein Interaction ◽

Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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Systematic Design and Evaluation of Drug Repurposing-Oriented Alzheimer’s Disease Ontology (Preprint)

10.2196/preprints.17944 ◽

2020 ◽

Author(s):

Fang Li ◽

Muhammad "Tuan" Amith ◽

Grace Xiong ◽

Jingcheng Du ◽

Yang Xiang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Targets ◽

Drug Repurposing ◽

Essential Elements ◽

Genomic Research ◽

Core Knowledge ◽

Disease Ontology ◽

Hybrid Strategy ◽

Complementary Strategy

BACKGROUND Alzheimer’s Disease (AD) is a devastating neurodegenerative disease, of which the pathophysiology is insufficiently understood, and the curative drugs are long-awaited to be developed. Computational drug repurposing introduces a promising complementary strategy of drug discovery, which benefits from an accelerated development process and decreased failure rate. However, generating new hypotheses in AD drug repurposing requires multi-dimensional and multi-disciplinary data integration and connection, posing a great challenge in the era of big data. By integrating data with computable semantics, ontologies could infer unknown relationships through automated reasoning and fulfill an essential role in supporting computational drug repurposing. OBJECTIVE The study aimed to systematically design a robust Drug Repurposing-Oriented Alzheimer’s Disease Ontology (DROADO), which could model fundamental elements and their relationships involved in AD drug repurposing and integrate their up-to-date research advance comprehensively. METHODS We devised a core knowledge model of computational AD drug repurposing, based on both pre-genomic and post-genomic research paradigms. The model centered on the possible AD pathophysiology and abstracted the essential elements and their relationships. We adopted a hybrid strategy to populate the ontology (classes and properties), including importing from well-curated databases, extracting from high-quality papers and reusing the existing ontologies. We also leveraged n-ary relations and nanopublication graphs to enrich the object relations, making the knowledge stored in the ontology more powerful in supporting computational processing. The initially built ontology was evaluated by a semiotic-driven and web-based tool Ontokeeper. RESULTS The current version of DROADO was composed of 1,021 classes, 23 object properties and 3,207 axioms, depicting a fundamental network related to computational neuroscience concepts and relationships. Assessment using semiotic evaluation metrics by OntoKeeper indicated sufficient preliminary quality (semantics, usefulness and community-consensus) of the ontology. CONCLUSIONS As an in-depth knowledge base, DROADO would be promising in enabling computational algorithms to realize supervised mining from multi-source data, and ultimately, facilitating the discovery of novel AD drug targets and the realization of AD drug repurposing.

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Loss of ferroportin induces memory impairment by promoting ferroptosis in Alzheimer’s disease

Cell Death and Differentiation ◽

10.1038/s41418-020-00685-9 ◽

2021 ◽

Author(s):

Wen-Dai Bao ◽

Pei Pang ◽

Xiao-Ting Zhou ◽

Fan Hu ◽

Wan Xiong ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Memory Impairment ◽

Iron Homeostasis ◽

Critical Role ◽

Gene Set Enrichment Analysis ◽

Molecular Characteristics ◽

Intracellular Iron

AbstractIron homeostasis disturbance has been implicated in Alzheimer’s disease (AD), and excess iron exacerbates oxidative damage and cognitive defects. Ferroptosis is a nonapoptotic form of cell death dependent upon intracellular iron. However, the involvement of ferroptosis in the pathogenesis of AD remains elusive. Here, we report that ferroportin1 (Fpn), the only identified mammalian nonheme iron exporter, was downregulated in the brains of APPswe/PS1dE9 mice as an Alzheimer’s mouse model and Alzheimer’s patients. Genetic deletion of Fpn in principal neurons of the neocortex and hippocampus by breeding Fpnfl/fl mice with NEX-Cre mice led to AD-like hippocampal atrophy and memory deficits. Interestingly, the canonical morphological and molecular characteristics of ferroptosis were observed in both Fpnfl/fl/NEXcre and AD mice. Gene set enrichment analysis (GSEA) of ferroptosis-related RNA-seq data showed that the differentially expressed genes were highly enriched in gene sets associated with AD. Furthermore, administration of specific inhibitors of ferroptosis effectively reduced the neuronal death and memory impairments induced by Aβ aggregation in vitro and in vivo. In addition, restoring Fpn ameliorated ferroptosis and memory impairment in APPswe/PS1dE9 mice. Our study demonstrates the critical role of Fpn and ferroptosis in the progression of AD, thus provides promising therapeutic approaches for this disease.

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Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease

Interdisciplinary Sciences Computational Life Sciences ◽

10.1007/s12539-019-00319-w ◽

2019 ◽

Vol 11 (4) ◽

pp. 645-654 ◽

Cited By ~ 7

Author(s):

Jiong Wu ◽

Linhui Chen ◽

Chaobo Zheng ◽

Shanhu Xu ◽

Yuhai Gao ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Analysis

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Microglia and its genetics in Alzheimer's Disease

Current Alzheimer Research ◽

10.2174/1567205018666211105140732 ◽

2021 ◽

Vol 18 ◽

Author(s):

Xinyan Liang ◽

Haijian Wu ◽

Mark Colt ◽

Xinying Guo ◽

Brock Pluimer ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Immune Cell ◽

Morphological Changes ◽

Association Studies ◽

Unmet Need ◽

Genome Wide Association Studies ◽

Protective Effects ◽

Risk Genes ◽

Main Component

: Alzheimer’s Disease (AD) is the most prevalent form of dementia across the world. While its discovery and pathological manifestations are centered on protein aggregations of amyloid-beta (Aβ) and hyperphosphorylated tau protein, neuroinflammation has emerged in the last decade as a main component of the disease in both pathogenesis and progression. As the main innate immune cell type in central nervous system (CNS), microglia play a very important role in regulating neuroinflammation, which occurs commonly in neurodegenerative conditions including AD. Under inflammatory response, microglia undergo morphological changes and status transition from homeostatic to activated forms. Different microglia subtypes displaying distinct genetic profiles have been identified in AD, and these signatures often link to AD risk genes identified from the genome-wide association studies (GWAS), such as APOE and TREM2. Furthermore, many of AD risk genes are highly enriched in microglia and specifically influence the functions of microglia in pathogenesis, e.g. releasing inflammatory cytokines and clearing Aβ. Therefore, building up a landscape of these risk genes in microglia, based on current preclinical studies and in the context of their pathogenic or protective effects, would largely help us to understand the complexed etiology of AD and provide new insight for the unmet need of effective treatment.

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