scholarly journals Sleep and Alzheimer’s Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects

2021 ◽  
Author(s):  
Dongze Chen ◽  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Sleep Duration ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Lipoprotein Particle ◽  
Level Data ◽  
Particle Clearance

Abstract Background: Alzheimer’s disease (AD) was associated with sleep-related phenotypes (SRPs). Whether they share common genetic etiology remains largely unknown. We explored the shared genetics and causality between AD and SRPs by using high-definition likelihood (HDL), cross phenotype association study (CPASSOC), transcriptome wide association study (TWAS), and bidirectional Mendelian randomization (MR) in summary-level data for AD (n = 79145) and summary-level data for seven SRPs (sample size ranges from 345552 to 386577). Results: AD shared strong genetic basis with insomnia (rg = 0.20; P = 9.70×10-5), snoring (rg = 0.13; P = 2.45×10-3), and sleep duration (rg = -0.11; P = 1.18×10-3). CPASSOC identifies 31 independent loci shared between AD and SRPs, including four novel shared loci. Functional analysis and TWAS showed shared genes were enriched in liver, brain, breast, and heart tissues, and highlighted the regulatory role of immunological disorders, very-low-density lipoprotein particle clearance, triglyceride-rich lipoprotein particle clearance, chylomicron remnant clearance and positive regulation of T cell mediated cytotoxicity pathways. Protein-protein interaction analysis provided three potential drug target genes (APOE, MARK4 and HLA-DRA) that interacted with known FDA-approved drug target genes. CPASSOC and TWAS demonstrated three regions 11p11.2, 6p22.3 and 16p11.2 may account for the shared basis between AD and sleep duration or snoring. MR showed AD had causal effect on sleep duration (βIVW = -0.056, PIVW = 1.03×10-3). Conclusion: Our findings provide strong evidence of shared genetics and causation between AD and sleep, and advance our understanding the genetic overlap between them. Identifying shared drug targets and molecular pathways can be beneficial to treat AD and sleep disorders more efficiently.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

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.

scholarly journals A transcriptome-wide association study of Alzheimer’s disease using prediction models of relevant tissues identifies novel candidate susceptibility genes

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yanfa Sun ◽  
Jingjing Zhu ◽  
Dan Zhou ◽  
Saranya Canchi ◽  
Chong Wu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Target Genes ◽  
Prediction Models ◽  
Association Studies ◽  
Strong Support ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer’s disease (AD), but the genes responsible for these associations remain largely unknown. Methods We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry. Results We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10−6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal. Conclusions Our study provides new insights into the etiology and underlying genetic architecture of AD.

Memapsin 2, a drug target for Alzheimer's disease

2003 ◽  
Vol 70 ◽  
pp. 213-220 ◽  
Author(s):  
Gerald Koelsch ◽  
Robert T. Turner ◽  
Lin Hong ◽  
Arun K. Ghosh ◽  
Jordan Tang
Keyword(s):  
Crystal Structure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transition State ◽  
Amyloid Precursor Protein ◽  
Therapeutic Target ◽  
Drug Target ◽  
Aspartic Protease ◽  
Precursor Protein ◽  
Memapsin 2

Mempasin 2, a ϐ-secretase, is the membrane-anchored aspartic protease that initiates the cleavage of amyloid precursor protein leading to the production of ϐ-amyloid and the onset of Alzheimer's disease. Thus memapsin 2 is a major therapeutic target for the development of inhibitor drugs for the disease. Many biochemical tools, such as the specificity and crystal structure, have been established and have led to the design of potent and relatively small transition-state inhibitors. Although developing a clinically viable mempasin 2 inhibitor remains challenging, progress to date renders hope that memapsin 2 inhibitors may ultimately be useful for therapeutic reduction of ϐ-amyloid.

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

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.

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

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
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.

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

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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